A method for rapid integration and management of cross-brand robots based on RUAPL

Abstract

This application discloses a method for rapid access and management of cross-brand robots based on RUAPL, belonging to the field of robot control technology. This method is applied to a multi-robot management platform and includes: receiving access requests from at least one target robot; extracting a skill list from the access requests to construct a skill semantic graph; abstracting the capabilities of the target robots based on the skill semantic graph to obtain a unified abstract description; performing RUAPL-based adaptation and conversion on the unified abstract description to generate downlink commands; verifying the downlink commands based on the target robot's digital twin sandbox; and, if the verification passes, sending downlink commands to the target robot through a control channel to configure the target robot. This method enables rapid access, semantic interoperability, secure control, and unified management of cross-brand robots in heterogeneous environments, reducing the operation and maintenance costs and integration complexity in multi-brand mixed scenarios.

Description

Technical Field

[0001] This application belongs to the field of robot control technology, and in particular relates to a method for rapid access and management of cross-brand robots based on RUAPL. Background Technology

[0002] With the rapid development of flexible manufacturing and smart logistics, multi-robot systems are increasingly being used in complex scenarios such as smart warehousing, production line assembly, and disaster relief. However, the current market offers a wide variety of robot brands and models, with significant differences in their underlying operating systems, communication protocols, and programming interfaces.

[0003] Existing robot management platforms typically lack a unified access standard, and different brands and models of robots use inconsistent communication protocols and interface standards.

[0004] This results in enterprises needing to conduct a large amount of customized development when integrating and managing robots from multiple brands. For each new brand or model of robot integrated, the development team needs to perform a lot of repetitive translation and bridging development work for its proprietary protocol, which increases deployment and maintenance costs. Summary of the Invention

[0005] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method for rapid access and management of cross-brand robots based on RUAPL, which realizes rapid access, semantic interoperability, security control and unified management of cross-brand robots in heterogeneous environments, reducing the operation and maintenance costs and integration complexity of multi-brand mixed scenarios.

[0006] Firstly, this application provides a method for rapid access and control of cross-brand robots based on RUAPL, applicable to a multi-robot management platform. This method includes:

[0007] The system receives access requests from at least one target robot via the control channel.

[0008] The skill list is extracted from the access request through a semantic channel to construct a skill semantic graph;

[0009] The skill abstraction module is invoked to perform capability abstraction on the target robot based on the skill semantic graph, resulting in a unified abstract description;

[0010] The unified abstract description is adapted and converted based on the RUAPL protocol using a bidirectional semantic adapter to generate downlink instructions.

[0011] The dynamic adaptation engine is invoked to verify the downlink command based on the digital twin sandbox of the target robot. If the verification passes, the downlink command is sent to the target robot through the control channel to configure the target robot.

[0012] According to one embodiment of this application, the step of extracting a skills list from the access request to construct a skills semantic graph includes:

[0013] Dependency parsing is performed on the access request to extract the core verbs and patient components in the access request;

[0014] Based on the skill classification system, the core verbs and the object components are classified and merged to obtain the skill list;

[0015] Based on the relationships between the skill elements in the skill list and the skill keywords in the skill list, graph nodes are extracted. The graph nodes include action nodes, object nodes, parameter nodes, and condition nodes.

[0016] The graph nodes are linked to the skill ontology in the target robot to establish entity links and relationship mappings, thereby determining semantic relationships.

[0017] Based on the semantic relationships, graph edges and semantic roles of graph nodes are obtained, wherein the graph edges include dependency type edges and weight edges;

[0018] Based on the semantic roles, the action node is used as the root node, the object node, the parameter node, and the condition node are used as child nodes, the dependency type edge is used as the connection edge between the root node and the child nodes, and the weight edge is used as the connection strength between the nodes to construct the skill semantic graph.

[0019] According to one embodiment of this application, the control channel includes an event-driven bus layer, a message routing layer, and at least one service channel layer;

[0020] The step of receiving an access request from at least one target robot via the control channel includes:

[0021] Through the event-driven bus layer, event listening rules are loaded to capture event messages sent by the target robot, thereby obtaining at least one event signal triggered by the target robot.

[0022] Through the message routing layer, the event signal is routed to the corresponding service channel layer according to the target identifier and service type carried in the event message;

[0023] The event signal is parsed and the protocol format is verified through the business channel layer. If the verification passes, key information is extracted and encapsulated into the access request. The key information includes the identity of the target robot, the type of service requested for access, the protocol field, and the skill list.

[0024] According to one embodiment of this application, after parsing the event signal and performing protocol format verification through the service channel layer, extracting key information if the verification passes, and encapsulating the key information into the access request, the method further includes:

[0025] The network and system conditions corresponding to the target robot are invoked to configure the environment for the access request;

[0026] Verify the protocol field in the access request, and configure a RUAPL message signature based on the RUAPL protocol for the access request;

[0027] Entropy weights are calculated for key information in the access request to determine the access quality evaluation index of the target robot, so as to evaluate the access quality of the target robot.

[0028] According to one embodiment of this application, the step of abstracting the capabilities of the target robot based on the skill semantic graph to obtain a unified abstract description includes:

[0029] Key skill features are extracted from the skill semantic graph, including skill type, skill parameters, and skill execution conditions;

[0030] The key skill features are mapped to a preset abstract template to generate an initial abstract description of the target robot;

[0031] The initial abstract description is semantically optimized to obtain the unified abstract description.

[0032] According to one embodiment of this application, the bidirectional semantic adapter includes a semantic translation engine, a neural symbol fusion translation engine, and an online few-shot learning component. Through the bidirectional semantic adapter, the unified abstract description is adapted and converted based on the RUAPL protocol to generate downlink instructions, including:

[0033] The unified abstract description is input into the semantic translation engine. Based on the syntax rules and semantic constraints of the RUAPL protocol, the unified abstract description is parsed and structured to obtain instruction elements. The instruction elements include operation type, operation object, operation parameters, skill action, execution object, parameter settings and condition constraints.

[0034] The neural symbolic fusion translation engine is invoked to perform semantic understanding and symbolic logic reasoning on the instruction elements, generating an intermediate representation based on the RUAPL protocol specification;

[0035] The intermediate representation is fine-tuned using an online few-shot learning component, based on the target robot's historical interaction data and the context information of the access request.

[0036] Gradient calculation is performed on the fine-tuned intermediate representation to obtain gradient information during the adaptation process, which is then aggregated to generate an alignment probability matrix.

[0037] Based on the alignment probability matrix, the unified abstract description is converted into the downlink instructions based on the RUAPL protocol.

[0038] According to one embodiment of this application, the downlink command is verified based on the digital twin sandbox of the target robot, including:

[0039] Based on the environmental perception information and spatial context of the target robot, the downlink command is analyzed to identify the operational intent and target state of the downlink command;

[0040] Based on the operational intent, the target state, the skill list and current state of the target robot, a task is programmed for the target robot to generate a task execution sequence;

[0041] The task execution sequence is optimally transmitted and aligned with the simulated operating environment in the digital twin sandbox of the target robot to determine the simulated feedback state of the target robot executing the downlink command in the digital twin sandbox.

[0042] The downlink command is verified based on the simulated feedback status.

[0043] According to one embodiment of this application, before verifying the downlink command based on the digital twin sandbox of the target robot, the method further includes:

[0044] Obtain real-time operating parameters and physical characteristic information of the target robot;

[0045] The real-time operating parameters and physical feature information are fused and analyzed to construct a three-dimensional virtual model of the target robot;

[0046] A state compression module is embedded in the three-dimensional virtual model to reduce the dimensionality of the target robot's operating state and extract key state variables.

[0047] Based on finite state automata, state division and transition rules are defined for key state variables to generate a state transition map of the target robot.

[0048] The state transition graph and edge computing resources are deployed in a distributed manner to construct the digital twin sandbox.

[0049] According to one embodiment of this application, after configuring the target robot, the method further includes:

[0050] The control channel receives the uplink command returned by the target robot in response to the downlink command;

[0051] The uplink command is parsed to extract the actual execution state data of the target robot;

[0052] The actual execution status data is compared with the simulated feedback status in the digital twin sandbox to identify the status deviation.

[0053] A transmission cost function is constructed based on the state deviation, and the path with the minimum transmission cost is determined by a dynamic programming algorithm.

[0054] The actual execution state data is mapped to the corresponding node of the digital twin sandbox along the path with the minimum transmission cost, so as to update the sandbox environment parameters of the digital twin sandbox.

[0055] According to one embodiment of this application, after parsing the uplink instruction, the method further includes:

[0056] Extract the feedback information of the target robot from the uplink command;

[0057] The feedback information is semantically parsed to extract task completion metrics and abnormal event markers;

[0058] The task execution sequence in the digital twin sandbox is adjusted according to the task completion index, and the subsequent operations of the target robot are replanned through the state transition graph.

[0059] The abnormal event markers are matched with a preset fault model to trigger the fault tolerance mechanism of the digital twin sandbox.

[0060] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application.

[0061] This application provides a method for rapid access and control of cross-brand robots based on RUAPL, which has the following advantages over existing technologies:

[0062] (1) By using a dual-channel mechanism of control channel and semantic channel, combined with technologies such as skill semantic graph construction, unified capability abstraction, RUAPL protocol adaptation and digital twin verification, the protocols of different brands of robots are adapted and encapsulated, realizing rapid access, semantic interoperability, security control and unified management of cross-brand robots in heterogeneous environments, reducing the operation and maintenance costs and integration complexity of multi-brand mixed scenarios, shortening the deployment cycle and improving the scalability and compatibility of the system.

[0063] (2) By performing deep semantic analysis on access requests, a structured skill semantic graph is constructed, realizing the automated conversion from unstructured or semi-structured natural language descriptions to computationally and reasonably expressible robot capability knowledge. This significantly improves the semantic understanding, interoperability and intelligent decision-making level of the multi-robot management platform in heterogeneous environments. Attached Figure Description

[0064] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0065] Figure 1 This is a flowchart illustrating the method for rapid access and management of cross-brand robots based on RUAPL, as provided in the embodiments of this application. Detailed Implementation

[0066] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0067] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0068] The following description, in conjunction with the accompanying drawings, details the method for rapid access and management of cross-brand robots based on RUAPL provided in this application, through specific embodiments and application scenarios.

[0069] Among them, the method for rapid access and control of cross-brand robots based on RUAPL is applied to multi-robot management platforms.

[0070] A multi-robot management platform is a centralized software system used to uniformly monitor, schedule, control, and coordinate the operation of multiple robots from different brands, models, or functional types in a specific environment. It enables unified access and effective management of robots of different brands and types based on the Robot Unified Access Protocol Layer (RUAPL). The platform achieves efficient resource allocation, intelligent task planning, and secure and stable system operation among all robots.

[0071] like Figure 1 As shown, this method for rapid access and management of cross-brand robots based on RUAPL includes:

[0072] Step 110: Receive an access request from at least one target robot via the control channel.

[0073] The target robot is a robotic device that is to be connected to a multi-robot management platform and has the ability to send access requests. It can be various automated entities such as mobile robots, robotic arms, and inspection equipment. These target robots may come from different brands and have different types and functions, but they can all achieve unified access and management through the cross-brand robot rapid access and management method based on RUAPL provided in this application.

[0074] The control channel is used to transmit instructions and provide status feedback between the multi-robot management platform (hereinafter referred to as the "platform") and the target robot. Through the control channel, the platform can reliably and stably receive access requests sent by the target robot, ensuring the integrity and timeliness of the request information. Simultaneously, after completing the configuration of the target robot, the control channel feeds back the configuration results and relevant status information to the platform, enabling the platform to monitor and manage the robot in real time.

[0075] An access request is a signal initiated by the target robot to the platform, requesting access to the platform and acceptance of its management. This request includes basic information about the target robot and its skills.

[0076] In order to achieve unified access and efficient management of robots from different brands and models in a multi-robot management platform, this application proposes an intelligent access and control mechanism based on the RUAPL protocol.

[0077] In step 110, when a target robot needs to access the platform, it sends an access request to the platform. The access request includes basic identity information such as the robot's device identification document (ID), brand and model, and communication address, as well as access authentication credentials. The platform uses the control channel to receive access requests from at least one target robot, establishes a stable communication connection with the target robot, and completes preliminary identity recognition and security verification.

[0078] Step 120: Extract a skill list from the access request through a semantic channel to construct a skill semantic graph.

[0079] The semantic channel is used to deeply extract and analyze skill information in access requests. It transforms skill information into a structured skill list through a Natural Language Processing (NLP) model and further constructs a skill semantic graph.

[0080] The skills list is a structured collection of information about the skills possessed by the target robot, extracted from the access request. It lists the various skills that the target robot can perform.

[0081] A skill semantic graph is a visual or logical network of skill relationships built on a skill list through semantic analysis and association. It is used to show the associations and dependencies between various skills and also includes richer and deeper semantic information.

[0082] In step 120, simultaneously with the establishment of the communication connection in the control channel, the platform enables the semantic channel to parse the functional capability information reported by the robot. In the access request, the target robot submits its skill list in a structured or semi-structured form, such as mobile navigation, object grasping, voice interaction, and environmental monitoring. The platform uses semantic analysis technology in the semantic channel to extract the type, parameters, execution conditions, and dependencies of these skills, performs deep parsing of the access request, extracts the skill list, and organizes it in the form of a knowledge graph to express the semantic relationships and logical hierarchy between skills, thus constructing a skill semantic graph.

[0083] Step 130: Invoke the skill abstraction module, and perform capability abstraction on the target robot based on the skill semantic graph to obtain a unified abstract description.

[0084] The skill abstraction module is a core component of the multi-robot management platform, used to abstract and summarize the capabilities of target robots based on skill semantic graphs. By calling the skill abstraction module, the platform can abstract the skills possessed by robots of different brands and types into a unified descriptive form, serving as a unified abstract description to shield the differences and complexities between robots.

[0085] The unified abstract description is a standardized and unified form of description obtained by abstracting the skills and capabilities of the target robot, which is independent of the specific brand and type of the robot.

[0086] In step 130, the platform invokes the skill abstraction module to perform in-depth processing on the constructed skill semantic graph. Using unified abstraction rules and standards, it abstracts, refines, and summarizes the capabilities of the target robot. This maps the heterogeneous terminology and interface specifications used by different robot brands into a unified capability model framework, achieving standardized expression of capabilities. This removes differences between different brands and types of robots, forming a unified abstract description that reflects only the functional essence of the robot. This enables cross-brand and cross-type representation of robot functional characteristics, facilitating unified management by the platform. For example, MoveToPosition from manufacturer A and NavigateTo from manufacturer B are uniformly abstracted as the "target point navigation" capability.

[0087] Step 140: Using a bidirectional semantic adapter, the unified abstract description is adapted and converted based on the RUAPL protocol to generate downlink instructions.

[0088] The bidirectional semantic adapter is used to achieve adaptation and conversion between the unified abstract description and the RUAPL protocol. It can convert the unified abstract description into an instruction format that conforms to the RUAPL protocol specification, and at the same time, it can parse the downlink instructions generated by the platform into the unified abstract description, so as to configure and manage the target robot.

[0089] The RUAPL protocol is a unified access protocol layer used to define standard specifications for communication and interaction between robots and platforms, including command formats, data transmission, and status feedback. It has good scalability and semantic expression capabilities, supports cross-brand semantic interoperability, defines a common message structure, has built-in security mechanisms, and an extensible message type system. Through the RUAPL protocol, robots of different brands and types can access the platform in a unified way and be subject to the platform's unified management and control.

[0090] Downlink commands are generated by the platform based on the RUAPL protocol and adapted through a bidirectional semantic adapter. They are used to configure and manage the target robot, and their format, fields, and semantics conform to the target robot's native protocol requirements. They contain specific operational requirements, parameter settings, and execution logic to guide the target robot in completing corresponding tasks or adjusting its working state.

[0091] In step 140, a bidirectional semantic adapter is used to dynamically load the corresponding protocol mapping rules according to the brand and model of the target robot. The unified abstract description is adapted and converted according to the RUAPL protocol, and translated into downlink instructions that can be recognized by the target robot's native system. The target robot can correctly understand and execute the downlink instructions, ensuring that the downlink instructions can be accurately recognized and executed on robots of different brands and types.

[0092] Step 150: Invoke the dynamic adaptation engine and verify the downlink command based on the digital twin sandbox of the target robot. If the verification passes, send the downlink command to the target robot through the control channel to configure the target robot.

[0093] The dynamic adaptation engine is used to dynamically verify downlink commands in a digital twin sandbox based on the target robot. It can analyze the execution effect of commands in the virtual environment in real time, predict potential conflicts or anomalies, and ensure the safe delivery of commands after verification.

[0094] A digital twin sandbox is a high-fidelity, interactive digital twin of a target robot built in a virtual environment. It simulates the target robot's operating environment and behavioral characteristics, reproduces the robot's geometric structure, motion characteristics, and sensor model, and simultaneously maps the spatial layout, obstacle distribution, and dynamic changes of its physical operating scene. This provides an accurate testing environment for command verification, enabling the platform to pre-rehearse and verify downlink commands without interfering with the actual operation of the robot, ensuring that no errors or conflicts occur when the commands are actually executed.

[0095] In step 150, before the downlink command is officially issued, to ensure the safety and feasibility of the operation, the platform invokes a dynamic adaptation engine to verify the downlink command in the target robot's digital twin sandbox. This simulates the execution process of the downlink command on the target robot, detecting potential conflicts, exceeding limits, path obstacles, or resource contention, and identifying and adjusting for any problems in advance to ensure the accuracy and feasibility of the command. After successful verification, the downlink command is sent to the target robot through the established control channel, completing the configuration and management of the target robot.

[0096] The target robot performs the corresponding operation and sends the status information back to the platform. The dynamic adaptation engine can also dynamically adjust and optimize the instructions based on the real-time status of the target robot and the feedback information to form a closed-loop control, thereby achieving more precise and efficient robot management.

[0097] According to the method for rapid access and management of cross-brand robots based on RUAPL provided in this application,

[0098] In some embodiments, extracting a skills list from the access request to construct a skills semantic graph includes:

[0099] Dependency parsing is performed on the access request to extract the core verbs and patient components in the access request;

[0100] Based on the skill classification system, the core verbs and the object components are classified and merged to obtain the skill list;

[0101] Based on the relationships between the skill elements in the skill list and the skill keywords in the skill list, graph nodes are extracted. The graph nodes include action nodes, object nodes, parameter nodes, and condition nodes.

[0102] The graph nodes are linked to the skill ontology in the target robot to establish entity links and relationship mappings, thereby determining semantic relationships.

[0103] Based on the semantic relationships, graph edges and semantic roles of graph nodes are obtained, wherein the graph edges include dependency type edges and weight edges;

[0104] Based on the semantic roles, the action node is used as the root node, the object node, the parameter node, and the condition node are used as child nodes, the dependency type edge is used as the connection edge between the root node and the child nodes, and the weight edge is used as the connection strength between the nodes to construct the skill semantic graph.

[0105] Understandably, dependency parsing is used to determine the core verbs and related patient components of a sentence by analyzing the dependency relationships between its constituent parts. When performing dependency parsing on access requests, it can accurately locate the core verbs describing robot skills, such as "grasp," "move," and "monitor," as well as the patient components that these verbs act upon, such as "object," "location," and "environmental parameters."

[0106] Core verbs are keywords that describe the actions performed by a robot's skills, representing its core functionalities. In dependency parsing, the core verb serves as the center of the sentence, and the surrounding patient and modifier elements together constitute a complete description of the robot's skills. By extracting core verbs, the platform can accurately identify the basic skill types possessed by the robot.

[0107] The patient component is the object or target of the action of the core verb, used to refine the specific application scenario of the robot's skill. For example, in the case of a skill description of "grasping an object," "grasping" is the core verb, and "object" is the patient component. The combination of the two clarifies the specific object on which the robot performs the grasping operation.

[0108] A skills classification system is a predefined framework covering various robot skill types, used to transform complex skills information into a structured skills list. Through classification and categorization, the platform can determine the types and number of skills possessed by a target robot.

[0109] Skill elements are the basic units that make up a skill list. In the process of constructing a skill semantic graph, skill elements mainly include action elements, object elements, parameter elements, and condition elements.

[0110] Among them, the action element corresponds to the action node in the graph, which is used to represent the specific operation or behavior that the robot can perform; the object element corresponds to the object node in the graph, which is used to represent the specific target or entity of the action; the parameter element corresponds to the parameter node in the graph, which is used to provide various parameter information required in the skill execution process, such as speed, force, angle, etc.; and the condition element corresponds to the condition node in the graph, which is used to describe the preconditions or constraints of skill execution.

[0111] By extracting skill elements and constructing semantic relationships between them, the platform can form a complete and accurate skill semantic graph.

[0112] Skill keywords are words in a skill list that can accurately summarize the core characteristics of a skill. They are used to reflect the main actions and objects of a skill, as well as to provide information such as the parameters and conditions for skill execution.

[0113] Graph nodes are the basic building blocks in a skill semantic graph, and are divided according to the different types of skill elements.

[0114] In the graph nodes, action nodes correspond to core verbs, representing the specific actions performed by the robot, such as "move," "grasp," and "rotate." Object nodes correspond to the recipient, representing the target or entity to which the action is directed. For example, in the "grasp object" skill, the "object" is the object node, used to define the specific scope of the action. Parameter nodes are used to describe the specific parameters or configuration information when the skill is executed, such as the speed of movement and the force of grasping. Condition nodes are used to describe the prerequisites, triggering scenarios, or limitations for skill execution. For example, a certain monitoring skill will only be activated under a specific ambient temperature; the condition node sets the boundary for the triggering of the skill.

[0115] A skill ontology is the formal definition and description of a target robot's own skills, including hierarchical and attribute relationships between skills. Through entity linking and relationship mapping, the platform can accurately understand the position and role of graph nodes in the skill ontology, thereby determining the semantic relationships between graph nodes.

[0116] Entity linking and relation mapping are the processes of associating and mapping graph nodes to skill ontologies in the target robot. Entity linking allows graph nodes to be accurately mapped to corresponding elements in the skill ontologies, ensuring that the skills represented by the graph nodes align with the robot's actual skills. Relation mapping establishes semantic connections between graph nodes, such as the action relationship between action nodes and object nodes, and the modification relationship between parameter nodes and action nodes.

[0117] Semantic relations are used to determine the type and properties of graph edges.

[0118] Graph edges are elements in a skill semantic graph used to connect various graph nodes.

[0119] In graph edges, dependency type edges are used to represent the syntactic and semantic dependencies between graph nodes based on dependency parsing, the hierarchical and subordinate structures between graph nodes, such as the execution relationship between actions and objects, the constraints of parameters on actions, etc.

[0120] Weighted edges reflect the strength or confidence of the association between nodes. They can be determined by the frequency of the semantic combination in historical requests or training corpora through context frequency statistics, by calculating the semantic matching degree between nodes through semantic similarity models (such as BERT, Sentence-BERT), or by the confidence score derived from volume inference. They are used to characterize the strength of the relationship between graph nodes based on factors such as the importance and frequency of use of skills in actual applications. Weighted edges add a quantitative dimension, rich semantic information and structured expression to the skill semantic graph, enabling the skill semantic graph to more accurately reflect the complex relationships between skills.

[0121] Dependency type edges ensure the logical consistency and accuracy among the various skill elements in the skill semantic graph, while weighted edges further refine these relationships, enabling the skill semantic graph to more accurately describe the skill characteristics of the target robot.

[0122] Semantic roles are the semantic functional roles undertaken by each node in a skill semantic graph. Examples include the executor and receiver of an action, the specific value or range of a parameter, and the threshold or state for conditional triggering. These roles clarify the various elements of a skill during execution and their interrelationships. Through dependency parsing and a skill classification system, skill elements in access requests can be accurately extracted, and their semantic roles in the skill semantic graph can be determined, enabling structured expression and visualization of skill information.

[0123] The root node is the starting point in the skill semantic graph, representing the core action or behavior of the entire skill.

[0124] Child nodes are the next level nodes of the root node in the process of constructing a skill semantic graph.

[0125] In actual execution, in the semantic channel, the natural language description or semi-structured text in the access request is analyzed by parsing the grammatical dependencies between words within the sentence to identify the core predicate structure of the expressive function, and then the core verbs describing the robot's capabilities and the patient components they act on are extracted. The core verbs and patient components together constitute the basic semantic units for expressing the robot's capabilities.

[0126] Based on a pre-built robot skill classification system, the semantic channel performs semantic classification and merging of the extracted core verbs and patient components across multiple functional dimensions, such as movement, operation, perception, and interaction, to obtain the original table.

[0127] The original expressions are mapped to standard skill categories, and synonyms, near-synonyms, or hierarchical expressions are merged. For example, "handling", "delivering", and "transportation" are uniformly classified into the "material movement" category, generating a standardized and redundant skill list.

[0128] Fine-grained semantic element analysis is performed on the relationships and keywords of each skill in the skill list, extracting four key graph nodes that constitute the semantics of the skills: action nodes, object nodes, parameter nodes, and condition nodes.

[0129] Entity linking is performed between graph nodes and a predefined robot skill ontology, mapping natural language words to standard conceptual entities in the ontology and assigning clear semantic meanings to the graph nodes. Simultaneously, semantic relationships between nodes are identified, such as "action-acts on-object," "action-has-parameters," and "action-constrained by-conditions," completing the relationship mapping and ensuring that the skill semantic graph possesses good semantic consistency and reasonability.

[0130] Based on the results of entity linking and relationship mapping, the semantic association types between each graph node are analyzed using predefined semantic role annotation systems such as PropBank, AMR, or a self-built robotics domain semantic framework, to characterize the functional logical relationships between the graph nodes. For example, if there is a "has parameter" relationship between "navigation" (action node) and "target point coordinates" (parameter node), the system classifies this semantic relationship as a standard dependency semantic type such as location parameter or hasParameter; if there is an "acts on" relationship between "grasp" (action) and "goods" (object), it is marked as an object dependency type.

[0131] Based on the above process, graph edges connecting the corresponding graph nodes are generated. Dependency types are represented by directed edges pointing from the root node to the child node, forming a semantic dependency structure. Weighted edges represent the strength or importance of the association between nodes in numerical form. The higher the weight, the more critical the parameter, object, or condition is to the execution of the skill.

[0132] Assign a semantic role to each graph node in the current skill expression. For example, "move" is the action role, "target location" is the location role, "maximum speed" is the parameter role, and "battery level above 20%" is the condition role.

[0133] All connections with dependency type edges and weight edges, along with the semantic roles of each node, are integrated into a structured directed semantic graph. This graph is rooted at action nodes, with the remaining nodes serving as their semantically dependent child nodes. The edge type represents the nature of the relationship, and the edge weight represents the strength of the association, together forming a complete skill semantic graph.

[0134] In this embodiment, by performing deep semantic parsing on access requests and constructing a structured skill semantic graph, an automated conversion from unstructured or semi-structured natural language descriptions to computationally and reasonably expressible robot capability knowledge is achieved, which significantly improves the semantic understanding, interoperability and intelligent decision-making level of the multi-robot management platform in heterogeneous environments.

[0135] In some embodiments, the control channel includes an event-driven bus layer, a message routing layer, and a service channel layer;

[0136] The step of receiving an access request from at least one target robot via the control channel includes:

[0137] Through the event-driven bus layer, event listening rules are loaded to capture event messages sent by the target robot, thereby obtaining at least one event signal triggered by the target robot.

[0138] Through the message routing layer, the event signal is routed to the corresponding service channel layer according to the target identifier and service type carried in the event message;

[0139] The event signal is parsed and the protocol format is verified through the business channel layer. If the verification passes, key information is extracted and encapsulated into the access request. The key information includes the identity of the target robot, the type of service requested for access, and the skill list.

[0140] Understandably, the dedicated communication link between the control channel robot management platform and the target robot is used to transmit key control information such as control commands, status feedback, and access requests. In the control channel, the event-driven bus layer is built on an event-driven architecture and serves as the underlying communication infrastructure of the control channel. Based on message middleware such as Message Queuing Telemetry Transport (MQTT), Kafka, and RabbitMQ, it listens for and receives asynchronous event messages from the robot, such as online notifications or fault alarms, and distributes messages through a publish / subscribe mechanism.

[0141] Event listening rules are a set of pre-configured conditions or policies that guide the platform's event-driven bus layer to selectively listen for and capture asynchronous event messages from the target robot. These rules explicitly define the types of events the platform should listen for, the range of devices from which the messages originate, and the execution conditions that trigger the processing flow, ensuring that the system can accurately and efficiently perceive and respond to device behaviors that meet business requirements.

[0142] Event messages are asynchronous notification messages proactively sent by the target robot to inform the platform of its status changes or to request services. They include fields such as event type, timestamp, source device ID, and payload.

[0143] Event signals are structured event instances extracted from event messages by the event-driven bus layer, and are an abstract representation of robot behavior within the platform.

[0144] The message routing layer is located above the event-driven bus layer. It is used to accurately distribute event signals to the corresponding business processing channels based on metadata such as the target address and business type in the message, thereby realizing message classification, orientation, and load balancing.

[0145] The target identifier is the recipient identifier specified in the event message, which indicates which service or module in the platform should handle the message.

[0146] The business type describes the functional category or application scenario to which the event belongs, such as access authentication, task assignment, status reporting, and remote control.

[0147] The business channel layer is the top-level logic processing module of the control channel, providing dedicated data processing channels for specific business scenarios such as device access, task execution, and state synchronization. Each business channel operates independently, responsible for protocol parsing, semantic understanding, security verification, and business logic processing of received messages to ensure the integrity and legitimacy of access requests.

[0148] Protocol format validation checks whether the parsed data conforms to the platform's predefined communication protocol specifications. For example, it checks if required fields are included, if field types are correct, and if the data conforms to the JSON Schema definition. If validation fails, the request is discarded or rejected to prevent illegal or erroneous data from entering the platform.

[0149] The key information is the core data fields that the platform must obtain during the robot access process, which are used to complete device identification, permission verification, capability modeling, and resource allocation.

[0150] Among the key information, the target robot's identity identifier is used to uniquely identify the target robot's device identity information, such as the device serial number (SN), physical (Media Access Control Address, MAC) address, and robot ID. The platform uses this identifier for device registration, authentication, and status tracking. The requested service type is the specific service category that the target robot wishes to use after requesting access to the platform, such as path planning service, task scheduling service, and remote monitoring service. The platform allocates corresponding resources and permissions according to the service type. The skills list is a list of functional capabilities declared by the target robot, such as autonomous navigation, object grasping, voice interaction, and environmental monitoring.

[0151] In actual execution, the event-driven bus layer initiates an event listening mechanism, pre-loading a set of event listening rules to identify specific event types related to robot access. When the target robot sends event messages to the platform, the event-driven bus layer captures these messages in real time and extracts the event signals triggered by the robot. The event signals contain basic metadata such as event type, timestamp, and source device address, used to characterize the robot's attempt to establish a connection with the platform.

[0152] The message routing layer intelligently routes captured event signals based on the target identifier and service type carried in the event message, using a distribution mechanism based on topic, tag, or content. This ensures that different types of events are accurately directed to the corresponding service channel layer modules. For example, "access request" events are routed to the "device access channel," while "task feedback" events are routed to the "task execution channel," achieving high concurrency, low latency, and orderly message distribution.

[0153] The event signal is transmitted to the corresponding business channel layer. The business channel layer performs protocol format verification on the received event signal to confirm whether it conforms to the communication specifications defined by the platform, preventing illegal or malformed data from entering the platform's core modules. After successful verification, the business channel layer further parses the event signal's payload to extract key information, including the target robot's identity, the requested service type, and the skill list.

[0154] The business channel layer encapsulates key information in a structured manner and generates standardized access requests.

[0155] In this embodiment, the control channel constructs an efficient, secure, and scalable robot access point through a three-level processing mechanism of event capture, intelligent routing, and protocol parsing, enabling efficient capture, accurate distribution, and secure parsing of access requests from target robots of different brands.

[0156] In some embodiments, after parsing the event signal and performing protocol format verification through the service channel layer, extracting key information if the verification passes, and encapsulating the key information into the access request, the method further includes:

[0157] The network and system conditions corresponding to the target robot are invoked to configure the environment for the access request;

[0158] Verify the protocol field in the access request, and configure a RUAPL message signature based on the RUAPL protocol for the access request;

[0159] Entropy weights are calculated for key information in the access request to determine the access quality evaluation index of the target robot, so as to evaluate the access quality of the target robot.

[0160] Understandably, network conditions refer to the technical parameters and operational characteristics of the target robot's communication environment, used to assess its data transmission capabilities with the management platform. These primarily include communication bandwidth, network latency, packet loss rate, supported communication protocols, and network topology.

[0161] System conditions refer to the target robot's own hardware and software operating environment and system capabilities, including operating system type, processor architecture and performance, firmware version number, security module support, storage space, and number of concurrent connections.

[0162] Environment configuration is the process of dynamically generating configuration scripts or parameter tables based on the target robot's network and system conditions through a configuration management engine, pushing them to the corresponding communication channels, and automatically setting communication parameters, loading adaptation modules, and allocating resource quotas. For example, it involves setting the number of message retries and timeouts, enabling or disabling data compression, loading the corresponding protocol parser, and allocating memory buffer size.

[0163] Protocol fields are structured fields defined in the access request according to the RUAPL protocol specification. They are used to characterize the semantic content of the request, including the unique identifier of the request, device ID, service type, timestamp, digital signature, and skills list.

[0164] RUAPL message signatures are digital signatures generated from key parts of RUAPL messages using asymmetric encryption algorithms, used to verify the integrity and authenticity of the message's origin.

[0165] Entropy weighting is an objective weighting method based on information theory. It reflects the degree of uncertainty of each indicator by calculating its information entropy. The smaller the entropy value, the more stable and important the information is, and the higher its weight. It is used to quantify the completeness and certainty of key information and avoid subjective weighting bias.

[0166] The access quality assessment index is a quantitative score calculated by comprehensively considering multiple dimensions such as identity integrity, clarity of skill description, and protocol compliance. It is used to measure the data quality level of this access request.

[0167] In actual execution, the platform invokes network and system conditions that match the target robot, automatically configures the optimal communication parameters for the access request, such as heartbeat interval, message compression method, retry strategy, etc., and initializes the corresponding driver modules and adaptation components to ensure efficient and stable subsequent interactions.

[0168] The protocol fields in the access request undergo deep validation, including field integrity, data type compliance, and value range legality, to ensure compliance with the RUAPL specification defined by the platform. After successful validation, the platform uses an asymmetric encryption algorithm and its private key to digitally sign the request header, key information digest, and timestamp, configuring a message signature based on the RUAPL protocol for the access request, which is then appended to the request message.

[0169] Key information is divided into multiple dimensions, such as the completeness of identity identification, the clarity of service type, the comprehensiveness of the skill list, and the standardization of parameter descriptions. Data for each dimension is normalized, and entropy values ​​are calculated for the information richness and uncertainty of each dimension. Based on the entropy values ​​of each dimension, a weighted method is used to calculate the comprehensive entropy weight, generating access quality assessment indicators for the target robot.

[0170] In this embodiment, the communication strategy is dynamically optimized according to the actual operating conditions of the robot through environmental adaptive configuration, thereby improving the access success rate. The RUAPL message signature mechanism is used to prevent illegal forgery and man-in-the-middle attacks. The access data quality is quantified through the information entropy model, supporting intelligent decision-making and anomaly warning. This enhances the robustness, security and intelligent management level of the multi-robot management platform in complex heterogeneous environments.

[0171] In some embodiments, the step of abstracting the capabilities of the target robot based on the skill semantic graph to obtain a unified abstract description includes:

[0172] Key skill features are extracted from the skill semantic graph, including skill type, skill parameters, and skill execution conditions;

[0173] The key skill features are mapped to a preset abstract template to generate an initial abstract description of the target robot;

[0174] The initial abstract description is semantically optimized to obtain the unified abstract description.

[0175] Understandably, key skill features are core semantic elements extracted from the skill semantic graph, used to characterize the essential attributes of a robot's functions. Within key skill features, skill type refers to the functional category the target robot can perform, such as movement, grasping, environmental perception, or voice interaction. This high-level classification of action nodes is typically based on a predefined skill classification system for identification and merging. Skill parameters are the specific configuration information required during skill execution, such as speed, distance, coordinates, and load weight—parameters that directly affect the operational behavior and execution effect. Skill execution conditions are the prerequisites or limiting factors upon which the skill operates, such as operation only indoors, battery power exceeding a certain threshold, or the need for manual authorization. In the skill semantic graph, these are represented as condition nodes connected to action nodes.

[0176] The pre-defined abstract template is a standardized data structure model predefined by the platform to uniformly describe the capabilities of all robots. The pre-defined abstract template specifies the field structure, semantic meaning, and formatting specifications that capability descriptions should include, ensuring that capability information from different sources can be mapped to the same framework and achieving semantic alignment across devices.

[0177] The initial abstract description is a preliminary ability expression generated by filling key skill features into the abstract template according to the mapping rules. It has a standardized structure, but may have problems such as inconsistent terminology, information redundancy or logical conflicts, which need to be further processed.

[0178] Semantic optimization is a process of fine-tuning the initial abstract description, including standardized replacement of terms, unified conversion of parameter units, merging and eliminating duplicate or contradictory conditions, and supplementing missing but reasonably inferred information, in order to improve standardization, consistency and completeness.

[0179] In actual implementation, within the skill abstraction module, the core semantic elements constituting the robot's functional essence are extracted from the skill semantic graph through traversal of the graph structure and determination of the semantic roles of nodes, serving as key skill features. These extracted key skill features are then used in conjunction with a skill ontology library to support synonym recognition and hierarchical reasoning, identifying equivalence or hierarchical relationships between terms from different manufacturers. For example, expressions such as "autonomous movement" and "path following" are uniformly categorized into the standard skill type of "navigation," and various parameters and constraints are converted and merged according to a unified unit and format. Based on semantic alignment rules, these are mapped to standard fields in a preset abstract template to obtain the initial abstract description.

[0180] The terminology in the initial abstract description is standardized, replacing non-standard expressions with authoritative terms from the ontology library; parameters are normalized, for example, converting kilometers per hour to meters per second; multiple similar or repetitive execution conditions are merged and simplified to eliminate logical contradictions; for some common but undeclared default parameters, reasonable completion is performed based on domain knowledge; finally, a unified abstract description is formed that is completely free from the language habits and interface differences of the original device, retaining only its essential functional semantics.

[0181] In this embodiment, by not losing key parameters and constraint information during the abstraction process, high-fidelity semantic preservation is achieved, enabling cross-brand interoperability, semantic-level collaboration, and intelligent management and control.

[0182] In some embodiments, the bidirectional semantic adapter includes a semantic translation engine, a neural symbol fusion translation engine, and an online few-shot learning component. Through the bidirectional semantic adapter, the unified abstract description is adapted and converted based on the RUAPL protocol to generate downlink instructions, including:

[0183] The unified abstract description is input into the semantic translation engine. Based on the syntax rules and semantic constraints of the RUAPL protocol, the unified abstract description is parsed and structured to obtain instruction elements. The instruction elements include operation type, operation object, operation parameters, skill action, execution object, parameter settings and condition constraints.

[0184] The neural symbolic fusion translation engine is invoked to perform semantic understanding and symbolic logic reasoning on the instruction elements, generating an intermediate representation based on the RUAPL protocol specification;

[0185] The intermediate representation is fine-tuned using an online few-shot learning component, based on the target robot's historical interaction data and the context information of the access request.

[0186] Gradient calculation is performed on the fine-tuned intermediate representation to obtain gradient information during the adaptation process, which is then aggregated to generate an alignment probability matrix.

[0187] Based on the alignment probability matrix, the unified abstract description is converted into the downlink instructions based on the RUAPL protocol.

[0188] Understandably, in a bidirectional semantic adapter, the semantic translation engine is a fundamental component, responsible for initially parsing the unified abstract description according to the grammatical rules and semantic constraints of the RUAPL protocol. It can identify and extract key semantic elements from the description, transforming unstructured functional expressions into structured data.

[0189] The Neural Symbolic Fusion Translation Engine is a hybrid reasoning system that combines neural networks and symbolic logic. It possesses the ability of deep learning models to perceive language patterns and integrates the rigorous deduction ability of formal logic for rules and constraints. It can perform security verification, conflict detection, and path optimization while understanding the semantics of instructions.

[0190] The online few-shot learning component is a lightweight adaptive learning module that can quickly capture the behavioral preferences and language features of a target robot with only a small number of historical interaction samples, enabling personalized fine-tuning of the instruction generation process. It is particularly suitable for scenarios where new devices are connected or the environment is dynamically changing.

[0191] Grammar rules are a formal set of rules that regulate the structure and expression of information; semantic constraints are restrictions imposed on the meaning and logical rationality of information content on the basis of grammatical legality.

[0192] Instruction elements are specific semantic units parsed from a unified abstract description. Among them, operation type is the category of behavior that the robot should perform, such as moving, grasping, or reporting status; operation object is the target entity to which the operation is applied, such as a certain goods, area, or user; operation parameters are the configuration information required to perform the operation, such as speed, angle, time, etc.; skill action is a further refinement of the operation type, reflecting the specific behavior pattern; execution object clarifies the actual subject executing the instruction; parameter settings cover the value range and default value of all adjustable parameters; condition constraints specify the prerequisite environment or state restrictions for operation execution, such as power requirements, area permissions, etc.

[0193] Semantic understanding identifies the intent and contextual relationships behind instruction elements, while symbolic logic reasoning is a formal deduction based on pre-defined rules to ensure the rationality and security of instructions.

[0194] Intermediate representations are transitional expressions generated during semantic transformation. They retain the integrity of high-level semantics while conforming to the structural requirements of the RUAPL protocol, serving as a bridge between the upper and lower levels.

[0195] Contextual information includes device model, network status, and task scenario.

[0196] Gradient calculation is used to analyze the sensitivity of the model to changes in each semantic layer during the adaptation process and to identify key influencing factors.

[0197] The alignment probability matrix is ​​a quantized mapping model generated by aggregating gradient information. It is used to measure the degree of matching between each semantic unit in the unified abstract description and the native instruction format of the target robot, and to guide the optimal path selection in the adaptation process.

[0198] In actual execution, the unified abstract description is input into the semantic translation engine. Based on the syntax rules and semantic constraints defined by the RUAPL protocol, the unified abstract description is deeply analyzed and structurally decomposed to identify the various instruction elements contained in the description.

[0199] The neural symbolic fusion translation engine is invoked to perform higher-level semantic understanding and logical reasoning on the received instruction elements, transforming them into an intermediate representation that conforms to the RUAPL protocol specification. For example, it can determine whether an operation violates security rules or select the optimal solution from multiple possible execution paths.

[0200] The online few-shot learning component utilizes the target robot's past communication records with the platform, as well as the context information in the access request, to locally optimize the aforementioned intermediate representation, making the intermediate representation more closely match the target robot's actual behavior patterns.

[0201] During the fine-tuning process, gradient calculations are performed on each layer of semantic expression in the intermediate representation to capture the changing trends and sensitive dimensions during the adaptation process. The resulting gradient information reflects the influence weight of different semantic units in the conversion process. These gradients are aggregated and analyzed to generate an alignment probability matrix that quantifies the mapping probability between each semantic element in the unified abstract description and the target robot's native instruction format.

[0202] Based on the alignment probability matrix, the entire semantic mapping process is guided, accurately converting the originally general unified abstract description into downlink instructions that conform to the RUAPL protocol standard.

[0203] In this embodiment, a semantic translation engine performs structured parsing, a neural symbol fusion translation engine achieves semantic understanding and logical reasoning, and an online few-shot learning component provides personalized adaptability. The three work together to enable the platform to maintain efficient, accurate, and safe instruction generation capabilities when facing diverse robotic devices.

[0204] In some embodiments, the downlink commands are verified based on the digital twin sandbox of the target robot, including:

[0205] Based on the environmental perception information and spatial context of the target robot, the downlink command is analyzed to identify the operational intent and target state of the downlink command;

[0206] Based on the operational intent, the target state, the skill list and current state of the target robot, a task is programmed for the target robot to generate a task execution sequence;

[0207] The task execution sequence is optimally transmitted and aligned with the simulated operating environment in the digital twin sandbox of the target robot to determine the simulated feedback state of the target robot executing the downlink command in the digital twin sandbox.

[0208] The downlink command is verified based on the simulated feedback status.

[0209] Understandably, environmental perception information refers to the state data about the space in which the target robot is located, which is obtained by the target robot through its own sensors, such as maps scanned by LiDAR, visual information captured by cameras, and distance data detected by ultrasound. This reflects the target robot's real-time cognition of the external environment.

[0210] Spatial context is a semantic-level spatial understanding of the target robot's current location in the environment, its orientation, its relative relationship with the target object, and the surrounding passable area.

[0211] Intent resolution is the process of identifying the underlying operational purpose and desired outcome state from downlink instructions.

[0212] Operational intent is the high-level behavioral purpose in downlink instructions, which includes a comprehensive semantic expression of the task background, execution logic, and functional objectives;

[0213] The target state is the final state that the target robot and its operating environment are expected to achieve, provided that the operational intent is executed correctly.

[0214] The current state is the actual operating status of the target robot at the moment the command is triggered, including dynamic attributes such as power level, whether it is performing other tasks, load status, and communication quality.

[0215] Task programming is the process of decomposing high-level downlink instructions into a series of ordered, executable low-level action steps based on the operational intent and target state, combined with the robot's skills and current operating status, considering the dependencies between actions, resource consumption, and safety strategies, thus obtaining a task execution sequence. For example, the downlink instruction for transporting goods can be broken down into a logically rigorous and optimally structured task execution sequence that includes multiple consecutive stages such as path planning, moving to the starting point, performing grasping, obstacle avoidance, reaching the destination, releasing the goods, and returning to the original position.

[0216] The simulation environment is a virtual world built within the digital twin sandbox, containing physical rules, dynamic models, and interaction mechanisms consistent with real-world scenarios. Within the simulation environment, task execution sequences are loaded and drive the digital twin sandbox through a full-process simulation.

[0217] Optimal transport alignment is the process of precisely matching each operation step in the task execution sequence with its spatiotemporal coordinates, execution timing, and resource calls in the simulation environment. This alignment ensures that the virtual simulation can realistically reproduce the entire process of instruction execution, avoiding verification distortion caused by temporal misalignment or spatial deviation.

[0218] Simulated feedback state refers to the dynamic response results generated after the task execution sequence runs in the digital twin sandbox. It includes multi-dimensional data such as the robot's motion trajectory, energy consumption changes, collision detection records, sensor outputs, task completion scores, as well as stability, efficiency and abnormal events during the execution process.

[0219] In actual execution, based on the target robot's digital twin sandbox, combined with its current environmental perception information and spatial context, the operational intent expressed by the downlink commands is analyzed, the surface actions are identified, and the complete task objectives behind the commands are restored. For example, the "transportation" behavior in "transporting goods from area A to area B", as well as the target state that the operation is expected to achieve, such as "the goods are accurately placed at the specified coordinates in area B and the robot returns to the standby position".

[0220] Subsequently, based on the identified operational intent and target state, combined with the target robot's own skill list and its current operating status, task programming is carried out to plan a feasible task execution sequence, which decomposes high-level instructions into a series of orderly low-level operation steps, such as: start the positioning system → plan the path → move to the cargo location → perform the grasping action → avoid obstacles and move forward → reach the target area → place the cargo → return to the standby point.

[0221] The generated task execution sequence is optimally aligned with the simulated operating environment in the digital twin sandbox. Using a spatiotemporal alignment algorithm, task steps are mapped to corresponding coordinates and time nodes within the sandbox. The simulation engine of the digital twin sandbox is then activated, driving the complete execution of the sequence within the virtual environment. This ensures a high degree of consistency between the virtual simulation environment and the real world in terms of structure, dynamics, and interaction logic. The digital twin sandbox provides real-time feedback on the robot's motion trajectory, energy consumption changes, sensor responses, collision detection results, and other multi-dimensional data, forming a complete simulated feedback state.

[0222] Based on the simulation feedback, the downlink command is verified, including whether the task can be completed successfully, whether there are path conflicts or obstacle avoidance failures, whether energy consumption exceeds the safety threshold, whether the operation sequence is reasonable, and whether the safety protection mechanism is triggered. If the simulation results show that there is a risk or potential failure, the system will automatically mark the problematic part and feed it back to the command generation module to trigger command optimization or replanning; if the simulation results meet expectations, the downlink command is determined to have passed the verification and is ready for safe issuance.

[0223] In addition, if path conflicts, insufficient power, or operation failures occur during simulation, the verification will fail and will trigger instruction optimization or process regeneration; if all indicators meet expectations, the instruction will be officially issued.

[0224] In this embodiment, the high-fidelity modeling capability of the digital twin sandbox for physical robots is utilized to simulate the entire process of instruction execution in virtual space, thereby identifying potential conflicts, logical errors, or security risks in advance and avoiding equipment failures or task failures in the real environment.

[0225] In some embodiments, before verifying the downlink instructions based on the digital twin sandbox of the target robot, the method further includes:

[0226] Obtain real-time operating parameters and physical characteristic information of the target robot;

[0227] The real-time operating parameters and physical feature information are fused and analyzed to construct a three-dimensional virtual model of the target robot;

[0228] A state compression module is embedded in the three-dimensional virtual model to reduce the dimensionality of the target robot's operating state and extract key state variables.

[0229] Based on finite state automata, state division and transition rules are defined for key state variables to generate a state transition map of the target robot.

[0230] The state transition graph and edge computing resources are deployed in a distributed manner to construct the digital twin sandbox.

[0231] Understandably, real-time operating parameters are used to characterize the dynamic behavior of the target robot at the current moment, including position, speed, battery level, load, joint angle, communication quality, etc., which are collected by sensors and reported periodically.

[0232] Physical characteristic information refers to the inherent static attributes of a robot, such as its external dimensions, mass distribution, drive method, wheeled or legged structure, sensor installation location, and maximum load capacity. These characteristics originate from the equipment's factory configuration or registration file and do not change or change very little over time.

[0233] A three-dimensional virtual model is a high-fidelity geometric and dynamic representation of a target robot in digital space. It not only reproduces its physical appearance but also includes physical characteristics such as kinematic constraints, mass distribution, and inertial parameters. This model can accurately simulate the robot's movement, turning, grasping, and other behaviors in a simulation environment.

[0234] The state compression module is a data processing unit embedded in the 3D virtual model. It is used to reduce the dimensionality of the high-dimensional state data continuously generated by the robot and to extract the key state variables that have the greatest impact on task execution and behavior prediction. For example, it can integrate multiple sensor readings into abstract indicators such as "positioning reliability", "system health" or "obstacle avoidance urgency", thereby reducing the amount of data transmission and improving simulation efficiency.

[0235] Key state variables are the result of state compression extracted from the original state data. They are core indicators that can effectively characterize the robot's operating mode and have the characteristics of clear semantics, low dimensionality, and strong interpretability.

[0236] Finite state automata are a formal system modeling method used to describe the switching logic of a system between different operating modes. They abstract the complex behavior of a robot into several discrete states, such as "idle," "moving," "task execution," "low battery," and "fault," and define the transition rules between states triggered by specific conditions.

[0237] State classification is the process of mapping key state variables to predefined discrete state categories based on their value ranges. For example, when the battery level is above 80%, it is classified as "normal," and when it is below 20%, it is classified as "low battery warning."

[0238] The transition rule definition specifies the conditions or events under which the target robot should switch from one state to another. For example, "receiving a new task and currently having no load" triggers the transition from "standby" to "task ready".

[0239] A state transition graph is a structured graph used to describe behavioral evolution paths. It is a structured expression of the behavioral logic of a target robot and can support simulation, prediction, and anomaly detection.

[0240] Edge computing resources are localized computing nodes deployed close to the physical location of the target robot, such as edge servers, gateway devices, or field controllers, and have low latency, high real-time performance, and localized processing capabilities.

[0241] In actual execution, real-time operating parameters and physical characteristic information of the target robot are collected via device reporting or platform monitoring interfaces. The acquired real-time operating parameters and physical characteristic information are then fused using a multi-source information fusion algorithm to align and correlate dynamic states and static attributes in a unified spatiotemporal coordinate system, eliminating data redundancy and noise, establishing a mapping relationship between dynamic states and static attributes, and generating a digital image of the geometric and physical data that can run in a simulation environment, resulting in a three-dimensional virtual model. For example, the robot's current posture can be matched with its three-dimensional geometry, or the energy consumption trend can be combined with its motor characteristic model, thereby forming a comprehensive understanding of the robot's overall behavior patterns.

[0242] To further improve simulation efficiency and reduce communication overhead, a state compression module is embedded in the 3D virtual model. This module performs dimensionality reduction on the high-dimensional operational state data continuously generated by the target robot, removing redundant information. Through compression algorithms, it identifies the most representative low-dimensional indicators from the high-dimensional data and extracts the key state variables that have the greatest impact on task execution and behavior prediction.

[0243] Formal modeling based on finite state automata is employed. Key state variables are extracted and their transition rules are defined. The behavior of the complex system is abstracted into several discrete states and the transition logic between them. Based on the possible operating modes of the target robot, a clear state range is set for each key state variable, and transition rules between states are defined under different triggering conditions. For example, when the battery level is below 20%, the system transitions from "normal operation" to "low battery warning"; when a new task instruction is received and there is currently no load, it transitions from "standby" to "task preparation". All states and their transition rules are organized into a state transition graph, serving as an abstract expression of its behavioral logic.

[0244] By distributing the generated state transition graph with edge computing resources, the digital twin sandbox can be run in a lightweight and localized manner, avoiding the latency and bandwidth pressure caused by concentrating all computing in the cloud.

[0245] In this embodiment, by constructing and deploying a digital twin sandbox for the target robot, a complete mapping from physical entity to virtual image is achieved, ensuring that the virtual environment can realistically and dynamically reflect the behavioral characteristics and operating status of the physical robot, thereby improving the safety, real-time performance, and intelligence level of the multi-robot system.

[0246] In some embodiments, after configuring the target robot, the method further includes:

[0247] The control channel receives the uplink command returned by the target robot in response to the downlink command;

[0248] The uplink command is parsed to extract the actual execution state data of the target robot;

[0249] The actual execution status data is compared with the simulated feedback status in the digital twin sandbox to identify the status deviation.

[0250] A transmission cost function is constructed based on the state deviation, and the path with the minimum transmission cost is determined by a dynamic programming algorithm.

[0251] The actual execution state data is mapped to the corresponding node of the digital twin sandbox along the path with the minimum transmission cost, so as to update the sandbox environment parameters of the digital twin sandbox.

[0252] Understandably, the control channel ensures that downlink commands can be issued in a timely manner, and that feedback information after the robot executes the commands can be transmitted back in real time.

[0253] An uplink command is a response message that the target robot actively sends to the platform after receiving and executing a downlink command. It includes the task execution result, changes in operating status, and abnormal event reports.

[0254] Actual execution status data is a collection of dynamic information generated by the target robot when it is actually running in the physical environment. It is used to describe the target robot's behavior under real conditions, including the target robot's position, speed, attitude, power consumption, task completion progress, sensor readings, obstacle avoidance behavior, and whether retries or interruptions occur at various time points.

[0255] The simulated feedback state is generated by the digital twin sandbox before issuing instructions, and is the simulation result of the robot's behavior under ideal or predicted conditions, including multi-dimensional information such as path planning, energy consumption curves, and action timing.

[0256] State deviation is a specific difference value obtained through comparison, including numerical deviations, such as the error between the actual distance traveled and the planned path, as well as nonlinear differences in the time dimension, such as delays, disordered behavior sequence, and abnormal state transitions.

[0257] The transmission cost function is a mathematical model used to quantify the cost of state data synchronization. It comprehensively considers the magnitude of state differences, the communication resources required for data updates, computational overhead, time latency, and the complexity of model tuning. The higher the cost, the more system resources are required to map the actual data back to the virtual model.

[0258] Dynamic programming is an optimization method used to solve multi-stage decision problems, finding the optimal sequence that minimizes the total cost from all possible paths. By analyzing the transition relationships between different time points and state nodes, a minimum transmission cost path that can achieve state synchronization at the lowest cost is calculated.

[0259] The path with the minimum transmission cost is the optimal alignment sequence consisting of a series of optimal data alignment points in time, state space, and state dimension.

[0260] In actual execution, the control channel receives the uplink command returned by the target robot in response to the downlink command. According to the semantic specification of the RUAPL protocol, the uplink command is parsed and processed to identify and extract the key content as the actual execution status data of the target robot.

[0261] The extracted actual execution state data is injected into the digital twin sandbox and compared item by item with the simulated feedback state in the digital twin sandbox to identify the state deviation between the two, reflecting the inconsistency between the physical system and the virtual model.

[0262] Based on the identified state deviations, a transmission cost function is calculated to measure the information correction cost required to map the actual state data back to the digital twin sandbox. Cost factors include the magnitude of the state differences, the frequency of data updates, communication bandwidth usage, computational resource consumption, and the complexity of model adjustments. A dynamic programming algorithm is used to optimize the transmission cost function, seeking an optimal path that achieves state synchronization with the minimum overall cost.

[0263] Along this path of least transmission cost, the actual execution state data is progressively mapped to corresponding nodes in the digital twin sandbox. Data replacement or parameter correction is performed at these nodes, making the behavior of the virtual model more closely resemble that of a real device. These nodes represent snapshots of the virtual model's state or key decision points at different times. Through precise data injection, the system dynamically adjusts the environmental parameters within the sandbox, such as updating the robot's kinematic model parameters, correcting sensor noise models, optimizing energy consumption prediction curves, or adjusting behavioral decision thresholds. This enables the digital twin sandbox to achieve online adaptive updates, continuously approximating the operating characteristics of a real robot and maintaining high-fidelity simulation capabilities.

[0264] In this embodiment, the simulation accuracy of the digital twin sandbox is continuously calibrated and optimized by transmitting actual execution data, ensuring that it can realistically reflect the dynamic behavior of the physical robot, thereby improving the reliability of subsequent command verification and the accuracy of control. This not only enhances the long-term availability of the digital twin sandbox but also strengthens the system's adaptability to environmental changes, equipment aging, or sudden failures, enabling continuously optimized intelligent control.

[0265] In some embodiments, after parsing the uplink command, the method further includes:

[0266] Extract the feedback information of the target robot from the uplink command;

[0267] The feedback information is semantically parsed to extract task completion metrics and abnormal event markers;

[0268] The task execution sequence in the digital twin sandbox is adjusted according to the task completion index, and the subsequent operations of the target robot are replanned through the state transition graph.

[0269] The abnormal event markers are matched with a preset fault model to trigger the fault tolerance mechanism of the digital twin sandbox.

[0270] Understandably, feedback information is specific content extracted from upward commands, including task progress reports, action confirmation signals, sensor readings, user interaction records, and system self-test results, used to characterize the target robot's actual response to downward commands.

[0271] Semantic parsing is the process of gaining a deeper understanding of feedback information. It uses natural language processing, rule-based reasoning, and contextual analysis to reconstruct the intent and state changes behind the information. For example, it can transform the text "Fetch failed, try again" into a structured event record and determine its impact on the overall task.

[0272] Task completion metrics are key parameters extracted through semantic parsing to quantify the quality of task execution. They are used to indicate whether a task is completed, as well as the accuracy, efficiency, and stability of the completion, such as the error in reaching the target location, the success rate of operational actions, and the deviation between the total task time and the expected time.

[0273] Abnormal event markers refer to the identification information of abnormal behaviors or potential faults identified in the feedback information, such as communication interruption, frequent obstacle avoidance triggering, motor overheating, path replanning more than three times, etc., to alert attention to risk points in the execution process.

[0274] The pre-defined fault models are a structured knowledge base that includes various typical fault modes and their corresponding characteristics, causes, impact range, and coping strategies. For example, "excessive communication latency" may correspond to the response measure of "switching to a backup communication link," and "two consecutive capture failures" may trigger the process of "reporting for manual intervention."

[0275] The fault tolerance mechanism is an automated response strategy built into the digital twin sandbox, used to ensure stable system operation when anomalies are detected.

[0276] In actual execution, structured or semi-structured data actively reported by the target robot during task execution is extracted from the uplink commands and used as feedback information. This information belongs to the robot and typically includes task phase reports, action confirmation signals, sensor alarms, user interaction records, and system self-check results.

[0277] By combining natural language understanding and rule-based reasoning, the extracted feedback information is semantically analyzed to identify the deep semantics in the feedback content, thereby obtaining task completion indicators and abnormal event markers.

[0278] Based on the extracted task completion metrics, the system dynamically adjusts the preset task execution sequence in the digital twin sandbox, modifying the order, parameters, or logical structure of the original operation steps. If the completion rate is lower than the expected threshold, it indicates that the current task strategy is insufficient. The system then re-plans the subsequent operations of the target robot using a state transition graph. The current state is identified through the state transition graph, and the optimal subsequent state path is deduced based on the task objective and environmental changes, generating an adjusted task sequence. For example, when the system detects that the robot's efficiency has decreased due to frequent obstacle avoidance, it can simulate a new path strategy in the virtual sandbox and distribute the optimized operation sequence to the physical device, achieving dynamic task rescheduling.

[0279] Simultaneously, the extracted abnormal event markers are compared with entries in the platform's preset fault model to determine if there is a semantic or logical correspondence. When an abnormal event marker successfully matches a fault mode, the fault tolerance mechanism of the digital twin sandbox is immediately triggered. This fault tolerance mechanism first simulates the execution effect of the fault response strategy in a virtual environment to verify its safety and effectiveness before issuing a confirmed recovery command to the real robot. For example, after detecting a "grasping failure" marker, the sandbox can simulate multiple solutions such as retrying the grasp, adjusting the gripper force, or switching to an alternative grasping point, and select the optimal path to execute. When "positioning drift" is detected, the sandbox can simulate operations such as restarting the positioning module, switching to assisted navigation mode, or pausing the task to wait for calibration, and select the path with the lowest risk to implement.

[0280] For example, at time t, the uplink command transmitted back from the target robot The actual state of the target robot in physical space is determined by the analysis as follows:

[0281]

[0282] in, It is the state vector of the actual state of the physical system; It is an uplink command; It is a decoding function used to decode... Perform semantic parsing and structured processing

[0283] To quantify the inconsistency between the virtual model and real behavior, the state deviation vector... for:

[0284]

[0285] in, It is the state vector of the simulated state in the digital twin sandbox.

[0286] To measure the semantic richness of state information, semantic density is calculated:

[0287]

[0288]

[0289] in, It is a state Learnable hash encoding is performed to map high-dimensional states into compact binary codes while preserving their semantic features; It is a learnable weight matrix used to highlight key semantic dimensions; This represents element-wise multiplication, implementing an attention mechanism. It is the most activated among the extracted hash results. The unit digit forms a sparse semantic representation. ; yes Norms are the number of non-zero elements and reflect semantic activity.

[0290] The higher the semantic density, the richer and more representative the information contained in the current state, and it is often used to adjust the sensitivity of the cost function.

[0291] To quantify at any given moment The overall cost borne by the system due to state deviations, the instantaneous cost function for:

[0292]

[0293] in, It is a double exponential explosion term, when the deviation As the cost increases, it grows superlinearly, reflecting a strong penalty for large deviations; This represents a penalty for semantic sparsity, when the semantic density... When information is scarce, the cost increases, encouraging the system to focus on information-rich states. It is a lifetime decay term, which increases with the remaining lifetime strength of the network. The decrease in costs coupled with the increase in expenses reflects the operational risks during periods of resource scarcity.

[0294] To assess the sustainability of communication links and the stability of system operation, the network remaining lifetime strength is introduced as follows:

[0295]

[0296] in, This is the target robot's current battery level. This is the initial battery level. Indicates the percentage of electricity already consumed; This is the current signal strength. It represents the maximum signal strength, and the ratio reflects the communication quality.

[0297] Network remaining lifetime strength integrates two dimensions: energy consumption and communication stability. The larger the value, the closer the system is to resource depletion and the higher the operational risk. As a decay factor in the cost function, it prompts the system to more actively update the model and adjust tasks when resources are scarce.

[0298] At any moment Based on the current information flow (arrive The expected value of the squared deviation from the final state based on all observed data up to this point is used as the martingale of confidence for predicting the terminal. for:

[0299]

[0300] in, It is the mathematical expectation operator, representing the operation of averaging a random variable; it is the square norm of the final state deviation. Used to quantify the degree of difference between the actual state of the physical robot and the simulated state in the digital twin sandbox at the end of the task (T). It is the physical state of the target robot at the end of the task, time T; It is the simulated state of the digital twin sandbox at the end time T of the same task.

[0301] The cumulative cost dynamics of the system over future time are described using value-backward stochastic differential equations:

[0302]

[0303] in, It is a value function, representing the value from Until the end of the mission The expected total cost; It is an instantaneous cost; It is the discount rate, which reflects the weighted decay of future costs; It is a control gain term; It is a Brownian motion increment that introduces random perturbations to simulate environmental uncertainty; It is a stopping penalty function used to quantify the maximum potential loss if the model is not updated at time t.

[0304] The optimal time for the system to perform model updates is the optimal stopping time. :

[0305]

[0306] in, It is the mathematical symbol for the infimum, representing the minimum time point at which the condition is satisfied.

[0307] Confidence-weighted gain for:

[0308]

[0309] in, In the optimal stopping time Terminal confidence at any given time. This is the preset upper limit of confidence. The gain factor increases as the current confidence level decreases: when... near (i.e., when the model is severely mismatched) Approaching 1 indicates that the model needs significant adjustments; when When it is close to 0 (i.e., the model is highly reliable), A value close to 0 indicates that only minor adjustments are needed.

[0310] The manifold projection is updated to:

[0311]

[0312] in, In the optimal stopping time After the model update is completed, the new simulation state of the digital twin sandbox Indicates at time The status is updated instantly afterwards; In the optimal stopping time The current simulation state of the digital twin sandbox before the event occurs; This represents projecting the physical state onto a predefined state manifold. Above, ensure that the updated state conforms to the system dynamics constraints; It is a logarithmic mapping on the Riemannian manifold that transforms the projected state into a direction vector in the tangent space; It is a confidence-weighted gain that controls the update magnitude; It is an exponential mapping that maps the update quantities in the tangent space back to the manifold to obtain the updated simulation state. The above updates maintain the integrity of the geometric structure and prevent model instability caused by direct value assignment.

[0313] Manifold projection update is used to describe the optimal stopping time. After triggering, how to integrate the physical state into the digital twin model.

[0314] In this embodiment, after parsing the uplink instructions and extracting the actual execution status data, the system further delves into the feedback information contained therein to achieve refined evaluation and intelligent response to the task execution process. This is achieved through closed-loop intelligent control, from extracting semantics from the feedback information, evaluating task quality, dynamically adjusting strategies, to proactively responding to anomalies. The entire process fully utilizes the simulation capabilities of the digital twin sandbox, completing strategy optimization and fault-tolerant drills without affecting the operation of physical equipment, significantly improving the robustness, adaptability, and task success rate of the multi-robot system.

[0315] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0316] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0317] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for rapid access and control of cross-brand robots based on RUAPL, characterized in that, The method, applied to a multi-robot management platform, includes: The system receives access requests from at least one target robot via the control channel. The skill list is extracted from the access request through a semantic channel to construct a skill semantic graph; The skill abstraction module is invoked to perform capability abstraction on the target robot based on the skill semantic graph, resulting in a unified abstract description; The unified abstract description is adapted and converted based on the RUAPL protocol through a bidirectional semantic adapter to generate downlink instructions. The RUAPL protocol is the robot unified access protocol layer protocol. The dynamic adaptation engine is invoked to verify the downlink command based on the digital twin sandbox of the target robot. If the verification passes, the downlink command is sent to the target robot through the control channel to configure the target robot. The step of abstracting the capabilities of the target robot based on the skill semantic graph to obtain a unified abstract description includes: Key skill features are extracted from the skill semantic graph, including skill type, skill parameters, and skill execution conditions; The key skill features are mapped to a preset abstract template to generate an initial abstract description of the target robot; The initial abstract description is semantically optimized to obtain the unified abstract description; The bidirectional semantic adapter includes a semantic translation engine, a neural symbol fusion translation engine, and an online few-shot learning component. Through the bidirectional semantic adapter, the unified abstract description is adapted and converted based on the RUAPL protocol to generate downlink instructions, including: The unified abstract description is input into the semantic translation engine. Based on the syntax rules and semantic constraints of the RUAPL protocol, the unified abstract description is parsed and structured to obtain instruction elements. The instruction elements include operation type, operation object, operation parameters, skill action, execution object, parameter settings and condition constraints. The neural symbolic fusion translation engine is invoked to perform semantic understanding and symbolic logic reasoning on the instruction elements, generating an intermediate representation based on the RUAPL protocol specification; The intermediate representation is fine-tuned using an online few-shot learning component, based on the target robot's historical interaction data and the context information of the access request. Gradient calculation is performed on the fine-tuned intermediate representation to obtain gradient information during the adaptation process, which is then aggregated to generate an alignment probability matrix. Based on the alignment probability matrix, the unified abstract description is converted into the downlink instructions based on the RUAPL protocol.

2. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 1, characterized in that, The step of extracting a skills list from the access request to construct a skills semantic graph includes: Dependency parsing is performed on the access request to extract the core verbs and patient components in the access request; Based on the skill classification system, the core verbs and the object components are classified and merged to obtain the skill list; Based on the relationships between the skill elements in the skill list and the skill keywords in the skill list, graph nodes are extracted. The graph nodes include action nodes, object nodes, parameter nodes, and condition nodes. The graph nodes are linked to the skill ontology in the target robot to establish entity links and relationship mappings, thereby determining semantic relationships. Based on the semantic relationships, graph edges and semantic roles of graph nodes are obtained, wherein the graph edges include dependency type edges and weight edges; Based on the semantic roles, the action node is used as the root node, the object node, the parameter node, and the condition node are used as child nodes, the dependency type edge is used as the connection edge between the root node and the child nodes, and the weight edge is used as the connection strength between the nodes to construct the skill semantic graph.

3. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 1, characterized in that, The control channel includes an event-driven bus layer, a message routing layer, and at least one service channel layer; The step of receiving an access request from at least one target robot via the control channel includes: Through the event-driven bus layer, event listening rules are loaded to capture event messages sent by the target robot, thereby obtaining at least one event signal triggered by the target robot. Through the message routing layer, the event signal is routed to the corresponding service channel layer according to the target identifier and service type carried in the event message; The event signal is parsed and the protocol format is verified through the business channel layer. If the verification passes, key information is extracted and encapsulated into the access request. The key information includes the identity of the target robot, the type of service requested for access, the protocol field, and the skill list.

4. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 3, characterized in that, After parsing the event signal and performing protocol format verification through the service channel layer, extracting key information if the verification passes, and encapsulating the key information into the access request, the method further includes: The network and system conditions corresponding to the target robot are invoked to configure the environment for the access request; Verify the protocol field in the access request, and configure a RUAPL message signature based on the RUAPL protocol for the access request; Entropy weights are calculated for key information in the access request to determine the access quality evaluation index of the target robot, so as to evaluate the access quality of the target robot.

5. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 1, characterized in that, Based on the digital twin sandbox of the target robot, the downlink commands are verified, including: Based on the environmental perception information and spatial context of the target robot, the downlink command is analyzed to identify the operational intent and target state of the downlink command; Based on the operational intent, the target state, the skill list and current state of the target robot, a task is programmed for the target robot to generate a task execution sequence; The task execution sequence is optimally transmitted and aligned with the simulated operating environment in the digital twin sandbox of the target robot to determine the simulated feedback state of the target robot executing the downlink command in the digital twin sandbox. The downlink command is verified based on the simulated feedback status.

6. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 1, characterized in that, Before verifying the downlink command in the digital twin sandbox based on the target robot, the following steps are also included: Obtain real-time operating parameters and physical characteristic information of the target robot; The real-time operating parameters and physical feature information are fused and analyzed to construct a three-dimensional virtual model of the target robot; A state compression module is embedded in the three-dimensional virtual model to reduce the dimensionality of the target robot's operating state and extract key state variables. Based on finite state automata, state division and transition rules are defined for key state variables to generate a state transition map of the target robot. The state transition graph and edge computing resources are distributed and deployed to construct the digital twin sandbox.

7. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 1, characterized in that, After configuring the target robot, the method further includes: The control channel receives the uplink command returned by the target robot in response to the downlink command; The uplink command is parsed to extract the actual execution state data of the target robot; The actual execution status data is compared with the simulated feedback status in the digital twin sandbox to identify the status deviation. A transmission cost function is constructed based on the state deviation, and the path with the minimum transmission cost is determined by a dynamic programming algorithm. The actual execution state data is mapped to the corresponding node of the digital twin sandbox along the path with the minimum transmission cost, so as to update the sandbox environment parameters of the digital twin sandbox.

8. The method for rapid access and control of cross-brand robots based on RUAPL according to claim 7, characterized in that, After parsing the uplink command, the method further includes: Extract the feedback information of the target robot from the uplink command; The feedback information is semantically parsed to extract task completion metrics and abnormal event markers; The task execution sequence in the digital twin sandbox is adjusted according to the task completion index, and the subsequent operations of the target robot are replanned through the state transition graph. The abnormal event markers are matched with a preset fault model to trigger the fault tolerance mechanism of the digital twin sandbox.

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