29 results about "Web retrieval" patented technology

The invention discloses an intelligent question and answer method and system, electronic equipment and an intelligent question and answer large model, and relates to the technical field of intelligent question and answer. The method comprises the steps of receiving query information reported by a user terminal, and obtaining a user intention according to the query information; if the user intention is the preset high-frequency question and answer intention, obtaining a pre-generated high-frequency full-text question answer corresponding to the user intention, and sending the high-frequency full-text question answer to the user terminal; if the user intention is not the preset high-frequency question and answer intention, performing multi-source heterogeneous knowledge retrieval, generating answer information according to a retrieval result, and sending the answer information to the user terminal, comprising at least one of retrieval in a pre-generated knowledge base according to the query information and network retrieval according to the query information.

A system configured to extract, classify, and order entity insights may include a computing system, a computer readable memory, at least one processor having instruction configured for ingesting data into the system from at least one external application programming interface configured to retrieve data over a network, integrating the data to form a cohesive data set using the at least one processor, storing the cohesive data set into the computer readable memory, processing the cohesive data set by the at least one processor using at least one machine learning model to generate a plurality of entity insights, each of the insights having at least one classification associated therewith, ordering the plurality of the entity insights based on a machine generated prioritization, and generating a presentation comprising the plurality of entity insights in human-readable form.

A computerized method for generating an answer to an input question, the method has the steps of: identifying, in a knowledge graph (KG), one or more entities included in the input question; finding, in the KG, a plurality of reasoning paths for the one or more entities based on comparison of a similarity between an embedding of each of the plurality of reasoning paths and an embedding of the input question; retrieving a plurality of quotes from a network, each quote being a piece of text related to the input question taken verbatim from the network; and sending the input question, and an input set to a foundation model (FM) to obtain the answer, the input set comprising the plurality of reasoning paths and the plurality of quotes.

The invention discloses an actinide alloy knowledge question-answering system based on a large language model and a storage medium, and belongs to the technical field of actinide alloy knowledge question-answering systems and artificial intelligence. The question-answering system comprises a knowledge base construction module, a user input enhancement module, a retrieval knowledge arrangement module and a reply module; the knowledge base construction module is used for constructing a knowledge base, collecting actinide alloy knowledge through network retrieval and carrying out structured processing on the actinide alloy knowledge; the user input enhancement module is used for performing semantic optimization on a user input problem; the retrieval knowledge arrangement module is used for finely sorting the retrieval and collection results; and the reply module is used for understanding the query intention of the user, recalling related professional contents from the knowledge base and generating answers. According to the system, non-professionals can quickly obtain required actinide alloy knowledge, interdisciplinary communication and cooperation are promoted, and popularization and innovative development of the actinide alloy technology are promoted.

The invention discloses a reinforcement learning system and method fusing multi-source deep retrieval and reasoning ability, and belongs to the technical field of artificial intelligence. The system comprises a large-model-driven reasoning agent which works in cooperation with a multi-source toolkit integrating local retrieval and network retrieval, and a large-model-driven reasoning agent which works in cooperation with the multi-source toolkit integrating local retrieval and network retrieval. Through a reinforcement learning framework designed by the invention, when the intelligent agent is trained to solve a complex problem, not only can the time of information retrieval be autonomously decided, but also which retrieval tool including a local knowledge base or network search can be intelligently selected and called, and a retrieval result is seamlessly fused into a multi-step reasoning chain. According to the method, the accuracy, timeliness and reliability of an artificial intelligence model in tasks such as question answering and reasoning are effectively improved, and the problems of knowledge illusion and static property of an existing large model are solved.

The invention discloses an automatic express parcel sorting system based on artificial intelligence, which relates to the technical field of automatic express parcel sorting and comprises a multi-mode sensing layer unit, an intelligent control module, a normal sorting execution mechanism, an abnormal sorting execution mechanism, an abnormal supplement identification module and an abnormal compensation processing device. The multi-mode sensing layer unit integrates double information acquisition modes of visual sensing and label scanning, and can effectively deal with complex scenes such as label abrasion, fouling and folding. And the abnormal sorting execution mechanism and the abnormal supplementary recognition module realize automatic distribution and multi-dimensional supplementary recognition of abnormal packages. Multiple sets of visual supplementary sensing modules and label supplementary scanning modules are used for omnibearing acquisition of six surfaces of the parcel, so that the problem of blind area identification of a limited area on the surface of the parcel by a traditional system is solved; and the network retrieval module carries out information completion through a compliance logistics information platform interface to form an automatic processing chain for identifying, shunting and supplementing identification information completion, so that the manual intervention demand is greatly reduced.

An attribution management system for rewarding view-based attribution on an online platform includes an attribution server that retrieves from a business manager over a data communication network using an Application Programming Interface (API), a set of candidate posts. Pixel traffic from the set of candidate posts is tracked based on first party data and social media posts. User interaction, including clicks and views, is identified on the set of candidate posts. Candidate posts below a pre-determined threshold value are boosted using a predetermined number of user interactions. A source of the one or more boosted candidate posts is identified using tags. At least one purchase from the views on one or more boosted candidate posts is identified. An attribution amount for each owner of the one or more boosted candidate posts is determined based on the at least one purchase using the source identified by the tags.

A method is disclosed. A data set including: (a) identifiers of a set of incidents occurring within a defined geographic region to which at least one service provider responded during a first time period and (b) address data identifying a location within the geographic region of each said incident of the set is retrieved over a network. An instruction to generate a heat map of the incidents occurring within the geographic region during the first time period is received from a user via a user interface generated to a display device. In response to the instruction to generate the heat map, the address data is converted to GPS data. A heat map of an aerial view of the geographic region based on the GPS data is generated. The heat map is displayed to the display device in a user interface.

The application discloses a fault maintenance method and device, equipment, a storage medium and a computer program product, and relates to the technical field of computers. The fault maintenance method comprises the following steps: when a query question about the cause of a fault of a target device is received, performing intention recognition on the query question by using a target language large model to obtain an intention recognition result; calling a vocabulary resource library according to the intention recognition result, and querying a fault maintenance knowledge graph generated based on a target document large model according to a target vocabulary obtained by calling, to obtain a plurality of answer cases; performing network resource retrieval according to the answer cases to obtain a network retrieval result of fault classification; determining fault classification according to the answer cases and the network retrieval result, and generating a fault solution recommendation scheme according to the fault classification. In this way, customized and accurate fault elimination suggestions are provided, and the efficiency and accuracy of fault maintenance are improved by relying on a knowledge graph and a large model.

The invention provides a large model event analysis enhancement method based on multi-dimensional retrieval enhancement generation, and relates to the technical field of artificial intelligence and information analysis crossing. After to-be-retrieved information is obtained, on one hand, a network retrieval result is inquired on a network, an image-text consistency evaluation result and a semantic authenticity evaluation result are obtained according to the network retrieval result, and first corpus data are obtained by integrating the network retrieval result, the image-text consistency evaluation result and the semantic authenticity evaluation result; on the other hand, the to-be-retrieved information generates retrieval vectors in multiple dimensions, a knowledge retrieval result of each dimension is obtained through retrieval in a corresponding preset knowledge database, and the knowledge retrieval results of multiple dimensions are integrated to obtain second corpus data. And combining the first corpus data and the second corpus data to obtain an enhanced corpus, and taking the enhanced corpus as semantic data to be analyzed by a large model. When a large model executes an analysis task by using the semantic data provided by the scheme of the invention, an analysis result with higher credibility can be obtained.

Techniques are described for providing multifactor authentication using randomly selected Digital Identity factors associated with a human user or individual seeking access to a facility, building, computer system, network, application, memory, etc. Digital Identity factors are or memory. Digital Identity factors are retrieved and stored by an authentication agent. The Digital Identity factors can be retrieved from a network such as the internet or can be previously provided by a customer or human individual requesting authorization. Upon receiving a request for authentication, a plurality of Digital Authentication factors are randomly selected. A request is sent to an environment associated with the human individual seeking access, the request including information regarding the randomly selected Digital Authentication factors. A reply is received having information regarding the randomly selected authentication factors. The information in the reply is compared with the randomly Digital Identity factors to determine whether to grant or deny access.

The invention relates to the technical field of artificial intelligence, in particular to a multi-round network retrieval self-error correction method, an intelligent question and answer method based on multi-round network retrieval self-error correction and a related device.The method comprises the steps that similarity matching is conducted on a given question and a retrieved preliminary answer, and a similarity level is obtained; in response to high similarity, a multi-round network retrieval self-error correction process is not triggered; and in response to similarity levels except for high similarity, triggering different rounds of network retrieval self-error correction processes according to a preset triggering mechanism. According to the method, the similarity matching result between the given question and the retrieved preliminary answer is determined based on the similarity label generation model, and different rounds of network retrieval self-error correction processes are triggered, so that the accuracy and efficiency of obtaining related knowledge blocks are greatly improved through network retrieval; and furthermore, through multi-round network retrieval self-error correction, the correlation between the knowledge blocks obtained through retrieval and the questions is improved, and the question and answer precision of a subsequent large language model is conveniently improved.

The present invention discloses a traditional Chinese medicine intelligent consultation method and system based on an AI large language model. The system includes a data acquisition module, an image processing module, a semantic analysis module, a disease database management module, a multimodal fusion module, a neural network retrieval module, an interaction optimization module, and an output module. The intelligent consultation method includes obtaining facial images, body images, and tongue coating images of a patient; constructing a disease database; based on the image data, collecting the voice responses of the patient, extracting the semantic content and language breath features in the voice, and the language breath features include speech rate, pitch, and pause frequency; converting the semantic content of the patient into traditional Chinese medicine professional terms, associating with the synonym list in the disease database, and normalizing inconsistent terms; fusing the image data, normalized terms, and language breath features through a multimodal neural network model; based on the comprehensive feature vector, performing a difference analysis on the remaining symptoms of the candidate diseases; and outputting the final diagnosis result.

The application discloses a video time retrieval method based on feature representation enhancement, and steps are as follows: generating a training set; constructing a video time retrieval network; training the video time retrieval network; and retrieving a video time. The application constructs a video time retrieval network based on feature representation enhancement, adds a query timing correction operation to a query feature extraction module, arranges all subject-predicate-object triples describing different actions in a to-be-queried sentence according to time sequences of the corresponding actions, realizes cross-modal feature sequence order matching, and reduces cross-modal semantic alignment difficulty. The application constructs a double-connection graph convolution module, takes candidate segment features as graph nodes, connects the candidate segment with global candidate segments of the front and rear parts through graph convolution, realizes fusion of the candidate segment features and the video global features, and improves candidate segment feature representation quality.

The invention relates to the field of network information fact verification research, in particular to a fact verification system relating to webpage content matching and natural language reasoning, which extracts key information from user input, retrieves and screens webpage content by combining the natural language reasoning capability of a large language model and a webpage retrieval enhancement technology, and finally verifies the webpage content. According to the method, webpage authority and semantic similarity are calculated, finally, the reasoning result of the large model and general knowledge judgment are synthesized, credible and related output content is generated, complex expression verification of the professional field can be met, and timeliness is achieved.

The invention discloses a text interaction method, system and device based on a large language model and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that a query statement is received, a query vector is obtained according to the query statement, and at least one local vector and a corresponding local text segment are obtained in a vector database according to the query vector; performing network retrieval according to the search keyword list of the query statement to obtain a network retrieval result, segmenting the network retrieval result to obtain at least one network text segment, and vectorizing the at least one network text segment to obtain a corresponding paragraph vector; determining a target text segment based on the sorting result; inputting the query statement and the target text segment into a large language model to obtain text interaction feedback information corresponding to the query statement; through multi-source retrieval and correlation coefficient reordering, the expansion of the retrieval coverage rate is realized, and the timeliness of the retrieval result is improved.

The invention discloses a network retrieval enhancement generation method and system oriented to large model cue word optimization, and belongs to the field of natural language processing. The method comprises the steps of obtaining an instruction input by a user; performing word segmentation on the instruction by using a word segmentation tool and extracting keywords; performing network retrieval through an external search engine according to the keywords to obtain a retrieval document set; performing text segmentation processing on the retrieval document set to obtain a plurality of text blocks; vectorizing the text blocks and storing the vectorized text blocks in a vector database; retrieving text blocks related to the instruction from a vector database to obtain a related text block set; reordering the related text block set and selecting text blocks as enhanced contexts; integrating the enhanced context with the user instruction to generate a final cue word; and inputting the final cue word into the large model, and obtaining a response result output by the large model. According to the invention, the answer accuracy, correlation and practicability of the LLM in diversified scenes are improved.

The invention discloses a joint optimization retrieval enhancement generation system and method, and relates to the technical field of artificial intelligence, and the system comprises a strategy network, a retriever and a reinforcement learning module; wherein the strategy network is used for determining whether retrieval is needed or not according to an input state, generating a retrieval query and generating a final answer; the retriever is used for returning related information according to a retrieval query generated by the strategy network; and the reinforcement learning module is used for performing joint optimization on the strategy network and the retriever through a reinforcement learning algorithm. The strategy network and the retriever are subjected to collaborative optimization through the reinforcement learning algorithm, so that the system can integrally optimize collaborative work among all the modules, the overall performance of the system is remarkably improved, retrieval efficiency, answer generation quality, response speed and the like are remarkably improved, and by dynamically adjusting behaviors of all the modules, the search efficiency of the system is improved. The system can be excellent in performance in different input states, and the requirement of a user for efficient and accurate knowledge acquisition is met.

A method is disclosed. A data set including: (a) identifiers of a set of incidents occurring within a defined geographic region to which at least one service provider responded during a first time period and (b) address data identifying a location within the geographic region of each said incident of the set is retrieved over a network. An instruction to generate a heat map of the incidents occurring within the geographic region during the first time period is received from a user via a user interface generated to a display device. In response to the instruction to generate the heat map, the address data is converted to GPS data. A heat map of an aerial view of the geographic region based on the GPS data is generated. The heat map is displayed to the display device in a user interface.

This invention relates to the field of sustainability, and particularly to the fields of sustainable production, end-product reuse, and end-of-life recycling. This disclosure relates to methods, apparatus, and computer elements for retrieving data from a distributed network associated with output products and / or input materials used to produce such output products.

An attribution management system for rewarding view-based attribution on an online platform includes an attribution server that retrieves from a business manager over a data communication network using an Application Programming Interface (API), a set of candidate posts. Pixel traffic from the set of candidate posts is tracked based on first party data and social media posts. User interaction, including clicks and views, is identified on the set of candidate posts. Candidate posts below a pre-determined threshold value are boosted using a predetermined number of user interactions. A source of the one or more boosted candidate posts is identified using tags. At least one purchase from the views on one or more boosted candidate posts is identified. An attribution amount for each owner of the one or more boosted candidate posts is determined based on the at least one purchase using the source identified by the tags.

An attribution management system for rewarding view-based attribution on an online platform includes an attribution server that retrieves from a business manager over a data communication network using an Application Programming Interface (API), a set of candidate posts. Pixel traffic from the set of candidate posts is tracked based on first party data and social media posts. User interaction, including clicks and views, is identified on the set of candidate posts. Candidate posts below a pre-determined threshold value are boosted using a predetermined number of user interactions. A source of the one or more boosted candidate posts is identified using tags. At least one purchase from the views on one or more boosted candidate posts is identified. An attribution amount for each owner of the one or more boosted candidate posts is determined based on the at least one purchase using the source identified by the tags.

A talking head digital identity immutable dual authentication method for use over a distributed network, comprising: downloading talking head and talking head show files from nodes of a distributed network; downloading hash values of a talking head and talking head show from a blockchain; sending a hash of a publisher's identification from the blockchain to a name lookup service; retrieving and confirming the identity of the publisher; recalculating hash values of the talking head and talking head show; comparing the recalculated hash values of the talking head and talking head show with the hash values of the talking head and talking head show retrieved from the blockchain; incorporating a 3D mesh model into the talking head show; starting playback of the talking head show if the hash values received from the blockchain match the recalculated hash values of the talking head and talking head show retrieved from the distributed network.

Embedded resource layouts are presented by rendering an interface document to cause a graphical interface to be displayed in accordance with appearance specifications for the graphical interface in the interface document, embedding, into the interface document in response to rendering the interface document, a resource layout document corresponding to a reference in the interface document and a user identity, retrieving, through a network in response to embedding the resource layout document, a plurality of resource values, each resource value among the plurality of resource values corresponding to a resource reference in the resource layout document, and arranging the plurality of resource values in the graphical interface in accordance with positioning specifications in the resource layout document.

The embodiment of the invention provides an information retrieval method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of information retrieval. According to the method, the target question input by the user and the historical dialogue are combined for local retrieval and network retrieval, and if a credible local retrieval result exists, the retrieval answer generated according to the local retrieval result is directly returned to the user, so that the response speed of retrieval is increased. And if the local retrieval result is not credible, waiting for the network retrieval result, and returning the retrieval answer generated according to the network retrieval result to the user, so that the retrieval quality and the response speed are effectively balanced. The local retrieval library is dynamically updated through the local retrieval result and the network retrieval result, linkage of the local retrieval library and network resources is achieved, local retrieval with higher reliability can be continuously provided while the updating cost of the local retrieval library can be reduced, and the response speed is further increased.

The present invention provides a cross-session recommendation method and system for a multi-interest fusion multi-modal knowledge graph, which relates to the field of data processing. Among them, the method includes: obtaining multiple historical session sequences and the current session sequence of the current user; constructing multiple historical interest graphs of the current user based on the multiple historical session sequences of the current user; constructing multiple current interest graphs of the current user based on the current session sequence; generating an aggregated embedding representation based on the multiple historical interest graphs and the multiple current interest graphs of the current user through a multi-modal knowledge graph attention mechanism; retrieving similar historical sessions of other users corresponding to the current session of the current user through a similar session retrieval network; generating a cross-session recommendation based on the aggregated embedding representation and the similar historical sessions of other users, which has the advantage of improving the effect of cross-session recommendation.

This invention discloses a multimodal agent-enhanced retrieval algorithm, belonging to the field of enhanced retrieval algorithm technology. The invention includes a multimodal text conversion unit, a vector database construction unit, and a multimodal knowledge graph (MMKG) construction unit. It enhances cross-modal reasoning capabilities, solving the modality disconnect problem in existing technologies: establishing visual-text associations through MMKG. It improves the efficiency of multi-source knowledge synthesis by employing plug-in parallel retrieval. Through consistency verification of the decision-making agent and expert refinement, it reduces the answer illusion rate, i.e., the proportion that conflicts with facts; MMKG embeds visual data storage paths, and web retrieval provides URL tracing, allowing users to trace the source of each fact in the answer, thus solving the black-box reasoning problem in existing RAGs.

The invention provides an interview data processing method and device based on a large model, electronic equipment, a storage medium and a computer program product, relates to the technical field of artificial intelligence, in particular to the technical fields of large models, natural language understanding, simulated interview and the like, and can be applied to simulated interview scenes. According to the specific implementation scheme, a retrieval request is generated through a first large model according to question and answer data in the interview process; performing network retrieval according to the retrieval request to obtain reference data; and through the second large model, generating answer optimization data of the question and answer data according to the reference data. According to the method, the artificial intelligence large model and the network retrieval are combined, so that the professionality and the accuracy of the answer optimization data are improved, the interview skill of the interviewer is improved, and the simulated interview requirement of the interviewer is met.

A computerized method for generating an answer to an input question, the method has the steps of: identifying, in a knowledge graph (KG), one or more entities included in the input question; finding, in the KG, a plurality of reasoning paths for the one or more entities based on comparison of a similarity between an embedding of each of the plurality of reasoning paths and an embedding of the input question; retrieving a plurality of quotes from a network, each quote being a piece of text related to the input question taken verbatim from the network; and sending the input question, and an input set to a foundation model (FM) to obtain the answer, the input set comprising the plurality of reasoning paths and the plurality of quotes.