Crowd intelligence robot virtual simulation experiment method and device

Abstract

The application provides a crowd intelligence robot virtual simulation experiment method and device, relates to the field of computer science and robot technology, and comprises the following steps: receiving a creation instruction of an experiment space, creating the experiment space in a container instance, defining basic information of an experiment in the experiment space, and setting a resource limit of the experiment space; receiving a creation instruction of an experiment space, creating the experiment space in a container instance, defining basic information of an experiment in the experiment space, and setting a resource limit of the experiment space; obtaining a log file of the experiment space, extracting a ROSBAG file in the log file, analyzing the ROSBAG file through a go-ros bag library, determining a topic category of the ROSBAG file, and generating post-analysis data; and storing the post-analysis data in different collections of a MongoDB database according to the topic category of the post-analysis data. The embodiment of the application is convenient for user management and query, and provides strong support for experiment monitoring and subsequent analysis.

Description

Technical Field

[0001] The present invention relates to the fields of computer science and robotics technology, and in particular to a swarm intelligence robot virtual simulation experiment method and system. Background Art

[0002] Virtual simulation experiments use computer technology to create virtual scenes that are similar to or even more idealized than real experimental environments, allowing users to perform various experimental operations and explorations in them.

[0003] Traditional experiments are subject to many limitations such as experimental conditions, resources, time and space. Virtual simulation experiments do not have the necessary equipment for experiments in the ordinary sense, but instead use simulation software on a computer to simulate real-life effects.

[0004] However, existing virtual simulation experiment methods are difficult to effectively manage the log data generated by the experiment, which will affect the monitoring and analysis of the experiment and cannot effectively provide a basis for the optimization and improvement of the experiment. Summary of the Invention

[0005] In order to solve the technical problem that the existing virtual simulation experiment methods in the prior art cannot effectively provide a basis for the optimization and improvement of the experiment, the embodiment of the present invention provides a method and device for the virtual simulation experiment of a group intelligence robot. The technical solution is as follows:

[0006] On the one hand, a virtual simulation experiment method of a swarm intelligence robot is provided, comprising:

[0007] After receiving the image usage right request input by the user, approving the image usage right request, obtaining the image after approval, updating the image information in the database, and allocating the image usage right to the user;

[0008] Receive user instructions, parse the user instructions, configure container parameters according to the user instructions, and create a container instance based on the parameter configuration through the run Docker method in the Channel Util class, or create a Kubernetes Deployment based on the parameter configuration and create a container instance in the Kubernetes cluster according to the Kubernetes Deployment;

[0009] receiving an instruction to create an experiment space, creating the experiment space in the container instance, defining basic information of the experiment in the experiment space, and setting resource limits for the experiment space;

[0010] Obtain the log file of the experimental space, extract the ROSBAG file in the log file, parse the ROSBAG file through the go-rosbag library, determine the topic category of the ROSBAG file, and generate parsed data;

[0011] According to the topic category of the parsed data, the parsed data is stored in different collections of the MongoDB database.

[0012] Optionally, the virtual simulation experiment method builds an experimental environment based on the front-end development framework Vue and the back-end development framework SpringBoot.

[0013] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0014] Encapsulates Docker CLI commands and provides a set of methods for managing and operating Docker containers and images through the Channel Util class;

[0015] The method in the method set is obtained according to the user instruction, and management operations are performed on the container instance and the image.

[0016] Optionally, obtaining the image includes:

[0017] Use the Docker CLI command docker pull to pull the image from the remote repository using the pull Image method in the Channel Util class; or

[0018] Use the docker build command to create a new image based on the build path.

[0019] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0020] In each experimental space, obtain container configuration requirements and parse container parameters in the container configuration requirements;

[0021] A container instance is created in the experiment space based on the container parameters, where each experiment space includes one or more container instances.

[0022] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0023] After receiving the experimental task allocation instructions input by the user, the Deployment resource is created using the Create MyRos Deployment method through the Kubernetes API to allocate container instances to multiple terminals.

[0024] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0025] When receiving the data operation instruction input by the user, the data in the MongoDB database is operated through the MongoTemplate encapsulation tool class.

[0026] On the other hand, a virtual simulation experiment device for a group intelligence robot is provided, and the virtual simulation experiment device for a group intelligence robot is used to implement the virtual simulation experiment method for a group intelligence robot provided in an embodiment of the present invention, and the device includes:

[0027] An allocation module, configured to receive an image usage right request from a user, approve the image usage right request, obtain the image after approval, update the image information into a database, and allocate the image usage right to the user;

[0028] A creation module is configured to receive user instructions, parse the instructions, configure container parameters according to the instructions, and create a container instance based on the parameter configuration using the run Docker method in the Channel Util class, or create a Kubernetes Deployment based on the parameter configuration and create a container instance in the Kubernetes cluster according to the Kubernetes Deployment.

[0029] a setting module, configured to receive an instruction to create an experimental space, create the experimental space in the container instance, define basic information of the experiment in the experimental space, and set resource limits for the experimental space;

[0030] A generation module is used to obtain the log file of the experimental space, extract the ROSBAG file in the log file, parse the ROSBAG file through the go-ros bag library, determine the topic category of the ROSBAG file, and generate parsed data;

[0031] The storage module is used to store the parsed data in different collections of the MongoDB database according to the topic category of the parsed data.

[0032] On the other hand, a group intelligence robot virtual simulation experimental device is provided, the group intelligence robot virtual simulation experimental device comprising:

[0033] processor;

[0034] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method provided by the embodiment of the present invention.

[0035] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided in the embodiment of the present invention.

[0036] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0037] The embodiment of the present invention records the experimental log files in the container in real time, automatically converts the log data into a unified format, and stores it in a MongoDB database, making it convenient for users to manage and query, and providing strong support for experimental monitoring and subsequent analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 This is a flow chart of a virtual simulation experiment method of a group intelligence robot provided by an embodiment of the present invention;

[0040] Figure 2 This is a framework diagram of a virtual simulation experiment method for a group intelligence robot provided by an embodiment of the present invention;

[0041] Figure 3 This is a schematic structural diagram of a group intelligence robot virtual simulation experimental device provided by an embodiment of the present invention;

[0042] Figure 4 This is a structural diagram of a group intelligence robot virtual simulation experiment equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0044] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0045] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0046] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0047] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0048] Existing virtual simulation experiment platforms mostly adopt traditional centralized architecture design solutions. Such systems have many common problems in setting up the experimental environment, resource allocation and isolation, operation convenience, and log management and analysis.

[0049] In terms of environment configuration and deployment, existing virtual simulation experiment methods and systems typically require the installation of a complex software environment on the local machine, including the operating system, programming language, and dependent libraries. This process is not only time-consuming and labor-intensive, but also prone to errors. For example, different experiments may require different versions of libraries, and the dependencies between these libraries may cause conflicts, making the environment configuration extremely complex. The complexity of the environment configuration leads to poor experimental repeatability. Each time a new environment is set up, it needs to be reconfigured, and the environment configuration of different users may be inconsistent, affecting the accuracy and reliability of the experimental results.

[0050] In terms of resource management and isolation, in most existing virtual simulation experiment methods and systems, multiple experiments often share the same physical resources, such as CPU, memory, and storage. This resource sharing can lead to interference between experiments. For example, a resource-intensive experiment can affect the performance of other experiments. Imperfect resource management reduces the stability and reliability of experiments.

[0051] In terms of experimental operation and recording, traditional experimental methods and systems are often limited to the experiments they can perform and can only provide a basic experimental environment. The experimental operations are complex, and the complexity of the operations limits the usability and popularity of the system. At the same time, it is difficult to effectively manage the log data generated by the experiments.

[0052] In terms of multi-user support and collaboration, existing experimental methods and systems generally do not support simultaneous multi-user operation. Multiple users must wait in line to conduct experiments one by one, which severely impacts experimental efficiency and collaboration. Furthermore, the system lacks user permission management, making it impossible to restrict and audit the operations of different users. This lack of multi-user support limits the system's collaborative capabilities and scope of application, preventing users from conducting experiments simultaneously. This lack of permission management can lead to the leakage and misuse of experimental data, compromising the security and reliability of experiments.

[0053] In order to solve the technical problem that the existing virtual simulation experiment methods in the prior art cannot effectively provide a basis for the optimization and improvement of the experiment, the embodiment of the present invention provides a method and device for the virtual simulation experiment of a group intelligence robot. The technical solution is as follows:

[0054] On the one hand, a virtual simulation experiment method of a group intelligence robot is provided, such as Figure 1 As shown, the method includes:

[0055] S1. After receiving a request for image usage rights from a user, the request is approved. After approval, the image is obtained, the image information is updated in a database, and the image usage rights are allocated to the user.

[0056] The user here can be an administrator or a normal user.

[0057] S2. Receive user instructions, parse the user instructions, configure container parameters according to the user instructions, and create a container instance based on the parameter configuration through the run Docker method in the Channel Util class, or create a Kubernetes Deployment based on the parameter configuration, and create a container instance in the Kubernetes cluster according to the KubernetesDeployment.

[0058] S3. Receive an instruction to create an experimental space, create the experimental space in the container instance, define basic information of the experiment in the experimental space, and set resource limits for the experimental space.

[0059] S4. Obtain the log file of the experimental space, extract the ROSBAG file in the log file, parse the ROSBAG file through the go-rosbag library, determine the topic category of the ROSBAG file, and generate parsed data.

[0060] S5. According to the topic category of the parsed data, the parsed data is stored in different collections of the MongoDB database.

[0061] Based on the underlying development framework Vue+SpringBoot framework, this paper combines container technologies such as Docker and Kubernetes to build an efficient virtual simulation experiment method and system for remote operation of group intelligence robots. The framework diagram of the virtual simulation experiment method and system for remote operation of group intelligence robots is as follows: Figure 2 As shown, the framework mainly includes five layers of architecture development: basic layer, data layer, service layer, business layer, and user layer.

[0062] User layer: The system sets up two roles: user and administrator. Administrators have higher-level permissions and can perform operations including but not limited to adding users one by one or in batches, modifying user information, image usage permissions, container experiment allocation, etc., thereby achieving fine-grained management of user access rights.

[0063] The business layer abstracts and implements system logic, defining the system's main business processes and functional modules. The system comprises four modules: user management, experiment management, container management, and log management. The user management module supports user login and information viewing and editing. Administrators can batch add, delete, and manage user accounts, set user permissions, and ensure that different users operate within their permissions, thus safeguarding system security and data integrity. The experiment management module supports full lifecycle management of Docker images. Administrators can create experiment spaces, set resource limits, and manage containers in a Kubernetes cluster, enabling the deployment of multi-container experimental environments. Furthermore, administrators can create containers based on images and distribute them to multiple users, each receiving a dedicated container instance in an independent user space. Meanwhile, regular users can create container experiments by applying for image permissions. The container management module supports full lifecycle management of containers, container filtering and searching, and port mapping, ensuring smooth communication between containers and with the outside world. The log management module captures experiment log files in real time and supports log upload, parsing, storage, and querying.

[0064] Service Layer: The service layer is the core functional layer of the system, providing container management and Kubernetes cluster management services. The service layer interacts with the business layer through APIs and handles the full lifecycle management of containers, including creating, starting, stopping, and deleting containers. Furthermore, the service layer supports container orchestration, allowing the creation and management of namespaces within a Kubernetes cluster, enabling multi-container deployment and resource isolation. The service layer is also responsible for log processing, including uploading, parsing, and storing ROSBAG log files, ensuring efficient management and querying of log data.

[0065] Data Layer: The data layer is responsible for the storage and management of system data, utilizing the MongoDB non-relational database to efficiently process both structured and unstructured data. The data layer stores structured data such as user information, container status, image details, and port mappings, ensuring data persistence and consistency. The data layer also processes unstructured data such as ROSBAG log files. Through a flexible data model, it supports log upload, parsing, and storage, facilitating user query and analysis, and providing a solid foundation for stable system operation and data security.

[0066] Infrastructure: The infrastructure layer is the underlying architecture of the system, providing the necessary hardware and software resources to ensure stable system operation. The infrastructure layer includes servers, storage devices, and network infrastructure, providing physical support for the system. The infrastructure layer ensures sufficient computing resources and storage space to support high-concurrency user operations and data processing. By optimizing network configuration, the infrastructure layer ensures efficient communication within and outside the system, providing users with a fast and stable access experience.

[0067] Using the swarm robot virtual simulation experiment method provided by the embodiment of the present invention, container creation, experiment publishing, and experiment log management and analysis can be performed in the following manner.

[0068] Container creation and experimental release process:

[0069] There are two main types of users involved in the entire process of container creation and experimental release, namely administrators and ordinary users. It implements the complete process from image management to container creation to experimental release, supports flexible operations of administrators and users, and provides an efficient, flexible and easy-to-manage experimental environment. The Channel Util class serves as a bridge for interaction between the system and Docker. Its design and implementation are crucial to the stability and performance of the entire system. By encapsulating the execution logic of Docker CLI commands, the Channel Util class provides the system with a flexible and reusable set of methods for managing and operating Docker containers and images. Container creation and experimental release process, such as Figure 2 The specific steps are as follows:

[0070] Administrators pull the required Docker images from remote repositories through Docker Hub, or upload custom Docker images to the image repository of the Kubernetes cluster. This process ensures the diversity and availability of images, providing a foundation for subsequent container creation. The system supports full lifecycle management of images, including creating, pulling, deleting, and listing images to ensure image consistency and availability. Use the pull Image method in the Channel Util class to pull images from remote repositories using the DockerCLI command docker pull, or use the docker build command to create a new image based on the build path and store the image information in the database.

[0071] The administrator configures container parameters and creates a container instance based on the existing image environment. By defining the container's configuration file, parameters such as the container's image, resource requests, and restrictions are specified. Kubernetes automatically creates container instances based on the configuration file, ensuring that the container can correctly access external resources and store data during runtime. Specifically, the run Docker method in the Channel Util class uses the Docker CLI command docker run to create a container and store the container information in a database. For example, the present invention provides two implementation methods for container construction.

[0072] The first implementation accepts user-submitted container creation parameters, including the container name, image ID, username, and port mapping. The container is created using a CLI command. The runDocker method in ChannelUtil is used to execute the Docker command, create the container, and obtain the container ID. The container information is then stored in a database.

[0073] The second implementation method creates a container within the Kubernetes cluster namespace environment and a Kubernetes Deployment based on the user-provided configuration. Users submit Deployment configurations via HTTP POST requests. The backend service receives the Deployment definition in JSON format, parses it into a MyRos object, validates the configuration, and then uses the Kubernetes client library to create the Deployment resources (i.e., containers). If an error occurs during the creation process, such as an invalid configuration or failed resource creation, the exception is captured and a corresponding error response is returned.

[0074] Administrators create an experiment space and define basic experiment information, such as the experiment name and description. Within the experiment space, administrators set resource limits, including CPU, memory, and storage. These resource limits ensure that experiments do not exceed predefined resource quotas during runtime, preventing resource overuse and ensuring stable system operation. An experiment space is a logical unit for experiments, used to organize and manage experiment-related resources. This is achieved through Kubernetes' namespace management functionality, using the Create Namespace method to create a new namespace resource and set resource quotas.

[0075] Administrators can create container instances by configuring container parameters based on existing image environments, tailoring different experimental spaces or specific requirements. Administrators can create multiple container instances for different experiments, ensuring each has its own independent runtime environment. Container instances can be created using the run Docker method in the Channel Util class.

[0076] Administrators can batch-publish created container instances to multiple regular users. Each regular user receives a separate container instance in their own user space. Kubernetes' namespace feature ensures that each regular user's container instances are isolated and do not interfere with each other. Administrators can use the system's interface to batch-assign container experiment tasks to regular users, improving the efficiency of experiment releases. Alternatively, use the Create My Ros Deployment method in the Kubernetes API to create a Deployment resource and assign container instances to regular users.

[0077] Regular users can apply to the administrator for image access through the system's interface. After submitting their application, the administrator reviews it and decides whether to allocate the image to the regular user. This process ensures the proper allocation and use of images, while also safeguarding system security and resource utilization. This is achieved through the user management module, where the administrator reviews the regular user's application, updates the image information in the database, and grants the user access.

[0078] Once the administrator approves a user's application, they gain access to the image and can use it to create container experiments in their own user space. They can define container configuration parameters, such as environment variables and port mappings, and create new container instances using the system's interface or command-line tools. After starting a container, they can run experiments, view results, and perform debugging. The system provides log management and monitoring capabilities to help users stay informed of their experiments. Use the runDocker method in the ChannelUtil class to create a container instance based on the image and start the container.

[0079] Implementation plan for experimental log management and analysis:

[0080] The workflow of the experimental log management and analysis implementation plan mainly includes the following four processes:

[0081] 1. Get the log file

[0082] The system can obtain log files in two main ways: one is for users to directly upload local ROSBAG files on the front-end page, and the other is to capture log files generated by ROS experiment containers in real time. Log files are saved to a designated storage directory and then returned to the file storage path for subsequent analysis. This ensures that log files are securely and accurately transmitted within the system, providing a foundation for subsequent data processing and analysis.

[0083] 2. Data Processing

[0084] After obtaining the file storage path, the system uses the go-rosbag library to parse the ROSBAG file. By traversing each record in the file and performing targeted processing based on the record type, each topic is accurately parsed. This step provides preliminary organization and structuring of log data, making it easier to store and query, and improving data usability and value.

[0085] 3. Data Storage

[0086] The system establishes a connection to the MongoDB database using the go.mongodb.org / mongo-driver library. Processed data is stored in separate MongoDB collections based on the topic category. For ROSBAG file storage, each topic is stored separately. This not only optimizes data organization but also improves query efficiency, facilitating subsequent rapid retrieval and analysis of specific topic data.

[0087] 4. Record the results

[0088] The system meets diverse user needs for experimental data queries. It integrates MongoDB with Spring Boot and utilizes MongoTemplate to encapsulate utility classes for addition, deletion, modification, and aggregate queries, enabling efficient querying of MongoDB data. Based on user-submitted query criteria, such as specifying a topic field or multiple field ranges, the system accurately retrieves the required data from the database, rapidly responding to user queries. This functionality ensures timely access to accurate experimental data, providing strong support for experimental analysis and decision-making.

[0089] Optionally, the virtual simulation experiment method builds an experimental environment based on the front-end development framework Vue and the back-end development framework SpringBoot.

[0090] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0091] Encapsulates Docker CLI commands and provides a set of methods for managing and operating Docker containers and images through the Channel Util class;

[0092] The method in the method set is obtained according to the user instruction, and management operations are performed on the container instance and the image.

[0093] Optionally, obtaining the image includes:

[0094] Use the Docker CLI command docker pull to pull the image from the remote repository using the pull Image method in the Channel Util class; or

[0095] Use the docker build command to create a new image based on the build path.

[0096] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0097] In each experimental space, obtain container configuration requirements and parse container parameters in the container configuration requirements;

[0098] A container instance is created in the experiment space based on the container parameters, where each experiment space includes one or more container instances.

[0099] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0100] After receiving the experimental task allocation instructions input by the user, the Deployment resource is created using the Create MyRos Deployment method through the Kubernetes API to allocate container instances to multiple terminals.

[0101] Optionally, the swarm intelligence robot virtual simulation experiment method further includes:

[0102] When receiving the data operation instruction input by the user, the data in the MongoDB database is operated through the MongoTemplate encapsulation tool class.

[0103] On the other hand, Figure 3As shown, a virtual simulation experiment device for a group intelligence robot is provided. The virtual simulation experiment device for a group intelligence robot is used to implement the virtual simulation experiment method for a group intelligence robot provided in an embodiment of the present invention. The device includes:

[0104] The allocation module 301 is configured to receive an image usage rights request from a user, approve the image usage rights request, obtain the image after approval, update the image information in a database, and allocate the image usage rights to the user;

[0105] A creation module 302 is configured to receive user instructions, parse the user instructions, configure container parameters according to the user instructions, and create a container instance based on the parameter configuration using the run Docker method in the Channel Util class, or create a Kubernetes Deployment based on the parameter configuration and create a container instance in the Kubernetes cluster according to the Kubernetes Deployment.

[0106] A setting module 303 is configured to receive an instruction to create an experiment space, create the experiment space in the container instance, define basic information of the experiment in the experiment space, and set resource limits for the experiment space;

[0107] A generation module 304 is configured to obtain a log file of the experimental space, extract a ROSBAG file from the log file, parse the ROSBAG file using the go-ros bag library, determine a topic category of the ROSBAG file, and generate parsed data;

[0108] The storage module 305 is configured to store the parsed data in different collections of the MongoDB database according to the topic category of the parsed data.

[0109] On the other hand, a group intelligence robot virtual simulation experimental device is provided, the group intelligence robot virtual simulation experimental device comprising:

[0110] processor;

[0111] A memory having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by the processor, implement the method provided by the embodiment of the present invention.

[0112] On the other hand, a computer-readable storage medium is provided, in which a program code is stored. The program code can be called by a processor to execute the method provided in the embodiment of the present invention.

[0113] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0114] The embodiment of the present invention records the experimental log files in the container in real time, automatically converts the log data into a unified format, and stores it in a MongoDB database, making it convenient for users to manage and query, and providing strong support for experimental monitoring and subsequent analysis.

[0115] The core of the virtual simulation experiment method of group intelligence robots provided by the embodiment of the present invention is to provide a comprehensive and in-depth solution from multiple dimensions such as rapid construction of experimental environment, efficient isolation and allocation of resources, simplification and optimization of operations, centralized management and analysis of logs, and collaboration and authority control of multiple users with the help of Docker containerization technology and Kubernetes cluster management technology, based on the front-end development framework Vue and the back-end development framework SpringBoot. A diversified experimental container environment is constructed, which can realize the full life cycle management of container experiments, including operations such as creation, start, stop and deletion, and supports multi-container deployment simulation experiments to ensure effective utilization and isolation of resources. In addition, the system also has command execution and log processing functions, which provide strong support for monitoring and subsequent analysis of the experiment. At the same time, the system supports multi-user operation and is equipped with a complete authority management mechanism to ensure data security and high efficiency of team collaboration.

[0116] As a preferred technical solution, this invention leverages Docker containerization technology to rapidly build and dynamically expand the software environment, eliminating the need for users to manually configure complex environments and ensuring repeatable and consistent experiments. Users simply pull the required images from a designated repository and conduct experiments in isolated containers, greatly simplifying the experimental preparation process and improving experiment startup efficiency.

[0117] As a preferred technical solution, this invention leverages Kubernetes cluster management technology to build an efficient and secure storage framework for resource management, enabling unified management and isolation of different experimental resources. By assigning each experiment a separate namespace, the system can rationally allocate resources such as CPU, memory, and storage, avoiding interference between experiments, ensuring the stable operation of each experiment, and improving resource utilization.

[0118] As a preferred technical solution, the present invention provides a rich API interface and an intuitive graphical user interface for easy operation and management of experiments. Whether creating, configuring, launching, or monitoring experiments, users can complete them through simple commands or graphical interfaces, without the need for a deep technical background. This lowers the threshold for system use and improves the convenience and efficiency of experimental operations.

[0119] As a preferred technical solution, this invention offers powerful log processing capabilities for log management. It automatically collects, parses, and stores ROSBAG log files, converts them to a unified format, and stores them in a MongoDB database. This allows users to easily query and analyze log data, promptly identifying experimental issues, providing a basis for optimization and improvement, and enhancing experimental traceability and reliability.

[0120] As a preferred technical solution, the present invention supports multi-user online operations at the same time in terms of multi-user collaboration and is equipped with a sophisticated authority management mechanism. The system can distinguish the permissions of different users, allowing administrators to add, delete and assign permissions to users in batches, ensuring that each user performs experimental operations within their own authority scope without interfering with each other. The administrator has multi-user distribution permissions and can add experimental containers based on specific images to ordinary users in batches according to preset image experiment tasks, which greatly simplifies the batch deployment process of the experimental environment and significantly improves the efficiency of experimental preparation and the manageability of the system.

[0121] Figure 4 Schematic diagram of a group intelligence robot virtual simulation experiment device provided by an embodiment of the present invention. Figure 4 As shown, optionally, the collective intelligence robot virtual simulation experiment device 410 may include a first processor 2001.

[0122] Optionally, the collective intelligence robot virtual simulation experiment device 410 may further include a memory 2002 and a transceiver 2003 .

[0123] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0124] The following combination Figure 4 The components of the group intelligence robot virtual simulation experiment device 410 are introduced in detail:

[0125] The first processor 2001 is the control center of the swarm intelligence robot virtual simulation experiment device 410 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), or application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs) or one or more field programmable gate arrays (FPGAs).

[0126] Optionally, the first processor 2001 can perform various functions of the swarm robot virtual simulation experimental device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0127] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 4 CPU0 and CPU1 are shown in FIG.

[0128] In a specific implementation, as an embodiment, the group intelligence robot virtual simulation experiment device 410 may also include multiple processors, such as Figure 4 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0129] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0130] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2002 may be integrated with the first processor 2001, or may exist independently and be accessed through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0131] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0132] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 4 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0133] Optionally, the transceiver 2003 may be integrated with the first processor 2001, or may exist independently and be connected to the first processor 2001 through the interface circuit ( Figure 4 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0134] It should be noted that Figure 4 The structure of the swarm intelligence robot virtual simulation experiment device 410 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0135] In addition, the technical effects of the group intelligence robot virtual simulation experimental equipment 410 can refer to the technical effects of the multimodal emotion recognition method described in the above method embodiment, and will not be repeated here.

[0136] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0137] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0138] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, motor drive, or data center to another website, computer, motor drive, or data center via infrared, microwave, or other means. The computer-readable storage medium can be any available medium accessible by a computer, or a data storage device such as a motor drive or data center that includes a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, or magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0139] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0140] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0141] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0142] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0143] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0147] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a motor driver, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0148] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A virtual simulation experiment method for group intelligence robots, characterized in that: include: After receiving the image usage right request input by the user, approving the image usage right request, obtaining the image after approval, updating the image information in the database, and allocating the image usage right to the user; Receive user instructions, parse the user instructions, configure container parameters according to the user instructions, and create a container instance based on the parameter configuration through the run Docker method in the Channel Util class, or create a Kubernetes Deployment based on the parameter configuration and create a container instance in the Kubernetes cluster according to the Kubernetes Deployment; receiving an instruction to create an experiment space, creating the experiment space in the container instance, defining basic information of the experiment in the experiment space, and setting resource limits for the experiment space; Obtain the log file of the experimental space, extract the ROSBAG file in the log file, parse the ROSBAG file through the go-ros bag library, determine the topic category of the ROSBAG file, and generate parsed data; According to the topic category of the parsed data, the parsed data is stored in different collections of the MongoDB database.

2. The method according to claim 1, characterized in that The virtual simulation experiment method is based on the front-end development framework Vue and the back-end development framework Spring Boot to build an experimental environment.

3. The method according to claim 1, characterized in that Also includes: Encapsulates Docker CLI commands and provides a set of methods for managing and operating Docker containers and images through the Channel Util class; The method in the method set is obtained according to the user instruction, and management operations are performed on the container instance and the image.

4. The method according to claim 1, wherein The obtaining of the image includes: Use the Docker CLI command docker pull to pull the image from the remote repository using the pull Image method in the Channel Util class; or Use the docker build command to create a new image based on the build path.

5. The method according to claim 1, wherein Also includes: In each experimental space, obtain container configuration requirements and parse container parameters in the container configuration requirements; A container instance is created in the experiment space based on the container parameters, where each experiment space includes one or more container instances.

6. The method according to claim 5, characterized in that Also includes: After receiving the experimental task allocation instructions entered by the user, the Deployment resource is created using the Create My RosDeployment method through the Kubernetes API to allocate container instances to multiple terminals.

7. The method according to claim 1, characterized in that Also includes: When receiving the data operation instruction input by the user, the data in the MongoDB database is operated through the MongoTemplate encapsulation tool class.

8. A virtual simulation experiment device for a group intelligence robot, wherein the virtual simulation experiment device is used to implement the virtual simulation experiment method for a group intelligence robot according to any one of claims 1 to 7, characterized in that: The device comprises: An allocation module, configured to receive an image usage right request from a user, approve the image usage right request, obtain the image after approval, update the image information into a database, and allocate the image usage right to the user; A creation module is used to receive user instructions, parse the user instructions, configure container parameters according to the user instructions, and create a container instance based on the parameter configuration through the run Docker method in the Channel Util class, or create a Kubernetes Deployment based on the parameter configuration and create a container instance in the Kubernetes cluster according to the KubernetesDeployment; a setting module, configured to receive an instruction to create an experimental space, create the experimental space in the container instance, define basic information of the experiment in the experimental space, and set resource limits for the experimental space; A generation module is used to obtain the log file of the experimental space, extract the ROSBAG file in the log file, parse the ROSBAG file through the go-ros bag library, determine the topic category of the ROSBAG file, and generate parsed data; The storage module is used to store the parsed data in different collections of the MongoDB database according to the topic category of the parsed data.

9. A virtual simulation experimental device for group intelligence robots, characterized in that: The group intelligence robot virtual simulation experimental equipment includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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