An Adaptive Optimization Method for CAD Drawing Parameters

Abstract

The present application provides an adaptive optimization method for CAD drawing parameters, which relates to the technical field of drawing parameter optimization, including: setting a dynamic drawing operation sequence; performing multi-target correlation analysis on the dynamic drawing operation sequence, configuring a drawing parameter adjustment vector, and correcting the parameter adjustment deviation; configuring an optimization instruction set; dynamically adjusting the underlying configuration parameters of the CAD drawing tool, generating a self-adaptively optimized drawing parameter configuration file, and monitoring the drawing smoothness and drawing error rate of each layer; synchronously matching and calling the drawing parameter configuration file, and dynamically feedback-adjusting the drawing smoothness and drawing error rate of each layer in combination with preset performance targets. Through this application, the technical goals of adaptive parameter adjustment and performance closed-loop control based on the optimization instruction set can be achieved, achieving the technical effects of continuously ensuring drawing smoothness, reducing drawing error rates, and improving overall rendering efficiency under different hardware environments and task load conditions.

Description

Technical Field

[0001] The present application relates to the technical field of drawing parameter optimization, and in particular to a method for adaptive optimization of CAD drawing parameters. Background Art

[0002] As the requirements for graphic expression accuracy, drawing efficiency and system response performance in industries such as architecture, machinery, and electronics continue to increase, the underlying parameter tuning of the mapping system has gradually become a key factor affecting the mapping experience and production efficiency.

[0003] Currently, most existing CAD systems use fixed parameter configurations or rely solely on static templates for performance control. These systems struggle to cope with the dynamic resource fluctuations and rapid changes in user operation patterns found in complex scenarios. This is particularly true for multi-layer, multi-viewport, and high-resolution rendering tasks, which can easily lead to resource imbalances, memory bandwidth congestion, graphics freezes, and even rendering errors. Furthermore, current technologies often rely on preset thresholds for texture compression rates and level-of-detail (LOD) switching strategies, lacking a real-time feedback mechanism for memory bandwidth utilization, which can easily lead to over-compression of image quality or performance waste.

[0004] In summary, the existing technology has technical problems such as the lack of real-time monitoring and dynamic parameter adjustment mechanism for system runtime resource status, which leads to the inability to effectively allocate video memory bandwidth, rendering load and layer priority in complex drawing scenarios, further affecting the stability, drawing quality and overall rendering performance of the CAD system in the high-concurrency, multi-view drawing process. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive optimization method for CAD drawing parameters to solve the technical problem in the existing technology that due to the lack of real-time monitoring and dynamic parameter adjustment mechanism of the system runtime resource status, the video memory bandwidth, rendering load and layer priority cannot be effectively allocated in complex drawing scenarios, which further affects the stability, drawing quality and overall rendering performance of the CAD system in high-concurrency, multi-view drawing processes.

[0006] In view of the above problems, the present application provides an adaptive optimization method for CAD drawing parameters, including: collecting user drawing behavior data and layer attribute data, and setting a dynamic drawing operation sequence; performing multi-objective association analysis on the dynamic drawing operation sequence, configuring a drawing parameter adjustment vector, and correcting the parameter adjustment deviation in combination with a material property database; configuring an optimization instruction set including a line type optimization scheme, a layer management strategy, and rendering configuration parameters based on the drawing parameter adjustment vector and the parameter adjustment deviation; dynamically adjusting the underlying configuration parameters of the CAD drawing tool based on the optimization instruction set, generating an adaptively optimized drawing parameter configuration file, and monitoring the drawing smoothness and drawing error rate of each layer; at the same time, according to different drawing tasks, performing synchronous matching calls on the drawing parameter configuration file, and performing dynamic feedback adjustment based on the drawing smoothness and drawing error rate of each layer in combination with preset performance targets.

[0007] Preferably, the adaptive optimization method of CAD drawing parameters also includes: collecting resource occupancy peak data and corresponding parameter configuration snapshots in historical drawing tasks; extracting task allocation characteristics and synchronization delay indicators of parallel computing tasks in the drawing pipeline based on the resource occupancy peak data and corresponding parameter configuration snapshots; optimizing the splitting nodes of the decision tree based on the task allocation characteristics and synchronization delay indicators so that the resource consumption ratio reduction rate of the generated optimization path meets the dynamic optimization threshold.

[0008] Preferably, the adaptive optimization method of CAD drawing parameters also includes: configuring performance monitoring indicators, the performance monitoring indicators including video memory bandwidth utilization, shader compilation time and geometry culling efficiency; obtaining inter-frame consistency deviation during synchronous rendering of multiple viewports, and using the instruction backlog depth and priority inversion number of the GPU computing queue as auxiliary indicators for measuring rendering scheduling efficiency.

[0009] Preferably, the adaptive optimization method of CAD drawing parameters also includes: based on the optimization instruction set, dynamically adjusting the texture compression rate and LOD level switching threshold according to the video memory bandwidth utilization; optimizing the pre-compiled shader cache capacity and compilation thread pool size based on the shader compilation time consumption, and enabling the asynchronous culling pipeline when it is detected that the geometry culling efficiency is lower than the efficiency set value.

[0010] Preferably, the adaptive optimization method of CAD drawing parameters also includes: constructing a configuration item dependency graph during the execution of the parameter optimization path; using the configuration item dependency graph, when a configuration conflict is detected, updating the planning constraints, and marking the optimization path after the conflict is resolved as a high-priority template, and associating it with the same type of hardware configuration file.

[0011] Preferably, the adaptive optimization method of CAD drawing parameters further includes: before dynamically adjusting the underlying configuration parameters of the CAD drawing tool, recording a snapshot of the drawing pipeline state and performance difference data to locate the key configuration items that cause the score to drop; based on the key configuration items, generating compensation adjustment instructions and inserting interruption points in the optimization path.

[0012] Preferably, the adaptive optimization method of CAD drawing parameters also includes: in a distributed rendering scenario, analyzing the hardware performance differences of each node, dynamically allocating rendering tasks based on the differences, and synchronously optimizing parameter configurations between nodes; when the frame generation time difference between nodes exceeds the time difference threshold, enabling the load migration mechanism and reconstructing the optimization path.

[0013] Preferably, the adaptive optimization method for CAD drawing parameters further includes: configuring an influence factor map against the configuration item dependency graph, the influence factor map being used to quantify the weight of each configuration item on drawing quality and performance; when a user inputs a modified parameter, the performance change trend is predicted based on the map and an alarm is generated, and redundant configuration items are identified through a map backtracking mechanism; based on the redundant configuration items, potential influencing factors are mined using the influence factor map, and the optimization instruction set is iteratively corrected until the drawing quality inspection is passed.

[0014] Preferably, the adaptive optimization method of CAD drawing parameters also includes: embedding a lightweight sandbox environment to simulate the adjusted drawing pipeline behavior, and when the simulation results meet the preset performance goals, retaining the configuration changes in batches to the drawing parameter configuration file; introducing performance deviation data, combining with the influencing factor map, and making targeted adjustments to the optimization instruction set.

[0015] Preferably, the adaptive optimization method of CAD drawing parameters also includes: extracting high-frequency operation areas and low-frequency idle areas; in the high-frequency operation area, dynamically allocating a first segment of the video memory bandwidth and enabling real-time anti-aliasing technology, while preloading geometric data of adjacent layers into the cache; in the low-frequency idle area, dynamically allocating a second segment of the video memory bandwidth and switching the rendering mode to deferred shading, and at the same time, enabling a dynamic unloading mechanism based on the viewing cone for non-visible areas.

[0016] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goals of adaptive parameter adjustment and performance closed-loop control based on an optimized instruction set, it achieves the technical effects of continuously ensuring drawing smoothness, reducing drawing error rates, and improving the overall rendering efficiency of the system under different hardware environments and task load conditions.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0019] Figure 1 A flowchart of a method for adaptive optimization of CAD drawing parameters according to the present application;

[0020] Figure 2 This is a flow chart of dynamically adjusting the underlying configuration parameters of a CAD drawing tool in a method for adaptively optimizing CAD drawing parameters in this application. DETAILED DESCRIPTION

[0021] This application provides an adaptive optimization method for CAD drawing parameters, resolving the existing technical issues of the inability to effectively allocate video memory bandwidth, rendering load, and layer priority in complex drawing scenarios due to the lack of real-time monitoring and dynamic parameter adjustment mechanisms for system runtime resources. This further affects the stability, drawing quality, and overall rendering performance of the CAD system during high-concurrency, multi-view drawing. This application achieves the technical goal of adaptive parameter adjustment and closed-loop performance control based on an optimized instruction set, achieving the technical effects of continuously ensuring drawing smoothness, reducing drawing error rates, and improving overall system rendering efficiency under different hardware environments and task load conditions.

[0022] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0023] Please see the attached Figure 1, this application provides a method for adaptive optimization of CAD drawing parameters, which specifically includes the following steps:

[0024] S1: Collect user mapping behavior data and layer attribute data, and set the mapping dynamic operation sequence.

[0025] Specifically, during the CAD drawing process, the user's operational behavior will leave behind a lot of data, such as the order in which drawing commands are called, the frequency of use of common tools, the method of selecting graphics elements, and the time interval between operations, etc., which are called user drawing behavior data. Layer attribute data refers to the basic properties of each layer in the drawing, such as layer name, color, line type, fill method, and the number of graphics contained in the layer. By synchronously collecting the two types of data, a detailed drawing behavior trajectory can be established as a dynamic drawing operation sequence, that is, a record of operation steps arranged in chronological order, reflecting the user's operating mode and habits when drawing, such as whether to establish a reference wireframe first, then fill in the area, and finally add dimension information.

[0026] S2: performing a multi-target correlation analysis on the mapping dynamic operation sequence, configuring a mapping parameter adjustment vector, and correcting a parameter adjustment deviation in combination with a material property database.

[0027] Specifically, a dynamic drawing operation sequence refers to the chronological sequence of various drawing operations performed by users in CAD software. It records the order of user operations, operation types, and corresponding parameter settings. Multi-objective association analysis of dynamic drawing operation sequences is performed to explore and model potential connections between operations from multiple dimensions, such as operation duration, the impact of operation results on graphics accuracy, and changes in resource consumption. Using association analysis algorithms, such as decision trees or Apriori rule algorithms, we can identify operations that have the greatest impact on final drawing quality and performance, operations that frequently occur in pairs, or operations with dependencies.

[0028] Next, based on the analysis results, a mapping parameter adjustment vector is generated. Each factor corresponds to a modulatable drawing parameter, such as line width, fill density, and layer priority. The parameter adjustment vector guides the system on how to automatically adjust according to user operating habits, such as setting commonly used line widths to default values ​​or automatically assigning specific layers to certain types of graphics.

[0029] However, relying solely on user operating habits can lead to errors, so corrections are also required in conjunction with a material property database. A material property database is a knowledge base containing the physical material properties corresponding to commonly used drawing objects, such as the structural characteristics, tolerance standards, and typical thickness ranges of different metals or plastics. When users draw mechanical parts or building components, they can automatically retrieve the database content based on the material information annotated in the drawing, determine whether the current parameter settings are reasonable, and then correct the values ​​in the drawing parameter adjustment vector to reduce deviations.

[0030] S3: configuring an optimization instruction set including a line type optimization scheme, a layer management strategy, and rendering configuration parameters according to the mapping parameter adjustment vector and the parameter adjustment deviation.

[0031] Specifically, by combining the drawing parameter adjustment vector and the parameter adjustment deviation, an optimization instruction set is generated, which includes adjustment plans for line types, layers, and rendering configurations. The optimization instruction set is a guidance plan consisting of a series of operating steps and parameter configurations, which is used to automatically adjust the underlying settings of drawing tools to achieve higher drawing efficiency or better drawing quality. It includes line type optimization solutions, that is, adjusting the line type and line style of different graphics or elements to ensure the visual effects and functional requirements of the drawing content. It also includes layer management strategies, which determine how to reasonably organize, manage, and allocate layers to ensure that each layer does not conflict during the drawing process and is easy to modify or review later. It also includes rendering configuration parameters, which are related to graphic display and rendering effects, such as the anti-aliasing effect, shadow effect, material rendering quality, etc. of the graphics. The parameters affect the graphic display effect and rendering efficiency during the drawing process. Through rendering configuration, users can improve rendering speed while ensuring drawing quality and avoid delays in complex graphics or high-precision requirements.

[0032] For example, if a user is found to frequently use thicker lines and complex layer structures during drawing, an adaptive line type optimization scheme and layer management strategy will be automatically configured in the optimization instruction set. If the user is drawing a large-scale architectural drawing, the layer allocation may be adjusted first so that each layer has the best rendering effect and display order. At the same time, appropriate adjustments will be made to the line type to ensure that complex lines can be rendered quickly without affecting the clarity of the drawing. If the rendering parameter configuration tends to be higher quality, the rendering configuration of texture compression or shadow effects may be increased to improve the visual effect of the graphics.

[0033] S4: Based on the optimization instruction set, dynamically adjust the underlying configuration parameters of the CAD drawing tool, generate a self-adaptively optimized drawing parameter configuration file, and monitor the drawing smoothness and drawing error rate of each layer.

[0034] Specifically, the optimization instruction set refers to a set of operating plans for improving drawing performance, covering multiple dimensions such as parameter adjustment, resource allocation, layer optimization, etc., which are used to maximize graphics processing efficiency under different hardware or task conditions. By optimizing the instruction set, the underlying configuration parameters of the CAD drawing tool can be dynamically adjusted, that is, the underlying system settings that affect drawing performance can be modified in real time, such as video memory cache allocation strategy, graphics API rendering priority, number of parallel processing threads, etc. Dynamic adjustment means real-time changes based on operating status, data input, user behavior and other conditions. After the adjustment is completed, an adaptively optimized drawing parameter configuration file is generated. The configuration file is a structured data set that records the current optimal system parameter combination for direct call in subsequent drawing processes or reuse as a template.

[0035] We then continuously monitor the drawing smoothness and drawing error rate of each layer. Drawing smoothness is used to evaluate the speed of screen updates. The drawing error rate measures the frequency of missing, misaligned, or color-distorted graphics during rendering.

[0036] S5: At the same time, according to different drawing tasks, the drawing parameter configuration file is synchronously matched and called, and dynamic feedback adjustment is performed based on the drawing smoothness and drawing error rate of each layer combined with the preset performance target.

[0037] Specifically, according to different drawing tasks, the drawing parameter configuration files are synchronously matched and called. When executing CAD drawing tasks, the corresponding parameter configuration will be automatically selected according to the characteristics of the current task. The drawing parameter configuration file records various parameter combinations related to graphics drawing, such as line width, layer order, anti-aliasing level, texture resolution, etc. Different tasks have different requirements for these parameters. For example, architectural sections focus on clear structural lines, while interior renderings pay more attention to rendering quality. Therefore, according to the task type, such as structural modeling, view annotation or three-dimensional display, the optimal configuration file is matched and called to ensure that the drawing process is both efficient and meets visual specifications.

[0038] Next, we use the drawing smoothness and error rate of each layer to determine whether the current configuration is suitable for the task. Drawing smoothness is usually expressed in frames per second. For example, a frame rate greater than 60 is considered very smooth, while a frame rate less than 30 is noticeable. The error rate measures whether there are issues such as dropped lines, misaligned layers, and rendering interruptions within the layer. By collecting real-time performance metrics, we can further evaluate the effectiveness of task execution.

[0039] At the same time, dynamic feedback adjustments are made based on preset performance targets. Performance targets are a set of predefined performance thresholds for different hardware or application scenarios. When actual performance deviates from these targets, a feedback mechanism is triggered to fine-tune current parameters, such as reducing texture quality, reducing the number of real-time lighting calculations, or adjusting layer compositing methods to re-align with performance requirements.

[0040] Furthermore, the present application also includes: collecting resource occupancy peak data and corresponding parameter configuration snapshots in historical drawing tasks; extracting task allocation characteristics and synchronization delay indicators of parallel computing tasks in the drawing pipeline based on the resource occupancy peak data and corresponding parameter configuration snapshots; optimizing the splitting nodes of the decision tree based on the task allocation characteristics and synchronization delay indicators so that the resource consumption ratio reduction rate of the generated optimization path meets the dynamic optimization threshold.

[0041] Specifically, we collect peak resource usage data from historical drawing tasks. Recording the maximum system resource usage during previous CAD drawing sessions, such as CPU utilization and video memory usage, can reveal performance bottlenecks under high load. At the same time, we also save snapshots of corresponding configuration parameters—the system configuration state at the time of peak resource usage, such as the number of rendering threads, texture compression rate, and layer caching strategy—providing context for subsequent analysis.

[0042] Based on historical data, we further extract task allocation characteristics for parallel computing tasks in the graphics pipeline. Task allocation characteristics indicate how tasks are distributed across a multi-core processor, such as whether the load of rendering a particular frame of graphics is evenly distributed across the four cores. Synchronization latency refers to the time it takes to synchronize data between parallel tasks after they complete execution. For example, if synchronization between two rendering tasks takes 50 milliseconds, this indicates significant latency, potentially causing frame delays or flickering.

[0043] Next, based on the task allocation characteristics and synchronization delay indicators, the split nodes of the optimization decision tree are adjusted. The optimization decision tree is a tree structure used to guide the automatic optimization behavior of the CAD system. Each split node represents a judgment condition, such as whether to enable deferred rendering or layer merging. Adjusting these nodes can change the structure of the entire optimization path, thereby ensuring that the resource consumption ratio of the generated optimization path meets the dynamic optimization threshold. In other words, after each optimization adjustment, system resource consumption (such as CPU, memory, or graphics card load) decreases by a certain percentage compared to the previous level. This percentage is dynamically set based on the current task complexity or hardware status.

[0044] Furthermore, the present application also includes: configuring performance monitoring indicators, which include video memory bandwidth utilization, shader compilation time and geometry culling efficiency; obtaining inter-frame consistency deviation during synchronous rendering of multiple viewports, and using the instruction backlog depth and priority inversion number of the GPU computing queue as auxiliary indicators to measure rendering scheduling efficiency.

[0045] Specifically, configuring performance monitoring indicators means setting a set of parameters in the CAD drawing system for real-time evaluation of graphics rendering and resource usage status, so as to promptly detect performance bottlenecks during operation, including video memory bandwidth utilization, shader compilation time, and geometry culling efficiency. Among them, video memory bandwidth utilization reflects the degree to which the graphics card reads and writes video memory data per unit time. For example, if the bandwidth utilization rate reaches 85% within one second, it means that the data throughput pressure is relatively high. Shader compilation time refers to the time required for the graphics shading program to be compiled from source code into GPU executable code, usually in milliseconds. A high time consumption may cause the first rendering to freeze. Geometry culling efficiency measures the ability to cull invisible graphics (such as occluded objects) during the rendering process. If the efficiency is low, graphics card resources may be wasted on meaningless primitives.

[0046] We further obtain inter-frame consistency deviations during synchronous rendering of multiple viewports. A multi-view viewport refers to a view that displays multiple angles or components simultaneously within a CAD interface. For example, when a plan, elevation, and perspective view coexist, updates to each view must be synchronized. Inter-frame consistency deviation refers to whether there is a synchronization delay between these views when each frame is generated. For example, if the left view generates 60 frames per second, while the right view can only generate 50 frames due to high load, the deviation is 10 frames, which affects the smoothness of user interaction. To further measure the efficiency of rendering scheduling, the instruction backlog depth and priority inversion count of the GPU compute queue are introduced as auxiliary indicators. The instruction backlog depth indicates the number of unprocessed rendering instructions in the GPU task queue. If the depth exceeds 100 instructions, it indicates a problem with scheduling efficiency. The priority inversion count refers to the situation where a low-priority task preempts the processing opportunity of a high-priority task due to a scheduling error, which can cause performance jitter.

[0047] Further, if Figure 2 As shown, the present application also includes: S41: based on the optimization instruction set, dynamically adjusting the texture compression rate and the LOD level switching threshold according to the video memory bandwidth utilization; S42: optimizing the pre-compiled shader cache capacity and the compilation thread pool size based on the shader compilation time, and enabling the asynchronous culling pipeline when it is detected that the geometry culling efficiency is lower than the efficiency setting value.

[0048] Specifically, memory bandwidth utilization refers to the bandwidth usage of the video memory in the graphics processing unit (GPU), that is, the ratio between the data transfer rate of the video memory and its maximum transfer rate during the rendering process. Higher memory bandwidth utilization means more efficient use of video memory when rendering graphics. The texture compression rate can be dynamically adjusted based on the memory bandwidth utilization. The texture compression rate refers to the degree of compression of image or texture data during storage. The higher the compression rate, the less video memory space is required, but it may also result in a decrease in graphics quality. Therefore, the texture compression rate is automatically adjusted according to the memory bandwidth utilization to ensure that the appropriate rendering quality is maintained without exceeding the memory bandwidth limit.

[0049] Furthermore, the LOD switching threshold refers to the critical point at which the graphics' Level of Detail (LOD) switches at different rendering distances. Different graphics may require different LODs at different viewing distances, with distant objects using a low LOD and nearby objects requiring a higher LOD. The switching threshold is dynamically adjusted based on video memory bandwidth utilization to strike a balance between optimizing performance and ensuring graphics quality.

[0050] Shader compilation time refers to the time the GPU spends generating and optimizing graphics shaders during the rendering process. Shaders are programs used to calculate the color of each pixel or vertex. Excessive compilation time can lead to inefficient rendering. To optimize rendering performance, optimize the cache capacity of pre-compiled shaders and the compilation thread pool size. Cache capacity refers to the amount of memory used to store compiled shader code. Increasing the cache capacity appropriately can reduce the overhead of repeated compilations. The compilation thread pool size determines the number of threads that can be used when compiling shaders in parallel. Increasing the thread pool can increase compilation speed.

[0051] Geometric culling efficiency refers to the efficiency with which unnecessary graphical elements are culled during rendering. For example, if an object is completely obscured, rendering should be avoided. Inefficient culling can lead to unnecessary resource waste, so when the geometric culling efficiency is detected to be below a set value, the asynchronous culling pipeline is enabled. The asynchronous culling pipeline parallelizes the culling process with the rendering process, preventing the rendering process from being slowed down by waiting for culling operations and improving overall rendering efficiency. Table 1 shows a recent record of CAD drawing parameter adjustments based on video memory bandwidth and shader compilation optimization.

[0052] Table 1: Recent CAD drawing parameter adjustment records based on video memory bandwidth and shader compilation optimization

[0053]

[0054] Furthermore, the present application also includes: constructing a configuration item dependency graph during the execution of the parameter optimization path; using the configuration item dependency graph, when a configuration conflict is detected, updating the planning constraints, and marking the optimized path after the conflict is resolved as a high-priority template, and associating it with similar hardware configuration files.

[0055] Specifically, during the execution of the parameter optimization path, the dependencies between various configuration parameters are modeled as a configuration item dependency graph. This graph presents the mutual influence of parameters in the form of a graph structure. For example, texture compression rate may be affected by video memory size, while the number of rendering threads is related to the number of CPU cores. This allows analysis of the causal relationships and conditional constraints between parameters, providing a logical foundation for subsequent optimization operations. The process of constructing the configuration item dependency graph can be continuously iterated to adapt to the dynamic changes between parameters in different task scenarios.

[0056] This configuration dependency graph is then used for real-time analysis. If a configuration conflict is detected during execution, such as a parameter being modified by two modules simultaneously, resulting in an inconsistency, planning constraints are automatically updated. Planning constraints refer to the prerequisites and boundaries set during the optimization process, such as a maximum video memory usage of no more than 4000 megabytes or a rendering time of no more than 30 milliseconds. When new conflicts are discovered, they are dynamically corrected based on their impact to avoid compromising the effectiveness of the optimization path.

[0057] To ensure that verified paths can be reused directly in similar hardware environments, conflict-resolved optimization paths are marked as high-priority templates. A high-priority template indicates that the path has been tested and has high applicability for the current hardware and scenario, and therefore is prioritized for parameter matching. To improve migration capabilities, high-priority templates are also linked to similar hardware configuration profiles, recording the configuration details of key hardware resources such as the processor, graphics card, video memory, and bandwidth, ensuring the inheritance and universality of the optimization path.

[0058] Furthermore, the present application also includes: before dynamically adjusting the underlying configuration parameters of the CAD drawing tool, recording the drawing pipeline status snapshot and performance difference data, locating the key configuration items that cause the score to drop; based on the key configuration items, generating compensation adjustment instructions and inserting optimization path interruption points.

[0059] Specifically, before dynamically adjusting the underlying configuration parameters of the CAD drawing tool, it is prioritized to record the state snapshot of the drawing pipeline and the corresponding performance difference data. The drawing pipeline state snapshot refers to the configuration and operating status of each key processing stage at a certain moment, such as the layer call order, rendering thread distribution, cache usage, etc. The performance difference data refers to the changes in indicators under different configurations, such as a drop in frame rate, an increase in video memory usage, or an increase in image jitter, etc., which are used to quantify whether performance degradation occurs at a certain stage, and then locate which configuration items have abnormalities, so as to find out the key factors affecting the overall score decline. A score decline means that the internal comprehensive evaluation indicators are lower than the standard. For example, if the drawing fluency score drops from 90 points to 65 points, it is necessary to find the root cause through retrospective analysis.

[0060] Subsequently, based on the key configuration items located, a set of compensating adjustment instructions are generated to fine-tune or replace key parameters to prevent them from continuing to cause performance problems. For example, if it is detected that the texture preloading mechanism frequently overflows due to insufficient cache, instructions may be automatically generated to increase the cache capacity from 500 megabytes to 800 megabytes. The compensating adjustment instructions will then be inserted into the interruption point of the original optimization path. The interruption point refers to the time node where new operations are allowed to be inserted during the optimization process. It is set before parameter switching, rendering task scheduling, or thread allocation changes to ensure that the compensation behavior has minimal interference with the system and maximum effect.

[0061] Furthermore, this application also includes: in a distributed rendering scenario, analyzing the hardware performance differences of each node, dynamically allocating rendering tasks based on the differences, and synchronously optimizing the parameter configurations between nodes; when the frame generation time difference between nodes exceeds the time difference threshold, enabling the load migration mechanism and reconstructing the optimization path.

[0062] Specifically, in a distributed rendering scenario, performance analysis is performed on each computing node involved in rendering, with a focus on hardware performance differences. Distributed rendering refers to the distribution of a complete drawing or modeling task to multiple devices or processing units for parallel execution to improve processing efficiency. Each node refers to an independent computing device or virtual processing unit, such as different graphics cards, different hosts or virtual machines, which may have differences in graphics card frequency, video memory size, number of computing cores, etc. Hardware performance difference analysis is to collect and compare these indicators to determine which nodes are suitable for processing heavy tasks and which nodes are suitable for processing auxiliary tasks. Rendering tasks are dynamically allocated based on the analysis results. For example, a large number of texture calculation tasks are assigned to high-memory nodes, while geometry culling work is assigned to nodes with high computing frequency. In order to make tasks work together efficiently, the parameter configurations between nodes are optimized synchronously, such as adjusting the layer cache consistency strategy or unifying the compiler version to ensure task collaboration efficiency.

[0063] Next, if it is detected that the time difference between nodes in generating graphics frames exceeds the preset time difference threshold, the load migration mechanism will be automatically activated. Frame generation time refers to the time required from the start of processing a frame to the generation of the complete output image. The time difference threshold is generally set to an acceptable range, such as within 100 milliseconds. Exceeding this range will cause visual freezes or data synchronization failures. The load migration mechanism is a dynamic adjustment method. When a node has a long processing time due to high resource pressure, some tasks are transferred to idle or lightly loaded nodes to balance the overall workload. Load migration is accompanied by the reconstruction of the optimized path. The optimized path refers to the pre-planned task execution order and resource allocation route. When the node structure or task allocation changes, the path must also be reconstructed to adapt to the current structure. Path reconstruction includes operations such as replanning data flow, adjusting parameter priorities, and updating cache strategies to ensure optimal performance during changes.

[0064] Furthermore, the present application also includes: configuring an impact factor map in accordance with the configuration item dependency graph, wherein the impact factor map is used to quantify the weight of each configuration item on drawing quality and performance; when the user inputs a modified parameter, the performance change trend is predicted based on the map and an alarm prompt is generated, and redundant configuration items are identified through a map backtracking mechanism; based on the redundant configuration items, the impact factor map is used to mine potential influencing factors and iteratively correct the optimization instruction set until the drawing quality inspection passes.

[0065] Specifically, based on the existing configuration item dependency structure, a map is further constructed to quantify the impact of each configuration item on drawing effects and computing performance. The configuration item dependency graph is a graphical structure that describes the interactions and logical dependencies between various CAD configuration items. For example, the video memory prefetch parameters may depend on the texture compression rate setting. On this basis, the impact factor map marks the contribution or influence weight of each configuration item on performance indicators such as frame rate, memory usage, rendering time and drawing accuracy, and realizes numerical expression through data analysis. For example, adjusting a certain parameter can increase the frame rate by 15 frames per second, or cause the error rate to increase by 3%, which can provide quantitative support for subsequent automatic optimization.

[0066] Then, when the user manually inputs and modifies certain CAD parameters, the performance change trend brought about by the change can be predicted based on the influencing factor map, and an alarm prompt can be generated. For example, if the user tries to increase the geometric accuracy to a very high level, the map will prompt that the adjustment may cause the frame rate to drop by more than 20 frames per second and occupy an additional 500 megabytes of video memory. The alarm mechanism can remind the user based on the prediction that the current operation may cause a performance bottleneck, thereby avoiding incorrect configuration. In addition, the map backtracking mechanism is started. The backtracking mechanism refers to starting from the current configuration, analyzing its upstream and downstream dependencies to see if there are redundant configuration items that are no longer needed. For example, after a texture enhancement is enabled, its related auxiliary filtering configuration items are no longer necessary but are still retained. Such items can be identified to streamline the configuration structure.

[0067] Furthermore, based on the identified redundant configuration items, the impact factor map is used to continue to explore potential influencing factors, that is, to find configuration paths that indirectly but significantly affect drawing performance or quality. For example, an overlooked lighting parameter may indirectly affect the overall material rendering load. Subsequently, the current optimization instruction set is iteratively revised based on this. The optimization instruction set is a set of specific parameters that guide the underlying CAD tools to adjust. It is dynamically updated to reflect new performance requirements and configuration strategies. The entire iterative process continues until it is detected that the drawing quality inspection has passed. The inspection criteria may include an error rate of less than 2%, a frame rate of more than 60 frames per second, and edge anti-aliasing meeting the set standards.

[0068] Furthermore, the present application also includes: embedding a lightweight sandbox environment to simulate the adjusted drawing pipeline behavior, and when the simulation results meet the preset performance goals, retaining the configuration changes in batches to the drawing parameter configuration file; introducing performance deviation data, combined with the influencing factor map, to make targeted adjustments to the optimization instruction set.

[0069] Specifically, embedding a lightweight sandbox environment to simulate the adjusted drawing pipeline behavior involves testing the modified parameter configuration using a low-resource, independent runtime environment, known as a sandbox, without impacting the actual CAD drawing system. Lightweighting means that this environment does not load the full rendering resources, retaining only the essential rendering core and selected modules, thereby improving simulation efficiency and reducing testing costs. Drawing pipeline behavior refers to the entire CAD image process, from input, modeling, layer processing, shading, geometry culling, to final rendered output. Simulating drawing pipeline behavior verifies system performance under the new configuration in terms of frame rate, memory usage, and rendering latency. When the simulated output meets pre-set performance targets (e.g., a minimum of 60 frames per second, a rendering error of less than 2%, and a response latency of less than 100 milliseconds), the parameter configuration is considered optimized and the configuration changes are batch-written to the drawing parameter configuration file, which records the current system default CAD drawing parameter combination for automatic subsequent recall.

[0070] Next, the difference between the simulation results and the expected performance indicators is introduced into the analysis model. Deviation data, such as the actual frame rate being 8 frames per second lower than the target value and the memory usage being 300 megabytes higher, can accurately reflect the shortcomings of the current configuration. Combined with the impact factor map, that is, the quantitative map constructed previously that reflects the influence weight of each parameter, it is possible to analyze the parameters that cause performance deviation. Subsequently, the optimization instruction set is adjusted in a targeted manner. The optimization instruction set refers to a set of commands that control the automatic adjustment of the underlying parameters of the CAD tool. Targeted adjustment means fine-tuning the key configuration items that cause the deviation, such as reducing the texture size, optimizing the culling strategy, or adjusting the rendering thread pool size, so that the parameters are more in line with the performance goals.

[0071] Furthermore, the present application also includes: extracting high-frequency operation areas and low-frequency idle areas; in the high-frequency operation area, dynamically allocating a first segment of the video memory bandwidth and enabling real-time anti-aliasing technology, while preloading geometric data of adjacent layers into the cache; in the low-frequency idle area, dynamically allocating a second segment of the video memory bandwidth and switching the rendering mode to deferred shading, and at the same time, enabling a dynamic unloading mechanism based on the viewing cone for non-visible areas.

[0072] Specifically, by analyzing user operation trajectories and interactive behaviors within the CAD system, we can identify frequently edited or viewed areas and areas that remain unchanged for extended periods of time. Frequently edited or viewed areas are typically those areas where users repeatedly focus on designing, modifying, dragging, or scaling. For example, in a building plan, these areas might be door and window nodes or pipeline interfaces. Low-frequency, unused areas, on the other hand, might be untouched background structures or areas beneath layer overlays.

[0073] In high-frequency operation areas, the first segment of the video memory bandwidth is dynamically allocated, that is, a higher proportion of video memory resources is allocated to this area to ensure its rendering speed and display quality. The video memory bandwidth determines the rate at which the GPU accesses video memory data. The first segment refers to a portion of dedicated bandwidth resources separated for high-frequency areas. At the same time, real-time anti-aliasing technology is enabled, which uses algorithms such as fast interpolation and sampling smoothing to eliminate line jaggedness and improve image edge clarity. To further improve interactive response efficiency, the geometric data of layers adjacent to the area will also be loaded into the cache in advance. The geometric data contains structural information such as the shape, size, and position of the object, and the cache is a temporary storage area with an access speed much faster than the main memory, which can reduce data reading latency.

[0074] In contrast, in low-frequency idle areas, the second segment of the video memory bandwidth is dynamically allocated, that is, lower video memory resources are provided to this area to avoid resource waste. In addition, the rendering mode will be switched to deferred shading. Deferred shading is an optimization strategy that only draws the geometric information of the scene first, and then performs shading processing after the final display content is determined, thereby reducing the computational overhead of pixel shading operations. At the same time, in order to further free up resources, a dynamic unloading mechanism based on the viewing cone is enabled for non-visible areas. The viewing cone is the visible area range established with the user's camera perspective as the center. Objects beyond the viewing cone will be identified as invisible. Through the dynamic unloading mechanism, the rendering resources of the object will be temporarily removed, thereby reducing the burden on the GPU.

[0075] To sum up, the adaptive optimization method of CAD drawing parameters provided in this application has the following technical effects: by realizing the technical goals of adaptive parameter adjustment and performance closed-loop control based on the optimization instruction set, the technical effects of continuously ensuring drawing smoothness, reducing drawing error rates and improving the overall rendering efficiency of the system under different hardware environments and task load conditions are achieved.

[0076] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. A method for adaptive optimization of CAD drawing parameters, characterized in that: include: Collect user mapping behavior data and layer attribute data, record the operation steps in chronological order, and set the dynamic mapping operation sequence; Performing a multi-objective association analysis on the dynamic mapping operation sequence, mining and modeling potential connections between operations from multiple dimensions, including the duration of the operation, the impact of the operation results on the graphics accuracy, and changes in resource consumption. Using an association analysis algorithm, identifying operations that have the greatest impact on the final drawing quality and performance, operations that frequently appear in pairs, or operations with dependencies, configuring a mapping parameter adjustment vector based on the analysis results, with each factor corresponding to a modulatable drawing parameter, such as line width, fill density, or layer priority, and correcting parameter adjustment deviations in conjunction with a material property database, including automatically retrieving database content based on material information annotated on the graphics, and correcting the values ​​in the mapping parameter adjustment vector; According to the drawing parameter adjustment vector and parameter adjustment deviation, a configuration includes a line type optimization scheme, the line type optimization scheme is used to adjust the line type and line style of different graphics or elements to ensure the visual effect and functional requirements of the drawing content, a layer management strategy and an optimization instruction set of rendering configuration parameters, the optimization instruction set is a scheme consisting of operation steps and parameter configuration, including the line type optimization scheme, the layer management strategy and the rendering configuration parameters, and is used to automatically adjust the underlying settings of the drawing tool; Based on the optimization instruction set, the underlying configuration parameters of the CAD drawing tool are dynamically adjusted to generate an adaptively optimized drawing parameter configuration file, and the drawing smoothness and drawing error rate of each layer are monitored; At the same time, according to different drawing tasks, the drawing parameter configuration file is synchronously matched and called, and dynamic feedback adjustment is performed based on the drawing smoothness and drawing error rate of each layer combined with the preset performance goals.

2. The method for adaptively optimizing CAD drawing parameters according to claim 1, wherein: The method further comprises: Collect resource usage peak data and corresponding parameter configuration snapshots in historical drawing tasks; Extracting task allocation characteristics and synchronization delay indicators of parallel computing tasks in the graphics pipeline based on the resource usage peak data and the corresponding parameter configuration snapshot; The splitting nodes of the decision tree are optimized based on the task allocation characteristics and synchronization delay indicators, so that the resource consumption ratio reduction rate of the generated optimization path meets the dynamic optimization threshold.

3. The method for adaptive optimization of CAD drawing parameters according to claim 2, wherein: Configure performance monitoring indicators, including video memory bandwidth utilization, shader compilation time, and geometry culling efficiency; The inter-frame consistency deviation during synchronous rendering of multiple viewports is obtained, and the instruction backlog depth and priority inversion number of the GPU computing queue are used as auxiliary indicators to measure the rendering scheduling efficiency.

4. The method for adaptively optimizing CAD drawing parameters according to claim 1, wherein: Based on the optimized instruction set, the underlying configuration parameters of the CAD drawing tool are dynamically adjusted, including: Based on the optimized instruction set, the texture compression rate and LOD level switching threshold are dynamically adjusted according to the video memory bandwidth utilization; Optimize the pre-compiled shader cache capacity and compilation thread pool size based on shader compilation time, and enable the asynchronous culling pipeline when it is detected that the geometry culling efficiency is lower than the efficiency setting value.

5. The method for adaptive optimization of CAD drawing parameters according to claim 4, wherein: include: During the execution of the parameter optimization path, a configuration item dependency graph is constructed; Using the configuration item dependency graph, when a configuration conflict is detected, planning constraints are updated, and an optimized path after conflict resolution is marked as a high-priority template and associated with similar hardware configuration files.

6. The method for adaptively optimizing CAD drawing parameters according to claim 5, wherein: Based on the optimization instruction set, the underlying configuration parameters of the CAD drawing tool are dynamically adjusted, which previously included: Before dynamically adjusting the underlying configuration parameters of the CAD drawing tool, record the drawing pipeline status snapshot and performance difference data to locate the key configuration items that cause the score to drop; Based on the key configuration items, compensation adjustment instructions are generated and interruption points in the optimized path are inserted.

7. The method for adaptively optimizing CAD drawing parameters according to claim 6, wherein: include: In distributed rendering scenarios, analyze the hardware performance differences of each node, dynamically allocate rendering tasks based on the differences, and simultaneously optimize parameter configurations between nodes; When the frame generation time difference between nodes exceeds the time difference threshold, the load migration mechanism is enabled and the optimized path is reconstructed.

8. The method for adaptively optimizing CAD drawing parameters according to claim 7, wherein: include: According to the configuration item dependency diagram, an impact factor map is configured, wherein the impact factor map is used to quantify the weight of each configuration item on drawing quality and performance; When users input modified parameters, the performance change trend is predicted based on the graph and an alarm is generated. Redundant configuration items are identified through the graph backtracking mechanism. According to the redundant configuration items, potential influencing factors are mined using the influencing factor map, and the optimization instruction set is iteratively corrected until the drawing quality inspection is passed.

9. The method for adaptively optimizing CAD drawing parameters according to claim 8, wherein: include: Embed a lightweight sandbox environment to simulate the adjusted drawing pipeline behavior, and when the simulation results meet the preset performance target, save the configuration changes in batches to the drawing parameter configuration file; The performance deviation data is introduced and combined with the impact factor map to make targeted adjustments to the optimization instruction set.

10. The method for adaptive optimization of CAD drawing parameters according to claim 9, wherein: Extract high-frequency operating areas and low-frequency idle areas; In the high-frequency operation area, dynamically allocating a first segment of the video memory bandwidth and enabling real-time anti-aliasing technology, while preloading geometric data of adjacent layers into a cache; In the low-frequency idle area, the second segment of the video memory bandwidth is dynamically allocated and the rendering mode is switched to deferred shading. At the same time, a dynamic offloading mechanism based on the viewing cone is enabled for the non-visible area.

Images