544results about How to "Improve matching accuracy" patented technology

The invention provides an employee post matching and deploying method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an optimized employee capability feature vector based on employee historical work data, extracting an employee capability development trend, and combining team cooperation data to obtain an optimized employee capability feature vector; a cross-modal contrast learning network is utilized to obtain an employee comprehensive ability vector, a development potential index is calculated, a post skill knowledge graph is constructed based on post instruction data and historical recruitment data, and a post skill feature matrix and a team structure feature vector are generated through a dynamic heterogeneous graph neural network and an adaptive graph attention network. Calculating an employee-post matching degree score matrix in combination with an employee comprehensive ability vector, inputting the matching degree score matrix and a development potential index into a distributed multi-agent reinforcement learning model, constructing a reward function, generating and evaluating a deployment scheme through hierarchical task decomposition and Monte Carlo tree search, and finally outputting a deployment execution scheme.

The invention discloses an electric power knowledge system construction and intelligent question and answer method based on a lightweight large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an electric power field knowledge system, and building a reasoning logic classification system to carry out the classification marking of a knowledge base in the electric power field knowledge system; based on the power domain knowledge system, a multi-dimensional similarity matching strategy is adopted to process a problem input by a user; generating a reasoning link based on a matching result of the multi-dimensional similarity matching strategy; the reasoning link is used for extracting and combining answers from the power domain knowledge system, and the generation process of the reasoning link comprises question decomposition and answer integration. According to the invention, systematic management of electric power professional knowledge is realized, and the matching accuracy of professional problems is improved; by generating the self-adaptive multi-hop reasoning link based on the question type and combining the question decomposition and answer integration technology, the intelligent question and answer ability of the system under the scenes of power equipment fault diagnosis, maintenance scheme recommendation, risk assessment and the like is improved.

The invention relates to the technical field of data processing and analysis, in particular to a management method and system based on a man-post intelligent matching algorithm model. The method comprises the following steps: acquiring post description data; standardizing the post description data to obtain standard post description data; performing semantic feature recognition on the standard post description data to obtain post semantic feature information; performing industry term information identification on the post semantic feature information to generate post industry term information; performing post industry type division on the standard post description data according to the post industry term information to obtain post industry type data; performing post capability demand determination on the post industry type data to obtain post capability demand data; through a semantic analysis technology, a natural language processing technology and a machine learning algorithm, the talent core capability information is accurately identified, and the post demand information is deeply analyzed, so that the talent post matching accuracy is improved.

The invention relates to the technical field of financial leasing systems, in particular to a financial leasing system and method based on a cloud platform, and the system comprises a leasing personnel input unit which is used for obtaining leasing demand information; the rental equipment data acquisition unit is used for acquiring equipment state information; the lease service management unit is used for processing lease matching and risk control; the demand unit is used for analyzing the lease demand; the leasing personnel registration unit is used for managing leasing personnel information; the rental platform resource library is used for storing system data; wherein the lease service management unit realizes lease matching and risk management through a dynamic multi-dimensional lease matching and risk control algorithm, and a state time sequence matching model greatly improves the accuracy of lease matching by combining historical data and real-time market demands; the multi-dimensional lease scoring system further considers a plurality of factors such as lessee credit and equipment conditions, and provides a comprehensive evaluation basis for subsequent decision making.

The invention discloses a stereo matching method based on context geometric features and distortion parallax optimization, which comprises the following steps of: firstly, shooting a calibrated left picture and a calibrated right picture through a binocular camera, extracting multi-scale context features of the two pictures, and calculating the similarity between feature points according to the feature points of the left feature graph and the right feature graph under different parallaxes; the matching cost is the matching cost, so that the constructed geometric cube contains rich geometric matching information. Meanwhile, context features are copied and expanded into context feature cubes with the same size according to parallax, and rich context information is contained. The geometric cube contains matching conditions of feature points of left and right views with different parallax errors, cost aggregation is guided through context features of the left view, and context feature information, feature point cost matching information under different parallax errors and geometric information of adjacent feature points are preliminarily fused to improve cost matching reliability. And generating a geometric attention weight cube. And filtering the contextual feature cube.

The invention discloses a big language model tool matching method and system based on a knowledge graph, belongs to the technical field of artificial intelligence, and solves the problem that an API (Application Program Interface) tool is difficult to efficiently manage and accurately select in a real-time information processing process of an existing big language model. According to the method, a multi-source data fusion technology is introduced to collect and sort information of external API tools of different data sources, and a dynamic relation weight adjustment mechanism is utilized to dynamically adjust the weight of the relation between different API nodes according to the actual use condition of the API tools and user feedback; representing the characteristics of each API node through a graph embedding technology based on an intelligent matching algorithm of deep learning, and calculating the matching degree of each candidate API and a request by using a sequence processing model; according to the method, the knowledge graph and the intelligent matching algorithm are combined, an external API tool used by a large language model can be dynamically managed and optimized, and the information processing accuracy and the system response speed are improved.

The invention discloses a large language model government affair intelligent question-answering method, system and device and a medium, and the method comprises the steps: obtaining warehousing data, carrying out the indexing processing of the warehousing data, obtaining index data, and storing the index data in a database; rewriting the original problem through the index data; splitting the rewritten question, and querying based on the split question to obtain a query result; based on the query result, querying the document data matched with the sub-questions by adopting multi-path recall, and determining the question matching degree of the document data; and positioning and extracting the original file from the database based on the matching degree, and generating an answer to the question. According to the method, the question and related content matching accuracy is improved by indexing the answer content needing to participate in the question, and the question and related content matching accuracy is improved by question rewriting, question splitting and multi-path recall. By positioning the original file content conforming to the question, the quality and accuracy of the answer result of the large language model are improved, and illusion is prevented from being generated when the large model answers.

The invention discloses a distributed multi-source image interactive fusion method based on multistage space-time calibration, and relates to the technical field of signal processing, comprising the following steps: step 1, time synchronization calibration; inputting an optical image and an infrared image, and synchronizing the acquisition time of the optical image and the infrared image through time synchronization calibration; step 2, performing time interpolation compensation calibration, and correcting time offset of the optical image and the infrared image; step 3, attitude calibration: aligning the optical image and the infrared image to a unified space coordinate system, and eliminating deviation caused by different visual angles; step 4, geometric distortion calibration: correcting the geometric distortion of the image; 5, interactive image fusion: fusing the optical image and the infrared image through a deep learning image processing method; according to the method provided by the invention, data deviation caused by time, space and attitude differences between different sensors and imaging systems is effectively solved.

The invention discloses a CAD drawing intelligent matching method and system based on multi-stage deep learning, and belongs to the field of image comparison, and the method mainly comprises the steps: S1, loading and preprocessing a CAD drawing, and constructing a triple data set through a data enhancement and sample balance technology; s2, constructing a target detection model and a target matching model, and training the target detection model and the target matching model by using the triple data set; wherein the target detection model adopts a YOLOv5 model, and the CAD drawing is detected in combination with sliding window detection and a non-maximum suppression technology; the target matching model adopts a double-end network structure; and S3, matching the CAD drawings to be identified by using the trained target detection model and target matching model. According to the invention, through a plurality of advanced technologies, intelligent matching and judgment of two or more CAD images in any different formats are realized.

The invention relates to the technical field of attendance management, and discloses an electronic class board attendance system for palm vein recognition, which comprises a palm vein data acquisition module for irradiating the palm of a user through a near-infrared light source and acquiring a palm vein image; the image optimization processing module is used for performing optical modeling optimization, noise removal and quality enhancement on the acquired palm vein image; the deep learning recognition module is used for extracting palm vein features and performing identity matching; the attendance record storage module is used for storing and managing user attendance data; and the control and communication module is used for managing cooperative work of all the modules and performing data interaction with an external attendance database. Through the technical scheme of combining near-infrared illumination and the light transmission equation, the palm vein feature image is accurately extracted, the effect of optimizing palm vein contrast and detail definition is achieved, the problems of palm vein feature edge blurring and detail loss are solved, and the image processing quality is improved.

The invention discloses an expert matching method and system based on large language model knowledge extension and fusion. The method comprises the following steps: firstly, collecting expert basic information, and enhancing through a pre-trained big language model to obtain expert extension information; and then, integrating the basic information and the extended information, and analyzing and fusing the basic information and the extended information by using RoBerta and a knowledge graph pre-training model to form expert fusion representation. According to enterprise technical requirements, requirement details are expanded through a large language model, and technical requirement fusion representation is generated in combination with representation analysis of the technical requirements and enterprise information. And finally, carrying out keyword correlation screening on the experts, narrowing a matching range, comparing fusion representation of the experts and requirements, calculating similarity and sorting, and recommending related matching experts. Through the method and the device, skill matching analysis can be effectively performed on external expert information, and expert matching precision and recommendation effect are improved based on semantic analysis and expert matching of technical requirements.

The invention provides a data enhancement method and device, electronic equipment and a storage medium. The invention relates to the technical field of artificial intelligence, in particular to the field of data enhancement optimization, and can be applied to application scenes such as intelligent search engines, intelligent question-answering systems, enterprise knowledge bases and intelligent customer service robots. The specific implementation scheme is as follows: determining a target problem of a target domain; generating a plurality of query statements for the target question by utilizing the first language model; executing each query statement by utilizing a second language model to obtain a query record corresponding to each query statement; analyzing each query statement and the corresponding query record by utilizing a first language model to obtain candidate query statements; generating a corresponding relation based on the target question and the candidate query statement; and adding the corresponding relationship to a sample library of the knowledge question-answering system.

The invention relates to the technical field of intelligent matching, in particular to an AI-based intelligent matching method for litigation materials, which comprises the following steps of: performing case text analysis and part-of-speech tagging on the basis of a case database, determining legal attributes of keywords, filtering non-critical stop words, and according to the frequencies of the keywords and legal concepts, determining the legal attributes of the keywords and the legal concepts; and obtaining a case feature vector set. According to the method, similar cases can be effectively and automatically clustered through multi-dimensional space mapping and case group recognition, an obvious advantage is brought to the accuracy of information retrieval, the accuracy of case similarity evaluation is further improved through introduction of cosine similarity measurement, it is ensured that a reference case is highly matched with a current case, and a community detection algorithm is applied, so that the accuracy of information retrieval is improved. Through the establishment of the legal quotation feature set and the quotation relation graph, the understanding of the relation between the legal documents is deepened so as to accurately judge the importance and the correlation of the materials, so that the efficiency and the accuracy of the matching of the litigation materials are improved.

The invention belongs to the field of model matching, and discloses a three-dimensional model matching method and device and electronic equipment, and the method comprises the steps: converting a to-be-matched three-dimensional model into first point cloud data, carrying out the preprocessing of the first point cloud data, setting a registration parameter according to a preprocessing result, extracting a first feature of the first point cloud data, and carrying out the matching of the first feature and the second feature; and registering the first feature with a second feature of the historical three-dimensional model according to the registration parameter to determine a matching result. Three-dimensional model matching is converted into feature matching, and the automation degree and efficiency of the matching process are improved. According to the method, feature extraction is performed on the first point cloud data to obtain the first feature, and then the first feature is registered with the second feature in the preset feature library, so that the similarity between the to-be-matched model and the historical model can be quickly and accurately evaluated. In the whole process, the dependence on professional modeling software is reduced, the consumption of manpower and computing resources is reduced, and meanwhile, the efficiency and accuracy of three-dimensional model matching are improved.

The invention provides a scientific research project expert matching method based on dynamic academic cooperation network graph representation learning, and aims to improve expert matching efficiency and quality in scientific research project management. According to the method, a dynamic complex network diagram is constructed, scholars serve as nodes, cooperation relations serve as directed edges, and the previous research history and cooperation experience of the scholars are comprehensively considered. And feature vector learning is carried out on scholar nodes by utilizing graph structure learning and a Transform architecture, so that accurate matching between scientific research projects and experts and scholars is realized. According to the method, more than 80% of matching precision and more than 89% of standardized discount cumulative earnings are obtained from data sets in the fields of artificial intelligence and data mining, the limitation of the prior art in the aspects of operation cost and scholar feature information utilization is effectively solved, and an innovative solution is provided for recommending appropriate partners for scientific research projects.

The invention relates to the technical field of image data processing, and provides a digital simulation pre-assembly method and system for a steel structure, and the method comprises the steps: obtaining models of a plurality of components in the pre-assembly process of the steel structure through point cloud scanning; for a plurality of points in the model of the same component, obtaining an initial neighborhood of each point, and obtaining a plurality of neighborhood smooth included angles of each point; amplifying the initial neighborhood of each point and obtaining the termination neighborhood of each point; obtaining segmentation integrity evaluation of each point; segmenting the model to obtain a plurality of to-be-matched edges of the model; acquiring an edge distribution array and a matched edge array in the two models; obtaining interface conformance evaluation between the two models; a spliced graph structure is constructed through threshold judgment; and the assembly sequence of the components in the steel structure is obtained. The invention aims to solve the problem that a plurality of successful matching positions are generated at the smooth position of the partial surface of the structural part to influence the matching precision and further influence the assembling progress.

The invention relates to the field of integrated circuit manufacturing, in particular to a layout pattern matching method, device and equipment and a storage medium. According to a preset geometric feature matching condition, searching and matching the target pattern in the to-be-matched layout to obtain a geometric matching pattern; obtaining a corresponding target airspace image and a matched airspace image; obtaining a matching frequency domain image corresponding to each geometric matching pattern and a target frequency domain image corresponding to the target pattern; respectively calculating a difference value between each matching frequency domain image and the target frequency domain image; judging whether the diffraction information difference value of each geometric matching pattern exceeds a preset first threshold value or not; and determining the geometric matching pattern of which the diffraction information difference value does not exceed the first threshold value as the photoetching matching pattern. According to the method, the geometric matching pattern is matched in the aspect of photoetching imaging characteristics, the pattern matching precision and matching accuracy are improved, and the practicability is improved.

On one hand, the invention provides a clinical test patient matching method based on a large model. The invention further discloses a clinical test patient matching system based on the large model. The clinical test patient matching system based on the large model is used for achieving the clinical test patient matching method based on the large model. According to the method, traditional patient containing and ranking are converted into case retrieval and problem-based filtering, a professional knowledge data set is constructed, a field large model is obtained based on large model fine tuning, and the cost can be greatly reduced and the accuracy can be improved by utilizing the understanding and processing capability of the large model for long texts. According to the method and the system, the answers of a plurality of containing and arranging standard questions can be more effectively integrated by fusing evaluation rules of various strategies, the medical semantic information is mined, the relationship between the patient and the clinical test is obtained, and the matching accuracy of the clinical test and the patient is effectively improved.

The invention relates to a method for clamping a cabin section A to complete butt-joint assembly with a cabin section B by a visual guidance robot. The mode that six degrees of freedom x, y, z, rx, ry and rz are measured and matched separately is adopted, measurement and matching of a circumferential rolling pin groove angle rx are completed through monocular vision, fixed alignment and matching of a pitching angle ry and a yaw angle rz are completed through a clamping tool and a fixing workpiece, distance adjustment in the y direction and the z direction in a butt joint plane is completed through monocular vision, and matching is completed. Adjustment of the distance in the x direction is completed through teaching to complete matching, finally six-degree-of-freedom adjustment and matching of the butt joint cabin sections A and B are achieved, and efficient and high-precision automatic assembly of the small-caliber and thin-wall cabin sections A and B is achieved.

The invention discloses a text-image pedestrian re-identification method for noise correlation. The method comprises the following steps: respectively carrying out global feature representation and local feature representation on an image and a text, and carrying out global feature marking and local feature marking; dividing the image and text data by adopting a dynamic selection and correction method specific to an instance; constructing a multi-granularity global feature learning module, and inputting the local features of the image and the text into the multi-granularity global feature learning module to obtain multi-granularity global features; and training an optimization network model through ID classification loss and similarity distribution matching loss, carrying out loss calculation on the identity ID, and completing similarity distribution matching loss calculation on the multi-granularity global features. Through the technical scheme of the invention, the matching precision of pedestrian re-identification based on text description is effectively improved.

The invention belongs to the technical field of remote rehabilitation guidance, and particularly relates to an intelligent remote rehabilitation guidance system. The system integrates the functions of man-machine interaction, information encryption, physiological and psychological assessment, rehabilitation grade division, physician professional assessment, rehabilitation knowledge base construction, rehabilitation matching planning, rehabilitation training monitoring and feedback, rehabilitation effect prediction and the like, and a fuzzy assessment algorithm and an emotional depression detection algorithm are integrated. The system comprehensively evaluates the physiological and psychological health conditions of a rehabilitative person, recommends appropriate rehabilitation doctors or health planning guidance suggestions for the rehabilitative person, monitors the standard degree and micro-expression changes of training actions in real time and gives timely feedback and adjustment suggestions in the rehabilitation process, and meanwhile, predicts a rehabilitation track by utilizing a machine learning algorithm, so that the rehabilitation effect is improved. Subsequent rehabilitation planning is optimized, and personalized precise rehabilitation is ensured; according to the invention, the participation degree and training scientificity of the convalescent are improved, and an effective solution is provided for remote rehabilitation guidance.

The invention relates to a document question-answer pair generation method and device, a question-answer method and device, computer equipment and a readable storage medium, and relates to the technical field of data generation, and the document question-answer pair generation method comprises the steps of obtaining a plurality of text slices of a document; inputting the text slices into a pre-trained knowledge graph model, and indicating the knowledge graph model to output at least one target question corresponding to the text slices based on format features and text types of the text slices; a knowledge graph associated with the knowledge field of the document is embedded in the knowledge graph model; inputting the same target question into a pre-trained question and answer model for multiple times to obtain multiple to-be-selected answers output by the question and answer model for multiple times; determining the to-be-selected answer with the maximum occurrence probability in the plurality of to-be-selected answers as a target answer matched with the corresponding target question; generating a plurality of question and answer pairs of the document based on each matched target question and target answer; and the matching accuracy of each question and answer pair is improved.

The invention relates to the technical field of data retrieval, in particular to a hardware image retrieval method based on deep learning. The method comprises the following steps: acquiring a multi-angle image of hardware to be retrieved, and generating hardware feature data after geometric correction and feature enhancement; and performing shape fuzzy retrieval on a preset hardware image database by using the feature data to generate a shape-like hardware data set. And carrying out surface grating structure construction and optical response simulation on the shape-like data set to generate hardware grating data. Based on the grating data, simulating a light field and collecting four-dimensional reflected light to obtain a four-dimensional data tensor; and associating the tensor with a preset hardware material attribute, and establishing a spectrum-material attribute relationship. Feature matching is carried out according to the relation, and target hardware data are accurately retrieved; according to the hardware image retrieval method, the hardware image retrieval method can be faster and more accurate.

The invention belongs to the technical field of network security, and discloses a feature enhanced community detection-based APT attack discovery method, which comprises the following steps of: collecting log data and constructing an origin graph; the source graph is segmented into a plurality of sub-graphs, a feature association vector set and a weight set are generated for each sub-graph, the sub-graphs with feature association vectors and weights are reconstructed, each reconstructed sub-graph comprises a plurality of triads, and the feature association vectors and the weights exist among the triads; performing feature enhanced community detection on each reconstructed sub-graph; and performing rule matching on the communities in each reconstructed sub-graph to obtain attack communities, and according to the timestamp attribute of each reconstructed sub-graph, connecting the attack communities in each reconstructed sub-graph to complete APT attack discovery. According to the method, the origin graph is constructed by using the collected logs, and accurate division of attack communities is performed by performing feature enhanced community detection on the origin graph.

The invention belongs to the technical field of image stitching, and discloses a multi-camera image stitching method and system. The method comprises the following steps: collecting data of a plurality of real-time images shot by a multi-camera device, and carrying out image preprocessing; collecting real-time image edge information of each piece of preprocessed real-time image data, and extracting a plurality of real-time feature points; performing feature point matching on the plurality of real-time feature points by using a pre-trained feature point matching model; performing image registration on all the preprocessed real-time image data according to a plurality of feature point matching results; carrying out image fusion on the real-time overlapping region of the real-time image data after all the images are registered by using an improved multi-band fusion algorithm; and according to the real-time splicing parameters, carrying out image splicing on the real-time image data after all the images are fused to obtain real-time holographic image data. The problems of poor visual effect, low accuracy, low processing speed, poor splicing quality and poor fusion effect in the prior art are solved.

The invention provides an image splicing method based on improved ORB and MLESAC, and belongs to the technical field of image splicing. According to the method, after a to-be-spliced image is obtained, firstly, feature point detection is performed on the image by using a rapid feature detection method, a descriptor is generated by using a binary robust independent basic feature method, and preliminary matching points are obtained after violent matching; secondly, re-extracting feature descriptors from the screened feature points by using a scale invariant feature transformation method, finding two optimal matches for each descriptor through a K-nearest neighbor algorithm, and adding a ratio test to filter out weak matches; and performing weighting processing on the data points by using a maximum likelihood estimation random consistency algorithm, and performing multiple iterations to obtain an optimal model. And finally, generating a spliced image through a weighted average image fusion technology. According to the method, ORB and SIFT are used in a cascading mode in the rough matching stage, a more efficient MLESAC algorithm is provided to complete fine matching, and the registration precision is remarkably improved while the image splicing real-time performance is ensured.

The invention provides a network construction energy storage linear quadratic optimization parameter identification method and system, and relates to the technical field of power system optimization control and identification, and the method comprises the steps: building a network construction energy storage control black box equivalent mathematical model, and abstractly representing a control structure and a to-be-identified parameter through an input and output relation; a hardware-in-the-loop simulation model is constructed, active disturbance, reactive disturbance, voltage disturbance and frequency disturbance are used as typical disturbance effects, the operation state of the power grid is continuously changed, and a time domain dynamic response database under massive operation conditions is constructed; constructing an identification error solver and a linear quadratic optimization parameter identification model; calculating and controlling the deviation between the prediction data of the black box equivalent mathematical model and the time domain dynamic response data; and the identification result is updated in a rolling manner through linear quadratic optimization calculation. According to the method, a traditional time domain identification mode is improved to be nested fusion of time domain data, a control model and an optimization model, and the accuracy of parameter identification under the wide operation condition is comprehensively improved.

The invention discloses an anaphora disambiguation method and system based on big language model enhanced text and structured query language generation. The method comprises the following steps: receiving a natural language question of a user, and analyzing and generating structured table field information through mode information; identifying and rewriting fuzzy time expression in the problem; extracting ambiguous entities, and replacing the ambiguous entities with database standard values through semantic matching and fuzzy matching; synthesizing the information to generate a structured query language statement and executing the structured query language statement; and if the execution result is null, automatically triggering an anaphora disambiguation process, performing error correction and re-matching on field values in the statement through a dual matching mechanism, and generating and executing a corrected query statement. According to the method, the problems of fuzzy anaphora, indefinite time expression, low matching accuracy, resource waste and the like in a traditional SQL system are effectively solved through a strategy of combining pre-processing and post-processing, and the accuracy, robustness and execution efficiency of complex query are remarkably improved.

The invention relates to the technical field of power electronics, in particular to a power circuit and power equipment, the power circuit comprises a control circuit, a reference current generating circuit and a charging current generating circuit, the control circuit is used for outputting a control signal, and the reference current generating circuit is electrically connected with the control circuit and used for generating reference current in response to the control signal; the charging current generating circuit comprises a plurality of groups of current generating units, each group of current generating units is electrically connected with the reference current generating circuit and the control circuit, the groups of current generating units are connected in parallel and are electrically connected with the battery at parallel connection points, and the target current generating unit responds to control signal generating unit current. The target charging current is output at the parallel connection point to charge the battery, the unit current and the reference current are in a preset proportion, and the target charging current is the sum of the unit currents generated by the target current generation unit. The precision of the charging current can be improved by forming the charging current by the plurality of groups of unit currents proportional to the reference current.

According to the multi-target tracking method and device, the storage medium and the computer equipment provided by the invention, in the to-be-tracked video of the non-first frame, the to-be-tracked target and the historical detection frame thereof are determined from the previous frame, the target position of the current frame is further predicted, and the target position of the current frame is determined according to the confidence of each detection target in the current frame. The detection targets with the confidence coefficients lower than a threshold value serve as candidate detection targets, and the detection targets with the confidence coefficients not lower than the threshold value serve as main detection targets; on the basis of the historical detection frame and the current prediction frame of each to-be-tracked target and the current detection frame of each main detection target, performing multiple times of matching on each current prediction frame and the current detection frame of each main detection target; and matching the current prediction frame which is not matched with the current detection frame of each candidate detection target, and taking each main detection target and the successfully matched candidate detection target as a to-be-tracked target of the current frame. Therefore, the tracking error can be reduced to the greatest extent under the conditions of shielding and detection loss.