597 results about "Inference engine" patented technology

The invention discloses an adaptive question-answering system based on knowledge distillation and multi-modal dynamic fusion, and the system comprises a knowledge distillation module which is used for migrating knowledge of a teacher model pre-trained on corpora in the communication field to a lightweight student model, achieving model compression through optimizing a distillation loss function, and obtaining a multi-modal dynamic fusion model; the loss function comprises a soft label output by the teacher model and a KL divergence constraint output by the student model; the multi-modal knowledge fusion module comprises a feature extraction unit, a self-adaptive weighting unit and an attention fusion unit; the self-adaptive inference engine comprises a semantic analysis unit; according to the cross-modal reasoning method and system, semantic alignment of equipment parameters, protocol texts and topological graphs is achieved through the multi-modal dynamic fusion technology, and the cross-modal reasoning accuracy is improved; compared with an original model, the lightweight student model has the advantage that the reasoning speed is increased in a protocol analysis task.

The invention relates to the technical field of medical image diagnosis, and discloses a medical image automatic diagnosis method and system based on deep learning. According to the method, multi-modal medical image data of a target object is acquired and standardized, a two-channel convolutional neural network is utilized to extract features, the features are processed through cross-modal feature fusion, adaptive attention weight distribution and other technologies, a cascaded two-way long-short-term memory network is adopted for modeling, abnormity is detected based on a probabilistic graph model, and the target object is identified. And the nidus is segmented by a multi-scale context information enhancement module, and finally a diagnosis suggestion is generated by a diagnosis inference engine driven by a knowledge graph. The system comprises a multi-modal image acquisition interface module, a distributed feature calculation cluster, a visual interaction terminal and a security audit module. According to the method, the accuracy and efficiency of medical image diagnosis can be improved, comprehensive diagnosis reference is provided for doctors, and meanwhile data safety and privacy are guaranteed.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention discloses a traffic large model construction and decision-making method and device based on multi-modal two-way map reasoning, and the method comprises the steps: constructing a multi-modal data set of a text, an image and a track, generating fusion features through spatial-temporal clustering and cross-modal Transform coding, carrying out the two-way map reasoning in combination with a traffic knowledge map, and carrying out the decision-making of the traffic large model. The method comprises the following steps: generating an embedded representation through a forward graph neural network, reversely mapping a decision scheme generated by a language model to a graph to verify consistency, outputting knowledge to enhance embedding, fusing multi-modal features and knowledge embedding by adopting an LoRA multi-task joint fine tuning technology, adapting to traffic field tasks, deploying a real-time inference engine, and carrying out real-time inference on the traffic field. And processing the dynamic data flow through an aging perception attention mechanism, and outputting traffic event identification, path planning and scene question and answer results in parallel. Compared with the prior art, the method has the advantages that the problems of insufficient multi-source heterogeneous data fusion, low knowledge utilization efficiency and poor real-time decision consistency can be solved, and the semantic understanding and decision accuracy of the traffic large model is effectively improved.

The invention relates to a synchronous line loss intelligent diagnosis and analysis system based on an electric power knowledge graph. The synchronous line loss intelligent diagnosis and analysis system comprises a data acquisition layer, a data access layer, a data storage layer, a data analysis layer and a visualization layer, the data acquisition layer is used for acquiring power grid node data; the data access layer is used for carrying out dynamic synchronization on power grid node data based on Apache Kafka; the data storage layer comprises a time sequence database and a graph database; the data analysis layer comprises a detection module and an inference engine; and the visualization layer is used for dynamically displaying a propagation path and node characteristics of an abnormal region based on a topological structure of the knowledge graph, and querying an abnormal reason of the node and the influence on the whole system by clicking the node of the device. According to the invention, intelligent detection and diagnosis of the line loss abnormal area are realized, and the problem root cause is rapidly identified.

The invention provides an intelligent decision-based distribution network fault positioning method and system, and the method comprises the steps: obtaining a target state data set collected by a power distribution network global intelligent perception network, carrying out the time-space semantic fusion processing, generating a multi-dimensional feature map, carrying out the context perception discrimination of the multi-dimensional feature map through a dynamic decision inference engine, and carrying out the fault positioning of a distribution network. Generating a fault risk judgment result, performing causal link tracking on the target state data set based on the fault risk judgment result, generating a fault section positioning result, generating a fault positioning instruction according to the fault section positioning result, sending the fault positioning instruction to the distribution network intelligent operation and maintenance platform, and triggering a precise maintenance process. Therefore, the global state data of the power distribution network are comprehensively utilized, the accurate evaluation of the fault risk of the power distribution network, the rapid positioning of the fault section and the efficient triggering of the accurate maintenance process are realized, and the operation reliability and safety of the power distribution network are remarkably improved.

The invention relates to the technical field of the Internet of Things, and discloses an equipment fault prediction and diagnosis system for the Internet of Things. The system comprises a multi-source data acquisition module which is used for acquiring heterogeneous sensing data of Internet of Things equipment in real time; the data purification module is used for carrying out noise suppression and abnormal value repair on the data and generating a standardized time sequence data stream; the feature enhancement module is used for extracting equipment state features through a multi-scale decomposition algorithm; the fault prediction module is used for constructing an equipment degradation prediction model based on the cascade residual network and generating a dynamic evolution graph of an equipment health index; and the diagnosis decision module is used for generating a fault positioning result and a maintenance strategy optimization instruction through a hybrid inference engine based on the atlas. In addition, the system also comprises an equipment life calibration model, and a prediction model is dynamically adjusted by considering the individual difference of equipment. The system can effectively process heterogeneous data, accurately predict faults, accurately diagnose and optimize a maintenance strategy, and improve the operation reliability and maintenance efficiency of the Internet of Things equipment.

The invention provides an intelligent early warning system fusing Internet of Things sensing data and a domain knowledge graph, aiming at the problems of data islands, high false alarm rate, response lag and the like of a traditional park early warning system, and is suitable for park safety prevention and control in industries such as chemical industry, logistics, manufacturing and the like. The knowledge graph is an ideal tool for modeling connection between objective objects in the real world, the data island problem can be effectively solved by constructing the knowledge graph oriented to the smart park safety management field and fusing an intelligent reasoning algorithm, and the accuracy and timeliness of park risk early warning are remarkably improved. Specifically, a whole set of pre-warning system is designed from bottom to top in three aspects of multi-modal knowledge graph modeling, a three-level pre-warning inference engine and a self-adaptive optimization mechanism, and the park pre-warning requirements which meet current intellectualization and manpower cost saving are constructed. The multi-modal knowledge graph relates to six types of ontology concepts, comprises different data types, and comprises an equipment topological relation, environmental parameter association, an emergency plan, risk analysis, attack behavior simulation and an asset attribute model. The third-level early warning reasoning comprises rule reasoning, sub-graph matching reasoning and link prediction reasoning. The self-adaptive optimization technology aims at constructing a feedback learning mechanism, incorporating each early warning processing result into a knowledge graph, and continuously optimizing the object relation weight. In an early warning analog simulation experiment, the scheme of the invention realizes the effects of reducing the false alarm rate by 42% and improving the emergency response speed by 60%, and the feasibility and effectiveness of the scheme are proved.

A real-time anomaly detection system for high-volume managed file transfers (MFT), consisting of: a secure, hardware-accelerated monitoring unit configured to interface with a managed file transfer server and intercept file transfer session data at wire-speed; a metadata extraction engine embedded in the hardware-accelerated unit, the metadata extraction engine configured to analyze protocol-specific session attributes, including, but not limited to, file size, transfer duration, encryption status, source and destination endpoints, transfer frequency, and payload entropy; a contextual AI inference engine communicatively coupled to the metadata extraction engine, the AI inference engine comprising a deep learning model trained on labeled historical MFT activity logs to detect contextual deviations from normative behavior; a federated learning architecture with a plurality of edge nodes, each hosting a local anomaly detection model trained on localized transmission metadata and configured to synchronize with a central aggregator using differentially private gradient updates; an Explainable AI (XAI) subsystem integrated into and configured to generate human-readable anomaly justifications, feature importance maps, and threat categorization labels; and a policy orchestration module configured to dynamically execute pre-configured or AI-based security responses, where the security responses include selective session termination, quarantining of transferred files, generation of alerts, or redirection of MFT workflows.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

An electronic circuit system implementing and executing machine learning inference engines. While ML inference engines are based on (architectures and parameters defined by) configured, trained and tuned machine learning models, our design has the novel ability to support data driven, on-the-fly-reconfigured model runs. Reconfiguration and tuning operations include dynamic computational graph modifications, define-by-run alterations, changes to network depth (number of layers) and width (neurons per layer), and adjustments to weights, biases, plus activation function parameters. Neural networks supported include Feed-Forward, RNN, CNN, and Hopfield architectures, plus Ensemble, Federated, Cooperating, Adversarial, and Swarm collections. Decision Trees and Forests are also supported, as are more esoteric approaches such as ART and KAN. Our invention is capable of running both standalone and cooperatively, the cooperative processing being local and / or remote / cloud based, interfacing with telemetry applications to feed data, and machine learning software to feed new or updated models.

The invention discloses a special disease queue data capturing method and system based on an intelligent medical knowledge graph, and relates to the technical field of medical information, and the method comprises the following steps: S1, constructing a special disease intelligent medical knowledge graph which comprises a bidirectional mapping relation between standard terms of a single disease category and clinical actual corpora, clinical text data is accumulated in a mode of combining manual annotation and machine learning, and a domain exclusive knowledge base containing symptoms, diagnosis and examination indexes is formed. According to the special disease queue data capturing method and system provided by the invention, by constructing the special disease intelligent medical knowledge graph, bidirectional mapping of single disease specification terms and clinical actual corpora is realized, and the problem of insufficient semantic understanding when non-standardized clinical corpora are processed by a traditional method is effectively solved; the entity information in the unstructured medical data can be accurately extracted by utilizing a natural language processing model and an inference engine.

The invention relates to the technical field of bridge structure health monitoring and maintenance, and discloses a bridge structure anti-fatigue performance predictive maintenance method based on digital twinning, and the method comprises the steps: constructing a bridge structure digital twinning model, and deploying a multi-mode sensing network; establishing a bridge fatigue mechanism knowledge graph, and structuring expert experience and historical cases; multi-source heterogeneous evidence fusion is realized based on a DS evidence theory, and evidence conflicts are eliminated; constructing a cause and effect inference engine based on a Bayesian network, and identifying a fatigue mechanism path; and a self-adaptive predictive maintenance decision-making system is realized, and an optimal maintenance strategy is generated. The problems that a traditional bridge fatigue analysis method is difficult to process multi-source heterogeneous data, the fatigue prediction precision is insufficient and the like are solved, and high-precision prediction and precise maintenance of the anti-fatigue performance of the bridge structure are achieved.

The invention provides an industrial control-oriented cloud-side collaboration system and an implementation method thereof, and relates to the technical field of cloud-side collaboration, and the system comprises an equipment layer which comprises an industrial equipment access gateway and is responsible for collecting equipment operation data and executing a control instruction; the edge layer comprises a data preprocessing unit used for processing the real-time data uploaded by the equipment layer; the lightweight inference engine is used for loading a lightweight model issued by the cloud layer and generating a control decision based on the real-time data; the deterministic scheduling engine is used for hierarchically controlling tasks according to task time sensitivity and distributing execution positions; the cloud layer comprises an industrial knowledge model center used for constructing a scenarized knowledge model based on the equipment operation data; the model lightweight unit is used for converting the scenarized knowledge model into a lightweight model; and the decision distribution unit is used for issuing the lightweight model to the edge layer. The technical problems of knowledge expression, model adaptation and task allocation in an industrial control scene can be solved.

The invention relates to the technical field of natural language processing, knowledge engineering and artificial intelligence, and particularly discloses a method and system for enhancing understanding of professional domain knowledge by a large model. The method comprises the steps that a professional domain entity classification system composed of a core entity, an auxiliary entity and a relation entity is constructed, attributes are expressed in a layered labeling and multi-granularity modeling mode, and semantic vectors are generated through ontology modeling and an embedding algorithm; based on a mixed extraction framework fusing expert rules and a neural network model, high-quality extraction of professional domain knowledge is realized; the method comprises the following steps: integrating multi-source heterogeneous data, and constructing a dynamically updated domain knowledge graph through semantic mapping, entity normalization and metadata weighting strategies; a knowledge graph is embedded into a Transform architecture, a knowledge perception attention mechanism and a multi-hop inference engine driven by reinforcement learning are introduced, and the knowledge fusion and inference ability of a large model is improved; and meanwhile, a triple check mechanism is designed to ensure entity consistency, relation logicality and numerical reasonability of the generated content. According to the method, the knowledge understanding and reasoning capability of a large model in professional scenes such as water conservancy is effectively improved, and the method has good universality and engineering application prospects.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

The invention relates to an unmanned aerial vehicle fault traceability analysis method and device, equipment and a storage medium. The method comprises the steps of defining entity types and relationship types among entities based on a predefined fault ontology model to construct a mode layer of an unmanned aerial vehicle fault knowledge graph; based on the mode layer, extracting a fault triple from the multi-source operation data of the unmanned aerial vehicle by using a mixed extraction model, and constructing a fault knowledge graph containing instance data; endowing a dynamic weight probability representing confidence to a relation edge in the fault knowledge graph, and generating a probabilistic fault knowledge graph; and mapping to-be-analyzed fault information to the probabilistic fault knowledge graph, performing traceability analysis by using a hybrid inference engine, and outputting a fault reason and a transmission path. According to the method, structured deep fusion of domain knowledge and data value is realized, and the traceability conclusion is improved from qualitative judgment to quantitative decision support with confidence measurement.

The invention provides a deep learning inference platform and an operation method and system of a deep learning inference engine. The deep learning inference engine is carried in a processor, and the operation method comprises the following steps: loading a trained inference model, establishing a tensor cache manager, and pre-distributing an output tensor; receiving input data, obtaining an initial tensor corresponding to the input data, performing tensor remodeling on the initial tensor to obtain a target tensor of a target dimension, and storing the target tensor into a first cache variable of a tensor cache manager; calling a target tensor in the first cache variable and performing mixed attention calculation on the target tensor to obtain a mixed attention calculation result; and writing the mixed attention calculation result into an output tensor for outputting. The method can dynamically adapt to multi-step decoding or high concurrency situations, can adapt to diversified texts, audios or other sequence data and other scenes, enables a deep learning inference engine to have higher universality and expandability, and improves the system performance.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention discloses a ship main power and auxiliary power real-time switching method and system based on large model optimization, and relates to the technical field of ship power system control, and the method comprises the steps: collecting the operation state data and environment parameters of a ship main power system and an auxiliary power system in real time through a multi-mode sensor array; and performing space-time alignment and noise suppression processing on the operation state data and the environment parameters, inputting the feature subset into a lightweight large model inference engine, generating a multi-target optimization decision set, executing a main and auxiliary power dynamic switching control sequence in a preset time window according to the multi-target optimization decision set, and outputting the main and auxiliary power dynamic switching control sequence. And generating a self-optimization log including a switching efficiency evaluation report and a model iteration suggestion based on the deviation degree of the switching feedback data and a preset performance index. According to the ship main and auxiliary power real-time switching method and system based on large model optimization, stable operation of a ship power system is guaranteed, and meanwhile the service life of equipment is prolonged.

The invention discloses a data private network state reasoning method based on an electric power production knowledge graph. The method comprises the following steps: 1) constructing the electric power production knowledge graph; 2) data acquisition; collecting operation state monitoring data of equipment from entity equipment of various power system components through a standardized interface, wherein the operation state monitoring data comprises the state, load data and environment data of the power system components; 3) associating the collected equipment operation state monitoring data with the knowledge graph through a data private network, and updating the power production knowledge graph based on the data collected in real time; and 4) analyzing the current state of the power system by adopting an inference engine, and predicting a potential fault. According to the method, the power production knowledge graph is constructed and data private network state reasoning is performed based on the power production knowledge graph, so that the operation and maintenance efficiency of the power system and the accuracy of fault diagnosis are remarkably improved.

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

A system for context-sensitive orchestration of autonomous agents in cloud platforms, consisting of: a hardware-based orchestration device configured for integration into a distributed cloud infrastructure; a context inference engine within the orchestration device, wherein the context inference engine is configured to receive and aggregate real-time telemetry data from a variety of distributed nodes, including at least one system-level parameter, at least one application-level parameter, and at least one environment parameter; a semantic inference module within the context inference engine, configured to generate a context-related state representation by correlating the parameters using a knowledge graph-based model of interdependencies; an optimization unit for machine learning within the orchestration device, which is communicatively connected to the context inference engine and is configured to predict resource requirements and operational states using reinforcement learning models trained on historical and real-time data streams; a policy-driven orchestration controller configured to translate the contextual state representation into actionable orchestration decisions by applying dynamic orchestration policies stored in a domain-specific policy repository; and a distributed agent interaction bus configured to delegate orchestration decisions to a variety of autonomous agents deployed on the cloud platform.

The invention discloses a digital intelligent tumor prevention and treatment management platform and management method, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: collecting the multi-modal data of a patient in real time, carrying out the standardized cleaning and space-time alignment processing, and generating a space-time tagging data set; inputting the multi-modal joint feature representation into an anti-fact inference engine, simulating potential effects under intervention of different treatment schemes, calculating individual processing effects, and generating an anti-fact rehabilitation suggestion set; according to the anti-fact rehabilitation suggestion set, a dynamic causal enhancement decision model is constructed, real-time physiological data feedback of the patient is continuously received through a near-end strategy optimization algorithm, and a treatment scheme is dynamically adjusted. According to the method, through multi-modal data space-time alignment and feature fusion, in combination with a tumor biological mechanism and a machine learning algorithm, the accuracy and clinical credibility of individualized tumor treatment scheme recommendation are remarkably improved.

The invention relates to the field of computer vision and deep learning, and discloses an image big data classification and recognition method and system based on deep learning, and the method comprises the steps: generating a gating matrix through the extraction of an image frequency domain energy coefficient, compressing a convolution kernel through the combination of asymmetric tensor decomposition, and carrying out the self-adaptive training through cross-modal semantic alignment and a meta-learning task. Efficient classification reasoning of dynamic path selection is realized, and the precision and the calculation efficiency are improved; the system comprises a frequency domain analysis module, a dynamic sparse gating module, an asymmetric tensor decomposition module, a meta-learning task generation module, a cross-modal alignment module and a dynamic inference engine module. According to the method, through cross-modal semantic alignment and meta-learning task optimization, in combination with lightweight parameter storage and edge calculation path selection, fine-grained classification precision improvement, model compression and high-efficiency reasoning are realized, and the calculation efficiency and generalization ability in a complex scene are remarkably enhanced.

The invention discloses an inference service deployment method, electronic equipment and a storage medium, and the method comprises the steps: responding to the creation of an inference service, reading a model information configuration table and an engine information configuration table, and carrying out the matching processing of the model information configuration table and the engine information table according to a pre-configured model name during the creation of the inference service, processing resources, inference engine mirror image identifiers and inference engine starting parameters which can be used for loading inference services on the container scheduling platform are obtained, and workload metadata used for bearing the inference services are created based on the processing resources, the inference engine mirror image identifiers and the inference engine starting parameters; the deployment of the inference service on the container scheduling platform is realized by distributing the workload metadata to the target cluster node. Through the method, the technical problem of relatively low deployment efficiency caused by manually configuring the parameters in the workload metadata depending on manpower in related technologies is solved, and the technical effects of simplifying the deployment process of the model reasoning service and improving the deployment efficiency are achieved.

The invention discloses a Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud cooperation, and relates to the technical field of agricultural intelligent irrigation, and the system comprises an edge equipment end which is deployed in a Chinese herbal medicine planting area, comprises a multi-mode sensor module, a communication module and an irrigation execution mechanism, and is used for collecting soil humidity, illumination intensity and meteorological data in real time, and sending the collected data to a cloud server; and the drip irrigation valve is controlled through the irrigation executing mechanism. According to the Chinese herbal medicine intelligent drip irrigation regulation and control system based on end-cloud collaboration, through an end-cloud collaboration architecture and a hierarchical decision-making mechanism, the drip irrigation regulation and control accuracy and real-time performance in a Chinese herbal medicine planting environment are improved. A local AI inference engine of the edge equipment end is combined with a dynamic learning module, an optimization instruction can be automatically generated during network fluctuation, dependence on real-time communication of the cloud end is reduced, and continuity and reliability of irrigation operation in complex terrains are ensured.

The invention relates to the field of artificial intelligence, discloses a crowd advertisement accurate putting determination method based on a knowledge graph, and aims to solve the problem of mismatching caused by interest model lag in traditional advertisement putting. The method comprises the following steps: constructing a dynamic evolution type user interest knowledge graph, fusing multi-source behavior flow and static portrait data, and introducing a time decay factor and an event triggering mechanism to realize node weight self-adaptive updating; a short-term interest pulse is accurately captured through a time sequence attention propagation and cross-domain association edge dynamic generation algorithm; and constructing a dual-channel graph neural network inference engine, respectively processing long-term stable and short-term sudden interest paths, and generating a delivery decision through confidence weighted fusion. According to the invention, the timeliness and accuracy of advertisement matching are improved, the mismatching rate is reduced, and the user experience and the self-calibration capability are enhanced.