115 results about "Shared knowledge" patented technology

The invention relates to the related technical field of digital data processing, in particular to a personalized federated learning method and system for a heterogeneous multi-source industrial internet, and the method comprises the steps: connecting a client, evaluating a load, time delay and modal similarity to generate a dynamic association table, deploying a hierarchical encryption protocol, and constructing a priority queue; a cache mechanism is set to coordinate distributed iterative optimization, so that the technical problem that network oscillation and computing resource waste are aggravated due to overhigh load of part of nodes caused by frequent access and exit of equipment and data volume difference in the industrial internet and repeated migration of clients and nodes is caused is solved, cross-equipment shared knowledge base vectors are extracted, and the computing efficiency is improved. The technical effects of reducing the influence of model isomerism on aggregation, dynamically scheduling high-frequency parameter local aggregation and low-frequency parameter cloud synchronization, optimizing the association weight of a client and a fog node in real time, realizing privacy protection and efficient personalized federated learning, and ensuring the privacy and security of user data in the training process are achieved.

The invention discloses a multi-agent cooperation enhancement method, system and equipment based on a knowledge graph, and the method comprises the steps: obtaining original data in an external environment, carrying out the preprocessing and feature extraction of the original data, generating a knowledge triple, storing the knowledge triple in a local knowledge graph, and submitting the knowledge triple to a shared knowledge graph for knowledge updating; when a to-be-executed task is received, decomposing the to-be-executed task by utilizing the large language model and querying global knowledge in the shared knowledge graph and local knowledge in the local knowledge graph to obtain a plurality of sub-tasks; a bipartite graph minimum cost matching algorithm is adopted to match a plurality of sub-tasks with the capability and availability of each agent to generate a preliminary task allocation scheme, and a large language model is utilized to optimize the preliminary task allocation scheme to generate an optimal task allocation scheme; and sending each task allocation knowledge fragment in the optimal task allocation scheme to a corresponding agent for collaborative execution through a semantic communication protocol.

The invention relates to the technical field of lithium battery safety management, and discloses a lithium battery fault diagnosis method based on a BMS, and the method comprises the steps: obtaining multi-dimensional battery operation data and real-time data, firstly extracting a local feature vector, generating a preliminary fault signal indication, then extracting an abnormal feature vector according to the preliminary fault signal indication, and carrying out the fault diagnosis according to the abnormal feature vector; and if the preset threshold is exceeded, compressing and transmitting to the adjacent management unit to form a shared data packet. According to the local feature vector and the shared data packet, updating a diagnosis model parameter to obtain an optimized fault recognition model for analyzing real-time data and calculating a fault matching degree, and determining a potential fault type if a threshold value is exceeded; and generating a collaborative query request to the distributed network to obtain a historical fault empirical data set, integrating the historical fault empirical data set, refining parameters to obtain an accurate fault probability, activating an alarm and recording a log if a warning threshold is exceeded, and finally updating the global shared knowledge base. According to the method, the problem of insufficient lithium battery fault diagnosis accuracy in a distributed scene is solved, and collaborative optimization of diagnosis accuracy and distributed collaboration is realized.

The invention provides a city calculation basic model and equipment based on a Mama time sequence, and relates to the technical field of data processing. In the model provided by the embodiment of the invention, a Mama framework is combined with multi-task time sequence analysis. Aiming at the non-stationarity of urban time series data, a frequency prompt network is designed for decoupling in combination with a Koopman operator theory and dynamic mode decomposition, a steady mode and non-stationary fluctuation are analyzed from the angles of time and frequency, and a frequency mode memory pool (FPMP) is introduced for realizing cross-scene feature reuse. The advantage of a Mama model in capturing long-term time dependence is utilized, and an attention mechanism similar to Mama is designed to simulate channel dependence in a multivariable time sequence. A learnable supplementary sequence and a multi-task prompt mark are introduced to support dynamic multi-task adaptation, and the contradiction between urban data isomerism and task diversity is relieved. Shared knowledge and specific task knowledge are balanced through shared experts and specific task experts of the multi-task attention module, the negative migration problem in multi-task learning is relieved, and the generalization ability of the model is enhanced.

The invention provides an incomplete multi-modal learning method based on prompt distillation, and aims to improve the robustness and generalization ability of a multi-modal model in a modal missing scene. According to the method, the advantages of knowledge distillation and prompt learning are fused, a collaborative framework composed of a hierarchical prompt generator, a teacher network and an inference network is constructed, and efficient migration of inter-modal shared knowledge and sample-level fine-grained information is realized. The method comprises the following steps: firstly, constructing and preprocessing a multi-modal data set; based on a prompt distillation mechanism, an efficient knowledge transmission mechanism is established between the teacher network and the reasoning network; and finally, inference is performed on the incomplete modal sample by using the trained prompt generator and the inference network, so that the accuracy and the adaptive capacity of downstream tasks are remarkably improved. According to the method, through collaborative optimization of the prompt generator and the double-distillation mechanism, the knowledge migration efficiency is remarkably improved while the light weight of the model is kept, and an efficient solution is provided for multi-modal learning in a complex real environment.

The invention provides a voice interaction method and device. The method comprises the following steps: receiving voice information input by a user; if it is determined that the intelligent agents need to be switched based on the voice information, determining a target intelligent agent from other intelligent agents except the current intelligent agent in the multiple intelligent agents; processing content corresponding to the voice information by adopting a large model corresponding to the target agent, a skill knowledge base and a shared knowledge base to obtain a reply; and outputting voice corresponding to the reply to the user. As the skill knowledge base queried when the user questions is processed is split into a plurality of sub-knowledge bases with different professional knowledge from a complete knowledge base, the data query amount is all reduced, and the voice interaction efficiency is improved. Moreover, the switched intelligent agent can check the voice information sent by the user to the intelligent agent before switching through the shared knowledge base, user historical voice information transmission between the intelligent agents is not needed, the speed of information sharing between the intelligent agents is increased, and the efficiency and accuracy of information interaction are improved.

The invention provides a septal tumor accurate diagnosis system based on artificial intelligence, a multi-scale attention fusion module receives multi-modal data, focuses a septal region, dynamically adjusts the weight and outputs a key feature vector, an adversarial self-supervision pre-training module digs tumor morphological features based on the vector, robustness and generalization ability are enhanced, and the accuracy of diagnosis is improved. And the causal intervention diagnosis decision-making module constructs a decision-making tree by using enhanced features to perform preliminary diagnosis and transmits causal information to the meta-learning enhanced diagnosis module, and the meta-learning enhanced diagnosis module adjusts parameters by using a small number of samples for rare subtypes, outputs a final diagnosis result and feeds back the final diagnosis result. According to the system, multi-mode and dynamic attention, cooperative confrontation self-supervision pre-training and causal reasoning are fused, rare tumor diagnosis is optimized through meta-learning, a closed-loop feedback and shared knowledge graph is formed, and the diagnosis precision, interpretability and rare case diagnosis capacity are integrally improved.

The invention provides a low-altitude multi-agent cooperative control method and system, and belongs to the technical field of cooperative control, and the method comprises the steps: carrying out the fusion of data collected by a plurality of sensors for any agent, and obtaining a fusion feature vector; wherein the plurality of sensors are deployed on the intelligent agent; performing semantic coding on the fusion feature vector, constructing a local knowledge graph, and performing semantic compression on the local knowledge graph to obtain compressed shared knowledge; transmitting the compressed shared knowledge to the other agents, and updating the local knowledge graph according to the received compressed shared knowledge transmitted by the other agents to obtain an updated knowledge graph; and performing multi-agent cooperative control according to the updated knowledge graph of all the agents. According to the method, the task execution efficiency of the multiple agents in the low-altitude scene is improved by combining the self-adaptive multi-modal fusion algorithm and a knowledge sharing mechanism among the multiple agents.

Disclosed herein are methods and systems for generating and using a shared knowledge language. In one embodiment, a method comprises retrieving data generated as a result of at least one computing device accessing one or more applications from a set of applications; generating a computer model comprising a set of nodes where each node having an identifier corresponding to a series of nouns and verbs generated in accordance with a schema associated with a shared knowledge language; when the processor receives a request: identifying a node that corresponds to the request; retrieving a pair of related nodes within the nodal data structure the processor identifying whether the relevance threshold has been satisfied using each respective node's identifier corresponding to the schema.

The invention provides a traceable federal incremental learning method based on grouping feature aggregation, and relates to the technical field of federal learning, and the method comprises the following steps: S1, carrying out end node traceable task learning; carrying out local pruning and fine tuning based on the initial sharing model, and judging whether tasks are repeated or not according to label distribution similarity; s2, constructing a cloud-end side collaborative grouping model; sending the global shared knowledge generated by the server model and the aggregation weight corresponding to the global shared knowledge to the end node; s3, at the end node, the client updates own model parameters based on the local cross entropy loss, the global shared knowledge generated by the server model of each group and the aggregation weight corresponding to the global shared knowledge, and outputs the updated model; and S4, replacing the new sub-model in the S1 with the updated model or updating the sub-model, repeating the steps S1 to S3, and stopping training until specified training times are reached. The method aims to effectively cope with challenges caused by storage limitation and task repeatability.

The invention discloses an incremental multi-language text recognition method and system based on shared knowledge mining, and relates to the technical field of text detection and recognition. Sending the playback set and the data set of the current language into all characteristic recognizers, and mining potential shared characters and shared words among languages based on the prediction consistency of all the characteristic recognizers; based on the shared characters, the shared words and a language domain discriminator, mining a dependency relationship between incremental languages to obtain language probabilities of character levels and word levels, and based on the language probabilities, weighting prediction probability distribution of each characteristic recognizer to obtain character level and word level probability distribution after relationship enhancement; and performing argmax operation on the word level probability distribution after relation enhancement, and selecting an index corresponding to a maximum probability value as a final recognition result. The forgetting of old language knowledge in the incremental learning process can be reduced, so that the continuous learning ability of the text recognizer is improved.

The embodiment of the invention provides a knowledge base processing method and device, computer equipment, a medium and a program product. The knowledge base processing method comprises the steps of receiving a sharing voucher of a shared knowledge base; in response to an access operation on the sharing voucher, displaying a shared sharing knowledge base which comprises at least one knowledge file; questioning information of the shared knowledge base is received; and displaying answer information of the question information, wherein the answer information is obtained by answering the question information based on the knowledge file in the shared knowledge base. By adopting the embodiment of the invention, the knowledge utilization rate of the knowledge base can be improved.

The invention provides a self-adjusting dialogue type multi-agent system and interaction method based on dynamic topology, and the system comprises a scheduling module which is used for receiving user input and routing a task to a corresponding field task agent in a conventional state; the domain task intelligent agents are used for processing the received tasks, evaluating the cognitive states in real time and sending out assistance signals when the cognitive states meet preset conditions; the shared knowledge management module is used for maintaining a dynamically updated distributed semantic knowledge graph; wherein the distributed semantic knowledge graph is used for sharing, associating and persisting structured knowledge entities and context information generated in the session process among all modules in real time; the topology control module is used for dynamically switching the communication control topology of the system from a first topology structure to a second topology structure in response to the received assistance signal; and when the assistance signal is eliminated, the communication control topology is recovered from the second topology structure to the first topology structure.

The invention belongs to the technical field of feature extraction, and more specifically relates to a federated learning method and device based on double adversarial training under an industrial Internet of Things. The method comprises the following steps: a server initializes a global model and sends the global model to a client; each client updates a local model based on the received global information; after the local model is updated, each client enters a local training stage; then, each client recalculates a local feature prototype, and the feature extractor uploads the local feature prototype in turns; then the following local information is uploaded to the server, and if the current round is in the uploading round set, the local feature extractor uploads the local information; and finally, the server aggregates the local feature prototype, the local classifier and the received local feature extractor, and then issues the aggregated information to each client. The method solves the problem that the existing federal learning method is not enough in balance mechanism design between individuation and shared knowledge and is difficult to adapt to the multi-source heterogeneous and dynamic change environment of an industrial field.

The invention relates to the technical field of model-driven system engineering modeling, in particular to an MBSE-oriented multi-agent collaborative automatic modeling method and system. The method comprises the following steps: acquiring a natural language modeling demand of a user, retrieving in a shared knowledge environment to obtain a knowledge sub-graph related to the demand, constructing a modeling context, and calling a generation agent to generate a current SysML v2 model; calling a verification agent to perform grammar verification and semantic verification based on the domain knowledge graph on the model, and outputting a verification result; when a verification result represents that grammar does not pass or semantic inconsistency exists or an engineering constraint rule is violated, calling a repair agent to execute deterministic repair and iterative verification according to the domain knowledge graph until a preset condition is met, and outputting a final SysML v2 model; notch marking or pocket bottom repairing can be carried out under the condition that the notch is covered. According to the method, the correctness, interpretability and convergence efficiency of complex system modeling are improved.

The invention discloses a multi-modal rumor detection system based on multi-field perception, relates to the technical field of rumor detection, and is used for solving the technical problem of low multi-field rumor detection precision in the prior art. The rumor detection system comprises a multi-view feature extraction module, a multi-domain perception module, a multi-level knowledge fusion module and a final classifier. Wherein the multi-view feature extraction module is used for respectively extracting corresponding single-mode features from a text mode and an image mode, and acquiring aligned image and text features as multi-mode view features; the multi-domain sensing module is used for learning cross-domain shared knowledge and domain specific knowledge at a text view angle, an image view angle and a multi-mode view angle; the multi-level knowledge fusion module is used for fusing cross-domain shared knowledge and domain specific knowledge of a text view angle, an image view angle and a multi-mode view angle; and the final classifier is used for obtaining a prediction result according to the cross-domain shared knowledge and the domain specific knowledge obtained by the multi-level knowledge fusion module.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The present application discloses a method of knowledge sharing between dialogue systems, including: receiving, by a first dialogue system, a knowledge sharing request sent by an external dialogue system, wherein the knowledge sharing request comprises at least feature information of a knowledge point to be shared by the external dialogue system; parsing the knowledge sharing request to determine the feature information of the knowledge point to be shared; and adding the feature information of the knowledge point to be shared to a knowledge base of the first dialogue system to form a shared knowledge base. The embodiment of the present application realizes the sharing of knowledge among different dialogue systems by sharing knowledge points among dialogue systems, and meets the cross-domain dialogue needs of users for dialogue robots to the greatest extent while minimizing costs.

The invention discloses a multi-modal knowledge base system construction method oriented to a large language model, which comprises the following steps: S1, creating a knowledge base instance, including setting a knowledge base name, description and visibility parameters; s2, adding a multi-modal document to the knowledge base, wherein the multi-modal document comprises a text, a PDF (Portable Document Format), an image and a table format; s3, configuring large language model parameters including a model identifier, an API key and a basic API address; s4, realizing a knowledge-based question and answer function, and enhancing a generation technology by combining a standardized API (Application Program Interface) with a retrieval; s5, realizing a dynamic scoring function of knowledge entries, and performing multi-dimensional scoring on the entries through a large language model; and S6, managing knowledge base authority, distinguishing a personal knowledge base mode and a shared knowledge base mode, and setting document visibility. According to the method, a multi-modal analysis engine is adopted, unified storage of multi-modal data is supported, lightweight document indexing is adopted, automatic scoring is performed through LLM, manual auditing dependency is reduced, and meanwhile high-quality knowledge sharing is ensured in combination with permission control.

The invention discloses a multi-agent-based task solving method and system and multi-agent control equipment, and belongs to the technical field of artificial intelligence, and the method comprises the steps: screening target task knowledge related to target task features in a shared knowledge base, and determining candidate agents based on the target task knowledge; each candidate agent calculates the corresponding task fitness based on the current environment state and the target task knowledge, the candidate agent with the maximum task fitness value is determined as the target agent, and the target agent executes the target task; according to the method, the candidate agents capable of executing the target task are determined by taking the knowledge as guidance, and then the specific and appropriate target agent is determined according to the actual situation, so that the knowledge is effectively utilized, and the task execution efficiency is ensured.

The invention discloses a robot autonomous assembly method and system based on imitation learning, a storage medium and computer equipment, and the method comprises the steps: constructing a universal expert strategy suitable for polygonal shaft hole assembly through analyzing and summarizing the polygonal shaft hole assembly experience of an expert; according to the strategy, task type images and human assembly demonstration data including assembly force / torque and assembly actions are collected; modeling a human assembly skill into a universal assembly skill model based on shared knowledge and specific knowledge; using a loss function guidance algorithm to train and optimize the general assembly model; and deploying the optimized general assembly model into a control system of the robot so as to drive the robot to execute humanization-like assembly. According to the method, the problems of poor generalization performance, slow convergence of an end-to-end learning mode and high cost of an imitation learning method can be solved, rapid learning of human skills can be realized, and humanoid robot autonomous assembly is realized.

The embodiment of the invention provides a business processing system based on multiple agents, and relates to the technical field of data processing. The system comprises an agent layer which is integrated with a plurality of professional agents, the professional agents comprise a business agent and a task scheduling agent, and the task scheduling agent is used for receiving a task request, dynamically allocating a task to a service agent corresponding to the task based on the task request, monitoring a processing state and a system load of each service agent, optimizing a task execution path, and coordinating cooperative work of a plurality of service agents to enable the service agents to process the task in parallel; the collaboration layer is used for providing information sharing and collaborative decision for a plurality of professional agents; and the resource layer is used for providing shared knowledge and tool support for a plurality of professional agents. Through one embodiment of the invention, the problem of low efficiency of the business process in the related technology is at least solved, and the effect of improving the efficiency of the business process in the related technology is further realized.

The invention relates to the technical field of liquid slip ring fault detection, and provides a liquid slip ring full life cycle fault detection system based on digital twinning. The system comprises the following modules: a detection module, a digital twinning module, a discrimination module, a diagnosis module and a shared knowledge base. The detection module is used for reading a detection signal of the liquid slip ring, processing the detection signal to obtain a digital detection signal, and storing the digital detection signal in the shared knowledge base; the digital twinning module is used for obtaining a predicted digital signal; the judgment module is used for updating the simulation model in the digital twinning module and generating a judgment result; the diagnosis module generates a diagnosis result according to the judgment result of the judgment module; and the shared knowledge base is used for storing all results generated in the detection module, the digital twin module, the judgment module and the diagnosis module. By designing the five modules, the degradation degree of each part of the liquid slip ring is quantified so as to adapt to a deep learning model.

The invention provides an intelligent optimization control method for a factory power station building cold source system, and the method comprises the steps: firstly, building an energy-saving optimization control model for the power station building cold source system through the combined application of a cold source system mechanism and factory actual operation big data; secondly, deeply analyzing a refrigerator operation mechanism and an energy consumption expression, determining the property of a problem to be optimized, and determining an equation or inequality constraint condition according to inherent attributes of refrigerator equipment and an industrial field environment; and secondly, designing a particle swarm optimization algorithm sharing knowledge archives, creating a learning optimization model to guide the direction of swarm intelligence search, calculating and solving to obtain the decoded optimal branch chilled water inlet / outlet water temperature, and finally providing the optimal branch chilled water inlet / outlet water temperature to a control part, so that the COP (Coefficient Of Performance) index is remarkably improved.

The invention belongs to the field of man-machine interaction, and particularly discloses an intelligent AI dialogue interaction method and system based on an RICH normal form, and the method comprises the steps: obtaining current dialogue context data and a user input signal, processing the data through an intention recognition model, and obtaining a user intention vector and a scene label; according to the user intention vector and the scene label, acquiring hierarchical state information in a preset multilayer interaction framework, and if the matching degree of the vector and the current hierarchical state is lower than a preset threshold value, activating an information sharing mechanism to obtain an updated shared knowledge graph; through the shared knowledge graph, a dynamic scene adaptation module is fused, a potential switching path and intention extension of a conversation are determined, and an optimized interaction path sequence is obtained; the objective of the invention is to solve the problems of inaccurate intention recognition, stiff interaction path switching and incoherent response logic of a traditional dialogue system in a complex scene in the prior art.

The invention relates to the technical field of electric vehicle charging infrastructure intelligent management, and discloses a charging pile load balancing method and system based on a neural network, and the method comprises the steps: analyzing the state, environment and user behavior data of a charging pile through a multi-mode auto-encoder network; generating a unified scene representation; recognizing a typical scene by using a variational auto-encoder and a density clustering algorithm; decomposing the charging load balancing knowledge into cross-scene shared knowledge and scene specific knowledge; automatically generating a neural network architecture adaptive to the current scene based on the scene similarity; using a meta reinforcement learning algorithm to train a meta strategy network to generate a load balancing decision; scene smooth transition is realized through gradual model switching; according to the method, multi-scene adaptation, knowledge sharing and smooth switching are realized, the problems of system redundancy, knowledge isolation, scene switching performance fluctuation and the like in a traditional method are effectively solved, and the operation efficiency of the charging infrastructure is improved.

The invention discloses a personalized federal learning method and system based on client clustering and sparse pruning, and the method comprises the steps: firstly constructing a central server-multi-client frame, initializing a full-size cluster model, a channel mask and an incremental pruning rate, and configuring SGD training, network slimming compression and encrypted transmission; performing dynamic re-clustering in each round of training, executing channel-level pruning by the client by using cluster structure information, performing local fine tuning, and uploading a sparse model; and the server carries out channel fusion on the model, and arithmetic averaging is carried out on the shared channel weight to generate a unified cluster model to be issued. After the target pruning rate is reached, the client continues personalized training and uploads, the server only executes FedAvg in the shared channel, the private channel keeps the original weight, and heterogeneous aggregation is achieved. A lightweight model with shared knowledge and private features can be obtained through iteration, the communication and storage overhead is remarkably reduced, and the method is suitable for a privacy-friendly intelligent reasoning scene of resource-constrained edge equipment.

The invention relates to a novel movilization split mirror generation method based on a large language model, which comprises the following steps of: identifying roles, scenes and key props appearing for the first time in a text, and extracting specific descriptions of the roles, the scenes and the key props; roles, scenes and key props are updated and supplemented; the recognized content, the extracted description and the updated and supplemented setting information are structurally stored in a setting library to form a dynamically-updated and globally-shared knowledge source, so that a dynamic setting library is constructed and maintained; the large language model receives the original text content of the current chapter and all setting information related to the chapter and retrieved from the dynamic setting library; the large language model follows the description in the dynamic setup library and converts the narrative text into a structured split script to generate a movie and television split. According to the method, the novel text can be automatically converted into the movie and television play split script, and the production efficiency of movie and television works is improved.

To provide a system.SOLUTION: A system includes: means for accumulating employee information as knowledge; means for suggesting answers or an organization or a person as a consultation partner based on input consultation content; means for promoting communication between employees; communication means for transmitting and receiving messages in real time; means for providing a forum function for sharing knowledge or experience; and means for analyzing emotions of users, and suggesting an optimal consultation partner.SELECTED DRAWING: Figure 1

The invention discloses a cross-theme composition scoring method and device based on interpretable knowledge capture and a medium, and the method comprises the steps: training a preset scoring model through a training data set, and updating the parameters of the scoring model; encoding the training data set by using the composition encoder with the updated parameters, and clustering according to an encoding result to obtain a plurality of clusters; performing clustering optimization based on the clustering cluster, and updating parameters of the composition encoder according to a clustering optimization result to obtain a target scoring model; sorting according to the clusters to obtain cue word information, and based on the cue word information, generating dominant shared knowledge representation of each cluster by using a preset semantic representation model; and performing composition scoring on the to-be-scored composition by using the target scoring model in combination with the dominant shared knowledge representation to obtain a scoring result of the to-be-scored composition. The scoring accuracy and interpretability of the new theme composition are improved, and the method can be widely applied to the technical field of data processing.