Method and device for collaborative rendering of H5 front end and RDP rear end, and storage medium

Abstract

The invention discloses a method and equipment for collaborative rendering of an H5 front end and an RDP rear end, and a storage medium, and belongs to the technical field of security services. The method comprises the following steps: in response to an operation and maintenance operation of a user at an H5 front end, performing semantic analysis on the operation and maintenance operation, and identifying an operation and maintenance task type corresponding to the operation and maintenance operation; determining a semantic priority corresponding to the operation and maintenance task type based on a preset rule base; according to the semantic priority, a collaborative scheduling strategy about the H5 front end and the RDP rear end is generated, and the collaborative scheduling strategy comprises a loading rendering strategy for an interface component of the H5 front end and a rendering transmission strategy for a picture area of the RDP rear end; executing the collaborative scheduling strategy, and monitoring a network state and a user behavior in the process of executing the collaborative scheduling strategy; and according to a monitoring result, triggering re-evaluation of the semantic priority and corresponding cooperative scheduling strategy adjustment. The resource scheduling efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of security service technology, and more specifically to a method, device and storage medium for collaborative rendering of H5 front-end and RDP back-end. Background Technology

[0002] With the development of cloud computing, hybrid cloud architecture, and remote work models, more and more enterprises are providing technical personnel with centralized management capabilities for resources such as servers, databases, and network devices through web-based operations and maintenance portals (H5 pages). Remote graphical operations typically rely on the Remote Desktop Protocol (RDP) and are embedded into the H5 page via WebAssembly or WebSocket wrappers, enabling lightweight access "without installing a client." However, in existing technologies, there is no linkage mechanism between the H5 frontend and the RDP backend, making unified resource scheduling impossible and resulting in low resource scheduling efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, storage medium, and computer program product for collaborative rendering of H5 front-end and RDP back-end, so as to solve the problem of low resource scheduling efficiency in the prior art.

[0004] To achieve the above objectives, a first aspect of this application provides a method for collaborative rendering of an H5 front-end and an RDP back-end, comprising: In response to user operations and maintenance operations on the H5 front end, semantic parsing is performed on the operations and maintenance operations operations to identify the corresponding operations and maintenance task types; Determine the semantic priority of the operation and maintenance task type based on a preset rule base; Based on semantic priority, a collaborative scheduling strategy for H5 front-end and RDP back-end is generated. The collaborative scheduling strategy includes a loading and rendering strategy for the interface components of H5 front-end and a rendering and transmission strategy for the screen area of ​​RDP back-end. Implement coordinated scheduling policies and monitor network status and user behavior during the execution of these policies. Based on the monitoring results, a reassessment of semantic priorities and corresponding adjustments to the collaborative scheduling strategy are triggered.

[0005] In this embodiment of the application, semantic parsing is performed on the operation and maintenance operation to identify the operation and maintenance task type corresponding to the operation and maintenance operation, including: matching the operation and maintenance operation with a preset operation and maintenance task template library through the operation and maintenance semantic parsing engine to determine the operation and maintenance task type corresponding to the operation and maintenance operation.

[0006] In this embodiment of the application, determining the semantic priority corresponding to the operation and maintenance task type based on a preset rule base includes: determining the semantic priority corresponding to the operation and maintenance task type based on at least one of the operation risk level corresponding to the operation and maintenance task type and the business criticality of the operation object, based on the preset rule base.

[0007] In this application embodiment, the loading rendering strategy includes the loading order and / or rendering timing and / or display detail level of the H5 front-end interface components; the rendering transmission strategy includes the rendering range of the screen area of ​​the RDP back-end and / or encoding parameters and / or transmission priority.

[0008] In this embodiment of the application, the method further includes: when the semantic priority is high, the loading rendering strategy includes immediately loading operation controls directly related to the operation and maintenance task type, and the rendering transmission strategy includes prioritizing the establishment of an RDP backend connection and transmitting image data of the area where the target application window is located.

[0009] In this embodiment of the application, the method further includes: when a decrease in network bandwidth or an increase in latency is detected, dynamically adjusting the transmission parameters of the RDP stream at the RDP backend, wherein the dynamic adjustment method includes at least one of the following: reducing the frame rate, reducing the color depth, increasing the compression rate, and switching from full frame update mode to differential update mode.

[0010] In this application embodiment, the triggering conditions for the re-evaluation of semantic priority and the corresponding adjustment of the collaborative scheduling strategy include: detecting that the user's operation focus leaves the interface area corresponding to the current operation and maintenance task, and / or detecting the generation of a new system alarm event.

[0011] A second aspect of this application provides a computer device, comprising: a memory configured to store instructions; a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the aforementioned method for collaborative rendering of an H5 front-end and an RDP back-end.

[0012] A third aspect of this application provides a machine-readable storage medium storing instructions that cause a machine to execute the aforementioned method for collaborative rendering of H5 front-end and RDP back-end.

[0013] The fourth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for collaborative rendering of H5 front-end and RDP back-end.

[0014] The aforementioned technical solution transforms user operation intentions into quantifiable semantic priorities, making resource scheduling more targeted and significantly shortening the first operational time for high-priority tasks. It deeply integrates the semantic priorities of user operations and maintenance behaviors with H5 front-end control and RDP graphics stream rendering, thus combining semantic understanding, front-end performance optimization, and remote graphics transmission. This breaks down the barriers between independent rendering of the H5 front-end and RDP back-end, achieving unified scheduling of resource allocation and loading timing, improving resource scheduling efficiency, optimizing overall resource utilization efficiency, and enhancing the availability and response efficiency of the remote operations and maintenance system. Through a real-time feedback mechanism, the rendering strategy can be dynamically adjusted based on user behavior switching and network fluctuations, improving robustness and user experience in complex environments.

[0015] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 The illustration shows a flowchart of a method for collaborative rendering of an H5 front-end and an RDP back-end according to an embodiment of this application. Figure 2 The schematic diagram illustrates a modular framework of an H5-RDP collaborative rendering system based on semantic priority according to an embodiment of this application; Figure 3 The illustration shows a schematic diagram of the semantic priority determination process according to an embodiment of this application. Detailed Implementation

[0017] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0018] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0019] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0020] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0021] Figure 1 The illustration schematically shows a flowchart of a method for collaborative rendering of an H5 front-end and an RDP back-end according to an embodiment of this application. Figure 1 As shown in the illustration, this application provides a method for collaborative rendering of an H5 front-end and an RDP back-end. Taking the application of this method to a processor as an example, the method may include the following steps: Step S102: In response to the user's operation and maintenance operation on the H5 front end, perform semantic parsing on the operation and maintenance operation to identify the operation and maintenance task type corresponding to the operation and maintenance operation.

[0022] Step S104: Determine the semantic priority corresponding to the operation and maintenance task type based on the preset rule base.

[0023] Step S106: Generate a collaborative scheduling strategy for the H5 front-end and the RDP back-end based on semantic priority. The collaborative scheduling strategy includes a loading and rendering strategy for the interface components of the H5 front-end and a rendering and transmission strategy for the screen area of ​​the RDP back-end.

[0024] Step S108: Execute the coordinated scheduling strategy, and monitor network status and user behavior during the execution of the coordinated scheduling strategy.

[0025] Step S110: Based on the monitoring results, trigger the re-evaluation of semantic priority and the corresponding adjustment of the collaborative scheduling strategy.

[0026] It's understandable that there's a one-to-one correspondence between operations and maintenance (O&M) tasks. Semantic parsing of O&M operations determines the corresponding O&M task type. For example, when the O&M operation is restarting the Apache service, the O&M task type could be "restart service." The default rule base is a pre-defined rule base storing O&M task types and their corresponding semantic priorities. Semantic priority can be understood as the priority of the O&M task; for example, the semantic priority for the O&M task type "restart service" can be set to "high." Different levels of semantic priority can correspond to different collaborative scheduling strategies between the H5 frontend and the RDP backend. The purpose of this strategy is to achieve collaborative rendering between the H5 frontend and the RDP backend. The specific content of the strategy can be pre-set and stored. Semantic priority re-evaluation refers to the reassessment of semantic priority. Loading and rendering strategies can include both loading and rendering strategies. Rendering and transmission strategies can include both rendering and transmission strategies. Network status, such as network jitter, can be considered. User behavior, such as a user switching to view monitoring charts, can also be considered.

[0027] Specifically, the processor can respond to user operations on the H5 front-end, perform semantic parsing on the operations, identify the corresponding operation task type, determine the semantic priority of the operation task type based on a preset rule base, and generate a collaborative scheduling strategy for the H5 front-end and RDP back-end based on the semantic priority. The collaborative scheduling strategy includes the loading and rendering strategy for the interface components of the H5 front-end and the rendering and transmission strategy for the screen area of ​​the RDP back-end. The processor then executes the collaborative scheduling strategy and monitors the network status and user behavior during the execution of the collaborative scheduling strategy. Based on the monitoring results, it triggers a re-evaluation of the semantic priority and an adjustment of the corresponding collaborative scheduling strategy.

[0028] The aforementioned technical solution transforms user operation intentions into quantifiable semantic priorities, making resource scheduling more targeted and significantly shortening the first operational time for high-priority tasks. It deeply integrates the semantic priorities of user operations and maintenance behaviors with H5 front-end control and RDP graphics stream rendering, thus combining semantic understanding, front-end performance optimization, and remote graphics transmission. This breaks down the barriers between independent rendering of the H5 front-end and RDP back-end, achieving unified scheduling of resource allocation and loading timing, optimizing overall resource utilization efficiency, and improving the availability and response efficiency of the remote operations and maintenance system. Through a real-time feedback mechanism, it can dynamically adjust rendering strategies based on user behavior switching and network fluctuations, improving robustness and user experience in complex environments.

[0029] In one embodiment, semantic parsing of operation and maintenance operations to identify the operation and maintenance task type corresponding to the operation and maintenance operation includes: matching the operation and maintenance operation with a preset operation and maintenance task template library through an operation and maintenance semantic parsing engine to determine the operation and maintenance task type corresponding to the operation and maintenance operation.

[0030] It's understandable that the operations and maintenance (O&M) task template library stores the correspondence between different O&M operations and O&M task types. The O&M semantic parsing engine can be used to receive and parse user input, understand user intent, and identify the corresponding O&M task type.

[0031] Specifically, the processor can use the operation and maintenance semantic parsing engine to match operation and maintenance operations with a preset operation and maintenance task template library to determine the operation and maintenance task type corresponding to the operation and maintenance operation.

[0032] In one embodiment, determining the semantic priority corresponding to the operation and maintenance task type based on a preset rule base includes: determining the semantic priority corresponding to the operation and maintenance task type based on at least one of the operation risk level corresponding to the operation and maintenance task type and the business criticality of the operation object, based on the preset rule base.

[0033] It is understandable that the semantic priority corresponding to the type of operation and maintenance task may be related to the level of operation risk and / or the business criticality of the operation object.

[0034] Specifically, the processor can determine the semantic priority of the operation and maintenance task type based on a preset rule base, according to at least one of the operation risk level corresponding to the operation and maintenance task type and the business criticality of the operation object.

[0035] In one embodiment, the loading rendering strategy includes the loading order and / or rendering timing of the H5 front-end interface components and / or the display detail level; the rendering transmission strategy includes the rendering range of the RDP back-end screen area and / or encoding parameters and / or transmission priority.

[0036] In one embodiment, the method further includes: when the semantic priority is high, the loading rendering strategy includes immediately loading operation controls directly related to the operation and maintenance task type, and the rendering transmission strategy includes prioritizing the establishment of an RDP backend connection and transmitting image data of the area where the target application window is located.

[0037] In one embodiment, the method further includes: when a decrease in network bandwidth or an increase in latency is detected, dynamically adjusting the transmission parameters of the RDP stream at the RDP backend, wherein the dynamic adjustment method includes at least one of the following: reducing the frame rate, reducing the color depth, increasing the compression rate, and switching from full-frame update mode to differential update mode.

[0038] In one embodiment, the triggering conditions for the re-evaluation of semantic priority and the corresponding adjustment of the collaborative scheduling strategy include: detecting that the user's focus leaves the interface area corresponding to the current operation and maintenance task, and / or detecting the generation of a new system alarm event.

[0039] In one embodiment, the H5 front-end includes a core operation layer, an auxiliary information layer, and a background layer; the execution of a collaborative scheduling strategy includes: according to the collaborative scheduling strategy, prioritizing the loading and rendering of the core operation layer, and delaying or asynchronously loading the auxiliary information layer and the background layer.

[0040] In one embodiment, the above method may further include a post-processing step: after the operation and maintenance task is completed, the user's operation behavior path and the corresponding system response data are recorded; based on the historical operation behavior path data, a pre-loaded template is generated for high-frequency or typical operation and maintenance tasks, and the pre-loaded template is cached in local storage to accelerate the response of subsequent identical or similar tasks.

[0041] In one specific embodiment, a method for collaborative rendering of an H5 front-end and an RDP back-end is provided. This method can dynamically coordinate the loading order, rendering granularity, and transmission parameters of H5 front-end components and RDP image streams based on the importance of the user's current operation intent under different network conditions. This significantly shortens the first operational time for high-priority tasks, improves user experience, and enhances system resource utilization. To achieve the above objectives, the embodiments of this application adopt the following technical solutions: This application provides an H5-RDP collaborative rendering system based on semantic priority, used to implement the aforementioned method for collaborative rendering of H5 front-end and RDP back-end, namely, the H5-RDP collaborative rendering method based on semantic priority. Figure 2 As shown, the system may specifically include the following core modules: 1. Operation and Maintenance Semantic Parsing Engine: Used to receive and parse user input, identify the corresponding operation and maintenance task type, and determine semantic priority.

[0042] 2. Collaborative Scheduling Controller: Communicates with the operation and maintenance semantic parsing engine and is used to generate resource scheduling strategies for H5 and RDP based on semantic priorities.

[0043] 3. Layered Progressive Rendering Module: Communicates with the Cooperative Scheduling Controller to execute cooperative rendering strategies, load H5 interfaces in layers, and render RDP images in blocks.

[0044] 4. Dynamic RDP Stream Optimization Module: Communicates with the Cooperative Scheduling Controller to monitor network status in real time and dynamically adjust the encoding and transmission parameters of RDP streams based on semantic priority.

[0045] 5. Feedback-based rendering adaptation mechanism: It communicates with the layered progressive rendering module, the dynamic RDP stream optimization module, and the operation and maintenance semantic parsing engine respectively, and is used to monitor user behavior and system status, and dynamically trigger semantic priority re-evaluation and rendering strategy update.

[0046] The above method may include the following steps: 1. Receive user input and perform semantic parsing on the input to identify the type of operation and maintenance task and its semantic priority; 2. Generate a collaborative rendering strategy between the H5 front-end and the RDP remote desktop based on semantic priority; 3. Implement a collaborative rendering strategy to load the H5 interface in layers and render the RDP screen in blocks. 4. Dynamically adjust the transmission parameters of the RDP stream based on real-time network status and semantic priority; 5. Monitor user behavior and system status changes to dynamically trigger semantic priority re-evaluation and rendering strategy adjustment.

[0047] The core of the system described in this application embodiment lies in understanding the user's intent through the operation and maintenance semantic parsing engine, and converting semantic priorities into specific rendering instructions through the collaborative scheduling controller. Figure 2 A schematic diagram of the overall modules of the system, such as Figure 2 As shown, the system mainly includes an operation and maintenance semantic parsing engine, a collaborative scheduling controller, a layered progressive rendering module, a dynamic RDP stream optimization module, and a feedback rendering adaptation mechanism. The modules work together to form a complete closed-loop adaptive system.

[0048] The following details the collaboration process for each module: 1. Operation and Maintenance Semantic Parsing Engine The engine is the "brain" of the system, responsible for understanding what the user wants to do and how urgent the matter is. Figure 3 The diagram illustrates the semantic priority determination process, and its specific steps are as follows: Input normalization: Regardless of whether the user operates through menu clicks, command line input, or search box, all input is converted into a uniform text description.

[0049] Task identification: A cascaded strategy of "rule matching (high-frequency scenarios) + NLP model (complex scenarios)" is adopted. For example... Figure 3 As shown, the rule engine prioritizes matching; if no match is found, it calls the NLP model for deep semantic understanding.

[0050] Entity extraction: Using sequence labeling models, key parameters (such as host IP and service name) are extracted from the statement.

[0051] Priority determination: An end-to-end classification model jointly outputs task type and semantic priority (high / medium / low). All rules and models support dynamic loading and hot updates from behavior cache / rule base.

[0052] 2. Cooperative Scheduling Controller This module is the "decision center" of the system.

[0053] Strategy initialization and template loading: Strategy templates are stored in Redis and hot updates are achieved using lazy loading and publish-subscribe mechanisms.

[0054] Task-Resource Mapping: Maintain a mapping table between task types and required resources, use a Trie tree for fast matching, and supplement it with similarity calculation to handle tasks that are not logged in.

[0055] Multi-objective policy generation: Policies are generated using a rule engine (such as Drools) and serialized via Protobuf. The policy content includes the loading sequence of H5 key components, the coordinates and transmission parameters of key RDP areas, and the priority-based bandwidth allocation ratio (e.g., high:medium:low = 70%: 20%: 10%).

[0056] Policy distribution and execution monitoring: Policies are distributed to the H5 client and RDP proxy gateway in real time via gRPC streaming channel, and execution metrics are monitored based on a sliding time window by a "watchdog" program to ensure that the policies take effect.

[0057] 3. Layered progressive rendering mechanism This mechanism is the "executor" of the strategy.

[0058] H5 UI rendering: H5 UI components are dynamically loaded based on strategy priority. During construction, components are marked with priority using custom plugins; at runtime, high-priority components are loaded first using dynamic import(), and lazy loading or idle loading within the viewport is implemented using technologies such as the Intersection Observer API.

[0059] RDP screen rendering: A "critical area first" block rendering strategy is adopted. First, the RDP screen is divided into regions and a priority queue is set; then, the FreeRDP client is modified to request image data of high-priority regions first; the first frame of the front end only displays the critical region (the rest of the region is blurred), and subsequent frames are updated incrementally according to priority, and the screen composition is completed in the Canvas.

[0060] 4. Dynamic RDP Flow Optimization Module This module is the "intelligent speed controller" for RDP transmission.

[0061] Real-time network status monitoring: Information such as bandwidth, latency, and packet loss rate can be obtained in real time by reporting to the RDP proxy gateway through Web Workers probes.

[0062] Priority-driven parameter decision-making: A decision engine is built based on a reinforcement learning model to dynamically adjust encoding parameters (such as resolution and frame rate) according to network status and task priority. High-priority tasks prioritize ensuring image clarity and smoothness when bandwidth suddenly drops.

[0063] Smooth transition and exception handling: A PID controller is used to smoothly adjust parameters. In cases of extreme network degradation, it automatically degrades to plain text transmission mode.

[0064] 5. Feedback-based rendering adaptation mechanism This mechanism enables the system to achieve "closed-loop self-adaptation".

[0065] Multi-dimensional behavior monitoring: The front end captures user interaction events and monitors network fluctuations and rendering anomalies.

[0066] Dynamic priority reassessment: When a sudden change in user behavior or a deterioration in network metrics is detected, the behavioral features are sent to the semantic parsing engine for rapid reassessment and semantic priority is updated.

[0067] Partial redrawing and hot policy switching: Based on the new priority, only the H5 components or RDP areas that have changed are updated. The collaborative scheduling controller generates incremental policies and distributes them to achieve seamless switching.

[0068] Feedback data closed-loop optimization: All behavior and policy mapping data are recorded and used for offline model training and continuous optimization of the rule base.

[0069] The following examples illustrate specific applications: An enterprise operations administrator accesses the cloud platform operations portal (H5 page) via a mobile browser, intending to perform the "restart Apache service" operation on an application server. This operation is identified as a high-priority task.

[0070] Step S1: User Behavior Capture and Semantic Analysis The user clicks the "Service Management" menu item and selects the "Restart Apache Service" button; The front-end event listener captures this operation and sends it to the back-end operations and maintenance semantic parsing engine; The parsing engine determines based on a predefined rule base: "Restarting the service" is a "high-risk operation" and its semantic priority is set to "high".

[0071] Step S2: Generate a cooperative scheduling strategy Upon receiving the priority signal, the collaborative scheduling controller initiates the high-performance rendering mode. The decision is as follows: H5 side: Immediately load the "Service Control Panel", "Operation Log Output Box", and "Confirmation Dialog Box"; RDP side: After establishing a connection, prioritize requesting image frames from the screen area (coordinate range [x1,y1,x2,y2]) where the command line terminal is located; Configure RDP parameters: Enable 30fps, 24-bit color depth, and LZ77 compression to ensure smooth interaction.

[0072] Step S3: Staged rendering output Phase 1 (0–1s): The H5 page only displays service control buttons and a waiting animation; Second stage (1–2s): RDP returns to the initial screen of the command line area, which is overlaid on the H5 interface; The third stage (2–3 seconds): The remaining non-critical areas (such as desktop icons and wallpapers) are gradually filled in; Users can start typing commands within 3 seconds, without waiting for the entire desktop to load completely.

[0073] Step S4: Dynamic adjustment during runtime If a user switches to "View Monitoring Charts" midway through the process, the semantic priority drops to "Medium", and the system automatically reduces the RDP frame rate by 10fps to free up bandwidth. If network jitter is detected, the system will automatically switch to the "keyframe + difference update" mode to reduce the amount of data.

[0074] Step S5: End and Cache After the operation is completed, the system records the behavior path for use in training the semantic model; Frequently used operation templates (such as "restart service") can be preloaded into the local IndexedDB to speed up the next response.

[0075] In summary, the embodiments of this application provide: a system architecture and method for joint resource scheduling of H5 front-end and RDP image stream based on user operation semantic priority; a mechanism for operation and maintenance task identification and semantic priority determination driven by a hybrid rule and NLP model; a method for dynamically mapping H5 key components and RDP key areas according to task type and generating collaborative rendering strategies; a priority-based block progressive rendering and transmission method for RDP screens, including priority request, transmission and screen composition of key areas; a dynamic RDP stream transmission parameter optimization method integrating semantic priority, especially the adaptive adjustment and degradation mechanism in weak network environments; and a closed-loop adaptation mechanism that dynamically re-evaluates semantic priority and triggers local rendering strategy updates based on user behavior and system status feedback.

[0076] The advantages of the embodiments of this application include: 1. Task-aware intelligent scheduling: Transforms user operation intentions (semantics) into quantifiable priorities, making resource scheduling more targeted and significantly shortening the first operational time for high-priority tasks.

[0077] 2. Deep cross-protocol collaboration: It breaks down the barriers between independent rendering of H5 and RDP, and realizes unified scheduling of resource allocation and loading sequence for both, thus optimizing the overall resource utilization efficiency.

[0078] 3. Adaptive rendering capability: Through a real-time feedback mechanism, the system can dynamically adjust the rendering strategy based on user behavior and network fluctuations, improving robustness and user experience in complex environments.

[0079] 4. High-efficiency bandwidth utilization: In weak network environments, priority-driven chunked rendering and stream optimization prioritize the smoothness of core operations and reduce the transmission of unnecessary data.

[0080] This application also provides a computer device, including: a memory configured to store instructions; a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement the method for collaborative rendering of H5 front-end and RDP back-end according to the above embodiments.

[0081] This application also provides a machine-readable storage medium storing instructions that cause a machine to execute the method for collaborative rendering of H5 front-end and RDP back-end according to the above embodiments.

[0082] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method for collaborative rendering of H5 front-end and RDP back-end according to the above embodiments.

[0083] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0088] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0089] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0091] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for collaborative rendering of H5 front-end and RDP back-end, characterized in that, The method includes: In response to the user's operation and maintenance operation on the H5 front end, the operation and maintenance operation is semantically parsed to identify the operation and maintenance task type corresponding to the operation and maintenance operation; The semantic priority corresponding to the operation and maintenance task type is determined based on a preset rule base; Based on the semantic priority, a collaborative scheduling strategy for the H5 front-end and the RDP back-end is generated, wherein the collaborative scheduling strategy includes a loading and rendering strategy for the interface components of the H5 front-end and a rendering and transmission strategy for the screen area of ​​the RDP back-end. The coordinated scheduling strategy is executed, and network status and user behavior are monitored during the execution of the coordinated scheduling strategy. Based on the monitoring results, a reassessment of the semantic priority and an adjustment of the corresponding collaborative scheduling strategy are triggered.

2. The method according to claim 1, characterized in that, The semantic parsing of the operation and maintenance operation to identify the operation and maintenance task type corresponding to the operation and maintenance operation includes: The operation and maintenance semantic parsing engine is used to match the operation and maintenance operation with a preset operation and maintenance task template library to determine the operation and maintenance task type corresponding to the operation and maintenance operation.

3. The method according to claim 1, characterized in that, The step of determining the semantic priority corresponding to the operation and maintenance task type based on a preset rule base includes: Based on a preset rule base, the semantic priority of the operation and maintenance task type is determined according to at least one of the operation risk level corresponding to the operation and maintenance task type and the business criticality of the operation object.

4. The method according to claim 1, characterized in that, The loading and rendering strategy includes the loading order and / or rendering timing and / or display detail level of the H5 front-end interface components; the rendering and transmission strategy includes the rendering range and / or encoding parameters and / or transmission priority of the RDP back-end screen area.

5. The method according to claim 1, characterized in that, The method further includes: When the semantic priority is high, the loading and rendering strategy includes immediately loading operation controls directly related to the operation and maintenance task type, and the rendering and transmission strategy includes prioritizing the establishment of an RDP backend connection and transmitting image data of the area where the target application window is located.

6. The method according to claim 1, characterized in that, The method further includes: When a decrease in network bandwidth or an increase in latency is detected, the transmission parameters of the RDP stream at the RDP backend are dynamically adjusted. The dynamic adjustment method includes at least one of the following: reducing the frame rate, reducing the color depth, increasing the compression rate, and switching from full frame update mode to differential update mode.

7. The method according to claim 1, characterized in that, The triggering conditions for the re-evaluation of semantic priority and the corresponding adjustment of the cooperative scheduling strategy include: The system detects that the user's focus leaves the interface area corresponding to the current operation and maintenance task, and / or detects the generation of new system alarm events.

8. A computer device, characterized in that, include: The memory is configured to store instructions; A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for collaborative rendering of an H5 front-end and an RDP back-end according to any one of claims 1 to 7.

9. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform a method for collaborative rendering of an H5 front-end and an RDP back-end according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method for collaborative rendering of H5 front-end and RDP back-end according to any one of claims 1 to 7.

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