119 results about "Work memory" patented technology

The invention relates to a child attention evaluation and training method and system, and the method comprises the steps: obtaining a brain wave mode, an eyeball movement track, an emotion activation level and autonomic nervous system function data of a child in real time, and generating multi-dimensional biological signal data; based on the multi-dimensional biological signal data, generating evaluation task data; generating dynamic task parameters according to biological signal data and behavior performance data in the evaluation task data; generating an attention feedback signal based on the dynamic task parameters; according to the attention feedback signal, a training task integrating work memory, suppression control and cognitive flexibility is constructed, and cognitive function training data is generated; and based on the cognitive function training data, synchronously sharing the attention performance data of the children to the parent terminal and the teacher terminal, generating life situation intervention suggestions, and iteratively optimizing evaluation parameters according to a long-term effect evaluation result. Therefore, the accuracy of children attention evaluation and training can be improved.

A sub-block-based storage device includes a memory device configured to include a plurality of memory blocks, a work memory storing instructions, and a memory controller communicatively coupled with the memory device and the work memory and configured to manage the plurality of memory blocks. Each block of the plurality of memory blocks includes a plurality of sub-blocks having different capacities. The memory controller is configured to execute the instructions to manage used sub-blocks which store valid data from among the plurality of sub-blocks, manage free sub-blocks which are empty from among the plurality of sub-blocks, select a garbage collection source block from the used sub-blocks, select candidate destination blocks by comparing a size of valid data stored in the garbage collection source block and each of sizes of the free sub-blocks, and select a garbage collection destination block from the candidate destination blocks.

The invention discloses a clinical intelligent decision-making method based on proxy workflow and a storage medium. Comprising the following steps: acquiring and preprocessing clinical information of a patient, and constructing a candidate disease set; and constructing a proxy directed workflow. In the retrieval stage, the diagnosis criteria corresponding to the candidate diseases are retrieved and aggregated from the diagnosis criteria library rechecked by the experts to form working memory. The preliminary diagnosis stage model node generates a preliminary candidate diagnosis set in combination with work memory and patient medical history and physical examination. And the final diagnosis stage generates a final diagnosis result based on the preliminary candidate diagnosis and the complete clinical information. And when the global confidence is lower than a threshold value, the model node pointedly checks an information source and updates reasoning and confidence. A successful reasoning track forms a demonstration set after manual auditing, the demonstration set is used for supervising a fine tuning model to obtain an initial strategy, multiple structured outputs are generated through grouping sampling, relative strategy updating is carried out in combination with reward signals and reference strategy regularization constraints, and optimization and stable improvement of the model diagnosis capability are achieved.

A multi-agent and human project management and collaboration tool can provide the ability to create and manage projects and software agents in a cohesive framework. The collaboration tool can provide a multi-agent, multi-player channel where agents and humans work together to achieve an outcome. The collaboration tool can initiate a new project, where the objective is defined, and a team is dynamically built to achieve this objective. The team roles are parsed, and an agent is created for each role. Each agent is equipped with a personalized skillset and a working memory, allowing them to effectively collaborate with humans and each other to progress autonomously through the project stages. The agents can complete tasks based on the project plan and the results are stored in the agent's working memory. The project status is updated, and a report can be generated after each stage, providing a record of the project's progress.

The invention discloses a robot training system and method based on plot memory in the technical field of artificial intelligence and robot learning, and solves the problems that an existing robot training method lacks an effective memory scheduling system, an empirical value quantification mechanism is not intelligent, and the multi-modal information fusion capability is insufficient. The system comprises a sensing module, a multi-modal unified memory encoder, a progressive memory scheduling system, a multi-dimensional value quantitative evaluation mechanism, an intelligent experience hierarchical scheduler, a multi-modal unified code retriever, a strategy generation module, an execution module and an anomaly detection module. The multi-modal unified memory encoder adopts a hierarchical dimensionality reduction Transform architecture, and fuses an RGB image, a depth image, force sensor data and joint angle information into a 576-dimensional unified feature vector; the progressive memory scheduling system comprises a working memory structure, a short-term memory structure and a long-term memory structure. The multi-dimensional value quantitative evaluation mechanism carries out quantitative scoring on experience based on reward evaluation, novelty evaluation and uncertainty evaluation.

The invention discloses an intelligent following robot object searching method and system based on graph structure memory. The method comprises the steps that the robot follows a user to move in real time, actively senses behaviors of the user and obtains an observation result; organizing a graph structure based on an observation result to form an initial pragmatic memory graph; forming structured memory based on the initial pragmatic memory graph, wherein the structured memory comprises continuously maintained and updated working memory and long-term memory for storing behavior habits of people; querying in the working memory or the long-term memory according to the question proposed by the user to obtain a query result; and outputting a voice prompt according to the query result. The system comprises an object observation module, a memory creation module, a structured memory storage module, a memory recall module and a reminding module. According to the method, the observation angle is flexible and active, the storage space is greatly saved, the query efficiency is improved, and based on graph structure memory, the method is closer to the memory and recall mode of human beings.

The invention provides a body navigation method, device and equipment based on brain inspiration structured spatial memory, and relates to the technical field of navigation.The method comprises the steps that a work memory module is used for determining a structured spatial memory module for searching a navigation instruction according to the complexity of the received navigation instruction, and the structured spatial memory module is used for searching the navigation instruction; the structured spatial memory module comprises at least one of a landmark memory module and a cognitive map module; responding to the navigation instruction, and performing hierarchical retrieval by using a structured spatial memory module of the navigation instruction to obtain a candidate coordinate set of the target; the candidate coordinate set comprises a plurality of candidate coordinates; determining an exploration sequence based on the confidence score of each candidate coordinate and the distance between each candidate coordinate and the coordinate of the current position of the body agent; the exploration sequence represents the exploration sequence of the candidate coordinates; and controlling the body agent to execute navigation operation based on the exploration sequence. According to the method, the robust space cognition and self-adaptive navigation capability of the intelligent agent with the body in a complex environment is realized.

A system for measuring the surface roughness of surfaces machined by electrical discharge machining (EDM) using a convolutional neural network, comprising: an image recording device with a camera module for recording images of a machined surface with depressions and burrs produced by electrical discharge machining; an image preprocessing module that is connected to the image capture device and is configured to improve the texture contours of the captured images and optimize the image; a Convolutional Neural Network module configured to receive images captured by the image preprocessing module, wherein the Convolutional Neural Network module comprises the following: a feature extraction submodule with multiple convolution and pooling layers, wherein this submodule is configured to analyze the distribution of light intensity variations in the acquired images, where darker areas correspond to valleys and lighter areas to edges of the processed surface, and extracts features from this; and a classification submodule with fully connected layers and an output layer, wherein the classification submodule is configured to predict an arithmetic mean roughness value (Ra) based on the extracted features; a computer device comprising a processor, a working memory and a storage unit, wherein the computer device is configured to execute the convolutional neural network module using the processor, the execution parameters being loaded from memory into the working memory; and an output module integrated into the computer device, wherein the processor is configured to create a database of samples with recorded surface roughness values ​​within the storage unit, the database containing the preprocessed image with timestamp and image metadata as well as the results obtained from the prediction, and wherein the processor is further configured to deliver the predicted arithmetic mean roughness value (Ra) from the output layer to a user interface.

The invention discloses a mild cognitive impairment recognition method and system based on fNIRS data under multi-task normal form fusion. The method comprises the steps that fNIRS data, obtained based on a resting state task and an n-back work memory task, of a testee are obtained; based on the obtained fNIRS data, extracting fNIRS related features; performing feature selection on the extracted fNIRS related features based on a feature selection algorithm to form a feature set; and inputting the formed feature set into the trained deep learning model, and outputting a mild cognitive impairment recognition result. According to the method, the data of the fNIRS under the resting state and the n-back work memory cognition task are obtained, the multi-dimensional fNIRS related features are calculated, feature selection is carried out based on a Gradient Boosting algorithm, and finally accurate MCI recognition is carried out in combination with a deep learning model. The method not only can effectively extract high-dimensional features, but also can realize automatic MCI diagnosis through model training and reasoning, and has relatively high accuracy and relatively strong popularization and application potential.

The invention discloses a cognitive function assessment data acquisition method and device, relates to the field of medical health, and is used for improving the convenience, efficiency and accuracy of assessment. Aiming at ADHD children, concentrated attention data, continuous attention data, selective attention data, alternate attention data, dispersive attention data, work memory ability data, suppression control ability data and cognitive flexibility ability data are respectively acquired by developing multiple rounds of interactive tasks. According to the invention, the portability of evaluation facilities is improved, and the accuracy and efficiency of data acquisition and evaluation are improved.

In various examples, temporal memory fusion in scene perception for autonomous and / or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use a temporal fusion model (e.g., the model) to generate representations—such as BEV images-associated with an environment. For instance, a working memory buffer may maintain working memory features, such as in a fixed-lag manner. A temporal overlap heatmap—such as a temporal overlap image—may then be temporally propagated to indicate a number of times individual grid cells fall within fields of view associated with temporal features. As such, to generate a representation using the model's temporal reasoning capabilities, the model may fuse the heatmap with the working memory features and / or the current features using one or more neural networks. The model may then process the fused output using one or more decoders to generate the representation.

The invention provides a maze robot brain-like cognitive system based on an emotion and memory mechanism, and the system comprises a perception and emotion generation module which obtains the environment state of a robot, and calculates the reflective fear value and cognitive fear value of the robot; the memory module is used for recording short-time memory, working memory and long-time memory of the robot in combination with an anti-thought memory mechanism; the behavior decision-making module is used for comparing the evaluation function based on the cognitive fear value with the energy margin to obtain a behavior decision-making result; the learning module is used for exploring and learning a target node and an energy node to obtain an updated target guide table and an updated energy guide table; and the action selection module is used for updating the target guide table and the energy guide table according to the behavior decision result, and outputting the next action. According to the invention, the survival rate of the robot in the environment learning process can be improved, and long-time learning is realized; and the robot can be prevented from repeatedly exploring in an invalid area, the learning efficiency is improved, the energy consumption is reduced, and self-survival is ensured.

The invention provides an intelligent dialogue method and device, equipment and a storage medium, and relates to the technical field of intelligent dialogues, and the method comprises the steps: obtaining the current input information of a user; when the current input information meets at least one of a preset reference condition, a semantic association condition, a task continuation condition and a predictive retrieval condition, judging that the current input information meets a historical retrieval condition, and extracting corresponding historical information from a short-term memory layer and a long-term memory layer; performing context splicing on the current input information and the historical information to generate integrated information; and generating reply information according to the integrated information and a matched preset reply template, and storing the current input information and the corresponding reply information in a working memory layer in the form of a key value pair. The interaction experience of the user can be improved.

The invention discloses a network equipment configuration file verification method and system based on a Drools rule engine, and the method comprises the steps: receiving a to-be-verified network equipment configuration file, carrying out the line-by-line analysis of the configuration file, and constructing a configuration line object; a configuration type mapping table is maintained, and a multi-stage matching strategy is adopted for configuration type identification; a Drools rule engine is loaded, a KieServices instance is created, and grouping management of verification rules is supported; inserting the analyzed configuration line object as a fact into a working memory of a Drools rule engine, performing verification rule matching and execution, and collecting a detailed verification result at the same time; and after verification is completed, statistical analysis and formatting processing are performed on a verification result, and a report is generated. According to the method and system, verification rules and program logic are separated, and dynamic management and execution of the rules are achieved through a Drools rule engine.

The invention relates to the technical field of artificial intelligence question and answer interaction, in particular to an agent question and answer interaction method based on a large model and generative artificial intelligence, and the method comprises the steps: screening out an information unit packet which needs to be stored in a short-term memory based on the time interval between each information unit packet and question content and the similarity of embedded vectors; obtaining the time delay of the intelligent agent at the current moment, and dynamically adjusting the capacity threshold value of the short-term memory in combination with the distribution of importance scores of all information unit packets in the short-term memory; and answering the question content based on the information unit packets stored in the working memory, the short-term memory and the long-term memory of the intelligent agent at the current moment. According to the method, the problems of long dialogue key information loss, cross-dialogue memory interruption and excessive context information in a traditional intelligent question and answer interaction method are solved, and the interaction continuity, the response speed and the question and answer interaction stability in long dialogue and cross-dialogue scenes are improved.

The invention discloses a large language model vulnerability testing method based on a working memory tree. The method comprises the following steps of 1, collecting malicious semantic texts; 2, constructing an automatic confrontation prompt; 3, testing vulnerabilities of the large language model; 4, evaluating a test result; and 5, carrying out iterative optimization on confrontation prompt. According to the method, antagonism prompts with concealment and attack diversity can be accurately constructed, and the limitation that a traditional large language model vulnerability testing method is limited in coverage range and insufficient in multi-prompt exploratory performance is improved.

A storage device includes a volatile memory and a storage controller, which is configured to control the volatile memory. The volatile memory includes a memory cell array, which has a plurality of sub-cell arrays therein, and a plurality of sub-wordline driver blocks, which are configured to drive sub-wordlines electrically connected to at least one of the plurality of sub-cell arrays. The storage controller includes: a volatile memory interface configured to transmit data to and receive data from the volatile memory, and detect an error bit(s) of data output from the volatile memory, a working memory configured to store a structure map table, which maps unit areas of the volatile memory, the sub-wordlines, and the plurality of sub-wordline driver blocks, and a processor, which is configured to: update an error count of a unit area of the volatile memory that corresponds to the error bit(s) detected from the volatile memory interface to the structure map table, detect at least one of a defective sub-wordline and defective sub-wordline driver block, by accessing the structure map table, and then repair at least one memory cell connected to the defective sub-wordline and / or repair at least one memory cell associated with the defective sub-wordline driver block.

A computer-implemented method for performing memory planning for a code generation for determining a code for computing a neural network includes (i) providing successive calculation steps of the neural network, wherein for each calculation step the size of an input data block and an output data block is determined, (ii) determining a condition for the memory planning for each specific computing step, in which an input data block in a memory area is at least partially contained in the output data block, wherein the condition indicates that the memory area associated with the output data block of the calculation step preceding the specific calculation step is contained in the memory area of the output data block of the specific calculation step, and (iii) performing memory planning in which the memory area of the respective input data block and output data block in the working memory is determined for each calculation step, taking into account the determined conditions.

The application discloses a product service network simulation platform based on multiple agents, and belongs to the field of service computing and simulation platform. The simulation platform comprises an agent template library, a digital twin interface, a topology manager and a simulation controller. The agent template library provides agents for representing roles in the product service network, and configures a predefined element attribute template for each agent. Each agent further comprises an agent memory set, which is divided into a short-term working memory, a long-term fact memory, a situational memory and a reflective memory. The digital twin interface acquires and records external simulation environment data in real time or periodically, ensures that the historical data is auditable, and inputs the data into the agents. The topology manager is used for mapping the interaction relationship of the roles in the product service network into the interaction relationship between the agents. The simulation controller is used for simulating each agent. The application breaks through the bottleneck of computational efficiency and scalability, and reduces the technical threshold of complex simulation construction and analysis.

Mechanisms are disclosed for detecting one or more defects in a system In-system and externally observable events are received. Active state information is derived from the in- system and externally observable events. If a memory update is required based on facts from the semantic memory, an episodic memory and a working memory are updated. Actions required from a procedural memory are extracted based on the updated episodic memory and the updated working memory. A prompt is executed on a generative language model to produce a model reasoning output. Reproduction steps are derived from the episodic memory, based on a determination that a defect is present from the model reasoning output. A defect signal is generated and provided to a testing user.

A system and method are disclosed for securely transferring or storing protected working memory outside an owner thread while maintaining full encryption throughout its lifecycle. The protected working memory has an encrypted data component and a keystream component containing dynamically updated encryption information used for per-cycle decryption and re-encryption. A hardened cryptographic subsystem, such as a Trusted Execution Environment (TEE), Secure Element (SE), TPM, or HSM, performs authenticated encryption using device-bound key material to wrap either (i) the keystream component alone or (ii) the entire protected working memory blob. The wrapped object is suitable for storage or transfer across threads or in external memory, without exposing plaintext or keystream material. When restoring, an authorized owner thread supplies a key handle to the hardened subsystem to securely decrypt and reconstruct the protected working memory only within a guarded heap. At no point are plaintext contents or keystreams available to the operating system or user-accessible memory.

The invention relates to a computer-implemented method for performing memory planning for code generation to determine a code for the computation of a neural network in a hardware environment (2), comprising the following steps: - Providing (S1) successive computation steps of the neural network, wherein several of the computation steps provide for the computation of a weight prefetching layer for which weight prefetching is applicable; - Assigning (S3) a possible weight prefetching to at least some of the multiple weight prefetching layers to obtain several different combinations of weight prefetching layers, wherein the weight prefetching involves preloading network parameters into a working memory of the hardware environment for the respective weight prefetching layer; - Performing (S4) memory planning for the code to be created for the several different combinations, wherein the respective memory planning for the successive computation steps of the neural network is carried out taking into account the respective combination of the weight prefetching layers for which weight prefetching is provided, wherein for the respective memory planning for each computation step the memory area of ​​the respective input data block, output data block and at least one network parameter block in main memory is determined and a maximum required memory space of main memory is determined; - Select (S5) one of the memory schedules depending on the maximum required memory space.

The invention discloses an operator switching method and device based on position information, and relates to the technical field of network switching, and the operator switching method based on position information comprises the steps: generating a multi-source position data set according to satellite positioning data, base station signaling data and IP address data; performing feature extraction and fusion processing on the multi-source position data set to obtain structured state data including country codes, region codes and network service levels; writing the structured state data into a working memory of a rule engine, and sorting business rules in a rule base of the rule engine to obtain a rule execution queue; and generating an operator switching instruction according to the structured state data in the working memory and the service rules in the rule execution queue, and completing operator switching according to the operator switching instruction. According to the invention, intelligent switching of cross-border operators can be realized based on multi-source location awareness and a dynamic rule engine.

This disclosure relates to methods and corresponding apparatus for managing convolutional computations. In one embodiment, a method for managing convolutional computations performed by a computing unit adapted to compute output data on an output channel based on convolutional kernels applied to input data blocks on at least one input channel, wherein computation for each input data block corresponds to output data on an output channel, and wherein computation for each convolutional kernel corresponds to output data on each output channel, the method comprising: identifying the size of a memory location available in a temporary working memory of the computing unit; preloading a maximum number of convolutional kernels that can be stored within the memory size in the temporary working memory; and controlling the computing unit to compute a set of output data that can be computed based on the preloaded convolutional kernels.

The invention discloses a self-adaptive illumination system and method for promoting cognitive efficiency based on task cognitive types, and relates to the technical field of intelligent illumination, and the method comprises the steps that a multi-mode task recognition module obtains user behavior data in an intelligent self-adaptive mode, extracts behavior characteristics of the user behavior data, and judges the task cognitive types of the user based on the behavior characteristics; the central processing and control module obtains an optimal lighting parameter through a reinforcement learning algorithm based on a task cognition type and user basic information in an intelligent self-adaptive mode, and sends a control instruction to the dimmable lighting module based on the optimal lighting parameter; the dimmable lighting module receives the control instruction and adjusts lighting parameters of the lamp according to the control instruction; the on-line efficiency verification and strategy optimization module quantifies the working memory efficiency of a user in the current light environment based on a micro-task in an intelligent adaptive mode, feeds back a working memory efficiency evaluation result to the central processing and control module, carries out algorithm optimization, and dynamically provides an optimal light environment capable of directly improving the working memory efficiency.

The optical-electro-mechanical system for enhancing the working memory comprises a transcranial supporting module, a phototherapy module, a blood oxygen and heart rate detection module and a main control module, and the phototherapy module is used for emitting treatment light waves to the head of a user; the blood oxygen and heart rate detection module is used for detecting blood oxygen and heart rate of a user and comprises the steps that PPG signals of the user are collected, a signal quality score is calculated, when the signal quality score is smaller than a score threshold value, if the absolute value of the light intensity amplitude difference value of a current sampling point and a previous sampling point of the PPG signals exceeds a first threshold value, the current sampling point is judged to be an abnormal point, and the current sampling point is judged to be an abnormal point. Repairing the current sampling point, otherwise, performing adaptive Kalman filtering on the PPG signal; calculating a blood oxygen value, a heart rate value and a standard deviation of an RR interval of the user according to the filtered PPG signal; the main control module is used for adjusting the light intensity, the duty ratio and the treatment time of the treatment light waves output by the phototherapy module according to the blood oxygen value, the heart rate value and the RR interval of the user.

The invention discloses a brain cognitive function analysis method based on an EEG-fNIRS cross-modal multilayer network. The method comprises the following steps: firstly, collecting multichannel EEG and fNIRS signals under a working memory training normal form, and preprocessing the EEG and fNIRS signals; source space reconstruction is carried out on the signals, corresponding position information is distributed for EEG and fNIRS source time sequences, and regions of interest are divided based on a Desikan-Killiany atlas; the method comprises the following steps: quantizing decomposable directed mutual information between systems by using IID-PSIT, and constructing an EEG-fNIRS multiplexing network; quantizing cross frequency coupling in the brain function network by using GCSIT, and constructing an EEG-fNIRS full-connection multi-layer network; topological characteristics of a multiplexing network and a multi-layer network are analyzed and extracted by using graph theory parameters, brain function changes under different cognitive loads are revealed, and the accuracy of cognitive load classification tasks is improved.

The invention discloses an attention training method, system and device based on a brain-computer interface, and a storage medium, and belongs to the technical field of brain-computer interfaces. The attention method is carried out on the basis of electroencephalogram signal data, measured in real time through a brain-computer interface, of a trainee, movement of a training role is controlled on the basis of an attention numerical value by providing the training role, and multiple training modes such as an attention intensity training task, an execution training task, a suppression training task and a work memory training task are further provided. The method, the system, the equipment and the medium brain-computer training are high in interactivity, a multi-task training paradigm is adopted, the defects of singleness and boring in the prior art are overcome, so that the compliance of trainees is improved, the training frequency is increased, a better training effect is achieved, and ADHD patients can not only be trained in hospitals, but also can be trained in the hospitals. And training can be carried out in a home environment, so that the device is more convenient, economical and efficient. Especially for school-age children and adolescents, the training compliance of patients is greatly improved, and the falling rate in the training process is reduced.

The invention relates to a computer-implemented method for executing a storage plan for determining code for computing a neural network for use in a hardware environment (2), comprising the following steps: providing (S1) successive computing steps of the neural network, wherein the size of at least one input data block (EB1, EB2, EB3, EB4) and at least one output data block (AB1, AB2, AB3, AB4) is determined for each calculation step; -determining (S2), for each calculation step, a maximum overlap region between the input data block (EB1, EB2, EB3, EB4) and the output data block (AB1, AB2, AB3, AB4); and performing (S3) a storage planning in accordance with the maximum overlap region for each calculation step, wherein the storage regions of the respective input data blocks (EB1, EB2, EB3, EB4) and output data blocks (AB1, AB2, AB3, AB4) are specified in the working memory.

The invention discloses a data processing method and device. The method comprises the following steps: acquiring a tool calling result generated by an interaction system according to client input; inserting a first content identifier in the tool calling result to obtain working memory data of the interactive system, and storing the working memory data to a context content cache; and replacing context content data in the working memory data with a content placeholder, wherein the content placeholder is used for indicating the interactive system to recover the context content data from the context content cache according to the first content identifier. According to the scheme, the context content data is marked and cached, and the context information is quickly recovered by using the first content identifier in the subsequent calling, so that efficient memory management and information reuse of the interaction system in multiple rounds of interaction are realized, the response speed is effectively improved, the repeated calculation amount is reduced, and the context consistency and the context integrity are ensured.