7 results about "Background factors" patented technology

The invention discloses a geological disaster monitoring and early warning method based on multi-factor dynamic coupling, belongs to the technical field of geological disaster monitoring and early warning, and aims to solve the problems that a traditional method cannot fuse multi-source factors in real time, is low in early warning precision, lags in response and the like. A geological environment static background factor is combined to construct a susceptibility evaluation model, a dynamic weight is analyzed and calculated by adopting a time sequence, a dynamic Bayesian network is utilized to carry out coupling analysis, and a geological disaster risk probability value is output in real time, so that a corresponding early warning level and an emergency response are triggered. The method is mainly used for real-time monitoring, accurate risk assessment and timely early warning of geological disasters.

The invention discloses a water conservation and ecological restoration area identification method and device. The method comprises the following steps: firstly, obtaining the water conservation amount and water conservation influence factors of a target area; screening and detecting the water conservation amount and the water conservation influence factors to obtain an optimal water conservation value, a dominant factor and a background factor; clustering, classifying and superposing the background factors, and performing partition processing on the background factors and the dominant factors to obtain a plurality of effective ecological partition units; the optimal water source conservation value is combined to count and quantify the water source conservation amount of each effective ecological partition unit, and a water source conservation potential value and a potential surplus amount are obtained; and finally, according to the water conservation potential value and the potential surplus, dividing a restoration priority level, and identifying a restoration area. According to the method, the defects that a traditional method is low in recognition accuracy and weak in priority pertinence are overcome, the zoning and evaluation scientificity is improved, and the water conservation and ecological restoration efficiency is effectively improved.

The application discloses a water source conservation and ecological restoration area identification method and device. The method comprises the following steps: first, obtaining the water source conservation amount and water source conservation influencing factors of a target area; screening and detecting the water source conservation amount and the water source conservation influencing factors to obtain optimal water source conservation values, dominant factors and background factors; clustering and classifying the background factors and the dominant factors after superimposing the background factors to perform partition processing on the background factors and the dominant factors, thereby obtaining a plurality of effective ecological partition units; combining the optimal water source conservation values to quantify the water source conservation amount of each effective ecological partition unit, thereby obtaining water source conservation potential values and potential surplus amounts; and finally, dividing restoration priority levels according to the water source conservation potential values and the potential surplus amounts to identify restoration areas. The application makes up for the problems of low identification accuracy and weak priority targeting of traditional methods, improves the scientificity of partition and evaluation, and effectively improves the water source conservation and ecological restoration efficiency.

The present invention relates to the fields of natural language processing and medical artificial intelligence technology, and discloses a large-scale model optimization method and system that integrates a medical knowledge graph. The method includes: constructing a temporal fuzziness-confidence joint modeling network model, wherein the input includes the time information corresponding to the current chief complaint, the context of the chief complaint, and individual patient information; the output is a normal estimated time prediction result; the normal estimated time prediction result is jointly calibrated based on the causal path information in the medical knowledge graph to obtain the time prior distribution on the graph side; a joint distribution is constructed based on the normal estimated time prediction result and the graph-side time prior distribution, and the expectation of the joint distribution is used as the time point prediction result. The time point prediction result is input into the large-scale model to output structured suggestions, which are then fed back into the medical knowledge graph for updating. This solves the problem of reasoning errors caused by failing to identify semantic dependencies or ignoring patient background factors.

The application discloses a wheat ear detection method based on an improved YOLOv8s, which is suitable for improving wheat ear detection problems in a complex field environment, so as to realize rapid identification and effective counting of wheat ears. First, the application replaces a C2f module in a Backbone network by using a FasterBiNetBlock, which can reduce model parameters, reduce the number of calculation redundancies and memory accesses, and refine features of the model, so that more attention is paid to the wheat ears during detection, and interference of background factors such as wheat stems and wheat leaves is inhibited. Secondly, a new network structure EQFPN is proposed to strengthen a Neck network, so that the model has the characteristics of being friendly to quantization and hardware. Finally, a triplet attention module is introduced to capture cross-dimension dependency and wheat ear features, and meanwhile, problems such as false detection and missed detection during wheat ear detection are reduced.

The application discloses a kind of heterogeneous compact reservoir difference evolution model construction methods, belong to oil and gas geological exploration technical field, this method includes five core steps: first, core, experiment, well logging and seismic multi-source data are standardized and preprocessed;Then quantitative analysis reservoir porosity, permeability temporal and spatial variation and pore throat characteristics;Then dynamic source rock diagenesis process and quantize its intensity;Further, key factors such as sedimentary microfacies, diagenetic transformation intensity, tectonic activity frequency and burial depth are coupled, and the correlation function between them and reservoir densification degree is established;Finally, a visual model is constructed and optimized by validation data and regional geological background factors.The application solves the problems of multi-source data integration, evolution law quantization and poor model applicability, realizes the integrated dynamic characterization from micro-pore throat to macro-reservoir, significantly improves the prediction accuracy of high-quality reservoir, and provides reliable technical support for compact oil and gas reservoir exploration and development.

The invention discloses a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of machine vision and image processing. Performing spatial position weighting on the high-resolution features by using background suppression guide information from a large receptive field branch, so that the model maintains small target texture and edge information and reduces the response ratio of a non-welding spot region, thereby improving the detectability of micro welding spots and fine-grained defects under a complex background; according to the method, a large receptive field feature extraction branch is set to provide a welding spot contour and context relationship, and welding spot enhancement guide information is generated by a high-resolution branch to participate in a feature fusion process of a second branch, so that the second branch adopts differential fusion weights for a welding spot candidate region and a background region in a fusion stage; and interference of background factors such as light reflection, silk screen and board texture on large target regression and classification is reduced.