362 results about "Target–action" patented technology

The invention provides a multi-mode intelligent robot control method and device, and the method comprises the steps: obtaining a current mode instruction which at least comprises one of a video instruction, a text instruction, a picture instruction and an audio instruction; on the basis of a pre-trained multi-modal large model, according to the current modal instruction, obtaining post-collapse characterization; on the basis of a pre-trained robot imitation learning network, according to the representation after collapse and current environment observation, predicting an output target action; the multi-mode intelligent robot with the body is controlled to operate according to the target action; wherein the robot imitation learning network is obtained by performing training optimization according to a training sample set formed by single-modal data samples, corresponding environment observation data and real actions of the robot. According to the method, the post-collapse representation is obtained by pre-training the multi-modal large model, and the post-collapse representation is utilized to predict the target action, so that the requirement on multi-modal manual annotation data when a training robot simulates a learning network is avoided in the process, the effect of multi-modal reasoning can be achieved only according to single-modal data training, and the efficiency of multi-modal reasoning is improved. Not only is data collection and labeling cost reduced, but also the reasoning ability of the model is improved.

Embodiments can relate to a system for automated exploit generation that receives input data representative of a target action to establish a target having a potential target vulnerability. The system can build a simulated target environment that includes the established target. The system can conduct an analysis method including a static, a concrete, a dynamic, and / or a symbolic analysis. The system can create a chainable sequence including an information disclosure, a read, a write, and / or an execution exploit primitive. The system can generate an exploit chain that, when executed by the processor in response to the target action, can transform the target action to a target failure within the simulated target environment and thereby expose the target vulnerability. The system can execute the exploit chain within the simulated target environment to examine coverage of the exposed target vulnerability. The system can generate an output representative of the exposed target vulnerability.

The invention provides an interaction method and device, electronic equipment, a storage medium and a program product, and the method comprises the steps: carrying out the semantic analysis of a to-be-processed problem, and generating a reply text and a target action of the to-be-processed problem; determining a first text matched with the target action from the reply text according to the reply text and the target action; performing time alignment on the execution starting time of the target action and the playing starting time of the first text; the reply text is played, and in the playing process of the reply text, according to the execution starting time of the target action and the playing starting time of the first text after time alignment, the target action is synchronously executed when the first text is played, so that synchronization of the action and the reply text can be realized; and the interaction performance is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as personal intelligence, financial science and technology and medical health, and discloses a mechanical arm task planning and control method, device, equipment and medium. Encoding visual information and task description language information to obtain visual features and language features, generating a target action sequence by using a language model, generating a basic path through dynamic planning under an environment model, adjusting the basic path according to environment change in combination with reinforcement learning, generating a final path, and decoding the target action sequence into a joint control instruction. And generating a motion control signal based on the final path, and optimizing a joint control instruction in combination with environment feedback. According to the method, through multi-modal information fusion and mixed path planning, the control instruction is dynamically optimized in combination with environment feedback, and the operation efficiency and task generalization ability of the mechanical arm in a complex environment are improved.

The invention provides a training method and device for a robot control model, a control method and device and electronic equipment, and relates to the technical field of equipment control, and the method comprises the steps: obtaining a preset motion data set, and employing a preset large language model in the robot control model to obtain a preset motion data set; generating an action sequence text and a prediction target direction corresponding to the sample natural language command based on the sample natural language command; and encoding the action sequence text by adopting an initial text encoder in the robot control model, determining a target action code corresponding to the action code from a preset action codebook, and performing action planning according to the target action code and a predicted target direction by adopting an initial action planning model in the robot control model. Generating action strategy information corresponding to the sample natural language command; and parameter adjustment training is carried out on the initial motion planning model in the robot control model. The generalization ability of the robot control model is improved, and control over the robot is more accurate.

The embodiments of the present application relate to the technical field of action capture. Disclosed are a method and apparatus for generating an action of a virtual object, and a cluster, a medium and a program product, which improve the flexibility of generating an action executed by a virtual object. The method comprises: acquiring first action data, which is used for indicating the movement situation of a reference point when a target object executes a target action; acquiring second action data, which is used for indicating the movement situation of the reference point when a virtual object executes the target action according to an action template, wherein the virtual object is used for representing the target object in a virtual space; on the basis of the first action data and the second action data, generating third action data, which is used for indicating the movement situation of the reference point after the target action is adjusted in view of the action template; and according to the third action data, generating an animation video, which includes clips of the virtual object executing the adjusted target action.

The embodiment of the invention provides an action sequence execution method, electronic equipment, a storage medium and a program product. The method comprises the steps that a global action sequence is generated based on semantic features representing user instructions, and the global action sequence comprises a plurality of node actions with an execution sequence; after executing each node action, performing anomaly identification on each node action to obtain an identification result corresponding to each node action; generating a target action sequence taking the target node action as a starting point under the condition that the identification result indicates that the target node action is abnormal; the target action sequence is used for updating the global action sequence; according to the updated global action sequence, interface elements in the operation interface are processed until the execution target of the user instruction is completed. According to the technical scheme of the embodiment of the invention, the technical problem that the success rate of the automatic execution process is not ideal in the prior art is solved.

The invention discloses a human-computer interaction method and device based on an intelligent body and the intelligent body, and belongs to the field of artificial intelligence. The method comprises the following steps: in response to a description text sent by a user for a target scene, determining an interaction task indicated by the description text; determining a target object associated with the interaction task; retrieving a pre-established object memory library corresponding to the target scene to obtain target sensing data corresponding to the target object; the object memory bank is used for storing perception data of all objects pre-identified from a target scene; determining at least one target action in a plurality of pre-configured candidate actions based on the target sensing data; and executing the target action on the target object to complete the interaction task. According to the method, the relevance between the object and the task in the complex scene can be understood, and accurate task execution is carried out based on the pre-constructed persistent object memory.

The invention relates to the technical field of data set construction, and particularly discloses a method for constructing a training data set for training an action recognition model. According to the method, the target action instance can be generated according to actual needs by utilizing the action generation large model, the initial data set is obtained according to the target action instance, and the initial data set is generalized by virtue of the visual large model to generate the augmented data set as the target training data, so that the use value of the data in the AI field is improved; the defects that traditional simulation data is poor in generalization, small in scale and high in generation cost are overcome; and the generation method does not depend on manual data labeling, so that the problems of wrong labeling, inconsistent labeling and the like caused by manual experience errors are avoided, the defects that manual data labeling is difficult to scale, the precision is poor, the cost is high and the efficiency is low are overcome, and the data scale, the accuracy, the data generalization and the construction efficiency of the data set are improved.

The invention provides a model training method, a silicon wafer processing method, a silicon wafer processing device, a storage medium and equipment. The model training method comprises the following steps: acquiring initial state information and target state information of a silicon wafer to be processed; through an action decision model, according to the initial state information and the target state information, machining action information for the machining machine table is generated; determining an award parameter corresponding to the processing action information, wherein the award parameter represents the processing quality and efficiency when the processing machine processes the to-be-processed silicon wafer based on the processing action information; if the reward parameter is not more than the preset condition, updating the initial action decision model according to the reward parameter to obtain an updated action decision model; and based on the updated action decision model, returning to execute the step of generating the processing action information through the action decision model according to the initial state information and the target state information until the reward parameter is greater than the preset condition, and obtaining a target action decision model. According to the invention, the processing machine table can be accurately adjusted.

The embodiment of the invention provides a reserved resource configuration method and device, equipment, a storage medium and a program product, and relates to the field of cloud computing. The method comprises the following steps: detecting a first state of a node server in a Kubernetes cluster, wherein the first state is a hardware resource which can be allocated to a container by the node server; determining a target action in actions corresponding to the first state by a pre-trained reinforcement learning model according to the first state, the actions including increasing or decreasing hardware resources reserved for a core process of an operating system of the node server and increasing or decreasing hardware resources reserved for a core service component of the Kubernetes system; and controlling the node server to execute the target action. According to the method provided by the invention, the hardware resources reserved by the node server for the core process of the operating system and the core service component of the Kubernetes system are dynamically adjusted according to the hardware resources which can be allocated to the container by the node server through the pre-trained reinforcement learning model.

The invention provides a robot control method, a robot and electronic equipment, and the method comprises the steps: planning an initial action sequence of the robot according to the environment information of the robot, correcting a part of action sequences in the initial action sequence according to a target action state of the robot at a current moment, and obtaining a target action sequence; and finally, the robot is controlled according to the target action sequence, the collection moment of the environment information is before the current moment, the initial action sequence comprises initial action states of the robot at multiple moments after the collection moment, and the partial action sequence is the action sequence located after the current moment in the initial action sequence. According to the method, the two steps of planning the action sequence and executing the action sequence can be executed at the same time, and the control efficiency is ensured.

The invention discloses a heterogeneous agent action strategy game method and a heterogeneous agent action strategy game system, which are used for solving the technical problem of poor adaptability of a heterogeneous agent system in a complex environment caused by an existing heterogeneous multi-agent-oriented action strategy game method. The method comprises the steps that when the number of heterogeneous agents meets static confrontation environment conditions, modeling is conducted on the multiple heterogeneous agents, a game problem is generated, and a synthetic unit intelligent game confrontation behavior model is constructed according to the game problem and preset dynamic change factors; solving the synthetic unit intelligent game confrontation behavior model according to the state information and the environment information corresponding to each heterogeneous agent, and generating a plurality of heterogeneous agent target actions; when the number of the heterogeneous agents meets the dynamic confrontation environment condition, a target reinforcement learning algorithm network and a hierarchical convolutional neural network are adopted to generate heterogeneous agent target actions corresponding to the target state information according to the target state information and the environment information of the target game confrontation environment.

Aspects of the present disclosure provide techniques for machine learning based action prediction. Embodiments include providing, as inputs to a recurrent neural network (RNN), an ordered sequence of strings representing actions performed by a user within a software application, the RNN having been trained through a supervised learning process to generate embeddings of the ordered sequence of strings and generate a numerical score relating to a target action based on the embeddings and an order of the ordered sequence of strings. Embodiments include providing, as respective inputs to a tree-based classification machine learning model, the numerical score and an additional feature relating to the user and receiving, as a respective output from the tree-based classification machine learning model in response to the respective inputs, a propensity score indicating a likelihood of the user to perform the target action.

The invention relates to the technical field of intelligent control, and discloses a dexterous hand motion planning and control method based on a visual language motion model, and the method comprises the steps: S1, collecting data, and carrying out the time sequence synchronization and consistency verification of multi-source data; s2, performing dynamic adaptive exponential moving average filtering and calculation processing on the joint sequence, and reconstructing a processing result into adaptive structured features; s3, fusing joint states, inputting the fused joint states into an action head network, splicing online and offline samples into a training batch according to a preset proportion, and determining results to jointly optimize model parameters; s4, performing model reasoning to obtain target action information, performing processing in sequence to generate a smooth low-jitter control sequence, and sending the smooth low-jitter control sequence to the dexterous hand for execution; and S5, the deployment thread issues a control instruction by aligning the annular buffer and the timestamp, triggers a rollback track and allows manual intervention when the control instruction exceeds a threshold value, and writes manual intervention data into an offline buffer. According to the method, efficient, stable and smooth motion control of the dexterous hand can be realized under the condition of extremely few teaching data sets.

The invention relates to a data processing method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: receiving a voice instruction to be responded; converting the voice instruction into text information; based on the text information and a vector matching result described by a plurality of preset instructions in a preset action classification library, determining a target action category corresponding to the text information; generating a target action instruction responding to the voice instruction according to the text information and the target action category; wherein the preset action classification library is used for representing a first mapping relationship between a plurality of preset action categories and preset instruction description. Therefore, the action category of the target action is determined based on vector matching, the process of constructing a grammar rule tree and a normalized word list in related technologies is effectively avoided, the data processing time of the voice instruction is shortened, and the problem caused by incomplete construction of the grammar rule tree and the like is effectively avoided.

The invention relates to the technical field of deep reinforcement learning, and discloses a deep reinforcement learning decision optimization method and device, computer equipment and a medium, and the method comprises the steps: detecting whether a candidate network cluster is in a strategy evaluation state or not; if in the strategy evaluation state, obtaining an evaluation result corresponding to each training node output by the strategy evaluation node; training nodes in the candidate network cluster are optimized according to the evaluation result until the candidate network cluster meets a preset termination condition, and a target action decision network is obtained; and determining a target action node in the target action decision network, and taking an action strategy corresponding to the target action node as a target action strategy. According to the method, the problems that a deep reinforcement learning decision optimization scheme is low in training efficiency, local optimum and experience forgetting are likely to occur, the intelligent agent correlation utilization of a multi-node scheme is insufficient, and a single-node scheme is not suitable for multi-node training are solved.

The invention relates to the technical field of front-end configuration, and discloses a declarative control configuration method and device, computer equipment and a storage medium, and the method comprises the following steps: configuring a corresponding target event structure body for a target control according to a predefined event structure body; the event structure body is used for configuring an action corresponding to the operation event; according to a predefined view structure body, configuring a corresponding target view structure body for the target control; the view structure body is used for configuring attributes of the control view; determining a target action corresponding to the target operation event according to a target event structure body of the target control; modifying attributes in the target view structural body according to the target action, and / or modifying attributes in view structural bodies of other controls; and analyzing and displaying the modified control by utilizing an interpreter. According to the method, control such as complex linkage of the control is uniformly abstracted into modification of the expression of the control through the operation event, so that functions such as a dynamic form can be realized, and more application scenes can be covered.

The invention relates to a reinforcement learning method, an action generation system, computer equipment and a storage medium. The method comprises the following steps: encoding historical state and action information within a preset time range to obtain encoding track information; generating a target action based on the coding track information and a preset deep learning network; determining a prediction return of the target action based on the target action and a current state corresponding to the target action; determining an exploration capability adjustment value based on the action probability distribution in the current state; and adjusting parameters of the deep learning network based on the prediction return and the exploration capability adjustment value. By adopting the method, the effect of improving the robustness of strategy generation can be achieved.

The invention provides a task processing method, device and equipment based on a virtual auxiliary screen, and the method comprises the steps: displaying a user interaction interface through a main screen, and collecting multimode data from the user interaction interface; determining a user intention based on the multimode data, and generating a task execution sequence based on the user intention; generating a target action sequence based on the task execution sequence; obtaining a target virtual secondary screen corresponding to the target action sequence, sending the target action sequence to the target virtual secondary screen, and performing task processing based on the target action sequence through the target virtual secondary screen; and obtaining a target execution result of the target virtual auxiliary screen for the target action sequence, and displaying the target execution result on a user interaction interface of the main screen. Through the technical scheme of the invention, rapid AI response can be realized, and smooth conversation between the user and the AI agent can be ensured even under the condition that the network is unstable.

The invention discloses a processing method and device, and relates to the technical field of artificial intelligence and equipment application, and the disclosed processing device comprises an action processing module which is used for obtaining function units of at least one application program on electronic equipment to form an action set, and the action set represents actions which can be executed by the electronic equipment; the intelligent processing module is used for responding to input data and determining a target action matched with the intention represented by the input data from the action set through a target model; the intention is an intention related to an action.

The embodiment of the invention discloses an action processing method, device, system and equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring point cloud data corresponding to each target object in a target space; recognizing the action of the target object based on the point cloud data to obtain the action corresponding to the target object; if the action is the target action, indicating the movable equipment to move to an area corresponding to the target object, and obtaining feedback information obtained by interaction between the movable equipment and the target object; and processing an event corresponding to the target action based on the target action and the feedback information. According to the method, action recognition is carried out by acquiring the point cloud data of the target object, a user does not need to wear any equipment, user privacy leakage is avoided while the action recognition accuracy is improved, meanwhile, the mobile equipment is linked to interact with the user, the event is processed according to the feedback information obtained through interaction, and the user experience is improved. And the action processing efficiency and the event processing capability are improved.

The application discloses a blockchain task allocation method and device based on DQN, equipment and medium, relates to the field of task allocation, comprising: determining the state vector of the DQN model, selecting the target action according to the state vector, and determining the predicted execution time and the actual execution time of the target action; the predicted execution time and the actual execution time are used as the input of the preset time length reward formula to obtain the time length reward; a plurality of experiences are randomly extracted, the target Q value of the DQN model is determined based on the plurality of experiences, and the DQN model is updated through the observation Q value and the target Q value of the DQN model; multiple rounds of updating are performed until the DQN model meets the preset requirements to obtain the target DQN model; the target blockchain task allocation model is constructed through the target DQN model, and after receiving the blockchain task allocation request, the target blockchain task allocation model is used for intelligent task allocation. Therefore, the task allocation efficiency can be improved, the dynamic environment change can be responded to, and the manual intervention can be reduced.

The invention provides an interaction method, an intelligent agent training method and device and electronic equipment, and the method comprises the steps: receiving a user instruction; generating a first target action associated with the first user interface according to the first page description information of the first user interface and the user instruction; and executing the first target action on the first user interface to realize the interactive response to the user instruction, thereby realizing the intellectualization and automation of user interface interaction, remarkably improving the speed and accuracy of the response to the user instruction, and improving the user experience. Meanwhile, user interfaces (including complex multi-element interfaces or simple single-function interfaces) with different structures and designs can be flexibly coped with, the consistency and satisfaction degree of user experience are enhanced, and operation under various application scenes is smoother and more natural.

The embodiment of the invention relates to the technical field of computers, in particular to a virtual object processing method, computing equipment, a storage medium and a computer program product.The virtual object processing method comprises the steps that in response to an action playing instruction for a target virtual object, a target action set corresponding to the target virtual object is determined; determining a target action from the target action set and playing the target action; when it is determined that the playing time of the target action reaches preset adjustment time, position difference information between the target virtual object and an associated virtual object of the target virtual object is determined; and adjusting the current position information of the target virtual object according to the position difference information.

The invention discloses a multi-step task reasoning and planning method and electronic equipment, and belongs to the technical field of robot control. The method comprises the steps of obtaining target task input, and determining a first action plan based on the target task input; candidate actions are obtained, a composite scoring function is generated, the candidate actions are associated with the first action plan, and the composite scoring function is used for scoring the candidate actions; decomposing the first action plan into multi-level tasks by using a multi-level planning algorithm, calculating the total cost, and selecting an execution path with the minimum total cost as a target action plan; and calculating a state difference value, and determining whether to trigger re-planning or not based on the state difference value. According to the invention, the robot control efficiency and accuracy are improved, and the robustness is enhanced.

The invention relates to the technical field of data set augmentation, and particularly discloses a data augmentation method for a training data set of an action recognition model. According to the method, the target action instance can be generated according to actual needs by utilizing the action generation large model, the initial data set is obtained according to the target action instance, and the initial data set is generalized by virtue of the visual large model to generate the augmented data set as the target training data, so that the use value of the data in the A I field is improved; the defects that traditional simulation data is poor in generalization, small in scale and high in generation cost are overcome; and the generation method does not depend on manual data labeling, so that the problems of wrong labeling, inconsistent labeling and the like caused by manual experience errors are avoided, the defects that manual data labeling is difficult to scale, the precision is poor, the cost is high and the efficiency is low are overcome, and the data scale, the accuracy, the data generalization and the construction efficiency of the data set are improved.

The invention discloses an available time pushing method and device and a storage medium, and the method comprises the steps: firstly receiving request information used for requesting to push target available total time, and then obtaining the predicted available time of a target object for carrying out a target action at each candidate place in each unit time period according to the request information, obtaining reachable information between every two candidate places; according to all the predicted available time and all the reachable information, the total available time of the target is obtained through calculation, dynamic time planning is carried out by comprehensively considering the space-time relation between time and places, and the sum of the available time of the target object for carrying out target actions in multiple unit time periods can be calculated more accurately and comprehensively. According to the method, the processing efficiency of time planning and the reasonability of time planning are effectively improved, so that a more accurate time planning scheme with a better optimization effect can be pushed to the target object, and the satisfaction degree of the target object for time planning is improved.

The application provides a virtual object control method, a virtual object control device, a computer readable storage medium and an electronic device, and relates to the technical field of virtual interaction. In the method, a user can trigger a sliding operation from a first control to a second control to control a virtual object to perform a target action related to the first control and the second control. Compared with the related art, the user does not need to press the first control and the second control respectively to trigger the target action. The user only needs to slide from the first control to the second control to achieve "one-key" triggering of the target action (such as a continuous attack action). This can improve the casting efficiency of the target action and reduce the cost of the user learning to cast the target action.

The embodiment of the invention discloses a task execution method and device, electronic equipment, a computer storage readable medium and a program product. The method comprises the following steps: acquiring a plurality of to-be-executed actions in a current interaction state; executing multiple rounds of action screening operation; wherein each action screening operation is used for screening candidate actions meeting the cost requirement in the current round from the action list determined in the last round, and determining the action list of the current round based on the candidate actions meeting the cost requirement in the current round; in the first round, candidate actions meeting the cost requirement in the round are obtained from a plurality of actions to be executed; taking a father node action corresponding to the candidate action screened out by the last round of action screening operation in the plurality of actions to be executed as a target action to be executed in the current interaction state; and in the current interaction state, executing the target task in response to the target action.