236 results about "Inference system" patented technology

The invention belongs to the technical field of artificial intelligence, and relates to an artificial intelligence-based electric energy quality disturbance identification and positioning method, which comprises the steps of constructing an electric energy quality disturbance signal data set, performing segmented preprocessing on electric energy quality disturbance voltage data, enhancing time-frequency joint features and encoding disturbance sensitive areas. And constructing a deep learning model for power quality disturbance identification and positioning, and identifying and positioning the power quality disturbance. According to the invention, through adaptive denoising processing, boundary detection and multi-resolution time-frequency feature extraction, the identification precision and positioning precision of power quality disturbance are significantly improved; self-adaptive wavelet denoising and dynamic segmentation are combined, noise interference is effectively suppressed, and the edge characteristics of voltage sudden change points are kept; according to the dual-task sharing network, disturbance identification and positioning tasks are cooperatively optimized, so that the network can consider disturbance classification and time positioning at the same time; and through Bayesian reasoning, the system can output confidence estimation, provides credibility quantification of identification and positioning results, and effectively improves the reliability of the system.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention relates to the technical field of network security, and provides an APT attack traceability and path restoration method and system, attack path validity is verified through a dynamic causal element path generator in combination with an anti-factual adversarial network, and fine-grained entity modeling is realized through a hierarchical multi-modal entity inference system. A causal attention mechanism is optimized to improve association analysis precision, a federal incremental learning framework is constructed to realize dynamic updating and privacy protection, and the problems of poor rule adaptability, coarse granularity of entity modeling, insufficient causal association distinguishing and low calculation efficiency in the traditional technology are effectively solved. The method has the technical effects of dynamically generating an effective attack path, improving attack traceability accuracy, reducing calculation overhead and enhancing privacy protection.

Various examples, systems, and methods are disclosed relating to an agentic curation pipeline. One system can process questions and other inquiries about video content by using a combination of models and stored information. The system can receive a query related to an event in a video, selects relevant portions of the video using embeddings, and apply the selected video data and a related sub-query to a video model. The output from the video model can be used by a language model, along with stored context, to generate an answer to the original query. The system can returns the answer to the requester.

The invention belongs to the technical field of intelligent question-answering systems, and particularly relates to an intelligent question-answering inference system based on a knowledge graph, which is characterized in that firstly, a knowledge graph construction module fuses multi-source data to generate a structured graph, and after a user inputs a natural language question, a question-answering analysis module completes intention classification and entity disambiguation and converts the question into structured query; an inference engine module fuses symbol rules and graph neural network inference through a hybrid inference sub-module, and a dynamic weight adjustment sub-module optimizes weights according to errors and attenuation factors to generate an inference result; the knowledge updating module incrementally updates the atlas in real time and detects conflicts, the interactive interface module visually presents a result, and the evaluation optimization module iteratively optimizes parameters in combination with offline evaluation and online feedback. The whole process is from user question asking to result output, accurate reasoning and continuous performance improvement are achieved, and multi-field question and answer requirements are met.

The invention relates to the technical field of network security and data processing, and discloses a violation short message recognition method and system based on deep semantic understanding, and the method comprises the steps: firstly cleaning an original short message, generating a mixed embedding vector through characters, sub-words and pinyin, and carrying out the recognition of the violation short message; then processing through a double-layer detection engine, wherein the first layer utilizes rules and a lightweight model for rapid preliminary screening; in the second layer, for suspected samples, a double-tower fusion neural network architecture is adopted, local and global features are combined, fusion is carried out through a gating unit, and a large language model is input to carry out deep semantic reasoning. The system executes strategies such as interception or flow limiting according to the risk score, and realizes model iteration through a dynamic knowledge base and incremental learning. According to the method, the resource consumption and the detection precision are balanced through the layered architecture, the antagonistic variants are effectively identified by utilizing multi-dimensional feature fusion, and the method has the adaptive evolution capability for a novel violation mode.

The invention discloses an industrial equipment fault inference system based on a knowledge graph, and the system comprises a knowledge graph construction module, a fault data collection module, an inference analysis module and a response processing module. The entity extraction unit extracts equipment components, fault types and maintenance record entities from an industrial equipment operation document, equipment manual unstructured data supplementation attributes are integrated, the attributes and association weights are marked, and the relationship construction unit establishes a fault causal relationship between the entities and a component association relationship to form a multi-level knowledge network; a knowledge verification unit verifies entity attribute consistency and relation rationality, a dynamic updating unit receives data updating nodes and relation strength of each unit and receives feedback data optimization weights, and in a fault data acquisition module, a real-time monitoring unit acquires operation parameters and state signals and associates equipment identifiers.

The invention belongs to the technical field of thinking chain reasoning, and particularly relates to a multi-modal reasoning method and system based on error attribution. The reasoning method comprises the following steps: on the basis of a current modal fusion weight, performing modal fusion on each piece of initial information in an initial information set, and then generating a thinking chain; after a reasoning dependency graph is constructed based on the thinking chain, check points are selected in the reasoning dependency graph; based on consistency, factuality and logicality, performing error possibility scoring on each check point, and if the error possibility scores of all check points in the current thinking chain are below a set threshold, outputting the current thinking chain; otherwise, marking the check points of which the error possibility scores exceed a set threshold value as error nodes; calculating relative contribution strength of different modes to error nodes; and on the basis of the relative contribution strength, updating the modal fusion weight, and regenerating the thinking chain. According to the invention, the accuracy of the reasoning result and the stability of the accuracy can be improved.

A variety of smart inference systems and robotic control devices are disclosed. A device networking system interconnects mobile devices, robotic devices, and others. A robotic controller secures and manipulates a controlled device user interface. A smart device system interprets data based on a plurality of models operating on a plurality of runtimes based on a plurality7 of capabilities. A smart payment processor system assigns payments accounts automatically as a function of a semantic matching between one or more inferred semantic identities. A robotic emulation device captures and analyzes a video signal from a target device and transmits manipulation signals emulating a peripheral input device. A system of carts are physically coupled in a charging configuration. A conveyor is configured to couple with another conveyor and having sensors to detect weight, size, location or other aspects of conveyed items, and implementing semantic analysis for management and augmentation.

The invention provides an inference system and method of a large language model based on multi-chip parallel computing, and relates to the technical field of artificial intelligence. The system comprises a pre-calculation module used for processing input instruction information to generate to-be-reasoned data, and the to-be-reasoned data is in a matrix form; the expert parallel module is used for sending the to-be-reasoned data to accelerator chips in the expert parallel module and determining sub-reasoning data processed by the activation expert units corresponding to the accelerator chips respectively, so that the activation expert units carry out calculation based on the corresponding sub-reasoning data and complete parallel calculation result data is determined. The input data is broadcasted to all the accelerator chips, each accelerator chip selects the corresponding input data for calculation according to the set activation expert unit, the same complete calculation result is obtained through global protocol operation among all the accelerator chips, and the overall operation performance and efficiency are improved.

The invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, and relates to the technical field of intelligent manufacturing and artificial intelligence. Natural language output of a large language model is converted into rules, facts and ontology fragments which can be directly consumed by an inference engine through knowledge obtaining and compiling, continuous increment updating of cross-domain knowledge is achieved in cooperation with metadata with sources and timestamps, and the limitation that a traditional static knowledge base is difficult to cover dynamic faults is overcome; secondly, a blackboard and agenda mechanism is used as a cooperative carrier, intermediate assertions of intelligent agents such as vibration, circuits and logs are published and subscribed in a structured mode, and a conflict resolution and consistency verification module carries out unified judgment according to specificity, time freshness and source credibility; therefore, delay and uncertain accumulation caused by long-chain natural language dialogues are avoided in a strong real-time scene.

The invention relates to the technical field of artificial intelligence, in particular to a browser-side AI reasoning system and method based on TensorFlow.js. The browser-side AI reasoning system and method based on TensorFlow.js comprises the following steps of model loading and initialization, equipment performance detection and model selection, input data collection and preprocessing, reasoning task scheduling and execution, result analysis output and visual export. Model cache updating maintenance and performance monitoring dynamic adjustment and optimization are carried out; the method has the beneficial effects that a set of complete front-end AI reasoning flow control mechanism is provided; realizing model dynamic adaptation based on equipment performance; various input forms are supported; a main thread is prevented from being blocked by utilizing multi-thread scheduling; a GPU acceleration and mixing precision calculation mechanism is introduced to improve the reasoning efficiency; establishing a model caching mechanism to improve the loading speed and the offline availability; performance monitoring and dynamic tuning functions are provided, and reasoning stability is ensured; providing a structured result output and visual display interface; a model version control and background hot update mechanism is realized; and the security, compatibility and expansibility of the system are improved.

The invention discloses a distributed intelligent deduction system and method for multi-source data fusion and dynamic scheduling, and relates to the technical field of deduction simulation. The deduction preprocessing module is used for carrying out task stage division on military scenarios and setting decision points, generating branch tasks according to tactical rules and carrying out model classification; the deduction management module is used for promoting deduction from an initial state based on a simulation engine, generating branch tasks at a decision point according to a real-time state, distributing initial weight coefficients by combining historical efficiency parameters and model types, and realizing parallel deduction and dynamic model switching; the deduction optimization module continuously updates a weight coefficient through a branch efficiency parameter, screens an optimal deduction path and iterates a state snapshot in a closed loop; the resource regulation and control module collects data in real time to support efficiency evaluation, resource distribution is dynamically optimized according to task loads, the deduction efficiency and accuracy are remarkably improved, and intelligent resource scheduling and system self-adaptive optimization are achieved.

An inference system applies a machine-learning transformer model to a batch of requests with variable input length or variable target length or variable internal sate length by selectively batching a subset of operations in the transformer model but processing requests in the batch individually for a subset of operations in the transformer model. In one embodiment, the operation to be processed individually is an attention operation of an encoder or a decoder of the transformer model. By selective batching, the inference system can allow batching operations to be performed for a batch of requests with variable input or target length or internal state length to utilize the parallel computation capabilities of hardware accelerators while preventing unnecessary computations that occur for workarounds that restrain the data of a batch of requests to a same length.

The invention relates to a cloud edge multi-unmanned aerial vehicle cooperative resource optimization method assisted by a large language model, and belongs to the technical field of unmanned aerial vehicle communication, and the method comprises the following steps: S1, constructing an edge-cloud unmanned aerial vehicle cooperative reasoning system; s2, establishing a joint optimization model for discriminating gain maximization; s3, deploying a large language model at a cloud node, and generating a global strategy through a planner, a memory bank and an reflection evaluator; s4, deploying a deep reinforcement learning model at each edge node, and executing real-time optimization according to a global strategy and local observation data; s5, a collaborative feedback mechanism is established, the edge node feeds back an execution result to the cloud node, the cloud node updates a global strategy according to the feedback result, and the edge node adjusts real-time optimization parameters according to the updated global strategy; and S6, adopting an actor-commentator resource allocation algorithm for dynamic knowledge flow collaborative optimization, and realizing collaborative optimization through a distributed sensing and centralized decision framework.

The invention relates to the technical field of soil pollution investigation and environment monitoring, and discloses a soil pollution low-carbon investigation system, which comprises a data acquisition and preprocessing module for acquiring site multi-source heterogeneous data and performing data quality evaluation and preprocessing; the feature extraction and fusion module is used for performing feature extraction and feature fusion; the pollution prediction and uncertainty quantification module is used for carrying out full-field pollution prediction and uncertainty quantification; the sampling optimization module is used for carrying out sampling value evaluation and gradient analysis; the carbon footprint monitoring module is used for carrying out carbon emission calculation based on an identification result and carrying out real-time monitoring on the carbon footprint; the path planning module is used for planning a sampling path and deploying an edge reasoning system to carry out real-time dynamic path decision; the scheme decision-making module is used for generating investigation candidate schemes and selecting an optimal scheme according to decision-making rules; according to the method, a multi-scale depth feature extraction and cross-modal fusion technology is adopted, and the collaborative value of multi-source heterogeneous data is fully mined.

The invention discloses a multi-agent cooperation method, system and device based on shared memory data, and a medium, mainly relates to the technical field of multi-agents, and aims to solve the problem that a traditional reasoning system cannot recognize semantic similarity between requests and cannot sense a context relationship of agent tasks, so that the efficiency is improved. The intelligent agent system is often deployed at multiple nodes, and the cache result cannot be shared across the nodes. Comprising the steps of obtaining an output result and an intermediate result generated by each path node; caching the semantic signature and the output result to a preset result cache layer; caching an intermediate result generated by each path node as context information to a preset context cache layer; and according to a preset time window, detecting inference frequencies of different semantic signatures generated by all the agents, and according to the inference frequencies, adjusting storage time of output results corresponding to the semantic signatures in a preset result cache layer.

The invention belongs to the technical field of cloud side-end collaborative reasoning, and discloses a cloud side-end model reasoning joint optimization method under air-ground collaboration. And designing a network architecture modeling module, an inference performance modeling module, a delay modeling module and a joint optimization module. An air-ground cooperative reasoning system is constructed, a thinking chain prompt mechanism is introduced to perform modeling on reasoning accuracy, and unmanned aerial vehicle selection, language model selection, reasoning task unloading decision and unmanned aerial vehicle trajectory are jointly optimized to minimize the total cost of the system. A continuous convex approximation method is adopted to optimize the trajectory of the unmanned aerial vehicle, and a multi-agent reinforcement learning method is combined to carry out distributed decision making and centralized training, so that low-delay and high-precision collaborative reasoning service is realized. According to the method provided by the invention, communication, calculation and resource reasoning are effectively coordinated in a cloud edge-end coordination scene with dynamic change of user requests and various task types, the overall service quality and resource utilization efficiency of the system are remarkably improved, and the method is superior to other existing methods.

Systems and methods for multi-turn collaboration for machine-learned inference are provided. A method can include receiving, by a computing system comprising one or more computing devices, a first input. The method can include generating, by the computing system based on the first input, structured data indicative of one or more target output properties for a machine-learned inference operation. The method can include receiving, by the computing system, one or more second inputs indicative of one or more changes to the one or more target output properties. The method can include updating, by the computing system, the structured data indicative of the one or more target output properties based on the second input to generate updated structured data. The method can include generating, by the computing system using a machine-learned model and based at least in part on the updated structured data, an output.

The invention relates to a method for realizing multi-modal large-model fine-grained privacy grading protection in an edge-cloud collaborative inference system, belongs to the technical field of large-model data security and privacy protection, and aims to solve the problems that privacy grading is rough, inference intermediate features are easy to leak and precision is damaged by protection measures in the prior art. According to the method, multi-modal data such as images and texts are received by edge computing nodes, deep semantic analysis and fine-grained segmentation are performed by using a local pre-trained multi-modal large model, and privacy scores are calculated to divide high and low privacy levels; uploading low-privacy data to a cloud computing center, and locally processing high-privacy data; and selectively shielding a middle layer feature map F generated by the edge based on the correlation degree and the contribution degree, then completing local reasoning, and uploading a result to be fused with the cloud. According to the method, precise fine-grained protection is achieved, the sensitive information leakage risk is remarkably reduced, and meanwhile the model reasoning precision and the system real-time performance are kept to the maximum degree.

The invention relates to an industrial automation optical detection method and device based on a YOLO network. The method mainly comprises the steps of image acquisition, wavelet decomposition and filtering preprocessing, lightweight YOLOv8 detection model reasoning, confidence coefficient triggered secondary detection mechanism, multi-dimensional feature classification, rule engine judgment and PLC control linkage. The small target detection precision is improved by introducing a CBAM attention module and a BiFPN structure, and low-power-consumption real-time reasoning is realized on embedded equipment in combination with an ONNX model. The system has the functions of dynamic threshold judgment, feature enhancement post-processing, CPK statistical analysis and automatic early warning. Compared with the prior art, the micro defect detection precision and the model reasoning efficiency are improved, and the method is suitable for high-speed production lines, embedded detection equipment and industrial closed-loop quality control scenes.

The invention provides a multi-field collaborative modeling and intelligent inference system oriented to battery running state evolution. The method comprises the following steps: acquiring electrical state data and the like acquired by a target battery in a test period to form multi-physical field original state data; and based on a preset state analysis window, processing the multi-physics field original state data, and generating a multi-physics field coupling state response sequence. And constructing a battery state evolution constraint description parameter according to the multi-physics field coupling state response sequence to obtain a state evolution consistency evaluation result. And performing adaptive adjustment on the battery state evolution constraint description parameters according to the state evolution consistency evaluation result to form an updated state evolution constraint parameter set, and performing inference calculation on the subsequent state evolution trend of the battery based on the constraint parameter set to generate a battery state evolution inference result. According to the method, the reliability and the stability of battery state evolution inference under complex working conditions are improved by constructing a self-adaptive inference mechanism driven by simulation constraints.

This invention discloses a method and system for scheduling large-scale model inference requests based on global state awareness, relating to the technical field of large language model inference systems. This method constructs a network and protocol layer, a session scheduling layer, and an inference engine layer within a single process. Requests are received and session objects are created via a network listening thread. Enqueueing and timeout cleanup are completed using a double-buffered queue and a polling thread. Worker threads claim sessions based on a global state-aware scheduling algorithm, complete protocol parsing and token encoding within the same process, and submit them to the inference engine. The inference engine performs continuous batch processing scheduling and KV cache reuse. Finally, the token is decoded, written back to the client, and session resources are released. This invention, through a co-process fusion architecture and a global state-aware scheduling algorithm, enables the service layer to obtain the internal state of the inference engine in real time and optimize scheduling decisions accordingly, eliminating cross-layer boundary overhead and information asymmetry problems.

The application discloses a kind of inference system and method for sequence recommendation system member inference attack, including label data generation module, difference feature construction module and attack model training module;Step 1, label data generation is carried out;Step 2, the difference feature construction of member and non-member is carried out;Step 3, the training of attack model is carried out.Compared with prior art, the application can guarantee the data privacy of user in a wider range of scenarios;Fill in the blank of member inference attack in more stringent scenarios;Significantly improve the attack inference effect.

The invention discloses a real-time closed-loop regulation and control method and system for the forming process of a stainless steel welded pipe. The method comprises the steps that multi-source heterogeneous data including a pipe shape image, a laser ranging sequence, temperature field distribution and roller pressure time sequence data in the forming process are synchronously collected through a distributed multi-source sensor network; performing space-time alignment and feature level fusion on the multi-source heterogeneous data, and constructing a multi-dimensional dynamic digital twinborn body in the forming process; on the basis of the multi-dimensional dynamic digital twins, an online rolling prediction model is adopted to deduce the development trend of weld forming quality and pipe diameter size deviation in real time; and according to the development trend, a cooperative regulation and control instruction set of the roller gap and the welding power is generated through a self-adaptive fuzzy inference system. By means of the embodiment of the invention, multivariable look-ahead perception and intelligent collaborative closed-loop regulation and control of technological parameters in the stainless steel welded pipe forming process can be achieved, and the stability and consistency of the forming quality are improved.

The disclosure leverages an endpoint management system, a local small foundation model, and a centralized foundation model to form an AI hybrid distributed inference system. The AI hybrid distributed inference system is used to provide directions based on data inputted from a system including factories, data centers, or other businesses. The system is hybrid because it leverages discriminative AI models and generative AI models. The system is distributed because the endpoint management system and the local small foundation model are located on an edge site where the data is generated, and the centralized large foundation system is located in a centralized system that is connected to a plurality of other edge sites. The endpoint management system interacts with the input data first and is used for simple issues. The local small foundation system is used for complicated issues. The centralized large foundation system is used for the most complicated issues.

The application discloses a PID control optimization method for a water turbine regulating system based on a PINN, which comprises the following steps: constructing and training a physical information neural network; deploying the trained physical information neural network to an online inference system, and simultaneously, equipping a PID controller as a main control loop of the water turbine regulating system; the PID controller outputs a real control signal, and performs state evolution based on a multi-state mechanism model and outputs a real-time state feedback signal; the real-time state feedback signal is input into the physical information neural network for state recursive prediction and output of a predicted state feedback signal; the real-time state feedback signal and the predicted state feedback signal are continuously compared, and the accuracy and reliability of the physical information neural network under the current operating condition are evaluated; a bypass verification loop is constructed based on the evaluation result, and the main control loop and the bypass verification loop jointly form a closed-loop intelligent control system. The application improves the control accuracy and operation reliability of the water turbine regulating system under nonlinear and time-varying operating conditions.

The application discloses a kind of geographical distribution type heterogeneous scene in big model inference architecture design method and system, first, the environment of big model inference system comprising multiple physical location computing nodes, big model inference task and its performance constraint are modeled, construct big model inference node architecture optimization model, using the heuristic node selection strategy based on cost benefit score Candidate hardware topology set satisfying basic constraint is screened;Second, based on dynamic programming idea, model parallel decision is executed to each candidate topology, using the hybrid parallel strategy of intra-node tensor parallel, inter-node pipelined parallel, determine optimal model layer distribution and tensor parallel degree;Finally, verify whether the inference delay of candidate topology meets the performance constraint, screen feasible scheme and select the inference architecture with the lowest total cost.The application can improve the flexibility of heterogeneous resource utilization in distributed environment, while ensuring the real-time performance requirements of big model inference, reduce the overall deployment cost of system.

Provided in the present application are a multi-agent reasoning system, a data reasoning method, apparatus and device, and a product. The method is applied to a multi-agent reasoning system comprising a plurality of agents, and comprises: a first agent performing key information extraction on reasoning reference data on the basis of a reasoning problem, so as to obtain reasoning key information (S101); a second agent generating a reasoning path on the basis of the reasoning problem and the reasoning key information (S102), wherein the reasoning path comprises prompt information for performing reasoning on the basis of the reasoning key information, so as to solve the reasoning problem; and a third agent performing integrated reasoning on the basis of the reasoning problem and the reasoning path, so as to generate a reasoning result corresponding to the reasoning problem (S103), wherein the reasoning result comprises a reasoning process and / or a reasoning conclusion. The method can improve the flexibility and reasoning efficiency of data reasoning.

This invention discloses a latency optimization method for end-edge collaborative inference systems based on fluid antenna assistance (FA). The method first establishes an FA-assisted system model and constructs DNN partitioning collaboration, IFD transmission, and inference accuracy models based on the system model to determine the total inference latency, including local inference, IFD wireless transmission, and edge inference delays. Then, a joint optimization problem is constructed with the objective of minimizing the total inference latency, jointly optimizing parameters such as DNN partitioning points, transmit power of each device, and computational resource allocation. The joint optimization problem must satisfy inference accuracy and energy consumption constraints. Finally, the problem is decomposed into four sub-problems, and the block coordinate descent method is used to alternately optimize until convergence, completing the latency optimization of the end-edge collaborative inference system. This invention is the first to integrate FA and collaborative inference frameworks, achieving multi-dimensional parameter joint optimization, effectively reducing the total inference latency, and demonstrating significant performance advantages in scenarios with high accuracy thresholds and adverse channel conditions.