170 results about "Embodied intelligence" patented technology

The invention belongs to the field of multi-mode sensing fusion, and relates to an intelligent multi-mode patrol sensing method, system and equipment and a storage medium, which are used for carrying out automatic patrol, real-time sensing and abnormity warning in a complex environment. Comprising the steps of natural language instruction analysis, task decomposition and distribution, intelligent agent scheduling and execution, multi-modal information collection and fusion, anomaly recognition and event response, result feedback and task closed loop, and through the integration of an OWL framework and a DeepSeek-R1 large model, a multi-modal data fusion technology and an NL-SLAM autonomous navigation algorithm, the multi-modal data fusion algorithm and the multi-modal data fusion technology are integrated. The problems that in the prior art, dynamic task understanding based on natural languages cannot be achieved, a sensing system lacks efficient multi-modal data fusion, so that the anomaly recognition precision is low, and complex tasks or emergencies are difficult to deal with are solved, various abnormal conditions in a complex environment are effectively dealt with, and the method is suitable for popularization and application. The stability and expansibility of task execution are improved, and the navigation success rate and the task completion rate of the system in an unstructured environment are enhanced.

The invention discloses a nuclear industrial robot body intelligent system based on a large model, which realizes autonomous sensing, decision making and execution of a robot in a nuclear industrial environment by integrating a sensing module, a learning model module, an execution module, a communication module and a security module. The sensing module adopts a multi-sensor fusion technology to collect environmental data such as temperature, radiation intensity and gas concentration in real time; the decision-making module dynamically generates a task execution strategy based on an open source large model base in combination with deep learning and reinforcement learning algorithms; the execution module completes complex tasks such as equipment maintenance, fault processing and nuclear waste carrying through a high-precision mechanical arm and a force feedback control technology; the communication module supports data interaction between the robot and a control center, and remote monitoring and emergency intervention are achieved; the safety module monitors the state of the robot in real time, multiple safety protection mechanisms are provided, and stable operation of the robot in the high-radiation environment is ensured. According to the invention, the operation efficiency and safety of nuclear industry facilities are obviously improved.

The invention discloses an air-ground cooperation system and an air-ground cooperation method based on intelligence, and relates to the field of intelligent unmanned system control. The system comprises an intelligent unit, a multi-element sensing unit, a communication unit and a task execution unit. Based on a Vision-Language-Action (VLA) framework, an intelligent unit with a body realizes end-to-end analysis of a natural language instruction and a visual instruction, fuses sensing data of an unmanned aerial vehicle and an unmanned vehicle, generates a joint feature code with time-space alignment, and dynamically allocates a cooperative task. The communication unit supports low-delay end-to-end communication and real-time data synchronization between clusters, and ensures efficient interaction between agents. The multi-element sensing unit is integrated with a visual camera, a laser radar and high-precision positioning equipment, and air-ground environment multi-mode sensing is achieved. And the task execution unit supports the unmanned aerial vehicle to complete coordination actions in a complex scene. According to the scheme, the problems of insufficient multi-mode perception, limited natural language interaction and low dynamic cooperation efficiency of the existing system are solved, and the cooperation capability and system robustness of the unmanned aerial vehicle and the unmanned aerial vehicle in a complex environment are remarkably improved.

The invention provides a reinforcement learning multi-mode body-equipped agent data generation method. According to the method, interaction between an intelligent agent and an environment and data collection are realized by establishing a data pre-acquisition and preprocessing module; learning similar features among different modes through joint representation learning; then on-line interaction between the intelligent agent and the environment is carried out based on reinforcement learning so as to optimize the current strategy; and finally, generating a new action sequence through the strategy network, and inputting the new action sequence into a conditional variable molecular encoder to generate corresponding multi-modal data. In addition, a modal discriminator is introduced for adversarial training, so that the quality of the generated data is further optimized. According to the method, high-quality and diversified multi-modal data can be generated, and powerful support is provided for research and application of the intelligent field.

The invention discloses a three-dimensional dynamic scene graph construction method based on 3D Gauss, and belongs to the field of computer body intelligence. The implementation method comprises the following steps of: realizing object perception and semantic feature extraction of open vocabularies by utilizing a visual basic model; a 3D Gaussian scene with high fidelity and continuous object semantics is constructed through multi-view multi-dimension optimization of 3D Gaussian representation; constructing a multi-level three-dimensional scene graph, extracting spatial levels and semantic relationships among objects by using 3D spatial positions and semantic tags of instance objects existing in a semantic Gaussian graph, and constructing a multi-level spatial semantic topology to accurately represent an environment layout; according to the method for realizing local updating for the Gaussian scene graph based on the environmental structural similarity, environmental change detection is carried out through real-time RGB-D observation and the structural similarity between high-quality rendering views of the Gaussian scene graph, and corresponding local updating is carried out by using rapid training and differentiable rendering of 3D Gaussian representation. And the capability of adapting to a complex dynamic environment of the 3D Gaussian scene graph is improved.

The invention relates to the technical field of robot control, and discloses a heterogeneous robot control system with intelligent and multi-modal perceptual driving, comprising: a quantum clock reference module used for generating a globally synchronized picosecond-level time reference signal and synchronizing a multi-modal sensor clock through a microwave pulse; the multi-mode sensing module comprises an event camera of which the dynamic range is greater than or equal to 120dB, a six-dimensional force sensor of which the noise density is less than or equal to 0.01 N / Hz and an inertial measurement unit of which the zero-bias stability is less than or equal to 0.8 degree / h; and the random differential manifold module adopts a four-layer full-connection neural network, and an input layer receives multi-modal data. According to the invention, a diamond NV color center system of the quantum clock reference module and a microwave pulse coupling technical scheme are adopted, so that a picosecond-level global signal synchronization effect is achieved. Compared with a scheme depending on a traditional crystal oscillator or a GPS clock in the prior art, the defects that electromagnetic interference is prone to occurring and signal jitter is large are overcome, and high-precision time sequence alignment of multi-module cooperation is ensured.

The invention relates to the technical field of body intelligence, and discloses a body intelligence-oriented sensing active control method and system, and the method comprises the steps: obtaining multi-modal sensing data, and determining a feature extraction unit and a control center node based on a heterogeneous data fusion framework; selecting a target coding module according to the spatial-temporal correlation index of the adaptive coding module, and generating a feature fusion path; determining a signal synchronization moment by combining the dynamic response delay and the sub-layer coupling degree, and marking a real-time fusion topological graph; and detecting a signal phase conflict, and planning a multi-modal control instruction based on a conflict result. The technology also relates to perception mode priority grading, feature fusion path dynamic generation, spectrum interference detection, path re-planning and the like. Microsecond-level time-space stamp marking and modal feature decoupling are realized through a distributed heterogeneous sensor array. According to the invention, the multi-modal data fusion precision, the signal synchronization control and the multi-modal coordination decision ability are improved, and the environmental adaptability and robustness of the intelligent system are enhanced.

The invention provides a full-autonomous cell operation system based on body intelligence, and relates to the field of cell operation, the system comprises a system hardware architecture and a system software architecture; the system hardware architecture comprises a micromanipulation execution system, a precision positioning system, a multi-mode sensing system and an intelligent calculation and control center. The micromanipulation execution system is used for cell environment interaction, the precision positioning system is used for sample bearing and environment control, the multi-modal sensing system is used for providing key information, and the intelligent calculation and control center is used for operation decision making; the system software architecture comprises a sensing layer, a planning layer, an execution layer and an interaction layer. According to the application, cell recognition, tool selection, path planning, operation execution and result feedback can be automatically completed without manual intervention; through tool autonomous switching and strategy dynamic adjustment, various operations such as cell clamping, injection, controllable deformation and micro-assembly can be completed, and the problems that traditional cell operation depends on manual work, efficiency is low, and consistency is poor are solved.

The invention discloses a body intelligent controller architecture based on an end side intelligent model and an operation method, and belongs to the technical field of industrial body intelligence. According to the architecture, a CPU, a GPU and an FPGA are integrated by adopting a heterogeneous multi-core processor, and high-speed interconnection is realized through a star-type + NoC hybrid topology; a multi-level task scheduling mechanism is constructed based on a real-time operating system, and it is ensured that control tasks are executed preferentially. End-side visual model lightweight (YOLOv5s model parameters are reduced to 3.5 M) and FPGA hardware acceleration (control instruction time delay is 10ns) are innovatively realized on an algorithm framework layer, and a transmission bottleneck is eliminated through a zero-copy data path. The system supports model hot switching less than or equal to 10ms and FPGA bit stream online updating less than or equal to 50ms, and has MD5 check and an automatic rollback mechanism. Compared with a traditional separation type scheme, the processing time delay is reduced by 25 times, the hardware cost is reduced, the size is reduced, and the real-time performance, reliability and deployment flexibility of the industrial robot and other intelligent agents with the body are remarkably improved.

The invention discloses a target-level three-dimensional point cloud cross-modal semantic retrieval method and device and electronic equipment. The target-level three-dimensional point cloud cross-modal semantic retrieval method comprises the steps that independent three-dimensional target point clouds are segmented from three-dimensional scene point clouds, and unique identifiers are given to the independent three-dimensional target point clouds; projecting each target point cloud to three orthogonal two-dimensional observation planes to generate a multi-view composite image; a pre-trained vision-language basic model is adopted to code and fuse the synthesized image, and a unified multi-modal feature vector is generated; a vector database in which the feature vectors are associated with their identifiers is constructed. And encoding a natural language query text into a text query vector by using the model, calculating the semantic similarity between the text query vector and the feature vector in the database, and positioning and returning the corresponding three-dimensional target point cloud. According to the method, accurate and efficient cross-modal retrieval from a natural language to a three-dimensional point cloud target is realized, the problem that a traditional method is difficult to support semantic fine-grained retrieval of the three-dimensional target point cloud is solved, and a key technical support is provided for training data management in the fields of intelligence and the like.

The invention discloses an intelligent experimental training system with a body based on a multi-modal large model, and the system comprises the following modules: a multi-modal sensing module collects visual, voice, motion and force sense data, and generates a state vector through synchronization and fusion; the semantic evaluation module is used for performing semantic matching and identifying knowledge blanks by using a multi-modal large model; the causal generation module generates a causal query after blank detection and forms a hypothetical experiment proposition; the tool body execution module executes an intervention action and collects multi-mode feedback; the effect verification module calculates causal effects before and after intervention and judges significance; the graph updating module generates new knowledge nodes and updates the causal relationship graph when verification is established; and the teaching management module performs causal chain visualization and result feedback according to the updated atlas. According to the method, a self intervention feedback mechanism is introduced into a multi-modal perception, semantic understanding and decision execution closed loop, so that the knowledge evolution and teaching intelligence level is remarkably improved.

The invention relates to the technical field of body intelligence, and provides a body intelligence hierarchical memory perception method and system and electronic equipment, and the method comprises the steps: collecting environment data through a perception processing module, executing feature extraction and sensor scheduling in a short-time perception layer, and outputting weighted fusion features; weighted fusion features are transmitted to a cognitive decision module through a communication interaction module, and the cognitive decision module receives the weighted fusion features, constructs a dynamic environment map in a mid-time situation layer, constructs an event memory chain and executes cross-task causal migration in a long-time cognitive layer, and outputs a long-term cognitive feature strategy. And generating a target instruction through the control module based on the weighted fusion feature, the dynamic environment map and the long-term cognitive feature, and transmitting the target instruction to a target mechanism through the communication interaction module. According to the method, the response and the decision are decoupled through a layered architecture, so that the dynamic environment adaptability is improved.

The invention relates to an intelligent method for controlling the shape of a fiber flexible body in the operation process, and belongs to the field of flexible body shape control. The method comprises the steps that point cloud data of a fiber flexible body and pose data of a manipulator tail end clamp holder are obtained; inputting the trained PIGNN model, and outputting shape prediction of the fiber flexible body; and a model prediction control method is adopted to calculate a motion track required by the mechanical arm, so that the mechanical arm controls the fiber flexible body to reach a target shape position. According to the method, the shape change predicted through the PIGNN model strictly follows the mechanical law of a fiber flexible body material, and the consistency and reliability of the optical fiber sensor can be greatly improved; model prediction control can predict and dynamically adjust the track of a mechanical arm in real time according to the shape, the processing requirements of optical fibers of different specifications are flexibly adapted, physical constraint and data driving are combined, so that the system can quickly adapt to a new scene, and a core technical foundation is laid for large-scale and high-precision application of an optical fiber sensor.

The invention discloses an industrial robot data acquisition method based on programming and intelligent cooperation, relates to an industrial robot control method, and provides an industrial robot data acquisition system and method based on programming and intelligent cooperation. The system comprises a programming mechanical arm (main arm), a robot demonstrator, five RGBD cameras and a humanoid robot (slave arm). The master mechanical arm and the slave mechanical arm conduct cooperative collection, the master arm executes pre-programming actions, and the slave arm receives the joint angle and the tail end pose of the master arm through the ROS and calculates the track of the slave arm. And efficient data acquisition and processing are realized by applying algorithms such as master-slave mechanical arm cooperation, image acquisition and processing, data conversion standardization, model training optimization and cloud edge cooperation. Meanwhile, an alarm mechanism is arranged, and data are recorded and analyzed. The method is high in expansibility, can be applied to the fields of industrial automation, intelligent manufacturing and the like, and helps to improve the industrial production efficiency and quality.

The invention relates to the technical field of ownership intelligence, in particular to a ownership intelligence fuzzy language instruction recognition method based on scene risk guidance. In order to solve the problem that a fuzzy language instruction recognition method in the prior art easily causes understanding deviation and potential safety hazards, the invention provides an intelligent fuzzy language instruction recognition method based on scene risk guidance, which comprises the following steps of: 1) constructing a scene risk map according to a basic risk and a distance risk; 2) identifying an instruction type; 3) instruction analysis and risk assessment; and 4) instruction rewriting and object identification replacement. According to the method, a risk perception mechanism is introduced, the high-risk behavior execution probability caused by semantic deviation of the intelligent robot is effectively reduced, the understanding accuracy and interaction safety of the intelligent robot in a real scene are improved, and the method is suitable for a complex application environment with high requirements for task reliability and behavior controllability of the intelligent robot.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as personal intelligence, financial science and technology, medical health and the like, and discloses an action instruction sequence generation method, device, equipment and medium. Generating an attention weight based on the visual feature vector and the tactile feature vector, fusing the visual feature vector and the tactile feature vector to generate a fusion feature vector, and constructing a reinforcement learning model state space containing environment state information and the fusion feature vector; a sequence of action instructions is generated by a reward function in response to a task target approach event or an obstacle collision event based on the state space. According to the method, through combination of multi-modal information fusion and reinforcement learning state space construction, the perception ability and action decision-making ability of the intelligent agent in a complex environment are improved, and autonomy, flexibility and stability of task execution are remarkably enhanced.

The invention discloses a wearable mechanical arm and an intelligent method thereof, relates to the field of artificial intelligence, and aims to solve the problems that a current wearable mechanical arm is insufficient in intelligence and limited in environment perception capability. According to the visual language information of the target task, the first action track is matched from the first database, or the visual language information is input into the visual language action model to obtain the second action track, then the mechanical arm action instruction is generated according to the obtained action track to be executed, and therefore the mechanical arm is driven to execute the target task. The system has the characteristics of high intelligent degree, strong self-adaption and strong environment perception capability.

The invention relates to a semantic dense vision SLAM (Simultaneous Localization and Mapping) method and system based on 3D (Three-Dimensional) Gaussian representation, and belongs to the field of body intelligence. The method comprises the following steps: acquiring image data of a scene through a sensor, constructing a scene model of 3D Gaussian representation, fusing semantic features and appearance features to generate high-dimensional semantic features, optimizing parameters of Gaussian points by combining appearance, geometry and semantic constraints, updating the scene model according to the optimized parameters, and optimizing a camera pose. And generating a rendered image through a differentiable rendering technology, tracking and positioning, detecting a loop by using image features, and optimizing the global map precision. According to the method, the mapping precision and robustness of the SLAM system are remarkably improved, so that the SLAM system can show more excellent performance in a complex environment.

The present application provides an embodied intelligence-based method and apparatus, a device and a medium for industrial part sorting processing. In this solution, firstly, a control signal for controlling a robotic arm to sort to-be-sorted parts is obtained by a task instruction understanding model based on part sorting description information inputted by a user, and then analysis processing is performed on an image of a to-be-sorted part by an intelligent perception model for parts according to the control signal to obtain a category and a grasp pose of the to-be-sorted part, the image being collected by an industrial camera. Finally, under obstacle-avoidance processing of an intelligent obstacle-avoidance neural network model, the robotic arm is controlled to sort the to-be-sorted part based on the control signal, the category and the grasp pose of the to-be-sorted part. This method helps reducing difficulty in controlling the robotic arm simply via natural language.

The invention discloses a task processing method and an intelligent device, and relates to the technical field of intelligent devices, and the method comprises the steps: obtaining multi-modal data which comprises task information of a to-be-processed task in a real-time interaction scene of a physical entity and an environment; performing feature extraction and feature alignment processing on different modal data in the multi-modal data to obtain a first feature after feature alignment; performing cross-modal data retrieval on a knowledge base based on the first feature to obtain target retrieval data; performing feature fusion on different modal data features in the first features to obtain second features; and making a decision based on the target retrieval data and the second feature by using a decision model to obtain an action instruction sequence, and executing the to-be-processed task based on the action instruction sequence.

The invention discloses a dynamic obstacle avoidance and path optimization method based on a body intelligence robot, and relates to the technical field of robot path planning, and the method comprises the following steps: collecting map data and task constraint data; performing path planning analysis on the map data and the task constraint data based on a set path optimization algorithm to generate initial planning path data; according to the intelligent robot dynamic obstacle avoidance and path optimization method, a robot actual driving path prediction model is constructed, and a target function is constructed for the deviation between the robot actual driving path prediction model and the real-time path planning data of the next time period to carry out minimization analysis; and the real-time path planning data of the next time period is optimized, so that the robot runs according to the optimized real-time path planning data to obtain an actual running path which fits the original real-time path planning data, and the running error of the robot is reduced.

The invention provides a teaching data conversion method and device oriented to intelligence and electronic equipment, and relates to the technical field of intelligence. The teaching data conversion method for the intelligent teaching system comprises the steps of obtaining to-be-converted teaching data in a first data format; reading metadata information of the to-be-converted teaching data declared in the configuration file; calling a target interface method aiming at the first data format according to the metadata information by utilizing a conversion engine, and obtaining feature data of the body agent during execution of the target task from the to-be-converted teaching data; wherein the conversion engine is a class which is defined by a parent class and is used for converting teaching data in a non-second format into teaching data in a second format; and obtaining the to-be-converted teaching data in the second format according to the feature data by using an aggregation method provided by the conversion engine. According to the method, standardized conversion of the intelligent teaching data with different sources can be realized efficiently at low cost.

The invention discloses a pipeline detection method and system based on an intelligent pipeline robot with a body, and relates to the technical field of pipeline detection. The method comprises the following steps: acquiring pose, environment and pipe wall sensing data acquired by a multi-mode sensor; a pipeline three-dimensional digital map is constructed, and real-time self-positioning is achieved; identifying a pipe wall defect based on the pipe wall sensing data, generating a defect data packet, and binding the defect data packet with a map space position for storage; when an adjacent robot is detected, constructing a temporary communication link; exchanging detection information containing a map abstract and task state information with an adjacent robot through the link; updating the three-dimensional digital map by using the map abstract of the adjacent robot; performing task negotiation by combining the task state information and the updated map; and adjusting the target operation area and the motion path according to the negotiation result. According to the invention, full-automatic high-precision detection and intelligent operation and maintenance decision-making in a complex pipeline environment can be realized.

The invention provides a robot evaluation method, a storage medium, electronic equipment and a program product, and relates to the technical field of intelligent equipment. The robot evaluation method comprises the steps of determining a weight corresponding to the degree of freedom of motion of a robot based on a to-be-executed task of the robot; based on the upper limit of the movement speed of the target joint, the weight corresponding to the target joint is determined, and the target joint comprises joints related to the to-be-executed task; based on the weight corresponding to the motion freedom degree of the robot and the weight corresponding to the target joint, operability evaluation information of the robot under the current configuration is calculated, and the operability evaluation information represents the ability of the robot to complete the to-be-executed task with the current configuration.

The embodiment of the invention provides an intelligent implementation method, device and equipment for a tool body, a storage medium and a program product. The method is applied to a client, the client is deployed on the edge side of a robot, a plurality of services are registered in the client, the services are called functions, and the plurality of services communicate based on a model context (MCP) protocol; the method comprises the following steps: receiving an instruction input by a user; sending the instruction and the available service information to a large language model to enable the large language model to generate service response information; service response information is received, to-be-called services in the service response information are called through a server side, a service execution result fed back by the server side is received, and the server side is deployed on the edge side of the robot; and sending the service execution result to the large language model to enable the large language model to generate a natural language response based on the service execution result, receiving the natural language response and displaying the natural language response to the user. According to the invention, the compatibility between services is improved, and the intelligent execution efficiency of the user is improved.

The invention relates to the technical field of intelligent equipment. A trajectory diffusion imitation learning method based on single human video demonstration comprises the steps that a demonstration video is analyzed to obtain an operation body posture motion trajectory and an object pose motion trajectory, and the demonstration video is a video of a human or machine operation object; respectively carrying out interpolation calculation on the positions and postures of the operation body posture motion trail and the object pose motion trail to obtain a training data set; using the training data set to train a diffusion strategy model to obtain a target diffusion strategy model; and during deployment, the real-time pose of an end effector of the robot serves as observation input of the target diffusion type strategy model, an operation body pose movement track and an object pose movement track in the deployment stage are formed, and a joint control instruction of a target operation body is obtained. And the task success rate is kept, and the practicability and the expandability of the imitation learning method are improved at the same time.

The invention discloses a teaching action generation method and system for assisting an intelligent robot with a body and a medium. The teaching action generation method comprises the steps that a first data set and a second data set are acquired; obtaining an action generation model according to the first data set and the second data set; and obtaining a to-be-processed teaching action, and generating a target teaching action according to the to-be-processed teaching action through the action generation model. The action generation model is obtained through training of the deep learning method, then the standard target teaching action is generated through the action generation model, the quality of the teaching action data set can be effectively improved, the training effect of the simulation learning model is improved, and the method can be widely applied to the technical field of artificial intelligence.

The invention relates to the field of artificial intelligence and self intelligence, and discloses a cross-space, cross-task and cross-ontology learning large model and reasoning method, the method comprises the following steps: a pre-training multi-modal large language model as a backbone network is used for processing visual and text input and generating a response; the intention bridging interface is used for compressing a hidden state output by the multi-mode large language model into semantic intention representation with a fixed length; the action strategy head is used for generating a continuous action sequence based on the semantic intention; the state encoder is used for encoding the body sensing state of the robot; and the motion encoder / decoder is used for embedding and reconstructing the motion. According to the method, cross-space migration, cross-task learning and cross-ontology generalization are realized in a single model, and the performance and generalization ability of digital space reasoning and physical space control are improved.

The invention discloses a magnetic attraction intelligent connection type autonomous operation and maintenance experiment node system, and the system comprises a sensing and modeling layer which is used for constructing a thinking digital twinning environment which is synchronous with a physics laboratory in real time, is centimeter-level in precision, can be interacted with the physics laboratory, and is rich in semantic information; the cognition and decision-making layer is used for upgrading simple and rule-based reactive behaviors into complex, logic-based and prospective active decisions; the collaboration and network layer is used for connecting all the distributed cognitive experiment nodes into an organic super organism capable of emerging'swarm intelligence '; and the interaction and operation and maintenance layer is used for seamlessly and immersively transmitting remote experts to a physical site by introducing generative artificial intelligence and own intelligence, so that the remote experts become a high-level commander and a lifelong learning partner of the whole cognitive experiment node system. By means of the magnetic attraction intelligent linkage type autonomous operation and maintenance experiment node system, the problems that current experiment equipment is lack of cognition on the physical environment, blank in complex logical reasoning, stagnant in adaptive capacity to dynamic tasks and the like can be solved, and the intelligent and systematic operation capacity of the experiment equipment is remarkably improved.

The invention provides a training method and device of a body intelligent model based on a multi-modal autoregression model, and relates to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction processing and feature fusion processing of an image feature set collected by a robot, and obtaining the overall visual feature information and a visual information feature set; performing text visual feature fusion processing on the visual information feature set and the instruction text to obtain visual text fusion features, and determining angle information features based on the overall visual feature information and the instruction text; and sending the visual text fusion feature and the angle information feature to a preset large language model to predict the action of the robot, training a body-equipped intelligent model of the robot according to a prediction result, and determining a target body-equipped intelligent model. According to the method, the object angle and environment sensing capability of the intelligent model can be remarkably improved, and the accuracy of model action prediction is further improved.