127 results about "Bayesian fusion" patented technology

The invention discloses a thunder and lightning monitoring and early warning method and system based on multi-source data fusion, and relates to the technical field of thunder and lightning early warning, and the method comprises the steps: extracting electric field time domain and frequency domain features, magnetic field change features, lightning activity modes and meteorological change features through obtaining atmospheric electric field, magnetic field, lightning activity and meteorological environment data in real time; and constructing multi-source feature data. A time sequence analysis and Bayesian fusion technology is adopted to calculate a correlation weight between data sources, and a fusion feature vector is generated. And establishing a weighted regression model based on the vector, calculating thunder and lightning occurrence probability through dynamic weight distribution, and generating a risk distribution map in combination with geographic information. The method has a closed-loop feedback optimization mechanism, model parameters and weights can be adaptively adjusted according to prediction errors and early warning accuracy, the accuracy, timeliness and environmental adaptability of lightning early warning are improved, and the method is widely applied to the fields of electric power, aviation, buildings and the like.

The invention belongs to the technical field of bridge monitoring, and relates to a system and a method for fusing multi-source data of a bridge structure. Comprising a heterogeneous topological graph construction and manifold embedding technology module, a multi-scale space-time cognitive convolutional neural network module, a continuous manifold space-time alignment and Bayesian fusion module and a structure health index calculation and state evaluation module. The heterogeneous topological graph construction and manifold embedding technology module is used for obtaining a heterogeneous topological graph, a node embedding vector and a manifold model parameter; the multi-scale space-time cognitive convolutional neural network module is used for performing deep feature extraction on the heterogeneous topological graph to obtain multi-scale fusion features; the continuous manifold space-time alignment and Bayesian fusion module is used for obtaining space-time alignment parameters and fusion state vectors; the structure health index calculation and state evaluation module is used for carrying out structure health monitoring and state evaluation on the bridge to obtain a final health evaluation result; therefore, the intelligence, automation and reliability levels of the structure monitoring system are improved.

The invention relates to the technical field of data processing, and discloses a geological disaster meteorological risk early warning method and system based on machine learning. The method comprises the following steps: acquiring rainfall intensity, soil saturation, underground water level change and slope runoff coefficient by a multi-source sensor, and constructing a geological disaster meteorological data set; performing sensitivity weight distribution on the meteorological factors according to geological conditions to obtain a weight matrix; carrying out weighted fusion on the weight matrix and meteorological time series data, and extracting features through a geological constraint long-short-term memory network to obtain a risk probability vector; dynamically adjusting an early warning threshold value based on the safety coefficient change rate; and carrying out Bayesian fusion on the risk probability vector and an adaptive early warning threshold to obtain a graded early warning result. The technical problem that an existing geological disaster early warning technology lacks a multivariate meteorological factor intelligent weight distribution and geological condition adaptive threshold adjustment mechanism is solved.

The invention discloses a water body new pollutant risk assessment method and system. Belongs to the technical field of environmental science. Comprising the steps of collecting multi-source heterogeneous data to generate an initial data set; performing data fusion in the initial data set by using a weighted Bayesian fusion algorithm and a space-time interpolation method to generate a target data set; importing the target data set into a deep learning model for prediction and obtaining a prediction result; and obtaining a preset early warning condition and combining with the prediction result to judge whether risk early warning needs to be carried out. Through constructing a pollutant data acquisition system and data fusion, the monitoring precision of the new pollutants in the water body is improved. The fusion algorithm is used for data preprocessing, and pollutant detection is comprehensive. And the space-time diffusion process of the pollutants can be accurately modeled.

The invention relates to the technical field of safety management and risk assessment, and discloses a laboratory safety intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, building a three-dimensional digital model of a laboratory; s2, deploying a sensor network covering a laboratory; s3, monitoring an operation behavior in real time through a sensor network, and comparing the operation behavior with the operation logic chain; s4, when the sensor network detects that the physical environment is abnormal; s5, collaborating the logic abnormal state, the position and strength of the risk source and the risk type; and S6, performing evolution prediction based on the risk type and the risk source. According to the method, Bayesian fusion reasoning is performed on context information such as multi-dimensional sensor evidence and real-time operation regulations, so that high-precision and high-confidence identification of risk types is realized, and the problem that the risk types cannot be identified due to lack of cognition on field operation activities is solved. And the risk qualitative is fuzzy, the false alarm rate is high, and the real dangerous case and compliance operation interference cannot be distinguished.

The invention relates to the technical field of energy consumption prediction, in particular to an energy consumption prediction method and system for coping with compressor driving, and the method comprises the following steps: collecting the operation data of a compressor through a plurality of sensors, and carrying out the data processing of the collected data through the combination of weighted Bayesian fusion and the dynamic adjustment of the weight of each sensor; constructing an energy consumption prediction model through the features captured by the multi-layer capture mechanism; a feedback adjustment mechanism is formulated to perform real-time monitoring on energy consumption prediction errors of an output result of the energy consumption prediction model, parameters of the model are automatically adjusted according to monitored error feedback information, and the adaptability of the energy consumption prediction model to real-time working conditions is improved; through incremental learning and a feedback regulation mechanism, the energy consumption prediction system can adapt to the change of the working condition of the compressor in real time; an energy consumption prediction model constructed through a multi-layer capture mechanism can capture time sequence dependence from the forward direction and the reverse direction of data at the same time, and capture the long and short time dependence relation of compressor energy consumption data.

The invention discloses a ground fracture extraction deep learning method based on direction perception and Bayesian fusion, which solves the problem of feature fracture caused by the fixed form of the traditional convolution kernel by constructing a direction priori knowledge extractor, strengthening the perception ability of a network to the direction and adjusting the receptive field along the fracture trend by adopting the dynamic deformation convolution kernel. And a Bayesian fusion module is designed, the weights of the global semantic features and the direction perception features are dynamically distributed based on a Bayesian probability model, the fusion effect between different features is optimized, and compared with other deep learning methods, the ground fracture extracted by the method is more complete and can adapt to a mining area environment with relatively complex geological conditions.

The invention provides an exoskeleton mountaineering intention recognition method and system and a storage medium, and belongs to the technical field of exoskeleton robot control, and the method comprises the steps of signal preprocessing and compensation, hierarchical feature extraction, multi-modal fusion recognition, control output, safety protection and the like. The method comprises the following steps: performing 50Hz power frequency filtering and 20-450Hz band-pass filtering on an electromyographic signal, and compensating signal distortion caused by muscle deformation by combining IMU (Inertial Measurement Unit) acceleration data; a dynamic four-dimensional channel is adopted to preferably extract myoelectricity features, and an improved attention mechanism model is utilized to extract multi-scale features; fusing multi-modal information such as myoelectricity and terrains based on a Bayesian fusion framework to realize intention recognition; meanwhile, triple safety protection strategies of joint angle hard limiting, falling detection and protection and signal loss fault-tolerant processing are set; the accuracy and control speed of motion intention recognition are improved, good safety performance is achieved, and the auxiliary effect and reliability of the exoskeleton in the mountaineering motion are effectively improved.

The invention discloses an intelligent fishing point dynamic prediction method and system based on multi-source marine environment data fusion. The method comprises the following steps: step 1, access, space-time alignment and pre-screening of multi-source heterogeneous marine environment data; step 2, priori knowledge base construction and suitability modeling based on target fish ecological habits; 3, constructing a fishing point prediction model fusing the multi-time-sequence environmental characteristics and deep learning; step 4, fusing two-channel prediction results under the Bayesian framework and quantifying uncertainty; 5, generating a dynamic mask of a real-time sea condition safety threshold value and fishery regulation space constraint; 6.1, constructing a comprehensive scoring function of the risk perception function. According to the method, multi-source heterogeneous data is constructed, a target fish ecological suitability model and a depth time sequence prediction model are combined, the fishing point posterior probability is generated through a Bayesian fusion mechanism, the prediction uncertainty is quantified, and dynamic fishing point recommendation with risk perception and compliance safety is realized.

The invention provides a model and data driving fused mechanical arm gravity compensation algorithm, and aims to solve the problem of poor mechanical arm gravity compensation precision caused by model uncertainty. According to the algorithm, firstly, a statics equation is established, secondly, multiple data are trained through a Bayesian neural network (BNN), a predicted torque and a standard are generated, then, through a Bayesian fusion method, a model calculation result and a neural network fitting result are subjected to Bayesian fusion according to a standard deviation, and a fusion estimation torque is generated. In order to solve the problem that the generalization ability of a data driving model is insufficient in an unseen scene, the algorithm uses a physical model as a constraint condition to limit the range of fusion torque, high deviation caused by insufficient model generalization is reduced, and then stability is ensured. Compared with a traditional modeling method, the fusion strategy is combined with the self-adaptability of physical constraint and data driving, the influence of modeling errors on the control precision is effectively reduced, and the precision and robustness of gravity compensation are remarkably improved.

The invention relates to the technical field of automobile electronics and intelligent control, and discloses an intelligent cabin mechanical arm system which comprises a folding mechanical arm body, a tail end sensing interaction module, a driver state monitoring subsystem and a central control unit which are installed in a cabin. The central control unit controls the storage or working mode of the mechanical arm according to the comprehensive fatigue index of the driver; in the working mode, visual gazing information is used as a gating signal to activate electroencephalogram analysis, the operation intention is confirmed in combination with the visual prior probability and electroencephalogram signals, and the mechanical arm impedance parameters are adjusted in real time based on the fatigue state. According to the method, through Bayesian fusion of vision and electroencephalogram signals, the problem that single-mode control is unstable is solved; by means of variable impedance control and collision detection of the momentum observer, active safety protection based on the physiological state of the driver is achieved, and non-sensitive and high-safety intelligent auxiliary experience is provided in a limited cabin space.

The invention relates to the technical field of information security, in particular to a network security event association detection method based on big data analysis. Comprising the following steps: data acquisition; feature extraction and fusion; correlation detection is carried out, wherein an improved Apriori-Bayesian fusion algorithm is adopted, and discretization processing is carried out on the event feature vectors; mining a frequent item set by using an improved Apriori algorithm; and risk assessment and result output. According to the method, an improved Apriori-Bayesian fusion algorithm is adopted, discretization processing is carried out on event feature vectors according to types, meanwhile, a security event weight factor is introduced to calculate the item set weighted support degree, and a minimum support degree threshold value is dynamically adjusted to mine a frequent item set; the association confidence coefficient is calculated in combination with the Bayesian network, and the confidence coefficient is corrected through the space-time association coefficient, so that the association relationship between the network security events can be scientifically judged, the problems of limited association judgment accuracy and lack of quantitative correction in the traditional technology are solved, and the association false alarm and missing report probability is reduced.

The invention discloses a power transmission and transformation project geological zoning automatic adjustment method and system, and relates to the technical field, and the method comprises the following steps: obtaining multi-source geological data, carrying out the data standardization preprocessing, and obtaining a high-dimensional feature data table from the preprocessed data based on a multi-source Bayesian fusion model; performing feature construction and standardization processing on the high-dimensional feature data table to obtain a high-dimensional geological feature set; performing feature compression on the high-dimensional geological feature set based on VAE, and performing unsupervised clustering based on GMM to construct a deep clustering model; controlling the power transmission project system to perform adaptive geological region division through a deep clustering model, and generating a path control strategy set based on reinforcement learning; according to the method, adaptive intelligent adjustment of the geological division and closed-loop optimization of a path control strategy are realized by calling a deep clustering model and reinforcement learning path optimization method, and the problems that the geological division is high in static property and lacks a real-time feedback mechanism and path control is not deeply coupled with a geological structure are solved.

The embodiment of the invention discloses a self-propelled hyperspectral imaging control method and system. The method comprises the following steps: based on an ultra-wideband wireless multilateral positioning method, acquiring ultra-wideband positioning information corresponding to a self-propelled hyperspectral imaging platform, and acquiring relative positioning information and a platform high-frequency vibration signal; based on a Bayesian fusion method, carrying out weighted fusion processing on the ultra-wideband positioning information and the relative positioning information; obtaining deformation data corresponding to the platform based on strain gauges arranged at a plurality of key positions of the platform; and according to the fused positioning information, the deformation data and the high-frequency vibration signal of the platform, performing adaptive closed-loop control on the platform so as to complete path correction of the platform, structural deformation offset, intelligent damping design between a vibration sensor and a pneumatic tire, and vibration compensation processing on a hyperspectral imaging module of the platform. According to the embodiment of the invention, the imaging precision is improved while the navigation positioning precision is improved.

The invention relates to the technical field of charging pile intelligent scheduling, and discloses a multi-model power transmission line channel three-dimensional fusion method, system and device based on BDF Bayesian data fusion and a medium, and the method comprises the steps: obtaining multi-source power transmission line data; performing data fusion on the registered multi-source data through a BDF Bayesian fusion strategy; generating a three-dimensional fusion model of the power transmission line channel; constructing a three-dimensional grid model, and carrying out object recognition through a deep learning algorithm to obtain various obstacles in the power transmission line channel; and constructing a multi-factor weighted scoring model in combination with the geometric features of the obstacles and meteorological data to obtain risk scores of the obstacles and perform hidden danger analysis. When data in a complex environment is processed, data inconsistency and noise interference are overcome, and the precision and robustness of the model are improved; the method effectively processes data changes in a dynamic environment, and adapts to various change factors in a complex scene.

The invention discloses a multi-source three-dimensional geological data fusion and management method and related equipment, and relates to the technical field of geological information, and the method comprises the steps: obtaining standardized geological data; fusing the standardized geological data based on a Bayesian fusion framework; constructing a multi-scale three-dimensional geologic model by adopting a potential field interpolation method based on probability constraints provided by posterior probability distribution; performing uncertainty quantification on the multi-scale three-dimensional geologic model by adopting a Monte Carlo simulation method; and storing the multi-scale three-dimensional geologic model and the uncertainty quantification result to a data management platform conforming to an OGC standard, and providing visualization, query and access of a geologic map layer through a Web service interface. According to the method, the sharing efficiency, the reuse value and the application convenience of the geological result data are greatly improved, and the intelligent level and the decision support capability of geological data analysis are enhanced on the whole.

The invention discloses a geological settlement monitoring method and system fusing deep learning and multi-source data, and relates to the technical field of geological settlement, and the method comprises the steps: collecting multi-source settlement observation and driving data, generating a settlement risk area mask based on historical records, and carrying out the self-adaptive grid division; a multi-source settlement field is generated through Kriging interpolation, and a driving factor grid field is mapped; performing spatial pyramid and time multi-scale decomposition on the settlement field and the driving factor to obtain a time-space sub-band; performing Bayesian fusion based on the sub-band confidence weight to obtain a fusion settlement field; inputting the fusion field and the driving factor into a deep learning model to train a multi-scale prediction sub-model, and reconstructing a global continuous settlement prediction field through cross-scale consistency; and dynamically optimizing the risk mask and the grid according to a prediction result to realize iterative monitoring. The problems of difficulty in multi-source data fusion, spatial scale heterogeneity and difficulty in accurate prediction of local high-risk area settlement in geological settlement monitoring are solved.

The invention relates to the field of power system automation, and discloses an intelligent power distribution network feed automation control system based on multi-source information fusion, which comprises a multi-source sensor module used for collecting current data in a power distribution network in real time, and an information fusion module used for receiving the preprocessed current data and sending the preprocessed current data to the power distribution network. The multi-source fusion module is used for carrying out multi-source fusion processing on the current data by adopting a Bayesian reasoning algorithm, the fault positioning module is used for receiving a fusion result, and the optimal control module is used for receiving a grounding fault occurrence position and adopting an optimal control theory and a dynamic planning algorithm; the invention further discloses an intelligent power distribution network feed automatic control method based on multi-source information fusion. The method comprises the following steps of an acquisition stage, a fusion stage, a positioning and decision-making stage and an execution and self-healing stage. According to the invention, by constructing the Bayesian fusion and dynamic programming control mechanism, the purposes of more accurate ground fault positioning, more efficient strategy removal and more adaptive response process are achieved.

The invention discloses an underground packer coupling integrity diagnosis system and a digital twinborn life prediction method. The system comprises a data acquisition layer, an edge computing layer, a cloud analysis layer with a built-in L4-level digital twin system and an application layer which are connected in sequence. According to the method, vibration, corrosion and other parameters are collected through a sensor, after preprocessing of an edge layer, a cloud calls a three-field coupling dynamic model, multi-field parameters, the failure probability and the remaining life are calculated in combination with an LSTM and Bayesian fusion algorithm, and an application layer outputs diagnosis results and maintenance suggestions and forms a closed loop. The problems that traditional methods depend on experience, multi-field coupling is difficult to quantify and the digital twinborn level is low are solved, the maintenance cost can be reduced, the well control accident risk is reduced, and a key technical support is provided for predictive maintenance of an oil and gas well.

The invention relates to the technical field of energy storage battery management, and provides an energy storage battery management system based on Bayesian fusion and model prediction control, and the system comprises a data collection and preprocessing module which collects the voltage, current and temperature data of an energy storage battery pack in real time; the electrochemical model prediction module is used for updating parameters by adopting a recursive least square method based on a second-order RC equivalent circuit model to obtain a capacity prediction value and prediction uncertainty; the data driving model prediction module is used for obtaining a capacity prediction value and prediction uncertainty through Monte Carlo dropout reasoning; the Bayesian fusion module is used for calculating an evidence weight based on the prediction uncertainty and carrying out Bayesian fusion on the capacity prediction value; and the model prediction control module is used for solving an optimal equalization strategy based on the fusion capacity prediction value and generating a control instruction to realize intelligent equalization scheduling of the energy storage battery pack. According to the invention, the accuracy of energy storage battery capacity prediction is improved, and adaptive battery management under complex working conditions is realized.

The invention relates to a plateau energy storage battery monitoring method and system based on physical data dual drive, and the method comprises the steps: firstly constructing a battery internal resistance physical model considering plateau environment correction, predicting the internal resistance in real time, and generating a failure prior probability; meanwhile, a support vector machine (SVM) and a random forest (RF) are used for constructing a data driving model, battery operation characteristics are extracted, and fault probability and uncertainty are output. On the basis, a Bayesian fusion method is used, the weights of a physical model and a data driving model are dynamically adjusted, the battery failure posterior probability is calculated, and the total uncertainty is quantified. And finally, setting a decision threshold, and when the posterior probability exceeds the threshold, triggering early warning and taking protection measures. The method effectively fuses the theoretical advantages of a physical model and the actual data mining capability of a data driving model, adapts to the plateau environment, and provides guarantee for safe and stable operation of the energy storage battery of the plateau photovoltaic power station.

The invention provides a Bayesian fusion-based 4D millimeter wave radar point cloud enhancement and target detection method, and the method comprises the steps: 1, projecting a 4D millimeter wave radar point cloud to an image plane, carrying out the matching of an image instance segmentation result, and obtaining an image semantic category and image segmentation confidence; 2, constructing a class conditional probability density model based on the physical characteristics of the radar points; based on a Bayesian probability framework, carrying out weighted fusion on the image segmentation confidence and the class probability density to obtain fusion confidence; 3, taking the fusion confidence and semantic category codes as augmented features, and performing feature splicing on the augmented features and the 4D millimeter-wave radar point cloud to generate a semantic enhanced point cloud; and sampling the semantic enhanced point cloud by adopting a sampling method fusing confidence guidance, and inputting the sampled semantic enhanced point cloud into a detection network for training and reasoning. According to the method, the technical problems of unidirectional dependence, shallow geometric matching and insufficient utilization of radar physical characteristics in the existing image and radar point cloud fusion technology are solved.

The invention discloses a dynamic task unloading method and system based on multi-dimensional perception, and relates to the technical field of edge computing and cloud computing collaborative optimization, and the method comprises the steps: carrying out the real-time monitoring of four key states of communication, computing power, energy consumption and environment of an intelligent video monitoring task of an intelligent construction site, and constructing a comprehensive unloading judgment system; respectively calculating a communication stability coefficient, a computing power congestion coefficient, an energy consumption cost coefficient and an environment perturbation sensitivity coefficient, performing evaluation, and automatically generating labels such as communication abnormity high risk, computing power insufficiency, energy consumption disqualification and environment perturbation; and then multi-source state comprehensive judgment is realized based on weighted Bayesian fusion, short-time resource trend prediction is carried out in combination with a lightweight GRU model, and finally, local, neighborhood and cross-domain optimal unloading decisions are formed. According to the method, intelligent switching of task unloading can be realized in a dynamic and sudden interference construction site environment, and the execution stability, the resource utilization efficiency and the energy consumption control capability of a video monitoring task are improved.

The invention discloses a narrow-body self-walking operation platform battery pack charging control method, and belongs to the technical field of battery management. Operating parameters of a lithium battery pack and working condition parameters of a working platform are collected in real time, corresponding charging stages are selected according to charge states and working condition types, and the charging process is divided into five stages including pre-charging, quick charging, optimized charging, equalizing charging and floating charging maintenance. A three-dimensional temperature field model is established based on multi-point temperature data, and a comprehensive temperature compensation coefficient is calculated to accurately adjust the charging current. The state of charge and the state of health of the battery are jointly estimated by adopting extended Kalman filtering and a long-short term memory neural network, and the estimation precision is improved through Bayesian fusion. The inclination angle and vibration acceleration of the platform are monitored in real time, and charging is adjusted or stopped according to safety conditions. The charging time is remarkably shortened, the charging efficiency is improved, the battery temperature rise is reduced, the battery cycle life is prolonged, and meanwhile the energy utilization efficiency and safety of the operation platform are improved.

The invention discloses an unmanned aerial vehicle load target tracking system and method based on multi-sensor fusion, and the method comprises the steps: obtaining visible light, infrared and laser radar data, carrying out the time sequence synchronization and attitude calibration, and obtaining a target fusion estimation value through a dynamic weighting Bayesian fusion algorithm; the improved Siamese neural network is used for feature matching, the LSTM trajectory predictor is combined to output a short-term motion state, and finally the adaptive mechanical control module drives the holder to realize stable tracking. According to the invention, the robustness, precision and continuity of target tracking in a complex environment are effectively improved.

The invention provides an operation state prediction method and system for a distribution network backup power supply. The running state prediction method of the distribution network backup power supply comprises the following steps: acquiring various target power supply parameters in real time, and performing fusion processing on the various target power supply parameters by adopting a Bayesian fusion algorithm to obtain a posterior decision probability; predicting working condition data of the distribution network backup power supply based on the target power supply parameter and the posterior decision probability; and further predicting the future operation state of the distribution network backup power supply according to the working condition data and outputting a prediction result. According to the method, the operation information of the distribution network backup power supply can be comprehensively acquired, association and uncertainty among various parameters are considered, dynamic computing power support can be carried out under different working conditions, and the future operation state of the backup power supply can be evaluated more comprehensively and accurately; besides, dynamic changes of various factors are considered, so that configuration, dynamic management and self-healing processing of the backup power supply are more scientific, and the problem of resource waste or insufficient power supply is avoided.

The invention discloses a bidirectional information retrieval enhancement generation method for a large language model, and belongs to the technical field of artificial intelligence. In order to overcome the defects that noise is introduced and key evidences are omitted due to the fact that traditional RAG only executes'query-document 'one-way retrieval, a two-way semantic perception retrieval enhancement generation model and a two-stage training framework are constructed, wherein in the first stage, the positive / negative example distance is increased in an embedded space in a contrast learning self-supervision mode; in the second stage, fine-grained correlation discrimination is carried out on query-document bidirectional sentences through supervised dichotomy, and probabilistic correlation scores are output; in the reasoning stage, the bidirectional probabilities are fused according to Bayesian to obtain final relevancy, document reordering is carried out, and plug and play can be achieved without fine adjustment of LLM in the whole process. According to the method, the accuracy and consistency of single-hop and multi-hop questions and answers and fact checking tasks are remarkably improved, and the method has the advantages of light weight and low deployment cost.

The invention discloses an unmanned aerial vehicle visual navigation method based on infrared semantic feature matching and inter-frame anchoring, and aims at the core requirement of unmanned aerial vehicle autonomous navigation in a GNSS denial environment, the stability of infrared imaging and the robustness of high-level semantic features are fully combined, and the high-precision, continuous and stable visual autonomous navigation capability is realized. The method is especially suitable for complex scenes such as low-illumination, weak-texture and large-area homogenized areas at night. Through innovation in the aspects of infrared semantic map construction, deep semantic feature extraction, multi-scale semantic matching, semantic anchoring key frame selection, inter-frame recursion positioning and closed-loop correction, Bayesian fusion estimation longitude and latitude solution and the like, the positioning precision and robustness of the unmanned aerial vehicle in GNSS denial, low-visibility and large-area homogeneous scenes are remarkably improved.

The invention provides a fault diagnosis method fusing adaptive wavelet threshold denoising and auto-encoder contribution degree weighting. The method comprises the following steps: carrying out wavelet denoising and standardization processing on training data in a training set based on an adaptive threshold function; using the trained residual error neural network ResNet to extract off-line related features; establishing an MSDAE off-line detection model, taking off-line related features corresponding to a plurality of modes as input for training, and calculating SPE and SPE control limits; acquiring an online test set, extracting online fault related features by using the trained residual neural network ResNet, calculating a Bayesian fusion index BIP according to the online fault related features, and judging whether online data have faults or not; and when a fault occurs, calculating the local linear propagation contribution degree of each process variable in the online data, establishing a contribution heat map, and performing fault diagnosis on the online data according to the contribution heat map. According to the invention, high-frequency noise components can be effectively filtered, and key variables causing process anomalies can be accurately positioned.

The invention discloses a zoning collaborative air pollution monitoring method and system, and the method comprises the steps: obtaining multi-source basic data of a monitoring region, calculating a pollution risk index and an attention index, and carrying out the weighting, thereby obtaining a comprehensive priority; dividing into sub-regions of different levels according to the priority, and configuring devices of different types or sensitivities to form a monitoring network; gas concentration and video image data are collected, pollution event comprehensive confidence is generated through fusion analysis, and when the pollution event comprehensive confidence exceeds a threshold value, a pollution event is judged and early warning is performed; the pollutant diffusion trend is predicted based on early warning, and a linkage control instruction for the treatment equipment is automatically generated; a two-factor dynamic partition model fusing pollution risks and regional attention is constructed, and a Bayesian fusion judgment algorithm that only results can be recognized by gas sensing data and videos is combined, so that complete closed-loop control from intelligent point distribution, accurate sensing and traceability prediction to automatic linkage is formed; and the overall efficiency and sustainability of regional pollution prevention and control are remarkably improved.