11results about How to "Shorten the analysis cycle" patented technology

The invention discloses a wafer defect atlas intelligent identification and classification method and system, and relates to the technical field of semiconductor integrated circuit manufacturing and detection, and the method comprises the steps: obtaining a detection point set, an original image and standard reference attribute data; constructing a space weight matrix based on reference data; calculating a generalized connection cost based on a path weighted cumulative value, constructing an initial graph model, and performing probability sparsification; determining verification points based on the weight matrix and extracting local gradient features of the original image; correcting the connection probability by using a Bayesian inference model, and updating a graph topological structure; and finally, outputting a classification result according to the connected sub-graph features. According to the method, layout physical attributes and image microscopic features are combined, and accurate reconstruction and classification of defect forms under a complex background are realized.

The invention provides a spark source atomic emission spectrometry determination method for acid-soluble aluminum and inclusion aluminum, and belongs to the technical field of assessment of inclusions in steel. The spark source atomic emission spectrometry determination method for acid-soluble aluminum and inclusion aluminum comprises the following steps: establishing a calibration curve of all aluminum, then determining integral intensities of inclusion aluminum, all aluminum and acid-soluble aluminum in a sample under corresponding conditions, and calculating the intensity of the inclusion aluminum by using a difference value between the integral intensities; selecting two standard samples with adjacent strength according to the strength of aluminum inclusion in the sample, and calculating a calibration coefficient K according to the aluminum content Al% lt; the excitation rate F is calculated according to the peak integral intensity of the inclusion aluminum and the integral intensity of the total aluminum in the 0.0007% standard sample, the inclusion aluminum content Als% is equal to K * F * (IAlt-IAls), and the content of the acid-soluble aluminum is equal to the difference between the content of the total aluminum and the content of the inclusion aluminum. According to the method, the accuracy of a measurement result is effectively improved while high analysis speed, low cost and simplicity and convenience in operation are ensured.

The invention relates to the technical field of bioinformatics and distributed computing, and discloses a millet gene screening method and system based on data mining and a storage medium, and the method comprises the steps: obtaining millet gene data, carrying out initial clustering processing, and dividing the millet gene data into a plurality of data segments to obtain a segment set; evaluating a resource demand index of each data segment in the segment set; obtaining current load state data of the computing nodes, and distributing the data segments to the corresponding computing nodes to generate task distribution mapping; starting a gene analysis task by adopting a parallel execution mechanism, monitoring an execution progress and generating a real-time synchronous adjustment signal; carrying out migration adjustment on the task according to the adjustment signal, optimizing a task execution sequence, collecting a gene analysis result of each calculation node, and fusing key site information to obtain an integrated analysis data set; performing verification processing on the analysis data set, and outputting a final millet gene screening result; the resource utilization rate and the analysis efficiency during gene data processing are improved.

This invention discloses a sleep analysis method that integrates spatiotemporal multi-scale features, comprising the following steps: Step 1, acquiring multi-channel raw physiological signal data from a polysomnography device and preprocessing it to obtain a standardized multi-channel signal data block of fixed length; Step 2, inputting the standardized multi-channel signal data block into a pre-trained deep learning feature extraction network, which includes parallel temporal feature extraction branches and spatial feature interaction branches, and outputting a multi-scale fused feature vector; Step 3, inputting the multi-scale fused feature vector into a pre-trained sleep event recognition model based on the Transformer architecture, which then outputs a sleep analysis report. This invention has the advantages of high efficiency, accuracy, and strong generalization.

The invention belongs to the technical field of ATE diagnosis, particularly relates to a test breakpoint control method and system for an ATE diagnosis process, and is used for solving the problems that existing ATE diagnosis breakpoint control depends on software codes, the operation threshold is high, hardware identifiers cannot be accurately associated, and the fault positioning efficiency is low. The method comprises the following steps: configuring breakpoint parameters, wherein the breakpoint parameters comprise identification information and trigger conditions of a target hardware unit; loading the breakpoint parameter into a test flow of an ATE diagnosis process; in the process of executing the test process, contextual information of a currently tested hardware unit is obtained in real time, and the contextual information comprises an identifier of the current hardware unit; matching the identifier of the current hardware unit with the identifier information of the target hardware unit, and if the identifier information of the current hardware unit is consistent with the identifier information of the target hardware unit and meets the trigger condition, triggering interruption to suspend the current test process; and in response to a recovery instruction, releasing the interruption and continuing to execute the test process.

The present application relates to the technical field of metallurgy, and discloses a kind of iron ore powder sintering reaction performance evaluation method and system, to solve the problems of fragmentation, strong experimental dependence, unable to measure key parameters and poor dynamic adaptability of existing method, scheme mainly includes: obtaining the chemical composition of the iron ore powder to be evaluated, and normalizing processing;Using the Equilib module of FactSage, the results under different temperature conditions are calculated when the reaction equilibrium is obtained;Extract liquid phase production, liquid phase chemical composition and reaction enthalpy data;And using the Viscosity module of FactSage, the liquid phase viscosity under different temperature conditions is calculated;Select the liquid phase production, reaction enthalpy and liquid phase viscosity under a certain temperature as evaluation parameters, carry out vector normalization processing and set weight, use TOPSIS multi-criteria decision method, respectively calculate the relative closeness of each group of evaluation parameters;Relative closeness is used as the evaluation index of iron ore powder sintering reaction performance.The present application improves the accuracy and efficiency of sintering reaction performance evaluation.

The invention relates to the technical field of defect detection, in particular to a 4D-STEM material defect automatic detection method and system, and the method comprises the steps: obtaining 4D-STEM data, carrying out the structural processing, and generating low-dimensional features inputted by a student network and privileged modal data inputted by a teacher network; an asymmetric teacher-student network is constructed, the teacher network processes the privilege mode to obtain physical semantic features, and the student network processes low-dimensional features; a compound loss function formed by DINO distillation and the like is used for pre-training a student network, and teacher parameters are updated through EMA; after the teacher network is removed, the decoder is accessed for fine tuning; and the deployment network inputs the to-be-detected data output result. By means of cross-modal distillation, students can learn physical priori in a network mode, the detection precision is improved, automatic detection of defects such as dislocation is achieved, and efficiency and robustness are both considered.

This invention relates to the field of electric drive system design and development for new energy vehicles, specifically to a method for testing and analyzing the dynamic stiffness of electric drive assembly mounts and the accuracy of simulation. The method involves standardized frequency response testing of the electric drive assembly mounts, identification of minimum dynamic stiffness, and the construction of a dynamic simulation model consistent with the test boundaries to extract the minimum dynamic stiffness on the simulation side. Then, it sequentially performs frequency and amplitude error analysis of the minimum dynamic stiffness points in the tests and simulations, as well as average dynamic stiffness error analysis. Finally, it comprehensively calculates accuracy performance coefficients based on multi-dimensional error coefficients and establishes a scoring system to achieve a quantitative evaluation of the test and simulation accuracy. This method solves the technical problems in existing technologies where evaluating the dynamic stiffness of electric drive assembly mounts requires multiple physical impact tests, which are resource-intensive, time-consuming, and lack a systematic and quantitative analysis method for testing and quantifying the accuracy of mount dynamic stiffness, thus failing to utilize the preliminary analysis results to guide subsequent mount design and development.

The invention discloses an ecological system health dynamic early warning platform and method based on sky-ground integration, and the platform comprises a data preprocessing layer which is used for obtaining sky satellite data, air unmanned plane data and ground sensor data of a target natural region in a preset historical time period; the model analysis layer is used for extracting a plurality of ecological parameters of each preset ecological attention object from the acquired data; dynamically determining the final weight of each preset ecological attention object based on the current environment state at the current moment; performing weighted summation on the plurality of ecological parameters and the final weight to obtain a current ecological health comprehensive value; the health early warning layer is used for determining a change trend curve graph of the ecological health comprehensive value in a future preset time period according to the current ecological health comprehensive value and the current environment state; and based on the change trend curve graph, determining whether to carry out early warning on the target natural area. By adopting the method and the device, the timeliness requirement of ecological protection can be met, and meanwhile, the accuracy of an early warning result is improved.

The present application relates to the technical field of industrial safety and chemical engineering design, in particular to a chemical engineering design and safety risk analysis integrated platform.The platform adopts a four-layer architecture of data base layer, intelligent engine layer, business component layer and human-computer interaction layer.The method comprises the following steps: intelligently analyzing chemical engineering drawings through an AI-P&ID recognition engine, extracting drawing element parameters and constructing topological relations; performing parameterized editing and conflict checking through a P&ID editing and checking component; performing process flow dynamic simulation through a process simulation engine; performing intelligent reasoning of risks through a HAZOP knowledge base large model; and finally automatically generating a structured risk analysis report through a report generation component.The present application realizes full-chain automation from drawing analysis to report generation, solves the problems of serious manpower dependence, obvious data defects, low efficiency and fragmented functions in traditional methods, improves the efficiency, accuracy and reliability of chemical safety risk analysis, and reduces the cost of manpower and software.

This invention discloses a precise and rapid identification device for elements in drilling cuttings, relating to the field of geological exploration equipment. It includes a housing, a sample processing component, a rotating multi-station sample tray, an X-ray detection component, a cleaning component, a data calibration module, and a human-machine interface component. The sample processing component sequentially feeds, grinds, and presses the drilling cuttings into standard samples. The rotating multi-station sample tray carries the samples and moves them between stations. The X-ray detection component irradiates the samples and acquires elemental data. The data calibration module uses a machine learning model to correct the raw data in real time. The cleaning component automatically cleans the sample cups after use. This invention achieves a fully automated, integrated workflow from sample feeding to analysis, solving the problems of non-standard sample pretreatment, low detection efficiency, and poor data accuracy in the field, significantly improving the speed and accuracy of element identification in drilling cuttings.