21 results about "Knowledge acquisition" patented technology

A blockchain-based knowledge-sharing intelligent customer service AI response accuracy optimization system, belonging to the fields of blockchain and artificial intelligence technology, is disclosed. The system includes a multi-source knowledge acquisition unit, a blockchain knowledge sharing unit, a knowledge quality assessment unit, an AI response optimization unit, and an interactive feedback and iteration unit. The multi-source knowledge acquisition unit collects customer service knowledge data from different departments, business lines, and application scenarios. The blockchain knowledge sharing unit receives pre-processed knowledge data and constructs a decentralized knowledge-sharing network based on distributed ledger technology. The knowledge quality assessment unit performs multi-dimensional quality assessments of the knowledge stored in the blockchain. The AI ​​response optimization unit trains and optimizes the intelligent customer service AI model based on high-quality knowledge samples. The interactive feedback and iteration unit collects user feedback from the intelligent customer service system. All units operate collaboratively to form a closed-loop mechanism.

The objective is to obtain a parameter adjustment content calculation device (10) capable of improving the accuracy of improving the communication quality of communications subject to quality control. The parameter adjustment content calculation device (10) comprises: an information acquisition unit (11) that acquires parameters set in one or more communication systems in which multiple communications take place, and quality information of communications subject to quality control; a design information knowledge acquisition unit (12) that acquires design information and knowledge used in the calculation of parameter adjustment content; an information storage unit (13) that stores the parameters and quality information acquired by the information acquisition unit (11) as information and stores the design information and knowledge acquired by the design information knowledge acquisition unit (12) as information; a parameter adjustment content calculation unit (14) that calculates parameter adjustment content based on the information stored in the information storage unit (13); and a parameter adjustment content output unit (15) that outputs parameter adjustment content to the communication system in which the parameters are adjusted according to the parameter adjustment content.

The application provides a robot cluster knowledge sharing method with body perception driving, belonging to the technical field of computer data processing, and generating a verifiable knowledge unit with a perception fingerprint containing sensor modalities and state parameters according to the perception data and task state of the robot. Through the on-demand accurate allocation of communication and computing resources, the overall operation efficiency of the robot cluster is greatly optimized, the passive global information broadcast is changed into the active knowledge acquisition of the robot individual based on the task demand, and the metabolic fusion mechanism based on the trust chain is combined to ensure that each robot only processes the most critical and most credible information for the current task, avoids information overload, significantly reduces the network bandwidth and individual processor load, and provides support for the deployment and application of large-scale robot clusters.

A biomimetic architecture-based intelligent iterative execution system and method are disclosed. The system includes an upper-layer main unit, a knowledge acquisition module, a security isolation module, and multiple lower-layer main units. This invention acquires external instructions through the active interaction unit of the upper-layer main unit, pre-generates a target result screen, and uses it as a unified business execution benchmark screen for the entire system. During task execution, the tool execution module plans the execution path based on this target result screen. Throughout the execution process, the benchmark screen is used as a verification standard to perform real-time numerical difference calculations between the real-time execution result and the target result screen, generating a deviation vector and continuously iterating and converging. This effectively solves the industry pain points of uncontrollable results, lack of real-time closed-loop correction, and lack of intelligent iterative capabilities during complex task execution, achieving high-precision, autonomously iterative, and highly stable intelligent task execution.

This invention relates to the field of terminology database technology in the construction engineering field, and discloses a method and system for constructing and applying a terminology database in the construction engineering field. The method includes preprocessing unstructured data to obtain structured construction engineering text data; extracting structured construction engineering text data based on a joint extraction architecture to obtain structured terminology data and construction engineering triple information; establishing a standard database and a terminology database; constructing a terminology-standard two-layer knowledge graph based on the inherent relationship between the standard database and the terminology database and the construction engineering triple information; and responding to user queries by using a hybrid decision-making mechanism based on rule-based reasoning and graph neural network learning to call the standard database, terminology database, and terminology-standard two-layer knowledge graph to obtain query results. This invention realizes an intelligent closed loop for the entire lifecycle of construction engineering terminology from extraction and management to application, solving the problems of difficult acquisition, complex management, and inefficient application of industry terminology knowledge.

The application belongs to the technical field of industrial digitization and artificial intelligence, and particularly relates to a kind of automobile parts enterprise knowledge generation and intelligent question answering system based on large model technology, through integrating multiple function platforms, the knowledge retrieval and generation path of "plug-in-knowledge base-large model" is constructed, and the large model technology is combined with the actual knowledge data of enterprise deeply, in the form of multi-modal of voice / text input / output + digital person audio picture synchronous dynamic interaction, the full-link intelligent service of "intention recognition-knowledge retrieval-business execution" is realized, the goals of efficiency improvement and digital operation agile iteration and continuous upgrading of manufacturing, research and development, operation and other businesses are achieved, the technical problems of poor adaptability of general model industry knowledge, split of multi-modal interaction and business system, low utilization rate of enterprise massive heterogeneous data and low efficiency of internal knowledge acquisition are solved.

The application discloses a question and answer method, a knowledge graph completion method and related devices, and the method comprises the following steps: using the representation of the first head text entity and the first text relationship of the tail entity to be supplemented in the target knowledge graph, and predicting a plurality of tail entity prediction representations; based on the plurality of tail entity prediction representations, a target tail text entity is found out from the target knowledge graph, and the first head text entity, the first text relationship and the target tail text entity are used to generate the supplemented knowledge of the target knowledge graph; a question to be answered is acquired; based on the question representation of the question to be answered, the knowledge matched with the question to be answered is found out from the target knowledge graph supplemented by knowledge; and based on the knowledge matched with the question to be answered, the answer to the question to be answered is obtained, so that the accuracy of the knowledge graph question and answer can be improved by supplementing the target knowledge graph and then answering the question to be answered.

This invention relates to the field of intelligent customer service, specifically disclosing a method and system for constructing a customer service knowledge base based on a knowledge graph. The method includes: identifying implicit needs by analyzing the spatiotemporal correlation between user actions and customer service dialogues, generating enhanced knowledge units that integrate explicit and implicit knowledge; constructing a knowledge graph, achieving adaptive optimization of the knowledge graph through real-time monitoring of relationship strength changes, forming a dynamic knowledge graph; capturing the user's current contextual information, matching it with the dynamic knowledge graph to generate contextualized knowledge subgraphs, and using multi-path reasoning to generate personalized response content; the system includes modules for implementing the above steps. This invention effectively solves the shortcomings of traditional customer service knowledge bases in implicit knowledge mining, dynamic updating, and deep contextual awareness, achieving improvements in knowledge acquisition from partial to comprehensive, knowledge base from static to dynamic, and responses from general to personalized, significantly enhancing the cognitive depth and service quality of intelligent customer service systems.

The utility model discloses a rocket grading display operating platform device relates to teaching demonstration tool technical field, include: support vertical fixed on operating platform, first guide rail device and second guide rail device are all vertically arranged on the support, first rocket model is connected through the first sliding block with first guide rail device sliding, parent arrow model is connected with the arrow model through separating device, the arrow model is connected through the second sliding block with second guide rail device sliding, the baffle is set up in second guide rail device one side, first motor and second motor are connected with first guide rail device and second guide rail device drive respectively, first travel switch and second travel switch are set up in first guide rail device one side and second guide rail device one side correspondingly, first motor, second motor, first travel switch and second travel switch all are connected with controller. Can intuit and can truly reflect the flight height difference of first rocket and multistage rocket, has improved the interest of student's study, has reduced the related knowledge acquisition difficulty.

The present application relates to computer bottom layer data processing and distributed feature operation technical field, specifically for a kind of multi-source gradient data processing system based on manifold reconstruction;Including: data and knowledge acquisition module, for obtaining the local model gradient update vector uploaded by multi-source data end, knowledge base contains gradient deviation typical mode feature;Ideal manifold reconstruction and simulation module, for generating gradient drift template and gradient anomaly template;Double-track difference feature extraction module, for calculating the theoretical drift deviation and theoretical anomaly deviation between gradient drift template, gradient anomaly template and quantization feature matrix respectively;Situation coupling decision module, for executing the trusted writing of multi-source gradient data or the release or isolated storage instruction of abnormal data rejection;The present application effectively improves the robustness of bottom layer multi-source feature data processing and the reliability of system memory update.

A software-based method for multimodal industrial knowledge throughout the product lifecycle includes the following steps: Step 1: Obtain the sources and requirements of multimodal industrial data throughout the product lifecycle, and perform cluster analysis on the multimodal industrial data; Step 2: Reuse and map existing ontology and standards to construct a hierarchical multimodal industrial knowledge ontology system; Step 3: Convert multimodal industrial data into text modality; utilize text knowledge mining methods to achieve multimodal industrial data knowledge mining, forming clusters of industrial knowledge triplets for each modality; Step 4: Globally encode the clusters of industrial knowledge triplets for each modality, and perform modal fusion and cross-modal alignment to achieve cross-modal industrial knowledge fusion; Step 5: Use the multimodal industrial knowledge graph throughout the product lifecycle as the knowledge reuse foundation to support the personalized configuration development of ubiquitous deployment industrial software for multi-scenario intelligent businesses, realizing a software-based standard process for industrial knowledge acquisition, mining, and reuse.

The application relates to the technical field of knowledge graphs, can be applied to business scenarios such as financial technology, and discloses a time-series knowledge graph driven knowledge retrieval method, device, equipment and medium, which comprises the following steps: subjecting unstructured text in a target field to field adaptability training to obtain a field-specific large model, using the model to extract entity relationship triples from business source text and construct a dynamic time-series knowledge graph, simultaneously performing comparative learning training on multi-modal data to establish cross-modal semantic indexing, and when a query request is received, performing joint retrieval and outputting feedback content. Through the fusion of field semantic modeling, time-series knowledge organization and cross-modal retrieval mechanism, the application enables scattered unstructured and multi-modal data to form a knowledge system that can evolve over time and be uniformly retrieved, thereby improving the accuracy of knowledge acquisition and the efficiency of business response.

This invention provides a fake face image detection method based on misleading learning. S1: Construct a prior knowledge acquisition module to build a basic fake artifact detection capability for the fake feature extractor and detector; S2: Build a misleading learning knowledge flow and a biased data knowledge flow; S3: Introduce a single-channel attention fusion network to enable the model to adaptively select the required multi-scale fake latent features and multi-scale real image latent features; S4: Construct a high-pass filter specifically for misleading learning to preprocess the fake images; S5: Introduce a misleading learning loss to constrain the training of the fake feature extractor; S6: Fine-tune the fake feature extractor using an external adaptor. This invention enables the model to minimize the feature dependence bias caused by irrelevant semantic features in the image, exhibiting excellent detection capabilities for fake images with different statistical labels, achieving optimal performance in both intra-domain and cross-domain tests.

This application discloses a multi-agent knowledge sharing method and computing device, relating to the field of artificial intelligence security technology. The method is applied to a computing device capable of coordinating multiple agents to execute tasks. The method includes: receiving a target task, which requires the cooperation of multiple agents; acquiring target knowledge to be shared by a first agent among the multiple agents; the target knowledge being knowledge generated by a preset model included in the first agent executing the target task; acquiring adjustment parameters; determining the target noise intensity corresponding to the target knowledge based on the adjustment parameters and the basic noise intensity; adding noise to the target knowledge based on the target noise intensity, and using the noise-added target knowledge for a second agent among the multiple agents to execute the target task. This allows for a precise balance between the privacy protection strength and the usability of the knowledge.

The application discloses a kind of based on multi-modal information's hemodialysis wisdom auxiliary diagnosis and treatment method and system, it is related to the field of diagnosis and treatment system, the method includes the following steps: step 1, collect prescription data and sign data, store into hemodialysis knowledge base, and the prescription data and sign data are carried out ETL preprocessing, respectively generate dialysis log and sign log;Step 2, prescription data is trained by means of LDA theme model, obtains the parameter and drug distribution of efficacy theme, the efficacy theme distribution of each dialysis;Step 3, using K-means++ algorithm is carried out cluster analysis to the efficacy theme distribution of each dialysis, the efficacy theme distribution of each dialysis is changed into an efficacy combination label sequence;Step 4, the parameter and weight of convolutional neural network are determined;Step 5, output patient dialysis prescription recommendation.The system includes knowledge acquisition module, reasoning module, explanation module and man-machine interaction module, also contain based on multi-modal information's hemodialysis wisdom auxiliary diagnosis and treatment method.

The application provides an enterprise knowledge question answering method, system and electronic equipment, the method comprises the following steps: obtaining a user's question request information, the question request information comprises a question text and a target identity identifier; generating a query vector based on the question text; searching according to the query vector and the target identity identifier to obtain a target text vector; and generating an answer text based on the target text vector and the question text; the method can realize fast searching while avoiding user's unauthorized access, effectively reducing the risk of data leakage, improving the security of enterprise knowledge question answering, realizing the leap from returning documents to direct answering, reducing the burden of employee information screening and integration, and improving the efficiency of knowledge acquisition.

The invention relates to the technical field of intelligent teaching, in particular to a Chinese classical aboriginal intelligent teaching system based on a knowledge graph and a construction method, and the method comprises the steps: a knowledge collection module collects classical aboriginal texts, literature research, multimedia teaching and teaching feedback data; the knowledge graph construction module is used for constructing a graph and mining hidden association between knowledge by applying natural language processing and a graph neural network algorithm on the basis of knowledge attributes and semantic association; the intelligent teaching center recognizes knowledge weak points by improving a BERT-GNN model and generates a personalized teaching scheme; the story situation interaction unit constructs a situation learning field domain, renders plots, figures and culture scenes, and triggers an exploration type task; the teaching adaptive regulation and control unit determines the knowledge mastering progress and adjusts the teaching content and the interaction mode according to the teaching scheme and the real-time data, and continuously optimizes the learning path and the emotional experience through meta-cognitive guidance and adaptive feedback. Therefore, the problems of poor interactivity, fragmentation of teaching content and the like in the prior art are solved.

This invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, relating to the fields of intelligent manufacturing and artificial intelligence. The invention constructs a multi-agent diagnostic system centered on explicit knowledge representation and collaborative reasoning. Through knowledge acquisition and compilation, the natural language output of the large language model is transformed into rules, facts, and ontology fragments that can be directly consumed by the inference engine. Combined with metadata containing source and timestamps, continuous incremental updates of cross-domain knowledge are achieved, overcoming the limitations of traditional static knowledge bases in covering dynamic faults. Secondly, using a blackboard and agenda mechanism as a collaborative carrier, intermediate assertions of agents such as vibration, circuits, and logs are structured for publication and subscription. A conflict resolution and consistency verification module makes unified decisions based on specificity, time freshness, and source credibility, thereby avoiding the accumulation of delays and uncertainties caused by long-chain natural language dialogues in highly real-time scenarios.

To provide technology that can generate explanations for data. [Solution] An information processing device according to one aspect of the present disclosure includes: a selection unit for selecting a type of description to be generated for data relating to equipment; a data acquisition unit for acquiring the data according to the selected type; a domain knowledge acquisition unit for acquiring domain knowledge of the equipment; a prompt creation unit for creating a prompt that instructs the generation of the description based on the data and the domain knowledge; and an explanation generation unit for generating information representing the description based on the prompt and a generation AI realized by a machine learning model including a large-scale language model.

This invention relates to the fields of industrial equipment fault diagnosis and artificial intelligence technology, and in particular to a method and apparatus for fault diagnosis of green hydrogen production equipment based on a large-scale model. The method includes responding to a query request generated by maintenance personnel based on alarm logs; retrieving external knowledge related to the query semantics from a fault knowledge base of all green hydrogen production equipment based on the query request; acquiring multimodal data within a preset time window prior to the triggering time of the query request; extracting features from the multimodal data to obtain data trend features and semantic features; constructing enhanced prompts using the external knowledge, data trend features, and semantic features; inputting the enhanced prompts into a trained target large-scale language model to obtain root cause analysis results. This invention can apply a domain-specific large-scale language model to the field of green hydrogen production, fully utilizing the contextual understanding capabilities of the domain-specific large-scale language model to make the reasoning results more accurate.

A method for truth-based, synthetic, partial-entity knowledge sourcing and tradeable asset creation based thereon, is provided. The method may receive a request to transact a long position or a short position with respect to a performance of a selected aspect of a larger entity. The method determines, using artificial intelligence (“AI”) running on a processor, a set of issuance characteristics. The issuance characteristics support the request to transact the long position or short position. The method data-mines historic information to retrieve legacy data representative of the set of issuance characteristics. The data-mining determines whether the set of issuance characteristics supports the request to transact. The method tests the legacy data to determine whether the issuance characteristics support the request to transact. In response to a determination that the issuance characteristics support the request, the method prompts the user to transact the requested long position or the requested short position.