121 results about "Threshold model" patented technology

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 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 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 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 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 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.

The invention discloses a multi-device state sensing driven intelligent storage abnormity early warning method, and relates to the field of storage cargo management, and the method comprises the steps: 1, collecting multi-source data through a multi-source sensor node, including odor intensity, volatile organic compound concentration, carbon dioxide or carbon monoxide content, and environment temperature and humidity, meanwhile, physical coordinates of the multi-source sensor nodes are obtained; 2, acquiring historical multi-source data in advance, constructing a dynamic threshold model based on the historical multi-source data, calculating odor fluctuation, a gas combination abnormal index and a temperature and humidity coupling risk value according to the multi-source data, and introducing time evolution trend analysis to obtain a gas combination abnormal index and a temperature and humidity coupling risk value; and a dynamic threshold model is combined to identify risk signals in the odor fluctuation degree, the gas combination anomaly index and the temperature and humidity coupling risk value, a space attenuation model is adopted to generate a three-dimensional anomaly thermodynamic diagram based on the physical coordinates of the multi-source sensor node and the risk signals, and a high-risk area is positioned.

The invention discloses a state monitoring method for a ring main unit, and relates to the technical field of dynamic monitoring. The method comprises the following steps: collecting and preprocessing historical data of ring main unit equipment; constructing an environment load dynamic threshold model by using a meta-learning algorithm, and after historical data training, inputting real-time environment data to obtain a threshold; comparing the real-time electrical data with a threshold value, judging abnormity and diagnosing a fault; acquiring an internal image of the ring main unit, and processing by using a multi-scale denoising method; constructing a model based on an open set recognition algorithm, and inputting a de-noised image to recognize physical defects; constructing a three-dimensional model according to the design drawing, and performing spatial registration with the physical defect to determine a physical fault; and grading early warning is carried out by integrating electrical and physical fault results. According to the invention, state monitoring of the ring main unit is realized through electrical and physical fault results.

The invention relates to the technical field of simulation, in particular to an infrared weapon perception capability simulation judgment method and system based on radiation transmission dynamic attenuation, equipment and a medium, and by adopting the method or system provided by the invention, attenuation intensity values of aircraft tail flame and infrared decoy projectile radiated to an attacking object through an atmospheric environment are dynamically calculated in real time; and comparing the calculated results, and comparing the compared maximum value with the perceived threshold value of the attacking object seeker. If the perceiving threshold value of the attacking object seeker is exceeded, the attacking object can perceive the target so as to hit the target; and if the perceiving threshold value of the attacking object seeker is not exceeded, the attacking object cannot perceive the target, so that the target is off-target. A more refined model is established through a dynamic infrared sensing model fusing three-dimensional attenuation of distance, time and atmosphere, so that a simulation result is more accurate compared with a traditional static threshold model.

The invention relates to the field of foundation construction monitoring, and discloses a multi-sensor fusion tubular pile sinking process real-time analysis system. The data acquisition and preprocessing module is connected with the sensor group; the real-time analysis module is connected with the data acquisition and preprocessing module and comprises a feature analysis sub-module used for generating a dynamic time sequence feature vector; the health diagnosis submodule is used for diagnosing the health state of the pile body; the dynamic threshold determination sub-module is used for determining a dynamic judgment threshold; a comprehensive judgment sub-module; the early warning module is connected with the real-time analysis module; and the data storage and visualization module is connected with the real-time analysis module. A dynamic early warning threshold value model is constructed through real-time geological changes and construction stages, the problems of false alarm and missing alarm caused by the fact that traditional fixed threshold value early warning cannot adapt to variable geology and working conditions are solved, the accuracy and reliability of early warning judgment are greatly improved, and the system can accurately recognize real risks.

The invention provides a dynamic threshold adjustment method based on a self-adaptive alarm rule engine, and belongs to the field of intelligent power grids, and the method comprises the steps: collecting equipment operation data in real time by an edge node, and building a personalized threshold model; the central server initiates a model aggregation request according to a preset period, each edge node uploads a personalized threshold model parameter increment, calculates an aggregated global model parameter, adds noise to the aggregated global model parameter to obtain the global model parameter, and establishes a global model; inputting the standardized feature vector into a global model to obtain a prediction probability, and calculating a basic threshold according to the prediction probability; if the prediction probability exceeds a basic threshold value, generating a weight vector for the feature vector after standardization processing to obtain a weighted feature; calculating an anomaly degree, calculating a smoothness anomaly degree, calculating an adjustment factor, and adjusting the basic threshold value to obtain a dynamic threshold value; the invention further provides a dynamic threshold adjusting system. The problem that a traditional fixed threshold value cannot cope with equipment parameter volatility enhancement is solved.

The invention relates to the technical field of big data mining, and particularly discloses a discrete particle swarm optimization-based influence maximization method on a hypergraph, which comprises the following steps of: S1, constructing a hypergraph model and a propagation rule: defining a hypergraph which is a node set and a hyperedge set, and satisfying hyperedges; a threshold model is adopted to describe the propagation process, a seed node set is activated at the beginning, other nodes and all hyperedges are not activated, the non-activated hyperedges are traversed, if the proportion of the activated nodes in the hyperedges is larger than or equal to a threshold value, all the nodes in the hyperedges are activated, and the process is repeated until no new hyperedges are activated; s2, initializing a particle swarm; s3, performing two-layer local influence evaluation; s4, particle speed and position updating; s5, local search optimization; and S6, iteration is terminated. By adopting the technical scheme of the invention, the global search capability, the convergence speed and the evaluation precision can be balanced, and efficient and accurate identification of high-influence nodes in the hypergraph is realized, so that the influence propagation effect and the algorithm expandability are improved.

The invention relates to the technical field of monitoring modeling, in particular to an electrostatic precipitation smoke emission dynamic modeling method based on multi-parameter coupling analysis, which comprises the following steps: calculating electric field rate and acceleration through sliding window difference, judging electric field abrupt change trigger parameter adjustment, normalizing particle size and charge density, and mapping disturbance intensity index. And calculating response delay and a gradient field to generate a disturbance coefficient, transferring a model weight when the disturbance exceeds a threshold value, performing fitting reconstruction, and performing regression weight reverse normalization to output a steady-state parameter group. According to the method, electric field dynamic characteristics are captured through equal-interval sampling in combination with a sliding window, an acceleration super-threshold model is constructed through second-order difference, a dynamic adjustment mechanism is triggered, particle size and charge density are normalized, disturbance intensity is mapped through linear interpolation, speed and density abrupt change are quantized through gradient weighting, and weight transfer and a least square method are introduced to reconstruct parameters. The transient analysis capability and robustness of the model are improved, and high-precision synchronous mapping of the emission trend and the equipment state is realized.

The invention provides an underground coal mine unmanned aerial vehicle dynamic path planning system. Comprising a map construction unit, a risk prediction unit, a cost calculation unit, a path planning unit and an execution feedback unit. The system collects underground environment information through a multi-wire-harness laser radar, a depth camera, a gas sensor and an inertial measurement unit, constructs a real-time three-dimensional voxel map through point cloud splicing and inertial navigation fusion, and records gas concentration, temperature, dust density and SLAM uncertainty; according to the system, gas concentration, temperature, dust density and SLAM uncertainty are continuously recorded by using a three-dimensional voxel map, and a future delta t-oriented space-time risk field is constructed through a gas convection diffusion model, a temperature time sequence model and a micro-seismic threshold model, so that the unmanned aerial vehicle can know a risk evolution trend in advance in a path planning stage; and the problem that in a traditional scheme, a person enters a future high-risk area by mistake while dodging local obstacles is avoided.

The invention provides a ground GNSS differential data correction method and device, and the method comprises the steps: obtaining a pseudo range and a carrier phase, calculating a whole cycle jump value of the carrier phase through employing an M-W combination function and an ionosphere delay change rate, and correcting a carrier phase value through employing the calculated whole cycle jump value of the carrier phase. The influence of station-satellite geometric distance is eliminated, the influence of ionosphere change is considered, and the method is suitable for cycle slip detection in a dynamic environment; according to the method, a self-adaptive threshold model is constructed on the basis of Turbo Edit cycle slip detection in a flight dynamic observation environment, cycle slip misjudgment at a low satellite elevation angle is reduced, and the influence of too low sampling rate on GF combined cycle slip detection performance is weakened to a certain extent; and a technical guarantee is provided for applying the GNSS differential data to the flight test.

The invention provides a method and a system for identifying fault data of a power transformer. The method comprises the following steps: acquiring online monitoring data of multi-dimensional operating parameters of the power transformer from a data input interface; effective monitoring data generated after the online monitoring data are preprocessed are input into a dynamic threshold value model for self-adaptive threshold value calculation, a threshold value calculation result is obtained, and the effective monitoring data with the threshold value calculation result not in a set dynamic threshold value range are marked as first-level abnormal data; performing physical constraint dual verification on the first-level abnormal data, and marking the first-level abnormal data which does not pass at least one verification as second-level abnormal data; and inputting a feature vector generated by feature extraction of the secondary abnormal data into a constructed noise classification model for noise type identification, and outputting a final identification result according to a confidence value and a noise type output by a noise classification module. According to the method and the system, redundant alarms are greatly reduced, and the fault positioning accuracy is improved.

The application belongs to the technical field of information technology, and specifically discloses an enterprise financial management auxiliary method and system based on artificial intelligence, wherein the method comprises the following steps: obtaining multi-layer data from transaction records, processing characteristic differences according to hierarchical classification, and obtaining hierarchical financial data sets; analyzing distribution rules by using the hierarchical financial data sets, adjusting a unified threshold range if the distribution shows loose and missed report signs, and determining an optimized threshold group; monitoring strict false report conditions according to the optimized threshold group, fusing time evolution factors by using a self-adaptive threshold algorithm, and obtaining a dynamic threshold model; and obtaining risk degree indexes in the dynamic threshold model; the application aims to solve the problem in the prior art that how to establish different abnormal judgment standards that can be continuously self-adjusted with time and detection effect according to the unique distribution characteristics of different hierarchical financial data and specific business scenarios, so as to become a truly intelligent and efficient enterprise financial risk early warning system.