1159 results about "Continual learning" patented technology

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a universal multi-modal key-value subsystem for sharing partial computations, implements hybrid placement strategies for dynamic memory management, and incorporates quantum-resistant secure enclaves. The architecture integrates hardware acceleration through GPU-FPGA hybrid caching and neuromorphic processors, applies adaptive energy and thermal management across hardware generations, and implements autonomous flash resource orchestration with multi-dimensional wear management. The system orchestrates tensor workflows using hierarchical scheduling, enables cross-agent collaboration with privacy preservation, and supports continuous learning without catastrophic forgetting. This integration delivers unprecedented computational efficiency and security in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.

The invention discloses a user behavior prediction system and method based on multi-modal data fusion, and particularly relates to the field of user behavior prediction, and the system comprises a multi-modal data collection module, a preprocessing and feature extraction module, a cross-modal fusion module, a user behavior prediction module, and a model optimization and feedback module. According to the system, multi-dimensional original data such as visual sense, auditory sense, text, physiological signals and environment context of a user are acquired in real time through a multi-modal data acquisition module; then, deep networks such as ResNet, VGGish and BERT are adopted to extract high-dimensional feature vectors of all modals, and contribution weights of features of different modals are dynamically learned through an attention mechanism; and finally, based on a time sequence model of Transform and LSTM, analyzing fusion features, and outputting probability distribution of future behavior intentions. And parameter joint optimization and continuous learning are realized through a multi-objective loss function and end-to-end back propagation.

The invention relates to the technical field of unmanned aerial vehicle remote sensing and artificial intelligence crossing, in particular to an intelligent monitoring system and method based on multi-modal remote sensing data and deep learning, and the system comprises an unmanned aerial vehicle cluster networking subsystem, a mixed feature matching subsystem and a multi-modal fusion and continuous learning subsystem. The method comprises the following steps: constructing an unmanned aerial vehicle cluster carrying a multispectral sensor and a laser radar LiDAR, and carrying out wireless networking among a plurality of unmanned aerial vehicles to realize sharing of acquired images; feature point extraction is carried out on collected images of different time phases, the extracted feature points are input into the generative adversarial network, and the feature points are matched; and receiving the matched collected images, dynamically fusing data of visible light, infrared and other multi-modal images through a space-time attention mechanism, and realizing high-precision target recognition and dynamic environment self-adaption in a small sample scene. According to the method, unmanned aerial vehicle multi-source remote sensing data acquisition, feature fusion and deep reinforcement learning are combined, and the method is used for intelligently monitoring a dynamic environment.

The invention discloses a green building intelligent lighting and energy collaborative optimization system based on multi-source data fusion. According to the system, firstly, multi-source information such as indoor and outdoor illumination, weather, personnel occupation and electricity price is subjected to weighted fusion through dynamic confidence, and an accurate real-time environment state is constructed; then, model predictive control is combined with a multi-objective optimization strategy, and a sunshade device, intelligent lighting and a heating ventilation air conditioner are cooperatively adjusted, so that the comprehensive energy consumption or operation cost is minimized on the premise that the indoor illumination and temperature comfort requirements are met; besides, the self-adaptive module continuously learns and adjusts the thermal characteristics and the use mode of the building through online model parameter correction and occupancy probability prediction, and it is ensured that the energy-saving effect is stable for a long time; the system drives each execution device through a standard building self-control interface, is easy to integrate in a newly built or reformed project, and can significantly improve the energy utilization efficiency and the indoor environment quality.

The invention belongs to the field of network security, and particularly discloses a network defense agent system based on a large language model. Through the design of the sensing layer, the decision analysis layer and the action execution layer, comprehensive protection of network threats is realized. The sensing layer is responsible for collecting original information from multiple channels and converting the original information into standardized data; the decision analysis layer performs modeling and threat reasoning on attack behaviors, evaluates a risk level and predicts subsequent actions; and the action execution layer specifically executes defense operation according to the defense strategy scheme output by the decision analysis layer. In addition, the application also constructs a data set oriented to attack and defense confrontation, records a complete attack sequence, defense response and effect evaluation thereof, and provides a reliable basis for continuous learning of defense agents. Experimental results show that the framework provided by the invention is superior to the traditional method in the aspects of attack detection accuracy, attack chain identification and defense strategy generation, and has stronger adaptability and real-time response capability.

Aiming at the problems of insufficient language understanding depth, weak personalized dialogue generation ability, lack of continuous learning and long-term user state modeling and the like in the current emotion recognition and psychological intervention technology, the invention provides a user emotion recognition and psychological intervention method combined with a large language model (LLM). According to the method, the potential emotional state is identified by analyzing free text information input by a user by utilizing the powerful capabilities of a large language model in the aspects of natural language understanding, emotional modeling and text generation; constructing a multi-round dialogue context, and reasoning a psychological change trend of the user; in combination with a psychological knowledge base, personalized and mild psychological intervention dialogue content with a dredging effect is generated. The system supports recognition and classification of various emotional states such as depression, anxiety and alonity, is suitable for various interaction scenes (such as APPs, webpages and social robots), and can greatly improve the precision of emotion recognition and the timeliness and effectiveness of psychological intervention. The emotion recognition and psychological intervention method based on the large language model provides solid technical support for constructing an intelligent, continuous and personalized psychological health management system, and has wide application prospects and profound social significance.

The invention discloses a talent matching method and system based on artificial intelligence, and aims to improve human resource configuration efficiency and decision intelligence. The method comprises the following steps: collecting talent and demand data in multiple channels, especially unstructured communication data including interview records and work communication records; processing data through technologies such as multi-mode resume analysis, extracting features and fusing the features; an enterprise talent knowledge base which is used for continuous learning and dynamic maintenance based on a system operation result and multi-source feedback and integrates structured and unstructured data is constructed; based on the natural language query of the user, providing intelligent question answering and decision support by using a retrieval enhancement generation model connected with the specific enterprise talent knowledge base; an advanced deep learning algorithm is adopted, a knowledge base is combined to carry out man-post matching and generate recommendation of context perception, and self-optimization of a matching strategy is realized through mechanisms such as reinforcement learning and the like. According to the invention, accurate and dynamic talent matching and intelligent decision making can be realized.

The invention relates to the field of civil engineering and artificial intelligence, in particular to a transfer learning optimization system and method for predicting the early-age strength of concrete, and the system comprises a multi-scale data sensing module, a physical information constrained deep neural network module, a formula adaptive transfer learning module, a Bayesian optimization prediction module and a federated learning feedback module. The whole process from data collection to model optimization is achieved, through the system, the concrete strength prediction errors of the extremely early age and the standard age are remarkably reduced to + / -5% and + / -3% respectively, meanwhile, the number of concrete test pieces for testing is reduced by 85%, the material and labor cost is greatly saved, and the method not only improves the prediction precision, but also reduces the construction cost. And through continuous learning and feedback, the prediction model is continuously optimized, and an efficient and economical concrete strength prediction solution is provided for actual engineering.

The invention discloses a tough city resource allocation method and system based on a block chain and edge computing, and relates to the technical field of city resource allocation, a federal protocol is utilized to exchange and shear a gradient abstract synchronization model version, then a prediction abstract containing fingerprints is written into a permission chain, and a time sequence is solidified through Byzantine consensus; an on-chain smart contract executes adaptive threshold evaluation based on sliding window prediction to generate a deployment intention with a verifiable path, the intention is sent to an execution subsystem through a side chain message bus in a low-delay mode, and cross-department resource rearrangement is completed through rolling optimization of an adaptive decision driver; an execution receipt is mapped into an incremental patch and written back to a local state tensor, a node trust weight is dynamically adjusted, and when the coverage degree and the weight rising range meet a triggering strategy, differential shearing training is started, and a new structure fingerprint and a model signature are generated; and efficient, credible and dynamic allocation and sustainable learning synchronous evolution of urban public resources in an emergency scene can be ensured.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a blood oxygen change monitoring algorithm fused with dynamic Bayesian modeling, which comprises the following steps: quantizing signal uncertainty by calculating entropy of pulse waveform harmonic energy distribution; and on the basis of the entropy value, driving a dynamic Bayesian network to carry out confidence coefficient evaluation and weighting processing on the blood oxygen estimation model, and finally outputting a decision pair containing a blood oxygen estimation value and the confidence coefficient thereof. According to the method, the signal uncertainty is converted into a computable entropy index, so that the system can autonomously distinguish real physiological changes and measurement noise, the problem of misjudgment caused by motion interference in traditional blood oxygen monitoring is avoided, meanwhile, non-inductive personalized calibration is achieved by utilizing the continuous learning ability of the Bayesian network, and the accuracy of the system is improved. And the reliability and the practicability of the medical wearable equipment are remarkably improved.

The invention relates to a long-range task-oriented VLA model method for a humanoid robot, which comprises the following steps of: S01, analyzing a natural language instruction through a space-time semantic analyzer to generate an atomic operation sequence with a space-time dependency relationship; s02, maintaining a task state machine by using a dynamic memory network, and tracking the task execution progress in real time; s03, integrating vision, language and sensor data through a multi-modal perception fusion engine; s04, calling a predefined action primitive based on an adaptive execution system and optimizing a motion track; and S05, performing online updating and optimization on the model through a continuous learning mechanism. According to the method, a natural language instruction is analyzed into a structured task sequence with space-time dependence through a space-time semantic analyzer, an execution sequence and preconditions are defined, the semantic understanding ability and the structuring degree of task planning are improved, and task decomposition and replanning in a dynamic environment are supported; according to the method, the LSTM and the knowledge graph are combined, and the task state is maintained in real time.

The invention discloses a log abnormal behavior detection method and system based on periodic pattern mining and incremental learning, and relates to the technical field of system abnormality monitoring. The method comprises the steps of obtaining original log data and executing preprocessing operation; main periodic frequency components are extracted through time frequency analysis to form a periodic set, a periodic stable part sequence and a transient change part sequence are divided, and a periodic modeling mechanism and a transient modeling mechanism are used for modeling; a joint prediction model is constructed, Monte Carlo Dropout is introduced to estimate uncertainty, and Bayesian weighting is adopted to generate a prediction value; abnormity is judged through a periodic residual error, a transient residual error and an overall residual error, and an abnormal causal path is analyzed and identified in combination with transfer entropy; a memory sample driven playback and distillation mechanism is adopted to execute incremental training, and modeling structure parameters are dynamically adjusted. The method has the capabilities of periodic rule modeling, unsteady behavior expression, prediction fusion, abnormal causal identification and continuous model learning.

The invention discloses an industrial time sequence prediction method based on adaptive continuous learning. The method comprises the following steps: firstly, dividing a non-stationary industrial time series data set to obtain a plurality of domains with the maximum distribution difference; different time domains are then modeled in sequence, and an improved empirical playback (DER + +) method is used to avoid catastrophic forgetting of previously accumulated knowledge. Meanwhile, a soft sample buffer area is introduced to promote memory and learning of key modes in the current field. And finally, the time-sensitive activation function TimeRelu enables the time convolutional network (TCN) to have a time evolution property, and the generalization ability of the prediction model is enhanced. According to the method, the continuous learning normal form is introduced into the time sequence prediction task, the limitations of huge resource overhead of traditional cumulative training, disastrous forgetting of an incremental learning mode and the like are overcome, and the method has theoretical and practical significance on industrial time sequence prediction.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention relates to the technical field of battery thermal management, and provides a dynamic thermal management method and system for a battery module, and the method comprises the following steps: carrying out the local environment heat dissipation capability evaluation when a preset triggering condition is satisfied, and generating an evaluation result; generating a heat dissipation adjustment parameter of the heat dissipation system based on the evaluation result and the current thermal state of the local battery; carrying out data monitoring and recording, and generating reported data information; the reported data information comprises data related to local environment heat dissipation capability evaluation, evaluation result data, heat dissipation adjustment parameter data of a heat dissipation system and battery thermal state change data after the heat dissipation system executes adjustment; and uploading the reported data information to a cloud platform, so that the cloud platform executes continuous learning and optimization operation of the thermal management strategy based on the reported data information. The method has the advantages that the adaptability and safety of heat management are improved, and continuous optimization of a cloud strategy is supported.

The invention discloses an employment and entrepreneurship support system based on artificial intelligence, relates to the technical field of occupational planning, and aims to solve the problem that a traditional support mode is insufficient in individuation and accuracy. The system comprises a multi-modal data acquisition module used for acquiring facial expressions, whole body dynamics and voice data of a user in at least 30 minutes of video dialogue in real time; a dialogue text is processed through a large language model based on a Transform architecture, continuous time sequence modeling is carried out on multi-modal dynamic behaviors by applying a liquid time constant network, and deep fusion reasoning is carried out in combination with multiple groups of multi-head Transform attention mechanisms. The core analyzes the real thought, psychological state, behavior pattern and core ability of the user through consistency verification, generates a structured dynamic user insight abstract, and customizes personalized vocational development or entrepreneurship planning according to the structured dynamic user insight abstract. According to the method, the potential of the user can be deeply informed, high-precision personalized planning is provided, the decision-making quality and success rate are improved, and the method has dynamic adaptation and continuous learning capabilities.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention relates to the field of intelligent automatic driving, in particular to a knowledge-driven end-to-end automatic driving method based on a sparse expert mechanism and a diffusion model. Comprising the following steps: S1, sensing information processing and state coding; s2, sparse expert module construction and multi-task training; constructing a sparse expert module composed of a plurality of experts, and obtaining a reusable driving skill through multi-task behavior cloning training; s3, generating a diffusion strategy network and an action sequence; and on the basis of a diffusion model, a future multi-step control action sequence is generated from the current state condition, and a continuous and stable driving decision is formed. And S4, a continuous learning and task migration mechanism. According to the method, a combinable and explainable modular driving knowledge structure is constructed, so that the strategy modeling capability is remarkably improved; a diffusion generation mechanism effectively improves the smoothness and stability of the decision process; and a continuous learning and task migration mechanism of structural decoupling improves the long-term adaptability and deployment efficiency of the system.

The invention provides a self-adaptive color depth optimization method for a liquid crystal display screen, which comprises the following steps of: acquiring manual adjustment operations of a user on brightness, contrast and color parameters of the liquid crystal display screen in different use scenes to form a user preference data set; according to the user preference data set, a data classification method is adopted to classify manual adjustment operations in different use scenes, and features of ambient light and content types are extracted to determine preference feature distribution of a user in a specific scene, and a personalized color preference model is constructed; preliminary calibration is carried out in combination with a dynamic balance algorithm, and display parameters are optimized through a continuous learning mechanism and secondary calibration. The problem that in the prior art, color adjustment of a liquid crystal display screen lacks individuation and scene self-adaption capacity is solved, continuous self-adaption of the display effect is achieved, manual intervention of a user is reduced, and the individuation degree and comfort degree of visual experience are improved.

The invention discloses a privacy protection federated basic model continuous learning method in a multi-source new energy main body collaborative scheduling scene, and the method comprises a device, a system and a medium, and aims to solve the security defect of user power data transmission, reduce the communication overhead in the transmission process and improve the generalization performance of a federated learning model. Differential privacy protection is carried out on power data of a new energy main body power terminal device, and a homomorphic encryption technology is adopted, so that safe sharing of data and power dispatching optimization are completed under the condition that user privacy is not leaked.

The invention relates to the technical field of electric bicycles, and discloses a data-driven electric bicycle battery fire hazard detection and protection method, which comprises the following steps: S1, multi-modal data acquisition and fusion, S2, data preprocessing and feature extraction, S3, data-driven intelligent detection: classifying normal and abnormal states through a supervised learning model, S4, hierarchical protection strategy, and S5, data-driven intelligent detection. S5, cloud data optimization and model continuous learning; S6, model data updating; S7, intelligent linkage and long-term optimization; S8, cloud risk data acquisition; and low, middle and high three-level protection mechanisms are adopted. Through dynamic risk assessment, charging and discharging power is limited in a medium risk, a charger is automatically disconnected, a user is prompted to stop operation, active protection is triggered in a high risk, and sound-light alarm and remote notification are triggered at the same time, so that accidents are prevented from further spreading.

The invention discloses a linkage control scene building method and system for smart home, and belongs to the technical field of smart home, and the method comprises the steps: S1, environment preparation and hardware deployment, S2, equipment configuration and protocol adaptation, S3, scene logic modeling and AI intention recognition, S4, edge side cooperative control, S5, AI model deployment and continuous learning, and S6, iterative optimization. On the basis of realizing intelligent home linkage control, network dependence can be reduced, and interaction can be simplified through AI intention recognition.

The invention relates to the technical field of grain detection, and discloses a grain broken rice rate online detection method and system based on image analysis. A grain broken rice rate online detection method based on image analysis comprises the steps of intelligent illumination control and high-speed synchronous imaging, self-adaptive image enhancement based on reinforcement learning, multi-stage parallel particle detection and segmentation, multi-modal feature deep fusion, robust classification based on ensemble learning, intelligent quality control and process optimization. And continuously learning and updating knowledge. A clear image is obtained through a multi-angle linear array camera and a stroboscopic light source, a reinforcement learning agent is adopted to carry out adaptive image enhancement, a deep learning network is used to carry out particle detection segmentation, multi-dimensional features are extracted, accurate identification is realized through an integrated classifier, and a digital twin model is established to carry out process parameter optimization. According to the invention, accurate online detection of the broken rice rate of grains can be realized in a high-speed flowing state, and the detection efficiency and accuracy are improved.

The invention discloses an information physical security defense method, system and device based on a distribution network digital twin simulation platform and a medium, and belongs to the technical field of information security and control defense of the power distribution Internet of Things, and the method comprises the steps: constructing a three-dimensional channel for interaction of a physical power grid, an information model and simulation data; on the basis of a three-dimensional channel, a three-state dynamic conversion model among a steady state, a transient state and a recovery state is constructed, and multi-state automatic switching simulation is achieved through sub-region division and parallel computing; a multi-level risk modeling system oriented to various risks is constructed based on simulation results, complex attack scenes are identified, and mapping of risk types and response paths is achieved; security risk assessment and defense strategy generation verification are completed, and intelligent collaboration and continuous learning of cross-domain defense strategies are achieved through collaborative optimization. According to the method, the construction efficiency of a complex risk scene is improved, quantitative analysis of cross-domain risks such as circuit breaker mis-tripping caused by network attacks is realized, and the limitation of traditional single-domain risk analysis is broken through.

A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time. This integrated approach offers significant advantages in data efficiency, security, and protocol flexibility, making it particularly suitable for complex, heterogeneous data environments such as IoT networks, cloud computing, and big data analytics.

The invention discloses a multi-modal data fusion-based intelligent management system for a thoracic surgery monitoring unit, belongs to the technical field of medical information and artificial intelligence, and aims to solve the limitation of an existing thoracic surgery monitoring system in the aspects of multi-modal data fusion, heterogeneous data semantic alignment and intelligent deep analysis and prediction decision. The system is characterized by comprising a multi-modal data acquisition unit, a heterogeneous data fusion and semantic alignment module, an intelligent analysis and prediction decision module, a man-machine interaction and visual presentation module and a secure storage and management module. By the adoption of the technical scheme, comprehensive multi-modal data fusion, high real-time performance, deep intelligent analysis and prospective prediction can be achieved, intelligent decision support, resource optimization, continuous learning and self-adaptive optimization are provided, and the intelligent level and patient management efficiency of the thoracic surgery intensive care unit are remarkably improved.

The invention discloses a multi-modal interaction control method for a Bluetooth knob screen, and relates to the technical field of multi-modal interaction control, and the method comprises the steps: synchronously collecting and forming a multi-modal feature vector set, and outputting a final operation instruction through a multi-modal fusion model; performing function mapping according to the final operation instruction, and identifying a control command; judging whether to resend the same instruction or adjust the instruction parameter based on the control command, and dynamically adjusting the tactile feedback parameter based on the function mapping of the current scene mode; performing low power consumption management based on the dynamically adjusted tactile feedback parameters, and triggering scene mode mapping update according to the wake-up instruction type; and optimizing the instruction output accuracy and the function mapping rule of the multi-modal fusion model based on the updated user interaction data. According to the method, deep fusion and intelligent cooperation of multiple interaction modes are realized, and accurate output of operation instructions, intelligent management of tactile feedback and adaptive updating of scene modes are achieved through dynamic function mapping, intelligent feedback adjustment and continuous learning optimization.

An AI-powered dispute triage and classification system (100) that includes: (a) a dispute input module configured to receive dispute inputs from multiple communication channels, including text, voice, email and chatbot interfaces; b) a Natural Language Understanding (NLU) module that uses machine learning models to extract intent, sentiment and contextual metadata from the dispute content; (c) a classification and prioritization module configured to categorize disputes into predefined taxonomies and assign priority levels based on predefined rules and AI-based predictions; (d) an intelligent routing module that routes disputes to specific human agents, automated systems or escalation paths based on classification results and organizational policies; (e) a solution assistance module configured to suggest or autonomously execute solution actions using AI-generated answers or predefined templates; and (f) a continuous learning module configured to retrain AI models using feedback data from dispute outcomes, user interactions and resolution performance, enabling automatic triage and classification of disputes in real time to optimise resolution time, accuracy and resource utilisation.