Simulation compiling method, system and medium based on cloud 3D digital space

Abstract

This application provides a simulation compilation method, system, and medium based on cloud-based 3D digital space. The method includes: obtaining the type of the target simulation compilation task; decomposing the target simulation compilation task according to the type to obtain a multi-layered compilation architecture; obtaining the resource allocation ratio corresponding to each layer of the compilation architecture; obtaining the computing power requirement corresponding to each layer of the compilation architecture; performing dynamic scheduling of elastic computing power; constructing a cloud-based compilation cache pool for the target simulation compilation task and executing a cache pool management strategy; monitoring the operation of the target simulation compilation task within a preset time period; extracting operation indicator data; judging the operation effect; and executing corresponding optimization measures, thereby realizing the simulation compilation technology based on cloud-based 3D digital space.

Description

Technical Field

[0001] This application relates to the field of simulation compilation technology, and more specifically, to simulation compilation methods, systems, and media based on cloud-based 3D digital space. Background Technology

[0002] With the rapid development of 3D digital space technology, simulation compilation tasks are becoming increasingly complex, and the requirements for compilation efficiency, computing power adaptation, and operational stability are constantly increasing. Existing simulation compilation solutions mostly rely on local computing power or fixed cloud configurations, which have problems such as inaccurate architecture decomposition and unbalanced resource allocation, making it difficult to match the differentiated needs of different types of simulation compilation tasks. At the same time, computing power scheduling lacks flexibility, which can easily lead to computing power redundancy or insufficiency. Moreover, the compilation process lacks an efficient caching mechanism, resulting in prominent repeated compilation and low compilation efficiency.

[0003] Furthermore, existing solutions lack the ability to monitor and dynamically optimize the operation process in real time, making it difficult to avoid operational failures and improve compilation results in a timely manner. Against this backdrop, there is an urgent need for a simulation compilation technology that is adapted to the characteristics of cloud-based 3D digital space and can achieve precise architecture decomposition, elastic computing power scheduling, efficient cache management, and dynamic optimization, so as to solve the existing technical bottlenecks and meet the requirements for efficient and stable operation of complex simulation compilation tasks. Summary of the Invention

[0004] The purpose of this application is to provide a simulation compilation method, system, and medium based on cloud-based 3D digital space. This method involves obtaining the type of the target simulation compilation task, decomposing the task according to its type to obtain a multi-layered compilation architecture, acquiring the resource allocation ratio and computing power requirements for each layer, and performing dynamic scheduling of elastic computing power. It also involves constructing a cloud-based compilation cache pool for the target simulation compilation task, implementing cache pool management strategies, monitoring the operation of the target simulation compilation task within a preset time period, extracting operational indicator data, judging the operational effect, and implementing corresponding optimization measures. This enables the technology of simulation compilation based on cloud-based 3D digital space.

[0005] This application also provides a simulation compilation method based on cloud-based 3D digital space, including the following steps: Obtain the type of the target simulation compilation task; The target simulation compilation task is decomposed according to the type to obtain a multi-layer compilation architecture, and the resource allocation ratio corresponding to each layer of the compilation architecture is obtained. Obtain the computing power requirements corresponding to each layer of the compilation architecture, and perform dynamic scheduling of elastic computing power; A cloud-based compilation cache pool is constructed for the target simulation compilation task, and a cache pool management strategy is executed. Monitor the execution of the target simulation compilation task within a preset time period, extract the execution index data, judge the execution effect, and implement corresponding optimization measures.

[0006] Optionally, in the cloud-based 3D digital space simulation compilation method described in this application, the type of obtaining the target simulation compilation task includes: Obtain the target simulation compilation task; The target simulation compilation task is categorized based on the scenario scale, target terminal, and operational requirements to determine its type. The scale of the scenarios includes individual devices, production lines, and city-level operations. The target terminal includes computers, mobile devices, and browsers; The operational requirements include real-time simulation and offline analysis.

[0007] Optionally, in the cloud-based 3D digital space simulation compilation method described in this application, the step of decomposing the target simulation compilation task according to the type to obtain a multi-layered compilation architecture and obtaining the resource allocation ratio corresponding to each layer of the compilation architecture includes: The target simulation compilation task is decomposed according to the type to obtain a multi-layered compilation architecture; The multi-layered compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging and deployment. Obtain the CPU utilization, GPU utilization, task execution time, and parallelism corresponding to each layer of the compilation architecture; Based on the CPU utilization, GPU utilization, task execution time, and parallelism, a preset resource allocation weighting algorithm is used to obtain the resource allocation ratio corresponding to each layer of the compilation architecture.

[0008] Optionally, in the cloud-based 3D digital space simulation compilation method described in this application, the step of obtaining the computing power requirements corresponding to each layer of the compilation architecture and performing elastic computing power dynamic scheduling includes: The real-time resource utilization rate of each layer of the compilation architecture is collected and compared with the corresponding resource allocation ratio to obtain the corresponding utilization rate deviation rate. If the occupancy deviation rate is greater than the first preset deviation rate threshold, the corresponding compilation architecture task is determined to be abnormal and an alarm needs to be triggered. If the occupancy deviation rate is less than or equal to the first preset deviation rate threshold and greater than the second preset deviation rate threshold, then the corresponding compilation architecture task is judged to be normal. If the occupancy deviation rate is less than or equal to the second preset deviation rate threshold, it is determined that the corresponding compilation architecture has excess resources and needs to be scaled down to release resources.

[0009] Optionally, in the cloud-based 3D digital space simulation compilation method described in this application, the step of constructing a cloud-based compilation cache pool for the target simulation compilation task and executing a cache pool management strategy includes: A cloud-based compilation cache pool is constructed for the target simulation compilation task, and corresponding cache pool management strategies are executed for each layer of the compilation architecture. The cache pool management strategy includes cache cleanup strategy, cache synchronization strategy, and cache hit rate improvement strategy; The cache cleanup strategy includes expired cache cleanup, LRU cache cleanup, and inefficient cache cleanup. The cache synchronization strategy includes multi-node synchronization and off-site disaster recovery. The cache hit rate improvement strategies include pre-caching, fuzzy matching, and statistical optimization.

[0010] Optionally, in the cloud-based 3D digital space simulation compilation method described in this application, the step of monitoring the operation of the target simulation compilation task within a preset time period, extracting operation index data, judging the operation effect, and executing corresponding optimization measures includes: Monitor the execution of the target simulation compilation task within a preset time period, and extract the execution index data, including the full compilation time, incremental compilation time, number of exceptions, total number of runs, number of executable tasks, and total number of tasks; The compilation efficiency coefficient is obtained by calculating the full compilation time and the incremental compilation time. The abnormality rate coefficient is obtained by calculating and processing the number of abnormalities and the total number of runs. The reliability coefficient is obtained by performing calculations based on the number of executable tasks and the total number of tasks. The runtime performance coefficient is obtained by weighting the compilation efficiency coefficient, runtime error rate coefficient, and reliability coefficient. The threshold comparison result is obtained by comparing the operation effect coefficient with the preset operation effect threshold. The running effect is judged based on the threshold comparison results, and corresponding optimization measures are implemented.

[0011] Secondly, this application provides a simulation compilation system based on cloud-based 3D digital space. The system includes a memory and a processor. The memory includes a program for a simulation compilation method based on cloud-based 3D digital space. When the program for the simulation compilation method based on cloud-based 3D digital space is executed by the processor, it performs the following steps: Obtain the type of the target simulation compilation task; The target simulation compilation task is decomposed according to the type to obtain a multi-layer compilation architecture, and the resource allocation ratio corresponding to each layer of the compilation architecture is obtained. Obtain the computing power requirements corresponding to each layer of the compilation architecture, and perform dynamic scheduling of elastic computing power; A cloud-based compilation cache pool is constructed for the target simulation compilation task, and a cache pool management strategy is executed. Monitor the execution of the target simulation compilation task within a preset time period, extract the execution index data, judge the execution effect, and implement corresponding optimization measures.

[0012] Optionally, in the cloud-based 3D digital space simulation compilation system described in this application, the type of obtaining the target simulation compilation task includes: Obtain the target simulation compilation task; The target simulation compilation task is categorized based on the scenario scale, target terminal, and operational requirements to determine its type. The scale of the scenarios includes individual devices, production lines, and city-level operations. The target terminal includes computers, mobile devices, and browsers; The operational requirements include real-time simulation and offline analysis.

[0013] Optionally, in the cloud-based 3D digital space simulation compilation system described in this application, the step of decomposing the target simulation compilation task according to the type to obtain a multi-layered compilation architecture and obtaining the resource allocation ratio corresponding to each layer of the compilation architecture includes: The target simulation compilation task is decomposed according to the type to obtain a multi-layered compilation architecture; The multi-layered compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging and deployment. Obtain the CPU utilization, GPU utilization, task execution time, and parallelism corresponding to each layer of the compilation architecture; Based on the CPU utilization, GPU utilization, task execution time, and parallelism, a preset resource allocation weighting algorithm is used to obtain the resource allocation ratio corresponding to each layer of the compilation architecture.

[0014] Thirdly, this application also provides a computer-readable storage medium storing a simulation compilation method program based on cloud-based 3D digital space. When the simulation compilation method program based on cloud-based 3D digital space is executed by a processor, it implements the steps of the simulation compilation method based on cloud-based 3D digital space as described in any of the above claims.

[0015] As can be seen from the above, the simulation compilation method, system, and medium based on cloud-based 3D digital space provided in this application obtain the type of the target simulation compilation task, decompose the target simulation compilation task according to the type to obtain a multi-layered compilation architecture, obtain the resource allocation ratio corresponding to each layer of the compilation architecture, obtain the computing power requirement corresponding to each layer of the compilation architecture, and perform elastic computing power dynamic scheduling. A cloud-based compilation cache pool is built for the target simulation compilation task, and a cache pool management strategy is executed. The operation of the target simulation compilation task is monitored within a preset time period, the operation index data is extracted, the operation effect is judged, and corresponding optimization measures are executed, thereby realizing the simulation compilation technology based on cloud-based 3D digital space.

[0016] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the simulation and compilation method based on cloud-based 3D digital space provided in this application embodiment; Figure 2 This is a schematic diagram of the architecture of the simulation compilation method based on cloud-based 3D digital space provided in the embodiments of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart of a cloud-based 3D digital space simulation and compilation method according to some embodiments of this application. This cloud-based 3D digital space simulation and compilation method is used in terminal devices, such as computers and mobile terminals. The cloud-based 3D digital space simulation and compilation method includes the following steps: S11. Obtain the type of the target simulation compilation task; S12. Decompose the target simulation compilation task according to the type to obtain a multi-layer compilation architecture and obtain the resource allocation ratio corresponding to each layer of the compilation architecture. S13. Obtain the computing power requirements corresponding to each layer of the compilation architecture and perform dynamic scheduling of elastic computing power; S14. Construct a cloud-based compilation cache pool for the target simulation compilation task and execute the cache pool management strategy; S15. Monitor the operation of the target simulation compilation task within a preset time period, extract the operation index data, judge the operation effect, and implement corresponding optimization measures.

[0022] In step S11, accurately obtaining the target simulation compilation task type is the core prerequisite for achieving efficient architecture decomposition, resource adaptation, and computing power scheduling in the cloud-based 3D digital space simulation compilation system.

[0023] In some embodiments, step S11 may include the following sub-steps: S111, obtaining the target simulation compilation task; S112, classifying the target simulation compilation task according to the scenario scale, target terminal, and operational requirements to obtain the type of the target simulation compilation task; S113, the scenario scale includes single device, production line, and city-level; S114, the target terminal includes computer, mobile terminal, and browser; S115, the operational requirements include real-time simulation and offline analysis.

[0024] In step S111, the system first obtains the basic information of the target simulation compilation task through the cloud interface and task submission channel, including key elements such as core task requirements, related data, and application scenarios, providing comprehensive data support for subsequent type classification. In step S112, based on the actual application requirements of the simulation compilation task, it classifies the task according to three core dimensions: scenario scale, target terminal, and operational requirements, thus determining the specific type of the target simulation compilation task. In step S113, the scenario scale dimension is divided into single-device level, production line level, and city level based on the simulation coverage, with different scales corresponding to different compilation complexities and resource consumption. In step S114, the target terminal dimension is divided into computer terminal, mobile terminal, and browser terminal based on the task deployment carrier, adapting to the operating environment and performance limitations of different terminals. In step S115, the operational requirements dimension is divided into real-time simulation and offline analysis based on the task execution mode; the former focuses on compilation speed and real-time response capability, while the latter emphasizes compilation accuracy and data processing efficiency.

[0025] In step S12, precise breakdown and resource allocation planning based on task type is the core prerequisite for improving compilation efficiency and optimizing resource allocation. The specific implementation process must closely match the characteristics of the task to ensure smooth connection between each link.

[0026] In some embodiments, step S12 may include the following sub-steps: S121, decomposing the target simulation compilation task according to the type to obtain a multi-layer compilation architecture; S122, the multi-layer compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging and deployment; S123, obtaining the CPU utilization rate, GPU utilization rate, task execution time, and parallelism corresponding to each layer of the compilation architecture; S124, processing the CPU utilization rate, GPU utilization rate, task execution time, and parallelism according to a preset resource allocation weight algorithm to obtain the resource allocation ratio corresponding to each layer of the compilation architecture.

[0027] In step S121, firstly, based on the clearly defined target simulation compilation task type (divided according to factors such as scene scale, target terminal, and operational requirements), the target simulation compilation task is specifically decomposed and processed. In step S122, the multi-layered compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging deployment. This layered architecture follows the entire simulation compilation process, with each layer having clearly defined functions and working in synergy: asset preprocessing is responsible for the standardized processing of basic resources such as 3D models and textures, providing compliant materials for subsequent compilation; logic compilation focuses on the parsing and transformation of simulation business logic, ensuring the accuracy of task execution logic; rendering adaptation optimizes rendering parameters for different target terminal characteristics, ensuring cross-terminal compatibility; and packaging deployment completes the encapsulation of the compilation results and cloud deployment adaptation, supporting the rapid deployment and operation of the task. In step S123, after decomposition, the core operating parameters of each layer of the compilation architecture are accurately collected, including the CPU utilization, GPU utilization, task execution time, and parallelism of each layer. The CPU and GPU utilization directly reflect the intensity of each layer's demand for cloud computing resources. For example, the rendering adaptation layer involves a large amount of graphics processing, resulting in a significantly higher GPU utilization than other layers. The task execution time is related to the compilation efficiency target of each layer, while the parallelism determines the concurrent processing capacity that each layer can handle. In step S124, the collected multi-dimensional parameters are input into a preset resource allocation weight algorithm. The algorithm, combined with the priority requirements corresponding to the task type, assigns differentiated weights to each parameter and completes comprehensive calculations, ultimately outputting the resource allocation ratio corresponding to each layer of the compilation architecture, achieving precise matching of resources with the compilation requirements of each layer.

[0028] In step S13, accurately controlling the computing power requirements of each layer of the compilation architecture and implementing dynamic scheduling of elastic computing power is a key step to ensure the efficient progress of compilation tasks and avoid resource waste or insufficient computing power. This step uses the resource allocation ratio of each layer of the compilation architecture as a benchmark and achieves accurate adaptation of computing power through real-time monitoring and dynamic judgment.

[0029] In some embodiments, step S13 may include the following sub-steps: S131, collecting the real-time resource occupancy rate corresponding to each layer of the compilation architecture and comparing it with the corresponding resource allocation ratio to obtain the corresponding occupancy rate deviation rate; S132, if the occupancy rate deviation rate is greater than a first preset deviation rate threshold, then the corresponding compilation architecture task is determined to be abnormal and an alarm needs to be triggered; S133, if the occupancy rate deviation rate is less than or equal to the first preset deviation rate threshold and greater than a second preset deviation rate threshold, then the corresponding compilation architecture task is determined to be normal; S134, if the occupancy rate deviation rate is less than or equal to the second preset deviation rate threshold, then the corresponding compilation architecture resources are determined to be excessive and resource reduction needs to be initiated.

[0030] In step S131, a cloud-based monitoring system continuously collects real-time resource utilization data for each layer of the compilation architecture (asset preprocessing, logic compilation, rendering adaptation, and packaging deployment), covering the real-time usage of core computing resources such as CPU and GPU. Subsequently, the collected real-time resource utilization is compared with the pre-determined resource allocation ratios for each layer, and the difference is calculated and normalized to accurately obtain the corresponding utilization deviation rate. This deviation rate directly reflects the degree of matching between the current computing power allocation and actual needs. In step S132, if the utilization deviation rate is greater than a first preset deviation rate threshold, it indicates that the resource utilization of the current compilation architecture task far exceeds expectations, and there is a high probability of task anomalies or resource contention. In case of an issue, an alarm mechanism must be triggered immediately to notify operations and maintenance personnel to intervene and investigate, preventing the anomaly from spreading and affecting the overall compilation progress. In step S133, if the utilization rate deviation rate is less than or equal to the first preset deviation rate threshold and greater than the second preset deviation rate threshold, it indicates that the current resource utilization and allocation ratio are basically matched, the compilation architecture task is running normally, and the existing computing power configuration can be maintained. In step S134, if the utilization rate deviation rate is less than or equal to the second preset deviation rate threshold, it is determined that the corresponding compilation architecture has a resource surplus. At this time, the scaling-down mechanism is activated to release redundant computing power resources and return idle resources to the cloud resource pool to achieve efficient resource reuse and ensure the optimized configuration of overall computing power resources.

[0031] In step S14, building a dedicated cloud-based compilation cache pool and implementing a refined management strategy are key measures to improve compilation efficiency and reduce redundant computing power consumption. At the same time, optimizing resource allocation by combining computing power scheduling feedback can further achieve efficient resource utilization.

[0032] In some embodiments, step S14 may include the following sub-steps: S141, constructing a cloud-based compilation cache pool for the target simulation compilation task, and executing corresponding cache pool management strategies for each layer of the compilation architecture; S142, the cache pool management strategies include cache cleanup strategies, cache synchronization strategies, and cache hit rate improvement strategies; S143, the cache cleanup strategies include expired cleanup, LRU cleanup, and inefficient cleanup; S144, the cache synchronization strategies include multi-node synchronization and off-site disaster recovery; S145, the cache hit rate improvement strategies include pre-caching, fuzzy matching, and statistical optimization.

[0033] In step S141, a dedicated cloud-based compilation cache pool is built for the target simulation compilation task. This cache pool can centrally store and manage the intermediate compilation results of each layer of the compilation architecture (asset preprocessing, logic compilation, rendering adaptation, and packaging deployment). In step S142, to ensure the efficient operation of the cache pool, corresponding cache pool management strategies need to be matched for each layer of the compilation architecture, covering three core dimensions: cache cleanup, cache synchronization, and cache hit rate improvement. In step S143, the cache cleanup strategy includes expired cleanup, LRU (Least Recently Used) cleanup, and inefficient cleanup: expired cleanup automatically deletes cached data that has exceeded its storage validity period, freeing up storage space; LRU cleanup prioritizes the removal of long-lived cached data. Unaccessed cache items are removed to ensure fast access to high-frequency data; inefficient cleanup removes cached data with low access efficiency, optimizing the overall performance of the cache pool; in step S144, the cache synchronization strategy covers multi-node synchronization and off-site disaster recovery. Multi-node synchronization ensures real-time consistency of cached data between different cloud nodes, while off-site disaster recovery ensures the security of cached data through off-site backup, avoiding data loss due to extreme failures; in step S145, the cache hit rate improvement strategy includes pre-caching, fuzzy matching, and statistical optimization: pre-caching predicts high-frequency demand data based on task type and stores it in advance, fuzzy matching enables cache reuse for similar compilation needs, and statistical optimization adjusts the cache storage structure by analyzing historical access data.

[0034] In step S15, conducting full-cycle operational monitoring, precise effect evaluation, and dynamic optimization of the target simulation compilation task is a key closed-loop link to ensure the stable and efficient progress of the task and continuously improve the compilation quality. This link, through systematic monitoring and quantitative analysis, promptly identifies operational shortcomings and optimizes them in a targeted manner.

[0035] In some embodiments, step S15 may include the following sub-steps: S151, monitoring the operation of the target simulation compilation task within a preset time period, and extracting operation index data, including full compilation time, incremental compilation time, number of exceptions, total number of runs, number of executable tasks, and total number of tasks; S152, calculating and processing based on the full compilation time and incremental compilation time to obtain a compilation efficiency coefficient; S153, calculating and processing based on the number of exceptions and total number of runs to obtain an operation exception rate coefficient; S154, calculating and processing based on the number of executable tasks and total number of tasks to obtain a reliability rate coefficient; S155, performing weighted processing based on the compilation efficiency coefficient, operation exception rate coefficient, and reliability rate coefficient to obtain an operation effect coefficient; S156, comparing the operation effect coefficient with a preset operation effect threshold to obtain a threshold comparison result; S157, judging the operation effect based on the threshold comparison result and executing corresponding optimization measures.

[0036] In step S151, the entire process of the target simulation compilation task is continuously tracked and data is collected within a preset time period. Core performance indicators reflecting task performance quality are comprehensively extracted, specifically including full compilation time, incremental compilation time, number of exceptions, total number of runs, number of executable tasks, and total number of tasks. Full compilation time and incremental compilation time are directly related to the core objective of compilation efficiency. Full compilation time refers to the total time from the start of the target simulation compilation task to the completion of full resource compilation (including asset preprocessing, logic compilation, etc.), reflecting the efficiency of the complete compilation process. Incremental compilation time, for scenarios involving task updates or partial modifications, is the time spent compiling only the changed parts, measuring the efficiency of incremental compilation. The core indicators of translation optimization effect are: the number of exceptions and the total number of runs, which reflect the stability of task operation. The number of exceptions is the total number of abnormal events such as errors, stutters, and interruptions that occur during the compilation task within a preset time period, directly reflecting the operational stability. The total number of runs is the cumulative number of times the target simulation compilation task is started and run within a preset time period, providing basic data for calculating the exception rate. The number of executable tasks and the total number of tasks are related to the reliability of task execution. The number of executable tasks refers to the number of tasks that can normally execute the simulation function after compilation, reflecting the effectiveness of the compilation results. The total number of tasks refers to the total number of all subtasks or single runs included in the target simulation compilation task, which is the core basis for calculating the reliability rate. Among them, in this step S152 In step S153, the compilation efficiency coefficient is derived by normalizing the compilation time based on the full compilation time and incremental compilation time, combined with the time benchmark value corresponding to the task type. In step S154, the runtime anomaly rate coefficient is obtained by calculating the ratio of the number of exceptions to the total number of runs; a lower coefficient indicates stronger runtime stability. In step S155, the reliability coefficient is calculated using the ratio of the number of executable tasks to the total number of tasks; a higher coefficient indicates higher reliability of task execution. In step S156, based on the core requirements of the simulation compilation task, differentiated weights are assigned to the compilation efficiency coefficient, runtime anomaly rate coefficient, and reliability coefficient, and a weighted summation is performed to obtain the overall runtime effect reflecting the task's runtime quality. The coefficient is used to compare the performance coefficient with a preset performance threshold in step S156 to obtain a threshold comparison result. In step S157, the performance of the task is judged based on the threshold comparison result, and corresponding optimization measures are implemented: if the performance coefficient is higher than the preset threshold, it indicates that the task is running well and the existing performance configuration can be maintained; if the performance coefficient is within the preset threshold range, it indicates that the task is basically up to standard, but there is room for optimization, and the caching strategy or computing power allocation ratio can be adjusted accordingly; if the performance coefficient is lower than the preset threshold, it indicates that there are obvious shortcomings in the task, and a deep optimization mechanism needs to be started immediately, such as re-disassembling the compilation architecture, optimizing the resource allocation algorithm, or repairing high-frequency abnormal nodes.

[0037] Please refer to Figure 2 , Figure 2 This is a schematic diagram of the architecture of the cloud-based 3D digital space simulation compilation method provided in the embodiments of this application. According to the embodiments of the present invention, this application proposes a cloud-based 3D digital space simulation compilation method: first, the task type is obtained, decomposed into a multi-layer compilation architecture and the resource allocation ratio is determined; then, elastic computing power is scheduled, a cloud-based compilation cache pool is constructed and managed; finally, the running indicators are monitored, the effect is judged and optimized to realize the simulation compilation in this scenario.

[0038] Secondly, the present invention also discloses a simulation compilation system based on cloud-based 3D digital space, including a memory and a processor. The memory includes a simulation compilation method program based on cloud-based 3D digital space. When the simulation compilation method program based on cloud-based 3D digital space is executed by the processor, it performs the following steps: Obtain the type of the target simulation compilation task; The target simulation compilation task is decomposed according to the type to obtain a multi-layer compilation architecture, and the resource allocation ratio corresponding to each layer of the compilation architecture is obtained. Obtain the computing power requirements corresponding to each layer of the compilation architecture, and perform dynamic scheduling of elastic computing power; A cloud-based compilation cache pool is constructed for the target simulation compilation task, and a cache pool management strategy is executed. Monitor the execution of the target simulation compilation task within a preset time period, extract the execution index data, judge the execution effect, and implement corresponding optimization measures.

[0039] In the cloud-based 3D digital space simulation and compilation system, accurately obtaining the target simulation and compilation task type is the core prerequisite for achieving efficient architecture decomposition, resource adaptation, and computing power scheduling.

[0040] In some embodiments, when the simulation compilation method program based on cloud-based 3D digital space is executed by the processor, it performs the following steps: obtaining the target simulation compilation task; classifying the target simulation compilation task according to the scene scale, target terminal, and operational requirements to obtain the type of the target simulation compilation task; the scene scale includes single device, production line, and city level; the target terminal includes computer, mobile terminal, and browser; the operational requirements include real-time simulation and offline analysis.

[0041] First, the system obtains complete basic information about the target simulation compilation task through cloud interfaces and task submission channels, including key elements such as core task requirements, related data, and application scenarios, providing comprehensive data support for subsequent type classification. Then, based on the actual application requirements of the simulation compilation task, it classifies the task into three core dimensions: scenario scale, target terminal, and operational requirements, thereby determining the specific type of the target simulation compilation task. The scenario scale dimension is divided into single-device level, production line level, and city level based on the simulation coverage, with different scales corresponding to varying compilation complexity and resource consumption. The target terminal dimension is divided into computer, mobile, and browser terminals based on the task deployment platform, adapting to the operating environment and performance limitations of different terminals. The operational requirements dimension is divided into real-time simulation and offline analysis based on the task execution mode; the former emphasizes compilation speed and real-time response capabilities, while the latter focuses more on compilation accuracy and data processing efficiency.

[0042] Among them, conducting precise task breakdown and resource allocation planning based on task type is the core prerequisite for improving compilation efficiency and optimizing resource allocation. The specific implementation process must closely match the characteristics of the task to ensure smooth connection between each link.

[0043] In some embodiments, when the simulation compilation method program based on cloud-based 3D digital space is executed by the processor, it implements the following steps: decomposing the target simulation compilation task according to the type to obtain a multi-layer compilation architecture; the multi-layer compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging and deployment; obtaining the CPU utilization rate, GPU utilization rate, task execution time, and parallelism corresponding to each layer of the compilation architecture; and processing the CPU utilization rate, GPU utilization rate, task execution time, and parallelism according to a preset resource allocation weight algorithm to obtain the resource allocation ratio corresponding to each layer of the compilation architecture.

[0044] First, based on the clearly defined target simulation compilation task types (divided according to factors such as scene scale, target terminal, and operational requirements), the target simulation compilation tasks are specifically broken down and processed. The multi-layered compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging deployment. This layered architecture follows the entire simulation compilation process, with each layer having clearly defined functions and working in synergy: asset preprocessing is responsible for the standardized processing of basic resources such as 3D models and textures, providing compliant materials for subsequent compilation; logic compilation focuses on the parsing and transformation of simulation business logic, ensuring the accuracy of task execution logic; rendering adaptation optimizes rendering parameters for different target terminal characteristics, ensuring cross-terminal compatibility; and packaging deployment completes the encapsulation of the compiled results and cloud deployment adaptation, supporting rapid task deployment. The process involves several steps. First, after decomposition, core operational parameters of each layer's compilation architecture are precisely collected. These parameters include CPU utilization, GPU utilization, task execution time, and parallelism for each layer. CPU and GPU utilization directly reflect the intensity of each layer's demand for cloud computing resources. For example, the rendering adaptation layer, due to its extensive graphics processing, has a significantly higher GPU utilization than other layers. Task execution time is related to the compilation efficiency targets of each layer, while parallelism determines the concurrent processing capacity that each layer can handle. The collected multi-dimensional parameters are then input into a pre-defined resource allocation weighting algorithm. This algorithm, combined with the priority requirements corresponding to the task type, assigns differentiated weights to each parameter and performs comprehensive calculations, ultimately outputting the resource allocation ratio corresponding to each layer's compilation architecture, achieving precise matching between resources and the compilation requirements of each layer.

[0045] Among them, accurately controlling the computing power requirements of each layer of the compilation architecture and implementing elastic computing power dynamic scheduling is a key link to ensure the efficient progress of compilation tasks and avoid resource waste or insufficient computing power. This link uses the resource allocation ratio of each layer of the compilation architecture as a benchmark and achieves accurate adaptation of computing power through real-time monitoring and dynamic judgment.

[0046] In some embodiments, when the simulation compilation method program based on cloud-based 3D digital space is executed by the processor, it performs the following steps: collecting the real-time resource occupancy rate corresponding to each layer of the compilation architecture and comparing it with the corresponding resource allocation ratio to obtain the corresponding occupancy rate deviation rate; if the occupancy rate deviation rate is greater than a first preset deviation rate threshold, it is determined that the corresponding compilation architecture task is abnormal and an alarm needs to be triggered; if the occupancy rate deviation rate is less than or equal to the first preset deviation rate threshold and greater than a second preset deviation rate threshold, it is determined that the corresponding compilation architecture task is normal; if the occupancy rate deviation rate is less than or equal to the second preset deviation rate threshold, it is determined that the corresponding compilation architecture resources are excessive and resource reduction needs to be initiated.

[0047] The system continuously collects real-time resource utilization data for each layer of the compilation architecture (asset preprocessing, logic compilation, rendering adaptation, and packaging deployment) using a cloud-based monitoring system, covering the real-time usage of core computing resources such as CPU and GPU. Then, the collected real-time resource utilization is compared with the pre-determined resource allocation ratios for each layer, and the difference is calculated and normalized to accurately obtain the corresponding utilization deviation rate. This deviation rate directly reflects the degree of matching between the current computing power allocation and actual demand. If the utilization deviation rate is greater than a first preset deviation rate threshold, it indicates that the resource consumption of the current compilation architecture task far exceeds expectations, and there is a high probability of task anomalies or resource contention. In case of an issue, an alarm mechanism must be triggered immediately to notify operations and maintenance personnel to intervene and investigate, preventing the anomaly from spreading and affecting the overall compilation progress. If the utilization rate deviation rate is less than or equal to the first preset deviation rate threshold and greater than the second preset deviation rate threshold, it indicates that the current resource utilization and allocation ratio are basically matched, the compilation architecture tasks are running normally, and the existing computing power configuration can be maintained. If the utilization rate deviation rate is less than or equal to the second preset deviation rate threshold, it is determined that the corresponding compilation architecture has a resource surplus. At this time, the scaling-down mechanism is activated to release redundant computing power resources and return idle resources to the cloud resource pool to achieve efficient resource reuse and ensure the optimized configuration of overall computing power resources.

[0048] Among these measures, building a dedicated cloud-based compilation cache pool and implementing a refined management strategy are key steps to improve compilation efficiency and reduce redundant computing power consumption. At the same time, optimizing resource allocation by combining computing power scheduling feedback can further achieve efficient resource utilization.

[0049] In some embodiments, when the simulation compilation method program based on cloud-based 3D digital space is executed by the processor, it implements the following steps: constructing a cloud-based compilation cache pool for the target simulation compilation task, and executing corresponding cache pool management strategies for each layer of the compilation architecture; the cache pool management strategies include cache cleanup strategies, cache synchronization strategies, and cache hit rate improvement strategies; the cache cleanup strategies include expired cleanup, LRU cleanup, and inefficient cleanup; the cache synchronization strategies include multi-node synchronization and off-site disaster recovery; the cache hit rate improvement strategies include pre-caching, fuzzy matching, and statistical optimization.

[0050] Specifically, a dedicated cloud-based compilation cache pool is built for the target simulation compilation task. This cache pool can centrally store and manage intermediate compilation results from each layer of the compilation architecture (asset preprocessing, logic compilation, rendering adaptation, and packaging deployment). To ensure the efficient operation of the cache pool, corresponding cache pool management strategies need to be matched for each layer of the compilation architecture, covering three core dimensions: cache cleanup, cache synchronization, and cache hit rate improvement. The cache cleanup strategy includes expired cleanup, LRU (Least Recently Used) cleanup, and inefficient cleanup: expired cleanup automatically deletes cached data that has exceeded its storage validity period, freeing up storage space; LRU cleanup prioritizes the removal of cached data that has not been accessed for a long time. The system includes cached items to ensure fast access to high-frequency data; inefficient cleanup removes cached data with low access efficiency, optimizing the overall performance of the cache pool; the cache synchronization strategy covers multi-node synchronization and off-site disaster recovery, multi-node synchronization ensures real-time consistency of cached data between different cloud nodes, and off-site disaster recovery ensures the security of cached data through off-site backup to avoid data loss due to extreme failures; the cache hit rate improvement strategy includes pre-caching, fuzzy matching and statistical optimization: pre-caching predicts high-frequency demand data based on task type and stores it in advance, fuzzy matching enables cache reuse for similar compilation needs, and statistical optimization adjusts the cache storage structure by analyzing historical access data.

[0051] Among them, conducting full-cycle operation monitoring, accurate effect evaluation and dynamic optimization of the target simulation compilation task is a key closed-loop link to ensure the stable and efficient progress of the task and the continuous improvement of compilation quality. This link, through systematic monitoring and quantitative analysis, can promptly identify operational shortcomings and optimize them in a targeted manner.

[0052] In some embodiments, when the simulation compilation method program based on cloud-based 3D digital space is executed by the processor, it performs the following steps: monitoring the operation of the target simulation compilation task within a preset time period, extracting operation index data, including full compilation time, incremental compilation time, number of exceptions, total number of runs, number of executable tasks, and total number of tasks; calculating and processing the full compilation time and incremental compilation time to obtain a compilation efficiency coefficient; calculating and processing the number of exceptions and total number of runs to obtain an operation exception rate coefficient; calculating and processing the number of executable tasks and total number of tasks to obtain a reliability coefficient; weighting the compilation efficiency coefficient, operation exception rate coefficient, and reliability coefficient to obtain an operation effect coefficient; comparing the operation effect coefficient with a preset operation effect threshold to obtain a threshold comparison result; judging the operation effect based on the threshold comparison result and performing corresponding optimization measures.

[0053] The process involves continuous tracking and data collection of the entire execution status of the target simulation compilation task within a preset time period. This comprehensively extracts core metrics reflecting task execution quality, specifically including full compilation time, incremental compilation time, number of exceptions, total number of runs, number of executable tasks, and total number of tasks. Full compilation time and incremental compilation time are directly related to the core objective of compilation efficiency. Full compilation time refers to the total time from the start of the target simulation compilation task to the completion of full resource compilation (including asset preprocessing, logic compilation, etc.), reflecting the efficiency of the complete compilation process. Incremental compilation time, for scenarios involving task updates or partial modifications, refers to the time spent compiling only the changed parts. Time is a core indicator for measuring the effectiveness of incremental compilation optimization; the number of exceptions and the total number of runs reflect the stability of task execution. The number of exceptions is the total number of abnormal events such as errors, stutters, and interruptions that occur during the execution of the compilation task within a preset time period, directly reflecting the operational stability. The total number of runs is the cumulative number of times the target simulation compilation task is started and run within a preset time period, providing basic data for calculating the exception rate; the number of executable tasks and the total number of tasks are related to the reliability of task execution. The number of executable tasks refers to the number of tasks that can normally execute the simulation function after compilation, reflecting the effectiveness of the compilation results. The total number of tasks refers to the total number of all subtasks or tasks in a single run of the target simulation compilation task. The core metrics for calculating reliability are as follows: The compilation efficiency coefficient is derived by normalizing the full compilation time and incremental compilation time based on the time baseline values ​​corresponding to the task type. The runtime anomaly rate coefficient is obtained by calculating the ratio of the number of exceptions to the total number of runs; a lower coefficient indicates stronger operational stability. The reliability coefficient is calculated using the ratio of executable tasks to the total number of tasks; a higher coefficient indicates higher task execution reliability. Finally, based on the core requirements of the simulation compilation task, differentiated weights are assigned to the compilation efficiency coefficient, runtime anomaly rate coefficient, and reliability coefficient. A weighted summation is then performed to obtain the overall runtime performance coefficient, which comprehensively reflects the task's operational quality. The performance coefficient is calculated by comparing it with a preset performance threshold. The threshold comparison result is then used to determine the task's performance and implement corresponding optimization measures: if the performance coefficient is higher than the preset threshold, the task is running well and the existing configuration can be maintained; if the performance coefficient is within the preset threshold range, the task is basically up to standard but has room for optimization, requiring targeted adjustments to the caching strategy or computing power allocation ratio; if the performance coefficient is lower than the preset threshold, the task has significant shortcomings and a deep optimization mechanism needs to be initiated immediately, such as re-disassembling the compilation architecture, optimizing resource allocation algorithms, or fixing frequently occurring abnormal nodes.

[0054] A third aspect of the present invention provides a readable storage medium storing a simulation compilation method program based on cloud-based 3D digital space. When the simulation compilation method program based on cloud-based 3D digital space is executed by a processor, it implements the steps of the simulation compilation method based on cloud-based 3D digital space as described in any of the preceding claims.

[0055] This invention discloses a simulation compilation method, system, and medium based on cloud-based 3D digital space. It obtains the type of the target simulation compilation task, decomposes the task according to the type to obtain a multi-layered compilation architecture, acquires the resource allocation ratio and computing power requirements for each layer, performs dynamic scheduling of elastic computing power, constructs a cloud-based compilation cache pool for the target simulation compilation task, executes cache pool management strategies, monitors the operation of the target simulation compilation task within a preset time period, extracts operational indicator data, judges the operational effect, and executes corresponding optimization measures, thereby realizing the technology of simulation compilation based on cloud-based 3D digital space.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0057] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0058] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0059] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A simulation and compilation method based on cloud-based 3D digital space, characterized in that, Includes the following steps: Obtain the type of the target simulation compilation task; The target simulation compilation task is decomposed according to the type to obtain a multi-layer compilation architecture, and the resource allocation ratio corresponding to each layer of the compilation architecture is obtained. Obtain the computing power requirements corresponding to each layer of the compilation architecture, and perform dynamic scheduling of elastic computing power; A cloud-based compilation cache pool is constructed for the target simulation compilation task, and a cache pool management strategy is executed. Monitor the execution of the target simulation compilation task within a preset time period, extract the execution index data, judge the execution effect, and implement corresponding optimization measures.

2. The simulation and compilation method based on cloud-based 3D digital space according to claim 1, characterized in that, The type of the target simulation compilation task to be acquired includes: Obtain the target simulation compilation task; The target simulation compilation task is categorized based on scenario scale, target terminal, and operational requirements to determine its type. The scale of the scenarios includes individual devices, production lines, and city-level operations. The target terminal includes computers, mobile devices, and browsers; The operational requirements include real-time simulation and offline analysis.

3. The simulation and compilation method based on cloud-based 3D digital space according to claim 1, characterized in that, The step of decomposing the target simulation compilation task according to the type to obtain a multi-layered compilation architecture and obtaining the resource allocation ratio corresponding to each layer of the compilation architecture includes: The target simulation compilation task is decomposed according to the type to obtain a multi-layered compilation architecture; The multi-layered compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging and deployment. Obtain the CPU utilization, GPU utilization, task execution time, and parallelism corresponding to each layer of the compilation architecture; Based on the CPU utilization, GPU utilization, task execution time, and parallelism, a preset resource allocation weighting algorithm is used to obtain the resource allocation ratio corresponding to each layer of the compilation architecture.

4. The simulation and compilation method based on cloud-based 3D digital space according to claim 3, characterized in that, The step of obtaining the computing power requirements corresponding to each layer of the compilation architecture and performing dynamic scheduling of elastic computing power includes: The real-time resource utilization rate of each layer of the compilation architecture is collected and compared with the corresponding resource allocation ratio to obtain the corresponding utilization rate deviation rate. If the occupancy deviation rate is greater than the first preset deviation rate threshold, the corresponding compilation architecture task is determined to be abnormal and an alarm needs to be triggered. If the occupancy deviation rate is less than or equal to the first preset deviation rate threshold and greater than the second preset deviation rate threshold, then the corresponding compilation architecture task is judged to be normal. If the occupancy deviation rate is less than or equal to the second preset deviation rate threshold, it is determined that the corresponding compilation architecture has excess resources and needs to be scaled down to release resources.

5. The simulation and compilation method based on cloud-based 3D digital space according to claim 1, characterized in that, The step of constructing a cloud-based compilation cache pool for the target simulation compilation task and executing a cache pool management strategy includes: A cloud-based compilation cache pool is constructed for the target simulation compilation task, and corresponding cache pool management strategies are executed for each layer of the compilation architecture. The cache pool management strategy includes cache cleanup strategy, cache synchronization strategy, and cache hit rate improvement strategy; The cache cleanup strategy includes expired cache cleanup, LRU cache cleanup, and inefficient cache cleanup. The cache synchronization strategy includes multi-node synchronization and off-site disaster recovery. The cache hit rate improvement strategies include pre-caching, fuzzy matching, and statistical optimization.

6. The simulation and compilation method based on cloud-based 3D digital space according to claim 1, characterized in that, The monitoring of the target simulation compilation task's operation within a preset time period, extraction of operational indicator data, assessment of operational effectiveness, and execution of corresponding optimization measures include: Monitor the execution of the target simulation compilation task within a preset time period, and extract the execution index data, including the full compilation time, incremental compilation time, number of exceptions, total number of runs, number of executable tasks, and total number of tasks; The compilation efficiency coefficient is obtained by calculating the full compilation time and the incremental compilation time. The abnormality rate coefficient is obtained by calculating and processing the number of abnormalities and the total number of runs. The reliability coefficient is obtained by performing calculations based on the number of executable tasks and the total number of tasks. The runtime performance coefficient is obtained by weighting the compilation efficiency coefficient, runtime error rate coefficient, and reliability coefficient. The threshold comparison result is obtained by comparing the operation effect coefficient with the preset operation effect threshold. The running effect is judged based on the threshold comparison results, and corresponding optimization measures are implemented.

7. A simulation and compilation system based on cloud-based 3D digital space, characterized in that, The system includes a memory and a processor. The memory contains a program for a simulation compilation method based on cloud-based 3D digital space. When the program for the simulation compilation method based on cloud-based 3D digital space is executed by the processor, it performs the following steps: Obtain the type of the target simulation compilation task; The target simulation compilation task is decomposed according to the type to obtain a multi-layer compilation architecture, and the resource allocation ratio corresponding to each layer of the compilation architecture is obtained. Obtain the computing power requirements corresponding to each layer of the compilation architecture, and perform dynamic scheduling of elastic computing power; A cloud-based compilation cache pool is constructed for the target simulation compilation task, and a cache pool management strategy is executed. Monitor the execution of the target simulation compilation task within a preset time period, extract the execution index data, judge the execution effect, and implement corresponding optimization measures.

8. The simulation and compilation system based on cloud-based 3D digital space according to claim 7, characterized in that, The type of the target simulation compilation task to be acquired includes: Obtain the target simulation compilation task; The target simulation compilation task is categorized based on scenario scale, target terminal, and operational requirements to determine its type. The scale of the scenarios includes individual devices, production lines, and city-level operations. The target terminal includes computers, mobile devices, and browsers; The operational requirements include real-time simulation and offline analysis.

9. The simulation and compilation system based on cloud-based 3D digital space according to claim 7, characterized in that, The step of decomposing the target simulation compilation task according to the type to obtain a multi-layered compilation architecture and obtaining the resource allocation ratio corresponding to each layer of the compilation architecture includes: The target simulation compilation task is decomposed according to the type to obtain a multi-layered compilation architecture; The multi-layered compilation architecture includes asset preprocessing, logic compilation, rendering adaptation, and packaging and deployment. Obtain the CPU utilization, GPU utilization, task execution time, and parallelism corresponding to each layer of the compilation architecture; Based on the CPU utilization, GPU utilization, task execution time, and parallelism, a preset resource allocation weighting algorithm is used to obtain the resource allocation ratio corresponding to each layer of the compilation architecture.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a simulation compilation method program based on cloud-based 3D digital space. When the simulation compilation method program based on cloud-based 3D digital space is executed by a processor, it implements the steps of the simulation compilation method based on cloud-based 3D digital space as described in any one of claims 1 to 6.

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