147 results about "Threshold model" patented technology

The embodiment of the invention provides a new energy automobile charging pile fault monitoring method and system, and relates to the technical field of charging pile fault detection. The new energy automobile charging pile fault monitoring method comprises the following steps: S1, collecting multi-source data in the operation process of a charging pile; s3, preprocessing the multi-source data to generate a multi-dimensional feature data set; s3, constructing a dynamic fault threshold model; s4, on the basis of the multi-dimensional feature data set and the dynamic fault threshold model, multi-level fault detection is executed; s5, establishing a fault prediction mechanism; and S6, generating a fault monitoring report according to the fault type and the fault severity evaluation index. Through multi-source data acquisition, dynamic threshold construction and a multi-level fault detection and prediction mechanism, real-time accurate monitoring and fault early warning of the operation state of the charging pile can be realized, and compared with a traditional method, the accuracy and the advancement of fault identification are remarkably improved.

The invention discloses an intelligent power distribution network line state real-time monitoring, analysis and evaluation system, and relates to the technical field of power system monitoring and control, the intelligent power distribution network line state real-time monitoring, analysis and evaluation system is constructed, data is collected in multiple dimensions, features are extracted through edge calculation, data is screened, a dynamic threshold model is constructed at a cloud end, and abnormity is identified. The method has the advantages that a three-dimensional digital twin network is constructed based on GIS data and an SCADA system through the power grid topology modeling module, and a unique'topology fingerprint 'containing electrical connection, physical position and logic control relation is generated for each piece of equipment; an upstream and downstream equipment list, a load influence range and a power flow transfer path are marked, and an association state evaluation module adopts a graph convolutional neural network and can accurately capture electrical coupling, physical proximity and historical fault association effects among equipment.

The invention discloses an exercise and cognition ability assessment method, system and device, and the method comprises the steps: obtaining real-time exercise information, including real-time joint angle information, real-time gait information, real-time attention information and real-time decision speed information, of a user; constructing a physiological-behavior coupling model based on a hidden Markov model, and generating a joint evaluation index by inputting real-time motion information; performing feature extraction on the real-time motion information by using a hierarchical feature extraction model to obtain a motion feature vector and a cognitive feature vector; performing fusion processing on the feature vector and the joint evaluation index, and outputting abnormal state information including motion abnormality and cognitive abnormality; behavior abnormity is detected through the dynamic threshold model, a real-time feedback instruction is generated in combination with a preset feedback rule, and finally user training task parameters are dynamically adjusted according to the instruction. According to the method, through multi-dimensional data fusion and a dynamic evaluation mechanism, cooperative monitoring and self-adaptive training regulation and control of motion and cognitive competence are realized.

The invention discloses a hydropower station workshop deformation monitoring method, which comprises the following steps of collecting multi-dimensional monitoring data and point cloud data of a hydropower station workshop, performing adaptive filtering and noise reduction on the multi-dimensional monitoring data, performing spatial registration on the multi-dimensional monitoring data and the point cloud data in a unified three-dimensional coordinate system, constructing a multi-channel time sequence deep learning model, and performing multi-channel time sequence deep learning on the multi-channel time sequence deep learning model; a dynamic early warning threshold model is constructed based on material characteristics, equipment operation parameters and historical monitoring data, risk assessment is carried out, a three-level response mechanism is triggered according to a risk assessment result, a sensor reliability evaluation index is established based on deviation analysis of monitoring data and an early warning result, and a data fusion weight is dynamically optimized. Adjusting the decision boundary of the classification model, and carrying out visualization and traceability analysis on the multi-dimensional monitoring data; the invention further discloses a hydropower station plant deformation monitoring system. According to the invention, a multi-dimensional and three-dimensional risk assessment system is established through data monitoring and real-time analysis, and the structural safety of the hydropower house is guaranteed to the maximum extent.

The invention relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention discloses a property security inspection method and system and a storage medium, and belongs to the technical field of property management. Through combination of intelligent inspection and manual inspection, the intelligent inspection adopts a multi-source data fusion and dynamic threshold determination mode to realize alarm of different risk levels of intelligent inspection abnormity; manual inspection is used for supplementing intelligent inspection and completing regular inspection tasks such as re-checking of intelligent inspection abnormity alarm and inspection of facilities and equipment without sensors; the dynamic threshold value model has the adaptive capacity of different scenes; through machine learning, the system can be continuously optimized, and multi-source data fusion is combined, so that the false alarm rate and the missing report rate of the system are effectively reduced, the accuracy and the dynamic adaptability of intelligent inspection are improved, and accurate alarm is realized; the cost problem caused by blind installation of the sensor can be avoided, and quantitative decision support is provided for property management; the management cost of the property can be reduced, and the intelligent management level and the working efficiency of the property can be improved.

The invention discloses an equipment fault early warning system and method based on the Internet of Things, and relates to the technical field of equipment fault early warning, and the method comprises the steps: collecting mechanical operation parameters and production process parameters of equipment, and setting each piece of equipment as an independent intelligent agent; establishing a three-level topological structure; based on the dual-threshold model, judging the running state of the equipment; when the equipment intelligent body detects that the equipment is abnormal, the equipment intelligent body immediately marks the state of the equipment intelligent body as abnormal and generates a feature code; the equipment intelligent agent receiving the feature code analyzes associated parameters in a process associated parameter atlas, and sends an analysis detection result to an adjacent equipment intelligent agent and a central management node; the central management node summarizes analysis results of all the equipment intelligent agents and judges the influence of faults on the fly ash recycling production process; and according to the severity and the influence range of the fault, performing graded early warning, and sending early warning information.

The invention relates to a method for intelligently monitoring the temperature of important parts of a server, which comprises the following steps: acquiring specified data of the server, inputting the specified data into a dynamic threshold model for training, acquiring various items in real time, inputting the trained dynamic threshold model, and adjusting the temperature threshold of each important part in real time according to the load change and environment change of the server; when the real-time temperature of a certain part is greater than or equal to the corresponding temperature threshold value, correcting the temperature threshold value of the associated part in real time, and performing coupling analysis on the temperature data of different parts under the same time dimension and the temperature change curve of different time nodes; when the real-time temperature of a certain part is greater than or equal to the temperature threshold determined by the dynamic threshold model, an early warning signal is sent out; and generating a temperature control strategy based on the early warning signal and executing a corresponding action by an execution component. The server temperature condition is comprehensively mastered through a dynamic threshold value model, correlation analysis and coupling analysis, an optimized control strategy is generated according to the early warning level, and rapid and accurate regulation and control of the server temperature are achieved.

The invention relates to the technical field of agricultural information, and discloses a visual analysis method and system based on an agricultural big data model. According to the system, crop growth time sequence data are processed by using a multi-scale feature extraction algorithm combining a recurrent neural network and wavelet transform, and key conversion points of a crop growth stage are automatically identified by using a deep learning architecture combining a variational auto-encoder and a hidden Markov model. A standard growth track model and a multi-dimensional deviation measurement index are constructed to evaluate the abnormal growth condition of crops, a dynamic threshold model based on reinforcement learning is applied to realize early warning of abnormal growth, and a multi-dimensional visual interface based on a clock metaphor is designed to visually display crop growth information. The crop growth stage recognition precision and early warning timeliness are improved, and the agricultural production management efficiency is remarkably enhanced.

The invention belongs to the technical field of circuit monitoring, and particularly relates to an AI fault identification method and system for an electric power circuit, and the method comprises the steps: constructing a cross-modal data flow with a space-time mark through the synchronous collection of a thermal imaging sequence and a three-phase current waveform, extracting the characteristics of a multi-physical field through combining the characteristics of a wire material, and carrying out the recognition of an AI fault. And establishing a thermal-electric coupling relationship by using space-time alignment convolution, further analyzing and calculating correlation strength between temperature anomaly and current fluctuation through a sliding window, constructing a dynamic threshold model, generating a fault probability thermodynamic diagram, and finally outputting a graded early warning signal. Compared with the prior art, the method has the advantages that deep fusion analysis of thermal imaging and electrical signals is realized, the recognition precision and response speed of potential fault points are improved, the abnormal state of the wire joint can be found and accurately positioned earlier, the misjudgment rate is effectively reduced, and the safety and stability of power grid operation are improved.

The invention relates to a motion evaluation method, device and equipment based on multi-modal physiological data and a medium, and the method comprises the steps: solving the problem of space-time mismatch of the multi-modal data through sampling timestamps of a hardware clock protocol for multi-source physiological signals such as a makeup rate, myoelectricity, blood lactic acid and the like; equipment interference and motion artifacts are eliminated, and the signal quality is improved; dynamic characteristics such as heart rate variability, myoelectricity root mean square and blood lactic acid gradient in the sliding window are calculated; dividing exercise intensity intervals based on the individually calibrated heart rate percentage and the myoelectricity activation degree threshold, and detecting conversion candidate points; and recognizing a motion intensity critical state in real time through a self-adaptive threshold model driven by historical data, and generating a comprehensive evaluation result containing a thermodynamic diagram and an early warning report. According to the method, the limitation of a traditional fixed threshold model is broken through, multi-modal data deep fusion and individual dynamic adaptation are achieved, the exercise intensity critical point detection precision is improved, and real-time decision support is provided for training load optimization and rehabilitation progress evaluation.

The invention discloses a tunnel deformation real-time early warning method and system based on an LSTM-CNN model, and belongs to the technical field of tunnel engineering safety monitoring. The method comprises four steps of data acquisition, spatio-temporal feature fusion processing, deformation prediction and risk assessment, and intelligent early warning and decision support: collecting multi-dimensional monitoring data through a distributed sensor network and recording spatio-temporal labels; after standardized noise reduction, extracting time trend and spatial distribution characteristics by using an LSTM-CNN fusion model, and constructing a time-space sequence data set; outputting a deformation prediction value based on the fusion features, and calculating a deviation degree in combination with a dynamic threshold model; and triggering multi-level early warning according to the deviation degree and performing visual display. The system comprises a data acquisition module, an edge calculation module, a cloud analysis module and an intelligent terminal module, and double-channel redundancy transmission is adopted to guarantee data continuity. Through spatio-temporal feature collaborative mining, dynamic threshold value adaptation and graded early warning, the deformation prediction precision and the risk assessment accuracy are improved, the transmission stability in an extreme environment is guaranteed, and the early warning decision efficiency is optimized.

The invention relates to a digital twinborn enabling dam body self-adaptive early warning method and system, and belongs to the technical field of hydraulic engineering safety monitoring, and the method comprises the following steps: constructing a digital twinborn model of a dam body; performing multi-source data fusion; the operation state of the dam body is simulated in real time, and a simulation result and actual monitoring data are compared and analyzed; establishing a dynamic early warning threshold model; when the monitoring data or the simulation analysis result exceeds an early warning threshold value, early warning information is sent out, and the early warning information is fed back to the digital twinborn model to be updated and optimized; the method has the beneficial effects that the early warning threshold value is automatically adjusted according to the historical operation data, the real-time monitoring data and the analysis simulation result of the dam body, the contribution of the historical operation data, the real-time monitoring data and the analysis simulation result is flexibly set by adjusting the weight and the coefficient of each part, and the early warning accuracy is improved. And the accuracy and the reliability of the early warning threshold are further improved.

The invention relates to the technical field of medical health information, and particularly discloses a heart rehabilitation training control method based on muscle force and electrocardiosignal real-time monitoring, which comprises the following steps: firstly, calculating real-time myoelectricity intensity to quantify muscle load, synchronously analyzing electrocardiosignals and respiratory frequency to obtain a cardiopulmonary state, and correcting physiological indexes in combination with a trunk motion state; then, a multi-parameter fusion result is generated through a weighted fusion algorithm, and the relevance between the muscle force load and the heart response is comprehensively reflected; then, the system judges the training intensity through a preset threshold model, generates a voice feedback instruction in real time to adjust the action of the patient, and adaptively updates training parameters to match the rehabilitation progress of the individual; meanwhile, the system continuously monitors multi-parameter changes, and automatically triggers an early warning mechanism when abnormal risks are detected, so that training safety is ensured. According to the method, the limitation that a traditional scheme depends on a single index is overcome, and personalized and dynamic heart rehabilitation training closed-loop control is achieved.

The invention relates to a glass flaw recognition method, system and equipment based on computer vision and a medium. The method comprises the following steps: firstly, acquiring original image data of a glass product, and performing feature extraction by adopting a deep learning model to obtain a defect candidate region set; extracting pixel-level boundary information from the defect candidate region set by using an instance segmentation algorithm, dividing a feature cluster after fusing multi-scale morphological features, and matching the feature cluster with a preset defect feature library to obtain a defect labeling result containing a defect category and a defect region feature vector; constructing a reference threshold model based on the defect category, and dynamically calibrating the model in combination with the defect region feature vector and the production condition parameter to generate a real-time detection threshold range; and performing secondary scanning on the original image data by using the real-time detection threshold range to generate a defect distribution map. According to the method, the accuracy of defect identification is improved, the adaptability to environment change is enhanced, and the glass defect identification task can be efficiently and stably completed.

The invention relates to the technical field of transportation, in particular to a vehicle emission anomaly detection and tracing method and device and electronic equipment, and the method comprises the steps: inputting a current operation condition and current operation data into a dynamic threshold model to obtain a first detection result, and inputting the first detection result into an emission prediction model to obtain a second detection result; fusing the first detection result and the second detection result to obtain a final detection result when the first detection result and the second detection result do not meet a preset vehicle emission abnormal judgment condition, judging that the vehicle emission is abnormal when the final detection result is greater than or equal to a preset threshold value, and judging that the vehicle emission is abnormal based on a vehicle emission abnormal knowledge graph database. And a Cosine similarity calculation strategy is utilized to obtain reasons and probabilities of abnormal emission. The problems that a traditional vehicle emission anomaly detection and tracing method is low in emission anomaly detection efficiency, high in misjudgment rate and difficult in tracing are solved, accurate detection of emission anomaly is achieved, closed-loop management from anomaly triggering to root cause tracing is completed, and operation and maintenance intelligence is improved.

The invention provides a data exception early warning method and device based on a dynamic threshold model. The method comprises the following steps: firstly, constructing an index time sequence library and an event library for storing index and event information; fusing the historical same-period base line, the trend term and the event influence value and generating a base line through a dynamic weight formula; calculating upper and lower limits of a dynamic threshold by combining the fluctuation acceleration and a basic fluctuation coefficient; and finally, comparing the real-time index value with a threshold range to judge whether to give an alarm, and optimizing the model or updating the event library according to a manual labeling result. According to the method, dynamic and accurate early warning of data exception can be realized, service scene changes can be adapted, the early warning accuracy is continuously improved through feedback optimization, and powerful support is provided for stable service operation and efficient exception disposal.

The invention discloses a key phase pulse signal synchronous acquisition and multi-axis centering analysis method, which belongs to the technical field of mechanical monitoring and diagnosis, and comprises the following steps of: configuring a synchronous acquisition system, acquiring a conditioned key phase pulse signal and a conditioned vibration signal, and extracting an axis speed and a load; constructing a time drift monitoring model to compensate the key phase pulse signal in real time, and ensuring the time synchronization precision; the compensation signal is input into a shaft centering correction model based on dynamics and finite element analysis, and radial deviation and axial deviation under the dynamic load are calculated; establishing a nonlinear tolerance threshold model by adopting a nonlinear regression model, and dynamically adjusting an allowable centering deviation threshold in combination with the real-time shaft speed and the load; through closed-loop comparison of the shaft centering deviation and a threshold value, a centering qualified conclusion or a correction suggestion is automatically output, and triggering signal resampling is supported to adapt to working condition changes; the real-time and self-adaptive analysis of the centering deviation of the multi-axis system is realized, and the operation stability and the maintenance efficiency of the rotating machinery are improved.

The invention discloses a medical image analysis method and system based on a visual language model, and belongs to the technical field of medical image intelligent diagnosis, and the system comprises an image preprocessing unit which carries out the down-sampling of an original retina OCT image to 256 * 256 and carries out the normalization of the original retina OCT image; the feature encoding unit comprises an image encoder based on RET Found in combination with LoRA optimization and a text encoder based on BioClinicalBERT; the class balance comparison learning unit is used for adjusting loss through class balance coefficients so as to relieve the class imbalance problem; the uncertainty estimation unit is used for calculating confidence quality and uncertainty scores based on Dirichlet distribution, and determining a threshold value in combination with an improved Youden index; and the model training unit adopts a total loss function of class balance loss and uncertainty loss, outputs a diagnosis result and an uncertainty score through transfer learning, and further comprises an image input module, a result display module and a data storage module. Rare disease classification performance and reliability are improved, training efficiency is improved through LoRA optimization, and an accurate and reliable scheme is provided for detection of the rare retina diseases.

The invention discloses a fault diagnosis method and system for a voltage regulation module, and belongs to the technical field of fault detection, and the method specifically comprises the steps: periodically collecting key operation parameters of the voltage regulation module, carrying out the preliminary anomaly detection of the collected key operation parameters through a multi-dimensional dynamic threshold model, and carrying out the fault diagnosis of the voltage regulation module; the initial anomaly detection is judged based on the deviation degree of the current operation state relative to a historical normal track, when the initial anomaly is judged, anomaly trend analysis in a time window is carried out, if the anomaly continuously exceeds a preset time threshold value, the current anomaly state is compared with a preset fault feature matrix, and if the anomaly does not continuously exceed the preset time threshold value, the current anomaly state is judged to be abnormal; identifying whether the voltage regulating module has a fault, and generating a corresponding fault label according to the identified fault type; according to the invention, latent, nonlinear or multi-dimensional coupling type faults can be accurately captured, the adaptive capability is high, early warning can be realized, and the operation reliability and maintainability of the voltage regulation module under complex working conditions can be significantly improved.

The invention relates to the technical field of natural disaster risk assessment, in particular to a multi-disaster disaster-inducing factor internal correlation analysis method, which comprises four steps of data preprocessing, Bayesian network correlation model construction, space-time dynamic evolution analysis and risk early warning threshold model construction. And deep coupling analysis of disaster-inducing factors of multiple disasters such as mountain torrent-debris flow and the like is realized. The method comprises the following steps: firstly, collecting 17 disaster-inducing factor data such as terrain and rainfall, performing dimensionality reduction through kernel principal component analysis, and calculating a dynamic weight by adopting an entropy evaluation method; then constructing a Bayesian network model to quantify conditional probability association; introducing a space-time attention mechanism to optimize a multi-scale coupling weight; and finally, establishing a coupling risk early warning threshold value and outputting a relevance intensity matrix. According to the method, the problem that a traditional single-disaster analysis method cannot capture the space-time linkage effect of disaster-inducing factors is solved, the risk early warning accuracy of multiple disasters is improved, and scientific decision support is provided for regional disaster prevention and reduction.

The invention discloses an equipment fault diagnosis method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The pre-training joint reasoning model is used for comprehensively analyzing the multi-dimensional data to generate an equipment fault probability prediction result, a composite fault mode can be effectively recognized, dynamic working condition changes can be adapted, meanwhile, tracing analysis is conducted on a fault propagation path in combination with the graph neural network, the effectiveness of feature extraction and modeling is improved, and therefore the fault probability prediction result is obtained. The technical problems that in an existing equipment fault diagnosis system, a threshold model is difficult to recognize a composite fault, a physical model cannot adapt to a dynamic working condition, and the fault traceability of a correlation analysis model is limited can be solved, and the purposes of improving the accuracy and adaptability of equipment fault diagnosis, enhancing the fault traceability and improving the reliability of equipment fault diagnosis are achieved. And the technical effect of promoting predictive maintenance to develop to higher-level intellectualization is achieved.

The invention discloses a global material intelligent management and control analysis platform based on multi-professional link data, relates to the technical field of material management, and solves the technical problems of insufficient consumption abnormality judgment accuracy and delayed inventory early warning and supply decision. According to the method, a weighted initial threshold model of historical data and business quota is innovatively constructed, a differential statistical method is adopted for periodic and non-periodic materials, meanwhile, a real-time influence factor is introduced, and a threshold is dynamically corrected through a weighting algorithm, so that the threshold can accurately adapt to business scene change, and the service scene change is accurately matched with the business scene change. The method effectively reduces the abnormal misjudgment rate and the missed judgment rate, achieves the precise monitoring of the consumption state, achieves the precise calculation of the basic remaining time, the scene-based remaining time and the safety buffer time through the scene-based consumption speed model and stock effective value measurement and calculation, achieves the scientific evaluation of the stock sufficiency through the combination with a preset threshold value, generates a purchase signal in advance, and improves the purchase efficiency. And the risks of stock overstock and material shortage are avoided.

The invention discloses an automatic testing method and system for credential equipment, and relates to the technical field of credential equipment test.The method comprises the steps that testing requirements input by a user are analyzed through a natural language interface, key parameters are extracted, historical cases are retrieved, and a structured testing requirement table is generated; based on the structured test demand table, constructing a multi-objective optimization model, and outputting a test strategy matrix after iterative updating by using an NSGA-III algorithm improved by chaotic mapping; performing feature extraction and anomaly detection on the test logs, and identifying anomaly features through a dynamic threshold model; constructing a fault propagation graph model based on the abnormal features, positioning a root cause node by applying an improved causal reasoning algorithm, and generating a strategy iteration scheme; the improved NSGA-III algorithm initializes a population and dynamic target space adaptation mechanism through chaotic mapping, so that the Pareto front coverage rate is improved, and meanwhile, in combination with a resource weight allocation formula of an aging-time decay factor, collaborative optimization of the test cost and the extreme scene coverage rate is achieved.

The invention relates to the technical field of intelligent agriculture and green plant protection, and discloses a corn tasseling period pest and disease damage green prevention and control system and method based on environment self-adaption, and the system comprises a prevention and control system which comprises an environment simulation unit, a pest and disease damage simulation database, a prediction analysis module, an early warning decision module and an acquisition unit. According to the method, a nonlinear mapping model of five-dimensional environment vectors (temperature, humidity, illumination intensity, rainfall and wind speed) and pest and disease damage activities is constructed, multiple regression analysis and Monte Carlo simulation are combined, pest and disease damage occurrence probability distribution under different climate scenes is generated, the limitation of a single threshold model is effectively overcome, and the method is suitable for popularization and application. The prediction reliability in a complex environment is obviously enhanced; according to the method, the disease and insect pest outbreak risk can be early warned 72 hours in advance, and the disaster loss is reduced by at least 30%; the dosage of chemical pesticides is reduced by more than 40% through a graded green prevention and control strategy, and the biodiversity of farmland and the ecological health of soil are protected.

The invention discloses a differential early warning method for a water turbine speed regulating system based on data driving, and aims to solve the problems of poor real-time performance, low accuracy, weak adaptability and lack of differential early warning of a traditional method. The method comprises the following steps: collecting data such as guide vane opening and unit frequency in real time, and preprocessing the data through median filtering and Kalman filtering; identifying working conditions by using a rule engine and fuzzy logic and processing according to the working conditions; extracting time domain and frequency domain characteristic parameters, and constructing statistical indexes; integrating statistics, nonlinearity and twinborn simulation to build a threshold model, and combining a production rule, a fault tree and a Bayesian network to build a rule model; and three types of early warning models are constructed, early warning levels are divided according to an expert scoring method, information is pushed, and a differentiation strategy is formulated. The method improves the early warning real-time performance and accuracy, adapts to complex working conditions, and guarantees the safe operation of the hydropower station.

The present invention relates to the technical field of quality management, and specifically provides a quality management method based on standard data. Specifically, it includes: constructing a dynamic standard database; collecting production data in real time; adopting a hybrid anomaly detection and dynamic interpolation strategy to handle data missing and noise, generating a standardized matching code based on hash and polynomial ring operations to ensure data consistency; determining quality deviations based on a dynamic threshold model and fuzzy logic, quantifying the deviation amount by combining the weighted Euclidean distance, realizing hierarchical early warning through a membership function, and triggering response measures according to the early warning level; using a scoring model and non-linear prediction to evaluate the effectiveness of measures and screening optimization plans; a closed-loop feedback mechanism continuously updates model parameters and the knowledge base. Through dynamic data integration, hybrid detection algorithms and matching code design, the present invention effectively solves the problems of inflexible static thresholds, single anomaly detection, and poor data consistency in traditional quality management, and significantly improves the detection accuracy and system robustness.

The invention belongs to the technical field of data analysis, and provides an abnormal data analysis method and system in a financial fast report scene based on artificial intelligence, and the method comprises the steps: comparing obtained target financial data with a pre-configured threshold value through employing a three-dimensional threshold value model, judging whether there is abnormal data or not according to a comparison result, and carrying out the analysis of the abnormal data in a financial fast report scene. The threshold value of the three-dimensional threshold value model is configured by integrating an industry financial benchmark, historical financial data of the enterprise and financial data of the enterprise in the current year; if the abnormal data exists, retrieving data associated with the abnormal data by using the updated knowledge graph, and identifying a business event having correlation with the abnormal data according to the retrieved data; and performing similarity analysis, statistical significance analysis and anomaly classification analysis on the abnormal data and the business event, and performing weighted fusion to obtain a final analysis result. The accuracy of financial abnormal data analysis in the prior art is improved.

The invention relates to the technical field of air separation production line health scoring, and provides an air separation production line health scoring method based on a multivariate heterogeneous model, and the method comprises the steps: S1, collecting the state data, working condition data and process data of an air separation production line; s2, in order to prevent the influence of abnormal data on the model construction of the system, carrying out abnormal value preprocessing on the collected data; s3, based on the preprocessed data, performing multi-dimensional early warning through adaptive threshold early warning, trend early warning and AI early warning; s4, after parameters in the air separation production line equipment trigger early warning, fault diagnosis is carried out only for rotary equipment and only in the vibration parameter or temperature parameter early warning scene of the bearing of the equipment; and S5, respectively calculating scores of the threshold model, the trend model, the A model and the mechanism model, and calculating a total health degree score of the system through weight configuration. The health management system for the space division industry is provided, and the overall operation condition of a production line can be effectively evaluated.

A power transformation equipment oil temperature prediction and abnormity early warning method based on a fuzzy algorithm comprises the following steps that real-time operation parameters and oil temperature historical data of power transformation equipment are collected, the data are preprocessed, and a high-quality input data set is constructed; constructing a fuzzy inference system considering multiple input factors, and setting a fuzzy membership function and an inference rule base; according to the fuzzy prediction result and the real-time oil temperature change, a self-adaptive dynamic safety threshold model is compared, a potential abnormal trend is identified, and automatic grading early warning is carried out according to the overtemperature grade; and corresponding exception type identifiers and disposal suggestions are generated and are pushed to the operation and maintenance platform through the communication module, so that remote operation and maintenance scheduling and response control are supported, and intelligent cooperative processing is realized. According to the invention, intelligent prediction of the oil temperature change trend under the influence of multiple factors is realized, the capability of sensing temperature rise abnormity in advance is improved, the accuracy of sensing the operation state of the equipment and the timeliness of early warning response are also remarkably improved, and the risk of equipment failure caused by the abnormal oil temperature is reduced.