Remote desktop control system and method based on cloud computing

Abstract

The invention discloses a remote desktop control system and method based on cloud computing, and relates to the technical field of digital information transmission, and the remote desktop control system comprises a cloud computing platform module, a data analysis module, a performance optimization module and a remote control strategy module. According to the method, the key factors influencing the remote desktop performance can be extracted from the interactive feature data, the accurate recognition capability of performance bottlenecks is improved, resource allocation and performance optimization strategies are helped to be optimized, the overall remote desktop control efficiency is improved, the future remote desktop control performance can be predicted, and the remote desktop performance can be effectively improved. According to the method and the system, the possible performance change trend is accurately pre-judged, so that a scientific basis can be provided for adjusting resource allocation and optimizing strategies in advance, delay can be reduced, user experience can be improved, meanwhile, according to the performance change trend and the real-time interaction state of a user, the system can dynamically adjust the control strategy of the remote desktop, and intelligent optimization of a transmission protocol is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information transmission, and in particular to a remote desktop control system and method based on cloud computing. Background Art

[0002] With the rapid development of science and technology, automated intelligent control has gradually become an indispensable part of people's lives, and remote desktop control is the concrete embodiment of this technology. Remote desktop control technology enables the operation of devices through remote connections, and various tasks can be completed without direct contact with the device screen, greatly improving work efficiency and convenience of life. This technology has been widely used in office, education, medical and other fields. Especially in the context of the increasing popularity of remote office and online education, remote desktop control has become an important means to improve resource utilization efficiency and save human resources.

[0003] In the existing technology, it is not convenient to extract the key factors affecting the remote desktop performance from the interactive feature data, improve the ability to accurately identify performance bottlenecks, optimize resource allocation and performance optimization strategies, improve the efficiency of the overall remote desktop control, and predict the future remote desktop control performance. It is impossible to accurately predict the possible performance change trend, which makes it difficult to provide a scientific basis for adjusting resource allocation and optimization strategies in advance, increases latency, and reduces user experience.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related art, the present invention proposes a remote desktop control system and method based on cloud computing to overcome the above technical problems existing in the existing related art.

[0006] To this end, the specific technical solution adopted by the present invention is as follows: According to one aspect of the present invention, a remote desktop control system based on cloud computing is provided, the remote desktop control system comprising: The cloud computing platform module is used to obtain the real-time interaction data of the remote desktop control based on the cloud computing platform, and process the real-time interaction data to obtain the interaction feature data of the remote desktop control; A data analysis module, used to analyze the obtained interactive feature data of remote desktop control by using a factor analysis algorithm, and identify key factors affecting the performance of remote desktop control; The performance optimization module is used to establish a performance optimization model based on key factors, and use the performance optimization model to predict the remote desktop control performance at future moments to obtain the change trend of the remote desktop control performance; The remote control strategy module is used to dynamically adjust the control strategy of the remote desktop and optimize the transmission protocol according to the changing trend and the real-time interaction status of the user.

[0007] Furthermore, the cloud computing platform module includes: The data acquisition module is used to establish a secure connection between the client and the remote host by using a remote communication protocol, and to collect real-time interactive data of the remote desktop control in real time; The data processing module is used to extract features from the collected real-time interactive data and generate interactive feature data for remote desktop control.

[0008] Furthermore, the obtained interactive feature data of remote desktop control is analyzed using a factor analysis algorithm, and the key factors affecting the performance of remote desktop control are identified as follows: Randomly generate an initial interactive feature data set, calculate the performance impact objective function value of each interactive feature, sort the interactive features and calculate the distribution density to form an optimal interactive feature data set; Using the interactive feature screening algorithm, the factor features that have the greatest impact on performance are selected from the preferred feature set, and the factor features are copied and expanded to form a new interactive feature data set; Reorganize and adjust the new interactive feature data set, remove the factor features that do not meet the performance constraints, and use the initialization function to regenerate the interactive feature data that meets the constraints; The primary interaction feature dataset is merged with the secondary interaction feature dataset to form a new optimal interaction feature dataset, and duplicate features are removed to finally generate a non-duplicate optimal interaction feature dataset; It is determined whether the number of iterations reaches a preset threshold. If so, the iteration is stopped and the key factors affecting the remote desktop control performance are output. Otherwise, the iteration is continued.

[0009] Furthermore, the interactive feature screening algorithm is used to select the factor features that have the greatest impact on performance from the preferred feature set, and the factor features are copied and expanded to form a new interactive feature data set including: Collect the optimal interactive feature data of remote desktop control, use the correlation analysis method to evaluate the correlation between the features, and generate a subset of interactive feature data classified by performance impact; Randomly select several sample training sets from the interaction feature data subset, calculate the gain value of each interaction feature on the performance, build a performance analysis model and screen key interaction features; Eliminate incomplete interactive feature data, verify the data with the highest integrity, and divide it into several feature data subsets as training sets; The performance analysis model is used to train the key feature data subsets, calculate their impact probabilities under different performance states, generate performance feature templates, screen out the subsets with the best performance, and form a new interactive feature data set.

[0010] Furthermore, the performance optimization module includes: A model building module for building a performance optimization model based on the identified key factors affecting the remote desktop control performance; The performance prediction module is used to predict the remote desktop control performance at future moments by using the established performance optimization model to obtain the change trend of the remote desktop control performance.

[0011] Furthermore, based on the identified key factors affecting the remote desktop control performance, a performance optimization model is constructed including: Obtain a set of key factors that affect remote desktop control performance, and divide the key factor set into a training set and a test set; Taking any key factor as the main reference feature, combined with other key factors, they are input into the integrated learning model to build multiple performance optimization sub-models; The performance of all performance optimization sub-models is evaluated on the training set, and the best-performing performance optimization sub-model is selected for fusion to form the final performance optimization model.

[0012] Furthermore, the established performance optimization model is used to predict the remote desktop control performance at future times, and the change trends of the remote desktop control performance include: Input the key factors that affect remote desktop control performance into the performance optimization model as the basic data for future performance prediction; The performance optimization model is used to calculate the performance indicators at future times and generate preliminary prediction results of remote desktop control performance; Based on the prediction results, the future change trend of the remote desktop control performance is analyzed, the performance fluctuation area is identified, and based on the performance fluctuation area, the change trend of the remote desktop control performance is obtained.

[0013] According to another aspect of the present invention, a remote desktop control method based on cloud computing is also provided, and the remote desktop control method comprises the following steps: S1. Based on the cloud computing platform, real-time interaction data of remote desktop control is obtained, and the real-time interaction data is processed to obtain interaction feature data of remote desktop control; S2. Analyze the obtained interactive feature data of remote desktop control using a factor analysis algorithm to identify key factors affecting remote desktop control performance; S3. Based on the key factors, a performance optimization model is established, and the remote desktop control performance at future moments is predicted using the performance optimization model to obtain the change trend of the remote desktop control performance; S4. According to the changing trend and in combination with the real-time interaction status of the user, the control strategy of the remote desktop is dynamically adjusted to optimize the transmission protocol.

[0014] The beneficial effects of the present invention are: 1. The present invention can extract key factors affecting remote desktop performance from interactive feature data, improve the ability to accurately identify performance bottlenecks, help optimize resource allocation and performance optimization strategies, improve the efficiency of overall remote desktop control, and can predict future remote desktop control performance and accurately predict possible performance change trends, thereby providing a scientific basis for adjusting resource allocation and optimization strategies in advance, helping to reduce latency and improve user experience. At the same time, according to performance change trends and real-time user interaction status, the system can dynamically adjust the remote desktop control strategy to achieve intelligent optimization of the transmission protocol. By optimizing the transmission protocol, data transmission delays can be further reduced, network bandwidth utilization can be improved, and the fluency and reliability of remote desktop control can be enhanced.

[0015] 2. The present invention uses a factor analysis algorithm to accurately identify key factors that affect remote desktop performance, optimizes feature screening and data reorganization processes, uses optimal feature data sets, eliminates redundant features and dynamically adjusts data structures, improves the validity of feature data and the prediction accuracy of the model, thereby improving the performance stability, resource utilization and cross-platform compatibility of remote desktop control, and providing users with a more efficient and smooth remote operation experience.

[0016] 3. The present invention uses a performance optimization model to accurately predict future remote desktop control performance. Through key factor input and model calculation, it generates performance change trends and identifies fluctuation areas. It can predict performance problems in advance and provide a scientific basis for optimizing resource allocation and dynamic policy adjustment, thereby meeting the needs of efficient remote operation in complex network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a principle block diagram of a remote desktop control system based on cloud computing according to an embodiment of the present invention; Figure 2The present invention is a flowchart of a remote desktop control method based on cloud computing according to an embodiment of the present invention.

[0019] In the figure: 1. Cloud computing platform module; 2. Data analysis module; 3. Performance optimization module; 4. Remote control strategy module. DETAILED DESCRIPTION

[0020] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0021] According to an embodiment of the present invention, a remote desktop control system and method based on cloud computing are provided.

[0022] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to a remote desktop control system based on cloud computing according to an embodiment of the present invention, the remote desktop control system includes: The cloud computing platform module 1 is used to obtain the real-time interaction data of the remote desktop control based on the cloud computing platform, and process the real-time interaction data to obtain the interaction feature data of the remote desktop control; It should be explained that lightweight data collection modules (Agents) are deployed on user terminals and remote desktop devices to capture the interaction behavior and device status between users and remote desktops in real time. These modules will upload the collected data to the cloud platform; the data is sent to the cloud computing platform through a secure and efficient transmission protocol (such as WebSocket, HTTP / 2 or gRPC). WebSocket is particularly suitable for low-latency data stream transmission, ensuring real-time upload of interactive data.

[0023] Specifically, real-time interaction data includes: 1) User operation data: records of user operations such as mouse movement, clicks, dragging, and keyboard input on the remote desktop.

[0024] 2) Screen update data: changes in the remote desktop screen, such as window movement, image refresh, video playback, etc.

[0025] 3) Network transmission data: real-time transmission performance indicators such as data packet delay, packet loss rate, bandwidth usage, network jitter, etc.

[0026] 4) System performance data: CPU, memory, GPU usage, load, temperature and other hardware performance information of the remote desktop device.

[0027] 5) Connection status data: real-time status of session connection, including connection time, disconnection times, recovery time, etc.

[0028] 7) User environment data: basic environment data such as the user terminal’s operating system, browser information, and device model.

[0029] Specifically, the interactive feature data includes: 1) Operation response time characteristics: the delay from user operation to remote desktop response.

[0030] 2) Screen refresh characteristics: screen update rate, frame rate, resolution and other characteristics related to visual smoothness.

[0031] 3) Network performance characteristics: bandwidth utilization, latency fluctuation, packet loss rate and other characteristics that affect the smoothness of interaction.

[0032] 4) Resource usage characteristics: CPU, memory, and GPU resource usage of the remote desktop and user terminal.

[0033] 5) Interaction stability characteristics: stability of packet transmission, session duration, error recovery rate, etc.

[0034] 6) User behavior characteristics: frequency of user operations, repetitive behaviors, and usage ratio of different functions.

[0035] Data analysis module 2, used to analyze the obtained interactive feature data of remote desktop control by using factor analysis algorithm, and identify key factors affecting remote desktop control performance; Specifically, the key factors include: 1) Network performance factors Bandwidth: The amount of available network bandwidth directly affects the data transfer rate.

[0036] Latency: The time interval from user operation to remote desktop response is an important factor affecting the smoothness of interaction.

[0037] Packet loss rate: The ratio of data packets lost during transmission, which may cause image freeze or operation failure.

[0038] Jitter: The degree of fluctuation in network delay, which affects the stability of real-time data.

[0039] 2) Remote desktop system performance factors Server CPU / memory usage: Whether the system resources are sufficient determines the response speed.

[0040] GPU processing power: For graphics-intensive operations such as video playback or 3D rendering, GPU performance is crucial.

[0041] Disk I / O performance: The speed of the storage during file transfer or read / write operations can affect task completion time.

[0042] 3) User terminal performance factors Terminal device performance: The CPU, memory, and GPU capabilities of the user device will affect the decoding speed and data rendering performance.

[0043] Screen resolution: Data transfer and rendering requirements are higher at higher resolutions.

[0044] Operating system and browser: The compatibility of different systems and browsers may affect performance.

[0045] 4) Interactive Behavior Factors Operation frequency: The frequency of user operations (such as the intensity of mouse clicks and keyboard input) affects the system load.

[0046] Interaction mode: Whether it is high-frequency input (such as typing) or complex operation (such as dragging and dropping files) has different impacts on performance.

[0047] 5) Image and video quality factors Compression algorithm: The compression rate of image and video data is inversely proportional to transmission efficiency and visual quality.

[0048] Refresh rate: The screen refresh rate has a significant impact on the smoothness of video playback and dynamic operations.

[0049] Color depth: High color depth will increase the data volume and affect the transmission speed.

[0050] 6) Security protocol factors Encryption algorithm: The higher the encryption strength, the better the data security, but it may increase calculation and transmission delays.

[0051] Authentication method: Complex authentication may increase the interaction initialization time.

[0052] The performance optimization module 3 is used to establish a performance optimization model based on key factors, and use the performance optimization model to predict the remote desktop control performance at future moments, and obtain the change trend of the remote desktop control performance; The remote control strategy module 4 is used to dynamically adjust the control strategy of the remote desktop and optimize the transmission protocol according to the change trend and the real-time interaction status of the user.

[0053] It needs to be explained that the performance trend of the remote desktop is analyzed in real time, and key performance fluctuation areas (such as increased latency, insufficient bandwidth, etc.) are captured as the basis for optimization; user operation frequency, input type (mouse, keyboard, etc.) and task complexity (file transfer, video playback, etc.) are collected to determine the interactive load characteristics; according to performance changes and user interaction needs, the image compression algorithm, resolution and color depth are dynamically adjusted to reduce the amount of data to improve transmission efficiency; the transmission protocol parameters are dynamically switched, such as adjusting the buffer size of WebSocket, data blocking strategy, and accelerated packet retransmission mechanism to reduce the impact of latency and packet loss; the allocation of CPU, GPU and memory resources is optimized in the cloud to ensure that high-load tasks (such as video rendering) receive priority support and improve overall performance; according to the real-time network bandwidth and latency fluctuations, the data transmission rate is adaptively adjusted to ensure the continuity and smoothness of the user experience; the effectiveness of the optimized strategy is regularly evaluated, and the control strategy is further adjusted through feedback loops to ensure that the transmission protocol always adapts to the user and network environment.

[0054] In this optional embodiment, the cloud computing platform module 1 includes: The data acquisition module is used to establish a secure connection between the client and the remote host by using a remote communication protocol, and to collect real-time interactive data of the remote desktop control in real time; The data processing module is used to extract features from the collected real-time interactive data and generate interactive feature data for remote desktop control.

[0055] In this optional embodiment, feature extraction is performed on the collected real-time interaction data to generate interaction feature data of remote desktop control, including: Obtain duplicate data from the real-time interaction data of the remote desktop, perform denoising, filtering and smoothing, connect the unprocessed interaction data rows, and generate a complete interaction data table; Based on the accurate interaction data set, the interaction feature trend is constructed, and the change feature index of the remote desktop interaction state is extracted by calculating the difference in the change amplitude of the feature trend at adjacent moments; A change amplitude threshold is preset. If the change amplitude difference exceeds the threshold, a new key interaction moment set is added; otherwise, the existing key interaction moment set is retained; Check the distribution of key interaction moments. If the distribution is too concentrated, integrate adjacent key interaction moments; otherwise, retain the current key moment set as the key feature data of remote desktop interaction.

[0056] In this optional embodiment, the obtained interactive feature data of remote desktop control is analyzed using a factor analysis algorithm to identify key factors affecting the performance of remote desktop control, including: Randomly generate an initial interactive feature data set, calculate the performance impact objective function value of each interactive feature, sort the interactive features and calculate the distribution density to form an optimal interactive feature data set; Using the interactive feature screening algorithm, the factor features that have the greatest impact on performance are selected from the preferred feature set, and the factor features are copied and expanded to form a new interactive feature data set; Reorganize and adjust the new interactive feature data set, remove the factor features that do not meet the performance constraints, and use the initialization function to regenerate the interactive feature data that meets the constraints; The primary interaction feature dataset is merged with the secondary interaction feature dataset to form a new optimal interaction feature dataset, and duplicate features are removed to finally generate a non-duplicate optimal interaction feature dataset; It is determined whether the number of iterations reaches a preset threshold. If so, the iteration is stopped and the key factors affecting the remote desktop control performance are output. Otherwise, the iteration is continued.

[0057] Specifically, the factor analysis algorithm is an improved NSGA-II algorithm. The NSGA-II algorithm is a classic multi-objective optimization algorithm. When used to deal with multi-objective optimization problems, it can effectively find a solution set that meets the multi-objective requirements. The present invention improves the basic NSGA-II algorithm, designs an effective integer encoding method, and introduces a deduplication operation.

[0058] In this optional embodiment, the interactive feature screening algorithm is used to select the factor features that have the greatest impact on performance from the preferred feature set, and the factor features are copied and expanded to form a new interactive feature data set including: Collect the optimal interactive feature data of remote desktop control, use the correlation analysis method to evaluate the correlation between the features, and generate a subset of interactive feature data classified by performance impact; Randomly select several sample training sets from the interaction feature data subset, calculate the gain value of each interaction feature on the performance, build a performance analysis model (i.e., decision tree classifier) ​​and screen key interaction features; Eliminate incomplete interactive feature data, verify the data with the highest integrity, and divide it into several feature data subsets as training sets; The performance analysis model is used to train the key feature data subsets, calculate their impact probabilities under different performance states, generate performance feature templates, screen out the subsets with the best performance, and form a new interactive feature data set.

[0059] Specifically, the interactive feature screening algorithm is an association rule algorithm, which is an algorithm used to mine the association relationships between items in a data set. Through the association rule algorithm, key association relationships can be discovered from massive interactive feature data, and the feature combinations that have the greatest impact on performance can be identified, providing strong support for building and optimizing performance analysis models.

[0060] In this optional embodiment, the formula for calculating the gain value of each interactive feature's impact on performance is: ; Where Z(T, A) represents the gain value of the interactive feature A to the performance target T; P(ti) represents the probability of the i-th global state appearing in the performance target T; It represents the probability of the jth conditional state in the performance target T appearing when the value of the interactive feature A is v; P(v) represents the probability that the value of feature A is v; n represents the total number of global states; m represents the total number of conditional states.

[0061] In this optional embodiment, the performance optimization module 3 includes: A model building module for building a performance optimization model based on the identified key factors affecting the remote desktop control performance; The performance prediction module is used to predict the remote desktop control performance at future moments by using the established performance optimization model to obtain the change trend of the remote desktop control performance.

[0062] In this optional embodiment, based on the identified key factors affecting the remote desktop control performance, constructing a performance optimization model includes: Obtain a set of key factors that affect remote desktop control performance, and divide the key factor set into a training set and a test set; Taking any key factor as the main reference feature, combined with other key factors, they are input into the integrated learning model to build multiple performance optimization sub-models; The performance of all performance optimization sub-models is evaluated on the training set, and the best-performing performance optimization sub-model is selected for fusion to form the final performance optimization model.

[0063] In this optional embodiment, the established performance optimization model is used to predict the remote desktop control performance at a future time, and the change trend of the remote desktop control performance includes: Input the key factors that affect remote desktop control performance into the performance optimization model as the basic data for future performance prediction; The performance optimization model is used to calculate the performance indicators at future times and generate preliminary prediction results of remote desktop control performance; Based on the prediction results, the future change trend of the remote desktop control performance is analyzed, the performance fluctuation area is identified, and based on the performance fluctuation area, the change trend of the remote desktop control performance is obtained.

[0064] It should be explained that, first, the key factors affecting the performance of remote desktop control are obtained, which may include network bandwidth, delay, packet loss rate, user operation frequency, screen refresh rate, terminal device performance, etc. These key factors are used as input parameters and input into the performance optimization model to form the basic data for predictive analysis; based on the input key factors, the performance optimization model calculates the performance indicators at future moments through established association rules and prediction algorithms (such as regression analysis, machine learning models, etc.) to generate preliminary prediction results. These performance indicators may include core performance parameters such as response time, data transmission rate, screen refresh rate, and resource utilization; according to the prediction results generated by the performance optimization model, the change trend of remote desktop control performance is analyzed, and the performance indicators at different time points are combined to identify the performance fluctuation areas that may occur in the future. These fluctuation areas may correspond to network congestion, high-frequency user operations, or insufficient system resources; on the basis of identifying the performance fluctuation areas, a more in-depth analysis of the change trend is conducted to further clarify the scope and possible duration of the fluctuation. For example, when network bandwidth fluctuates, its specific impact on screen refresh rate and response time, as well as possible recovery time, is predicted; performance change trends are output in the form of data reports or visualizations to help operation and maintenance personnel or system automatic optimization modules understand performance changes in real time. At the same time, based on the prediction results, it is recommended to adjust the control strategy, such as optimizing image compression rate, reducing refresh rate, or adjusting transmission protocol parameters, to reduce the impact of fluctuations on user experience.

[0065] According to another embodiment of the present invention, Figure 2 As shown, a remote desktop control method based on cloud computing is also provided, and the remote desktop control method includes the following steps: S1. Based on the cloud computing platform, real-time interaction data of remote desktop control is obtained, and the real-time interaction data is processed to obtain interaction feature data of remote desktop control; S2. Analyze the obtained interactive feature data of remote desktop control using a factor analysis algorithm to identify key factors affecting remote desktop control performance; S3. Based on the key factors, a performance optimization model is established, and the remote desktop control performance at future moments is predicted using the performance optimization model to obtain the change trend of the remote desktop control performance; S4. According to the changing trend and in combination with the real-time interaction status of the user, the control strategy of the remote desktop is dynamically adjusted to optimize the transmission protocol.

[0066] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A remote desktop control system based on cloud computing, characterized in that: The remote desktop control system includes: The cloud computing platform module is used to obtain the real-time interaction data of the remote desktop control based on the cloud computing platform, and process the real-time interaction data to obtain the interaction feature data of the remote desktop control; A data analysis module, used to analyze the obtained interactive feature data of remote desktop control by using a factor analysis algorithm, and identify key factors affecting the performance of remote desktop control; The performance optimization module is used to establish a performance optimization model based on key factors, and use the performance optimization model to predict the remote desktop control performance at future moments to obtain the change trend of the remote desktop control performance; The remote control strategy module is used to dynamically adjust the control strategy of the remote desktop and optimize the transmission protocol according to the changing trend and the real-time interaction status of the user.

2. A remote desktop control system based on cloud computing according to claim 1, characterized in that: The cloud computing platform module includes: The data acquisition module is used to establish a secure connection between the client and the remote host by using a remote communication protocol, and to collect real-time interactive data of the remote desktop control in real time; The data processing module is used to extract features from the collected real-time interactive data and generate interactive feature data for remote desktop control.

3. A remote desktop control system based on cloud computing according to claim 2, characterized in that: The step of extracting features from the collected real-time interactive data to generate interactive feature data of remote desktop control includes: Obtain duplicate data from the real-time interaction data of the remote desktop, perform denoising, filtering and smoothing, connect the unprocessed interaction data rows, and generate a complete interaction data table; Based on the accurate interaction data set, the interaction feature trend is constructed, and the change feature index of the remote desktop interaction state is extracted by calculating the difference in the change amplitude of the feature trend at adjacent moments; A change amplitude threshold is preset. If the change amplitude difference exceeds the threshold, a new key interaction moment set is added; otherwise, the existing key interaction moment set is retained; Check the distribution of key interaction moments. If the distribution is too concentrated, integrate adjacent key interaction moments; otherwise, retain the current key moment set as the key feature data of remote desktop interaction.

4. The remote desktop control system based on cloud computing according to claim 1, characterized in that: The factor analysis algorithm is used to analyze the obtained interactive feature data of the remote desktop control, and the key factors affecting the remote desktop control performance are identified, including: Randomly generate an initial interactive feature data set, calculate the performance impact objective function value of each interactive feature, sort the interactive features and calculate the distribution density to form an optimal interactive feature data set; Using the interactive feature screening algorithm, the factor features that have the greatest impact on performance are selected from the preferred feature set, and the factor features are copied and expanded to form a new interactive feature data set; Reorganize and adjust the new interactive feature data set, remove the factor features that do not meet the performance constraints, and use the initialization function to regenerate the interactive feature data that meets the constraints; The primary interaction feature dataset is merged with the secondary interaction feature dataset to form a new optimal interaction feature dataset, and duplicate features are removed to finally generate a non-duplicate optimal interaction feature dataset; It is determined whether the number of iterations reaches a preset threshold. If so, the iteration is stopped and the key factors affecting the remote desktop control performance are output. Otherwise, the iteration is continued.

5. A remote desktop control system based on cloud computing according to claim 4, characterized in that: The interactive feature screening algorithm is used to select the factor features that have the greatest impact on performance from the preferred feature set, and the factor features are copied and expanded to form a new interactive feature data set, including: Collect the optimal interactive feature data of remote desktop control, use the correlation analysis method to evaluate the correlation between the features, and generate a subset of interactive feature data classified by performance impact; Randomly select several sample training sets from the interaction feature data subset, calculate the gain value of each interaction feature on the performance, build a performance analysis model and screen key interaction features; Eliminate incomplete interactive feature data, verify the data with the highest integrity, and divide it into several feature data subsets as training sets; The performance analysis model is used to train the key feature data subsets, calculate their impact probabilities under different performance states, generate performance feature templates, screen out the subsets with the best performance, and form a new interactive feature data set.

6. A remote desktop control system based on cloud computing according to claim 5, characterized in that: The formula for calculating the gain value of each interactive feature's impact on performance is: ； Where Z(T, A) represents the gain value of the interactive feature A to the performance target T; P(ti) represents the probability of the i-th global state appearing in the performance target T; It represents the probability of the jth conditional state in the performance target T appearing when the value of the interactive feature A is v; P(v) represents the probability that the value of feature A is v; n represents the total number of global states; m represents the total number of conditional states.

7. The remote desktop control system based on cloud computing according to claim 1, characterized in that: The performance optimization module includes: A model building module for building a performance optimization model based on the identified key factors affecting the remote desktop control performance; The performance prediction module is used to predict the remote desktop control performance at future moments by using the established performance optimization model to obtain the change trend of the remote desktop control performance.

8. The remote desktop control system based on cloud computing according to claim 7, characterized in that: The construction of a performance optimization model based on the identified key factors affecting the remote desktop control performance includes: Obtain a set of key factors that affect remote desktop control performance, and divide the key factor set into a training set and a test set; Taking any key factor as the main reference feature, combined with other key factors, they are input into the integrated learning model to build multiple performance optimization sub-models; The performance of all performance optimization sub-models is evaluated on the training set, and the best-performing performance optimization sub-model is selected for fusion to form the final performance optimization model.

9. The remote desktop control system based on cloud computing according to claim 8, characterized in that: The remote desktop control performance is predicted by using the established performance optimization model at a future time, and the change trend of the remote desktop control performance is obtained, including: Input the key factors that affect remote desktop control performance into the performance optimization model as the basic data for future performance prediction; The performance optimization model is used to calculate the performance indicators at future times and generate preliminary prediction results of remote desktop control performance; Based on the prediction results, the future change trend of the remote desktop control performance is analyzed, the performance fluctuation area is identified, and based on the performance fluctuation area, the change trend of the remote desktop control performance is obtained.

10. A remote desktop control method based on cloud computing, used to implement the remote desktop control system based on cloud computing according to any one of claims 1 to 9, characterized in that: The remote desktop control method comprises the following steps: S1. Based on the cloud computing platform, real-time interaction data of remote desktop control is obtained, and the real-time interaction data is processed to obtain interaction feature data of remote desktop control; S2. Analyze the obtained interactive feature data of remote desktop control using a factor analysis algorithm to identify key factors affecting remote desktop control performance; S3. Based on the key factors, a performance optimization model is established, and the remote desktop control performance at future moments is predicted using the performance optimization model to obtain the change trend of the remote desktop control performance; S4. According to the changing trend and in combination with the real-time interaction status of the user, the control strategy of the remote desktop is dynamically adjusted to optimize the transmission protocol.

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