8results about How to "Efficient culling" patented technology

The present application belongs to the field of refractory material resource utilization, and particularly relates to a comprehensive treatment process for resource utilization of used refractory materials, which comprises: pretreatment, wherein the used refractory materials are sorted, surface cleaned and impurities removed to obtain raw materials for recycling; the raw materials are sequentially subjected to primary coarse crushing, secondary medium crushing, tertiary fine crushing and matched closed circuit screening to obtain crushed materials with target particle size; the qualified particle size materials are sent into a hydration and drying integrated equipment, and after hydration in a water vapor environment, vacuum drying is performed; the dried materials are sent into a wheel milling equipment through a quantitative feeder set, and selective crushing of pseudo-particles is performed under an applied pressure of 15-25 MPa, and finally, classification screening is performed to obtain regenerated materials with different particle size specifications. The process has the advantages that: the step-by-step accurate refinement from large waste materials to target particle size is realized, the particle size is accurately controlled, and the aggregate loss rate is effectively reduced. Compared with traditional single-stage or two-stage crushing, the over-crushing phenomenon is effectively controlled, and the aggregate recovery rate is improved.

This invention discloses a method for detecting encrypted communication of malicious software based on autonomous learning, characterized by the following steps: Step S1: Preprocessing network traffic data, including traffic reassembly, protocol filtering, and structured log generation, to extract SSL / TLS encrypted sessions; Step S2: Extracting session statistical features, including TCP connection layer features, SSL / TLS protocol layer features, and certificate layer features, forming a multi-dimensional feature vector; Step S3: Constructing a host session heterogeneous graph, which includes two types of heterogeneous nodes: host nodes and session nodes, and two types of edge types: session-related edges and master-slave-related edges; Step S4: Using the hierarchical aggregation algorithm HSGraphSAGE to embed nodes into the heterogeneous graph, and obtaining host node representations containing spatiotemporal features through multi-hop neighbor aggregation; Step S5: Inputting the host node representations into a classifier, and outputting the detection results of malicious communication behavior of the host. This invention achieves accurate detection of encrypted malicious traffic through a five-step collaborative mechanism. The entire process effectively improves the detection rate of APT attacks and significantly reduces the false positive rate while ensuring privacy compliance (without decrypting the original data).

The invention discloses a power system safety assessment method, device and equipment and a medium, and relates to the technical field of power system management, and the method comprises the steps: constructing an initial explicit constraint set with unit output as a decision variable; screening the initial explicit constraint set to obtain an effective domain boundary set; establishing a probability distribution model of the uncertain power factors, and extracting a first probability feature of each uncertain power factor by using the probability distribution model; transmitting the uncertainty of each power grid node to a corresponding effective domain boundary based on the effective domain boundary set and the first probability feature to obtain a second probability feature of each effective domain boundary; and obtaining a probabilistic feasible region boundary based on each second probability feature, and evaluating the power system according to the probabilistic feasible region boundary. The power system security risk probabilistic assessment with clear physical meaning is realized by transferring the source load uncertainty from the power grid node to the effective boundary and calculating the probability density function.

The application discloses a biological age evaluation method and system based on whole life cycle DNA methylation and application, and belongs to the technical field of bioinformatics. First, the obtained original DNA methylation data is subjected to quality control and standardization processing; then, differential methylation sites significantly related to calendar age are screened as features; next, taking calendar age as a target variable, a regression model is constructed by adopting a LightGBM gradient boosting framework to obtain a methylation clock model; subsequently, the model is used to predict the DNA methylation age of an individual, and an epigenetic age acceleration value is calculated based on the deviation of the DNA methylation age from the calendar age; finally, the age acceleration value is subjected to correlation analysis with health or physiological indexes in different life stages, so as to evaluate the biological aging state of the individual and predict related health risks. The application covers the whole life cycle, is suitable for Chinese population, and can provide an effective tool for clinical disease risk prediction, health management and anti-aging intervention effect evaluation.

A method for removing outliers of three-dimensional high-density electrical data, comprising: arranging high-density original data in columns, respectively, coordinates of electrodes A, B, M and N, natural electric potential SP, voltage VP, current IP and resistivity RO; calculating electrode AB distance AB, electrode AB midpoint distance XAB, electrode MN distance MN, electrode MN midpoint distance XMN, normalized electric potential LOGUI and normalized electric potential and distance ratio LOGUIMNAB according to the high-density original data; establishing a mapping relationship between the spatial positions of data points and each measurement value according to the calculated values of electrode AB distance AB, electrode AB midpoint distance XAB, electrode MN distance MN, electrode MN midpoint distance XMN, normalized electric potential LOGUI and normalized electric potential and distance ratio LOGUIMNAB, and drawing scatter plots of different dimensions; removing outlier data in the scatter plots according to the drawn scatter plots of different dimensions, and saving the data after removing outliers. The present application considers the spatial relationship of each data point and the physical relationship of each measurement value, and has the advantages of high efficiency and high precision.

The utility model discloses a paper die cutting technical field's a kind of defective product paper automatic rejection die cutting device, to solve the problem of inconvenient rejection of defective product generated in the paper die cutting process in prior art. It includes paper feeding mechanism, cutting mechanism and material receiving mechanism, at least one group of sensors is provided between paper feeding mechanism and cutting mechanism, both sides of material receiving mechanism are equipped with mounting bracket, feed shaft is rotatably arranged between two mounting brackets, adhesive tape roll is installed on feed shaft, the outside of adhesive tape roll is adhesive surface, feed shaft can move in the vertical direction of mounting bracket, material receiving shaft is also installed between two mounting brackets, and material receiving roll is installed on material receiving shaft;The utility model is used to detect the position of defective product in paper die cutting work, and automatically rejects corresponding paper according to the position of defective product paper, since adaptive automation equipment, convenient operation, can avoid secondary damage, with excellent application prospect.

This invention relates to a non-contact liquid level detection method and system, belonging to the field of industrial detection technology. The method includes: receiving a ranging command; simultaneously triggering a millimeter-wave radar sensor and an ultrasonic sensor to measure the liquid level in the same container, obtaining raw echo signals respectively; performing signal processing and liquid level calculation on the raw echo signal from the millimeter-wave radar sensor to obtain a first liquid level value from the millimeter-wave radar sensor, and performing signal processing and liquid level calculation on the raw echo signal from the ultrasonic sensor to obtain a second liquid level value from the ultrasonic sensor; and determining the liquid level in the container based on the first and second liquid level values. This method achieves rapid and efficient removal of invalid data, preventing contamination from flowing into subsequent data systems, and solves the problem that a single sensor cannot identify invalid data in complex engineering environments in the liquid industry.

The application belongs to the field of materials, and particularly relates to a crystal structure generation method based on a diffusion model and a self-attention attenuation graph network, aiming to solve the problems of low matching degree of crystal structure generation and target properties and limited prediction accuracy. The method comprises the following steps: obtaining real crystal structure data, and performing feature extraction to construct a crystal structure dataset; inputting the crystal structure dataset into a diffusion model for training under the condition of target material properties; converting the crystal structure dataset into a crystal graph, and inputting the crystal graph into a graph neural network for training; in the message passing process, combining a multi-head self-attention mechanism and a weight attenuation function based on atomic distance to update node features and edge features, so as to obtain a prediction model; inputting target material properties into the trained diffusion model to generate candidate crystal structures; inputting the candidate crystal structures into the trained prediction model to output property prediction values; comparing the property prediction values with the target material properties, and screening to obtain a target crystal structure.