611 results about "Match algorithm" patented technology

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to the field of human resource management, and discloses a tobacco enterprise human resource management auxiliary calibration method based on big data, and the method comprises the steps: obtaining multi-source heterogeneous data related to enterprise internal human resources, and constructing a structured human resource data model in combination with a data standardization processing mechanism and an abnormality elimination strategy; post portrait modeling is carried out on the structured human resource data model, a capability dimension nesting analysis method is introduced, key capability factors and weight distribution required by each post are extracted, and a post capability demand graph is constructed; based on the post capability demand map, fusing the staff portraits and the historical job data, and identifying the deviation between posts and the staff through a multi-dimensional feature matching algorithm to form a preliminary calibration suggestion set; and a dynamic service association analysis method is introduced, and key matching parameters in the preliminary calibration suggestion set are dynamically corrected in combination with latest service demand data and real-time task assignment information. The method has the advantage of improving the management efficiency.

The invention discloses a multi-agent cooperation enhancement method, system and equipment based on a knowledge graph, and the method comprises the steps: obtaining original data in an external environment, carrying out the preprocessing and feature extraction of the original data, generating a knowledge triple, storing the knowledge triple in a local knowledge graph, and submitting the knowledge triple to a shared knowledge graph for knowledge updating; when a to-be-executed task is received, decomposing the to-be-executed task by utilizing the large language model and querying global knowledge in the shared knowledge graph and local knowledge in the local knowledge graph to obtain a plurality of sub-tasks; a bipartite graph minimum cost matching algorithm is adopted to match a plurality of sub-tasks with the capability and availability of each agent to generate a preliminary task allocation scheme, and a large language model is utilized to optimize the preliminary task allocation scheme to generate an optimal task allocation scheme; and sending each task allocation knowledge fragment in the optimal task allocation scheme to a corresponding agent for collaborative execution through a semantic communication protocol.

The invention relates to the technical field of rehabilitation training detection, in particular to a rehabilitation training detection method and system based on artificial intelligence, a standard action library is constructed through standard action videos shot at multiple angles, and track and angle features of key joints are extracted for user training comparison; the skeleton key points of the user are extracted in real time through a MoveNet network, and efficient posture recognition in a home scene is achieved; analyzing position difference, angle change and acceleration characteristics by combining a space-time sequence matching algorithm, generating a dynamic matching degree index, and positioning a deviation joint to generate a correction prompt; introducing an attention mechanism model, learning the contribution degree of each joint to cycle recognition, dynamically selecting a dominant joint for action counting, recognizing starting and ending points of an action cycle through an acceleration curve, and finishing effective action statistics in combination with a dynamic threshold value, so that the counting accuracy and the self-adaptive capability are improved; therefore, the training cost is reduced, the evaluation credibility is enhanced, and accurate statistics and analysis of rehabilitation training data are realized.

The invention relates to the technical field of wind power equipment monitoring, and discloses a wind power gear box intelligent fault early warning method and system based on machine learning. The method comprises the steps that multi-source monitoring data such as vibration signals, temperature data and oil analysis data of the wind power gear box are acquired, and multi-scale operation characteristics are extracted through time-frequency conjoint analysis; key fault sensitive features are determined through an adaptive feature selection algorithm, and a dynamic fault feature weight matrix is constructed in combination with a historical fault case library; multi-modal data fusion is adopted to generate an enhanced fault feature set, and modal decomposition is carried out on the enhanced fault feature set to obtain a trend component and a fluctuation component; a fault evolution feature space is constructed by using a deep neural network based on two components, then a fault development mode is identified by using a time sequence mode matching algorithm, and finally a graded early warning signal is generated according to a matching degree with a preset mode, so that fault features can be comprehensively captured, and safe operation of a wind power gear box is ensured.

The invention relates to an underwater topographic measurement method based on laser radar and visual fusion, which comprises the following steps: synchronously acquiring underwater point cloud, images and physical environment sensing data, and endowing a unified space-time label to realize the space-time consistency of multi-source data; according to the method, the data quality of different modes is improved by means of preprocessing, denoising, scale normalization, feature enhancement and the like, an environment interference weight matrix is constructed in combination with an environment sensing model, feature extraction and matching algorithm parameters are adaptively adjusted according to different environment states, and multi-scale feature description, spatial consistency and physical constraint criteria are established, so that the multi-modal data quality is improved. According to the scheme, high-reliability feature matching between the point cloud and the image is achieved, finally, through environment-driven iterative optimization and fusion, the robustness and precision of space registration are improved, high-precision multi-modal data automatic registration can be stably achieved in the underwater dynamic environment, adaptability is high, and environment perception and space measurement quality is effectively improved.

The invention discloses a time-space clustering-based instant retail intelligent distribution scheduling method, medium and system, and the method comprises the steps: obtaining the geographic information of a hierarchical geographic unit through an electronic fence, collecting the data of a to-be-distributed order, and calculating a distribution time window through a time window prediction model, so as to generate time-space clustering information; according to the method, orders with overlapped delivery time windows in the same geographic unit are aggregated into task packages with the same address, the task packages are dynamically allocated through a matching algorithm based on the real-time state of a rider, and algorithm parameters are updated according to delivery feedback. According to the invention, the accuracy of order aggregation and the rationality of task allocation are obviously improved, and the refined scheduling of transport capacity resources and the optimization and improvement of the overall distribution efficiency are realized.

The embodiment of the invention discloses a multi-sensor fusion anti-degradation SLAM mapping method and system. The method can effectively solve the problem of pose drift of a robot in a mapping process in structure degradation environments such as an indoor long corridor, constructs a globally consistent three-dimensional point cloud map and a robot trajectory, and comprises the following steps: realizing depth coupling of an IMU and a wheel speedometer based on extended Kalman filtering, and generating high-frequency pose prediction; denoising, down-sampling and motion distortion correction are carried out on the 4D laser radar point cloud, and the normal vector and intensity characteristics of the point cloud are extracted; a normal vector and intensity feature enhanced scanning matching algorithm is adopted, and a target function is optimized through a multi-feature weight, so that the matching precision in a degradation scene is improved; loopback detection is realized through candidate key frame screening and geometric registration verification, and a closed-loop constraint is incorporated into a factor graph for global correction; and finally, incrementally updating the global point cloud map and carrying out consistency optimization, and outputting a robust three-dimensional point cloud map and a high-precision robot track.

The invention relates to the technical field of crane line fault diagnosis, in particular to a crane line fault diagnosis system and method based on multi-source data fusion, which comprises a data acquisition and processing unit, a mechanical and electrical coupling characteristic unit and a characteristic fusion and fault quantification unit, the three-axis vibration acceleration and the three-phase current waveform of the track are obtained through the data collecting and processing unit, the mechanical and electrical coupling characteristic unit conducts three-dimensional vector synthesis and wavelet packet decomposition on vibration data, and a time-space incidence matrix of harmonic distortion and vibration is constructed. And the feature fusion and fault quantification unit outputs coupling factors by using a bidirectional long-short-term memory network and an attention mechanism, and outputs a fault probability value through a dynamic time warping matching algorithm after time-frequency domain analysis and sample entropy judgment, so that time-space correlation modeling and dynamic fault matching of multi-source data are realized. And the fault positioning precision and the diagnosis accuracy are improved.

The invention discloses a multi-mode driven cross-industry digital twinning universal platform architecture and an implementation method, and relates to the technical field of digital twinning and artificial intelligence. The method comprises the following steps: establishing a multi-modal driven cross-industry digital twinning universal platform, deploying a multi-source heterogeneous data acquisition component in a data access layer to access text, image and time series data, and converting unstructured data into a unified feature space by adopting a Transform-GNN cross-modal encoder in a multi-modal fusion layer; in the large model scheduling layer, feature vectors are analyzed through a multi-modal large model center based on an industry knowledge graph, an algorithm is dynamically matched, and an initial decision strategy is generated; the method comprises the following steps of: establishing a parameterized template library, and supporting security cooperative training of a third-party algorithm scheduling engine on a third-party algorithm, and deploying a lightweight digital twin engine in a twin engine layer: establishing the parameterized template library: pre-defining three templates of geographic space, equipment assets and business processes; deploying the twin model to an edge node by adopting a knowledge distillation method; in the interactive application layer, loading the BIM / GIS model in a lightweight manner through a low-code tool, and completing scene construction through a dragging component; and rendering a twin state in real time through a three-dimensional cockpit, analyzing a natural language instruction and performing corresponding operation.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source geological data processing method and system of a three-dimensional geological model.The method comprises the following steps that mountain landform and river valley images are obtained, gray frequency characteristics are extracted, a frequency energy gradient layer is constructed, a frequency continuous response area is screened to generate a structure boundary set, and a structure boundary set is constructed; the method comprises the following steps of: extracting a boundary normal vector by utilizing principal component analysis, identifying boundary sections with consistent directions, estimating a physical property parameter gradient direction, judging an included angle screening blocking region, generating a space attribute limiting layer, carrying out space alignment analysis on an overlapping region vector included angle, updating a boundary label, and generating an available attribute path structure set in three-dimensional geological modeling through a Dijkstra algorithm. According to the method, a conduction model is constructed through frequency domain decomposition and logarithmic transformation enhanced recognition, frequency window analysis noise reduction, principal component extraction vector analysis direction and center difference estimation, dynamic matching is promoted through alignment, a Dijkstra algorithm optimizes a path, and the geological model bedding characterization and conduction simulation precision is improved through cooperation of a multi-dimensional technology.

The invention relates to the technical field of high temperature sensing and data fusion, in particular to a method for array thermocouple multi-mode compensation and DIC stress field space-time coupling fusion, which comprises the following steps: step 1, in a thermotechnical signal intelligent processing and compensation unit, completing hardware and algorithm collaborative design of an electronic cold junction compensation module; 2, constructing a multi-algorithm fusion compensation module, integrating nonlinear correction, drift compensation and interference suppression functions, and accurately coping with various interference signals through a dynamic weighting strategy; step 3, adopting a sub-pixel-level matching algorithm and a homography matrix calibration technology to realize high-precision space alignment of the temperature and stress measurement units; and establishing a nonlinear incidence relation between the temperature and the stress based on an improved Gaussian process regression model. According to the invention, based on collaborative design of the thermotechnical signal intelligent processing and compensation unit and the DIC vision and temperature data conjoint analysis module, the core precision problem of temperature and stress detection in a high-temperature environment is solved through hardware optimization and algorithm innovation.

The invention aims to provide a speed anomaly detection and fraud identification method and system based on trajectory data, and belongs to the technical field of road traffic safety, the method realizes abnormal trajectory detection and fraud identification through multi-stage data processing: firstly, preprocessing original trajectory data, and removing invalid data; noise points are filtered based on a DBSCAN algorithm; track segments are divided according to vehicle speed changes; matching the moving track segment to a map road through a map matching algorithm; calculating an error ratio between the calculation speed and the equipment uploading speed, and identifying an abnormal track segment; and finally, analyzing the speed distribution of the abnormal track section, and identifying a counterfeit behavior. The system comprises a data preprocessing module, a trajectory noise filtering module, a trajectory division module, a map matching module, an abnormal speed detection module and a speed verification and forgery identification module. According to the method, the accuracy of speed anomaly detection is improved, the recognition capability of a hidden speed forgery behavior is enhanced, and the authenticity and credibility of trajectory data are improved.

The invention relates to the technical field of ship manufacturing, and discloses a ship block assembly man-hour matching method based on multi-modal process feature recognition. Multi-modal process data including structured process parameters, unstructured process documents and the like are collected, each modal process feature vector is obtained through feature extraction, and then a fusion feature vector is generated through a multi-modal feature fusion algorithm. And constructing a man-hour matching knowledge base according to historical assembly order data, and determining an assembly process sequence and a man-hour allocation scheme by using a dynamic matching algorithm. According to the method, a process set can be optimized through a graph traversal algorithm, real-time sensor data are analyzed to deal with abnormal fluctuation, and process compliance is verified by means of a process rule knowledge graph. According to the method, multi-modal data can be effectively fused, the man-hour matching precision is improved, the assembly process is optimized, the ship block assembly efficiency and quality are improved, and the cost is reduced.

The invention discloses a watershed water resource supply and demand risk prediction and evaluation method based on a multilevel Bayesian causal network, and relates to the technical field of water resource supply and demand risk management.The watershed water resource supply and demand risk prediction and evaluation method comprises the steps that a water resource supply and demand risk diagnosis knowledge graph is constructed according to key variables and interrelations input by a user; constructing a multi-level Bayesian causal network structure; estimating conditional probability distribution among the nodes, and performing parameter learning and structure training on the Bayesian causal network; carrying out risk path identification through a reverse Bayesian reasoning method; outputting a posterior probability of water resource supply and demand risk prediction; based on a preset fuzzy character string matching algorithm, typical risk events and risk features are extracted; and according to the posterior probability and the risk characteristics, comprehensively evaluating the water resource supply and demand risk level. The method can improve the systematicness and scientificity of risk identification, is suitable for multi-link and multi-scale risk assessment and scheme comparison and selection in a complex drainage basin, and has high practical value and popularization prospect.

The invention discloses an AI-based child story video generation method and system, and relates to the technical field of artificial intelligence and multimedia crossing, and the method comprises the steps: generating a script from an original text input by a user through constructing an AI model fusing an emotion modeling capability; constructing an image generation combination model, defining a joint loss function, calculating an edge intensity graph of the contour image by using a Sobel edge detection algorithm, calculating an optical flow field of frame change by using a block matching algorithm, and performing color image dynamic frame alignment; a fine tuning WaveNet model is used to generate audio; through constructing an image generation combination model, combining a StyleGAN3-T model and an LDM model, defining a joint loss function, and using a Sobel edge detection algorithm and a block matching algorithm to calculate an edge intensity graph and an optical flow field, dynamic frame alignment of a color image is realized, and inter-frame continuity of a generated video is improved.

The invention relates to a BIM-based water conservancy project safety early warning method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source data such as an osmotic pressure gradient field based on a predefined hydraulic engineering monitoring point space coordinate list, and generating a three-dimensional osmotic field matrix after noise reduction, standardization and interpolation processing; physical characteristics of the matrix are extracted by using a pre-trained space-time convolution model, and abnormal parameter labels are determined through threshold comparison and deviation calculation; associating the exception to a BIM component node through a spatial topology matching algorithm; and finally, performing visual early warning based on the BIM abnormal coordinate vector, and generating a structured alarm instruction set by combining risk levels and abnormal type matching treatment measures. According to the method, through multi-source data fusion modeling, feature analysis and extraction, BIM space mapping, three-dimensional visualization decision making and the like, the precision, efficiency and intuition of hydraulic engineering safety early warning are improved, and a reliable technical scheme is provided for guaranteeing safe and stable operation of hydraulic engineering facilities.

The invention relates to the technical field of software architecture design, and discloses a software architecture design method based on intelligent bionic implementation of a large model group. The method comprises the steps of obtaining software design requirement input; performing multi-level semantic analysis on software design requirement input to generate an initial component set and context environment description; based on the context environment description, candidate component knowledge related to the initial component set is retrieved from a distributed knowledge base; performing consistency verification on the candidate component knowledge and the initial component set by utilizing a dynamic inference engine to generate a verified component map; according to the verified component atlas, candidate software architectures are generated through an architecture template matching algorithm; and performing iterative adjustment on the candidate software architecture by adopting an optimization strategy, and outputting a target software architecture design. Through collaborative analysis and intelligent reasoning of the large model group, automation and intelligence of software architecture design are achieved, and the efficiency and quality of software design are improved.

Proposed is a mobile robot utilizing the uncertainty of map matching poses for localization. The mobile robot includes a point cloud sensor which acquires point cloud data through scanning, a matching pose calculation part which applies a pre-registered point cloud map and the point cloud data to a pre-registered scan-map matching algorithm to calculate a map-matching pose of the mobile robot, and an uncertainty estimation part which estimates uncertainty of the map-matching pose on the basis of a probability distribution of a preset pose space around the map-matching pose. Through this, the uncertainty of the map-matching pose is estimated to evaluate the reliability of the map-matching pose, enabling more precise localization.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention relates to the technical field of remote sensing agricultural data management, and discloses a remote sensing agricultural big data management system based on a block chain. According to the system, image data is subjected to radiation, atmosphere and geometric correction preprocessing to generate a standardized remote sensing image; the feature extraction module performs multi-scale segmentation on the crop feature region, identifies the crop feature region, extracts texture, spectrum and shape feature values, and fuses the feature values to form a multi-dimensional feature vector; the block chain storage module performs hash operation on the vector to generate a feature fingerprint, constructs a new block in combination with a timestamp and a preorder block hash value, and forms a non-tampering agricultural data chain after verification of a consensus mechanism; a path analysis module traverses the block sequence, extracts a storage path and screens a high-relevance feature path set; and the intelligent classification module extracts feature tags, and generates an agricultural data classification structure through a multi-dimensional matching algorithm. The system realizes standard processing, safe storage and intelligent application of remote sensing agricultural data, and meets the requirements of agricultural modernization development.

The invention belongs to the technical field of engineering material detection, and particularly discloses an asphalt mixture dynamic water stability comprehensive detection system which comprises a standard test piece preparation module, a dynamic load loading module, a diffusion behavior analysis module, a sealing failure verification module and a water stability evaluation module. A detection area is limited through closed sealing treatment, a staged progressive increase dynamic load is applied to simulate an actual working condition, a three-dimensional moisture diffusion map is constructed to analyze diffusion behavior parameters, and a dynamic water stability grade evaluation result is generated in combination with an intelligent matching algorithm. According to the method, edge water seepage interference can be effectively avoided, the moisture diffusion process is accurately monitored, the performance of the asphalt mixture is comprehensively evaluated, and a scientific basis is provided for pavement structure design and material optimization.

The invention relates to the technical field of production matching recommendation, in particular to a textile fabric online production recommendation system based on a knowledge graph, and the system comprises a knowledge graph construction module which is used for integrating and storing multi-dimensional information of textile fabrics, and constructing a textile fabric knowledge graph; the user demand receiving module is used for receiving cloth demand information input by a user, wherein the cloth demand information comprises cloth types and performance requirements; the recommendation algorithm module is used for analyzing the matching degree between the user demand and the cloth characteristics through a matching algorithm based on the knowledge graph, and recommending an optimal cloth production scheme for the user; and the scene analysis module is used for collecting related environment parameters of an expected application scene of a user, constructing an influence model between cloth performance and the environment parameters, evaluating performance of recommended cloth under a demand environment condition, and optimizing cloth recommendation according to an evaluation result. And the user can obtain the cloth recommendation which most meets the requirements.

The invention discloses an AI-based cooperative communication method and system for dynamic spectrum allocation and interference elimination, and the method comprises the following steps: 1, collecting historical data of power equipment, including power line environment data collected by a sensor and power line data collected by a dual-mode communication module; 2, marking the historical data, and inputting the marked historical data into the AI model for training; 3, predicting communication performance through the trained AI model, and dynamically adjusting spectrum allocation according to a prediction result; and step 4, the AI model analyzes interference signal characteristics in a prediction result, and interference is eliminated for different interference types by adopting a matching algorithm. According to the invention, the AI technology is utilized to dynamically optimize the power and frequency spectrum selection of the HPLC and the HRF under the condition of meeting the upper limit of equipment energy consumption and the upper limit of transmission power of the dual-mode communication module, and the interference in a complex power line environment is eliminated, so that the optimal communication performance is ensured.

The invention provides a reverse osmosis membrane cleaning system which is characterized in that a variable-frequency water pump control system is adopted to adjust water flow momentum according to cleaning parameters output by a matching algorithm, differential impact strength control of different areas on the surface of a membrane is realized through a flow adjusting valve group, and a stress distribution mode adaptive to pollution distribution is obtained; mechanical stress data of the membrane surface in the cleaning process are collected in real time through a membrane material stress monitoring sensor, if the stress value exceeds a membrane material safety threshold value, a stress protection mechanism is triggered, and the impact strength of a corresponding area is automatically reduced; establishing an adaptive parameter optimization algorithm according to pressure feedback data and a pollutant stripping effect evaluation result in the cleaning process, and calculating a parameter adjustment scheme of the next cleaning period through a tradeoff function of the cleaning effect and mechanical damage; a membrane performance degradation prediction model is established through accumulation of historical cleaning data, the remaining service life of a membrane material is predicted according to accumulated mechanical stress and cleaning frequency data, and a quantitative judgment basis of the membrane replacement opportunity is obtained.

The invention relates to the field of geographic information mutual analysis, and discloses a multi-source dynamic collaborative geographic information mutual analysis method and device, equipment and a medium. The method comprises the following steps: collecting time-synchronized static geographic information, dynamic GPS positioning data and a plurality of pairs of starting and ending point unstructured text data sets, and constructing a dynamic spatio-temporal data pool; high-quality spatio-temporal data is obtained through dual anomaly verification and Kalman filtering cleaning correction; optimizing an address longitude and latitude mapping table in combination with a three-level administrative range and a dynamic high-frequency word; a non-standard address is analyzed into a structured text through a BERT-CRF model, and a precise coordinate and a standardized text are obtained through a hierarchical dynamic matching algorithm. By adopting the method, meter-scale positioning is realized, the non-standard address resolution accuracy is greatly improved, the duplicate name address mismatching rate is reduced, a dynamic scene is adapted, the resolution efficiency and reliability are considered, and an efficient technical support is provided for bidirectional accurate mutual resolution of geographic coordinates and text addresses.

The invention discloses a clinical decision interaction method and system based on structured evidence reasoning, and relates to the technical field of computer technology and medical informatization. The method obtains a user input query; the method comprises the following steps: performing mixed entity identification from a query input by a user based on an entity identification model and a rule matching algorithm to obtain a candidate entity set, and performing conflict resolution and entity standardization processing on the candidate entity set to obtain a core entity; performing multi-granularity intention recognition based on confidence calculation between the classification systems of the core entity and the medical exclusive intention to obtain a target intention; performing screening query in a pre-constructed structured knowledge graph according to the core entity and the target intention to obtain a corresponding structured knowledge fragment; generating a cue word based on the structured knowledge fragment, user input query, instruction constraint and multiple verification; and inputting the cue word into the large language model, and outputting an answer. According to the invention, more accurate and more reliable dialogue service can be provided for the medical field.

The invention discloses an adaptive education textbook generation method and device based on a large model, and a medium, and relates to the technical field of intelligent education, and the method comprises the steps: determining a knowledge point set which needs to be expanded currently by a learner through a knowledge graph retrieval and course outline matching algorithm based on a learner portrait data set, and generating a teaching task list; according to the individualized evidence Prompt script, generating individualized teaching content by using a controlled decoding mode of a large language model, performing content consistency verification, and rearranging the content into a customized teaching material; carrying out conflict discovery-retrieval supplement-local regeneration-format recombination on the customized textbook through a ReAct reasoning framework to obtain a high-adaptability education textbook; the teaching material can be continuously corrected and optimized in the use process, so that the generation of the high-adaptability personalized education teaching material which is correct and smooth in teaching, fit and sustainable in evolution is realized.

The invention discloses a hybrid expert model training optimization method based on expert calculation load balancing scheduling, and the method comprises the steps: firstly predicting current iteration global expert load data, and obtaining sampling data; and secondly, designing a multi-dimensional performance perception expert placement evaluation method, and realizing quantitative comparison and decision in a stage of searching an expert placement scheme by converting multi-dimensional communication and calculation into a unified time delay index. And then designing an expert placement strategy by adopting an expert grouping search algorithm of dynamic load feature perception and a matching algorithm of expert grouping and resource perception based on the sampling data and an expert placement evaluation method. Finally, on the basis of an expert placement strategy, an asynchronous communication strategy is adopted to implement expert and optimizer parameter scheduling, and training optimization is completed. According to the method, by improving the computing load balance and asynchronous communication concealment between the devices, synchronous waiting is reduced, and the training efficiency and the overall performance of the hybrid expert model are effectively enhanced.