Adaptive optimization method for CAD (Computer Aided Design) drawing parameters

Abstract

The invention provides an adaptive optimization method for CAD (Computer Aided Design) drawing parameters, and relates to the technical field of drawing parameter optimization, comprising the following steps: setting a dynamic drawing operation sequence; performing multi-target correlation analysis on the drawing dynamic operation sequence, configuring a drawing parameter adjustment vector, and correcting a parameter adjustment deviation; configuring an optimization instruction set; dynamically adjusting bottom configuration parameters of the CAD drawing tool, generating a drawing parameter configuration file after adaptive optimization, and monitoring drawing fluency and drawing error rate of each layer; and carrying out synchronous matching calling on the drawing parameter configuration file, and carrying out dynamic feedback adjustment through the drawing fluency and the drawing error rate of each layer in combination with a preset performance target. According to the method and the device, the technical targets of adaptive parameter adjustment and performance closed-loop control based on the optimized instruction set can be realized, and the technical effects of continuously guaranteeing the drawing fluency under different hardware environments and task load conditions, reducing the drawing error rate and improving the overall rendering efficiency are achieved.

Description

Technical Field

[0001] This application relates to the technical field of drawing parameter optimization, and particularly to an adaptive optimization method for CAD drawing parameters. Background Art

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

[0003] Currently, most existing CAD systems adopt fixed parameter configurations or rely only on static templates for performance regulation, making it difficult to cope with dynamic resource fluctuations and rapid changes in user operation modes in complex scenarios. Especially in multi-layer, multi-viewport, and high-resolution rendering tasks, it is easy to cause unbalanced resource allocation, video memory bandwidth congestion, graphic lags, and even rendering errors. In addition, current technologies mostly rely on preset thresholds for texture compression ratios and level of detail (LOD) switching strategies, lacking a feedback adjustment mechanism for the real-time utilization rate of video memory bandwidth, and prone to problems such as excessive image quality compression or performance waste.

[0004] In summary, there are technical problems in the prior art that due to the lack of real-time monitoring of the resource status during system operation and a dynamic parameter tuning mechanism, it is impossible to effectively allocate video memory bandwidth, rendering load, and layer priorities in complex drawing scenarios, further affecting the stability, drawing quality, and overall rendering performance of CAD systems during high-concurrency, multi-view drawing processes. 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 problems in the prior art that due to the lack of real-time monitoring of the resource status during system operation and a dynamic parameter tuning mechanism, it is impossible to effectively allocate video memory bandwidth, rendering load, and layer priorities in complex drawing scenarios, further affecting the stability, drawing quality, and overall rendering performance of CAD systems during 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 correlation analysis on the dynamic drawing operation sequence, configuring a drawing parameter adjustment vector, and combining with a material property database to correct the parameter adjustment deviation; configuring an optimization instruction set including a line type optimization scheme, a layer management strategy, and rendering configuration parameters according to 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 fluency and drawing error rate of each layer; at the same time, synchronously matching and calling the drawing parameter configuration file according to different drawing tasks, and dynamically feedback-adjusting according to the drawing fluency and drawing error rate of each layer and a preset performance target.

[0007] Preferably, the adaptive optimization method for CAD drawing parameters further 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 the 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 for CAD drawing parameters further includes: configuring performance monitoring indicators, where the performance monitoring indicators include video memory bandwidth utilization rate, shader compilation time, and geometry culling efficiency; obtaining the inter-frame consistency deviation during multi-viewport synchronous rendering, and using the instruction backlog depth and priority inversion count of the GPU computing queue as auxiliary indicators for measuring the rendering scheduling efficiency.

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

[0010] Preferably, the adaptive optimization method for CAD drawing parameters further includes: constructing a configuration item dependency graph during the execution of the parameter optimization path; using the configuration item dependency graph, when detecting a configuration conflict, updating the planning constraint conditions, marking the optimized path after conflict resolution 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 the drawing pipeline state snapshot 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 of 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, predicting the performance change trend based on the map and generating an alarm prompt, and identifying redundant configuration items through a map backtracking mechanism; based on the redundant configuration items, using the influence factor map, mining potential influencing factors, and iteratively correcting the optimization instruction set until the drawing quality inspection passes.

[0014] Preferably, the adaptive optimization method of CAD drawing parameters also includes: embedding a lightweight sandbox environment, simulating the adjusted drawing pipeline behavior, and when the simulation results meet the preset performance goals, batch retaining the configuration changes 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, while enabling a dynamic unloading mechanism based on a viewing cone for non-visible areas.

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

[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, it can be implemented according to the contents of the specification, and in order 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 cited 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 the present 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 in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a method for adaptive optimization of CAD drawing parameters in this application;

[0020] Figure 2 This is a schematic diagram of a process for dynamically adjusting underlying configuration parameters of a CAD drawing tool in a method for adaptively optimizing CAD drawing parameters in the present application. DETAILED DESCRIPTION

[0021] This application provides an adaptive optimization method for CAD drawing parameters, which solves the technical problem that the existing technology cannot effectively allocate video memory bandwidth, rendering load and layer priority in complex drawing scenes due to the lack of real-time monitoring and dynamic parameter adjustment mechanism of system runtime resource status, further affecting the stability, drawing quality and overall rendering performance of the CAD system in high concurrency and multi-view drawing processes. The technical goal of adaptive parameter adjustment and performance closed-loop control based on optimized instruction sets is achieved, achieving the technical effect of continuously ensuring drawing fluency, reducing drawing error rate and improving the overall rendering efficiency of the system under different hardware environments and task load conditions.

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

[0023] Please see attached Figure 1, the present 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, in the CAD drawing process, the user's operation behavior will leave a lot of data, such as the order of drawing command calls, the frequency of use of common tools, the selection method of graphics elements, and the time interval of operations, which are called user drawing behavior data. Layer attribute data refers to the basic attributes 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 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 operation 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 multi-objective correlation analysis on the mapping dynamic operation sequence, configuring a mapping parameter adjustment vector, and correcting parameter adjustment deviations in combination with a material property database.

[0027] Specifically, the dynamic operation sequence of drawing refers to the time sequence of various drawing operations performed by users in CAD software, which records the order of user operations, operation types and their corresponding parameter settings. Multi-objective association analysis is performed on the dynamic operation sequence of drawing, and the potential connections between operations are mined and modeled from multiple dimensions. The dimensions may include the duration of the operation, the impact of the operation results on the graphics accuracy, the changes in resource consumption, etc. Through association analysis algorithms, such as decision trees or Apriori rule algorithms, the operations that have the greatest impact on the final drawing quality and performance, operations that often appear in pairs, or operations with dependencies can be identified.

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

[0029] However, relying solely on user operating habits may lead to errors, so it is also necessary to combine the material property database for correction. The material property database is a knowledge base that contains the physical material properties corresponding to commonly used drawing objects, such as the structural properties, tolerance standards, typical thickness ranges, etc. 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 graphics, 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 a rendering configuration parameter 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 schemes for line types, layers and rendering configurations. The optimization instruction set is a guidance scheme consisting of a series of operation 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 schemes, that is, adjusting the line types and line styles of different graphics or elements to ensure the visual effects and functional requirements of the drawing content. It also includes layer management strategies to determine how to reasonably organize, manage and allocate layers to ensure that each layer will not conflict during the drawing process and is easy to modify or consult later. It also includes rendering configuration parameters, which are related to graphic display and rendering effects, such as anti-aliasing effects, shadow effects, material rendering quality, etc. 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 under 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, the optimization instruction set will automatically configure the adaptive line type optimization scheme and layer management strategy. 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, and the line type is adjusted appropriately to ensure that complex lines can be rendered quickly without affecting the clarity of the drawing. If the rendering parameter configuration is biased towards 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 an 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 solutions 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. The optimization instruction set can dynamically adjust the underlying configuration parameters of the CAD drawing tool, that is, to make real-time modifications to the underlying system settings that affect drawing performance, 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 or template reuse in subsequent drawing processes.

[0035] Then, the drawing smoothness and drawing error rate of each layer are continuously monitored. Drawing smoothness is used to evaluate the screen update speed. The drawing error rate measures the frequency of missing, misaligned or color deviation problems in the graphics rendering process.

[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 through 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, building profiles 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, the drawing smoothness and drawing error rate of each layer are used to determine whether the current configuration is suitable for the task requirements. Drawing smoothness is usually expressed in frames per second. For example, more than 60 frames per second is considered very smooth, and less than 30 frames per second will feel obvious lag; the drawing error rate measures whether there are problems such as lost lines, misaligned layers, and rendering breaks in the layer. By collecting real-time performance indicators, the task execution effect can be further evaluated.

[0039] At the same time, dynamic feedback adjustment is performed in combination with preset performance targets. Performance targets refer to a series of performance indicator thresholds predefined for different hardware or application scenarios. When it is detected that the current actual performance deviates from these targets, the feedback mechanism will be triggered to fine-tune the current parameters, such as reducing texture quality, reducing the number of real-time lighting calculations, or adjusting the layer synthesis method, so as to re-match the 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 the 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 the resource usage peak data in historical drawing tasks and record the maximum value of system resource usage in previous CAD drawing processes, such as CPU usage and video memory usage, to reflect the performance bottleneck of the system under high load. At the same time, we also save the corresponding parameter configuration snapshot, that is, the system configuration status when resource usage reaches the peak, such as the number of rendering threads, texture compression rate, layer cache strategy, etc., to provide background conditions for subsequent analysis.

[0042] Based on historical data, we further extract the task allocation features of parallel computing tasks in the graphics pipeline. The task allocation features indicate how tasks are allocated on a multi-core processor, such as whether the load distribution of a certain frame of graphics rendering task is balanced among the four cores. The synchronization delay index refers to the time consumed when data is synchronized between parallel tasks after execution. For example, if it takes fifty milliseconds for two rendering tasks to synchronize, it means that there is a large delay, which may cause frame delay 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 delayed rendering, whether to enable layer merging, etc. Adjusting these nodes can change the structure of the entire optimization path, so that the resource consumption ratio of the generated optimization path meets the dynamic optimization threshold. That is to say, after each optimization adjustment, the system resource consumption (such as CPU, memory or graphics card load) has a certain degree of decrease compared to before, and the percentage is dynamically set according to the current task complexity or hardware status.

[0044] Furthermore, this application also includes: configuring performance monitoring metrics, which include video memory bandwidth utilization rate, shader compilation time consumption, and geometric culling efficiency; obtaining the inter-frame consistency deviation during multi-viewport synchronous rendering, and using the instruction backlog depth and priority inversion count of the GPU computing queue as auxiliary metrics to measure the rendering scheduling efficiency.

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

[0046] Furthermore, obtain the inter-frame consistency deviation during multi-viewport synchronous rendering. A multi-viewport refers to a view that simultaneously displays multiple angles or components in the CAD interface. For example, when a plan view, elevation view, and perspective view coexist, the update of each view must be time-synchronized. The inter-frame consistency deviation refers to whether there is a synchronization delay when these views are generated for each frame. For example, if the left view generates sixty frames per second, while the right view can only generate fifty frames due to high load, the deviation is ten frames, which will affect the smoothness of user interaction. To further measure the efficiency of the rendering scheduling, the instruction backlog depth and priority inversion count of the GPU computing queue are introduced as auxiliary metrics. The instruction backlog depth represents the number of unprocessed rendering instructions in the GPU task queue. If this depth exceeds one hundred instructions, it indicates that there are problems with the 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 scheduling errors, which will cause performance jitter.

[0047] Furthermore, as Figure 2 shown, this application also includes: S41: Based on the optimized instruction set, dynamically adjust the texture compression ratio and LOD level switching threshold according to the video memory bandwidth utilization rate; S42: Optimize the pre-compiled shader cache capacity and the size of the compilation thread pool based on the shader compilation time consumption, and enable the asynchronous culling pipeline when it is detected that the geometric culling efficiency is lower than the set efficiency value.

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

[0049] In addition, the LOD level switching threshold refers to the critical point at which the level of detail (LOD) of a graphic switches at different rendering distances. Different graphics may require different levels of detail at different viewing distances. Distant objects can use a low level of detail, while nearby objects require a higher level of detail. The switching threshold is dynamically adjusted according to the utilization of the video memory bandwidth to find a balance between optimizing performance and ensuring graphic quality.

[0050] The shader compilation time refers to the time taken by the GPU to generate and optimize graphic shaders during the rendering process. A shader is a program used to calculate the color of each pixel or vertex. An overly long compilation time will result in low rendering efficiency. To optimize the rendering performance, the cache capacity of the pre-compiled shaders and the size of the compilation thread pool are optimized. The cache capacity refers to the amount of memory used to store the compiled shader code. Appropriately increasing the cache capacity can reduce the overhead of repeated compilations, while the size of the compilation thread pool determines the number of threads that can be used when compiling shaders in parallel. Increasing the thread pool can improve the compilation speed.

[0051] The geometric culling efficiency refers to the efficiency of culling graphic elements that do not need to be drawn during the rendering process. For example, when an object is completely occluded, the rendering of that object should be avoided. Low culling efficiency may lead to unnecessary resource waste. Therefore, when it is detected that the geometric culling efficiency is lower than the set value, an asynchronous culling pipeline is enabled. The asynchronous culling pipeline refers to processing the culling process in parallel with the rendering process, thus preventing the rendering process from slowing down due to waiting for the culling operation and improving the overall rendering efficiency. Table 1 shows the adjustment record of CAD drawing parameters based on the optimization of video memory bandwidth and shader compilation for the most recent time.

[0052] Table 1: Adjustment Record of CAD Drawing Parameters Based on the Optimization of Video Memory Bandwidth and Shader Compilation for the Most Recent Time

[0053]

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

[0055] Specifically, during the execution of the parameter optimization path, the dependency relationships between various configuration parameters are modeled as a configuration item dependency graph. The configuration item dependency graph presents the mutual influence between parameters in the form of a graph structure. For example, the texture compression ratio may be affected by the video memory size, and the number of rendering threads is related to the number of CPU cores, and then the causal relationships and conditional constraints between parameters are analyzed, providing a logical basis 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] Next, the configuration item dependency graph is used for real-time analysis. When a configuration conflict is detected during actual execution, such as a certain parameter being modified simultaneously by two modules resulting in inconsistency, the planning constraints will be automatically updated. The planning constraints refer to the preconditions and boundaries set during the optimization process, such as the maximum video memory occupancy not exceeding 4000 megabytes or the rendering time not exceeding 30 milliseconds. When new conflicts are discovered, they will be dynamically corrected according to the impact of the conflicts to avoid affecting the effectiveness of the optimization path.

[0057] At the same time, to ensure that the verified path can be directly reused in a similar hardware environment in the future, the optimized path with resolved conflicts is marked as a high-priority template. A high-priority template means that this path has passed tests and has high applicability in the current hardware and scenario, so it is preferentially called for parameter matching. To improve the migration ability, the high-priority template is also associated with the same type of hardware configuration file, recording the configuration details of key hardware resources such as the processor, graphics card, video memory, and bandwidth, ensuring the inheritance and generality of the optimization path.

[0058] Furthermore, this application also includes: before dynamically adjusting the underlying configuration parameters of the CAD drawing tool, recording the snapshot of the drawing pipeline state and the performance difference data, and locating the key configuration items that cause the score to drop; based on the key configuration items, generating a compensation adjustment instruction and inserting it at the optimization path breakpoint.

[0059] Specifically, before dynamically adjusting the underlying configuration parameters of the CAD drawing tool, a status snapshot of the drawing pipeline and the corresponding performance difference data are preferentially recorded. The status snapshot of the drawing pipeline refers to the configuration and running status of each key processing stage at a certain moment, such as the layer call order, the distribution of rendering threads, the cache usage, etc. The performance difference data refers to the index changes shown under different configurations, such as a decrease in frame rate, an increase in video memory occupancy, or an increase in image jitter, etc., which is used to quantify whether performance degradation occurs at a certain stage, and further locate which configuration items are abnormal, so as to find out the key factors affecting the overall score decline. The score decline means that the internal comprehensive evaluation index is lower than the standard. For example, the drawing fluency score drops from 90 points to 65 points, and root causes need to be found through backtracking analysis.

[0060] Subsequently, based on the located key configuration items, a set of compensation adjustment instructions are generated to fine-tune or replace the key parameters to avoid 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. Then, the compensation adjustment instructions will be inserted into the breakpoint position of the original optimization path. The breakpoint refers to the time node where new operations are allowed to be inserted during the optimization process, which is set before parameter switching, rendering task scheduling, or thread allocation change to ensure that the compensation behavior has the least interference and the greatest effect on the system.

[0061] Furthermore, this application also includes: in a distributed rendering scenario, analyzing the hardware performance differences of each node, dynamically allocating rendering tasks according to the differences, and synchronously optimizing the parameter configuration 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, the performance of each computing node participating in the rendering is analyzed, with a focus on the hardware performance differences. Distributed rendering means that a complete drawing or modeling task is distributed 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 aspects such as graphics card frequency, video memory size, and the number of computing cores. The hardware performance difference analysis is to collect and compare these indicators to determine the nodes suitable for handling heavy tasks and the nodes suitable for handling auxiliary tasks. According to the analysis results, the rendering tasks are dynamically allocated. For example, a large number of texture calculation tasks are assigned to high-video-memory nodes, while the geometry culling work is assigned to nodes with high computing frequencies. In order to make the tasks cooperate efficiently, the parameter configuration between each node is synchronously optimized, such as adjusting the layer cache consistency strategy or unifying the compiler version to ensure the task cooperation efficiency.

[0063] Next, if the time difference between the nodes in generating the graphic frames exceeds the preset time difference threshold, the load migration mechanism will be automatically enabled. The frame generation time refers to the time required from the start of processing a picture to the generation of a complete output image. The time difference threshold is generally set within a tolerable range, such as within 100 milliseconds. Exceeding this range will cause visual stuttering or data synchronization failure. The load migration mechanism is a dynamic adjustment method. When a certain node has a long processing time due to high resource pressure, part of the tasks will be transferred to idle or less-loaded nodes, so as to balance the overall workload. Load migration is accompanied by the reconstruction of the optimization path. The optimization path refers to the pre-planned task execution order and resource allocation route. When the node structure or task assignment changes, this path also needs to be reconstructed to adapt to the current structure. Path reconstruction includes operations such as re-planning the data flow, adjusting parameter priorities, and updating the cache policy to ensure optimal performance is maintained during the change.

[0064] Furthermore, this application also includes: configuring an influence factor map according to the configuration item dependency graph, where the influence factor map is used to quantify the weights of each configuration item on the drawing quality and performance; when the user inputs modified parameters, predicting the performance change trend based on the map and generating an alarm prompt, and identifying redundant configuration items through the map backtracking mechanism; according to the redundant configuration items and the influence factor map, mining potential influencing factors and iteratively correcting the optimization instruction set until the drawing quality inspection passes.

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

[0066] Next, when the user manually inputs to modify certain CAD parameters, the performance change trend brought about by the changes can be predicted based on the impact factor map, and an alarm prompt can be generated. For example, if the user attempts to increase the geometric accuracy to a very high level, the map will prompt that this 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, thus avoiding misconfiguration. In addition, start the map backtracking mechanism. The backtracking mechanism means starting from the current configuration and analyzing whether there are redundant configuration items that are no longer needed in its upstream and downstream dependencies. For example, after a certain texture enhancement is enabled, its related auxiliary filtering configuration item is no longer necessary but is still retained, and such items can be identified to streamline the configuration structure.

[0067] Furthermore, based on the identified redundant configuration items, continue to explore potential influencing factors that may exist using the impact factor map, that is, find configuration paths that indirectly but significantly affect the drawing performance or quality. For example, a neglected lighting parameter may indirectly affect the overall material rendering load. Subsequently, the current optimization instruction set is iteratively corrected accordingly. The optimization instruction set is a set of instructions that guide the underlying CAD tool to adjust specific parameters and 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 can include an error rate lower than 2%, a frame rate higher than 60 frames per second, and edge anti-aliasing reaching the set standard, etc.

[0068] Further, this application also includes: embedding a lightweight sandbox environment to simulate the behavior of the adjusted drawing pipeline. When the simulation results meet the preset performance goals, batch retain the configuration changes to the drawing parameter configuration file; introduce performance deviation data and, in combination with the impact factor map, make a directional adjustment to the optimization instruction set.

[0069] Specifically, embedding a lightweight sandbox environment to simulate the behavior of the adjusted drawing pipeline means that, without affecting the operation of the actual CAD drawing system, a relatively resource - consuming independent operating environment, namely the sandbox environment, is used to conduct simulation tests on the modified parameter configuration. Lightweight means that this environment does not load complete rendering resources, but only retains the necessary rendering cores and some modules, thereby improving the simulation efficiency and reducing the test cost. The behavior of the drawing pipeline refers to the entire process from the input, modeling, layer processing, shading, geometric culling to the final rendering output of the CAD image. Simulating the behavior of the drawing pipeline can verify the performance of the system in terms of frame rate, memory occupancy, rendering latency, etc. under the new configuration. When the simulation output results reach the preset performance goals, that is, the performance standards set early, such as the number of frames per second is not less than 60 frames, the rendering error is less than 2%, and the response latency is less than 100 milliseconds, etc., it is considered that the parameter configuration meets the optimization requirements, and this configuration change is batch - written into the drawing parameter configuration file to record the current system - default CAD drawing parameter combination for subsequent automatic invocation.

[0070] Immediately afterwards, the difference value 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 occupancy being 300 megabytes higher can accurately reflect the deficiencies of the current configuration. Combining with the influence factor map, that is, the quantitative map constructed previously that reflects the influence weights of each parameter, it is possible to analyze the parameters that caused the performance deviation. Subsequently, the optimization instruction set is adjusted directionally. The optimization instruction set refers to the command set that controls the automatic adjustment of the underlying parameters of the CAD tool, and directional adjustment means making fine - tuning for the key configuration items that cause the deviation, such as reducing the texture size, optimizing the culling strategy, or adjusting the size of the rendering thread pool, etc., so that the parameters further conform to the performance goals.

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

[0072] Specifically, by analyzing the operation trajectories and interaction behaviors of users in the CAD system, the graph areas that are frequently edited or viewed and the graph areas that remain unchanged for a long time are identified. The high - frequency operation area is usually the part that users repeatedly focus on during operations such as design, modification, dragging, or scaling. For example, in an architectural floor plan, parts such as door and window nodes and pipeline interfaces may be high - frequency operation areas. The low - frequency idle area may be the background structure or the lower - layer area of the layer superposition that has not been involved for the time being.

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

[0074] Relatively, in the low-frequency idle region, the second segment of the video memory bandwidth is dynamically allocated, that is, a lower video memory resource is provided for this part of the region to avoid resource waste. In addition, the rendering mode will be switched to deferred shading. Deferred shading is an optimization strategy that first only draws the geometric information of the scene and then performs the shading process when the final display content is determined, thereby reducing the computational overhead of pixel shading operations. At the same time, to further release resources, a frustum-based dynamic unloading mechanism is enabled for non-visible regions. The frustum is the visible region range centered on the user's camera perspective, and objects outside the frustum will be recognized as invisible. Through the dynamic unloading mechanism, the rendering resources of the object will be temporarily removed, thus reducing the GPU burden.

[0075] In summary, an adaptive optimization method for CAD drawing parameters provided by this application has the following technical effects: By achieving the technical goal of adaptive parameter adjustment and performance closed-loop control based on an optimized instruction set, the technical effects of continuously ensuring drawing fluency, reducing the drawing error rate, and improving the overall system rendering efficiency under different hardware environments and task load conditions are achieved.

[0076] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of this application and its equivalent technologies, this application also intends to include these changes 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, and set dynamic mapping operation sequences; Performing multi-objective correlation analysis on the mapping dynamic operation sequence, configuring mapping parameter adjustment vectors, and correcting parameter adjustment deviations in combination with a material property database; According to the mapping parameter adjustment vector and parameter adjustment deviation, an optimization instruction set including a line type optimization scheme, a layer management strategy and a rendering configuration parameter is configured; 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 through the drawing smoothness and drawing error rate of each layer combined with the preset performance goals.

2. A method for adaptively optimizing CAD drawing parameters as claimed in claim 1, characterized in that: 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 occupancy peak data and the corresponding parameter configuration snapshots; The split 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. A method for adaptively optimizing CAD drawing parameters as claimed in claim 2, characterized in that: Configure performance monitoring indicators, including video memory bandwidth utilization, shader compilation time, and geometry culling efficiency; The inter-frame consistency deviation is obtained when rendering multiple viewports synchronously, and the instruction backlog depth and priority inversion times 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, characterized in that: Based on the optimization instruction set, the underlying configuration parameters of the CAD drawing tool are dynamically adjusted, including: Based on the optimized instruction set, dynamically adjust the texture compression rate and LOD level switching threshold 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. A method for adaptively optimizing CAD drawing parameters as claimed in claim 4, characterized in that: 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, the planning constraints are updated, and the optimized path after the conflict is resolved is marked as a high priority template and associated with the same type of hardware configuration files.

6. A method for adaptively optimizing CAD drawing parameters as claimed in claim 5, characterized in that: Based on the optimization instruction set, the underlying configuration parameters of the CAD drawing tool are dynamically adjusted, including: 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 optimized path interruption points are inserted.

7. A method for adaptively optimizing CAD drawing parameters as claimed in claim 6, characterized in that: include: In distributed rendering scenarios, analyze the differences in hardware performance 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. A method for adaptively optimizing CAD drawing parameters as claimed in claim 7, characterized in that: include: According to the configuration item dependency diagram, an impact factor map is configured, and the impact factor map is used to quantify the weight of each configuration item on drawing quality and performance; When the user inputs the modified parameters, the performance change trend is predicted based on the graph and an alarm is generated. The redundant configuration items are identified through the graph backtracking mechanism. According to the redundant configuration items, potential influencing factors are mined with the influencing factor map, and the optimization instruction set is iteratively corrected until the drawing quality inspection passes.

9. A method for adaptively optimizing CAD drawing parameters as claimed in claim 8, characterized in that: include: Embed a lightweight sandbox environment to simulate the adjusted drawing pipeline behavior, and when the simulation result meets 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 influencing factor map to make targeted adjustments to the optimization instruction set.

10. A method for adaptively optimizing CAD drawing parameters as claimed in claim 9, characterized in that: Extract high-frequency operating areas and low-frequency idle areas; In the high frequency operation area, dynamically allocating a first segment of 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. Meanwhile, a dynamic unloading mechanism based on a viewing cone is enabled for the non-visible area.

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