879 results about "Extraction algorithm" patented technology

The invention discloses a multi-source heterogeneous data knowledge graph construction method for railway disaster prevention monitoring, and relates to the technical field of knowledge graph construction, and the method comprises the steps: gathering multi-source heterogeneous data related to railway disaster prevention monitoring, and constructing a domain ontology model used for guiding knowledge extraction and fusion; extracting entities, attributes and relationships among the entities from different modal data after standardization preprocessing by using a targeted extraction algorithm; obtaining fused structured knowledge based on a multi-strategy knowledge fusion process of domain ontology constraint and confidence evaluation; the fused structured knowledge is stored in a graph database, and construction of the knowledge graph in the railway disaster prevention monitoring field is completed; through combination of domain ontology construction, a mixed knowledge extraction engine and a multi-strategy knowledge fusion technology, deep semantic fusion of multi-source heterogeneous data in the railway field is realized. The invention aims to construct a knowledge graph capable of comprehensively and accurately reflecting complex characteristics in the railway disaster prevention field.

The disclosure discloses a surface defect detection method, system, equipment, and terminal, comprising: S1, LBP operator: before comparing the benchmark image with the actual shot image, a LBP texture feature extraction algorithm is first used for pre-processing; S2, Sift feature-point matching: adding a Sift feature-point matching algorithm to calculate key feature points in the image and compare; S3, defect detection: when the local feature points of the shot image are successfully matching with a certain image in the benchmark image library, a detailed comparison will be made and complete defect detection. The disclosure integrates various image processing methods in advance without any setting by users. After running the system with one click, it can automatically perform detection, greatly reducing personnel training costs. The disclosure has simple structure and low cost. The basic structure only needs a computer, camera, and light source, making it easy to use and with highly mobile.

The invention discloses an engineering safety progress intelligent monitoring method based on multi-source data collaboration, which relates to the technical field of intelligent engineering monitoring, and comprises the following steps of: mapping multi-source engineering monitoring data into nodes and edges of a graph in real time by utilizing an incremental graph updating algorithm, generating a dynamic knowledge graph, and generating a dynamic mapping result; performing graph traversal on the dynamic knowledge graph through an association subgraph extraction algorithm, extracting a security event matrix and a project progress matrix, calculating an SPI index value by using a dynamic weighted fusion algorithm, synchronously performing multi-threshold interval grading on the SPI index value, generating an SPI early warning level, performing state coding on the SPI early warning level, and generating a comprehensive feature vector; according to the method, data of different types can be effectively integrated and a basis is provided for formulating a targeted engineering safety progress solution through an incremental graph updating algorithm and a Bayesian causal graph model, and node probability distribution characteristics are extracted by using a forward propagation layer. The method is advantaged in that the incremental graph updating algorithm and the Bayesian causal graph model are utilized to effectively integrate data of different types and provide a basis for formulating a targeted engineering safety progress solution.

The invention discloses a carbon emission prediction and optimization method, and the method comprises the steps: obtaining multi-source heterogeneous data including historical carbon emission data, meteorological data, economic indexes, energy consumption data, and Internet of Things sensor data, and constructing a three-dimensional feature matrix through employing an improved spatial-temporal feature extraction algorithm; based on a mixed architecture of a graph neural network GNN and a long and short term memory network LSTM, a prediction model is established in combination with an attention mechanism, and training is performed through an adaptive learning rate optimization algorithm; a prediction result is input into an improved NSGA-III algorithm, and three targets of total carbon emission, economic cost and social benefits are optimized at the same time; and establishing a feedback closed loop through reinforcement learning RL, and updating the model and the strategy on line according to real-time monitoring data. According to the method, multiple advanced technologies such as accurate data processing, dynamic prediction, multi-objective optimization and cross-domain collaboration are integrated, and a comprehensive and effective solution is provided for carbon emission management.

The invention provides a power transmission line inspection data asset management method and system, and the method comprises the steps: obtaining the multi-modal data of a power transmission line through a plurality of sensors disposed on the power transmission line and the surrounding environment, and sensors carried by a helicopter and an unmanned plane; and processing the multi-modal data based on the space-time correlation model to generate a fusion data set. Analyzing the fused data set by using an adaptive feature extraction algorithm, generating structured power transmission line data assets including environmental features, defect features and hidden danger features, and evaluating the possibility of triggering defects by environmental conditions based on the association of the environmental features and the defect features; according to the hidden danger characteristics and the environmental conditions, constructing a graph network model which takes a power transmission line as a node and sensor data as an edge weight and is used for analyzing a defect propagation path and predicting time and space distribution of hidden dangers; defect risk level early warning is generated based on a prediction result, a power transmission line hidden danger distribution diagram is generated in combination with environmental meteorological data, and accurate assistance is provided for patrol and maintenance plans.

The invention relates to the technical field of electrical engineering and sensors, and provides a high-precision current sensor electrical performance detection method, which comprises the steps of collecting an original output signal of a current sensor and performing multi-dimensional signal preprocessing; extracting multi-dimensional feature parameters from the preprocessed signals based on a dynamic feature extraction algorithm; establishing a dynamic compensation model according to the multi-dimensional characteristic parameters, and generating a compensation control signal; a compensation control signal is injected into the detected current sensor to form closed-loop feedback, and a compensated output signal is collected; calculating a sensor performance degradation parameter according to the signal difference before and after compensation; an electrical performance assessment report is generated based on the performance degradation parameter. According to the invention, the measurement precision and reliability of the sensor can be improved, the service life is prolonged, the maintenance cost is reduced, and a powerful guarantee is provided for safe and stable operation of a power system and electronic equipment.

The invention relates to the technical field of data processing, and discloses a financial report generation method and system based on artificial intelligence. The method comprises the steps of preprocessing multi-source financial data to obtain target data; performing semantic understanding through a capital extraction algorithm to obtain a knowledge base; intelligent matching template filling is carried out to generate a preliminary report; verifying and analyzing the logic relationship by applying a causal relationship; optimizing the content based on the verification result; and performing multi-level auditing and updating to obtain a final report. According to the method, intelligent processing, deep semantic understanding and logic verification of financial data are realized by introducing a knowledge-based capital extraction algorithm and a causal relationship extraction model, so that a high-quality financial report which is strict in logic and professional in content is generated.

The invention discloses a VLM model intelligent decision-making-based driving method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: extracting key visual features from continuous multi-frame driving scene images based on a preset visual feature extraction algorithm; inputting a user instruction and a time sequence visual Token corresponding to the key visual features into a preset VLM model for multi-modal alignment, and generating a target planning Token; inputting the target planning Token and the time sequence vision Token into a preset trajectory generation model to obtain a predicted trajectory; and converting the predicted trajectory into a control signal, and controlling the mobile device to complete a moving action based on the control signal. The problem of modal difference between a semantic space and an action space is solved.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention discloses an intelligent bid evaluation expert extraction and management system, and relates to the technical field of expert extraction systems. Comprising a data cleaning and compression module, a database logic and table relation design module, an expert information collection standard and automation module, an information real-time updating and principal management module, an extraction algorithm design module, a safety and authority management module and a real-time monitoring and auditing function module. Through a self-adaptive sparse auto-encoder and a dynamic clustering technology in the data cleaning and compression module, abnormal values in a bid evaluation expert database can be detected and cleaned, data compression is performed by adopting a self-adaptive dictionary coding algorithm, and the storage efficiency and query performance of the database are improved; based on a multi-modal information fusion and reinforcement learning method, accurate collection, dynamic updating and intelligent management of expert information are realized. The randomness and fairness of the bid evaluation expert extraction process are ensured by using a mode of combining deep reinforcement learning and a quantum random number generator in an extraction algorithm design module.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention relates to the technical field of data processing, in particular to an accelerometer vibration rectification error analysis method, which comprises the steps of receiving multiple paths of accelerometer original current signals, processing acquired data and outputting a feature point coordinate sequence with confidence; according to the confidence and the optimization algorithm combination in the frequency band distribution characteristic dynamic scheduling knowledge base, calculating a local statistical data set; inputting the data set into a mechanical dynamics model to execute trajectory simulation, and inputting a control model reconstruction signal to compare a dual-path positioning drift distance difference to generate an error correction coefficient matrix; outputting a knowledge base updating instruction and a parameter resetting instruction by adopting a matrix reconstruction vibration rectification error quantized value; a quantized value is imported to calculate theoretical angle deviation and verify convergence, and the neural network is triggered to be retrained when the deviation exceeds a threshold value. Through a non-smooth feature extraction algorithm and a closed-loop feedback mechanism, the problem of signal distortion caused by smooth operation in the prior art is solved.

The invention provides a small sample respiratory disease pattern recognition method based on a diffusion model. The method comprises the following steps: firstly, for common respiratory disease types, collecting and classifying data of three respiratory modes: normal respiration, pause respiration and deep respiration which respectively correspond to respiratory diseases such as normal respiration, obstructive apnea and asthma in medicine; secondly, learning an internal distribution rule of data by using a radio frequency diffusion model, and generating diversified and high-quality radio frequency time sequence data on the basis of limited real samples so as to expand a data set; and thirdly, combining the generated data with real data, extracting an accurate respiration signal through a one-dimensional time sequence extraction algorithm, and converting a time sequence into a two-dimensional image by utilizing a Gramian Angle Field (GAF) method, so that the time dependency relationship of the time sequence is intuitively presented in the image. And finally, a convolutional neural network (CNN)-Transform deep learning model is adopted to carry out feature extraction and classification on the image data, and then multi-scale feature analysis and breathing mode recognition are carried out. According to the method, the recognition precision and generalization ability of the model are remarkably improved under the small sample condition, and an accurate and reliable solution is provided for non-contact breathing mode recognition.

The invention provides a structure surface multi-disease three-dimensional measurement method and device, equipment and a medium. The method comprises the steps that an image sequence carrying GPS coordinate information is collected; performing depth calculation on the image sequence by using a three-dimensional reconstruction technology, and performing three-dimensional reconstruction on a scene to generate an initial three-dimensional model; carrying out disease determination and point cloud processing to obtain a three-dimensional point cloud model of a specified color and each disease point cloud instance; using a skeleton extraction method and a boundary point extraction algorithm to extract a boundary point set and a skeleton point set of the skeleton point set corresponding to the crack point cloud, and according to each obtained skeleton point set and the corresponding boundary point set, performing quantitative calculation on the length and the width of each crack; generating a spalling surface grid model by using a Poisson surface reconstruction algorithm and a hole filling algorithm, and calculating and determining the volume of the three-dimensional spalling instance according to the spalling surface grid model; and determining the normal direction of each point of the corrosion cloud instance, generating a grid model by using a Poisson surface reconstruction algorithm, and calculating the corrosion area.

The invention discloses an external parameter calibration method for a laser radar and camera system, and the method specifically comprises the steps: synchronously collecting the laser radar point cloud and camera image data of a circular plane calibration plate through employing the circular plane calibration plate with a two-dimensional coding pattern printed at the center; segmenting laser radar point cloud subsets belonging to the calibration plate from the laser radar point cloud through a plane extraction algorithm, and calculating centroid coordinates of the point cloud subsets as three-dimensional feature points; detecting angular points of the two-dimensional coding pattern in the camera image, and calculating geometric centers of the angular points as two-dimensional feature points; multiple groups of three-dimensional-two-dimensional feature point corresponding relations are established through multiple times of scanning, and initial external parameters are solved by using a PnP algorithm; and minimizing a re-projection error through a nonlinear optimization algorithm to obtain a final external parameter. The method provided by the invention is suitable for application scenes such as robots, automatic driving or three-dimensional reconstruction, and accurate, efficient and automatic external parameter calibration can be realized.

The invention discloses a writing brush calligraphy practice correction system based on real-time handwriting analysis. The system comprises a sensing layer used for collecting handwriting tracks, physiological signals, environmental parameters and ink mark characteristics; the edge calculation layer is used for executing noise filtering, coordinate system normalization, multi-modal data alignment and feature primary extraction through an Apache Kafka data pipeline; and the algorithm analysis layer is used for carrying out super-long calligraphy stroke sequence modeling by adopting an S4 architecture time sequence model, introducing a Neural ODE module for modeling, capturing dynamic characteristics in a continuous pen wielding process, constructing a multi-physics-field coupled PINN framework, constraining neural network prediction through a physical loss function, deploying a dual-stage characteristic extractor to extract high-order characteristics, and extracting the high-order characteristics. Book style features are extracted, and the current practicing book of the user is classified in real time; the intelligent correction layer is used for generating a dynamic correction suggestion, constructing a personalized learning path, realizing self-adaptive scoring and providing aesthetic dimension feedback at the same time; and the user interaction layer is used for providing an AR correction interface.

The invention discloses a data output method and device based on fixed-point number extraction and a storage medium, and relates to the technical field of computers, and the method comprises the steps: carrying out the calculation and normalization processing of a non-zero positive number of an input value through a precision formula, and generating a valid number corresponding to the non-zero positive number; querying in a compression lookup table according to a compression index corresponding to the effective number to obtain a reciprocal square root approximate value corresponding to the effective number; performing iterative optimization, index parity adjustment, remainder feedback correction and boundary specialization processing on the basis of a reciprocal square root approximate value corresponding to the effective number to generate an effective result; and carrying out reverse normalization processing on the effective result, generating a fixed point number corresponding to the effective result, and outputting the fixed point number. According to the invention, through conversion between the fixed-point number form and the effective number form, the technical problem of distributed environment amplification errors in a fixed-point number extraction algorithm in related technologies is solved, and the performance and accuracy of fixed-point number extraction are improved.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention discloses a driving motor fault diagnosis method and device based on an artificial intelligence technology, and relates to the technical field of fault diagnosis, and the method comprises the steps: arranging a multi-modal sensor at a preset part of a driving motor, and obtaining a multi-dimensional data signal collected by the multi-modal sensor; performing real-time filtering, amplification and noise reduction processing on the multi-dimensional data signal, and performing feature extraction on the multi-dimensional data signal by using a feature extraction algorithm to obtain a plurality of key feature parameters; based on a preset dynamic weighted fusion algorithm, fusing the plurality of key feature parameters to obtain fused key feature parameters; and establishing a hybrid deep network architecture consisting of a long-short term memory network and a convolutional neural network, and inputting the fusion key feature parameters into the pre-trained hybrid deep network architecture to obtain corresponding fault information of the driving motor. The problem of low fault diagnosis accuracy of the driving motor in the prior art is solved.

The invention provides a neurosurgery tumor patient personalized nursing intervention method based on big data. The neurosurgery tumor patient personalized nursing intervention method comprises the steps that physiological parameters, tumor characteristic data, behavior data and environment data of a patient are collected through a multi-mode sensor and an electronic medical record system; preprocessing the data to generate a standardized data set; extracting dynamic time sequence features and spatial correlation features based on a multi-modal fusion feature extraction algorithm; calculating an intensity parameter, a frequency parameter and a priority parameter of nursing intervention through a personalized intervention parameter generation algorithm according to the features; generating a personalized nursing intervention scheme in combination with the nursing rule base; a dynamic adjustment scheme is fed back through real-time data, and intervention parameters are corrected through a feedback compensation mechanism; and outputting the corrected intervention scheme to the nursing terminal equipment for execution. Accurate personalized nursing intervention can be achieved, the nursing scheme is dynamically adjusted, and the nursing effect and rehabilitation quality of neurosurgical tumor patients are improved.

The invention provides a calcium imaging neural signal extraction method and product based on self-supervised pre-training, and relates to the technical field of artificial intelligence. Based on a pre-training data set containing a large amount of unlabeled two-photon calcium fluorescence imaging data, a pre-training decoding network and a corresponding pre-training task are self-supervised, and a pre-trained space-time coding network is trained by taking minimization of a pre-training loss function as a target; the method comprises the following steps of: firstly, establishing an encoder and decoder network, establishing fine-tuning data with accurate space-time marked neuron calcium signal positions through simulation, simultaneously performing fine-tuning on the encoder and decoder network by taking minimization of a fine-tuning loss function as a target, and checking the algorithm extraction precision based on various actual imaging data to obtain a calcium imaging signal extraction algorithm network with strong generalization performance. Therefore, a calcium signal extraction model which can be generalized in various imaging conditions and model biological brain regions can be obtained through training without manual labeling, a high-accuracy neural signal extraction result is obtained, the application difficulty that a deep learning model lacks data generalization ability is overcome, and the accuracy of a neural signal extraction result is improved. Calcium imaging neural activity analysis based on self-supervised pre-training and wide in application is achieved.

The invention relates to the field of fault early warning, in particular to a fault early warning method for a tailing paste filling system. Comprising the steps of monitoring and storing parameters in real time to form a parameter matrix; a sliding time window technology is introduced, standardization processing is carried out on the parameters at each moment, and standardized parameter values are obtained; on the basis of the standardized parameter values, a dynamic feature extraction algorithm is adopted for processing to obtain feature values; based on the characteristic value, a fault probability value is calculated through a dynamic gain adjustment fault probability calculation algorithm, the fault probability value is compared with a fault threshold value, and a fault judgment result is obtained; according to the characteristic value and the fault judgment result, a saliency score is calculated through a fault positioning algorithm, and a fault source is determined according to the saliency score; and performing fault early warning based on the fault source. The technical problems that in a tailing paste filling system, multiple monitoring points exist, so that real-time data processing becomes complex, and unified comparison and analysis are difficult to conduct are solved.

The invention provides a multi-unmanned aerial vehicle adaptive migration method for a sudden no-fly zone, and relates to the technical field of unmanned aerial vehicles. And acquiring fusion environment data through cooperative sensing of multiple unmanned aerial vehicles, processing the data by adopting a semantic boundary extraction algorithm based on deep learning, and outputting a sudden no-fly zone map and unmanned aerial vehicle safety state codes. On the basis, the unmanned aerial vehicles are shunted according to the safety state codes and the sudden no-fly zone map, real-time obstacle avoidance tracks are generated for the non-intruding unmanned aerial vehicles, and cluster collaborative separation is executed for the intruding unmanned aerial vehicles. For unmanned aerial vehicles whose tasks are interrupted due to obstacle avoidance or separation, task decomposition is realized based on the sudden no-fly zone map and space-time isolation, and continuous execution of cluster tasks is realized through an energy consumption constraint auction mechanism, so that a plurality of unmanned aerial vehicles can better cope with a complex and changeable sudden no-fly zone environment.

The invention discloses an intelligent pressure regulation and control method and system for artery compression, and the method comprises the steps: collecting the arterial blood pressure waveform and vascular elasticity parameters of a puncture part of a patient in real time, and the multi-dimensional pressure data of a pressure sensor; constructing an initial pressure parameter set containing a pressure distribution gradient and a vascular response coefficient through a dynamic feature extraction algorithm; carrying out collaborative analysis on the initial pressure parameter set and blood flow characteristic parameters obtained through blood flow monitoring, and generating a regulation and control matrix containing an optimal compression force range and a time decay function; performing multi-target optimization processing on the regulation and control matrix through a parallel optimization algorithm to generate a real-time pressure control instruction set; based on the deviation value of the real-time pressure execution data fed back by the user interface and the blood flow monitoring data, a grading alarm mechanism is triggered, and a compression strategy is automatically corrected. By utilizing the embodiment of the invention, accurate regulation and control of the artery compression process can be realized, and the hemostatic effect is optimized and the medical quality is improved on the premise of ensuring the safety of a patient.

The invention relates to the technical field of integrated circuit board finished product detection, in particular to a computing processing module finished product monitoring method and system, and the method comprises the steps: scanning a plurality of computing processing module finished products passing through a finished product conveying belt in real time through a linear array laser scanner disposed above the finished product conveying belt, and carrying out the real-time monitoring of the plurality of computing processing module finished products; further obtaining a spectrum pattern corresponding to each calculation processing module finished product; identifying local spectrum patterns corresponding to the plurality of chip assemblies in the spectrum pattern; and sequentially detecting whether each local spectrum pattern has a plane flaw, if so, evaluating the damaged surface of the plane flaw, and evaluating the damage degree of the damaged chip assembly based on the proportion of the damaged surface. According to the monitoring method, the spectral pattern representing the height information is generated in real time through the linear array laser scanner, the local areas of the multiple chip assemblies are accurately recognized in combination with the RGB color threshold value and the image feature extraction algorithm, interference caused by dense arrangement of the multiple assemblies is effectively avoided, and the positioning precision and the anti-noise capacity of plane defect detection are improved.

The invention relates to the technical field of medical surgical navigation, in particular to a real-time intraoperative navigation system and method based on optical and inertial navigation. The system comprises an optical data acquisition module which is used for acquiring images, identifying surgical instruments and anatomical structures of patients, and extracting corresponding feature points by adopting a mode of combining a feature point extraction algorithm based on deep learning with a traditional algorithm; calculating pose information by using a mode of fusing a pose resolving algorithm based on deep learning and a traditional algorithm; the inertial data acquisition module is used for integrating the data of each component of the IMU by using the IMU integrated component, establishing a state model and the observation module, and acquiring the attitude information of the surgical instrument; and the data fusion processing module is used for carrying out time synchronization and calibration of optical camera and IMU acquisition, and adopting different data fusion strategies according to the reliability and availability of optical data and inertial data. According to the technical scheme, the reliability of the intraoperative navigation system can be improved.

The invention relates to the technical field of digital image processing, in particular to a digital image mixed watermarking method based on time domain and frequency domain synchronization. According to the method, a time domain and frequency domain synchronous watermark embedding algorithm and a multi-stage watermark extraction algorithm are innovatively combined, so that the concealment and robustness of the watermark are remarkably improved, and the problems of digital image copyright protection in the face of printing and scanning attacks and network attacks are effectively solved; the method ensures the security and reliability of digital image copyright protection, provides a more advanced and effective technical means for digital image copyright protection, has the advantages that a time domain and frequency domain synchronous watermark embedding algorithm enables watermarks to be fused into image details and is high in concealment, the watermarks are effectively protected in the face of various attacks, and the security and reliability of digital image copyright protection are improved. The multi-stage watermark extraction algorithm ensures accurate extraction when the image quality is reduced, and the watermark performance is integrally improved.

The invention provides a construction method of a compact sandstone core natural fracture characteristic parameter characterization model. The construction method specifically comprises the following steps: step 100, self-making a compact sandstone core natural fracture image data set; step 200, establishing an efficient self-adaptive Frac-YOLOv8 rock core natural fracture segmentation network; step 300, training the network by using the data set to obtain a rock core natural fracture segmentation model; step 400, inputting a core image to be segmented into the model, and segmenting cracks; step 500, generating a binary crack data set; step 600, applying a crack skeletonization extraction algorithm to quantify the crack length; step 700, using a pixel statistical algorithm to quantify the actual area of the crack and calculating the average width of the crack; and finally, calculating the maximum width pixel value of the crack by using a crack variable circle algorithm, and quantifying the maximum width through the diameter of an inscribed circle. The method provides an effective basis for evaluation and analysis of natural rock core cracks of tight sandstone.

The invention relates to a knowledge base content recommendation method and system based on large model and subdivision field matching. The method comprises the steps that key content feature words are automatically extracted from original content currently read by a user based on a feature word weighted extraction algorithm; forming cue words based on the feature words and the names of the subdivided fields, and determining professional subdivided fields according to the cue words; aiming at each determined subdivision field, searching contents related to the feature words in a database or a knowledge base by adopting a semantic similarity retrieval algorithm, and extracting first M most related results; on the basis of the feature words and the search results of the subdivision fields, prompt words are formed, the first L most relevant results are calculated from the M search results to serve as recommendation content, and corresponding recommendation reasons are generated; summarizing the recommended contents determined in each subdivided field, and storing the summarized recommended contents in a database; and calling the recommended content of each subdivided field from the database. According to the method, automatic and low-cost accurate recommendation can be realized, and a large amount of manual data annotation is not needed.

The invention belongs to the technical field of strip mine step line automatic extraction, and discloses a strip mine step line automatic extraction algorithm based on double RANSAC optimization shape detection, and the method comprises the steps: introducing a block processing strategy, and controlling the scale of each sub-point cloud through dividing sub-regions; samples are randomly extracted from the disordered point cloud according to plane sample data, shapes are extracted through probability selection and connection of the multiple samples, and finally optimization and storage of the shapes are completed; and setting a normal vector threshold value, a point set number threshold value and a point cloud density threshold value so as to effectively filter out an inclined plane, a curved surface and a residual point set and reserve a target shape area. According to the invention, full-automatic extraction of the step line is realized by using the point cloud data, and the extraction can be completed within half a workday. And large subjective errors exist in manual extraction, the precision and efficiency of the extraction result can be greatly improved by utilizing a computer technology based on the high-precision point cloud data, the labor and time cost is reduced, and the engineering benefit and the economic benefit are remarkable.