157 results about "Early warning model" patented technology

The present application relates to the technical field of material forming prediction, in particular to a real-time regulation and early warning method in a composite material forming process, comprising acquisition of unstable production composite material morphology images and determination of corresponding processes, analysis of process factors corresponding to each unstable production result by using a grey correlation analysis method, establishment of an early warning model based on a large amount of data, real-time acquisition and analysis of the morphology in actual production, and repeated adjustment of the corresponding process when abnormal production occurs. The model can automatically identify unstable production conditions in the composite material forming process, quickly locate the problem source according to the weight of the process factors, provide early warning information, so as to timely adjust the production parameters and ensure stable production of the composite material.

The application belongs to the technical field of marine environment monitoring and early warning, and specifically relates to a multi-channel internal wave flow early warning system and an early warning measurement method, which comprises the following steps: a buoy body is fixedly arranged through a fixed anchor, and a marine profile comprehensive detection module synchronously collects multi-dimensional profile data such as flow velocity, temperature and salinity. A main control unit is embedded with a mixed driving early warning model, a phase velocity of an internal wave is solved based on a physical model, a nonlinear correction is performed through a machine learning residual correction module, a high-precision internal wave phase velocity is obtained, and then early warning time, shear flow and displacement amplitude are calculated and an alarm level is determined. Alarm information is redundantly sent to a shore station receiving system through a ground mobile communication link and a satellite communication link of a multi-channel communication module, and the shore station receiving system regularly updates the machine learning model by taking measured data as incremental samples. The application realizes high-precision and low-delay early warning of internal wave flow, has high communication reliability, and is suitable for marine engineering, underwater vehicles and safety guarantee of offshore operations.

The application provides a kind of data-driven thermal power unit equipment health early warning method and device, comprising: constructing historical sample data set;The feature engineering extraction is carried out to historical sample data set, determine a plurality of sensitive characteristic parameters of the equipment health state, and construct the equipment health benchmark baseline based on sensitive characteristic parameters;From real-time operation data, the real-time characteristic parameters corresponding to sensitive characteristic parameters are extracted;The multidimensional health deviation of real-time characteristic parameters relative to health benchmark baseline is calculated;Multi-dimensional health deviation is input into the health early warning model pre-trained, and the current equipment health state score and predicted degradation trend are output by health early warning model;Combined with the preset grading early warning rule, determine whether to trigger early warning and the early warning level triggered.The application can effectively solve the problem that the traditional fixed threshold method is poor in adaptability under variable working conditions, the false alarm rate is high, improve the equipment fault prediction ability and the intelligent level of operation and maintenance decision.

The application is suitable for the technical field of financial early warning, and provides a financial early warning system, comprising a heterogeneous data fusion module, a dynamic knowledge graph construction module, a risk perception and prediction module and a self-adaptive early warning generation module. The system solves the technical problem that the existing financial early warning system has a single data dimension, is difficult to depict a risk correlation network, leads to one-sided early warning, has insufficient early warning capability for "contagious" crises caused by associated risks, and has a static and rigid early warning model, which lacks self-adaptation and evolution capability, leading to gradual degradation of long-term early warning performance, poor explainability of early warning results, limited decision support, and inability to develop accurate and effective risk mitigation measures, greatly weakening the actual decision support value of the early warning system. The application achieves the technical effects of accurate, explainable and self-adaptive early warning of associated and dynamic financial risks.

The invention relates to the technical field of battery safety management, and particularly discloses a water system battery thermal runaway early warning method and system based on multiple sensors, and the method comprises the steps: continuously analyzing the real-time cross correlation among sensor data flows, such as voltage, temperature, pressure and hydrogen concentration, and comparing the real-time cross correlation with a preset health state reference model; and isolated abnormal signals which are caused by single-point instantaneous interference and are inconsistent with other data streams can be accurately identified. Besides, a dynamic trust score with historical inertia is established for each sensor, so that the system can judiciously adjust the data weight of the sensor according to the long-term performance of the sensor, and the defect that the early warning model is polluted and missed in report due to the fact that the continuous slow failure of the sensor cannot be identified is overcome. Through the self-adaptive weight-descending fusion of the sensor data with low reliability, a judgment which is more robust and more accurate to the battery state can be finally generated, and the reliability of the early warning system in a complex real environment is improved.

This invention relates to the field of medical big data analysis technology, and particularly to a machine learning-based method for predicting the risk of drug-induced liver injury (HILI) related to traditional Chinese medicine (TCM). The invention includes the following steps: Step S1: Collect clinical data and divide it into training and test sets; use literature case reports as an external validation set; Step S2: Perform statistical inter-group comparisons based on standardized training set data to screen for independent risk factors; Step S3: Based on the XGBoost model, construct early warning models for HILI related to TCM and select the optimal prediction model; Step S4: Use the SHAP interpreter to interpret the optimal prediction model; Step S5: Input clinical data into the optimal prediction model and output the patient's risk probability. The purpose of this invention is to provide a machine learning-based method for predicting the risk of HILI related to TCM, addressing the problems of low prediction accuracy, lack of interpretability in existing models, and difficulty in accurately predicting the risk of HILI.

The application provides a charging facility fault early warning model construction method based on multi-source data perception, and belongs to the technical field of charging facility fault early warning.The application forms a multi-source time series data set by collecting charging pile operation state data and environmental data and performing time series alignment, expands fault samples by using an adversarial generative network and a semi-supervised clustering algorithm to form a balanced sample data set, constructs a fault early warning model containing a time series memory pool encoding layer and a frequency domain convolution feature extraction layer, cooperatively optimizes early warning accuracy and response time delay by using a double-layer game optimization framework to obtain an optimal parameter combination, deploys the optimized model to an edge computing module to realize real-time fault early warning, establishes an online incremental learning mechanism based on sliding time window detection data distribution offset, and uses an elastic weight consolidation technology to update model parameters and retain historical knowledge, thereby solving the problem of early warning performance degradation of charging facility fault early warning in a data distribution evolution environment.

This invention provides a method for early warning and self-healing of material blockages on production lines that integrates the Internet of Things (IoT). It relates to the fields of industrial intelligent manufacturing and automation technology. The method involves deploying multiple types of sensors at key equipment nodes on the production line to collect multi-source heterogeneous data. This data is then transmitted to edge computing nodes for preprocessing, outputting a multimodal feature dataset. A production line blockage early warning model is constructed based on this multimodal fusion deep learning network model. Blockage monitoring is performed using this model. Based on the monitoring results, corresponding self-healing strategies are executed, generating control commands that are sent to the production line actuators. The entire chain of data for each early warning and self-healing event, along with its final result labels, is collected to form a feedback dataset. This dataset is then used for periodic incremental training and optimization of the production line blockage early warning model. This invention achieves early warning and autonomous handling of blockages, improving production continuity and intelligence.

The present application relates to a kind of lung nodule early warning method and system based on multi-model fusion, belong to health management field.Therein, the method includes collecting lung nodule CT image and implementing standardization processing, generates lung nodule analysis image;The feature recognition of lung nodule analysis image is executed to generate the lung nodule label image with multi-dimensional pathological feature label, and the lung nodule time series data set is structured;Lung nodule label image and lung nodule time series data set are used to construct lung nodule fusion prediction model and output lung nodule relabeling pathological image, lung nodule fusion prediction model integrates time series probability prediction model, learning supervision model and relabeling model;Based on lung nodule relabeling pathological image, construct three-dimensional convolutional neural network early warning model, quantify deterioration risk coefficient, when deterioration risk coefficient breaks through preset alert threshold, trigger clinical early warning mechanism, the present application realizes the relabeling of patient lung nodule potential state by fusion model, completes lung nodule early warning.

This invention discloses an infection early warning model and prediction system for critically ill patients based on multimodal monitoring data, belonging to the field of medical artificial intelligence and clinical decision support technology. The system collects multi-source heterogeneous physiological signals, laboratory test indicators, imaging features, and nursing records from patients. Through a self-designed cross-modal feature fusion algorithm and dynamic weight adjustment mechanism, it constructs a nonlinear infection risk prediction model. Furthermore, time-series segmented coding and adaptive threshold correction methods are introduced during model training to achieve early and accurate infection early warning. Compared with existing technologies, this invention significantly improves the sensitivity and specificity of prediction and maintains stable performance across different departments and diseases.

This invention discloses a method and system for integrated end-to-end data management of complex equipment. First, the server acquires structured and unstructured data from the design, manufacturing, and operation / maintenance stages of the system via a data acquisition module. Second, edge nodes perform data preprocessing, unifying data formats and time bases. Subsequently, the system constructs entity attribute and time-series models through a unified data modeling module, and achieves multimodal information alignment and representation through a data fusion module. The system also incorporates a quality risk early warning model, combining historical data and real-time status to achieve risk scoring and fault prediction. Finally, the application service module supports business functions such as intelligent scheduling, quality traceability, predictive maintenance, and performance analysis. This invention integrates the entire data flow across design, manufacturing, and operation / maintenance, solving problems such as data silos, low system collaboration efficiency, and difficulty in quality traceability, thereby improving the intelligence level and decision-making capabilities of complex equipment manufacturing.

This application discloses an intelligent diagnostic method and system for leakage risk of metal roof nodes based on image recognition. The method includes: S1, multimodal data acquisition and preprocessing; S2, node region localization and feature extraction; S3, multi-scale visual Transformer feature fusion; S4, leakage feature identification and classification; S5, leakage risk assessment and early warning; and S6, model iteration and optimization. This application utilizes simultaneous acquisition and pixel-level registration of visible light and infrared thermal imaging to simultaneously acquire the surface texture information and temperature distribution information of nodes. By fusing global context and local detail features using a multi-scale visual Transformer network, it can effectively correlate weakly correlated features across modalities, thereby overcoming the limitations of single-modal detection. It can detect both visible surface defects and hidden problems such as internal water accumulation, reducing false alarm and missed detection rates.

The application discloses a cigarette silk production data fusion quality tracking management method and system. The method comprises the following steps: collecting multi-modal data, establishing data correlation mapping with batch ID and equipment ID, and obtaining pretreated data with unique coding; sequentially performing data cleaning, structuring and correlation fusion processing on the pretreated data, and performing multi-level automatic correlation to output a standardized data set; constructing a material flow quality tracking model, a process quality tracking model and a multivariate abnormal early warning model according to the standardized data set, and outputting full-process quality tracking result data; hierarchically storing the data and establishing a three-dimensional data maintenance mechanism; and calling the stored data to generate a visual report. The system comprises modules corresponding to the above steps. The application solves the problems of data chain loss, non-transparent production process and insufficient multi-modal data fusion capability in the prior art.

The application discloses a deep learning-based time-space fusion early warning method for deep rock burst, and constructs a dual-branch Transformer-CNN time-space feature fusion early warning model. The model captures local mutation features in a microseismic sequence through a parallel multi-scale CNN network branch, combines a Transformer network branch to extract global evolution features, realizes comprehensive mining of rock burst precursor information and identification of key precursor information, and further enhances the dynamic adaptability of the early warning model to complex microseismic sequences by using a self-adaptive gating fusion mechanism, thereby enhancing the adaptability of the early warning model in a complex monitoring environment. In addition, a learnable position coding is introduced to replace a traditional fixed coding mode, so that the early warning model can more flexibly capture time sequence dependence in mining stress evolution, and the problems of gradient vanishing and low calculation efficiency of a traditional cyclic network in long sequence modeling are overcome. Finally, the classification accuracy of the rock burst danger level and the early warning reliability are effectively improved.

This invention belongs to the field of open-pit coal mine safety monitoring and data fusion technology, and specifically provides a multi-source data fusion method for open-pit coal mines based on BP neural networks. This method includes five steps: data acquisition, preprocessing, BP neural network fusion model construction, fusion inference and early warning application, and online model updating. Data acquisition covers geological, equipment status, and environmental parameters. Preprocessing includes data cleaning, noise suppression, feature selection, and standardization. The model construction adopts an input layer-double hidden layer-output layer structure, trained using the Adam optimizer and physical constraint loss function. Fusion inference outputs equipment status or slope displacement results and provides early warnings. The model is updated incrementally on a daily / weekly basis, and a full retraining is performed when the evaluation index degrades by more than 10%. This method solves the problems of weak adaptability and limited fusion accuracy in existing algorithms, and features high accuracy, strong robustness, good real-time performance, and practical applicability. It can improve the safety monitoring level of open-pit coal mines and reduce the incidence of disasters.

This invention discloses a joint early warning method and system for agricultural disasters based on meteorological data elements. The method includes constructing a multi-source meteorological data acquisition system to collect meteorological data from the target agricultural area; preprocessing the collected multi-source meteorological data; constructing a targeted agricultural disaster early warning indicator system based on the main types of agricultural disasters in the target area and the growth characteristics and maturity stages of different crops; constructing an agricultural disaster early warning model integrating machine learning algorithms and training and optimizing the model; classifying agricultural disasters into early warning levels according to preset early warning level thresholds and combining the types and maturity stages of crops in the target area, and generating early warning information; and disseminating the generated early warning information through preset channels. This invention integrates multi-source meteorological data to construct a multi-dimensional early warning indicator system and a targeted early warning model, achieving accurate and real-time early warning of agricultural disasters.

The application discloses a multi-parameter vital sign monitoring and early warning system based on an intelligent ambulance, and relates to the technical field of signal alarm devices.The system comprises a task receiving and parameter determining module, a model reconstruction and compression module, and a model issuing and monitoring and early warning module.The specific steps include receiving first-aid task information to dynamically determine a list of target vital sign parameters;reconstructing and compressing a cloud-integrated early warning model based on the list to generate a lightweight early warning model;finally, issuing the lightweight model to the target ambulance to perform monitoring and early warning, and adopting a local and cloud collaborative early warning mechanism based on confidence.The application solves the technical problems of insufficient early warning accuracy caused by the lack of task adaptability, the difficulty in deploying complex models due to the limitation of computing resources, and the impact of the local and cloud alarm logic on the collaborative efficiency, and realizes accurate, real-time and reliable early warning of vital signs in a mobile first-aid scene with limited resources.

This invention discloses a method for constructing a risk warning model for medical device testing based on big data. This invention relates to the field of data processing and risk warning technology, and solves the technical problem of failing to effectively correlate the deep nonlinear relationships between personnel behavior, equipment status, environmental fluctuations, and final test results, resulting in a large number of risk signals latent in process data going undetected. This invention introduces deep process features such as equipment stability indicators, personnel pass rate deviation, report modification rate, and environmental parameter deviation, enabling it to capture weak risk signals that traditional methods cannot detect. Five-dimensional feature engineering covers all key aspects of laboratory operation, and the unsupervised learning model can discover hidden anomalies beyond preset rules, compensating for the shortcomings of supervised learning in recognizing unknown patterns. The warning threshold automatically floats with recent data distribution, adapting to seasonal changes, new project launches, and other scenarios, avoiding frequent false alarms caused by a one-size-fits-all approach.

The present application provides a kind of coastal nuclear power biodiversity monitoring method, belong to the technical field of coastal nuclear power ecological influence, the present application is by in the nuclear power plant surrounding coastal area layout multi-sensor array acquisition site synchronous acquisition water quality parameter, acoustic signal, image data and plankton density such as multidimensional ecological information, constructs multidimensional ecological characteristic data set, utilizes the dense connection network architecture of marine ecological correlation analysis model to learn the nonlinear mapping relationship between multi-source data and outputs biodiversity evaluation index and community structure characteristic parameter vector, combines ecological dynamic early warning model to carry out future ecological change trend prediction, when detecting ecological abnormal signal, start ecological risk comprehensive regulation mode and carry out adaptive optimization adjustment of monitoring parameter, solve the technical problem that coastal nuclear power station ecological monitoring data is multi-source heterogeneous and insufficient in space-time correlation analysis capability.

The application discloses a kind of based on digital twinning unmanned mine car reliability rapid early warning method, system, it is related to unmanned mine car technical field, including the following steps: the digital twin simulation model of unmanned mine car is constructed;Based on the digital twin simulation model, the digital twin reliability model that the reliability state change of each module of the unmanned mine car is characterized is constructed;Based on the measured data of the unmanned mine car, the consistency of the digital twin simulation model and the digital twin reliability model is judged, and after judging consistent, reliability state database is constructed;Based on the reliability state database, digital twin reliability rapid early warning model is obtained by training, and the early warning model is used to identify and output the current reliability state of the unmanned mine car according to real-time acquisition vehicle-mounted sensor observation data, to realize early warning.The reliability state of each subsystem and module of unmanned mine car is rapidly and accurately warned.

This invention provides a training method for a psychological crisis early warning model and a psychological crisis early warning method, relating to the field of natural language processing technology. Through the psychological crisis early warning model, implicit expression data undergoes style transformation to obtain style transformation results. These results are then used to classify psychological crises, yielding target crisis classification results. The psychological crisis early warning model is then trained using the style transformation results, the target crisis classification results, and the explicit expression data corresponding to the implicit expression data. This training enables the target psychological crisis early warning model to possess strong style transformation and psychological crisis classification capabilities, thereby reducing the difficulty of detecting implicit psychological crisis data and improving the accuracy of detection.

The present application relates to the technical field of generator set, specifically to a generator set monitoring and early warning diagnosis method based on principal component analysis and random forest, comprising: collecting historical operation data of the unit, extracting data characteristic values, and forming a data characteristic set by using principal component analysis; using a random forest model to construct a health state evaluation model according to the data characteristic set; calculating the dynamic reference value of the data characteristic set through the health state evaluation model, setting the deviation threshold value by using the dynamic reference value, and constructing an early warning model; forming a unit monitoring and early warning sheet according to the deviation value of the unit measuring point and the preset deviation threshold value, intelligently tracking the abnormal situation of the unit through an abnormal tracking mechanism; and analyzing the unit operation state by using a unit knowledge graph module according to the unit monitoring and early warning sheet, and outputting operational specification guidance to the operating personnel for adjustment. The present application realizes intelligent tracking and real-time operation guidance of the abnormal situation of the unit, significantly improving the efficiency of fault handling and the safety of unit operation.

The application discloses a rotating machinery fault early warning method based on a multi-modal large model, comprising the following steps: 1, obtaining a one-dimensional original time sequence vibration signal, and constructing a multi-modal supervised data set; 2, building a multi-modal fault early warning network, inputting a group of vibration signal slices, time domain waveform images and preset text prompts, and obtaining a text sequence containing a prediction label; 3, calculating an SFT loss based on the text sequence containing the real label, and iteratively obtaining a multi-modal alignment model; 4, calculating a GRPO loss based on the real label and the prediction label, training the multi-modal alignment model after reinforcement learning, and iteratively obtaining an optimal fault early warning model; 5, inputting a test sample into the optimal fault early warning model, and generating a text sequence containing a prediction label. The application adopts a two-stage training strategy, can enhance the semantic understanding ability of the model to the time sequence vibration signal, can reduce the dependence on the thought chain annotation, and can improve the accuracy of fault diagnosis and the interpretability of the model output.

This invention provides a method and system for analyzing and providing early warning of environmental carrying capacity, applied in the field of data processing technology. This application performs data forwarding and standardization processing on cleaned environmental carrying capacity information to generate target indicator features; processes the target indicator features to generate cloud model parameter information; performs weight allocation processing on the cloud model parameter information to generate weight information corresponding to each indicator; processes the target indicator features based on the weight information corresponding to each indicator to generate the average membership degree, the fluctuation range value of the membership degree, and the noise level value of the membership degree; and processes the average membership degree, the fluctuation range value of the membership degree, and the noise level value of the membership degree based on the target environmental carrying capacity early warning model to generate environmental carrying capacity early warning information for the area to be evaluated.

This invention discloses a method, system, device, and storage medium for power grid resource clustering early warning in new power systems. The method includes: aggregation of multi-source, multi-modal power grid resource data; digital conversion to form a vectorized representation; cluster analysis; outlier detection based on the distance of cluster center changes; construction of a multi-dimensional fault space; multi-dimensional cluster space early warning analysis; processing of fault information with missing dimensions; and submission to users for comprehensive analysis. This invention fully leverages the value of data from new power systems, identifying, warning of, and timely handling of potential power grid resource hazards before faults occur, establishing a proactive early warning model for power grid resources based on prevention. Based on the digital space and multi-dimensional fault space of the power grid, it proposes a power grid resource clustering analysis early warning method, improving the timeliness and accuracy of power grid resource early warning analysis. It effectively assists power companies at all levels in making power grid emergency repair decisions, improving users' electricity experience, and has significant practical implications for improving the reliability of power grid supply.

This invention discloses a method and system for determining vibration-induced cavitation in micro-gap insulating oil, relating to the field of power equipment condition monitoring technology. First, a micro-gap fluid pressure analysis model under vibration is established. By considering a nonlinear model with multi-field coupling, the fluid pressure in the micro-gap is obtained. Then, to analyze the cavitation pressure threshold of bubble nuclei, a multi-parameter coupled analytical model incorporating dynamic gas-liquid balance correction is established to obtain the cavitation pressure threshold. Finally, a dynamic cavitation determination and critical state early warning model for insulating oil is established to obtain the cavitation risk factor and cavitation determination results. The method proposed in this invention considers the influence of multiple dynamic factors, has good dynamic adaptability, and the introduction of a multi-field coupled nonlinear model improves the accuracy of cavitation determination, providing a new approach for micro-gap cavitation research.

The application relates to the technical field of battery monitoring, in particular to a construction method of a battery fault early warning model, which comprises the following steps: S1, collecting new energy automobile lithium battery full life cycle operation data; S2, screening actual characteristic segments of internal short circuit fault precursors; S3, screening simulation characteristic segments of internal short circuit fault precursors; S4, completing construction of a high-quality fault database; S5, generating a composite input sequence of a Transform encoder; S6, processing through the Transform encoder and outputting a fault determination result, and training and optimizing to obtain the battery fault early warning model. The battery fault early warning model constructed by the application solves the problems of fault sample scarcity and early warning lag in the prior art.

The application discloses a high-pressure oil pump fault early warning method, system and device, and relates to the technical field of ship diesel engine fault diagnosis. The early warning method comprises the following steps: in response to the high-pressure oil pump being in a steady pressure working condition, sample data of the high-pressure oil pump are acquired; the sample data comprise a front end temperature and rail pressure of the high-pressure oil pump; the sample data are input into a pre-trained early warning model to acquire detection parameters; and in response to the detection parameters being greater than a first threshold value, early warning information is generated. In this way, by monitoring the front end temperature and rail pressure of the high-pressure oil pump and inputting the data into the pre-trained early warning model, online fault early warning of the high-pressure oil pump can be realized, potential fault conditions can be discovered early, fault further deterioration can be avoided, downtime and maintenance costs can be reduced, and the reliability and stability of the diesel engine can be improved. Furthermore, by generating the detection parameters and the first threshold value, when the detection parameters exceed the first threshold value, the early warning information is generated, thereby improving the accuracy of fault detection and reducing false positives and false negatives.

The application discloses a kind of wind turbine gearbox vibration signal enhancement and fault early warning system and method in the technical field of wind power equipment fault diagnosis, comprising: obtaining the health vibration signal of wind turbine gearbox under multiple working conditions and pre-processing;End-to-end deep neural network model is constructed, the model includes cascaded vibration signal enhancement module and fault early warning module, the vibration signal enhancement module is configured to extract fault sensitive features irrelevant to working condition, and the fault early warning module is configured to reconstruct health vibration signal based on the features.The application actively eliminates the influence of speed, load and other working condition factors at the feature level by introducing a working condition invariant feature extractor based on adversarial training, so that the fault early warning model is not sensitive to variable working conditions, greatly improving the accuracy and reliability of early warning in complex wind field environment.

This invention discloses a method and system for early warning of reservoir dam risks based on multi-source data fusion, belonging to the field of dam monitoring technology. The method includes: acquiring multi-source data of the target dam; performing differentiated feature extraction and hidden hazard enhancement on the multi-source data to generate a multi-source feature set; performing feature filtering on the multi-source feature set to generate a core feature set; performing fusion on the core feature set to obtain fused features; inputting the fused features into a trained early warning model to obtain a risk quantification value; acquiring heterogeneous data of the target dam and creating heterogeneous features; performing scenario adaptation quantification on the heterogeneous features to generate multi-dimensional heterogeneous features; performing enhancement on the multi-dimensional heterogeneous features to obtain enhanced multi-dimensional heterogeneous features; constructing a threshold dynamic optimization model; and performing risk assessment based on the target dam's early warning dynamic threshold and risk quantification value to generate an early warning classification for the target dam. This invention improves the accuracy and reliability of dam risk early warning.