Integrated application method and system for realizing cloud desktop office based on network set-top box

Abstract

The present invention relates to the field of cloud computing, and discloses an integrated application method and system for realizing cloud desktop office based on a network set-top box, including: determining the network set-top box of the cloud desktop, analyzing the compatibility coefficient of the network set-top box, and configuring the KVM virtualization software of the network set-top box; constructing a data compression module of the network set-top box, constructing an intelligent flow control module of the virtualization layer, and constructing a distributed architecture cloud desktop of the cloud desktop; configuring a fault transfer mechanism of the distributed architecture cloud desktop, calculating the fault recovery coefficient of the fault transfer mechanism, and when the fault recovery coefficient meets a preset fault recovery threshold, analyzing the operation preference of the distributed architecture cloud desktop corresponding to the target user, using a user preference analysis model to analyze the preferred desktop usage mode of the target user, and executing the integrated application of the distributed architecture cloud desktop based on the preferred desktop usage mode, the data compression module, and the intelligent flow control module. The present invention can improve the collaborative efficiency of the user's cloud desktop office.

Description

Technical Field

[0001] The present invention relates to an integrated application method and system for realizing cloud desktop office based on a network set-top box, and belongs to the field of cloud computing. Background Art

[0002] The integrated application of cloud desktop office refers to the application of cloud computing technology in the desktop computing environment, which integrates various office resources and functions to provide users with a centralized, efficient and convenient office solution. The integrated application of cloud desktop office aims to achieve standardization, automation and intelligence of the office environment through cloud computing technology, thereby improving work efficiency, reducing operating costs and ensuring business continuity.

[0003] Traditional cloud desktop office integrated applications usually rely on installing and maintaining a complete operating system and applications on each client device. This approach cannot centrally manage and share resources, and cannot provide an efficient, secure, and more flexible office environment, resulting in reduced user mobility and collaboration efficiency. Summary of the invention

[0004] The present invention provides an integrated application method and system for realizing cloud desktop office based on a network set-top box, the main purpose of which is to improve the accuracy of security authentication while reducing the workload of path detection.

[0005] To achieve the above object, the present invention provides an integrated application method for realizing cloud desktop office based on a network set-top box, comprising: Obtaining office requirements of the cloud desktop, determining a network set-top box of the cloud desktop according to the office requirements, analyzing a compatibility coefficient of the network set-top box, and configuring KVM virtualization software of the network set-top box when the compatibility coefficient meets a preset compatibility threshold; Based on the KVM virtualization software, a data compression module of the network set-top box is constructed, and a data transmission influencing factor of the virtualization layer corresponding to the KVM virtualization software is marked; Based on the data transmission influencing factor, construct the intelligent flow control module of the virtualization layer, and based on the data compression module and the intelligent flow control module, construct the distributed architecture cloud desktop of the cloud desktop; Configuring a failover mechanism of the distributed architecture cloud desktop, calculating a failure recovery coefficient of the failover mechanism, and when the failure recovery coefficient meets a preset failure recovery threshold, collecting user operation data of the distributed architecture cloud desktop; Based on the user operation data, the operation preferences of the target user corresponding to the distributed architecture cloud desktop are analyzed; according to the operation preferences, the preferred desktop usage pattern of the target user is analyzed using a trained user preference analysis model; based on the preferred desktop usage pattern, the data compression module and the intelligent flow control module, the integrated application of the distributed architecture cloud desktop is executed.

[0006] Optionally, analyzing the compatibility coefficient of the network set-top box includes: Defining compatibility analysis indicators of the network set-top box; Collecting set-top box data of the network set-top box, wherein the set-top box data includes set-top box hardware data and set-top box software data; Analyzing the compatibility analysis indicator score of the compatibility analysis indicator based on the set-top box data; Determining the dynamic weight of the compatibility analysis indicator; Based on the indicator dynamic weight and the compatibility analysis indicator score, the compatibility coefficient of the network set-top box is determined.

[0007] Optionally, constructing the data compression module of the network set-top box based on the KVM virtualization software includes: defining data compression requirements of the network set-top box; Determining the data type of the network set-top box; Define the type data compression algorithm of the data type; Based on the type of data compression algorithm, construct a data compression module architecture of the network set-top box, wherein the data compression module architecture includes an input interface, a compression engine, an output interface, and configuration management; According to the data compression module architecture, a data compression module of the network set-top box is constructed.

[0008] Optionally, the type data compression algorithm for defining the data type includes: Analyze the offset, length and characters of the text type corresponding to the data type; Defining a text compression algorithm for the text type according to the offset, length, and characters; Mark the pixel brightness value and frequency variables of the image data corresponding to the data type; Based on the pixel brightness value and frequency variable, an image compression algorithm for the image data is constructed using the following formula; Determining that the data type corresponds to intra-frame analysis, inter-frame analysis, and transform coding of video data; Constructing a video compression algorithm for video data corresponding to the data type; Based on the text compression algorithm, the image compression algorithm and the video compression algorithm, a type data compression algorithm of the data type is determined.

[0009] Optionally, the constructing the intelligent flow control module of the virtualization layer based on the data transmission influencing factor includes: Determining the intelligent flow control target of the virtualization layer; Establishing a data model of the virtualization layer; Based on the data transmission influencing factor and the intelligent flow control target, construct an intelligent flow control algorithm for the virtualization layer; Building a decision engine for the virtualization layer; Based on the data model, the intelligent flow control algorithm and the decision engine, an intelligent flow control module of the virtualization layer is constructed.

[0010] Optionally, constructing the intelligent flow control algorithm of the virtualization layer based on the data transmission influencing factor and the intelligent flow control target includes: Collecting the traffic of the virtualization layer; Based on the data transmission influencing factor and the intelligent flow control target, determine the bandwidth adjustment factor of the traffic, wherein the bandwidth adjustment factor includes bandwidth difference, delay difference, packet loss rate difference and network congestion level; Based on the bandwidth difference, delay difference, packet loss rate difference and network congestion level, the following formula is used to construct an intelligent flow control algorithm for the traffic.

[0011] Optionally, configuring the failover mechanism of the distributed architecture cloud desktop includes: Analyze the cloud desktop application environment of the distributed architecture cloud desktop; Based on the cloud desktop application environment, determining the failover requirements of the distributed architecture cloud desktop, wherein the failover requirements include a recovery time objective and a data recovery point objective; Based on the failover requirements, construct a failover architecture for the distributed architecture cloud desktop; Through the failover architecture, a failover mechanism for the distributed architecture cloud desktop is established.

[0012] Optionally, the calculating a failure recovery coefficient of the failover mechanism includes: Obtaining the mean recovery time and failure frequency of the failover mechanism; A failure recovery coefficient of the failover mechanism is calculated based on the failure mean recovery time and the failure frequency.

[0013] Optionally, analyzing the operation preference of the target user corresponding to the distributed architecture cloud desktop based on the user operation data includes: Preprocessing the user operation data to obtain preprocessed user operation data; Analyzing the access application of the target user based on the pre-processed user operation data; Recording the access frequency of the access application; marking the task complexity of accessing the application; Based on the access frequency and the task complexity, the operation preference of the target user is analyzed.

[0014] In order to solve the above problems, the present invention also provides an integrated application system for realizing cloud desktop office based on a network set-top box, the system comprising: A virtualization software configuration module, used to obtain the office requirements of the cloud desktop, determine the network set-top box of the cloud desktop according to the office requirements, analyze the compatibility coefficient of the network set-top box, and configure the KVM virtualization software of the network set-top box when the compatibility coefficient meets a preset compatibility threshold; An impact factor analysis module, used to construct a data compression module of the network set-top box based on the KVM virtualization software, and mark the data transmission impact factor of the virtualization layer corresponding to the KVM virtualization software; A distributed cloud desktop construction module, used to construct an intelligent flow control module of the virtualization layer based on the data transmission influencing factor, and to construct a distributed architecture cloud desktop of the cloud desktop based on the data compression module and the intelligent flow control module; A failover mechanism construction module is used to configure the failover mechanism of the distributed architecture cloud desktop, calculate the failure recovery coefficient of the failover mechanism, and when the failure recovery coefficient meets a preset failure recovery threshold, collect user operation data of the distributed architecture cloud desktop; The user preference analysis module is used to analyze the operation preferences of the target user corresponding to the distributed architecture cloud desktop based on the user operation data, and analyze the preferred desktop usage pattern of the target user using the trained user preference analysis model according to the operation preferences, and execute the integrated application of the distributed architecture cloud desktop based on the preferred desktop usage pattern, the data compression module and the intelligent flow control module.

[0015] Compared with the problems described in the background technology, firstly, by accurately obtaining office needs and analyzing the compatibility coefficient of the network set-top box, the accuracy of hardware selection is ensured, so that the configured KVM virtualization software can run stably, and the overall performance and reliability of the system are improved. Secondly, the construction of the data compression module effectively reduces the amount of data transmission, reduces the network bandwidth demand, and improves the data transmission efficiency, especially in an environment with limited network conditions. This effect is particularly obvious. Furthermore, the introduction of the intelligent flow control module performs dynamic traffic management based on data transmission influencing factors, further optimizes the allocation of network resources, ensures the priority transmission of key business data, and significantly improves the user experience. In addition, the configuration of the fault transfer mechanism and the calculation of the fault recovery coefficient ensure that the service can be quickly restored when a fault occurs, reaching the preset fault recovery threshold, greatly reducing the downtime caused by system failures. By collecting user operation data and analyzing operation preferences, the system can automatically adjust to the target user's preferred desktop usage mode, improving the user's office efficiency and satisfaction. Finally, the execution of the integrated application closely combines user preferences, data compression and intelligent flow control algorithms to form an efficient, personalized and stable cloud desktop office environment, which not only improves office efficiency, but also reduces the enterprise's IT operation and maintenance costs. Therefore, the present invention improves the collaboration efficiency of users' cloud desktop office. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of an integrated application method for implementing cloud desktop office based on a network set-top box provided in one embodiment of the present invention; Figure 2 A schematic diagram of a module for implementing an integrated application method for cloud desktop office based on a network set-top box provided in an embodiment of the present invention.

[0017] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0018] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0019] The embodiment of the present application provides an integrated application method for cloud desktop office based on a network set-top box. The execution subject of the integrated application method for cloud desktop office based on a network set-top box includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the integrated application method for cloud desktop office based on a network set-top box can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0020] Embodiment 1: Reference Figure 1 FIG. 1 is a flow chart of an integrated application method for implementing cloud desktop office based on a network set-top box according to an embodiment of the present invention. In this embodiment, the integrated application method for implementing cloud desktop office based on a network set-top box includes: S1. Obtain office requirements of a cloud desktop, determine a network set-top box of the cloud desktop according to the office requirements, analyze a compatibility coefficient of the network set-top box, and configure a KVM virtualization software of the network set-top box when the compatibility coefficient meets a preset compatibility threshold.

[0021] The present invention obtains the office demand of the cloud desktop, and according to the office demand, determines that the network set-top box of the cloud desktop can select the cloud desktop service suitable for the enterprise demand, and matches the corresponding network set-top box to realize efficient and safe remote office. The office demand refers to a series of specific requirements of a specific enterprise or individual for computing resources, software applications, data storage and transmission, etc. in a remote working environment, and the network set-top box refers to a hardware device that is connected to a display and an input device (such as a keyboard and a mouse) to allow a user to remotely access the cloud desktop service.

[0022] The present invention analyzes the compatibility coefficient of the network set-top box and can comprehensively analyze the compatibility coefficient of the network set-top box to ensure that it can meet specific office needs.

[0023] In detail, the analyzing the compatibility coefficient of the network set-top box includes: Defining compatibility analysis indicators of the network set-top box; Collecting set-top box data of the network set-top box, wherein the set-top box data includes set-top box hardware data and set-top box software data; Analyzing the compatibility analysis indicator score of the compatibility analysis indicator based on the set-top box data; Determining the dynamic weight of the compatibility analysis indicator; Based on the indicator dynamic weight and the compatibility analysis indicator score, the compatibility coefficient of the network set-top box is determined.

[0024] Among them, the compatibility analysis index refers to the key parameters used to evaluate the compatibility of the network set-top box with specific systems, applications and environments, including hardware performance, software compatibility, network performance and other indicators; the set-top box hardware data refers to the physical components and performance parameters of the network set-top box; the set-top box software data refers to the relevant information of the operating system, application and driver running on the network set-top box; the compatibility analysis index score refers to the result of quantitative scoring of each compatibility analysis indicator according to pre-defined standards; the indicator dynamic weight refers to the importance coefficient assigned to each compatibility analysis indicator according to the use environment and user needs of the network set-top box; the compatibility coefficient refers to a comprehensive indicator calculated based on the compatibility analysis index score and the indicator dynamic weight, which is used to quantify the overall compatibility of the network set-top box.

[0025] In the present invention, when the compatibility coefficient meets the preset compatibility threshold, the KVM virtualization software configured with the network set-top box can be used to run a virtual desktop or other virtualization applications. Wherein, the KVM virtualization software refers to an open source virtualization technology based on the Linux kernel. The compatibility threshold refers to the lowest compatibility standard of the network set-top box.

[0026] S2. Based on the KVM virtualization software, construct a data compression module of the network set-top box, and mark the data transmission influencing factor of the virtualization layer corresponding to the KVM virtualization software.

[0027] The present invention constructs the data compression module of the network set-top box based on the KVM virtualization software to ensure that it can correctly process the data stream.

[0028] In detail, the data compression module of the network set-top box is constructed based on the KVM virtualization software, including: defining data compression requirements of the network set-top box; Determining the data type of the network set-top box; Define the type data compression algorithm of the data type; Based on the type of data compression algorithm, construct a data compression module architecture of the network set-top box, wherein the data compression module architecture includes an input interface, a compression engine, an output interface, and configuration management; According to the data compression module architecture, a data compression module of the network set-top box is constructed.

[0029] Among them, the data compression demand refers to the specific requirements of the network set-top box for the compression function when processing data, the data type refers to the type of data that the network set-top box needs to process, the type data compression algorithm refers to the compression algorithm designed for a specific data type, the input interface refers to the part of the data compression module that receives the original data, which defines how to obtain data from the data source, the compression engine refers to the core part of implementing the compression algorithm, which is responsible for performing data compression operations, the output interface refers to the part of the data compression module that outputs compressed data, which defines how to send compressed data to the destination, the configuration management refers to the part that allows users or system administrators to configure the parameters of the data compression module, and the data compression module refers to the software component that implements the above architecture, which integrates the input interface, compression engine, output interface and configuration management to provide complete data compression functions.

[0030] Furthermore, the type data compression algorithm for defining the data type includes: Analyze the offset, length and characters of the text type corresponding to the data type; Defining a text compression algorithm for the text type according to the offset, length, and characters; Mark the pixel brightness value and frequency variables of the image data corresponding to the data type; Based on the pixel brightness value and the frequency variable, an image compression algorithm for the image data is constructed using the following formula, wherein the image compression algorithm: in, Represents image data coefficient, Represents image data Changing frequency variables , Represents image data Changing frequency variables , Represents frequency variable The scaling factor, Represents frequency variable The scaling factor, Represents image data The brightness value of the position, represents the cosine function, represents pi, Represents image data Positional The value of the axis, Represents image data Positional The value of the axis; Determining that the data type corresponds to intra-frame analysis, inter-frame analysis, and transform coding of video data; Constructing a video compression algorithm for video data corresponding to the data type; Based on the text compression algorithm, the image compression algorithm and the video compression algorithm, a type data compression algorithm of the data type is determined.

[0031] Among them, the offset refers to the distance from the current scanning position to the starting position of the repeating sequence, the length refers to the length of the repeating sequence, the character refers to the new character immediately following the repeating sequence, the text compression algorithm refers to the text compression algorithm built based on the offset, length and character, the pixel brightness value refers to the brightness or color value of each pixel in the image, the frequency variable refers to the variable used in the transform coding process, such as u and v, which represent the position of the image data in the frequency domain, the image compression algorithm refers to the algorithm for compressing the image, and the The coefficient refers to the value obtained in the discrete cosine transform process, the scaling factor refers to the value used to adjust the coefficient in the DCT transform and inverse transform process, and the brightness value refers to the brightness component of each pixel in the image.

[0032] It should be explained that the data transmission influencing factors refer to factors that can affect the performance and efficiency of data transmission in a virtualized environment, such as network bandwidth, network delay, data packet loss rate and other factors.

[0033] S3. Based on the data transmission influencing factors, construct an intelligent flow control module of the virtualization layer, and based on the data compression module and the intelligent flow control module, construct a distributed architecture cloud desktop of the cloud desktop.

[0034] The present invention constructs the intelligent flow control module of the virtualization layer based on the data transmission influencing factors, which can significantly improve the overall performance and efficiency of the network and provide users with more stable, fast and reliable network services.

[0035] In detail, the intelligent flow control module of the virtualization layer is constructed based on the data transmission influencing factor, including: Determining the intelligent flow control target of the virtualization layer; Establishing a data model of the virtualization layer; Based on the data transmission influencing factor and the intelligent flow control target, construct an intelligent flow control algorithm for the virtualization layer; Building a decision engine for the virtualization layer; Based on the data model, the intelligent flow control algorithm and the decision engine, an intelligent flow control module of the virtualization layer is constructed.

[0036] Among them, the intelligent flow control target refers to a series of performance and efficiency goals aimed to be achieved through the intelligent flow control module, the data model refers to a structured representation used to describe network status, traffic characteristics and flow control parameters, the intelligent flow control algorithm refers to a set of rules and calculation steps used to dynamically adjust network traffic based on the information in the data model, the decision engine refers to the component in the intelligent flow control module responsible for making flow control decisions based on the algorithm output, and the intelligent flow control module refers to an integrated system component that combines the data model, the intelligent flow control algorithm and the decision engine to achieve intelligent management of network traffic.

[0037] Furthermore, the constructing of the intelligent flow control algorithm of the virtualization layer based on the data transmission influencing factor and the intelligent flow control target includes: Collecting the traffic of the virtualization layer; Based on the data transmission influencing factor and the intelligent flow control target, determine the bandwidth adjustment factor of the traffic, wherein the bandwidth adjustment factor includes bandwidth difference, delay difference, packet loss rate difference and network congestion level; Based on the bandwidth difference, delay difference, packet loss rate difference and network congestion level, the following formula is used to construct an intelligent flow control algorithm for the traffic, wherein the intelligent flow control algorithm is: in, Indicates the traffic bandwidth of the traffic, Indicates the available bandwidth for traffic. Indicates the bandwidth difference of the traffic. Indicates the delay difference of the traffic. Indicates the packet loss rate difference of the traffic. Indicates the network congestion level of the traffic, represents the adjustment factor calculation function, Indicates the priority weight of traffic.

[0038] Among them, the traffic refers to the collection of data packets transmitted in the network, the bandwidth difference refers to the difference between the expected bandwidth demand of the traffic and the current available bandwidth, the delay difference refers to the difference between the acceptable delay of the traffic and the current network delay, the packet loss rate difference refers to the difference between the acceptable packet loss rate of the traffic and the current network packet loss rate, the network congestion level refers to the severity of congestion in the network, the traffic bandwidth refers to the amount of bandwidth allocated to specific traffic, the traffic available bandwidth refers to the maximum bandwidth currently available to traffic in the network, the adjustment factor calculation function is a function for calculating the bandwidth adjustment factor, which is determined based on the bandwidth difference, delay difference, packet loss rate difference and network congestion level, and the priority weight refers to the weight assigned according to the priority of the traffic or quality of service (QoS) requirements.

[0039] The present invention constructs a distributed architecture cloud desktop based on the data compression module and the intelligent flow control module. The cloud desktop can build an efficient, scalable and secure cloud desktop distributed architecture, which can optimize user experience and resource utilization by using data compression and intelligent flow control technology. The distributed architecture cloud desktop refers to a virtualized desktop running on a cluster composed of multiple servers.

[0040] S4. Configure the failover mechanism of the distributed architecture cloud desktop, calculate the failure recovery coefficient of the failover mechanism, and when the failure recovery coefficient meets the preset failure recovery threshold, collect user operation data of the distributed architecture cloud desktop.

[0041] The present invention configures the failover mechanism of the distributed architecture cloud desktop to ensure the safe operation of the cloud desktop.

[0042] In detail, the configuration of the failover mechanism of the distributed architecture cloud desktop includes: Analyze the cloud desktop application environment of the distributed architecture cloud desktop; Based on the cloud desktop application environment, determining the failover requirements of the distributed architecture cloud desktop, wherein the failover requirements include a recovery time objective and a data recovery point objective; Based on the failover requirements, construct a failover architecture for the distributed architecture cloud desktop; Through the failover architecture, a failover mechanism for the distributed architecture cloud desktop is established.

[0043] Among them, the cloud desktop application environment refers to the specific scenarios in which users use cloud desktops for work, study and entertainment, including user types, usage habits, application requirements, network conditions, etc. The recovery time objective refers to the maximum time interval allowed from the occurrence of a failure to the resumption of normal operation of the business. The data recovery point objective refers to the maximum time interval allowed for data loss when a failure occurs. The failover architecture refers to a set of system components, network configurations and processes designed to achieve failover, so as to ensure seamless switching to the backup system when a failure occurs in the main system. The failover mechanism refers to the specific process for implementing failover.

[0044] Optionally, based on the failover requirement, the failover architecture storage redundancy technology and network redundancy technology of the distributed architecture cloud desktop are constructed.

[0045] The present invention calculates the failure recovery coefficient of the failover mechanism to determine the recovery capability of the failover mechanism, thereby improving the operational security of the distributed architecture cloud desktop.

[0046] In detail, the calculating of the failure recovery coefficient of the failover mechanism includes: Obtaining the mean recovery time and failure frequency of the failover mechanism; Based on the mean fault recovery time and the fault frequency, the fault recovery coefficient of the failover mechanism is calculated using the following formula: in, represents the failure recovery factor of the failover mechanism, Indicates the failover mechanism corresponding to the recovery time objective, It represents the mean time to recover from a fault. Indicates the failover mechanism corresponds to the data recovery point objective, Indicates the amount of data corresponding to the failover mechanism, Indicates the fault frequency.

[0047] Among them, the mean fault recovery time refers to the average time required from the occurrence of a fault to the system recovering to a normal operating state, the fault frequency refers to the number of times the system fails within a specific time period, and the fault recovery coefficient refers to the ability of the failover mechanism to restore system services after a fault occurs.

[0048] It should be explained that the fault recovery threshold refers to a threshold used to determine whether the fault recovery coefficient (RC) has reached an acceptable recovery performance level, and the user operation data refers to data generated by the user during the interaction with the cloud desktop.

[0049] S5. Based on the user operation data, analyze the operation preferences of the target user corresponding to the distributed architecture cloud desktop; according to the operation preferences, use the trained user preference analysis model to analyze the preferred desktop usage pattern of the target user; based on the preferred desktop usage pattern, the data compression module and the intelligent flow control module, execute the integrated application of the distributed architecture cloud desktop.

[0050] The present invention analyzes the operation preferences of the target users corresponding to the distributed architecture cloud desktop based on the user operation data, and can deeply understand the operation preferences of the target users, and optimize the cloud desktop environment accordingly, thereby improving user satisfaction and work efficiency.

[0051] In detail, the analyzing the operation preference of the target user corresponding to the distributed architecture cloud desktop based on the user operation data includes: Preprocessing the user operation data to obtain preprocessed user operation data; Analyzing the access application of the target user based on the pre-processed user operation data; Recording the access frequency of the access application; marking the task complexity of accessing the application; Based on the access frequency and the task complexity, the operation preference of the target user is analyzed.

[0052] Among them, the pre-processed user operation data refers to the user operation data after processing steps such as cleaning, formatting and de-privacy, the accessed application refers to the specific software program used by the user in the cloud desktop environment, the access frequency refers to the number of times the user accesses a specific application within a specific time period, the task complexity refers to the level of steps, skills and cognitive effort required to complete a task in a specific application, and the operation preference refers to the user's behavioral tendency in the cloud desktop environment.

[0053] Optionally, marking the complexity of the task of accessing the application program may be implemented by utilizing static code analysis.

[0054] The present invention can improve the user experience by analyzing the target user's preferred desktop usage pattern based on the operation preference using a trained user preference analysis model. The user preference analysis model refers to a model based on machine learning or statistical analysis methods, which is trained to identify and predict the user's behavior patterns and preferences in a cloud desktop environment, and the preferred desktop usage pattern refers to the pattern of specific behaviors and usage habits exhibited by the user in the cloud desktop environment.

[0055] Finally, based on the preferred desktop usage mode, the data compression module and the intelligent flow control module, the present invention executes the integrated application of the distributed architecture cloud desktop to realize the integrated application of the distributed architecture cloud desktop and provide efficient, personalized and reliable cloud desktop services.

[0056] Compared with the problems described in the background technology, firstly, by accurately obtaining office needs and analyzing the compatibility coefficient of the network set-top box, the accuracy of hardware selection is ensured, so that the configured KVM virtualization software can run stably, and the overall performance and reliability of the system are improved. Secondly, the construction of the data compression module effectively reduces the amount of data transmission, reduces the network bandwidth demand, and improves the data transmission efficiency, especially in an environment with limited network conditions. This effect is particularly obvious. Furthermore, the introduction of the intelligent flow control module performs dynamic traffic management based on data transmission influencing factors, further optimizes the allocation of network resources, ensures the priority transmission of key business data, and significantly improves the user experience. In addition, the configuration of the fault transfer mechanism and the calculation of the fault recovery coefficient ensure that the service can be quickly restored when a fault occurs, reaching the preset fault recovery threshold, greatly reducing the downtime caused by system failures. By collecting user operation data and analyzing operation preferences, the system can automatically adjust to the target user's preferred desktop usage mode, improving the user's office efficiency and satisfaction. Finally, the execution of the integrated application closely combines user preferences, data compression and intelligent flow control algorithms to form an efficient, personalized and stable cloud desktop office environment, which not only improves office efficiency, but also reduces the enterprise's IT operation and maintenance costs. Therefore, the present invention improves the collaboration efficiency of users' cloud desktop office.

[0057] Embodiment 2: like Figure 2 As shown, it is a functional module diagram of an integrated application system for realizing cloud desktop office based on a network set-top box according to the present invention.

[0058] The integrated application system 200 for realizing cloud desktop office based on network set-top box of the present invention can be installed in an electronic device. According to the functions realized, the integrated application system for realizing cloud desktop office based on network set-top box can include virtualization software configuration module 201, influence factor analysis module 202, distributed cloud desktop construction module 203, failover mechanism construction module 204, and user preference analysis module 205. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0059] In the embodiment of the present invention, the functions of each module / unit are as follows: The virtualization software configuration module 201 is used to obtain the office requirements of the cloud desktop, determine the network set-top box of the cloud desktop according to the office requirements, analyze the compatibility coefficient of the network set-top box, and configure the KVM virtualization software of the network set-top box when the compatibility coefficient meets the preset compatibility threshold; The impact factor analysis module 202 is used to construct a data compression module of the network set-top box based on the KVM virtualization software, and mark the data transmission impact factor of the virtualization layer corresponding to the KVM virtualization software; The distributed cloud desktop construction module 203 is used to construct the intelligent flow control module of the virtualization layer based on the data transmission influencing factor, and to construct the distributed architecture cloud desktop of the cloud desktop based on the data compression module and the intelligent flow control module; The failover mechanism construction module 204 is used to configure the failover mechanism of the distributed architecture cloud desktop, calculate the failure recovery coefficient of the failover mechanism, and when the failure recovery coefficient meets the preset failure recovery threshold, collect user operation data of the distributed architecture cloud desktop; The user preference analysis module 205 is used to analyze the operation preferences of the target user corresponding to the distributed architecture cloud desktop based on the user operation data, and analyze the preferred desktop usage pattern of the target user using a trained user preference analysis model according to the operation preferences, and execute the integrated application of the distributed architecture cloud desktop based on the preferred desktop usage pattern, the data compression module and the intelligent flow control module.

[0060] In detail, the modules in the integrated application system 200 for realizing cloud desktop office based on network set-top box in the embodiment of the present invention are used in the same manner as above. Figure 1 The integrated application method for realizing cloud desktop office based on network set-top box described in the text is the same as the technical means and can produce the same technical effects, so it will not be repeated here.

[0061] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An integrated application method for realizing cloud desktop office based on a network set-top box, characterized in that: The method comprises: Obtaining office requirements of the cloud desktop, determining a network set-top box of the cloud desktop according to the office requirements, analyzing a compatibility coefficient of the network set-top box, and configuring KVM virtualization software of the network set-top box when the compatibility coefficient meets a preset compatibility threshold; Based on the KVM virtualization software, a data compression module of the network set-top box is constructed, and a data transmission influencing factor of the virtualization layer corresponding to the KVM virtualization software is marked; Based on the data transmission influencing factor, construct the intelligent flow control module of the virtualization layer, and based on the data compression module and the intelligent flow control module, construct the distributed architecture cloud desktop of the cloud desktop; Configuring a failover mechanism of the distributed architecture cloud desktop, calculating a failure recovery coefficient of the failover mechanism, and when the failure recovery coefficient meets a preset failure recovery threshold, collecting user operation data of the distributed architecture cloud desktop; Based on the user operation data, the operation preferences of the target user corresponding to the distributed architecture cloud desktop are analyzed; according to the operation preferences, the preferred desktop usage pattern of the target user is analyzed using a trained user preference analysis model; based on the preferred desktop usage pattern, the data compression module and the intelligent flow control module, the integrated application of the distributed architecture cloud desktop is executed.

2. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The analyzing the compatibility coefficient of the network set-top box includes: Defining compatibility analysis indicators of the network set-top box; Collecting set-top box data of the network set-top box, wherein the set-top box data includes set-top box hardware data and set-top box software data; Analyzing the compatibility analysis indicator score of the compatibility analysis indicator based on the set-top box data; Determining the dynamic weight of the compatibility analysis indicator; Based on the indicator dynamic weight and the compatibility analysis indicator score, the compatibility coefficient of the network set-top box is determined.

3. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The data compression module of the network set-top box is constructed based on the KVM virtualization software, including: defining data compression requirements of the network set-top box; Determining the data type of the network set-top box; Define the type data compression algorithm of the data type; Based on the type of data compression algorithm, construct a data compression module architecture of the network set-top box, wherein the data compression module architecture includes an input interface, a compression engine, an output interface, and configuration management; According to the data compression module architecture, a data compression module of the network set-top box is constructed.

4. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The type data compression algorithm defining the data type includes: Analyze the offset, length and characters of the text type corresponding to the data type; Defining a text compression algorithm for the text type according to the offset, length, and characters; Mark the pixel brightness value and frequency variables of the image data corresponding to the data type; Based on the pixel brightness value and frequency variable, an image compression algorithm for the image data is constructed using the following formula; Determining that the data type corresponds to intra-frame analysis, inter-frame analysis, and transform coding of video data; Constructing a video compression algorithm for video data corresponding to the data type; Based on the text compression algorithm, the image compression algorithm and the video compression algorithm, a type data compression algorithm of the data type is determined.

5. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The intelligent flow control module of the virtualization layer is constructed based on the data transmission influencing factor, including: Determining the intelligent flow control target of the virtualization layer; Establishing a data model of the virtualization layer; Based on the data transmission influencing factor and the intelligent flow control target, construct an intelligent flow control algorithm for the virtualization layer; Building a decision engine for the virtualization layer; Based on the data model, the intelligent flow control algorithm and the decision engine, an intelligent flow control module of the virtualization layer is constructed.

6. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 5, characterized in that: The constructing the intelligent flow control algorithm of the virtualization layer based on the data transmission influencing factor and the intelligent flow control target includes: Collecting the traffic of the virtualization layer; Based on the data transmission influencing factor and the intelligent flow control target, determine the bandwidth adjustment factor of the traffic, wherein the bandwidth adjustment factor includes bandwidth difference, delay difference, packet loss rate difference and network congestion level; Based on the bandwidth difference, delay difference, packet loss rate difference and network congestion level, the following formula is used to construct an intelligent flow control algorithm for the traffic.

7. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The configuring the failover mechanism of the distributed architecture cloud desktop includes: Analyze the cloud desktop application environment of the distributed architecture cloud desktop; Based on the cloud desktop application environment, determining the failover requirements of the distributed architecture cloud desktop, wherein the failover requirements include a recovery time objective and a data recovery point objective; Based on the failover requirements, construct a failover architecture for the distributed architecture cloud desktop; Through the failover architecture, a failover mechanism for the distributed architecture cloud desktop is established.

8. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The calculating the failure recovery coefficient of the failover mechanism includes: Obtaining the mean recovery time and failure frequency of the failover mechanism; A failure recovery coefficient of the failover mechanism is calculated based on the failure mean recovery time and the failure frequency.

9. The integrated application method for realizing cloud desktop office based on network set-top box according to claim 1, characterized in that: The analyzing, based on the user operation data, the operation preference of the target user corresponding to the distributed architecture cloud desktop includes: Preprocessing the user operation data to obtain preprocessed user operation data; Analyzing the access application of the target user based on the pre-processed user operation data; Recording the access frequency of the access application; marking the task complexity of accessing the application; Based on the access frequency and the task complexity, the operation preference of the target user is analyzed.

10. An integrated application system for cloud desktop office based on a network set-top box, characterized in that: The system comprises: A virtualization software configuration module, used to obtain the office requirements of the cloud desktop, determine the network set-top box of the cloud desktop according to the office requirements, analyze the compatibility coefficient of the network set-top box, and configure the KVM virtualization software of the network set-top box when the compatibility coefficient meets a preset compatibility threshold; An impact factor analysis module, used to construct a data compression module of the network set-top box based on the KVM virtualization software, and mark the data transmission impact factor of the virtualization layer corresponding to the KVM virtualization software; A distributed cloud desktop construction module, used to construct an intelligent flow control module of the virtualization layer based on the data transmission influencing factor, and to construct a distributed architecture cloud desktop of the cloud desktop based on the data compression module and the intelligent flow control module; A failover mechanism construction module is used to configure the failover mechanism of the distributed architecture cloud desktop, calculate the failure recovery coefficient of the failover mechanism, and when the failure recovery coefficient meets a preset failure recovery threshold, collect user operation data of the distributed architecture cloud desktop; The user preference analysis module is used to analyze the operation preferences of the target user corresponding to the distributed architecture cloud desktop based on the user operation data, and analyze the preferred desktop usage pattern of the target user using the trained user preference analysis model according to the operation preferences, and execute the integrated application of the distributed architecture cloud desktop based on the preferred desktop usage pattern, the data compression module and the intelligent flow control module.

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