13635results about How to "Improve consistency" patented technology

The invention belongs to the technical field of multi-agent collaboration, and provides an electric power cross-modal knowledge fusion multi-agent collaboration processing method and system, and the technical scheme is as follows: analyzing a task execution command instruction input by a user; key elements of the task are extracted, the task is decomposed into a plurality of sub-tasks according to the extracted key elements, and logic association and priorities among the sub-tasks are identified; according to the logical association and priority among the subtasks, allocating the subtasks to the corresponding agents; each agent executes the corresponding subtask according to a set execution path, the executed task result is shared to other agents to cooperatively complete the complex task, and finally a cooperative processing result is obtained through integration. Efficient execution of complex tasks such as document generation, review and management is realized, and smooth completion of the tasks is ensured.

The invention relates to the technical field of data processing, and discloses a coal and gas outburst dynamic monitoring and intelligent early warning method and device. The method comprises the following steps: collecting and preprocessing multi-source sensor data arranged in a target area to obtain a multi-dimensional data set; performing multivariable time sequence analysis on the multi-dimensional data set to obtain coal seam gas outburst risk prediction data; based on coal seam gas outburst risk prediction data, performing dynamic risk assessment and spatial interpolation processing on the multi-dimensional data set to obtain real-time risk distribution data of the target area; performing multi-dimensional threshold analysis on the real-time risk distribution data to obtain preliminary early warning data; performing multi-modal information fusion on the preliminary early warning data to obtain target early warning data; and performing intelligent decision tree analysis on the target early warning data to obtain a target early warning scheme. According to the invention, the accuracy and reliability of dynamic monitoring and intelligent early warning of coal and gas outburst are improved.

The invention provides a three-dimensional point cloud data segmentation method and system based on multi-scale point features, and relates to the technical field of point cloud processing, and the method comprises the steps: obtaining original point cloud data, constructing a three-dimensional Hash index table, carrying out the self-adaptive hierarchical division through a local maximum value of a density distribution curve, and obtaining a local maximum value of a density distribution curve; noise point elimination is carried out based on a region density count value and a geometric distribution coefficient, an enhanced feature vector containing a main feature direction, a main curvature value and the like is constructed for non-eliminated points, a local geometric descriptor is constructed by adopting four spherical search regions with increasing radiuses, a three-branch space attention network is input to extract spatial features, and the spatial features are extracted; and fused feature data are obtained through a multi-level geometric feature extraction network and a bidirectional feature propagation network. And obtaining a final segmentation result through segmentation energy function optimization and a region merging strategy.

The invention relates to the technical field of model construction, in particular to an obstetric delivery decision model construction method based on a knowledge graph. The method comprises the following steps that multi-dimensional information collection is conducted on a pregnant woman to be delivered, individualized data are obtained, and the multi-dimensional information comprises a medical record text, a laboratory examination result, an ultrasonic image and fetal heart rate monitoring data; performing standardization and structural processing on the individualized data to generate a standardized multi-modal data set; performing entity identification and relation extraction processing on the standardized multi-modal data set according to a preset obstetrical domain ontology model to obtain an initial knowledge graph; and carrying out priori knowledge-based mapping expansion on the initial mapping knowledge domain to obtain a multi-modal mapping knowledge domain. Through an attention mechanism and a decision support interface, the interpretability of the model is enhanced, and doctors can understand and trust decision suggestions.

The invention relates to a display screen backlight brightness adjusting method and system based on a Mini QLED technology. The method comprises the following steps: dividing a display image signal into a brightness signal, a chrominance signal and a saturation signal, and generating image feature data; performing sub-pixel-level boundary detection to obtain a plurality of initial backlight sub-regions, and calculating region brightness distribution data; executing region clustering operation and region merging to obtain a plurality of target backlight sub-regions and calculating backlight partition mapping data; carrying out nonlinear feature transformation, and calculating backlight unit control data of the Mini LED backlight unit; outputting the backlight compensation amount of each target backlight sub-region by adopting a deep Q network; and performing real-time feedback correction, calculating a deviation value between an actual display parameter and a target parameter of each target backlight sub-region, and outputting a backlight optimization control signal. According to the invention, the backlight brightness is accurately adjusted, and the consistency of the display effect is improved.

The invention relates to the field of inductive control of illuminating lamps, in particular to an inductive control system of an inductive LED illuminating lamp. The method comprises the following steps: the lighting scene module is used for identifying all LED lighting lamps in an area; calculating node positions of all the LED lighting lamps in the area one by one, and identifying use scenes in the area to construct a plurality of LED lighting scene sub-areas; the ambient light feature module is used for acquiring ambient light parameters of a scene sub-region; performing light spatial distribution tracking on the ambient light parameters of the scene sub-regions, and performing ambient light state feature mining to generate ambient light comprehensive state features; and the multi-area lighting cooperation module is used for carrying out dynamic light source output parameter adjustment and multi-area lighting cooperation smoothing on the plurality of LED lighting scene sub-areas based on the environment light comprehensive state characteristics so as to generate multi-area lighting cooperation control parameters. According to the invention, rapid and accurate induction control of the LED illuminating lamp is realized.

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention discloses a live-action three-dimensional model construction method, system and device based on a digital city, and the method comprises the steps: obtaining the multi-source sensing data of a city, and carrying out the data preprocessing, and the multi-source sensing data comprises city image data, point cloud data and GIS data; converting the preprocessed multi-source sensing data in different formats into a preset standard format, and performing registration and fusion of the multi-source sensing data to obtain fused multi-source data; performing data optimization on the fused multi-source data, and constructing a city-level three-dimensional real scene model according to the optimized fused multi-source data and a preset three-dimensional space analysis tool; the preset three-dimensional space analysis tool provides space analysis and three-dimensional space visualization functions; according to the invention, efficient and accurate real-scene three-dimensional data of a city is provided, and three-dimensional and visual space information is provided.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention provides a marine pollution tracing and monitoring method based on satellite remote sensing and an unmanned aerial vehicle, and relates to the technical field of marine pollution tracing and monitoring, and the method comprises the steps: configuring a pollution diffusion path prediction model and a data driving model based on range pollution data, precise pollution data and deepwater data after multi-modal data fusion, and obtaining a pollution prediction result and pollution error data, carrying out hybrid drive fusion based on the pollution prediction result and the pollution error data, and tracking an initial source of pollutants to obtain pollution source tracing data. Through preprocessing and fusion of multi-modal data, in combination with range pollution data, precise pollution data and deep water data, a pollution diffusion path prediction model and a data driving model are dynamically adjusted, so that a more precise pollution prediction result and pollution error data are generated, and meanwhile, through reverse diffusion simulation and pollution traceability analysis, a more precise pollution prediction result is obtained. The high efficiency of pollution source positioning and decision support is improved, and an accurate and decision basis is provided for coping with marine pollution events.

The utility model discloses a poker machine and a card lifting device thereof, the card lifting device comprises a plurality of card lifting seats arranged around a card receiving seat, a card receiving box and the card lifting seats are arranged in a one-to-one correspondence manner inside and outside, the card lifting seats are provided with card lifting grooves, and card lifting plates are vertically connected with the card lifting seats in a sliding manner and can lift up and down in the card lifting grooves; the mahjong tile lifting device further comprises a rotating ring, the rotating ring penetrates through the mahjong tile lifting seats, a plurality of lifting cams are arranged on the top of the rotating ring, the lifting cams correspond to the mahjong tile lifting seats and the mahjong tile lifting plates in a one-to-one mode, and each lifting cam comprises an upward protruding part used for lifting the corresponding mahjong tile lifting plate to the top and a downward concave part used for descending the corresponding mahjong tile lifting plate to the bottom. And when the rotating ring rotates in a reciprocating manner, the tile lifting plate can be driven to lift up and down in the tile lifting groove. The scheme is simple in structure, reliable in operation, low in manufacturing cost and convenient to install and debug.

The invention provides a textile defect positioning method, which comprises the following steps: according to an extracted defect feature map, fusing feature maps of different scales, extracting multi-scale defect features, and comprehensively depicting space distribution and combination information of defects; aiming at the condition that multiple flaws exist at the same time, a multi-task learning model is constructed, flaw detection, classification and positioning tasks are carried out at the same time, and a back propagation algorithm is adopted to optimize the multi-task learning model; positioning the defect area by adopting an image segmentation algorithm, obtaining the contour of the defect area according to the positioning result of the defect area and by adopting a Canny edge detection algorithm, and calculating the area and position information of the defect; and based on an evaluation result, a dynamic threshold adjustment algorithm is adopted, a defect judgment threshold is adjusted according to a production batch and a product type, and a threshold parameter is dynamically updated by analyzing historical data and a current production condition.

The invention discloses a building measurement method and system based on three-dimensional scanning, a medium and a product, and relates to the field of exploration, and the method comprises the steps: obtaining BIM model data and a measurement parameter threshold value of a target building, and determining a subarea scanning path according to the BIM model data; scanning the target building according to the subarea scanning path to obtain a point cloud data set of the target building; generating a three-dimensional digital model of the target building according to the point cloud data set, and dividing the three-dimensional digital model into a plurality of feature regions; each feature region comprises at least one feature component; extracting a feature point set in the feature region, and establishing a mapping relationship with a corresponding point set of a corresponding region in the BIM model data; calculating actual measurement parameters of the feature region based on the mapping relation; and comparing the actual measurement parameter with the measurement parameter threshold, determining an abnormal area and generating a measurement technical report. By implementing the method, the building data can be more comprehensively acquired, and the precision of building measurement is improved.

The invention discloses an intelligent question answering system based on a large language model, and relates to the technical field of natural language processing and information retrieval. According to the system, a retrieval enhancement generation (RAG) and knowledge reordering (Re-rankig) technology is utilized, and the knowledge processing capacity for mechanical manufacturing process design, industrial standards and related specifications is improved. The system is mainly composed of four parts, wherein the first part is used for constructing a knowledge base covering machine manufacturing knowledge; in the second part, a LoRA + fine-tuned Qwen2-7B model is used for guaranteeing high-precision requirements such as process parameter calculation; in the third part, the RAG technology is integrated to effectively retrieve and process domain knowledge; and 4, introducing a reordering model to optimize answers. The system is remarkably superior to a traditional large language model LLM in BLEU-4 and ROUGE text matching evaluation indexes, the reliability and speciality of application of the system in the mechanical manufacturing field are verified, an effective scheme is provided for intelligent question answering in the field, and the prospect is wide.

The invention provides a drainage basin flood runoff forecasting system and forecasting method based on water conservancy big data, relates to the technical field of flood runoff forecasting, and constructs the drainage basin flood runoff forecasting system based on the water conservancy big data by integrating the advantages of a physical model and a data driving model. The adopted bidirectional LSTM network is used for abnormal value interpolation, so that the data quality can be effectively improved; the HEC-HMS model is combined with a terrain correction factor to optimize the accuracy of the runoff process; the LSTM network enhances the data driving capability of flow prediction through the fusion of geographic space features and time series data, the model fusion module introduces an attention mechanism and water conservation constraints, the dynamic weight balance of a physical model and a data driving model is realized, the reliability and consistency of a prediction result are comprehensively improved, and the prediction efficiency is improved. And the model verification module realizes continuous comparison and verification of a forecast result and actually measured data through a closed-loop feedback mechanism of a total error and a dynamic threshold value.

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention relates to the technical field of laser cutting control, in particular to a laser cutting control system and method for a lens assembly. The method comprises the following steps that a lens assembly is scanned in an omnibearing mode to obtain point cloud data, then abnormal points are removed, three-dimensional reconstruction is carried out, a lens assembly model is generated, cutting task data are obtained, a cutting target is recognized, optimal cutting space-time arrangement is carried out on the lens assembly model on the basis, and an optimized cutting blueprint is generated. Cutting path planning is conducted according to the optimal blueprint, candidate cutting paths are obtained and dynamically compensated, a compensation cutting path is formed, mapping of control parameters is conducted according to the compensation cutting path, change control parameters are obtained, then parameter replacement is conducted, and accurate intelligent laser cutting control is achieved. The laser cutting control method for the lens assembly is more accurate and more efficient.

The invention provides a composition scoring cue word optimization method based on a large language model. The method comprises the following steps: obtaining composition topic information and a labeled composition sample set; constructing an initial scoring cue word according to the composition topic information, and evaluating the performance of the scoring cue word; using the current scoring cue word to guide the large language model to score each composition in the composition sample set; calculating a difference value between the large language model score and the manual annotation score; guiding the large language model to analyze the composition with the maximum difference value and obtain a score difference reason, so as to guide the large language model to modify the score cue words and generate candidate score cue words; evaluating the performance of the candidate scoring cue word, and comparing the performance of the candidate scoring cue word with the performance of the current scoring cue word to judge whether the current scoring cue word is updated; and carrying out loop iteration optimization on the scoring cue word step until an iteration termination condition is reached, and outputting a final scoring cue word. According to the method, the scoring cue word is optimized, so that the composition scoring accuracy and consistency of the large language model are improved.

The invention relates to an automatic optimization method for a model cue word, which comprises the following steps of: acquiring an initial cue word and a corresponding training data set which comprises input data and a corresponding real label; and then, inputting the initial cue word and the input data into a target model together to generate a corresponding prediction label. And on the basis, guiding a target model by using a feedback prompt word, and generating language prompt feedback by comparing the predicted tag with the real tag. The feedback is used for identifying potential defects in the initial cue word, optimizing the initial cue word based on the feedback, and finally generating a target cue word. The cumbersome process of manually adjusting the cue words is reduced, gradual improvement of the cue words is achieved through a feedback mechanism, and high accuracy and consistency of model output are ensured.

The invention provides an electromagnetic field composite powder feeding type plasma arc additive manufacturing formability regulation and control method and system, and relates to the technical field of plasma arc additive manufacturing. The flowing direction and speed of liquid metal in a molten pool in the deposition process are regulated and controlled through the target composite electromagnetic field, and the morphology and the internal organization structure of a deposition layer are controlled; in the process of regulating and controlling the liquid metal in the molten pool, process parameters and molten pool dynamic image data in the deposition process are collected in real time, and a pre-trained state recognition model is used for conducting self-adaptive adjustment on the process parameters of a subsequent deposition layer; and after deposition of each layer is completed, a laser scanning system is adopted for measuring the actual geometric size and surface appearance of the deposition layer, deposition manufacturing of the next layer is executed based on the optimized composite electromagnetic field control parameters, the corrected layered slicing data and the process path, and the execution is executed circularly till manufacturing of the whole workpiece is completed.

The invention discloses a method and a system for realizing data synchronization between heterogeneous databases based on logs. The method comprises the following steps: acquiring connection information of a source database and a target database; querying a table structure of the source end database and generating an SQL statement to establish a data mapping rule; matching a corresponding rule model from a mapping rule model library; if the matching is not successful, the system abnormally quits and prompts the user to create a new mapping model and add the new mapping model to the mapping rule model base; converting a table structure of the source end database into a JSON character string based on a standard field type; an SQL statement required by the target database is output through the data mapping engine module; the message monitoring program reads the data in the temporary storage area and converts the data into data meeting the requirements of the target database; formatting the converted data into SQL statements supported by a target database; and sending the generated SQL statement to a target database to complete data migration. Accurate and controllable synchronization of the data and the model in a heterogeneous environment is realized.

The invention relates to the technical field of data processing, in particular to a data management method for automatically detecting data abnormal values based on deep learning, which comprises the following steps of: based on an input data set, performing synchronization and missing value filling on a plurality of data sources, and performing standardization and normalization processing on data to obtain a preprocessed data set; according to the method, the data preprocessing is executed, the consistency and quality of data input are optimized, feature extraction and model layer-by-layer training are utilized, the recognition capability of the model for abnormal data is enhanced, the precision and efficiency of data anomaly detection are improved, calculation of residual errors and reconstruction errors is combined, the accuracy of abnormal data scoring is improved, and the accuracy of data anomaly detection is improved. The detection threshold value is dynamically adjusted by utilizing data characteristics, the false alarm rate is reduced, and the reliability and the stability of the enhanced data processing flow are ensured by combining time sequence analysis, evaluating the abnormal development trend, performing comparative analysis on abnormal data and normal data modes and performing data correction.

The invention discloses a brightness and chrominance correction method and system for an LED display screen. The method comprises the following steps: acquiring ambient light data and original brightness and chrominance data of the LED display screen; performing preprocessing operation on the ambient light data to obtain ambient light standardized data; inputting the original brightness and chrominance data and the ambient light standardized data into a pre-trained ambient light depth compensation model to obtain a brightness and chrominance correction coefficient matrix; inputting the brightness and chrominance correction coefficient matrix and the original brightness and chrominance data into a pre-trained display correction optimization model to obtain a correction instruction; and sending the correction instruction to the LED display screen to adjust the brightness and chromaticity of the LED display screen. According to the method, through application of ambient light compensation and display correction optimization, the brightness and the chromaticity of the display screen can be automatically corrected under different ambient light conditions, and the consistency and the stability of the brightness and the chromaticity are improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a multi-mode remote sensing data driven refined city three-dimensional reconstruction method and system, and the method comprises the steps: obtaining an original satellite optical image for providing city surface information; synthetic aperture radar (SAR) data is obtained and used for assisting reconstruction of a point cloud data missing region; the method comprises the following steps: acquiring laser radar data carried by a flight aerial carrier for acquiring high-precision three-dimensional point cloud; performing data preprocessing; carrying out fusion on the preprocessed multi-modal data; on the basis of the fused features, complementing space detail information; on the basis of the refined features, progressive reconstruction is carried out, vertexes and edges are gradually added from the initial vertexes, and a grid model is constructed; in the reconstruction process, performing semantic segmentation on the grid model by using a multi-category segmentation network, and judging the category of each triangular patch; time sequence reconstruction is completed through fusion of multi-temporal data, and a dynamic city change model is generated; and complementation of advantages of different modal data is realized.

The invention provides a heat input regulation and control method and system for electric arc additive continuous forming, and relates to the technical field of additive manufacturing, and the method comprises the following steps: collecting process parameters in real time, inputting pretreatment process data into a self-adaptive heat input quantity calculation model, and constructing and calculating to obtain a real-time heat input quantity based on a thermodynamic principle and a heat transfer theory; starting the multispectral imaging equipment, performing real-time temperature field scanning, recording temperature field scanning data, extracting temperature gradient and cooling rate information, performing correlation calculation with real-time heat input quantity, and determining an influence relation evaluation result; generating a heat input sequence according to the influence relation evaluation result; and inputting the heat input sequence into a self-adaptive control system constructed based on a reinforcement learning algorithm, calculating an optimal parameter scheme according to a preset target sedimentary layer performance index and a current temperature field state, adjusting corresponding spatial-temporal distribution of control energy input according to the optimal parameter scheme, and performing control heat input on different positions and different layers of the sedimentary layer.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention discloses an LLM capability enhancement method of a hybrid enhancement strategy, and particularly relates to the technical field of natural language processing. The method comprises the following steps: obtaining corpus data and external literature data of a target application field, carrying out denoising, word segmentation and semantic indexing processing, and establishing a searchable heterogeneous knowledge base; secondly, classifying the corpus data by adopting a topic analysis and word vector clustering method to form a basic corpus set; performing multi-round parameter updating on the large language model by using a domain fine tuning algorithm based on the basic corpus set, and setting a fusion threshold value between a new parameter and an original parameter; and retrieving related literature fragments from the heterogeneous knowledge base through a matching algorithm, screening content conforming to field correlation by utilizing semantic similarity evaluation, fusing the content with the fine-tuned model, processing a fusion result by adopting a context fusion algorithm and an entity linking method, completing syntactic analysis, and generating a precise solution oriented to the target application field.