24 results about "Action Code" patented technology

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 present disclosure relates to systems, methods, and non-transitory computer-readable media that perform text-to-image editing using executable code generated from natural language text input. For instance, in one or more embodiments, the disclosed systems receive, from a client device, a digital image and natural language text input providing instructions for modifying the digital image. The disclosed systems also generate, using a large language model, executable action code for modifying the digital image in accordance with the instructions of the natural language text input, the executable action code being compatible with an editing application. The disclosed systems further modify the digital image by executing the executable action code via the editing application and provide the modified digital image for display via a graphical user interface of the client device.

The invention relates to the technical field of elevator safety risk analysis, in particular to an elevator safety risk self-adaptive analysis method and system. The method comprises the following steps: performing misoperation code execution unit mapping on an elevator operation logic code according to an elevator historical operation log to obtain a misoperation code execution unit; performing abnormal speed change code logic analysis on the misoperation code execution unit, and performing trigger condition backtracking to obtain abnormal speed change trigger condition data; performing causal relationship network construction based on the abnormal speed change triggering condition data to obtain a potential risk causal relationship network; and building an elevator safety risk self-adaptive analysis model for the potential risk causal relationship network based on a random forest algorithm to obtain the elevator safety risk self-adaptive analysis model. The elevator safety risk analysis technology is optimized, so that the elevator safety risk analysis technology is more perfect.

The invention provides a method, a device and a computer readable medium for action decision based on a self-adjusting mechanism. A set of previous states associated with a target application and a set of previous rewards corresponding to the set of previous states may be obtained. An action in a natural language form for the target application may be generated based on the set of previous states and the set of previous rewards. A set of predefined rules may be utilized to verify whether the action is reasonable. In response to verifying that the action is reasonable, action code in the form of a computer language corresponding to the action may be generated. The target application may be caused to execute the action code.

The invention relates to the technical field of video synthesis, and discloses an efficient sign language video synthesis system which comprises a data uploading and processing module, a natural language processing module, an action code generation module, an action optimization module, a virtual human animation system, a user interface and a display module. The data uploading and processing module is used for receiving and preprocessing text, voice input, keyboard input and handwriting input provided by a user; the natural language processing module is used for performing word segmentation, part-of-speech tagging and semantic understanding on an input text, translating the input text into a sign language symbol sequence, and enhancing understanding in combination with multi-modal data; and the action code generation module is used for predicting and generating a sign language action sequence by using a time sequence model and a generative adversarial network, and generating expressions and body languages based on text emotion. By combining text and video data, richness and accuracy of sign language translation are remarkably improved, and the generated sign language video can be closer to a real communication scene.

The invention relates to the technical field of multi-modal interaction, in particular to a multi-modal interaction control method and system for a sweeping robot, and the method comprises the following steps: collecting voice and gesture input, extracting voice trigger information and action features, generating a modal frequency mark and an instruction list, matching a task path, and generating a modal identification table. The method comprises the following steps: extracting and comparing action sequences to recognize a master control mode, analyzing continuity to generate a response mark, and screening consistent input to generate an interaction instruction result. In the method, through extracting time, quantity and confidence characteristics of voice and gesture input, establishing frequency and trigger marks of multi-mode input, realizing accurate recognition of an input state; a task path and semantic content are combined to carry out space and semantic matching, the task relevance judgment capability is improved, based on action code comparison, dominant modes are identified, conflict instructions are eliminated, the master control status of each mode in continuous tasks is dynamically balanced, the control response accuracy is improved, and an interaction mechanism with intelligent judgment capability is constructed.

The invention discloses a self-calibration IMU intention recognition method and system, and belongs to the technical field of man-machine interaction. The method aims at solving the problems that in the prior art, due to drifting of a sensor, the long-term stability is poor, and due to the fact that an action process and a static posture cannot be distinguished, an instruction is mistakenly triggered and repeated. In order to solve the problem, the method provided by the invention comprises the following steps: acquiring a motion data stream of a sensor, and segmenting the motion data stream into analysis windows; independently determining an action code for representing a dynamic process in the window based on the dynamic characteristics in the analysis window; independently determining a state code for representing the static posture of the tail end of the window based on the static characteristics in the analysis window; and generating a control instruction based on the logic association verification between the action code and the state code. In addition, drift is suppressed through a zero-speed updating mechanism, and drift is actively compensated by resetting a yaw reference when the upright posture is detected.

The invention provides a fine-grained acquisition method, equipment and system for equipment action data, and relates to the technical field of digital information transmission. The method comprises the following steps: when an action starting signal or an action ending signal of production line equipment is monitored, triggering an action recording event, encoding the action recording event through a preset encoding protocol to obtain an action code, and obtaining encoding time according to the triggering time of the action recording event; obtaining action record data of the action record event according to the action code and the coding time; transmitting the action record data to a data storage terminal; and the data storage terminal is used for analyzing the action record data to obtain the action code and the coding time, and obtaining equipment action data according to the action code and the coding time. According to the method provided by the invention, the fine granularity of the equipment action data is improved, and the association between the equipment action data and the actions of the production line equipment is ensured.

An example computer implemented method for generating an artificial intelligence (AI) model from a self-creating script coded in a programming language includes receiving an objective for the AI model and obtaining at least first action code corresponding to a first action, where the first action is associated with an action objective similar to the objective. The method further includes generating at least a second action code based on one of the first action code and a specification of the programming language and comparing a first outcome of the first action code and a second outcome of the second action code. The method further includes inserting one of the first action code and the second action code into the self-creating script based on the comparing the first outcome of the first action code and the second outcome of the second action code and executing the self-creating script including the one of the first action code and the second action code to satisfy the objective.

The invention belongs to the technical field of software debugging, and particularly relates to a software debugging method, a controller, an upper computer and a system. The software debugging method comprises the following steps: receiving a trigger source-action pair; when the trigger source-action pair contains the self-defined trigger source-action pair, temporarily storing the self-defined trigger source-action pair in the random access memory; the user-defined trigger source-action pair comprises a user-defined trigger condition and a user-defined action type, and correspondingly comprises a user-defined trigger condition code and a user-defined action code; when a trigger condition in the trigger source is established, executing a corresponding action code; and feeding back an execution result. The software debugging system comprises a controller and an upper computer, and the upper computer is connected with the controller through a communication interface except for a JTAG interface. The method gets rid of dependence on a JTAG interface, covers troubleshooting of a whole life cycle, and adapts to troubleshooting requirements of endless new fault problems by dynamically loading a user-defined trigger source-action pair.

The present disclosure proposes a method, apparatus and computer-readable medium for action decision based on self-tune mechanism. A set of previous states associated with a target application and a set of previous rewards corresponding to the set of previous states may be obtained. An action in natural language for the target application may be generated based on the set of previous states and the set of previous rewards. It may be verified with a set of predefined rules whether the action is reasonable. Action code in computer language corresponding to the action may be generated in response to verifying that the action is reasonable. The target application may be caused to execute the action code.

The invention discloses an intelligent routing jump recommendation method based on reinforcement learning, and relates to the technical field of computer networks. Comprising the steps that 1, multi-dimensional data are collected, the multi-dimensional data comprise user role information, page types and user feedback data, 2, a state space S is defined, the collected data are subjected to structured coding, the state of each system is represented by a three-dimensional vector, all dimensions correspond to user role codes, page type codes and user feedback state codes respectively, and the user role codes, the page type codes and the user feedback state codes are stored in the state space S; 3, creating a Q table, initializing a Q value table according to the size of the state space S and the size of the action space A, the dimension of the Q table being S * A, and initializing each element Q (s, a) into a fixed value Q0, 4, carrying out action coding, carrying out unique coding on a jump target of each page, and establishing a mapping relation table of action codes and actual page jump logic, 5, optimizing the jump action based on a recommendation strategy of Q-Learning; and 6, generating and outputting a recommendation result, and receiving feedback.

Securing a file in a computer network includes processing a file system request that identifies a user entity, a file entity, and a file-directed action. Responding to the request with an indication of permission to reveal a portion of the file system, an indication of permission to reveal at least a portion of the file, and an indication of permission to perform the file-directed action. The response being based on access and use constraints of the file entity, as well as a portion of user entity-context and action code entity-context.

This invention discloses a method and apparatus for cross-project security remediation detection and migration in the blockchain ecosystem, relating to the field of blockchain technology. The method includes: constructing a code submission knowledge graph with code submission as the central node and edge relationships such as submitted modified functions, submitted related topics, submitted related merge requests, submission belonging to a project, project reuse of a project, and merge request discussion topics; determining the editing action based on the syntax tree differences of the affected functions to which the code submission belongs, and determining the action code sequence by backfilling the original source code; outputting the remediation probability of a code submission belonging to a security remediation submission using the submission message and line-level differences, the code submission evidence subgraph of the code submission knowledge graph, and the action code sequence; and constructing cross-project migration rules and performing project migration detection when the remediation probability is greater than a probability threshold to determine the cross-project vulnerability status. Based on the above scheme, the identification and migration performance of cross-project security remediation in the blockchain ecosystem is improved.

The invention discloses an operation performance obtaining method and device, equipment, a storage medium and a program product, and relates to the technical field of computers. The method comprises the following steps: displaying a virtual component in a virtual scene; in response to the received component editing operation for the virtual component, displaying a component operation code corresponding to the virtual component; under the condition that the execution component operation code drives the virtual component, component performance information corresponding to the virtual component is displayed; the component performance information comprises operation execution information used for representing the execution condition of the ith operation code in the at least one operation code, and i is a positive integer. The execution condition of the operation code in the component operation code can be displayed in a more targeted manner through the operation execution information, so that the operation code with a performance problem can be efficiently positioned, and the code adjustment efficiency is improved. The method and the device can be applied to various scenes supporting code creation, such as social media scenes, game content creation scenes, programming learning scenes and multimedia sharing scenes.

Securing a file in a computer network includes processing a file system request that identifies a user entity, a file entity, and a file-directed action. Responding to the request with an indication of permission to reveal a portion of the file system, an indication of permission to reveal at least a portion of the file, and an indication of permission to perform the file-directed action. The response being based on access and use constraints of the file entity, as well as a portion of user entity-context and action code entity-context.

The invention provides a method for realizing an action, a related device and a computer program product, and the method comprises the steps: determining an action code based on an action destination Probuf file capable of realizing the action in response to the detection that a corresponding action code is not configured for the action in Hvigor; an action code is provided for the Hvigor; and executing the action code in the swan environment by utilizing Hvigor so as to realize the action in the swan environment. Therefore, the code support capability for the action can be provided in the swan-gap environment, and the execution capability for the action in the swan-gap environment is improved while the code configuration requirement for realizing the action is simplified and the implementation difficulty of the action is reduced.

The application discloses a continuous action semantic coding and translation method and system for a humanoid robot, and the method comprises the following steps: establishing a unified reference time axis, and setting a sampling and coding element action data structure and a decoding element action data structure; sampling data of each actuator according to the sampling and coding element action data structure; coding the sampling points according to action semantics, and outputting the coding and storing the coding in an action library after the coding is completed; taking out the action coding stream from the action library when the robot moves, and decoding the action coding stream; calculating the continuous action coding stream by using an action smoothing algorithm after the decoding, and filling each action point of the continuous action coding stream according to the decoding element action data structure; and outputting the decoded action coding stream and executing the decoded action coding stream according to the unified time axis. The joints of the humanoid robot can independently move at the same time point, and the linkage action of the humanoid robot can be realized on the whole.

The present application provides a method, device and system for fine-grained collection of equipment action data, which relates to the field of digital information transmission technology. The method includes: when an action start signal or an action end signal of a production line equipment is monitored, an action recording event is triggered, the action recording event is encoded through a preset encoding protocol to obtain an action code, and the coding time is obtained according to the triggering time of the action recording event; the action recording data of the action recording event is obtained according to the action code and the coding time; the action recording data is transmitted to a data storage terminal; the data storage terminal is used to parse the action recording data to obtain the action code and the coding time, and the equipment action data is obtained according to the action code and the coding time. The method of the present application improves the fine-grainedness of the equipment action data and ensures the association between the equipment action data and the action of the production line equipment.

The invention provides a crushing working condition identification method and device and electronic equipment. The method comprises the following steps: acquiring working condition data of the excavator in a corresponding preset historical duration; according to the working condition data, the engine rotating speed of the excavator and first time sequence data corresponding to the composite action coding information are extracted; determining second time sequence data corresponding to the associated single-action coding information according to the rotating speed of the engine and the first time sequence data; and according to the second time sequence data, determining a crushing condition identification result of the excavator. The accuracy of recognizing the crushing working condition can be improved, and the working efficiency of the excavator can be improved.

A method for analyzing at least one sequence of movements performed by a person, including steps of segmenting the sequence of movements into time units, each associated with a time (t0 to tn), determining for each instant, positions of at least some characteristic points of the person using an acquisition system providing raw data, assigning at each instance at least one position code for the characteristic points as a function for each of them of the determined position to form a combination of position codes at each instant, assigning at least one elementary action code for each given instant corresponding to the combination of position codes at the given instant, and syntactically verifying the elementary action codes and / or position codes from at least one structured language to refine the raw data relating to the positions of the characteristic points.

The present invention provides a method for dividing the action cycle of a hydraulic support in a fully mechanized mining device and extracting information, including: importing the original support column pressure data and action code data; preprocessing the action code data; performing a preliminary division of the action cycle of the support based on the support column pressure data; using the action code data to correct and supplement the preliminary divided action cycle of the support, numbering the divided action cycles, and determining the coal mining complete cutter to which the action cycle of each support belongs; extracting the median support pressure and end resistance of the support in each action cycle, and establishing the corresponding relationship between the action cycle of each support and the complete cutter in combination with the complete cutter data. The present invention uses the column pressure data as input, uses the action code data for correction, and determines the coal mining complete cutter to which each action cycle belongs, and finally obtains a support action cycle information table containing relevant data information of each support action cycle.

The invention relates to an algorithm visualization method based on a large model. The method comprises the steps of converting to-be-visualized target data through a large model to obtain an original code, and analyzing the original code to obtain an analysis result; identifying the analysis result to obtain at least one insertion point in the original code and the position and the type of the insertion point; matching a target operation code template from a preset code library according to the type of each insertion point in sequence; sequentially generating calling codes according to the positions of the insertion points and the corresponding target operation code templates, inserting the calling codes into the positions of the insertion points, converting the calling codes into operation objects, and arranging the operation objects according to an execution sequence to generate an operation queue; and visualizing the target data based on the operation queue. According to the scheme, semantic analysis, insertion point positioning and insertion code generation are automatically performed in the visualization process by using the large model, so that the insertion accuracy and consistency of the insertion codes in the visualization operation are ensured, and the applicability of the visualization technology is enhanced.

The application discloses a method for training an action generation model, an action generation method, an action generation device and an action generation equipment, and relates to machine learning of artificial intelligence. The method comprises the following steps: calling an action encoder to perform action feature extraction on an action state of a virtual character to obtain an action code; calling a style encoder to perform style feature extraction on the action state of the virtual character to obtain a style code; calling a decoder to decode the action code and the style code to obtain a predicted action of the virtual character; calculating a style loss according to the style codes of different action states; calculating an action loss according to the action codes of different action states; calculating a reconstruction loss according to a real action corresponding to an action state and the predicted action; and training the action generation model according to the action loss, the style loss and the reconstruction loss. The method can distinguish the action styles of different virtual characters and improve the action quality of the generated virtual characters.