47 results about "Behavior control" patented technology

The invention discloses a digital twinning-oriented animation linkage method and device, equipment and a storage medium. The method comprises the steps that based on classes in a domain ontology model and attributes of the classes, operation data of the Internet of Things system are converted into first semantic data, and the domain ontology model comprises an environment class, an equipment class and a behavior class; performing event identification on the first semantic data to obtain a first event; reasoning the first event based on a preset reasoning rule set to obtain a first reasoning result, the first reasoning result comprising a first response behavior for the first event; and controlling a digital twinborn body corresponding to the Internet of Things system to execute an animation matched with the first response behavior. Therefore, a closed-loop control chain of data driving, semantic recognition, event reasoning and animation linkage is formed, high reliability is achieved, complex dynamic linkage is supported, and therefore intelligent, automatic and visual expression of the system is effectively improved.

The application discloses a kind of based on behavior control's bionic robot action control method, it is related to technical bionic robot control field, including the following steps: establishing global terrain coordinate system, completing multi-sensor calibration, robot starts autonomous navigation task, vision sensor starts to collect surrounding environment image;Image is handled, and the color, texture feature in image is extracted;Interference object identification module identifies the interference object therein, and extracts its feature information;Interference index analysis module calculates interference index according to the feature information of interference object;Terrain environment complexity evaluation module real-time collection terrain environment information, evaluates the complexity of terrain environment;Action control decision module comprehensively interference index and terrain environment complexity evaluation result, makes action control decision, and sends control instruction to motion execution mechanism, the application solves the problem of bionic robot in complex environment motion intelligence and safety.

This invention relates to the field of distributed computing technology, and in particular to a unified registration and scheduling system for AI agent capabilities in edge-cloud collaboration, comprising: a dynamic behavior mapping module for capturing client interaction behavior features and mapping them to behavior control topology, performing path adjustment when deviations occur, and registering the control behavior to a unified MCP capability center; a semantic feature compression module for dimensionality reduction and compression of the original multimodal data stream to generate low-dimensional semantic feature stubs; an asynchronous state alignment module for converting feature stubs into asynchronous stateless message streams and reconstructing context mirror data on the cloud side; an inference stream scheduling module for generating inference stream scheduling strategies based on multi-dimensional states; a thought chain flow control module for dynamically truncating the streaming output of the large model according to the strategy and extracting core control segments and confidence levels; and a residual collaborative execution module for sending the core control segments to the edge for residual logic completion, driving the topology to execute control behaviors.

This invention provides a control device that reduces system costs by using message data in a distinctive format and controls in-vehicle equipment appropriately according to the vehicle's condition using a table. [Solution] The control device 10 includes a state conversion unit that inputs message data including a first state representing a basic vehicle state and a second state representing other vehicle states; a first storage unit that stores a first table which defines the control content to be performed on multiple control targets for each of the multiple first states that the vehicle can take; a behavior control unit that outputs information regarding the control to be performed on multiple control targets based on the first state and the first table; a second storage unit that stores a second table which shows information for changing the control content defined in the first table; and a second behavior determination unit, an additional behavior determination unit, and an additional control unit that change the control information output by the behavior control unit based on the second state and the second table when the message data includes a second state.

The invention discloses a humanoid robot controller training method, a humanoid robot controller control method and a humanoid robot controller training device. A two-stage training framework is adopted, and in the first stage, a reinforcement learning algorithm is adopted to independently train a plurality of single-behavior controllers which are specially used for different sports skills (such as walking and standing); then, the capabilities of the single-behavior controllers are integrated into a unified multi-behavior controller through a behavior distillation method, so that the unified multi-behavior controller has the capability of autonomously switching skills according to states. And in the second stage, in order to improve the robustness of the multi-behavior controller in a complex environment, a task environment for simulating various use terrains is constructed, and reinforcement learning fine tuning is performed on the controller in parallel in the environment, so that a controller capable of adapting to the complex terrains and realizing multi-skill smooth switching is finally obtained. The adaptive capacity of the humanoid robot controller and the reliability of practical application can be improved. The robot can be widely applied to the technical field of robots.

The application provides a virtual object control method and device and a storage medium, relates to the technical field of Internet, can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving, and comprises the following steps: obtaining an object behavior tree of a target virtual object in a target game; a single node in the object behavior tree can perform data transmission with an AI system data center of the target game, and different nodes perform data communication through the AI system data center; through the object behavior tree, state information and environment information of the target virtual object in a virtual environment of the target game are obtained; in the case that an event triggering condition is reached, a corresponding target virtual event is triggered in the virtual environment, and a target behavior sub-tree corresponding to the target virtual event in the object behavior tree is activated; and the behavior of the target virtual object is controlled based on the target behavior sub-tree and the AI system data center. The application can effectively improve the flexibility of virtual object behavior configuration and behavior control.

The application discloses a kind of dimension self-adapting human behavior prediction method and equipment, the method first acquires the D-dimensional time series containing human behavior, carries out modal decomposition processing, obtains each dimension mode.Secondly, for each mode, the internal relationship between the implied state, activity contribution and observation event is constructed based on the dynamics equation.Then estimate each parameter of the dynamics equation, and construct total parameter set.Finally, the prediction sequence of each mode is inferred based on the total parameter set, and is reconstructed as D-dimensional time series, realize the prediction of human behavior.In the equipment, the central processing module processes the human behavior data collected by the perception module, the abnormal behavior control interaction module alarms, the communication module is used to send the abnormal data back to the personal computer end, and the data is transmitted to the storage module for saving when power failure or network interruption.The application does not need prior knowledge when carrying out human behavior prediction, low cost and high accuracy.

The invention discloses a ship lockage simulation method of self-adaptive time step length in a digital twinning scene, and belongs to the technical field of digital twinning and simulation. The method comprises the following steps: collecting and preprocessing ship motion state, ship lock structure and navigation rule data; a system architecture comprising a simulation layer, a control layer and a rule layer is constructed, the simulation layer adopts a multi-level physical engine cooperative computing mechanism, high-precision three-dimensional geometric modeling and real-time dynamic simulation are combined, and a self-adaptive time step control algorithm is introduced to dynamically adjust a simulation step in a key area so as to improve the precision; the control layer supports multi-ship-type dynamic parameter configuration through a modular ship behavior control model library; the rule layer drives navigation logic control through a rule engine, and flexibly executes rules such as single-line and double-line lockage and priority scheduling. According to the method, the problem of mold penetration caused by inaccurate collision detection in the prior art is effectively solved, and the universality, the flexibility and the intelligent level of multi-ship-type and multi-rule scenes are improved.

A behavior determination unit determines, as a behavior of an avatar, any one of plural types of avatar behaviors including performing no operation by using at least one of a user state, a state of electronic equipment, an emotion of a user, or an emotion of an avatar representing an agent for having a dialogue with the user, and a behavior determination model at a predetermined timing; and a behavior control unit that displays the avatar in an image display region of the electronic equipment, in which the avatar behaviors include provision of advice on health to the user, and in a case where the behavior determination unit determines, as the behavior of the avatar, to provide advice on health to the user, the behavior determination unit autonomously determines a behavior corresponding to a health condition of the user based on a parameter representing the health condition of the user.

This invention discloses a smart pet ball based on behavior recognition and adaptive reward interaction, and its control method, belonging to the field of smart pet supplies technology. The smart pet ball includes a spherical shell, a drive mechanism, a control module, a posture detection module, an obstacle avoidance detection module, a reward delivery module, an interactive feedback module, a power supply module, and a communication module. The control module receives detection data from the posture detection module and the obstacle avoidance detection module, and controls the drive mechanism to perform rolling, bouncing, turning, stopping, or escape actions based on the ball's motion state, the external environment state, and the pet's interactive behavior. When preset conditions are met, the control module releases a fixed amount of pet food, and the control module outputs light, sound, or vibration feedback. This invention can form a closed loop between motion interaction and reward delivery, and automatically adjust the interaction mode according to the pet's touch frequency, chasing duration, collision state, or stress behavior characteristics, thereby improving the pet's interactive enthusiasm, training effect, and emotional soothing ability when alone.

The invention discloses a pet behavior control method and a pet electronic fence device. The method comprises the following steps: acquiring a first position of a target pet; when the first position of the target pet is located outside the first range, initial label parameters of the target pet are obtained, the initial label parameters are obtained by inputting attribute information of the target pet into a personalized pet model, and the personalized pet model is obtained by training attribute information of multiple pets; and sending a first stimulation instruction to a target actuator of the target pet, the first stimulation instruction including the initial tag parameter, so that the target actuator stimulates the target pet based on the initial tag parameter. Personalized management of pets is realized, pertinence and effectiveness of electronic fence control are improved, and suitability of the pets during stimulation is taken into consideration.

The application provides a behavior control method and device; the method comprises the following steps: determining a current behavior state of a virtual object and a state transition matrix of the virtual object in a current time period; determining a first state transition probability distribution corresponding to the current behavior state from the state transition matrix; in response to the first state transition probability distribution, transferring the behavior state of the virtual object from the current behavior state to a first target behavior state in a behavior state set, generating a first behavior event queue based on a Poisson rate corresponding to the first target behavior state; and controlling the virtual object to execute behavior events in the first behavior event queue in sequence. Through the application, accurate control of the behavior of the virtual object can be realized, thereby improving the fidelity of the behavior of the virtual object.

The application relates to a bionic robot interactive behavior control method and device, a bionic robot and a medium, wherein the method comprises the following steps: in a scene in which the bionic robot interacts with an interactive object, acquiring interactive scene information and pose information of the bionic robot; obtaining an interactive behavior instruction according to the interactive scene information and the pose information and a preset behavior generation model, the behavior generation model being obtained by training an initial behavior generation model according to sample interactive scene information, sample pose information of the bionic robot and a gold standard interactive instruction, the gold standard interactive instruction being generated based on sample video data of the bionic object of the bionic robot interacting with the interactive object, and interactive behavior data of the bionic object from the perspective of the bionic object being reconstructed; and generating a corresponding interactive behavior based on the interactive behavior instruction; thus, the bionic robot can make continuous and lively interactive behaviors according to specific conditions.

The invention provides a data processing method and device, a storage medium and a program product, and the method comprises the steps: constructing a mapping relation between each scene identifier and a query behavior control parameter in a corresponding downloading scene, so as to generate full-amount configuration; in response to a received downloading service request, acquiring a corresponding target query template, a target parameter rule and target routing information from the full-amount configuration according to a scene identifier carried in the downloading service request, and splicing a downloading condition carried in the downloading service request into the target query template based on the target parameter rule; and generating a data object for asynchronous downloading based on the target routing information and the updated target query template. According to the method, a plurality of query templates are configured for a downloading scene, and matching can be performed according to downloading conditions, field column information and the like transmitted by a user, so that the query efficiency of the data object is improved, meanwhile, the coupling between a service scene and processing logic is reduced, and the maintenance is more convenient and the expansion is more flexible.

The application discloses a kind of active behavior control system and method based on dynamic parameter hysteresis feedback.The system takes the feedback loop of dynamic parameter as one of main control mechanisms, optionally combined with hysteresis mapping to prevent state frequent switching, and can integrate multi-modal emotion adaptive cooling, explainability generation, compliance audit, positive feedback incentive and social collaboration, atomic ability management and value realization, etc. The whole link closed loop containing task execution and value realization is realized in optional embodiment. The application is helpful to improve at least one of response sensitivity, control stability and feedback attribution accuracy of the system by means of asymmetric response, hysteresis interval, causal reasoning, feedback attribution and event binding, safety fuse and autonomous authority boundary definition, intention safety pre-check, offline autonomy, resilience engineering and disaster recovery, cross-cultural adaptation and fairness protection, etc. It provides an active behavior control scheme that can be independently deployed and extended as needed.

The application discloses a network intrusion detection method, device and equipment and a storage medium. The method comprises the following steps: obtaining a target vehicle control instruction and instruction feature information corresponding to the target vehicle control instruction, wherein the instruction feature information represents a behavior control sequence corresponding to the target vehicle control instruction, so as to obtain first detection information according to the instruction feature information and standard instruction feature information, wherein the standard instruction feature information represents a standard behavior control sequence corresponding to the target vehicle control instruction, and then obtaining a network intrusion detection result according to the first detection information. The technical scheme provided by the application can improve the accuracy of network intrusion detection, and thus improve the security of network transmission.

A vehicle behavior control method implementing functions of not affecting a driving safety of a vehicle through a pedal, comprises acquiring a pedal duration of the pedal, determining a custom instruction matched with the pedal duration of the pedal, and controlling the vehicle to perform a vehicle behavior corresponding to the custom instruction. A vehicle behavior control system and a vehicle computer are also provided.

The application relates to the field of home safety, in particular to an AI intelligent home safety control system; the AI intelligent home safety control system comprises a management center, the management center is in communication connection with a home user behavior module, a home user control module and a safety detection control module. Historical instruction data of the user to the intelligent home are acquired, the historical instruction data are analyzed, a user instruction graph is constructed, and user control data are generated in combination with the user instruction graph; a network path link is constructed in combination with the historical instruction data and the user control data; the network path link is analyzed to generate a standard path link template; real-time home path links are generated through the acquired current control behavior of the user, a home behavior control model is obtained according to the user instruction graph and the network path link; real-time instruction data collected are analyzed in combination with the home behavior control model to generate a risk mapping index table, and the intelligent home is controlled in safety through the risk mapping index table. The application can improve the safety of the intelligent home.

The application relates to a multi-modal digital human behavior control method and system for document explanation, which comprises the following steps: obtaining a document to be explained, and structurally analyzing the document; obtaining a document explanation instruction, generating an overview description, and determining an initial explanation position; generating explanation content and teaching behavior labels according to a current document anchor point and the initial explanation position; further generating streaming voice and behavior parameters; in the process of real-time synchronous explanation, collecting voice signals and performing pretreatment to obtain real-time voice signals; analyzing the matching probability of the voice signals and preset keywords; performing voice activity detection on the real-time voice signals to obtain an effective voice duration; performing semantic analysis on the real-time voice signals to obtain an intention confidence; if any index exceeds a corresponding preset index, interruption is triggered, and after the interruption ends, the document explanation is continued from the breakpoint. Compared with the prior art, the application has the advantage of high interruption detection precision.

The invention relates to a bionic robot interaction behavior control method and device, a bionic robot and a medium, and the method comprises the steps: obtaining interaction scene information and pose information of the bionic robot in a scene where the bionic robot interacts with an interaction object; an interaction behavior instruction is obtained according to the interaction scene information, the pose information and a preset behavior generation model, and the behavior generation model is obtained by training an initial behavior generation model according to the sample interaction scene information, the sample pose information of the bionic robot and a gold standard interaction instruction; the gold standard interaction instruction is generated based on sample video data of interaction between a bionic object and an interaction object of the bionic robot and interaction behavior data of the bionic object under the perspective of the reconstructed bionic object; generating a corresponding interaction behavior based on the interaction behavior instruction; in this way, the bionic robot can make continuous and vivid interaction behaviors according to specific conditions.

The invention discloses a humanoid robot for dispensing medicine in a pharmacy and a medicine dispensing method. The humanoid robot comprises a walking base, a trunk part, humanoid mechanical arm mechanisms, humanoid manipulator mechanisms and a behavior control unit. The trunk part is connected to the top of the walking base, the two opposite sides of the trunk part are connected with humanoid mechanical arm mechanisms correspondingly, the sides, away from the trunk part, of the humanoid mechanical arm mechanisms are connected with humanoid manipulator mechanisms used for grabbing medicine, and the trunk part is provided with a display screen; the behavior control unit is used for controlling the walking base to move to the medicine shelf area; the humanoid mechanical arm mechanism is controlled to move, the humanoid mechanical arm mechanism is driven to grab the target medicine in the medicine shelf area, and the target medicine is temporarily stored in the storage box; and the walking base is controlled to move to the medicine dispensing window, the humanoid manipulator mechanism is controlled to dispense the medicine at the medicine dispensing window, the medication information of the target medicine is displayed through the display screen, and the working efficiency of medicine dispensing of the pharmacy is improved through the humanoid robot.

The application relates to a behavior guidance data generation method, system, device and computer equipment. The method comprises the following steps: collecting a multi-modal physiological perception signal of a target object; analyzing the multi-modal physiological perception signal based on a behavior recognition model to obtain effective behavior data in a target time period; processing the effective behavior data based on a state recognition model to obtain a behavior execution state; determining a behavior control strategy corresponding to the behavior execution state in a corresponding relationship between execution states and control strategies; and generating guidance information based on the behavior control strategy and outputting the guidance information. The method can improve the accuracy of the prompt for user diet management.

Computer-implemented system (100) for controlling an autonomous vehicle (10), aF (10), wherein the system (100) comprises: a non-volatile, computer-readable storage medium (32, 46) containing a set of instructions for programming at least one computer (34), wherein the at least one computer (34) comprises: a neural network (635, NN) with a large number of nodes with context-to-vector, context2vec, context-mediated embeddings to enable the operation of the autonomous vehicle (10); a variety of coded context2vec-aF words (610) in a sequence of time intervals to capture context and behavior data derived from autonomous vehicle sensor data (10) in time sequences, wherein the context and behavior data include: at least map, situation and behavior data of the autonomous vehicle operation (10); a set of inputs to the neural network (635, NN) which includes the following: at least one current, one previous and one subsequent coded context2vec-aF word (610), each represented in a one-hot scheme of a set of possibilities of the context2vec-aF words (610), wherein at least one context2vec-aF word (610) of the set is designated with an ON state, while other context2vec-aF words (610) of the set are designated with an OFF state; a neural network solution applied by the at least one computer (34) to determine a target context aF word of each set of inputs based on the current context2vec aF word (610); an output vector computed by the neural network (635, NN) representing the embedded distributed one-hot scheme of the input-encoded context2vec-aF word (610); and a set of behavior control operations for controlling the behavior of the autonomous vehicle (10) based on the context2vec-aF word (610) by the at least one computer (34); the map, situation and behavior data embedded in the context2vec-aF word (610) further include the following: a first, a second, a third, a fourth and a fifth part; the first part comprising the following: junction and stop map data; the second part includes the following: distance and time data until the collision; the third part includes the following: speed target and lane intention situation data; the fourth part comprises the following: data for forward clearance and expected maneuverability; and the fifth part includes the following: relative and expected maneuver behavior data to enable a control action by the at least one computer (34) of the autonomous vehicle (10).

The invention relates to a UML (Unified Modeling Language) model-oriented event-driven behavior code automatic generation method and system. The method comprises the following steps: exporting an XMI file containing class, activity and state machines from a UML (Unified Modeling Language) modeling tool, constructing a document tree through DOM (Document Object Model) analysis, and extracting meta-model elements and relationships thereof; and generating attributes and methods of the main class and the associated class and Java source codes of the main method according to an analysis result, taking the main method of the main class as a simulation / operation entry, and calling the main method of the associated class during operation. Aiming at the behavior type of the classifier: when the behavior type is Activity, traversing an identification sequence, branching / merging, parallelizing / synchronizing and circulating from an InitialNode, constructing a behavior control flow diagram, decomposing the behavior into SimpleTask and LoopTask, and generating a Java code containing an Execute method and a scheduling statement; and when the state conversion diagram is the StateMach, reconstructing the state conversion diagram and generating a state machine instance and a StateMach Runner operation management type instance code, thereby realizing migration execution driven by a signal event and a time event and mixed triggering of the signal event and the time event and the activity. The method is suitable for the field with high requirements for model consistency and rapid verification.

The application provides a game character behavior control method and device and electronic equipment; the method comprises the following steps: obtaining current state data of a target game; determining a target model from a plurality of behavior control models which are pre-trained, inputting the current state data into the target model, and obtaining an output result; the plurality of behavior control models are obtained through the following training method: transforming model parameters of an initial model based on preset transformation parameters to obtain a plurality of intermediate models; at least part of the model parameters of different intermediate models are different; the plurality of intermediate models are trained to obtain the plurality of behavior control models; based on the output result, a target behavior operation is determined, and the game character is controlled to perform the target behavior operation. This method can improve the randomness of the behavior of the game character, avoid the player identifying that the game character is controlled by game AI, and avoid the player observing the behavior rule of the game character, thereby improving the game experience.

The application discloses a device for generating a reaction closed-loop evaluation automatic driving model, which comprises a behavior controller and a generative renderer, wherein the behavior controller is based on a classic rule system IDM, and the generative renderer is based on a diffusion model; the automatic driving generation model workflow is as follows: S1: an end-to-end automatic driving model takes a current frame of a look-around picture as input and outputs a planned future driving route of a self vehicle; in the framework, the simulation framework proposed in the application utilizes the generation capability of the generative model in a self-recurrence manner, realizes high-frequency interaction with the end-to-end driving model, and generates time-consistent images; the time-consistent module, the object-level control based on projection and the scene-level control based on retrieval can significantly improve the model generation fidelity.

This behavior control system comprises: a communication device that communicates with a subject; a subject information acquisition unit that acquires subject information of the subject; an empathy level prediction unit that predicts an empathy level of the subject on the basis of the acquired subject information; an anxiety level prediction unit that predicts an anxiety level of the subject on the basis of the acquired subject information; and a behavior generation unit that generates a behavior for the communication device on the basis of the predicted empathy level and anxiety level.

The invention discloses an intelligent CRM management method based on reinforcement learning, and the method comprises the following steps: S1, obtaining customer relationship data, and carrying out the preprocessing of the customer relationship data; s2, constructing a Markov decision process, defining a state and action mapping relation, and labeling behavior feedback to generate a triple set; s3, constructing a reinforcement learning framework, executing strategy sampling and experience playback on the triple set, and calculating a strategy updating direction; s4, performing convolution extraction and residual aggregation on the state sequence, and generating a context fusion vector in combination with cross-layer attention; s5, generating action distribution, estimating a dominant function and updating strategy parameters; s6, selecting a strategy action according to action distribution, mapping the strategy action into a structured CRM instruction, and constructing a behavior control flow diagram; and S7, recording execution feedback, and continuously optimizing the strategy and value network parameters by using a residual error correction mechanism. According to the method, closed-loop optimization of customer policy and CRM process execution is realized, and the response and conversion efficiency is improved.

Embodiments of the present disclosure disclose a virtual character behavior control method and device, an electronic device and a medium. A specific embodiment of the method comprises: generating virtual character setting information according to preset virtual character knowledge base information, a preset behavior generation process tool and input dialogue information of a corresponding target virtual character; generating behavior execution information according to the input dialogue information, preset prompt word text information and the preset behavior generation process tool; generating target behavior execution information according to the preset behavior generation process tool, the behavior execution information, preset requirement information and interaction likeability information; changing virtual character dialogue information according to the target behavior execution information to obtain target virtual character dialogue information; and controlling the virtual character to perform operations corresponding to the target behavior execution information and the target virtual character dialogue information. This embodiment improves the real-time performance and accuracy of virtual character behavior dialogue generation, thereby improving the user's interactive experience.