319results about How to "Exact search" patented technology

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map searching method and system based on local semantics and global communitization, and the method comprises the steps: extracting entities and relationships thereof from a knowledge map to construct an undirected graph; the undirected graph is divided into multiple levels of community information, and a community report is obtained by generating a community information table; selecting an instance triple containing data source information from the knowledge graph, constructing an instance triple information source table, extracting IDs, names and attributes of entities, and generating an entity attribute table to obtain an entity attribute vector table; key entities in a to-be-queried question are extracted and subjected to vector representation, the similarity between the key entities and an entity attribute vector table is calculated, first N related entities are selected to generate contexts (including an entity context, a relation context, a data source context and a community context), and finally a search result of the to-be-queried question is obtained. According to the method, the information related to the to-be-queried question can be accurately retrieved from the knowledge graph.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

The invention discloses a personalized medical record analysis recommendation method and system based on a patient portrait. The method comprises the following steps: S1, generating a preprocessed health data set; s2, according to the preprocessed health data set, detecting a health event of the patient through a predefined health event triggering rule and an event classification model; s3, based on the priority of the health event, setting a trigger condition to start an updating process of the patient portrait; s4, the preprocessed health data set is combined with health event information, and a dynamic patient portrait is generated through a multi-modal data fusion technology and a time sequence modeling technology; s5, calculating a similarity index and selecting an optimally matched similar medical record; s6, according to the intelligent medical record analysis result and the medical knowledge graph, a diagnosis and treatment suggestion for the current patient is generated in combination with the rule base. The intelligent level and the precision level of the personalized medical record analysis recommendation system are remarkably improved.

The invention relates to the technical field of question answering systems. The question answering system comprises a language understanding generation module, a multi-agent coordination module, a knowledge graph retrieval module, an emotion analysis adaptation module, a virtual reality interaction module, a time travel simulation module, a cross-language processing module and a future prediction reasoning module. The language understanding generation module is used for semantic analysis and response generation; the multi-agent coordination module is used for agent distribution and agent interaction; according to the multi-agent question answering system based on the large language model, through deep semantic analysis of the large language model, the intention and key information of a user question can be accurately understood, and according to the type and field of the question, the most suitable agent can be dynamically allocated for answering; necessary communication and cooperation can be carried out among a plurality of agents, information circulation and answer consistency are ensured, a cross-domain knowledge graph is constructed, and rich knowledge support is provided for answer of complex questions.

The invention discloses an equipment fault natural language question and answer method and system based on cue word optimization. The method comprises the steps of obtaining an equipment fault problem which is input by a user and described by a natural language; performing natural language understanding and semantic representation on the question, and identifying core words and dimensions; generating an optimal query prompt word based on the core word and the dimension; combining the optimal cue word with a pre-constructed equipment fault knowledge base, and constructing query and retrieving related knowledge; generating a natural language reply based on the retrieval knowledge and presenting the natural language reply to the user; obtaining user feedback information; and based on the feedback information, optimizing the cue word generation strategy and updating the knowledge base to form a closed-loop learning process. The system further comprises innovation modules such as cross-modal fusion and understanding, intelligent labeling and knowledge extension. According to the method, innovative mechanisms such as cue word optimization generation, knowledge base closed-loop optimization, multi-source heterogeneous data fusion utilization and low-cost knowledge extension are provided, and highly-intelligent fault diagnosis and guidance are realized.

The invention provides a feedback pushing method and system based on background quotation data, and relates to the technical field of data processing, and the method comprises the steps: receiving a quotation request uploaded by a client, carrying out the semantic analysis, obtaining a commodity name keyword, carrying out the extension of the commodity name keyword, carrying out the standardization processing of a commodity attribute, and determining the feature of the commodity attribute; determining customer quotation preference features, and comprehensively obtaining multi-dimensional quotation request feature vectors; constructing a combined retrieval condition, extracting feature vectors of the target quotation data, performing weighting, performing screening through a multi-criterion decision model, generating a comprehensive matching degree matrix, and performing screening to obtain high-quality target quotation data; and sorting, determining optimal quotation data, pushing the optimal quotation data, receiving feedback information and feedback scores, if the feedback information is acceptance, generating corresponding order information and sending the order information to a supplier, and if the feedback information is rejection, pushing the optimal quotation data again.

The invention discloses an intelligent retrieval management method and system based on OCR and document analysis, and relates to the technical field of intelligent retrieval management. The system comprises a document scanning and processing module, a semantic analysis and label generation module, a feature extraction and mapping module and a user demand analysis and retrieval module. The document scanning and processing module is used for converting a paper document into an image file by using a scanner and forming a text data set; the semantic analysis and label generation module performs semantic analysis on each text in the text data set to form a semantic label; the feature extraction and mapping module extracts keywords in the text data, generates word vectors of a keyword set, and constructs a mapping matrix of tags and text word vectors; and the user demand analysis and retrieval module analyzes the user demand data, generates a feature matrix queried by the user, evaluates the matching degree between the text data and the user demand, and sorts and outputs retrieval results.

The invention provides a medical AI question and answer method based on retrieval enhancement, which comprises the following steps: acquiring a medical professional knowledge base, and constructing an entity recognition model and a medical knowledge graph; the method comprises the following steps: splitting a medical professional knowledge base, and creating a vector index and a full-text retrieval reverse index; intention classification is carried out, and initial processing is completed; performing multi-path mixed retrieval on the medical professional knowledge base to obtain a user question related text block; a filtering rule is preset, and the user question related text blocks are filtered; answering the user question according to a preset second prompt word, and answering the user question according to a preset third prompt word when the user question exceeds the range of the medical professional knowledge base or the multi-path mixed retrieval has no result. According to the method, related context information can be accurately searched, reliable reference information is provided for a generative large model to answer professional questions, the interactive experience of a medical question answering system is improved, and the accuracy and specialty of answer generation of the system are remarkably improved.

The invention discloses a multi-modal model-based RPA element anchor point automatic searching method. The method comprises the following steps of S1, acquiring coordinates, text information and regional screenshots of target elements and candidate anchor point elements in a webpage; s2, according to the area screenshot, obtaining element categories of target elements and candidate anchor point elements through a target detection model; s3, inputting the coordinates, the text information, the regional screenshots and the element categories of the target elements and the candidate anchor point elements into a multi-modal model, performing vector conversion, vector alignment, element distinguishing, element attention and element judgment by utilizing the multi-modal model, and detecting and determining the target anchor point elements; s4, recording a relative relation between the target element and the anchor point element, wherein the relative relation is used for positioning the target element when the robot process automatically runs; the anchor point elements of the webpage are dynamically recognized through the webpage screenshot, the element coordinates and the element types, and the method has the advantages of being wide in application range and reliable in recognition.

The invention discloses a network strategy intention translation system and method based on retrieval enhancement. The system comprises an intention acquisition module, a retrieval enhancement module, an intention translation module, a network strategy generation module, a network strategy verification module, a network strategy issuing module and the like. The method comprises the following steps: (1) an intention acquisition module acquires a network strategy intention input by a user in a natural language; (2) a retrieval enhancement module retrieves a retrieval document highly related to the business scene; (3) an intention translation module generates an expected intermediate intention template; (4) a network strategy generation module converts the expected intermediate intention template into a specific network configuration strategy; (5) a network strategy verification module verifies the network configuration strategy; and (6) the network strategy issuing module converts the network configuration strategy into a specific network configuration command and pushes the specific network configuration command to corresponding network equipment.

The present invention discloses a laser positioning method and a robot based on image information. The execution subject of the laser positioning method is a robot equipped with a structured light module. The structured light module includes a line laser transmitter and a camera without an infrared filter, so that the imaging information of infrared light and the imaging information of visible light are retained in the image captured by the camera. The laser positioning method includes the following steps: the robot controls the camera to capture an image of light reflected back from the surface of an object to be measured by the line laser emitted by the line laser transmitter, and detects the brightness and darkness type of the image captured by the camera. When the robot detects that the current frame image captured by the camera is a bright frame image, the robot searches for the position of the line laser from the current frame image by executing an inter-frame tracking algorithm. When the robot detects that the current frame image captured by the camera is a dark frame image, the robot extracts the position of the line laser from the current frame image by executing a brightness centroid algorithm.

The invention discloses an intelligent question and answer method and a related device, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a question input by a user and a target document corresponding to the question; the method comprises the following steps: firstly, determining a vector of a problem and vectors of a plurality of text fragments corresponding to a target document, wherein the plurality of text fragments corresponding to the target document are obtained by segmenting the target document from a plurality of granularities based on a document structure; on the basis of the similarity between the vector of the problem and vectors of a plurality of text segments corresponding to the target document, candidate text segments are determined; and finally, based on the candidate text fragments, generating answers corresponding to the questions. According to the method and the device, through the multi-granularity segmentation strategy based on the document structure, the target document can be more efficiently analyzed and analyzed in the subsequent steps on the premise that the integrity of original text information of the target document is not lost, so that information related to the question can be more accurately retrieved, and the accuracy and the reliability of answers are improved.

The invention discloses a thinking chain-based software vulnerability automatic repair method and a storage medium. The method comprises the following steps of S1, preprocessing training data; constructing input and output of the model, including a vulnerability program, a repair position and a repair program; s2, model training; taking a vulnerability program as the input of the model, and constructing a sequence of {a repair position + a repair program} as the expected output; step S3, vulnerability restoration; and S2, inputting a given bug program to be repaired into the trained model obtained in the step S2 after utilizing the input in the step S1, searching possible outputs by utilizing a cluster search technology, and arranging the outputs from high to low according to the possibility to obtain candidate repair programs generated by CotRepair. The storage medium is used for storing a computer program for executing the method. The method has the advantages of simple principle, wide application range, high automation degree, high repairing efficiency and precision and the like.

The retrieval method comprises the following steps: performing semantic enhancement on acquired question information to obtain question enhancement information; extracting a target core word from the analyzed question information by utilizing a preset core word database; retrieving in a preset vector database to obtain a first retrieval result; vectorizing the problem enhancement information by adopting a preset vectorization model; retrieving target vector data similar to the problem vector data from a preset vector database to obtain a second retrieval result; performing retrieval in a preset ES database by using the question enhancement information to obtain a third retrieval result; deduplication and rearrangement are conducted on paragraph information in the three retrieval results, and one or more pieces of target paragraph information are selected from the paragraph information; and splicing the questions and answers, inputting the spliced questions and answers into a preset cue word framework, and inputting the obtained question and answer cue words into a preset language model to obtain answers. According to the method, retrieval is performed from multiple dimensions such as the target core word, the problem vector data and the problem enhancement information, so that the retrieval accuracy is improved.

The invention relates to the technical field of supply chain management data analysis, and discloses a supply chain management data analysis system and method based on machine learning, and the system comprises a supplier object search module, a supplier object service quality analysis module, and a supplier object selection module. The method comprises the following steps: accurately searching a qualified historical supply quality number of a material purchasing target supplier based on target supplier object parameters in combination with an intelligent search algorithm and supplier historical supply behavior information, and accurately analyzing a historical supply quality behavior completion result of the material purchasing target supplier in combination with numerical analysis; supplier service level accurate data analysis is performed by adopting an intelligent search algorithm in combination with the historical supply quantity of a material purchasing target supplier, quality behavior analysis parameters and different supply behavior supplier service level parameters, so that enterprise supply chain supplier service quality dynamic and accurate analysis is realized; and the intelligence and quality of enterprise supply chain management are improved.

The invention relates to the technical field of data processing and analysis, and particularly provides a power dispatching data processing and analysis method and system based on a large language model, a terminal and a storage medium, and the method comprises the steps: S1, receiving information inputted by a user; s2, judging whether a file exists in the information input by the user or not: if not, judging the type of the information according to a prompt word in the information input by the user, if the information is non-query type information, executing a step S31, and if the information is query type information, executing a step S32; if the file exists, executing the step S33; and step S31, converting the non-query information into a target vector, matching the target vector with a vector in a pre-stored vector library, performing similarity judgment, and outputting a result based on judgment. According to the invention, efficient and accurate power data processing and analysis can be realized.

The invention discloses a two-dimensional MUSIC spectrum peak searching method based on an improved Archimedes algorithm, and the method comprises the steps: carrying out the one-dimensional MUSIC spatial spectrum function derivation of a signal model, and expanding the one-dimensional MUSIC spatial spectrum function to a two-dimensional MUSIC spatial spectrum function; secondly, taking a spatial spectrum function of the two-dimensional MUSIC as a target function of an Archimedes optimization algorithm; and then, in combination with an objective function, introducing a fusion chaos algorithm strategy to initialize a population, introducing a mutation algorithm, disturbing positions, and improving an Archimedes optimization algorithm. And finally, carrying out two-dimensional MUSIC spectral peak search through an improved Archimedes optimization algorithm, and taking the direction of arrival, namely, a pitch angle and an azimuth angle, obtained in a mode of solving maximum and minimum values through a target function as spectral peak values. The two-dimensional MUSIC spectrum peak searching method has the advantages that the optimization speed is higher, the optimization precision is higher, the robustness is better, and the two-dimensional MUSIC spectrum peak searching can be quickly and accurately carried out.

The invention discloses a hot upflow positioning method based on data driving. For a long time, unmanned aerial vehicles are always indispensable in the industries of monitoring tasks, geographic surveying and mapping, target reconnaissance and the like. Flight endurance is a key limit for wide use of unmanned aerial vehicles. However, although researchers effort to modify and optimize the aerodynamic shape to reduce glider drag or replace battery reactants, use of these methods to increase flight endurance still has limitations. The invention aims to utilize the energy of the nature by collecting wind energy. According to the method, upflow estimation is considered, and then the position of the wind field is estimated through a sparse recognition algorithm of nonlinear dynamics. The thermal positioning is realized through the SINDy-based method and the characteristic of accurately identifying the mathematical physical specification model, so that the capability of identifying the hot upflow is improved, and the energy of the aircraft is increased.

The utility model relates to the technical field of wireless microphones, in particular to a wireless microphone with a positioning function, which comprises a microphone main body, a protective lens is arranged at the bottom of the microphone main body, a video transmission module is arranged in the microphone main body, a micro camera is arranged at the bottom of the video transmission module, and a wireless communication module is arranged in the microphone main body. The bottom of the video transmission module is provided with a plurality of night vision lamps, the top of the microphone main body is provided with a net head, the outer side of the microphone main body is provided with a display screen, and the outer side of the microphone main body is provided with a power switch. According to the utility model, a scene outside the microphone main body is shot through the miniature camera, and the picture is transmitted to a user, so that the user can accurately search the microphone main body according to the shot picture, the difficulty of searching the microphone main body is greatly reduced, and the efficiency of searching the microphone main body is improved.

The invention discloses a similar surgery picture retrieval method, system and equipment for minimally invasive surgery based on deep learning and a medium, and relates to the technical field of image processing. Respectively extracting original image features in the preprocessed video stream and original text features in the preprocessed text content; performing interactive fusion on the original image features and the original text features by adopting a feature fusion module; using a feature codebook module to map the fusion feature into a codebook vector; a mapping feature decoding module is adopted to decompose the codebook vector into compressed image features and compressed text features, finally, the compressed image features are decoded into similar images, and the compressed text features are decoded into text descriptions corresponding to the similar images; and the operation picture can be quickly and accurately retrieved.

The present invention relates to the technical field of computing power resource allocation data processing, and discloses a multi-cloud heterogeneous computing power scheduling system and scheduling method, wherein the system comprises a computing power capacity analysis module, a cloud computing power platform object evaluation module, and a computing power scheduling execution module; the target cloud computing power platform type is efficiently analyzed and processed by standard storage of cloud platform objects corresponding to different computing power capacities combined with an intelligent search algorithm and computing power capacity parameters of tasks to be processed, so as to realize accurate search of target cloud computing power platform objects based on multi-cloud heterogeneity; the service prices of multiple target cloud computing power platform objects are digitally analyzed, and the cloud computing power platform with the minimum service price is scientifically analyzed, so as to realize accurate screening of the best cloud computing power platform object that meets the tasks to be processed, thereby improving the accuracy and reliability of scheduling operations of the computing power scheduling system, and improving the scheduling operation quality of the computing power scheduling system.

The invention provides a customer service interaction optimization method and device, equipment, a storage medium and a program product, and relates to the technical field of computers. The customer service interaction optimization method comprises the following steps: in response to a received user question, performing intention recognition on the user question based on a mental model; determining a corresponding domain knowledge base for semantic enhancement based on a result of intention recognition; rewriting the user question based on a query operation on the semantic space alignment spectrogram to obtain to-be-retrieved information; retrieving a corresponding field knowledge base based on the to-be-retrieved information to obtain a business knowledge fragment for enhancing the user question; and inputting the business knowledge fragments into the large language model for response, and outputting reply information of the user question. Through the technical scheme disclosed by the invention, the semantic barrier between the knowledge base and the questions of the user can be effectively made up, the understanding accuracy of the big language model on the intention of the user is improved, and the use experience of the user in interaction of the intelligent customer service system is further improved.

The invention provides a data query method, a data query device, equipment, a storage medium and a product, which can be applied to the technical field of computers, and the method comprises the following steps: in response to a data query instruction, obtaining a first query identifier corresponding to to-be-queried information; on the basis of the first query identifier, query operation is executed in a cache database, a first query result is obtained, and the first query result comprises a parameter value corresponding to the first query parameter; under the condition that the parameter value of the first query parameter is a first preset numerical value, generating a second query identifier according to the first query parameter and the parameter value; executing the query operation in the cache database based on the second query identifier to obtain a second query result; according to the first query result and the second query result, a target query result is generated, and the target query result is used for representing the conformity relation between the to-be-queried information and the data index requirement corresponding to the first query identifier.

The invention discloses a data processing method and device, a data retrieval method and device and a storage medium, and relates to the technical field of document retrieval, and the method comprises the following steps: constructing a plurality of data nodes corresponding to non-linear structure data according to a hierarchical relationship of the obtained non-linear structure data; determining description information corresponding to each data node; generating semantic descriptions corresponding to the data nodes of which the hierarchical relationship is a father-son relationship; segmenting the nonlinear structure data according to each data node, the description information corresponding to each data node and each semantic description to obtain corresponding fragments; determining a fragment vector corresponding to each fragment; and storing all the fragment vectors and the hierarchical relationship into a vector database, and constructing an index corresponding to each fragment vector. Semantic fragmentation and vectorization optimization processing can be performed on nonlinear structure data of a tree structure and a graph structure, a data basis is provided for retrieval of the nonlinear structure data, and accurate retrieval of the nonlinear structure data is realized.

The invention discloses a multi-modal Hash retrieval method, system and equipment based on a multi-view center structure and a medium, belongs to the technical field of artificial intelligence and multi-modal Hash retrieval, and aims at achieving the technical purpose of obtaining balance between intra-class compactness and inter-class separability in the multi-modal Hash retrieval process. According to the technical scheme, the method comprises the following steps: constructing a multi-modal data set; constructing a multi-modal Hash retrieval model based on a multi-view center structure; training the model; wherein the construction of the multi-modal Hash retrieval model based on the multi-view center structure specifically comprises the following steps: modal specific prototype learning: respectively extracting refined features from corresponding modals by using an image modal depth multilayer perceptron and a text modal depth multilayer perceptron; calculating the average value of the refined features of the image modality and the text modality to obtain a modality specific prototype, and further obtaining the unique features of the image modality and the unique features of the text modality; carrying out multi-modal integrated prototype learning; and performing multi-view semantic enhanced hash learning.

The invention is suitable for the field of advertisement auditing, and discloses an advertisement content isolation auditing method, system and device and a medium, and the method comprises the steps: receiving advertisement content; the advertisement content is classified, and text advertisements, picture advertisements and video advertisements are identified and classified; the advertisement contents of different classifications are audited respectively; wherein when the picture advertisements are classified, digital watermarks are embedded in the picture advertisements, and the digital watermarks comprise category information of the picture advertisements. In the picture advertisement classification and auditing process, the digital watermarking technology is innovatively adopted in the picture advertisement classification and auditing method. By embedding the category information of the picture advertisement into the digital watermark and adding the watermark into the picture advertisement, quick classification and efficient management of the picture advertisement are realized, the accuracy and efficiency of picture advertisement classification are improved, convenience is provided for subsequent auditing work, and the user experience is improved. And the overall quality and efficiency of advertisement auditing are further improved.

The invention provides a power line impedance matching method and system based on space division, and belongs to the technical field of power line impedance matching based on deep learning. Acquiring a power line terminal load value; based on a database query function, querying capacitance and inductance element values and an optimal target function value during optimal matching corresponding to the obtained power line terminal load value in a database; wherein the database stores the optimal values of capacitance and inductance devices under the optimal matching condition corresponding to different power line terminal load values obtained by using a deep reinforcement learning algorithm model for optimization. According to the method, the program running time is effectively shortened, the problem of unsatisfactory agent optimization caused by too large search space is improved, and the problem of poor optimization result caused by insufficient training of the agent is avoided; the search space is reduced, and the search accuracy is improved; the space is subjected to non-uniform segmentation, and compared with multi-agent distributed optimization based on uniform segmentation, the search effect of the agents is obviously better.

The invention discloses a personal data retrieval method and device based on a deep neural network, and relates to the technical field of data retrieval. The personal data retrieval method and device based on the deep neural network specifically comprise the following steps: S1, data preprocessing and index construction; s2, designing a deep neural network model; s3, performing strategy retrieval and fragmentation processing; s4, displaying a retrieval result; s5, performing feedback and optimization; the semantic relation between the file names is learned through the deep neural network, the intelligent retrieval capacity exceeding traditional keyword matching is achieved, the intention of a user can be accurately understood, and even if the file names are not completely matched, related files can be found; after complete file name retrieval fails, a fragmentation processing strategy is adopted, deep neural network similarity calculation is combined, the retrieval range is effectively expanded, the retrieval recall rate and accuracy are improved, and meanwhile the input burden of a user is reduced.

The invention relates to the technical field of data processing, in particular to a large transport vehicle identification method and system based on radar and vision fusion. The method comprises the following steps: collecting a monitoring radar signal on the way of a transport vehicle through radar monitoring equipment; carrying out transport vehicle characteristic analysis on the radar signals according to the transport vehicle on-the-way monitoring radar signals, and generating transport vehicle characteristic data; collecting on-way visual data of the transport vehicle by using high-resolution monitoring equipment; carrying out transport vehicle on-way visual feature analysis according to the transport vehicle on-way visual data, and generating transport vehicle on-way visual feature data; and according to the transport vehicle characteristic data and the transport vehicle on-way visual characteristic data, carrying out transport vehicle external heterogeneous data mapping and transport vehicle dynamic characteristic mode identification, and respectively generating a transport vehicle external heterogeneous data matrix and transport vehicle dynamic characteristic mode data. According to the method, the vehicle identification of the large transport vehicle is carried out based on radar and vision fusion, and the vehicle information of the large transport vehicle is identified more comprehensively.