284results about How to "The recognition effect is accurate" patented technology

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention relates to the field of image processing, in particular to a welding quality comprehensive evaluation method and system, and the method comprises the steps: obtaining an ultrasonic image of a welding joint, carrying out the contour detection, and extracting a defect region of the welding joint; gradient features and gray features of pixel points in the defect area are calculated, feature vectors of target pixel points in the defect area are constructed, and the feature vectors serve as the expression degree of the defect area; edge information of the defect area is obtained so as to determine defect types, the defect types comprise cracks and bubbles, the shape and size of a window are set according to the defect types, bilateral filtering processing is carried out, defect information is extracted, and comprehensive evaluation is carried out on the welding quality. According to the method, the appropriate window shape and size are selected according to the defect type, the detail information of the defect is effectively reserved, the definition of the ultrasonic image is remarkably enhanced through the self-adaptive filtering strategy, and therefore the welding quality is better evaluated.

The invention discloses a text keyword desensitization method, system and equipment based on a large model and a medium, belongs to the technical field of large model and natural language processing, and aims to solve the technical problem of how to automatically identify and desensitize sensitive information, improve the accuracy of sensitive information identification and further improve the text processing efficiency. According to the technical scheme, the method comprises the steps of data collection; preprocessing the data; marking sensitive information; segmenting the data; selecting a basic model; finely adjusting the model; training the fine-tuned large language model, and obtaining and storing the trained fine-tuned large language model; preprocessing the to-be-processed text data; detecting sensitive information, formulating a desensitization strategy and implementing desensitization processing, performing desensitization verification on the text after desensitization processing, and optimizing and improving the fine-tuned large language model according to a verification result; and packaging the optimized and improved fine-tuned large language model into an API (Application Program Interface) service for an application program to call.

The invention discloses a conflict detection processing method and system based on a BIM model, a medium and a product, and relates to the technical field of constructional engineering, and the method comprises the steps: obtaining a construction project design scheme; based on a preset model framework, determining a BIM model according to the construction project design scheme; predicting potential conflict points in the BIM model based on a preset conflict mode library; matching the potential conflict points with the building structure, the electromechanical pipeline and the equipment layout by using a space mapping algorithm, and determining possible conflict areas; on the basis of a natural language processing technology, operable solution suggestions are generated according to the possible conflict areas; after the operable solution suggestions are sent to the user, the solution suggestions determined by the user are received; and adjusting the BIM model based on the solution suggestion. By implementing the method, the accuracy and intelligence of conflict detection can be improved.

The invention belongs to the technical field of fault prediction, and discloses a fault prediction method and system for an intelligent accessory monitoring platform. The method comprises the following steps: an intelligent accessory monitoring platform constructs a knowledge graph, a fault prediction model and a fault maintenance strategy generation model; performing knowledge mapping on the real-time monitoring log by using a knowledge graph to obtain a semantic enhanced real-time monitoring log; performing fault prediction by using a fault prediction model according to the real-time operation monitoring data and the semantic enhanced real-time monitoring log to obtain a real-time fault prediction result; generating a fault maintenance strategy by using a fault maintenance strategy generation model according to the real-time fault prediction result to obtain a real-time fault maintenance strategy; and visually displaying the real-time fault prediction result and the real-time fault maintenance strategy of the attachment. According to the invention, the problems of insufficient fault prediction accuracy, insufficient data processing capability and low intelligent degree in the prior art are solved.

The invention discloses a seismic source inversion data driven rock burst prediction method and system, and the method comprises the steps: carrying out the quantitative inversion calculation of the core parameters of a coal rock fracture seismic source mechanism through original sound wave data collected on site; a rock burst seismic source data prediction model is constructed based on an Informer algorithm, and the occurrence time and position of rock burst are accurately predicted. A rock burst risk evaluation model is constructed by combining the predicted occurrence time and position of a future fracture source and seismic source mechanism core parameters, the magnitude and property of rock burst are predicted, and a dual-drive rock burst intelligent model for seismic source data prediction and rock burst risk evaluation is established; and multi-dimensional intelligent prediction of sequential response, spatial distribution and evolution attributes in the rock burst catastrophe process is realized. According to the method, seismic source data prediction and risk evaluation are combined, the rock burst catastrophe risk can be predicted, and the rock burst evolution process and the disaster-causing mechanism can be visually represented.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention discloses a novel noise HV spectrum ratio imaging method based on polarization. The method comprises the following steps: collecting background noise data in a predetermined monitoring area; preprocessing the acquired background noise data, wherein the preprocessing comprises data format conversion, data splicing, mean value removal, trend removal, waveform pinching and band-pass filtering processing; preprocessed data are processed by adopting a polarization analysis technology, a wave field is separated by setting a threshold value and analyzing a power spectrum, and a background noise area is divided into a linear body wave component and a nonlinear surface wave component. According to the polarization-based noise HV spectrum ratio imaging method, the precision and efficiency of underground structure analysis are remarkably improved through the precise separation of the wave field and the automatic processing flow of the HV spectrum ratio, and by means of polarization analysis optimized data processing, the automatically generated frequency and depth profile and the automatically picked excellent frequency and peak value, the imaging precision and efficiency of the underground structure are greatly improved. And the analysis of the underground velocity structure and the wave impedance interface is more accurate.

The invention relates to the technical field of geological surveying and mapping, in particular to a multi-source data fusion geological surveying and mapping system which comprises a multi-dimensional data collection module, a data calibration and synchronization module, a geological image enhancement module, a data fusion and model generation module and a visualization and interaction module. The data calibration and synchronization module carries out timestamp calibration and geographic position processing on data, the geological image enhancement module enhances geological images, the data fusion and model generation module fuses the processed data and constructs a geological model by using a modeling technology, and the visualization and interaction module provides a multi-dimensional data interaction platform. Through the geological texture recovery sub-module, visual quality of images is improved, reliability and accuracy during geological data and resource evaluation are enhanced, polarized light data are collected through the polarized image processing sub-module, key information such as surface material roughness is revealed, and surface and underground geological processes can be better understood and explained.

The invention discloses a blood vessel segmentation method and device based on large model fine tuning, equipment and a medium, and the method comprises the steps: obtaining a blood vessel image, carrying out the preprocessing of the obtained blood vessel image, and obtaining a training set; performing fine tuning on a pre-trained image segmentation model by adopting the training set, so that the image segmentation model learns related features of blood vessel segmentation; wherein the low-rank adaptive fine tuning technology is adopted to perform fine tuning on the image segmentation model: two parallel low-rank matrixes are inserted into a Transform layer in the image encoder, the input is processed by the two low-rank matrixes in sequence, and the processing result and the original output are added to obtain the final output. According to the method, the defects of a large model in a blood vessel segmentation task are overcome through efficient parameter fine tuning and a multi-scale feature fusion module. Accurate blood vessel segmentation can be realized under limited annotation data, the difficulty and cost of medical image segmentation tasks are greatly reduced, and the method can be widely applied to the technical field of medical image processing.

The invention relates to the technical field of animal husbandry, and discloses an intelligent live pig behavior recognition system based on multi-modal data fusion, which comprises a data acquisition module, a data preprocessing module, a feature extraction module, a multi-modal fusion module, a behavior recognition module, a data storage and management module and a user interaction module. According to the intelligent live pig behavior recognition system and method based on multi-modal data fusion, the limitation of a single-modal recognition method and the defect that audio information can supplement visual information are overcome by combining data of two modals of video and audio, the recognition accuracy is improved, and meanwhile, by using an improved AVSlowFast model and a GCT channel attention mechanism, the recognition accuracy is improved. The system can effectively fuse visual and auditory features, and further enhance the recognition capability in a complex environment, so that the system can accurately recognize and classify different behaviors of live pigs under various breeding conditions, and the recognition reliability is greatly improved.

The invention discloses an unmanned aerial vehicle inspection defect image recognition system and method, and relates to the technical field of image recognition, and the system comprises an unmanned aerial vehicle platform, an image collection module, an image processing module, a defect detection and recognition module, and a report generation module. The unmanned aerial vehicle platform comprises an unmanned aerial vehicle body, a high-definition camera, an infrared thermal imager, a cloud processing center, a GPS positioning system and a data transmission module. According to the method, the image collected by the unmanned aerial vehicle is subjected to preprocessing such as denoising, then feature extraction is performed, the influence of factors such as environment and weather on the shot image is effectively eliminated, the defect type of the inspection target is accurately identified through the defect identification model, the identification result is more accurate, the influence on defect identification judgment of the inspection target is avoided, and the inspection efficiency is improved. The efficient, accurate and real-time inspection and defect identification of the target area are realized, the model is optimized and updated regularly, and the accuracy and efficiency of identification can be improved.

The invention discloses a meat product foreign matter on-line detection method and system, and belongs to the field of visual detection.The detection method comprises the steps that a fixed detection area is set, a multi-mode sensor is installed above the fixed detection area, and the multi-mode sensor collects data and transmits the data to a data processing center through a wireless network; the data processing center decompresses and preprocesses the received data and fuses the image data twice to obtain a final fused image; according to the feature information in the final fusion, extracting fusion feature data; analyzing the feature data by using a support vector machine, and judging whether foreign matters exist or not and classifying the types of the foreign matters; a detection result is fed back to a production line control system in real time, and meat products containing foreign matters are positioned and removed. According to the method, the multi-mode sensor and an efficient data analysis technology are combined, and the accuracy and the real-time performance of meat product foreign matter detection are effectively improved.

The invention discloses a short wave signal detection, identification and training method based on deep learning. The method comprises the following steps: extracting IQ data of a narrowband signal from target signal data through a deep learning model; the modulation features and protocol features of the narrowband IQ data are analyzed through a deep learning model, and signal modulation mode identification and signal protocol type identification are carried out; storing all processed signal data and related label information; labeling the signal data based on a deep learning model and a preset rule, and storing a finally confirmed label as a final labeling result in a database; and collecting labeled data from the database, performing learning model training and parameter adjustment based on a deep learning framework, and performing retraining by taking a first time interval as a period. The invention also discloses a short wave signal detection, identification and training device based on deep learning, corresponding equipment and a storage medium. According to the invention, the short-wave signal can be efficiently and accurately detected and identified.

The invention relates to a microbial index-based estuary and coastal environment ecological evaluation method, which comprises the following steps of: selecting representative sampling points, collecting sediment samples, analyzing and evaluating pressure gradients generated by human activities to estuary and coastal environments through physicochemical indexes, and constructing pressure indexes according to a reference standard, the comprehensive environment pressure index of each sampling point is constructed and calculated to reflect the overall condition of the target area environment; evaluating the health condition of large invertebrate communities in estuary and coastal environments and the response of the communities to human pressure, and calculating a marine organism index AMBI, so as to display the ecological health condition of a region; a high-throughput sequencing technology is used for identifying microorganism composition, pollution indicator bacteria are defined, a microorganism index M-AMBI value is calculated according to the pollution indicator bacteria, and the ecological health condition of each sampling point is graded; and finally, verifying the reliability of an ecological evaluation result through statistical analysis. According to the method, multi-dimensional data are integrated, the ecology of the estuary and coastal environment is comprehensively evaluated, a scientific basis is provided for revealing an environment pressure source and an ecological effect thereof, formulation of a precise environment management strategy is assisted, pollution risks are effectively prevented and controlled, and sustainable development of an estuary and coastal ecological system is promoted.

The invention provides a substation safety management and control method and system based on a three-dimensional reconstruction model, and relates to the technical field of safety management and control, and the method comprises the steps: obtaining real-time point cloud data inputted by a laser point cloud sensor and a real-time image collected by a camera; comparing the real-time point cloud data with a background point cloud model, and extracting a changed dynamic region by using a background difference algorithm; the dynamic reconstruction module is used for identifying a first position of a first dynamic target in the dynamic region based on the real-time point cloud data; calculating a second position of a second dynamic target contained in the real-time image; if the first position is successfully matched with the second position and the sparse structure of the real-time point cloud data is successfully matched with dense pixels of the real-time image, determining that the first dynamic target and the second dynamic target are both dynamic flight targets; and determining the position of the dynamic flight target according to the first position and the second position, and marking in the three-dimensional model. The safety management and control precision of the transformer substation can be improved.

The invention discloses a data management system based on a monitoring video stream, belongs to the field of data analysis, and aims to solve the problems of camera connection acquisition, image quality optimization, pedestrian related information analysis and data storage retrieval and backup in video monitoring. The system comprises a data acquisition module, a target detection module and a data storage module. The data acquisition module is used for identifying and connecting a video monitoring camera, performing video stream data acquisition, adjusting an acquisition frame rate by estimating a data size, and evaluating and optimizing image quality to obtain a detection image data set; the target detection module is used for carrying out feature extraction, classification and marking on pedestrians in the detection image data set, recording pedestrian trajectory information, carrying out real-time tracking and updating, identifying a behavior mode according to the trajectory information, dividing a gathering area and obtaining the pedestrian flow; and the data storage module is used for classifying and labeling the data, constructing an index structure to guarantee efficient retrieval, dividing data grades according to multi-dimensional features, and dynamically adjusting a backup time interval.

The invention discloses a document proofreading processing method based on a large model. The document proofreading processing method comprises the following steps: step 1, preprocessing a document and identifying the document as a text; step 2, segmenting the text into independent text blocks; step 3, performing vectorization processing on the text blocks; step 4, performing retouching on the text block by using the random degree large model to obtain a new text block; step 5, performing vectorization processing on the new text block; 6, comparing the vector similarity of the text block and the new text block, if the similarity is high, entering the next step, and if the similarity is low, repeating the steps 4-6; 7, comparing the text of the text block with the text of the new text block, and marking difference characters in pairs; and 8, proofreading the text blocks and the new text blocks according to the document, and integrating and outputting the text blocks and the new text blocks. According to the method, more accurate and comprehensive document proofreading service can be provided, dynamic change of languages and diversified document requirements can be met, and the specialty and credibility of documents are remarkably improved.

The invention discloses a lightweight crack penetration degree detection method based on ground penetrating radar and Swin Transform optimization, and the method comprises the following steps: 1, collecting ground penetrating radar data, and carrying out the preprocessing and feature extraction; step 2, constructing a multi-feature fusion data set; step 3, designing a two-stage through crack detection model based on Swin Transform optimization; 4, a first-stage through crack detection model optimized on the basis of Swin Transformer-YOLOv8 is designed, and a second-stage through crack detection model is designed on the basis of Swin Transformer-YOLOv8 optimization; and step 5, training and predicting the data of the training set and the test set according to the model architecture in the step 3 and the step 4. According to the invention, the accuracy of crack penetration degree detection is improved, and a scientific basis is provided for road maintenance; the detection speed is greatly improved, and the requirement of large-area rapid detection is met; the calculation complexity is reduced, the equipment performance pressure is reduced, and the detection efficiency is improved; the generalization ability of the model is enhanced, and the method can adapt to different road conditions; and the development of an intelligent road detection technology is promoted.

The invention relates to the technical field of man-machine interaction data, in particular to man-machine interaction equipment and a control method thereof. The method comprises the following steps: acquiring equipment screen operation data, performing time window segmentation, generating a discrete touch event sequence and a dynamic touch feature sequence, clustering and processing the features by utilizing interaction features, forming user operation behavior grouped data, and combining equipment historical interaction data to form a user operation behavior group. Frequent item set mining is carried out to identify a high-frequency operation mode, a user operation preference model is constructed, user operation habits are identified, redundant actions are eliminated, main interaction action data are extracted, interaction intention prediction is carried out on main interaction actions based on historical data of equipment, and dynamic response is carried out on the man-machine interaction equipment according to a prediction result. And effective control is realized. According to the invention, a more personalized and smoother man-machine interaction equipment control method is realized.

The invention discloses a large model reasoning method and device based on sensitive information protection and related equipment. The method comprises the steps that firstly, an original input text is obtained, sensitive entities in the original input text are detected through a named entity recognition NER model, relation extraction and attribute extraction are conducted, and a sensitive information knowledge graph is constructed; then, screening sensitive information in the original input text according to the knowledge graph, and replacing the sensitive information to generate a desensitized input text; next, calling a first large model of the server side, and executing a specified task by using the desensitized input text to obtain a result text; and finally, calling a second large model of the user side, and restoring the sensitive information in the result text by using the original input text, the desensitized input text and the result text to obtain a complete text. Large models configured at different ends are utilized, sensitive information is protected, and meanwhile practicability of result texts is taken into account.

The invention discloses a data fusion method and system for a fire detection sensor, relates to the technical field of data fusion, remarkably improves the reliability and accuracy of the system, and solves the problems of false alarm, missing report, environmental interference, sensor fault and the like. By introducing a detailed anomaly detection module and a multi-level threshold evaluation mechanism, the system can accurately distinguish normal fluctuation and real fire signals, and reduces false alarm and missing alarm; in addition, the environment monitoring module calculates an environment interference coefficient in real time and compares the environment interference coefficient with a threshold value, so that the influence of environment factors on the detection accuracy is effectively dealt with, and the detection reliability is improved. The safety fault-tolerant module monitors the state of the sensor and the data transmission process in real time to ensure that a data compensation mechanism is started when the sensor fails or data is lost, so that the fault-tolerant capability of the system is enhanced; the comprehensive measure ensures the stability and accuracy of the system under various interference and fault conditions.

The invention relates to the technical field of remote maintenance of a power distribution cabinet, in particular to a remote maintenance system of an intelligent power distribution cabinet, which constructs a component operation parameter matrix by collecting current, voltage, component temperature, starting time and internal and external environment data of the power distribution cabinet in real time, and combines environment parameters of the power distribution cabinet to maintain the power distribution cabinet. And carrying out deep learning training by using a residual neural network algorithm. The method not only can detect common fault modes, but also can identify abnormal conditions caused by component aging, so that the problems of false alarm and missing alarm in a traditional fault detection method are solved. Through the residual neural network, the system can automatically learn normal and abnormal operation modes from historical data, and the accuracy of fault detection is improved. And the exception type is further pushed to the operation and maintenance personnel and is visually displayed, so that the judgment time and the round-trip times of the operation and maintenance personnel on the maintenance component are saved, and the maintenance efficiency is improved.

The invention relates to the technical field of power system fault detection, and discloses a 10kV cable single-phase earth fault detection method based on an adaptive algorithm, and the method comprises the following steps: S1, employing a voltage collector and a current collector to collect the power frequency and transient signals of a switching station integrated intelligent terminal strip; s2, filtering and de-noising the collected signals through a self-adaptive digital signal processing algorithm; s3, adaptively determining a single-phase earth fault signal component, and identifying the most significant frequency band; s4, extracting features of the fault signal by using a multi-band multi-criterion adaptive algorithm; and S5, based on the extracted features, selecting the most significant criterion for single-phase earth fault detection. Through an adaptive algorithm and a deep learning model, the detection and recognition precision of a complex single-phase earth fault is improved, real-time intelligent processing is realized through an integrated monitoring module, misjudgment is reduced, the operation and maintenance efficiency is improved, the system adapts to multi-scene application, and the intelligentization and power supply reliability of the system are enhanced.

The invention relates to a deep learning-based workpiece processing path feature identification method for a numerical control system. According to the method, a G code generated by a numerical control machine tool is analyzed and processed to obtain features of a tool path when a workpiece is machined. Particularly, the method not only performs basic data preprocessing and cleaning, but also performs deeper analysis by establishing cutter path characteristic indexes such as path length, angle, curvature and the like. The data classified through the feature indexes are used as input of a deep learning algorithm, and efficient recognition of the tool path features is further achieved. According to the numerical control machining tool path feature recognition method provided by the invention, the efficiency and the accuracy of feature recognition are improved, the perception capability of a complex machining path is further enhanced through application of a deep learning algorithm, and support is provided for intelligent recognition of the machining path of a numerical control system.

The invention discloses a visibility prediction method and system, and the method employs a dynamic window division and abnormal value processing technology based on data variability, solves a problem that meteorological data abnormal values are difficult to accurately recognize, achieves the precise recognition and processing of the abnormal values, and improves the data quality. By adopting the missing value interpolation method based on the weighted distance, the problem that the missing value of the meteorological data is difficult to fill is solved, the accurate interpolation of the missing value is realized, and the integrity and the reliability of the data are enhanced. According to the method, a dynamic normalization processing method is adopted, the problems of different scales of meteorological factor data and local characteristic differences are solved, adaptive normalization of the meteorological factor data is achieved, important fluctuation information is reserved, and the sensitivity of a model to data details is improved.

The invention relates to the technical field of iPSCs colony pluripotency evaluation, in particular to an iPSCs colony pluripotency evaluation method based on hierarchical Transform attention feature fusion, and the method comprises the following steps: collecting images in an iPSCs culture process, and establishing a mapping relation between stem cell pluripotency and cell colony morphology; constructing an iPSCs image data set, enabling each pixel in the image to correspond to a pluripotent state category, and marking a pixel level; building an evaluation model based on Transform attention feature fusion, wherein the evaluation model comprises a Gabor edge feature enhancement module, a layered Transform trunk feature extraction module and an attention feature fusion module; the obtained feature layers are fused after being subjected to up-sampling and normalization, and then a prediction image, namely an evaluation result, is obtained after decoding; training the model by using the iPSCs image data set; and carrying out pluripotency evaluation on the image in the iPSCs culture process by using the model obtained by training. According to the method, human factor interference can be prevented, and the precision of iPSCs set pluripotency evaluation is high.

The invention provides a strip-shaped long-distance unmanned aerial vehicle sensing countering system and method along a railway, and relates to the technical field of rail traffic safety. The system comprises an unmanned aerial vehicle detection device arranged at a key node along a railway and used for detecting an unmanned aerial vehicle; the unmanned aerial vehicle countering device transmits a controllable interference signal to interfere with the unmanned aerial vehicle; the signal receiving and directional transmitting equipment is used for receiving, amplifying and redirectionally transmitting the interference signal or the radio signal transmitted by the unmanned aerial vehicle; the rear-end intelligent management and control platform is used for receiving the signal information, analyzing the threat level in real time, selecting a countering strategy and implementing directional interference through equipment; the method comprises the steps of equipment deployment, detection and early warning, data analysis, instruction issuing, signal emission and interference, and response and dynamic adjustment. According to the invention, the problems of insufficient coverage, poor identification precision and single countering means in long-distance and strip-shaped scenes along the railway in the prior art can be solved, and real-time sensing and dynamic countering of the unmanned aerial vehicle are realized.

The invention relates to the technical field of intelligent traffic, and discloses a deep learning-based expressway traffic flow detection method and system, and the method comprises the steps: obtaining multi-modal traffic data and environment data in real time from each key road section of an expressway, and carrying out the preprocessing of the multi-modal traffic data obtained in real time, including the time-space alignment of the multi-modal traffic data; inputting the preprocessed multi-modal traffic data and environment data into a pre-trained deep fusion network model, and outputting various vehicle parameters for representing expressway traffic flow characteristics; inputting the various vehicle parameters into a pre-trained high-speed traffic flow detection model, and outputting traffic flow grade information; dynamically updating the deep fusion network model at the key road section by adopting a model fusion mode according to the vehicle parameter change condition at each key road section within the preset time; and updating the high-speed traffic flow detection model according to external conditions introduced by the traffic flow grade information in actual use. And the accuracy and real-time performance of high-speed traffic flow detection are improved by combining deep learning.

The invention discloses a large model semantic coding BIM data and engineering entity matching system, which comprises a knowledge graph construction module used for establishing a knowledge graph according to building project historical data; the BIM data and engineering entity coding module is used for coding the current BIM data or the current engineering entity into a vector by using a Chinese pre-training language model; the BIM data and engineering entity similar semantic retrieval module is used for retrieving a plurality of similar vectors of the current BIM data vector or the current engineering entity vector in the knowledge graph by using a retrieval enhancement generation model; and the BIM data and engineering entity matching module is used for matching the current BIM data vector or the current engineering entity vector with the corresponding similar vector by using cosine similarity. The invention further discloses a BIM data and engineering entity matching method for large model semantic coding. Compared with manual operation, the technical scheme of the invention has the advantages of high accuracy, high efficiency and high flexibility.