11results about How to "Reliable basis for decision-making" patented technology

The application provides a sea area efficiency evaluation method and system based on geospatial analysis technology, which comprises the following steps: acquiring multi-source evaluation indexes of each unit, calculating first-order statistical moments, calculating spatial Gini coefficients and local information entropy of each index domain, correcting a preset resolution coefficient by using the local information entropy when the Gini coefficient exceeds a threshold, calculating deviation sequences of each unit index value and neighborhood first-order statistical moments, setting an asymmetric processing function according to the local information entropy, amplifying the absolute value of a negative deviation by using an exponential function, and compressing the value of a positive deviation by using a logarithmic function, wherein the local information entropy is positively correlated with the base number of the function, calculating an initial efficiency value based on the processed deviation sequences and the corrected resolution coefficient by using a comprehensive grey correlation degree, calculating a local Moran index based on the initial efficiency value to determine a spatial clustering type, correcting the resolution coefficient twice according to the spatial clustering type, recalculating the comprehensive grey correlation degree, and obtaining a sea area efficiency evaluation result.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

This invention belongs to the field of mine safety monitoring technology, specifically involving an intelligent management system based on miners' positioning data processing. The system includes a wearable precision inertial measurement device, a multi-source data fusion gateway, an underground wireless transmission network, a biomechanical feature extraction server, a behavioral quality evaluation management platform, and a multi-dimensional early warning terminal. The wearable device collects high-frequency motion vector data of miners in real time, performs preliminary noise reduction using the gateway, and the server establishes a gait cycle segmentation model to accurately extract gait feature parameters such as stride length, ground contact time interval, and body center of gravity oscillation frequency. This invention realizes a shift from simple spatial location monitoring to in-depth monitoring of biological signals and behavioral quality, filling the monitoring blind spots caused by passive displacement and improving the proactiveness of mine risk early warning and the accuracy of safety supervision.

The application discloses a kind of cable health state evaluation method and system based on single-end open circuit impedance spectrum, belong to nondestructive testing and impedance spectrum analysis technical field.This method only needs to carry out frequency sweep test at one end of cable and keep open circuit at the end, after obtaining wideband input impedance spectrum, extract real part data and identify resonance extreme point;The upper and lower envelope lines are constructed by extreme point interpolation, and the high-frequency characteristic impedance and attenuation constant of the full frequency band are derived;Further, the distributed parameters of the cable are calculated by combining the phase constant.By comparing the characteristics of the distributed parameters, the health status of the cable can be accurately evaluated, and the physical mechanism can effectively distinguish between thermal aging and moisture damage.The application eliminates the cumbersome of double-end cooperation test, does not need to intercept samples, greatly saves the test time, has wide applicability and strong anti-interference ability.

This invention relates to the field of power distribution protection technology, specifically disclosing a gas leakage monitoring device for power distribution equipment suitable for extremely cold environments. The device includes: a data acquisition unit comprising multiple gas sensor probes deployed at vertical heights within the monitored area; a data processing unit that performs temperature compensation correction on the gas concentration signal to obtain corrected discrete-point concentration data; a fusion calculation unit that reconstructs the spatial concentration field of the monitored area based on the corrected discrete-point concentration data, performs dynamic data assimilation on the spatial concentration field to obtain an optimal estimated concentration field, calculates at least two characteristic parameters characterizing the gas state based on the optimal estimated concentration field, and outputs a decision output result; and a result application unit that identifies and warns of leakage status based on the decision output result. This invention can avoid false alarms and missed alarms caused by gas liquefaction or stratification, making it suitable for gas leakage monitoring of power distribution equipment in extremely cold environments.

This application discloses a digital management method and system for hospital nursing quality indicators, relating to the field of medical and nursing quality information technology. The method includes the following steps: setting assessment rules for evaluating nursing process quality indicators; acquiring patient nursing records and constructing a patient-centered event time series; based on the assessment rules, filtering nursing events related to the corresponding nursing process from the event time series; dynamically adjusting the strictness of the assessment rules based on the identity information of nursing staff and the clinical status information of patients; verifying the event time series according to the adjusted assessment rules and obtaining verification results; determining the compliance of the nursing process based on the verification results, and generating a compliance conclusion and audit report for the nursing process. This application can intelligently distinguish between genuine nursing quality defects and discrepancies in record appearances caused by differences in personnel and context, solving the problems of fragmented processes, isolated data, and delayed assessment in traditional nursing quality management.

This invention provides a method and apparatus for evaluating the credibility of generated content, relating to the fields of computer science and artificial intelligence. The method includes: collecting information sources from multiple platforms; extracting features from the information sources to obtain multi-dimensional features; defining graph nodes and edges, assigning weights, and learning graph neural networks from the multi-dimensional features to obtain a directed weighted information source authority graph; performing end-to-end self-supervised learning on the information source authority graph using a graph convolutional network or graph attention network to obtain the graph neural network authority score for each node; performing conflict graph modeling and confidence propagation iterative algorithm processing on the information source authority graph to obtain a confidence propagation score; setting a timeliness factor, and weightedly fusing the graph neural network authority score, confidence propagation score, and timeliness factor to obtain a credibility score. This method achieves dynamic, accurate, and multi-platform collaborative evaluation of information source credibility.

This invention belongs to the field of data processing technology, specifically disclosing a safety assessment method for energy storage power stations based on real-time monitoring data. The method includes: performing multimodal feature analysis on voltage and environmental data sequences of the energy storage power station to obtain static voltage feature tensors and static environmental feature tensors; conducting collaborative analysis of the static voltage and environmental feature tensors to obtain an environmental voltage feature correlation group; performing entropy weight dynamic analysis on the environmental voltage feature correlation group to construct a multidimensional state vector; performing time-series deduction on the multidimensional state vector to obtain a safety situation evolution matrix; and performing residual analysis between the safety situation evolution matrix and real-time monitoring time-series data to obtain a comprehensive deviation index; and performing root cause assessment on the comprehensive deviation index to obtain comprehensive risk information. This invention enables more accurate, dynamic, and efficient safety assessment of energy storage power stations, allowing for comprehensive situation perception and rapid identification of risk root causes.

The present application belongs to the technical field of urban ecology and plant protection, and discloses a plant growth health state intelligent monitoring method and system based on multi-dimensional spectral feature analysis. The method comprises the following steps: constructing a specific multi-stress pathological spectral feature library in advance; acquiring target plant canopy multispectral image data in real time, calling the feature library after pretreatment to complete spectral matching comparison, and outputting canopy physiological state judgment conclusion; simultaneously starting the acoustic flow control aerosol detection device, using the sound field to enrich and focus the biological aerosol particles in the air, performing spectral analysis on the captured particle flow, extracting spectral dimension digital features, and independently judging the aerosol abnormality level; fusing the canopy judgment conclusion and the aerosol abnormality level for two-dimensional cross verification, and outputting the plant health state classification result and the grading early warning instruction. Through the dual-mode parallel monitoring and strong verification fusion decision of canopy spectrum and microenvironment aerosol spectrum, the present application realizes the early accurate classification and automatic early warning of plant stress.

This invention discloses a method and system for time-series demand perception in the poultry industry supply chain, relating to the field of time-series data analysis technology. The method includes: extracting and clustering common pattern libraries with semantic labels from historical time-series sales data and semantic attribute data through multimodal coding and contrastive learning. In the perception phase, the time-series and semantic data of the object to be perceived are mapped to a unified space, and reference common patterns are retrieved based on semantic and sequence similarity in two stages. Then, using personalized time-series features as the primary driver and common patterns as a reference, adaptive fusion is achieved through an attention mechanism to enhance feature representation. Finally, a decoder is used to generate a time-series demand perception sequence for future target periods. This invention exhibits higher perception stability and interpretability in data sparse and cold-start scenarios, effectively supporting production scheduling, inventory, and sales decisions in the poultry industry supply chain.

This invention proposes a mechanical drilling rate prediction method based on physical consistency constraints. By collecting and preprocessing drilling time-series data from multiple wells, a sliding window is used to construct historical operating conditions and future prediction sequences. A prediction model integrating a gated cyclic unit and a multilayer perceptron is constructed to achieve joint modeling of historical features and future controllable parameters. Simultaneously, a physical consistency loss function based on a mechanical specific energy model is introduced and jointly optimized with data error loss. A dynamic weighting mechanism enables collaborative learning of data-driven and physical constraints. This method can achieve multi-step prediction of mechanical drilling rate, improve the accuracy and physical rationality of the prediction results, and enhance the model's generalization ability under complex operating conditions.