288 results about "Best matching" patented technology

The invention relates to the technical field of retrieval enhancement generation, in particular to a method for automatically adjusting retrieval enhancement generation parameters based on content feature modeling. The method comprises the following steps: receiving an original query text of a user, performing component analysis, identifying terminologies, general vocabularies and question entities, and quantifying to form query fingerprints; acquiring a historical behavior sequence of the user, and constructing a score reflecting the level of the user by combining the query fingerprints and adopting a time decay weighting algorithm; the user level score is converted into specific retrieval parameter configuration, and a retrieval strategy blueprint is formed; guiding document library retrieval according to the retrieval strategy blueprint, and screening out a candidate knowledge set which is most matched with the professional level of the user; and according to the user level score, a preset instruction template is intelligently filled, and a situational generation instruction is constructed. According to the method, the problem of non-uniform cognitive load caused by a traditional system is solved through a retrieval enhancement generation technology, and the technical knowledge transmission efficiency and the user satisfaction are remarkably improved.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention discloses a heterogeneous computing system, a cache consistency maintenance method and device, equipment and a medium, and relates to the technical field of heterogeneous computing. The method comprises the step of redefining a cache coherence protocol of the heterogeneous computing system as a metadata index protocol, a multicast synchronization protocol and a hybrid response protocol. Entropy weights of different feature dimensions are determined according to feature values of all data blocks of the heterogeneous computing system, and a protocol most matched with the task load running state at the current moment is determined from a metadata index protocol, a multicast synchronization protocol and a mixed response protocol according to the entropy weights of the different feature dimensions; and determining whether to switch the protocol according to whether the current protocol is consistent with the selected protocol. According to the method, the problem that the protocol cannot be effectively optimized in related technologies can be solved, and the consistency protocol which is most matched with the current task load of the heterogeneous computing system can be accurately determined.

The invention provides an agent strategy optimization method based on multi-modal fusion, an agent and electronic equipment. The method comprises the following steps: selecting a best matching fusion strategy for a target task in a pre-constructed fusion strategy library; wherein the fusion strategy library comprises a plurality of predefined fusion strategies; fusing the multi-modal data required by the target task by using a best matching fusion strategy; when the target task is executed, learning is carried out based on the fused multi-modal data corresponding to the target task, and a task decision is generated. According to the method, information loss possibly caused by simple splicing can be avoided, a foundation is laid for the accuracy of subsequent task decision making, and meanwhile, the task decision making can be dynamically adjusted according to the interaction condition with other agents, so that rapid and efficient learning is realized in a complex scene, and the accuracy and adaptability of agent decision making are greatly improved.

The invention provides a multi-stage progressive enhanced knowledge retrieval method for industrial process operation and maintenance, and relates to the field of natural language processing and information retrieval, and the method comprises the following steps: constructing a knowledge base in the field of industrial process operation and maintenance; obtaining user query and classifying; screening the documents in the knowledge base through the classified user query and a BM25 algorithm, and determining a first candidate document; screening the first candidate document through a lightweight filtering method combining TF-IDF word frequency-inverse document frequency and cosine similarity to obtain a second candidate document; the second candidate documents are accurately sorted through retrieval based on dense vectors, answers most matched with user query are obtained, the retrieval process is optimized layer by layer through retrieval strategies of four stages of query routing, keyword recall, lightweight filtering and vector fine arrangement, and therefore the retrieval efficiency and the retrieval precision are improved.

A tallying data processing method and system for an intelligent port, a medium and a product relate to the field of electric digital data processing, and the method comprises the following steps: obtaining a ship loading and unloading plan, tallying operation records and cost accounting data according to a preset period, and generating standard tallying data; extracting feature data in a pre-time window from the standard tallying data; inputting the feature data into a prediction calculation model, and generating a plurality of groups of loading and unloading plan change prediction scenes according to a historical operation mode; performing parallel calculation on the multiple groups of prediction scenes to obtain scene prediction data; comparing the real-time tallying data acquired at the port site with the scene prediction data, and calculating to obtain a data goodness of fit; selecting the most matched prediction scene as a current execution scene based on the data goodness of fit, and storing other prediction scenes into a standby scene library; and generating a tallying operation instruction and expense settlement data according to the current execution scene. By implementing the application, the efficiency of port multi-link data processing can be improved.

An example device for decoding video data includes a memory configured to store video data; and a processing system comprising one or more processors implemented in circuitry, the processing system being configured to: generate a prediction block for a current block of video data using a neural network; determine an equivalent intra mode for the prediction block from a set of available intra-prediction modes using decoder-side intra mode derivation (DIMD), the equivalent intra mode representing one of the intra-prediction modes that would generate an intra-prediction block that would best match the prediction block generated using the neural network; decode a residual block for the current block of the video data based on the equivalent intra mode; and combine the prediction block with the residual block to decode the current block of the video data.

The invention provides a well drilling and completion report self-adaptive SQL generation and intelligent question and answer data processing method. The method comprises the steps that S1, natural language query input of a user is received; s2, inputting user query input into a 'Zhongqi-7b-chat-1m' model, understanding a query intention, and outputting a most matched database table name; s3, accessing database metadata according to a table name output by the model, and obtaining structure information corresponding to the table; s4, inputting the natural language query and the obtained table structure information into a 'Zhongs-Code-32B-Instruct' model together, and generating an SQL (Structured Query Language) query statement conforming to semantic logic by the 'Zhongs-Code-32B-Instruct' model; and S5, executing the generated SQL query statement, and returning a structured query result. According to the method, the advanced natural language processing technology and the deep learning model are combined, so that automatic conversion from natural language query to standardized SQL is realized. According to the method, the query efficiency and accuracy are greatly improved, and the method is particularly suitable for non-technical personnel to perform complex drilling data query.

The embodiment of the invention provides a marketing short video generation method and device based on a large language model, and the method comprises the steps: carrying out the matching degree analysis of a hot title and product data based on the large language model, and outputting a hot topic which is most matched with a product according to the sequence of the matching degree from high to low; searching a hot video related to the most matched hot topic in a video platform, and extracting an oral copy in the hot video based on an automatic voice recognition technology; generating a creation copywriting by using a large language model based on the oral copywriting, the hot topics and the product data; and performing similarity matching on the embedded vector corresponding to the created copywriting and the semantic vector of the video slice in the database, and sorting and generating a mixed video meeting the duration requirement according to the similarity.

The invention provides a code reuse method and device based on AI drive, equipment and a medium. When a code submission event is detected, a code scanning process is triggered, a code analysis module driven by AI carries out multi-dimensional analysis on submitted codes, quantitative evaluation is carried out on the basis of a preset reusability evaluation model, and code snippets meeting a reuse standard are stored in a code knowledge base with a semantic index structure; analyzing annotation semantics based on a natural language processing technology through an AI retrieval engine, calculating semantic similarity between demand description and code snippets in a knowledge base in combination with a deep learning model, and positioning the code snippets which are optimally matched; after code multiplexing execution, performing incremental modification on the multiplexing code snippets and automatically generating change records; and carrying out value layering on the code snippets based on the Pareto analysis principle by using frequency trend prediction. Through combination of the AI technology and code reuse, the development efficiency can be remarkably improved.

A data management system receives updates to records of a source dimension. Some records of the source dimension reference target dimensions. The data management system identifies template records from existing records in the source dimension for modeling changes to connections with the target dimensions based on the updated records in the source dimension. The template records are discovered using rules-driven processes, AI-driven processes, or a serial or parallel hybrid processes including rules and AI. These processes use ancestor information from the updated records to find best-matching template records. The rules-driven processes additionally rely on matching fields, and the AI-driven processes additionally rely on vector embeddings and optionally clustering. Updates are made to the target records in the target dimensions, including any roll-up structures indicated for data propagation, identified using the template records, and downstream applications using the target records may consume the updates.

The invention discloses a heterogeneous computing system, a cache consistency maintenance method and device, equipment and a medium, and relates to the technical field of heterogeneous computing. The method comprises the following steps: determining a hotspot data access path at the current moment according to an access sequence and dependency relationship strength of each data block of the heterogeneous computing system at the current moment; generating current running state information according to the hotspot data access path, the workload information of the heterogeneous computing system at the current moment, the cache effective information and the cross-device communication information; and based on the relationship between the running state of the heterogeneous computing system and the cache coherence protocol type, determining the cache coherence protocol most matched with the current running state of the heterogeneous computing system according to the current running state information. According to the method, the problem that the cache consistency protocol cannot be effectively and dynamically optimized in related technologies can be solved, the cross-device data dependency relationship can be dynamically captured, the dynamic change can be accurately sensed, and the high performance and data consistency of the system can be ensured.

The invention relates to the field of artificial intelligence, in particular to an intelligent diagnosis system for crop diseases and insect pests and a self-evolution method of the intelligent diagnosis system. The diagnosis system extracts features from a disease and insect pest image and a description text of a user for disease and insect pests, performs multi-modal fusion, generates a diagnosis report by using the fused features, and further comprises a gating network and a LoRA adapter which is specific for various disease and insect pest tags. And the gating network selects a corresponding LoRA adapter according to the description text of the plant diseases and insect pests, the corresponding LoRA adapter is dynamically injected into a Transform layer of the basic model, a reasoning path of the LoRA adapter is temporarily reconstructed, and the model with updated parameters is utilized to generate a diagnosis text. According to the method, the most matched expert capability can be loaded in time, so that the adaptability and the diagnosis accuracy of complex and changeable diagnosis scenes are remarkably improved.

The embodiment of the invention provides a CSI (Channel State Information) performance monitoring report triggering method based on an AI / ML (Artificial Intelligence / Markup Language) model and a related device, and provides a channel environment adaptive cumulative sum CUSUM algorithm, the algorithm configures different CUSUM parameter sets for different states of UE (User Equipment) respectively, each CUSUM parameter set comprises a relaxation factor k and a decision threshold h, and the relaxation factor k and the decision threshold h correspond to each other. The values of k and h in different parameter sets are different; the UE can select the most matched CUSUM parameter according to the current state (or scene), so that a performance monitoring mechanism of the UE can intelligently balance the detection sensitivity and robustness. Moreover, according to the scheme, performance differences are accumulated through a CUSUM algorithm and then compared with a decision threshold value to judge whether a performance degradation report is triggered or not, instantaneous jitter of a channel is effectively filtered through accumulation of the performance differences, and it is ensured that the report is triggered only when the performance of the AI / ML model continuously declines. Therefore, the uplink signaling overhead caused by mistaken touch and the resource waste caused by subsequent network response (such as mode switching) are reduced.

The invention provides a three-dimensional flow velocity field inversion method and system based on coherence-assisted SAR adaptive offset tracking, and a storage medium, so as to improve the problem of decoherence caused by time heterogeneity in a synthetic aperture radar interferometry (InSAR) technology, and relates to the technical field of electric power engineering geological disaster monitoring. The method comprises the following steps: establishing a space-time baseline network between SAR images; obtaining coherence data after geocoding; selecting a pair of SAR images from the space-time baseline network as a reference unit, calculating an offset result through a pixel offset tracking technology for the reference unit, and determining an optimal matching window parameter of the reference unit; establishing a mapping function between coherence and optimal matching window parameters; according to the established mapping function, calculating an optimal matching window parameter of each remaining SAR image pair; and in combination with the line-of-sight average deformation rate fields of the ascending and descending rails, inverting a surface three-dimensional flow velocity deformation field of the target area through a geometric vector decomposition model.

The invention relates to the field of data processing, in particular to an injection mold temperature control method and system, and the method comprises the steps: uniformly dividing the surfaces of injection molds of the same model and different batches into regions based on the area, and obtaining a historical temperature sequence and a real-time temperature sequence of each region; a sliding window is preset for the historical temperature sequence, the temperature fluctuation degree is calculated, and the temperature sensitivity degree of each area is determined. And calculating the similarity between the real-time temperature sequence and the historical production stage temperature sequence, selecting the historical stage with the maximum similarity as the best matching stage, and calculating the comprehensive temperature error. And according to the temperature sensitivity degree and the comprehensive temperature error, a temperature control signal of the PID control center is corrected, a comprehensive temperature control signal is obtained, and accurate temperature control is realized. By calculating the temperature sensitivity degree and dynamically adjusting the temperature control strategy, accurate and stable temperature control is achieved, manual intervention is reduced, and the production efficiency and the product quality are improved.

The invention discloses a building construction progress intelligent monitoring method and device and electronic equipment, and belongs to the technical field of intelligent monitoring, and the method comprises the steps: comparing a label of a building construction image with a building information model library, and obtaining a two-dimensional visual angle image data set; performing semantic segmentation on a bounding box of the building construction image to obtain a segmentation mask set; calculating the cosine similarity of the two-dimensional visual angle image data set and the segmentation mask set according to the feature vector cosine similarity, and determining the angle corresponding to the maximum value of the cosine similarity as the best matching angle; based on the optimal matching angle, a two-dimensional optimal matching view angle image and an optimal matching mask are determined, and the construction progress of the building construction image is determined; semantic segmentation is carried out on the bounding box, and parts related to the building are extracted in a targeted mode; and by calculating the cosine similarity between the two-dimensional visual angle image data set and the segmentation mask set, similarity matching is carried out on the building construction image and the building information model library, so that the monitoring accuracy is further ensured.

According to the efficient matching method for the GIS multi-scale basic geographic space data, a multi-scale matching scheme based on geographic space database optimization is improved, firstly, a spatial database model based on a relational database and a spatial database engine is used as a method for storing the multi-scale basic geographic space data, and the method is high in storage speed and flexible in application; 2, establishing a new object selection method for multi-scale geographic space data matching, organizing a basic geographic entity in a data structure form of a multi-way tree based on a multi-scale semantic change rule of geographic space data, and establishing a category matching method for semantic change of the same element category of different proportional scales; a category matching relationship under a multi-scale condition is effectively constructed; and thirdly, an optimization scheme for multi-scale basic geographic space data matching is established, the matching effect is good, and the operation efficiency is high. Spatial data fusion can be efficiently realized, the spatial data quality is improved, and the map database updating efficiency is improved.

The invention relates to the technical field of industrial big data processing and algorithm scheduling, and particularly discloses an industrial algorithm model scheduling method and system for industrial big data, and the method comprises the steps: constructing an algorithm dependency graph of an execution pipeline composed of a plurality of industrial algorithms, bottleneck nodes influencing the overall performance are identified through a critical path analysis algorithm; carrying out operation fusion on algorithm pairs with close dependency relationships; optimal data partitioning strategies are determined for different algorithms, and an elastic execution pipeline is realized; allocating the most matched heterogeneous computing resources for the algorithm operation on the critical path, and adjusting the execution priority of the algorithm operation in real time according to the system load and the operation scene; a fine-grained synchronization system is realized; the industrial algorithm execution efficiency is improved, and the high-performance requirement of an industrial big data processing scene is met.

The invention discloses a storage goods allocation dynamic optimization method based on AI, and belongs to the technical field of intelligent storage and logistics management.The method comprises the steps that firstly, multi-dimensional data of goods, storage, business and the like are obtained; then, calculating a time-sensitive goods location-goods matching degree score integrating future delivery frequency of goods, association degree with adjacent goods, physical distance and a dynamic time urgency factor changing along with approaching of expected sorting time; a reinforcement learning model is used, the matching degree score is used as a key decision basis, a composite reward function considering overall layout gain, physical handling cost and high-urgency-degree task reward is used for guidance, and a dynamic goods allocation optimization scheme with globality and perspectiveness is output; according to the method, continuous self-adaptive adjustment of the storage layout can be realized, and the sorting efficiency and the response capability of the warehouse are remarkably improved.

The invention belongs to the technical field of artificial intelligence, and particularly discloses a retrieval enhancement generation method and system based on port equipment maintenance, and the method comprises the steps: obtaining equipment data according to a knowledge carrier of port equipment, carrying out the data preprocessing and data indexing of the equipment data, and storing the processed data in a vector database or an index; converting a fault query text input by a user into a high-dimensional real value vector by utilizing a pre-trained text embedding model; searching a fault knowledge fragment most matched with the high-dimensional real value vector in a vector database, and optimizing the fault knowledge fragment to obtain an optimal knowledge fragment set; and combining the optimal knowledge fragment set with the fault query text to generate an enhanced prompt, inputting the enhanced prompt into a large language model for reasoning analysis, and generating a fault positioning result and a processing suggestion. According to the invention, the accurate positioning of the fault mode can be realized, and the fault processing efficiency is improved.

The invention relates to the technical field of artificial intelligence and data engineering, in particular to an unstructured data universal structured acquisition method and system based on RAG. The method comprises the following steps: firstly, constructing a knowledge base for storing a table structure and field information of a service table; carrying out index identification and extraction on the input unstructured text by utilizing the large model; secondly, searching and matching a plurality of most similar business fields in a knowledge base aiming at each extracted index by utilizing a search enhancement generation technology; based on the input unstructured text and the most similar business field information of each index, assisting a large language model to elect a most matched business field of the index in a specific business scene; and finally, storing the index value of the index in a knowledge base in combination with the most matched business field. The universal data acquisition method for the unstructured data is suitable for index extraction universal for all service lines in the field, can automatically align and screen service fields in a specific service scene and store the service fields in the specific service scene, and realizes the universal data acquisition method for the unstructured data.

The invention relates to the technical field of conveyor regulation and control, in particular to a pipe chain conveyor operation regulation and control method suitable for bulk material conveying. Obtaining time sequence data of motor working states and monitoring point running states of the pipe chain conveyor in a preset time period and a historical time period, and screening out historical reference time most matched with working conditions at all moments in the preset time period by comparing difference characteristics of the motor working states in the historical time period and the preset time period; carrying out multi-dimensional operation state characteristic comparison on the material blocking probability and each moment in a preset time period, dynamically calculating the material blocking probability, and screening material blocking moments; the material blocking probability difference of all the monitoring points at the material blocking moment and the time relation of the material blocking moment are analyzed, an abnormal transmission chain is constructed, and a material blocking propagation path is revealed; on the basis, feature vectors are constructed, misjudgment is reduced in combination with a neural network, and a refreshed abnormal transmission chain is obtained; and finally, calculating the self-adaptive regulation and control intensity at the current moment based on the refreshed abnormal transmission chain to realize closed-loop optimization of a regulation and control strategy.

A method, computer system, and a computer program product are provided. A first data sample is received via a computer. The first data sample is compared to a collection of models to select one of the models that is determined as a best match for the first data sample. The comparing includes (A) the computer comparing metrics of data summarization for the first data sample to metrics of data summarization of the models and (B) the computer comparing a neural network metric for the first data sample to respective neural network metrics for the models.

The embodiment of the invention provides an object 6D attitude estimation method and system, electronic equipment and a storage medium, and belongs to the technical field of computer vision. According to the method, in the first stage, an optimal matching template of a target observation image in a 3D template set is analyzed, and meanwhile, a rough corresponding relation between the optimal matching template and the target observation image is determined; in the second stage, an affine transformation matrix between the target observation image and the optimal matching template is determined according to the rough corresponding relation, and a smooth corresponding relation between the target observation image and the optimal matching template is determined based on the affine transformation matrix, so that corresponding relation noise and outliers are filtered out; in the third stage, the coordinate offset is determined according to a regression offset block between the target observation image and the feature map of the optimal matching template, the smooth corresponding relation is updated according to the coordinate offset, and the accurate corresponding relation is obtained; and determining the 6D attitude of the object according to the accurate corresponding relation. According to the method, the precision of attitude estimation is improved through gradual pixel-to-pixel corresponding learning.

The invention provides a contract generation method and system based on artificial intelligence. Belongs to the technical field of artificial intelligence and comprises the steps that a user inputs basic information and personalized requirements of a contract through an interactive interface, and a management system analyzes the input information, extracts key features and constructs a requirement vector; constructing an intelligent decision model based on a reinforcement learning algorithm; a database containing various contract templates is constructed, and the management system selects the most matched template from the template database as a starting point of contract generation according to the output of the reinforcement learning model. Through an artificial intelligence technology, especially natural language processing and a reinforcement learning algorithm, user demands can be rapidly analyzed and contract texts meeting the requirements can be generated, so that the time of traditional manual contract writing is greatly shortened.

The invention provides an information matching method, a recruitment system testing method, a recruitment system testing system, a terminal and a medium. The method comprises the following steps: respectively encoding an extracted job seeker feature vector and an extracted recruiter feature vector into a job seeker behavior code and a recruiter behavior code; based on a preset gradient descent algorithm, importance weights of the job seeker behavior codes and the recruiter behavior codes in final matching are determined in an adaptive learning mode; and fusing the weighted job seeker behavior code and the weighted recruiter behavior code, and calculating a matching degree between the comprehensive matching code of the job seeker obtained by fusion and the feature code of the potential matching object so as to determine an optimal matching result between the recruiter and the job seeker based on the matching degree. According to the invention, through a dynamic adaptive learning weight distribution mechanism and a behavior coding fusion strategy, independent preferences of the job seeker and the recruiter can be fully considered and satisfied, and the accuracy of the matching process can be ensured.

The invention belongs to the field of labor dispatch data analysis, and discloses a labor dispatch data analysis processing method and system and a storage medium. According to the invention, through dynamic matching and multi-scheme comparison of the personnel capability labels and the post demand labels, the optimal matching scheme can be accurately generated, and the labor dispatch matching success rate and the matching effect are improved. According to the method, the matching coefficient is generated by combining actual working data such as the efficiency coefficient and the stability coefficient, upgrading from static label matching to dynamic data driving matching is achieved, adaptability abnormity can be accurately recognized, the situation that the adaptability of matched personnel posts is insufficient is avoided, and the problems that the task completion rate is low, and client feedback is poor are prevented. According to the invention, a bidirectional correction system is constructed based on customer feedback and personnel feedback, and the mechanism can respond to business changes in real time and autonomously trigger a correction mechanism so as to quickly respond to actual business requirements, reduce manual intervention, greatly improve delivery management efficiency and reduce resource waste.

The invention relates to the technical field of knowledge distillation, and discloses a teacher-student model automatic matching method based on iterative pruning, comprising the following steps: S1, obtaining a pre-trained teacher model; s2, executing an iterative pruning process on the teacher model, wherein the iterative pruning process specifically comprises the steps of performing pruning operation on the teacher model; performing fine adjustment on the teacher model after pruning; storing the fine-tuned teacher model as a potential student model; judging whether a preset iteration termination condition is met or not, and if not, returning to the pruning operation; and S3, based on a preset scoring standard, selecting an optimal student model from the plurality of stored potential student models. By introducing a strategy based on iterative pruning, student models of different scales and performances can be automatically generated from a teacher model, and a student model most matched with the teacher model is selected from the student models by using a preset scoring standard; by means of the automatic generation and selection mode, the uncertainty of manual design is reduced, and high calculation overhead of a traditional NAS method is avoided.