701 results about "Decision tree model" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses a time-space alignment fusion processing method and system for multi-source physiological signals, and relates to the technical field of data processing.The method comprises the steps that the multi-source physiological signals in the limb movement state are synchronously collected, and a heterogeneous time sequence data set is obtained; performing space-time alignment processing on the heterogeneous time sequence data set to generate a synchronous physiological signal matrix; performing signal quality evaluation based on the synchronous physiological signal matrix, constructing a weighted decision tree model, performing confidence fusion on the synchronous physiological signal matrix according to the weighted decision tree model, and generating multi-parameter joint monitoring data; and performing motion artifact suppression processing on the multi-parameter joint monitoring data, outputting a physiological parameter index set, and transmitting the physiological parameter index set to a first-aid equipment monitoring terminal. Therefore, the technical effects of eliminating signal distortion, improving monitoring data quality and ensuring first-aid monitoring precision are achieved.

The invention provides a combined wind power prediction method suitable for a distributed wind power plant, and the method comprises the steps: collecting the real-time meteorological data and historical power data of a wind power plant cluster, carrying out the cross-wind-plant data collaborative cleaning, and generating a time-space aligned standardized data set. Constructing an adaptive spatio-temporal feature extractor, outputting a spatio-temporal feature matrix, and inputting the spatio-temporal feature matrix into the spatio-temporal adaptive neural network, the graph attention prediction model and the physical constraint decision tree model to generate three prediction sequences. And the sequences are fused through a space-time collaborative attention mechanism to generate a dynamic weighted combination prediction result. And performing physical constraint correction on the result by using a space-time residual error correction network to generate a final prediction sequence. And updating the neural network topological structure based on the prediction error distribution, and outputting a prediction result with uncertainty evaluation to a power grid dispatching system. According to the method, the precision and reliability of wind power prediction of the distributed wind power plant can be improved, and the stability and economy of power grid dispatching are improved.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

An approach is provided for organizational transformation from a current state to a target state. Common language model(s) can dynamically perform interviews with stakeholders as part of a cooperative game to use disparate stakeholder insights to define the target state, projects, milestones, tasks, and resource use / availability. Lookalike Models can be used to model the organization as a dissipative system and calculate an organizational entropy score. A Markov model identifies possible task completion pathways between current and target state. An optimal project completion path through the Markov model may be identified using Decision Tree Models to identify magnitude of contribution to organizational transformation towards target state for each project and likelihood of successful project completion for each project using Fault Tree Models. Project completion resource allocation plans can be generated based on optimal Markov path. Bayesian Priors can be calculated based on performance measured using micro-behaviors analysis.

The invention provides an environment detection method and system based on a sample target detection model. The method comprises the following steps: synchronously acquiring an environment image, a video stream and physical parameters by using a multi-mode sensor; decomposing the data into image features and environmental parameter components through a dual-time sequence control signal, and realizing space-time alignment by adopting a linear phase filter; constructing a foreground region template based on the depth information, and generating target recognition feature representation containing an abnormal blurred target; adversarial training is carried out on the lightweight target detection network in combination with a transfer learning strategy, the network integrates convolutional features and a Transform attention mechanism, and the weight is dynamically adjusted through environmental parameters; fusing a target result and sensor data in real-time detection, and inputting a decision tree model for risk grading; and after the early warning is triggered, reconstructing a false detection sample through an online learning mechanism and iteratively optimizing the model. The system correspondingly comprises a multi-modal data acquisition module, a data enhancement and annotation module, a model training module, a real-time detection and fusion module and an early warning and optimization module. According to the invention, through multi-source data fusion, dynamic data enhancement and an adaptive compensation mechanism, the small target detection precision, the environmental adaptability and the real-time early warning capability are significantly improved.

A dynamic real estate valuation system based on multiparametric market indicators, which includes the following: a valuation engine configured to generate real-time results for property valuation; a multitude of distributed data ingestion and processing units configured to capture heterogeneous data sources, including historical property transaction data, real-time property listings, zoning and land use records, macroeconomic indicators, geospatial data, environmental sensor outputs, and sentiment-derived metrics; a model orchestration control unit comprising a stack of machine learning models, wherein the models include at least a gradient boosting decision tree model, a long-short-term memory (LSTM) time series forecaster, and an enhancement learning module that iteratively optimizes the model parameters based on the observed evaluation accuracy; a data contextualization controller configured to apply dynamic weighting to each input parameter based on the geographic, temporal, and market context by executing decay functions and location-specific rule matrices; a physical property valuation terminal (PVT) that includes an edge processing unit (EPU), geolocation circuitry, secure communication interfaces and a touch-based user interface; a valuation book subsystem configured to hash the valuation output, timestamp, and signatures of the input record into a blockchain-based distributed ledger; wherein the system is designed to continuously recalibrate its valuation results by comparing the predicted valuations with the actual sales or rental prices, and wherein the physical terminal is designed to produce a legally certifiable valuation document with embedded provenance data.

The invention relates to the technical field of pipe gallery fire analysis, in particular to an underground pipe gallery fire feature correlation analysis system based on an improved Apriori algorithm, and aims to comprehensively collect underground pipe gallery data through multi-modal sensing equipment and pre-process the underground pipe gallery data into a standardized three-dimensional time sequence data set to provide a high-quality data basis for subsequent analysis; a multi-dimensional feature association matrix is constructed based on the three-dimensional time sequence data set, feature association strength is quantified, and a multi-dimensional feature association basis is provided for fire risk mining; a fire risk rule knowledge base is constructed through a space-time weighted support degree optimization Apriori algorithm, effective association rules are accurately extracted, and the accuracy and reliability of fire risk early warning are improved; based on the fire risk rule knowledge base, a decision tree model is established, association rule weights of branches of the decision tree are analyzed, fire risk grades are divided, a composite early warning instruction set is generated, different coping strategies can be adopted for risks of different grades, and the pertinence and effectiveness of emergency response are improved.

The invention discloses a power transmission line icing intelligent early warning method, system and device and a medium, and the method comprises the steps: obtaining icing state data and meteorological data, and dynamically adjusting the collection frequency and a dormancy strategy; performing data preprocessing and cleaning on the acquired data; extracting time-frequency features through wavelet packet transformation and a self-attention mechanism, and fusing the spatial dependency relationship and cross-modal interaction information of multiple monitoring points by using a graph neural network to obtain enhanced icing state characterization; performing icing risk prediction by adopting a gradient boosting decision tree model to obtain an icing risk prediction result; and analyzing an icing risk prediction result by using an interpretable tool, identifying a key factor which has the greatest influence on icing risk prediction, dynamically adjusting an early warning level according to the key factor, and generating an early warning and maintenance suggestion. Therefore, the monitoring real-time performance and the early warning timeliness are improved.

The invention discloses an unstructured storage hierarchical strategy optimization method based on machine learning, and relates to the technical field of data storage management, and the method comprises the steps: monitoring a file access event in real time, generating an access log, and extracting a multi-dimensional feature data set; utilizing the trained multi-dimensional decision tree model to distribute a corresponding storage hierarchy for the storage object to obtain a storage hierarchy decision; according to a storage level decision, distributing the storage objects to different storage layers, and carrying out resource configuration and storage operation; monitoring the access condition of the storage object in the new storage hierarchy in real time, and collecting file access performance, storage cost and response time to obtain feedback data; by monitoring the file access event in real time and extracting the multi-dimensional feature data set containing the basic attribute, the access behavior and the context information, efficient response of hotspot data and reasonable utilization of resources are ensured, and the overall performance utilization rate and the cost effectiveness of storage are remarkably improved.

The invention relates to the technical field of street lamps, and provides a solar street lamp fault self-inspection and cloud alarm system, which comprises a cloud control platform and a local module, and is characterized in that the local module comprises a wireless communication unit, a data processing unit with a built-in dynamic decision algorithm, a data acquisition unit and an execution unit; the method comprises the following steps: periodically collecting real-time operation data and real-time environment data of the solar street lamp; outputting a fault type code and an emergency disposal instruction through the fault classification decision tree model; sending alarm information to the cloud control platform; the execution unit executes the emergency disposal instruction. According to the method, the dynamic threshold value interval is generated through the dynamic threshold value adjustment algorithm, the influence of dynamic environment parameters such as illumination intensity and temperature on the running state of the solar street lamp is fully considered, misjudgment caused by fixed threshold value judgment is avoided, the system false alarm rate is greatly reduced, and unnecessary manual troubleshooting cost is reduced.

The invention relates to the technical field of intelligent equipment, in particular to an intelligent interactive method and system for pet emotion pacifying based on the Internet of Things, and the method comprises the steps: collecting the audio data, physiological data, environment data and owner feedback data of a pet, and carrying out the data cleaning of the collected data; performing feature extraction; combining a decision tree model, a recurrent neural network, a variant of the recurrent neural network, a Transform model and a graph neural network, constructing a hierarchical fusion model, introducing a transfer learning and reinforcement learning mechanism, designing a dynamic model fusion weight adjustment strategy, and judging pet emotion according to extracted features; different pacifying measures are adopted according to the types of the abnormal emotions of the pet judged by the hierarchical fusion model; and establishing a multi-index evaluation system. According to the invention, through the hierarchical fusion model of technology fusion, the pet emotion recognition precision and speed are improved.

The invention relates to the technical field of data encryption, and discloses a data encryption method, encryption equipment and a storage medium. In the method, an encryption device obtains to-be-encrypted data uploaded by a terminal, and extracts a plurality of data features from the to-be-encrypted data, the data features including a data sensitive feature, a business risk feature and a user behavior feature; performing quantitative scoring on the plurality of data features to obtain a plurality of feature scores, and calculating a total data score according to the plurality of feature scores; inputting the total data score into a preset trained decision tree model to determine the security level of the to-be-encrypted data; according to the security level, determining a target encryption algorithm from a preset corresponding relationship between the security level and the encryption algorithm; and encrypting the to-be-encrypted data according to the target encryption algorithm and the dynamically generated encryption key. By means of the method, the problems that differential protection is difficult to achieve according to data characteristics in a related encryption method, and security risks are caused by key management staticization are solved.

The invention provides a three-dimensional setting printing correction method and device, equipment and a storage medium, and the method comprises the steps: scanning a three-dimensional setting, and obtaining a three-dimensional point cloud model, color grid data and a multispectral texture mapping graph; based on the three-dimensional point cloud model and the color grid data, determining an offset defect labeling graph and a parallax offset matrix; performing two-dimensional mapping and global correlation calculation on the multispectral texture mapping graph to generate a texture anomaly thermodynamic graph and an LAB color space deviation vector; inputting the parallax offset matrix, the offset defect labeling diagram, the texture anomaly thermodynamic diagram and the LAB color space deviation vector into a preset decision tree model, and generating a root cause classification label and a printing quality score; and based on the root cause classification label and a preset historical printing parameter database, constructing a reinforcement learning model, taking the printing quality score as a reward function of the reinforcement learning model, and outputting a printing correction scheme.

The invention discloses a database malicious behavior detection and blocking method and system based on multi-dimensional features, and the method comprises the steps: collecting performance index data in real time, and sequentially carrying out the smoothing and normalization of the performance index data, and constructing a time sequence feature vector; performing analysis and feature extraction on the network flow data to obtain an abnormal connection mode feature vector; inputting the collected multi-dimensional feature vectors into an improved factorization machine, and performing feature crossing and dimension reduction processing by using an MFB pooling technology to obtain dimension-reduced feature vectors; inputting the dimension reduction feature vector into a dual-mode collaborative detection mechanism composed of an XGBoost decision tree model and a DRL model, and outputting a dynamic risk detection result; starting a hierarchical blocking strategy based on the risk level in the dynamic risk detection result; the method realizes closed-loop protection of feature extraction, risk rating and hierarchical blocking, has adaptive threshold adjustment and batch detection acceleration capabilities, and ensures continuity and high efficiency of database services.

The invention discloses an automatic suction removal method, device and equipment for welding smoke and a medium, and relates to the technical field of machine learning. Linear interpolation and capacity expansion are conducted on a temperature matrix of a welding area; inputting the interpolated and expanded temperature matrix into a decision tree model, outputting predicted position information of the smoke source, and calculating spatial position coordinates; converting the point cloud data of the welding area into a grey-scale map, fusing the grey-scale map and the interpolated and expanded temperature matrix data to obtain a temperature space distribution matrix, inputting the temperature space distribution matrix into a first convolutional neural network, and outputting obstacle avoidance point cloud data; calculating parameters of the obstacle avoidance area, inputting the parameters, the spatial position coordinates and the initial joint rotation angle of the actuator into a second convolutional neural network, and outputting a target joint rotation angle; therefore, the welding smoke in the welding area can be automatically sucked by the actuator, the sucking efficiency is improved, the smoke concentration in the welding working area is reduced, the conflict condition of the smoke sucking disc working area is avoided, and the interference to the welding work and the sucking leakage rate are reduced.

The invention relates to the technical field of power equipment state monitoring, and discloses a cable state fault prediction method based on multi-source data fusion. According to the method, a digital twinborn model is constructed by acquiring multi-source data, and a theoretical health baseline changing along with working conditions is simulated and calculated in real time. And comparing the base line with the measured data to generate a thermoelectric coupling matrix with quantitative deviation, and diagnosing the degradation state according to the thermoelectric coupling matrix. And when the deviation exceeds a threshold value, performing attribution analysis by using a gradient boosting decision tree model, and generating a visual spectrogram associated with the partial discharge and the root cause. And finally, the defect type is judged by intelligently identifying a high-risk visual mode on the spectrogram, and the high-risk state is predicted. According to the method, working condition interference is filtered through dynamic reference, causal diagnosis is performed, the problem of high false alarm rate in the prior art is solved, and the prediction accuracy is remarkably improved.

The invention belongs to the field of query optimization, and particularly relates to a fragmentation storage and query optimization method and system for a high-concurrency database, and the method comprises the steps: analyzing business features through a preset decision tree model, and selecting an optimal fragmentation key, constructing a fragmentation rule engine and initializing a database in combination with hash, range and list fragmentation rules and a fragmentation splitting-merging strategy; generating a query abstract syntax tree by using an ANTLR4 analyzer, and detecting whether a preset Cube is hit or not to directly return a result; if not, dynamically routing to a target fragment list based on a fragment key field, load balancing and a failover strategy, accelerating data retrieval in combination with a high-frequency index, and combining fragment-level results through a T-TopK algorithm; according to the method, the self-adaptive matching of the fragmentation strategy and the service requirement, the calculation push-down of the query process and the result optimization are realized, and the low delay and the high availability in a high-concurrency scene are ensured.

The invention relates to the technical field of self-adaptive starting, and discloses a self-adaptive starting method and system based on voltage and current monitoring, and the method comprises the steps that an MCU (Microprogrammed Control Unit) triggers an auxiliary silicon controlled rectifier and a main silicon controlled rectifier through preset starting logic to start a motor, and carries out the high-frequency sampling of current and voltage data in real time; once starting failure is detected, short-time voltage sequences and short-time current sequences before and after a failure occurrence point can be automatically captured and stored. Then, key features are extracted from the sequences, and failure causes are determined through decision tree model analysis. And based on the analysis result and the current retry times, the MCU outputs a corresponding retry strategy to ensure that the motor can be successfully started or enter a safety mode. Thus, the reliability of motor starting is improved, subsequent fault diagnosis is supported through detailed data recording, and the maintainability and safety of the system are greatly improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a coastal salt marsh vegetation carbon sink estimation method and system based on multi-temporal phenological characteristics. The method comprises the following steps: acquiring a remote sensing image of a target coastal salt marsh area in a key phenological period; extracting an initial vegetation contour of a vegetation coverage range in the target area from the remote sensing image by adopting an unsupervised classification method; constructing a phenological decision tree model by taking an NDVI threshold method as a core, and integrating a machine learning enhanced node division mechanism and a phenological period slope analysis method to enhance the phenological decision tree model; inputting the initial vegetation contour into a phenology decision tree model, and performing multi-level classification on vegetation types according to the initial vegetation contour through the phenology decision tree model to distinguish vegetation coverage ranges of various types of vegetation in the target coastal salt marsh area; and vegetation carbon density parameters are obtained, and the estimation of the total carbon sink amount of the target coastal salt marsh area is completed through spatial superposition calculation in combination with a vegetation coverage range, so that accurate classification of vegetation and efficient estimation of carbon sink are realized.

The invention relates to the technical field of power equipment fault diagnosis, and discloses a power plant equipment multistage fault diagnosis method and system based on a dynamic decision tree, and the method comprises the steps: 1, collecting equipment operation data through multiple sensors, and carrying out the preprocessing of multi-source data; comprising noise filtering, outlier elimination and missing value filling; step 2, extracting feature values according to the time domain features and the frequency domain features, constructing a state decision tree model based on a C4.5 algorithm, optimizing attribute split points by an information gain rate, and optimizing generalization ability by an REP post pruning strategy; and step 3, monitoring operation data of the power plant equipment based on the state decision tree model, diagnosing and predicting equipment faults according to the monitoring data, outputting fault levels according to monitoring results, and triggering corresponding grading responses. The fuel power plant equipment fault diagnosis method provided by the invention has the advantages of high precision, low false alarm, capability of effectively distinguishing fault levels and dynamic self-adaptive capability.

The invention discloses a hydrological data model fusion analysis method based on water resource scheduling decision support, and relates to the field of water resource scheduling, and the method comprises the steps: loading an HMM-BP noise detection model, obtaining a multi-source data fusion model, and outputting a space-time grid data cube; constructing a water consumption behavior prediction unit through a TPB decision tree model, adding a water right transaction engine, and outputting a water consumption prediction value, a behavior of an intelligent contract protocol and a transaction cooperation model; an incremental method is adopted to generate N combined scene matrixes of rainfall and temperature rise correlation adjustment, an ecological stability evaluator is combined, a climate and ecological coupling model is constructed, and a scene and risk mapping table and a resource re-allocation weight are output; and constructing an intelligent rule base platform to obtain an executable decision logic tree. The system has the advantage that full-chain intelligentization of water resource scheduling is realized through modular integration. Therefore, social and market collaborative prediction, climate and ecological risk quantification and adaptive decision support are achieved.

The invention discloses an SDN (Software Defined Network)-oriented virtual network topology dynamic construction method, which comprises the following steps of: 1, acquiring multi-modal network data in a teaching scene in real time through an embedded probe, and constructing a multi-modal topology sensing matrix; step 2, inputting the multi-modal topology perception matrix into a pre-trained deep decision tree model to generate a dynamic topology descriptor; 3, converting the dynamic topology descriptor into a standardized configuration instruction set compatible with multiple protocols; step 4, dynamically adjusting the density parameters of the virtual nodes according to the stage characteristics of the teaching process, and realizing progressive reconstruction from basic single-domain topology to complex cross-domain topology; and 5, triggering a topology self-healing mechanism when detecting that the entropy value is suddenly changed, and synchronously updating the abnormal operation mode feature library in the knowledge graph. Through cooperative work of multiple links, the efficient, intelligent and flexible virtual network topology dynamic construction method is provided for a teaching scene, and the teaching effect and the network management efficiency are effectively improved.

The invention discloses a waistband type falling protection early warning device for old people living alone, and belongs to the technical field of intelligent wearable equipment. The problems that an existing device is single in early warning mode and cannot effectively guarantee the safety of old people living alone are solved, corresponding action results are matched for various data of the old people through the convolutional neural network and the lightweight decision tree model, and if a preliminary judgment result conforms to a normal mode, a judgment result is directly output; if not, triggering a secondary analysis mechanism to further judge whether the old people fall down or not; the GPS positioner is also used for analyzing whether falling behaviors occur at the same position for many times, and if a high-risk position is found, when the old people approach the high-risk position, the warning lamp and the alarm are used for reminding and paying attention to action behaviors; the fall detection accuracy is improved, the possibility of false alarm and missing alarm is reduced, the old people can be actively prevented from falling again in a high-risk area, and a more comprehensive safety guarantee is provided for the old people living alone.

The invention discloses an electrical equipment part defect detection method and device, electronic equipment and a storage medium, and belongs to the field of defect detection.The method comprises the steps that visible light and infrared thermal images of all parts of electrical equipment are obtained; fusing the visible light and infrared image features of each part through a multi-modal model, and detecting whether defects exist or not and the types of the defects by using the fused features; the defective part is marked as a target part; extracting visible light apparent characteristics and infrared thermal characteristics of each target part; and generating a defect severity level based on a preset decision tree model by integrating the defect prediction result, the visible light appearance and the infrared thermal characteristics of each target part. Therefore, by implementing the method and the device, the problem of inaccurate or missing defect type identification caused by insufficient multi-source information comprehensive analysis capability in the prior art can be solved.

The invention relates to the technical field of anti-fatigue of steel parts, and discloses an anti-fatigue system and method for a high-hardness steel part based on ultrasonic-shot blasting composite strengthening. The method comprises the steps that the macroscopic geometrical morphology, the microstructure and the dynamic stress-strain data of a target steel part are collected through a multi-mode sensing system, and a multi-physics field digital twinborn model is constructed through fusion; on the basis of fatigue damage feature distribution in the model, sorting by using an enhanced decision tree model, and triggering parameter configuration by using a priority over-critical value; synchronously calculating ultrasonic field regulation and control parameters and shot blasting trajectory planning parameters through an adaptive neural network algorithm; the parameters are injected into a digital twin model to simulate material response and energy transfer, data such as a microdefect closed state are output, and a parameter correction signal is generated through multi-dimensional consistency inspection. According to the method, precise control over composite strengthening is achieved, and the anti-fatigue performance of the high-hardness steel part is effectively improved.

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

The invention provides a multi-modal fusion fatigue screen monitoring recognition and reminding method and system, and the method comprises the steps: data collection: collecting the visual data of a screen monitoring person in real time, and synchronously collecting the physiological data; multi-modal data fusion: adopting a feature weighted fusion method based on an entropy weight method to adaptively distribute weights and generate a fusion fatigue index FFI by quantifying dynamic information entropy of each visual data and physiological data; fatigue grade classification: realizing three-level fatigue state judgment based on a fusion fatigue index FFI obtained by multi-modal data fusion and a dynamic decision tree model; and dynamic intervention: based on a fatigue grade classification result, adopting a double-channel intervention mechanism of bracelet touch alarm and automatic telephone call value length. When fatigue and inattention of a monitoring screen watchman occur, the monitoring screen watchman can be timely and accurately identified and a reminding intervention mechanism is started, so that the continuity and the safety of the monitoring screen work are ensured, and bad safety production events caused by human reasons are avoided.

The invention relates to a migration method and system for a database table structure mapped by a metadata type, and the method comprises the following steps: obtaining table structures in a source database and a target database, and calling an interface to obtain metadata fields in the table structures; performing mapping analysis on the metadata fields to obtain candidate field mapping and corresponding mapping vectors; inputting the mapping vector into a gradient decision tree model to calculate a migration confidence coefficient mapped by each candidate field, wherein the migration confidence coefficient is a probability value for predicting successful mapping of the candidate field mapping; screening the candidate field mapping based on the migration confidence; if the migration confidence difference value mapped by the screened candidate fields is within a preset range, conflict arbitration is carried out according to a preset conflict resolution strategy, and a final migration decision is generated; and migrating the table structure of the source database to the target database based on the final migration decision.

The invention discloses a method and a system for automatically adjusting the rotation angle of an engine blade. The method for automatically adjusting the rotation angle of the engine blade comprises the following steps: collecting historical parameters of an engine, collecting real-time data of the engine, carrying out multi-dimensional data fusion on the data to extract key information, screening remarkable influence factors, and constructing a parameter influence network diagram; a four-channel long-short-term memory prediction model is established, four channels output corresponding prediction results respectively, and constraint and error correction are carried out on the prediction results; a corner angle is calculated according to a four-channel output prediction result, constraint limitation and a parameter influence network diagram, and an output corner instruction is directly mapped into actuator displacement; through compensation of a nonlinear actuator, the adjustment precision is guaranteed, and it is ensured that the actuator accurately executes a rotation angle instruction; and the model is lightened, so that the finally obtained decision tree model can efficiently run in an embedded system. The rotating angle of the engine blade can be automatically adjusted under different working conditions, and the adjusting efficiency of the rotating angle of the engine blade is improved.