A containerized deployment method based on a lightweight simulation engine

Abstract

The application discloses a containerization deployment method based on a light-weight simulation engine, and comprises the following steps: S1, light-weight processing of the simulation engine; S2, optimization of the computing resources of the simulation engine, resource consumption is reduced by means of parallel computing, algorithm optimization and parameter tuning; S3, optimization of network resources, measures such as front-end and back-end separation, data compression transmission, incremental data synchronization and adaptive data frame rate are adopted; S4, optimization of storage resources; and S5, optimization of a database; the containerization deployment method based on the light-weight simulation engine can significantly improve the running efficiency and resource utilization of the simulation engine by means of multi-dimensional technologies for optimizing computing resources, network resources, storage resources and database resources; the cross-platform deployment of the simulation engine can be realized by means of the containerization technology; and the flexibility and high availability of the simulation engine are enhanced by means of the dynamic resource allocation and elastic scaling mechanism.

Description

Technical Field

[0001] This invention relates to the field of containerized deployment technology for simulation engines, specifically a containerized deployment method based on a lightweight simulation engine. Background Technology

[0002] With the continuous development of modern simulation technology, especially the widespread application of simulation engines in war games, various exercises and other fields, traditional simulation engines often face a series of problems such as large executable program size, high resource consumption, and difficulty in deployment and migration between different platforms. These problems make the deployment, maintenance and expansion of simulation engines complex and inefficient, which seriously restricts their application scope and real-time requirements.

[0003] Therefore, how to lightweight and containerize the simulation engine to solve the problem of excessive resource consumption and improve its cross-platform compatibility and flexibility has become an urgent problem to be solved in the industry. By introducing lightweight technology and containerized deployment methods, the resource consumption of the simulation engine can be effectively reduced, the deployment process can be simplified, and it can better adapt to the needs of the domestic software and hardware environment and meet the requirements of real-time data transmission and processing. Summary of the Invention

[0004] This invention provides a containerized deployment method based on a lightweight simulation engine, which can effectively solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a containerized deployment method based on a lightweight simulation engine, comprising the following steps: S1. Lighten the simulation engine; S2. Optimize the computing resources of the simulation engine by using parallel computing, algorithm optimization, and parameter tuning to reduce resource consumption; S3. Optimize network resources by adopting measures such as front-end and back-end separation, data compression transmission, incremental data synchronization, and adaptive data frame rate. S4. Optimize storage resources; S5. Optimize the database; S6. The lightweight simulation engine and related dependencies are packaged into a Docker container image to form a portable and standardized deployment runtime environment; S7. Deploy on a cluster platform that supports container orchestration, and use the container platform to achieve resource scheduling, automatic fault removal and high availability management of nodes; S8, based on DevOps methodology, integrates an automated operation and maintenance system to achieve continuous integration, automatic deployment, elastic scaling, and lifecycle management of simulation engine applications.

[0006] According to the above technical solution, in the process of lightweighting the simulation engine in S1, the model simplification technology is first used to sort out and eliminate non-core functions, redundant code and irrelevant simulation processes in the original simulation model. Secondly, regarding the module structure of the simulation engine, the front-end and back-end are separated by decoupling the interface layer and the business logic layer.

[0007] According to the above technical solution, in terms of network resource optimization in S3, the simulation engine adopts a front-end and back-end separated data interaction architecture, which transmits situational data, command data and service control signals separately. For high-frequency, large-volume situational data, the system first performs real-time compression processing, selecting an efficient data compression algorithm that balances compression ratio and decompression speed. For scenarios where data changes are minimal, an incremental synchronization mechanism can be used to transmit only the smallest data packets containing the changed data. In addition, an adaptive frame rate algorithm is used to dynamically adjust the data push frequency based on the client hardware performance, the user's current field of view, and dynamic network bandwidth conditions. In terms of storage resource optimization, all large-volume, low-frequency access data such as equipment information, geographic information, and historical simulation data are migrated to the remote server. The client adopts an on-demand loading strategy and improves the access speed of frequently used data through a local caching mechanism. When the local cache hit rate is lower than the preset threshold, background data synchronization and cache update are automatically triggered.

[0008] According to the above technical solution, in the database optimization stage of S5, the existing simulation engine database table structure is sorted out and standardized and reconstructed to eliminate redundant fields, unify index rules, and establish multi-level indexes for frequently accessed business tables to improve the response speed of complex query statements.

[0009] According to the above technical solution, the algorithm optimization in S2 includes adaptive resource allocation based on two factors: model complexity and real-time load. Specifically, during the computational resource management process of the simulation engine, for each simulation task, the system monitors its current model complexity C and node load L in real time, and determines the final allocation coefficient F through the following adaptive formula:

[0010] Where C0 is the complexity before optimization, C1 is the complexity after optimization, and L is the load of the current node. max The maximum load of the node is α, and β are weighting coefficients. The larger the allocation coefficient F, the more CPU and memory resources the system allocates to the simulation task; The system automatically determines whether to adopt multi-threaded concurrency and scheduling priority improvement measures according to F, and realizes refined resource allocation for simulation models with different complexities and nodes with different loads.

[0011] According to the above technical solution, the network resource optimization in S3 adopts joint control of bandwidth dynamic threshold and incremental synchronization, specifically: When the simulation platform is transmitting data, the system real-time detects the network bandwidth B and packet loss rate P, and uses the following adaptive formula to calculate the actual frame size S of this round of data synchronization n :

[0012] where S0 is the size of the original data packet, D is the data update ratio, P is the current packet loss rate, P max is the maximum acceptable packet loss rate, B is the current bandwidth, B tef is the reference bandwidth; The platform automatically adjusts the data packet size and synchronization frequency of this round of transmission frame according to S n When the bandwidth deteriorates and the packet loss rate increases, the system automatically reduces the frame and reduces the volume of synchronization packets, and only sends important data.

[0013] According to the above technical solution, the elastic scaling management in S8 adopts a dual-threshold elastic adjustment mechanism, specifically: The system periodically counts the current load L of the cluster, the preset low threshold T1 and high threshold T2, and automatically adjusts the number of container replicas N according to the following criterion:

[0014] [[ID=3,0]]where Nnew is the number of new replicas, Ncur is the current number of replicas, and γ, δ are adjustable coefficients; Automatically expand when L>T2, and automatically scale down when L<T1; Through this dual-threshold elastic strategy, the system can intelligently cope with high-concurrency bursts and resource idleness, and realize automatic resource expansion and recovery.

[0015] According to the above technical solution, the container image construction in S6 further includes hierarchical optimization and automatic configuration injection, specifically: When constructing the container image of the simulation engine, first set the application core files, dependent libraries, and running environment as multi-layer images respectively. The bottom layer is the operating system base image, the middle layer is the running environment and dependent libraries, and the upper layer is the main program and configuration files of the simulation engine; During the image construction process, a hierarchical caching mechanism is adopted. If the bottom layer and the middle layer have no changes, only the application layer image needs to be rebuilt.

[0016] According to the above technical solution, the high-availability management in S7 includes fault self-healing and multi-level disaster tolerance, specifically: All simulation engine nodes deployed in the cluster environment have health checks and heartbeat monitoring mechanisms enabled. The system periodically sends heartbeat signals to each node. If a node is detected as unresponsive, it is immediately and automatically removed from the load balancer pool and the corresponding container instance is automatically restarted on other available nodes to achieve self-healing. For critical simulation services, the platform can enable a multi-level disaster recovery strategy: when the primary node fails, the system automatically switches the service to the backup node and restores the execution progress of the simulation task. The platform supports automatic backup and rollback of simulation tasks through log archiving, status snapshots, and other methods, ensuring the integrity and traceability of critical data.

[0017] According to the above technical solution, a containerized simulation engine deployment system includes: The module includes a lightweight simulation engine module, a computing resource optimization module, a network and storage optimization module, a database optimization module, a containerized deployment module, a high availability and elastic scaling module, and an automated operation and maintenance module. Each module is responsible for the lightweight reconstruction of the corresponding simulation business, parallel computing and algorithm optimization, data transmission and storage management, database table structure and index optimization, container image layering and encapsulation, fault self-healing and automatic scaling control.

[0018] Compared with the prior art, the beneficial effects of the present invention are: the present invention has a scientific and reasonable structure, is safe and convenient to use, and the containerized deployment method based on a lightweight simulation engine can significantly improve the operating efficiency and resource utilization of the simulation engine by optimizing computing resources, network resources, storage resources and database resources in multiple dimensions. Containerization technology enables cross-platform deployment of the simulation engine, and dynamic resource allocation and elastic scaling mechanisms enhance the flexibility and high availability of the simulation engine, enabling it to adapt to different operating environments and load conditions. Furthermore, the automated operation and maintenance management system of this invention simplifies the operation and maintenance process of the simulation engine, and improves the stability and reliability of the system through container lifecycle management and fault recovery mechanisms. This method is applicable to simulation systems of various sizes, effectively reduces operation and maintenance costs, and improves system scalability, thus having broad application prospects. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0020] In the attached diagram: Figure 1 This is a schematic diagram of the method steps of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0021] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0022] Example: Figure 1-2 As shown, the present invention provides a technical solution: a containerized deployment method based on a lightweight simulation engine, comprising the following steps: S1. Lightweighting the simulation engine, specifically including optimizing the simulation model, streamlining redundant modules, separating the interface display layer from the core logic, and refactoring the business code structure; S2. Optimize the computing resources of the simulation engine by using parallel computing, algorithm optimization, parameter tuning and other methods to reduce resource consumption; S3. Optimize network resources by adopting measures such as front-end and back-end separation, data compression transmission, incremental data synchronization, and adaptive data frame rate to reduce bandwidth consumption; S4. Optimize storage resources, including migrating equipment data, geographic information data, etc. to the server, and loading and caching them on the client as needed; S5. Optimize the database, including table structure optimization, index optimization, and introducing an in-memory database to improve data access efficiency; S6. The lightweight simulation engine and related dependencies are packaged into a Docker container image to form a portable and standardized deployment runtime environment; S7. Deploy on a cluster platform that supports container orchestration, and use the container platform to achieve resource scheduling, automatic fault removal and high availability management of nodes; S8, based on DevOps methodology, integrates an automated operation and maintenance system to achieve continuous integration, automatic deployment, elastic scaling, and lifecycle management of simulation engine applications.

[0023] According to the above technical solution, in the process of lightweighting the simulation engine in S1, the model structuring simplification technology is first adopted to sort out and eliminate non-core functions, redundant code and irrelevant simulation processes in the original simulation model, so as to ensure that the main functions are complete and the execution logic is efficient. Secondly, regarding the module structure of the simulation engine, the front-end and back-end are separated by decoupling the interface layer and the business logic layer, which significantly reduces the size of the core executable file. Furthermore, in the stage of optimizing computing resources, multi-threaded and multi-process parallel computing technology is used to split the simulation tasks that were originally executed serially into modules and distribute them to multiple CPU cores or distributed computing nodes to run in parallel, thereby improving the overall simulation efficiency of the system. Meanwhile, for complex algorithm engines, the optimal solution path is dynamically selected by combining domain-specific efficient algorithms or adaptive optimization algorithms to minimize the amount of invalid computation in each simulation process. The combined application of the above optimization measures can effectively reduce the system's consumption of computing resources such as CPU and memory, and improve the computing speed of the simulation engine, meeting the requirements of distributed deployment and operation in high real-time scenarios. Furthermore, by optimizing parameters, performing algorithm preprocessing and postprocessing, the accuracy and stability of the model results are further improved, providing a fundamental guarantee for subsequent containerized deployment and elastic scaling.

[0024] According to the above technical solution, in terms of network resource optimization in S3, the simulation engine adopts a front-end and back-end separated data interaction architecture, which transmits situational data, command data and service control signals separately. For high-frequency, large-volume situational data, the system first performs real-time compression processing, selecting an efficient data compression algorithm that balances compression ratio and decompression speed to ensure terminal rendering efficiency. For scenarios where data changes are minimal, incremental synchronization mechanisms can be used to transmit only the smallest data packets containing the changed portions, significantly reducing network bandwidth consumption. In addition, based on the client hardware performance, the user's current field of view, and the dynamic conditions of network bandwidth, an adaptive frame rate algorithm is adopted to dynamically adjust the data push frequency to avoid data accumulation or delays and packet loss. In terms of storage resource optimization, all large-volume, low-frequency access data such as equipment information, geographic information, and historical simulation data are migrated to the remote server. The client adopts an on-demand loading strategy and improves the access speed of frequently used data through a local caching mechanism. When the local cache hit rate is lower than the preset threshold, background data synchronization and cache update are automatically triggered, effectively relieving pressure on local storage space. By comprehensively applying the above-mentioned multi-dimensional network and storage optimization strategies, not only is the smooth operation of the simulation engine in low-bandwidth, high-concurrency environments guaranteed, but data reliability and access efficiency in large-scale distributed simulation scenarios are also improved.

[0025] According to the above technical solution, in the database optimization phase of S5, the existing simulation engine database table structure is sorted out and standardized and reconstructed to eliminate redundant fields, unify index rules, and establish multi-level indexes for frequently accessed business tables to improve the response speed of complex query statements. Introducing a high-performance in-memory database caches core business data in memory, reducing the I / O bottleneck of traditional disk databases and improving the overall concurrency of the system; Entering the containerized deployment stage, mainstream container technologies such as Docker are adopted to build layered images of the lightweighted simulation engine, packaging the application, dependency libraries, configuration files and runtime environment into a portable image. Container images enable one-click distribution, rapid startup, and horizontal scaling during deployment, greatly improving deployment and maintenance efficiency. By leveraging container orchestration platforms (such as Kubernetes), declarative resource scheduling strategies can be used to achieve automatic container orchestration, automatic node discovery, health checks, and automatic removal of faulty nodes, ensuring high availability of the cluster. Based on DevOps methodology, the system can achieve continuous integration of the simulation engine, automated testing and canary release, and supports elastic scaling capabilities: adjust the number of container replicas in real time according to the current system load to maximize resource utilization and automate operation and maintenance. The entire process is centered on full lifecycle management, covering all stages such as deployment, operation, monitoring, expansion, upgrade and rollback, ensuring the efficiency, stability and maintainability of the simulation engine system.

[0026] According to the above technical solution, the algorithm optimization in S2 includes adaptive resource allocation based on two factors: model complexity and real-time load. Specifically, during the computational resource management process of the simulation engine, for each simulation task, the system monitors its current model complexity C and node load L in real time, and determines the final allocation coefficient F through the following adaptive formula:

[0027] Where C0 is the complexity before optimization, C1 is the complexity after optimization, and L is the load of the current node. max The maximum load of the node is α, and β are weighting coefficients. The larger the allocation coefficient F, the more CPU and memory resources the system allocates to the simulation task; The system automatically determines whether to adopt measures such as multi-threaded concurrency and scheduling priority enhancement based on F, so as to achieve fine-grained resource allocation for simulation models with different complexities and nodes with different loads, thereby improving overall computing efficiency and response capability.

[0028] According to the above technical solution, network resource optimization in S3 adopts a joint control of dynamic bandwidth threshold and incremental synchronization, specifically as follows: During data transmission, the simulation platform monitors network bandwidth B and packet loss rate P in real time, and uses the following adaptive formula to calculate the actual frame size S of this round of data synchronization. n :

[0029] Among them, S0 is the size of the original data packet, D is the data update ratio, P is the current packet loss rate, and P max is the maximum acceptable packet loss rate, B is the current bandwidth, and B tef is the reference bandwidth; The platform automatically adjusts the data packet size and synchronization frequency of the transmission frame in this round according to S n When the bandwidth deteriorates and the packet loss rate increases, the system automatically reduces the frame rate and reduces the volume of the synchronization packet, and only sends important data to ensure the smoothness of key services, reliable data, and improve the stability and response speed in a large-scale distributed simulation environment.

[0030] According to the above technical solution, the elastic scaling management in S8 adopts a dual-threshold elastic adjustment mechanism, which specifically is: The system periodically counts the current load L of the cluster, the preset low threshold T1 and the high threshold T2, and automatically adjusts the number of container replicas N according to the following criteria:

[0031] Among them, Nnew is the new number of replicas, Ncur is the current number of replicas, and γ, δ are adjustable coefficients; Automatically expand when L > T2, and automatically scale down when L < T1; Through this dual-threshold elastic policy, the system can intelligently handle high-concurrency bursts and resource idleness, realize automatic resource expansion and recycling, ensure the continuous high availability of simulation services, and reduce the overall resource cost.

[0032] According to the above technical solution, the container image construction in S6 further includes hierarchical optimization and automatic configuration injection, which specifically is: When building the container image of the simulation engine, first set the application core files, dependent libraries, and running environment as multi-layer images respectively. The bottom layer is the operating system base image, the middle layer is the running environment and dependent libraries, and the upper layer is the main program and configuration files of the simulation engine; During the image construction process, a hierarchical caching mechanism is adopted. If the bottom layer and the middle layer have no changes, only the application layer image needs to be rebuilt, which greatly improves the image update and distribution efficiency; To achieve fast cross-environment deployment, the system automatically detects the target host environment variables at startup and dynamically injects parameter configuration files such as network, storage, and database, and can be started with one key without manual intervention; the above hierarchical optimization and automatic injection mechanism not only ensures the compactness of the image package and efficient network transmission, but also ensures that each startup configuration is up-to-date, enhances the flexibility and maintainability of container deployment, and effectively supports the large-scale elastic deployment and operation and maintenance management of the simulation engine.

[0033] According to the above technical solution, the high-availability management in S7 includes fault self-healing and multi-level disaster tolerance, which specifically is: All simulation engine nodes deployed in the cluster environment have health checks and heartbeat monitoring mechanisms enabled. The system periodically sends heartbeat signals to each node. If a node is detected as unresponsive, it is immediately and automatically removed from the load balancer pool and the corresponding container instance is automatically restarted on other available nodes to achieve self-healing. For critical simulation services, the platform can enable a multi-level disaster recovery strategy: when the primary node fails, the system automatically switches the service to the backup node and restores the simulation task execution progress, minimizing business interruption time. The platform supports automatic backup and rollback of simulation tasks through log archiving, status snapshots, and other methods, ensuring the integrity and traceability of critical data; The aforementioned high availability and multi-level disaster recovery mechanisms effectively improve the robustness of the simulation engine in complex scenarios and the system's continuous service capabilities, reducing the risks caused by single points of failure.

[0034] According to the above technical solution, a containerized simulation engine deployment system includes: The module includes a lightweight simulation engine module, a computing resource optimization module, a network and storage optimization module, a database optimization module, a containerized deployment module, a high availability and elastic scaling module, and an automated operation and maintenance module. Each module is responsible for the lightweight reconstruction of the corresponding simulation business, parallel computing and algorithm optimization, data transmission and storage management, database table structure and index optimization, container image layering and packaging, fault self-healing and automatic scaling control; Through the collaboration between the above modules, the system supports the efficient deployment, operation and maintenance of multi-instance containerized simulation engines, enabling simulation services to be quickly launched, automatically scaled up, migrated across platforms and be intelligently operated and maintained. The system is compatible with multiple mainstream cloud platforms and local server environments, and has the advantages of easy deployment, high availability, easy expansion and security. It is suitable for large-scale distributed simulation and digital twin business scenarios.

[0035] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A containerized deployment method based on a lightweight simulation engine, characterized in that: Includes the following steps: S1. Lighten the simulation engine; S2. Optimize the computing resources of the simulation engine by using parallel computing, algorithm optimization, and parameter tuning to reduce resource consumption; S3. Optimize network resources by adopting measures such as front-end and back-end separation, data compression transmission, incremental data synchronization, and adaptive data frame rate. S4. Optimize storage resources; S5. Optimize the database; S6. The lightweight simulation engine and related dependencies are packaged into a Docker container image to form a portable and standardized deployment environment. S7. Deploy on a cluster platform that supports container orchestration, and use the container platform to achieve resource scheduling, automatic fault removal and high availability management of nodes; S8, based on DevOps methodology, integrates an automated operation and maintenance system to achieve continuous integration, automatic deployment, elastic scaling, and lifecycle management of simulation engine applications.

2. The containerized deployment method based on a lightweight simulation engine according to claim 1, characterized in that, In the process of lightweighting the simulation engine in S1, the model simplification technique is first used to sort out and eliminate non-core functions, redundant code and irrelevant simulation processes in the original simulation model. Secondly, regarding the module structure of the simulation engine, the front-end and back-end are separated by decoupling the interface layer and the business logic layer.

3. The containerized deployment method based on a lightweight simulation engine according to claim 1, characterized in that, In terms of network resource optimization, the simulation engine adopts a front-end and back-end separated data interaction architecture in S3, which transmits situational data, command data and service control signals separately. For high-frequency, large-volume situational data, the system first performs real-time compression processing, selecting an efficient data compression algorithm that balances compression ratio and decompression speed. For scenarios where data changes are minimal, an incremental synchronization mechanism can be used to transmit only the smallest data packets containing the changed data. In addition, an adaptive frame rate algorithm is used to dynamically adjust the data push frequency based on the client hardware performance, the user's current field of view, and dynamic network bandwidth conditions. In terms of storage resource optimization, all large-volume, low-frequency access data such as equipment information, geographic information, and historical simulation data are migrated to the remote server. The client adopts an on-demand loading strategy and improves the access speed of frequently used data through a local caching mechanism. When the local cache hit rate is lower than the preset threshold, background data synchronization and cache update are automatically triggered.

4. The containerized deployment method based on a lightweight simulation engine according to claim 1, characterized in that, In the database optimization phase of S5, the existing simulation engine database table structure is sorted out and standardized to eliminate redundant fields, unify index rules, and establish multi-level indexes for frequently accessed business tables to improve the response speed of complex query statements.

5. The containerized deployment method based on a lightweight simulation engine according to claim 1, characterized in that, The algorithm optimization in S2 includes adaptive resource allocation based on a dual factor of model complexity and real-time load. Specifically, during the computational resource management process of the simulation engine, for each simulation task, the system monitors its current model complexity C and node load L in real time, and determines the final allocation coefficient F through the following adaptive formula: Where C0 is the complexity before optimization, C1 is the complexity after optimization, and L is the load of the current node. max The maximum load of the node is α, and β are weighting coefficients. The larger the allocation coefficient F, the more CPU and memory resources the system allocates to the simulation task; The system automatically determines whether to adopt multi-threaded concurrency and scheduling priority enhancement measures based on F, so as to achieve fine-grained resource allocation for simulation models with different complexities and nodes with different loads.

6. The containerized deployment method based on a lightweight simulation engine according to claim 5, characterized in that, The network resource optimization in S3 adopts joint control of bandwidth dynamic threshold and incremental synchronization, specifically as follows: During data transmission, the simulation platform monitors network bandwidth B and packet loss rate P in real time, and uses the following adaptive formula to calculate the actual frame size S of this round of data synchronization. n : Where S0 is the original data packet size, D is the data update ratio, P is the current packet loss rate, and P max The maximum acceptable packet loss rate, where B is the current bandwidth. tef Reference bandwidth; The platform is based on S n The system automatically adjusts the data packet size and synchronization frequency of the current transmission frame. When the bandwidth deteriorates or the packet loss rate increases, the system automatically reduces the frame size and the synchronization packet size, sending only important data.

7. A containerized deployment method based on a lightweight simulation engine according to claim 6, characterized in that, The elastic scaling management in S8 adopts a dual-threshold elastic adjustment mechanism, specifically as follows: The system periodically statistics the current load L of the cluster, the preset low threshold T1 and high threshold T2, and automatically adjusts the number of container replicas N according to the following criteria: in, For the new number of copies, The current number of replicas is γ, and δ are adjustable coefficients. Automatically expand when L>T2, and automatically scale down when L<T1; Through this dual-threshold elastic strategy, the system can intelligently handle high-concurrency bursts and resource idleness, and achieve automatic resource expansion and recycling.

8. The containerized deployment method based on a lightweight simulation engine according to claim 1, characterized in that, The container image construction in S6 further includes hierarchical optimization and automatic configuration injection, specifically as follows: When constructing the simulation engine container image, first set the application core files, dependent libraries, and running environment as multi-layer images respectively. The bottom layer is the operating system base image, the middle layer is the running environment and dependent libraries, and the upper layer is the simulation engine main program and configuration files; During the image construction process, a hierarchical caching mechanism is adopted. If the bottom layer and the middle layer do not change, only the application layer image needs to be rebuilt.

9. A containerized deployment method based on a lightweight simulation engine according to claim 1, characterized in that, The high-availability management in S7 includes self-healing and multi-level disaster tolerance, specifically as follows: For the simulation engine nodes deployed in the cluster environment, health check and heartbeat monitoring mechanisms are enabled; the system periodically sends heartbeat signals to each node. If a node is detected to be unresponsive, it will be immediately automatically removed from the load balancing pool, and the corresponding container instance will be automatically restarted on other available nodes to achieve self-healing; For key simulation services, the platform can enable a primary-standby multi-level disaster tolerance strategy: when the primary node is abnormal, the system automatically switches the service to the standby node and resumes the execution progress of the simulation task; The platform supports automatic backup and rollback of simulation tasks through methods such as log archiving and status snapshots, ensuring the integrity and traceability of key data.

10. A containerized simulation engine deployment system implemented based on the method according to any one of claims 1 to 9, comprising: A simulation engine lightweight module, a computing resource optimization module, a network and storage optimization module, a database optimization module, a containerized deployment module, a high-availability and elastic scaling module, and an automated operation and maintenance module; Each of the above modules is responsible for the lightweight reconstruction, parallel computing and algorithm optimization, data transmission and storage management, database table structure and index optimization, container image hierarchical encapsulation, self-healing and automatic scaling control of the corresponding simulation service.

Images