340results about How to "Efficient capture" patented technology

The invention discloses a transformer area distribution transformer reverse heavy overload prediction method and system, and the method comprises the steps: obtaining data which comprises the historical electrical quantity characteristics of a photovoltaic user, the numerical weather forecast characteristics of the geographic position of a transformer area, and a prior graph structure formed according to the topological graph of the transformer area; preprocessing the data to construct a data set; the method comprises the steps of establishing a space-time diagram prediction model, inputting historical electrical quantity characteristics and numerical weather forecast characteristics of N photovoltaic users and a prior diagram structure into the model to obtain a future K-step area distribution transformer load prediction result, performing heavy overload judgment based on a prediction value, and finally obtaining a future K-step area distribution transformer reverse heavy overload prediction result. According to the method, the topological structure of the power distribution network, the electrical characteristic data and the numerical weather forecast characteristics are comprehensively utilized, high-precision prediction of the multi-time-step power of the photovoltaic grid-connected system is achieved through the space-time diagram neural network model, and the method is suitable for application scenes such as operation optimization and early warning management of the power distribution network with distributed photovoltaic access.

The invention relates to the technical field of government affair services, in particular to an NLP entity recognition method based on semantic extension, which comprises the following steps: collecting multi-source time sequence data and generating an entrepreneurship ecological data graph through a dynamic time warping algorithm; dynamic evolution of a term library is realized by using a generative adversarial network, and a term semantic association graph is constructed in combination with a graph convolutional network; a double-path attention mechanism is deployed in a Transform encoder, and global semantics and local attention guided by terms are fused through a gating loop unit; modeling fuzzy query analysis into a Markov decision process, and optimizing a semantic extension strategy by adopting a reinforcement learning agent; multi-granularity features are extracted through a feature pyramid network, and model parameters are optimized in combination with a bidirectional long-short-term memory network and an online learning feedback loop; and finally, calculating entity service association strength by using a graph attention network, and generating an interpretable report based on a Shapley value attribution algorithm. A closed-loop learning system is formed, and the business starting policy matching accuracy is improved.

The invention discloses an electroencephalogram feature recognition method for motion intention prediction in a lower limb alternate stepping process. The electroencephalogram feature recognition method comprises the following steps: acquiring an electroencephalogram signal of a subject before the subject executes an action under the requirement of a lower limb alternate stepping motion execution normal form; preprocessing the electroencephalogram signal to obtain an initial signal, performing label labeling on the initial signal according to an action type, and forming a data set by a label and the initial signal; constructing a neural network model, wherein the neural network model comprises a multi-scale grouping space-time convolution module, a self-attention mechanism module for updating external memory based on Top-k gating and a grouping causal time convolution module; and training the neural network model in a supervised manner by using the data set to obtain a prediction model for predicting the action type. According to the method provided by the invention, the movement intention of the patient can be recognized by the movement brain-computer interface before the lower limb alternate movement, the control of the rehabilitation robot on the specific lower limb movement by the brain intention is realized, and the method has an important value on the clinical application of a brain-controlled rehabilitation robot system.

The invention discloses an electric power bid inviting and purchasing scale prediction method and system. The method comprises the following steps: firstly, acquiring historical electric power bid invitation purchase data, including unstructured text data and structured data, and establishing a cross-modal fusion model and a purchase scale prediction model; thirdly, dividing the historical data into a plurality of meta-learning task sets according to a preset electric power bid invitation purchasing scene, and performing meta-training on the model parameters by adopting a meta-learning framework to enable the model to obtain learning ability; in the real-time prediction application, the system monitors a preset condition, and when the condition is triggered, the newly acquired real-time data and the real purchase scale are used as small samples, and the purchase meta-model parameters are quickly updated online. According to the method, deep mining of multi-source heterogeneous data is realized, the adaptability and prediction precision of the model to a new bid inviting scene are enhanced, and powerful technical support is provided for accurate purchase decision making of the power industry.

The invention relates to a lightweight multi-scene disease and pest detection method and system based on RT-DETR. According to the scheme, firstly, a standardized image processing link is constructed, multi-band feature extraction is carried out on an input image by using a convolutional backbone network introduced with wavelet transform, an effective receptive field is expanded in a frequency domain through wavelet decomposition and an inverse reconstruction mechanism, and feature capture of a tiny insect pest target is enhanced while calculation redundancy is reduced. Furthermore, a bidirectional feature pyramid network including global and local double-branch collaborative modeling is adopted, cross-level dynamic interaction and gating fusion are performed on multi-scale features, and environmental noise interference such as veins and illumination under a complex farmland background is effectively inhibited. And finally, establishing a homography mapping model from a pixel plane to a geographic space according to camera calibration parameters, converting a visual detection result into a spatial distribution diagram layer with latitude and longitude information, and realizing dimension crossing of pest and disease damage monitoring from single-point identification to region-level risk assessment.

The invention discloses an action sports scoring method and device based on big data analysis, and relates to the technical field of computer vision, and the method comprises the following steps: S1, constructing a human body posture tensor manifold; s2, generating aligned action track characteristics; s3, generating symmetrical positive definite manifold features; s4, generating deep manifold distribution parameters; s5, quantifying global distribution deviation characteristics; s6, calculating a residual vector based on the deep manifold distribution parameters and the standard action distribution parameters, inputting the residual vector into the improved AGCN model for processing, constructing an adaptive topology based on the residual vector, and performing aggregation analysis to obtain a joint-level physical angle error after multi-scale space-time convolution and manifold enhancement; and S7, outputting a comprehensive score vector. According to the method, the limitation that a traditional method only depends on local geometric features and ignores global topological structure constraints and statistical distribution priori is overcome, and an efficient solution is provided for intelligent scoring of sports actions.

The invention relates to the technical field of X-ray tube manufacturing, in particular to an X-ray tube shell, a manufacturing method and an X-ray tube, the X-ray tube shell comprises a shell used for containing an anode target disc, a heat dissipation layer is attached to the inner wall of the shell, and the heat dissipation layer at least surrounds the periphery of the anode target disc; the heat dissipation layer is of an interpenetrating network structure formed by compounding functional metal and diamond, and the material of the functional metal at least comprises one of molybdenum and tungsten. Based on the device, the shell can absorb thermal radiation energy by using the high absorption characteristic of molybdenum or tungsten in an infrared band, and can quickly conduct heat out by means of the high thermal conductivity of diamond, so that the heat dissipation efficiency of the shell is improved, and heat accumulation is effectively prevented.

The invention discloses a dam abutment slope structure health monitoring system and method based on distributed strain optical cables. Comprising the following steps: obtaining zero strain data of each distributed measuring point of the dam abutment slope by subtracting initial stable strain data of each distributed measuring point of the dam abutment slope from initial stable temperature data of the corresponding distributed measuring point of the dam abutment slope; obtaining real-time strain data of each distributed measuring point of the dam abutment slope by subtracting the strain data at each moment of each distributed measuring point of the dam abutment slope from the temperature data at the corresponding moment of each distributed measuring point corresponding to the dam abutment slope; performing strain calculation according to the real-time strain data of each distributed measuring point of the dam abutment slope and the zero strain data of the corresponding distributed measuring point of the dam abutment slope to obtain the strain variation of each distributed measuring point of the dam abutment slope along with the time change; and evaluating the structural health state of the dam abutment slope according to the development trend of the strain variation of each distributed measuring point of the dam abutment slope along with the time change and a preset periodic environment interference curve.

The biomass particle hot blast stove comprises a stove body, a feeding assembly, a combustion assembly, a heat exchange assembly and a smoke exhaust assembly. The combustion assembly is arranged in the furnace body, a combustion chamber of the combustion assembly is sequentially divided into a drying pyrolysis area, a main combustion area and a burnout area in the conveying direction of biomass particles, and ordered partitioned combustion of preheating, sufficient combustion and complete burnout of fuel is achieved; the heat exchange assembly comprises a first heat exchange set arranged above the combustion assembly and a second heat exchange set arranged in the circumferential direction of the combustion assembly, and a three-dimensional and multi-layer waste heat recovery structure is formed. The combustion efficiency and the burn-off rate of the biomass particles are obviously improved; through the first heat exchange set and the second heat exchange set, efficient and sufficient recovery of combustion waste heat is achieved, and the overall heat efficiency and the energy utilization rate of the hot blast stove are greatly improved.

This invention discloses a ferroelectric-based teeth whitening material, its preparation method, and its application, belonging to the field of teeth whitening technology. The preparation method of the ferroelectric-based teeth whitening material includes the following steps: S1. Sodium alginate, gelatin, and water are mixed to obtain a first mixed solution; S2. 2,2,3,3,4,4-hexafluoropentane-1,5-diol is added to the first mixed solution, and the mixture is mixed again to obtain a second mixed solution; S3. The second mixed solution is allowed to stand at low temperature, then immersed in a calcium ion aqueous solution for cross-linking and curing to obtain the ferroelectric-based teeth whitening material. The teeth whitening material of this invention enables HFPD molecular crystals to remain stable in a clean water environment for teeth whitening, resulting in excellent teeth whitening effects. The HFPD molecular hydrogel prepared by this invention can achieve a high efficiency degradation of over 95% of indigo carmine within 25 minutes without polarization treatment, with a piezoelectric catalytic reaction rate constant of 0.132 min. ‑1 .

The application relates to the field of optical fiber sensing, and more particularly to an event detection method, system and device for optical cable detection and a storage medium. The method comprises the following steps: inputting optical fiber data of an optical cable into a first processing unit for processing to obtain space-time features; inputting the optical fiber data into a second processing unit for processing to obtain frequency domain features; inputting the space-time features and the frequency domain features into an attention processing unit after splicing for feature enhancement to obtain enhanced features; inputting the enhanced features into a bottleneck layer processing unit for processing to obtain bottleneck features; wherein the bottleneck layer processing unit comprises at least two bottleneck layer processing subunits, and the bottleneck layer processing subunits can perform feature transformation processing on the enhanced features; and inputting the bottleneck features into a backbone network for optical fiber event detection to obtain an optical fiber event detection result corresponding to the optical fiber data. The application can improve the accuracy of optical fiber event detection in an optical cable.

The invention provides an audit text classification filing method and system suitable for audit large model training, and relates to the technical field, the method comprises the following steps: carrying out entity identification and relation extraction on an unstructured audit document to generate a knowledge graph, and extracting a sub-graph structure as a feature representation; performing node embedding learning by using a graph neural network, generating a document vector containing structure and semantic information, and determining a document attribution category through an attention mechanism; and a natural language retrieval request can be analyzed, multi-hop path reasoning is executed, and a document corresponding to an implicitly associated extension node is obtained. The audit document classification accuracy is improved, efficient association query is realized, and the audit knowledge mining capability is enhanced.

The invention discloses a multi-modal ultrasonic image recognition method and device, which effectively combines an ultrasonic image and a corresponding task text, and improves the recognition precision of the ultrasonic image by utilizing the interaction of visual information and language information. By preprocessing the ultrasonic image and the task text and generating a visual Token sequence and a language Token sequence, data of different modalities can be effectively fused to generate a multi-modal sequence. The sequences are subjected to context interactive fusion of a multi-head attention model to obtain a multi-modal embedded representation containing visual and language information, so that the model can accurately analyze a task instruction while understanding the image content, and the recognition accuracy is improved. And furthermore, through selection and weighted fusion of dynamic experts and in combination with background knowledge provided by static experts, the processing capability of the model is improved. And finally, the output result is adjusted and optimized by using the fusion control parameters, so that the efficiency and the accuracy of ultrasonic image recognition are improved.

The application discloses a preparation method of polyurea porous material with flower cluster structure and application thereof. The material prepared by the application is a flower cluster porous polyurea material prepared by a self-assembly reaction in a mixed solvent of acetone and water, with hexamethylene diisocyanate as a reaction monomer. Nucleophilic addition reaction occurs between isocyanate groups and water molecules to generate a urethane intermediate, the intermediate rapidly decomposes to release CO2 gas and generate an amine group; the newly generated amine group immediately reacts with the remaining isocyanate groups in the system to form a polyurea oligomer, and then self-assembles into a flower cluster porous structure. The polyurea flower cluster structure has a high specific surface area, rich pores and controllable surface chemical properties, is an ideal material as an adsorbent, and can effectively capture dye molecules in a solution. The application solves the key technical problems of low adsorption capacity and poor selectivity of traditional adsorption materials in dye wastewater treatment.

The invention provides flame-retardant tread rubber as well as a preparation method and application thereof. The flame-retardant tread rubber is prepared from the following raw materials in parts by weight: 100 parts of matrix rubber, 24-35 parts of a composite flame retardant, 40-100 parts of filler, 1-4 parts of a silane coupling agent, 2-6 parts of an anti-aging agent, 1-3 parts of sulfur, 1-3 parts of an accelerant, 1-3 parts of stearic acid and 3-5 parts of zinc oxide, the composite flame retardant comprises a halogen flame retardant and an inorganic flame retardant, the inorganic flame retardant comprises an inorganic coating flame retardant, and the inorganic coating flame retardant comprises an antimony trioxide core and a molybdenum disulfide coating layer coating the surface of the antimony trioxide core. In the flame-retardant tread rubber disclosed by the invention, a molybdenum disulfide coating layer in the inorganic coating flame retardant can improve the stability of antimony trioxide at a high temperature, and can enhance the interfacial compatibility with a non-polar rubber matrix at the same time, and the synergistic reaction with the halogen flame retardant further improves the flame-retardant effect; therefore, the flame retardancy of the rubber is improved, and excellent mechanical properties are kept at the same time.

The invention belongs to the technical field of electric power meteorology, and provides a medium and long term wind and light resource prediction method and system considering climate remote correlation factors, and the method comprises the steps: obtaining global climate index historical data, and carrying out the standardization processing of the data, and obtaining a standardized climate index; for the historical resource sequence of the target station, calculating the mutual information value of the standardized climate index, and constructing a forecasting factor set by taking the lag time corresponding to the maximum value of the mutual information as the optimal early warning window period; extracting wind and light resource measured data of the same period in historical years to construct a reference probability density function; and taking the reference probability density function as prior distribution, combining a preset dynamic mode prediction result as a likelihood function, and outputting a prediction result containing a deterministic value and a confidence interval. By means of the characteristic that ocean signals change slowly, the method breaks through the 15-day prediction limit of an atmospheric mode, effective trend prediction from the quarterly level to the annual level is successfully achieved, and the requirement for medium and long term resource evaluation is met.

This application relates to a method and apparatus for time-resolved electrochemical Raman spectroscopy. The method includes: acquiring a delay time parameter, potential waveform data, and an initial trigger signal; performing event trigger detection on the initial trigger signal to obtain an event trigger timestamp; at the event trigger timestamp, converting the potential waveform data based on the initial trigger signal to obtain a potential pulse signal; the potential pulse signal is used to indicate the initiation of an electrochemical reaction; at the event trigger timestamp, determining the laser trigger delay amount corresponding to the potential pulse signal based on the delay time parameter; at the event trigger timestamp, transforming the potential pulse signal based on the laser trigger delay amount to generate a laser trigger pulse signal; wherein the laser trigger pulse signal is used to indicate the emission of a pulsed laser; acquiring the reaction current signal corresponding to the electrochemical reaction and the Raman spectrum corresponding to the electrochemical reaction under pulsed laser irradiation. This method can improve the tracking accuracy of rapid electrochemical processes.

The utility model belongs to the technical field of roadway dust removal, and relates to an air flow purification device of a fully-mechanized face air return crossheading, which comprises a frame type component, a lifting component, a lower rotating component, an upper rotating component and a spraying component, the frame type component is used for allowing an operation vehicle to pass through, and the lifting components are arranged on the two sides of the frame type component and used for achieving lifting of an upper structure in the frame type component; a spraying component is arranged in the middle of the upper structure of the frame type component and used for treating dust in an area where a vehicle passes through. The rotating lower part is hinged with one side of the lower structure of the frame type part, and the spraying part is arranged at the upper part of the rotating lower part; the rotating upper part is hinged to the side, close to the rotating lower part, of the upper structure in the frame type part, and a dust catching net is arranged between the rotating upper part and the frame type part. Sufficient passing space is provided for vehicles, obstruction of a traditional dust catching net to traffic is avoided, different roadway heights and environment conditions can be flexibly adapted, and the stability of the treatment effect is ensured.

The invention belongs to the technical field of coal mine safety monitoring, and discloses a coal mine tunnel roof disaster real-time early warning system and method. Comprising the following steps: performing time sequence reasoning on monitoring data acquired in real time, respectively judging whether the key physical quantity of the roof triggers primary early warning or secondary early warning by acquiring an expected value prediction curve and an uncertainty boundary curve of the key physical quantity of the roof in a future time step, and acquiring corresponding early warning information when the early warning is triggered; constructing a dynamic causal graph, inputting the early warning information and the currently acquired key physical quantity into the dynamic causal graph for reasoning, calling candidate intervention measures based on the acquired current state node to perform anti-fact reasoning in the dynamic causal graph, and acquiring a risk evolution trajectory and a risk suppression efficiency index of the candidate intervention measures; and the risk suppression efficiency indexes are sorted and preferentially selected, and a causal decision early warning report containing the optimal intervention scheme is output, so that the scientificity and effectiveness of risk suppression are improved.

This invention discloses a method for preparing high-elongation bars for cold forging, belonging to the field of aluminum alloy preparation technology. Through optimization of composition and process, the prepared bars possess high purity, uniform and fine equiaxed grain structure, and excellent plasticity, with significantly improved elongation. This meets the stringent requirements of cold forging for high material plasticity, resulting in a bright, crack-free surface and uniform grains after cold forging, overcoming problems such as oxide scale, coarse grains, and poor dimensional accuracy caused by hot forging. Furthermore, the introduction of modified talc and sodium nitrate as refining agents further promotes uniform microstructure, achieving high elongation and excellent cold forging performance.

This invention relates to the field of navigation and timing technology, specifically to a GNSS timing data prediction method and related equipment. The method involves acquiring Roland timing data when GNSS timing data is invalid and preprocessing it. The preprocessed data is then input into a trained single-hidden-layer extreme learning machine model. This model calculates the hidden-layer output matrix by randomly initializing the input layer weight matrix and bias vector, and solves for the output weights using regularized least squares. The model outputs a normalized GNSS timing data prediction value, which is then denormalized to obtain the final GNSS timing prediction result. This method combines the high stability of the Roland system with the efficient computational power of the extreme learning machine to achieve accurate GNSS timing data prediction. It is suitable for enhancing the resilience of positioning, navigation, and timing systems in critical infrastructure scenarios, ensuring the continuous operation of the system even when GNSS signals are interfered with or fail.

This invention discloses a method and system for predicting solar radio bursts based on a complete solar magnetograph, belonging to the field of solar radio technology. It includes: acquiring a complete solar magnetograph; inputting the complete solar magnetograph into a pre-trained improved ResNet model to obtain image feature vectors; dividing the image feature vectors into pseudo-time series and inputting them into an improved LSTM network to obtain solar radio burst prediction results; the improved ResNet model introduces a custom residual layer to extract small-scale features from the complete solar magnetograph, and the improved LSTM network introduces a gating mechanism to process the image feature vectors at different time scales. This method can improve the accuracy of solar radio burst prediction without utilizing solar radio spectrum data; it solves the problem that existing predictions using solar radio spectra are easily interfered with by the accuracy of the input data observations.

The invention relates to a positive electrode active material and a preparation method thereof, a positive electrode and a lithium ion battery. The positive electrode active material comprises lithium manganate particles and a sodium phytate layer coating the surfaces of the lithium manganate particles. According to the technical scheme, in the positive electrode active material, the sodium phytate layer serves as a coating layer, and six phosphate groups in molecules of the sodium phytate layer and manganese ions dissolved out of lithium manganate particles can form a stable complex, so that the dissolved-out manganese ions are efficiently captured and prevented from being further migrated into an electrolyte to damage the performance of a battery; meanwhile, the sodium phytate layer forms a layer of compact physical barrier on the surfaces of the lithium manganate particles, so that direct contact between the lithium manganate particles and an electrolyte can be effectively blocked, and dissolution of manganese ions is inhibited from the source. Through a synergistic effect of a chelation effect and a physical barrier effect of the sodium phytate layer, the dissolution amount of manganese ions can be remarkably reduced, and the crystal structure of lithium manganate particles is prevented from being distorted, so that the structural stability of the positive electrode active material is improved.

The application discloses a titanium nitride supported osmium platinum catalyst with a gradient polarization effect, a preparation method of the catalyst and application of the catalyst in fuel cells, and belongs to the field of new energy materials and electrocatalysis technology. The catalyst comprises a TiN carrier and a Pt-Os alloy cluster supported on the carrier, wherein Os is doped in the form of a single atom in a Pt nanocluster. A gradient polarization architecture is constructed inside the catalyst, electrons are directionally transferred in TiN-Pt-Os, and electron-rich Os single atoms act as oxygenophilic sites to capture OH ‑ , adjacent Pt sites dissociate H2, through two-site cooperation and interface water network regulation, significantly accelerating the kinetics of alkaline hydrogen oxidation reaction. The mass activity of the catalyst reaches 11337 A / g, which is 24.7 times that of commercial Pt / C; when applied to an AEMFC single cell, the peak power density reaches 1.49 W / cm 2 , which is 2.7 times that of commercial Pt / C, and has excellent practical application prospect.

The invention discloses a sleep state closed-loop deep brain stimulation method and system based on a multi-modal signal, and the method comprises the steps: preprocessing an obtained electroencephalogram signal and an electromyographic signal, extracting multi-dimensional candidate features, and obtaining a core feature vector through screening; constructing a sleep state prediction model, inputting the core feature vector into the sleep state prediction model, and outputting a probability result of sleep state classification and a corresponding reliability index; executing multi-level physiological safety verification to obtain a target state, and allowing to output a state switching instruction when and only when the multi-level physiological safety verification is passed and the target state meets a preset stable condition; and continuously outputting a corresponding stimulation parameter set to the deep brain stimulation electrode based on a final target state corresponding to the state switching instruction. The problems that in the prior art, in the closed-loop regulation and control process, the sleep state recognition result is excessively relied on, and physiological safety constraint and stimulation continuity control are lacked are solved.

The application discloses a kind of ball grid array package chip solder ball bubble defect automatic detection method and system, it is related to chip intelligent detection technical field, the method is guided by designing semantic cross-layer fusion framework, realizes multi-scale feature balanced fusion in neck network by introducing feature alignment and redistribution module, strengthens small target details and semantic information using local semantic enhancement module, and the positioning accuracy of bubble is optimized in combination with smooth geometry positioning loss function.The application significantly improves the detection capability of small, fuzzy and occluded bubbles, has strong robustness and generalization, and can be widely used in solder ball bubble, material porosity and surface defect industrial vision detection scene.

This invention belongs to the field of medical signal processing technology, specifically a method and prediction system for constructing a three-dimensional tumor motion trajectory prediction model. It involves collecting temporal data of point clouds from the human chest and abdomen, as well as temporal location information of the tumor, and establishing a unified spatial coordinate system. The temporal data of the point clouds are divided into n time periods of total duration T1, and the temporal location information of the tumor is divided into n time periods of total duration T2, resulting in n training samples. The point clouds of the training samples are further divided into several pseudo-voxel sub-blocks according to the human projection plane, and K pseudo-voxel sub-blocks are selected based on motion saliency to obtain the temporal feature vector of the tumor motion. The temporal feature vector of the tumor three-dimensional motion trajectory prediction is used as input, and the corresponding tumor temporal location information is used as output to train an attention-enhanced dilated convolutional prediction network. This invention enables high-precision, non-invasive prediction of the three-dimensional tumor motion trajectory.

The utility model provides a kind of wall drilling device for fire engineering, including bottom plate, the upper end right side of bottom plate is symmetrically provided with sliding slot, the inside of sliding slot is slidably provided with slide column, the inside of sliding slot is provided with the drive unit for driving slide column to move, the upper end of two slide columns is jointly connected with fixed plate, the right end of fixed plate is provided with mounting plate, and drilling wall piece is provided on mounting plate, the front and rear ends of mounting plate are both provided with fixed frame, rotatingly provided with rotating column in the inside of fixed frame, the both ends of rotating column extend to the outside of fixed frame and are jointly connected with rotating plate, through the wall drilling device for fire engineering of the utility model, not only dust generated in the process of drilling can be adsorbed by dust cover, improve working environment quality, but also the angle of dust cover dust suction can be adjusted by rotating rotating plate, ensure that it is always in the best dust suction position, effectively capture dust generated in the process of drilling, avoid dust to escape.

This invention relates to the field of chip inspection technology, and more particularly to a chip non-destructive testing system based on embedded current scanning. The system includes a current scanning module, an information acquisition module, a data analysis module, an abnormal scanning region generation module, and a scanning path generation module. Instead of simply performing a higher-density secondary scan of the entire chip, this invention intelligently filters out abnormal scanning regions requiring special attention based on the initial scan results and calculates their scanning priority. This allows valuable inspection time and computing resources to be concentrated on the most suspicious and severe areas, avoiding unnecessary repeated scanning in normal areas. The system dynamically selects the optimal scanning path based on the regularity of the region shape; for example, parallel lines are used for regular regions, and radial lines are used for irregular regions. This makes the scanning path more closely match the geometric features of the defect, obtaining a more complete region characterization with fewer scanning points, resulting in higher path planning efficiency.

The application discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, relates to the field of metamorphic rock P-T-t trajectory reconstruction, and comprises the following steps: acquiring electronic probe micro-area mapping data and internal standard sample data of a metamorphic rock sample, and performing multi-stage data correction; performing mineral phase identification and chemical component quantitative interpretation, generating a mineral classification mapping, an element concentration distribution diagram and a whole rock major component data set; performing thermodynamic phase equilibrium simulation, generating a P-T apparent profile diagram, and extracting a P-T trajectory; performing micro-area in-situ chronology analysis on selected minerals in the mineral classification mapping to obtain chronology data; coupling the P-T trajectory and the chronology data in time and space, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visualized reconstruction report. The application realizes full-process automation and standardization of the metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction, and improves the precision, efficiency and reliability of trajectory analysis.