1726 results about "Data reliability" patented technology

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

The invention discloses an intelligent control system for a current transformer, and particularly relates to the technical field of power system monitoring, which comprises a fluctuation identification unit, a redundancy acquisition unit, an error fitting unit, a protection delay unit and a fault response unit, realizes real-time identification and classification of current fluctuation, and has standby path high-speed sampling and fusion signal construction capabilities. The fluctuation trend is judged through error regression, a protection strategy is dynamically set, response is delayed to avoid misoperation, finally, a high-level protection instruction is triggered and a fault event is output when offset overruns or alignment fails, and the data accuracy and the system response stability in the power supply switching process are guaranteed; according to the method, accurate identification of current fluctuation is realized through multi-dimensional feature extraction, data reliability is enhanced by using redundant sampling and error fitting, and by combining delay control and a hierarchical response mechanism, the risk of misoperation is effectively reduced, the fault judgment accuracy and the system protection stability are improved, and the method is suitable for a complex power supply switching scene.

The invention relates to the technical field of forklift risk management, in particular to a multi-scene-oriented forklift self-adaptive safety early warning decision-making method, which comprises the following steps of: through multi-scene dynamic modeling, constructing multi-dimensional scene feature vectors according to forklift operation environment spatial features, cargo physical attributes and driver behavior parameters; establishing a scene classification model by means of transfer learning; a multi-modal sensor data fusion technology is utilized, a heterogeneous sensor network is deployed to collect data, and data reliability is optimized through space-time alignment and confidence evaluation; a dynamic safety threshold value is generated through fuzzy logic and reinforcement learning, and a driver behavior correction factor is introduced for real-time adjustment; calculating a comprehensive risk index by using a dynamic weight distribution algorithm, and triggering graded early warning and response; a federated learning closed-loop optimization risk assessment model is adopted, robustness is verified in combination with digital twinning, and cooperative obstacle avoidance of multiple forklifts is achieved through inter-vehicle communication. According to the method, the operation safety and decision-making accuracy of the forklift in a complex and changeable scene are remarkably improved.

The invention, which relates to the technical field of disaster monitoring and early warning, discloses a landslide and debris flow disaster monitoring and early warning system comprising a multi-source data acquisition module, an intelligent processing module, an analysis decision module and an early warning response module. The space-time reference unified framework is constructed, the high-precision time synchronization and space projection conversion technology is adopted, the space-time matching problem of the satellite-ground heterogeneous data in the prior art is solved, the multi-source parameter fusion proportion is adaptively adjusted according to indexes such as signal quality and environment interference degree through a dynamic weight distribution mechanism based on data reliability, and the space-time matching precision of the satellite-ground heterogeneous data is improved. In addition, a multi-model collaborative decision-making framework is established, the advantages of three models of mechanism driving, probability statistics and intelligent learning are fused, a comprehensive research and judgment result is generated through a confidence quantitative evaluation and conflict resolution algorithm, an early warning response mechanism is matched, a pre-arranged plan library is automatically matched according to a risk level, and an external control system is linked. And a closed-loop management chain of monitoring early warning, analysis decision and emergency disposal is formed.

The invention relates to the technical field of hydrogeological monitoring, in particular to a hydrogeological dynamic monitoring and analysis system and method based on big data. The method specifically comprises the steps that hydrogeological data are collected in real time, edge calculation is combined for data denoising, space-time calibration and error correction, variational mode decomposition, geologic model constraint and wavelet transform are adopted for eliminating noise and drift errors, storage and transmission efficiency are optimized through incremental coding and Huffman coding, and quadratic form calibration codes are generated to ensure data reliability; a hydrological evolution trend is predicted by fusing a space-time deep learning model and a self-attention mechanism, and adaptive anomaly detection is realized by combining DBSCAN clustering and prediction error analysis; and optimizing a water resource allocation strategy through reinforcement learning, hierarchically evaluating a risk level and generating a management scheme, and finally visually displaying monitoring data, an abnormal score and a decision result. According to the method, multi-source data fusion, space-time modeling and intelligent decision making are integrated, and hydrological monitoring precision and response efficiency are improved.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

The invention belongs to the technical field of power systems, and particularly relates to a pressure sampler on-line unblocking control method and system, and the method comprises the steps: collecting the pressure difference fluctuation data of the inner wall of a pipeline in real time based on a quantum pressure difference sensor, associating historical flow field parameters through a pre-trained quantum neural network model, and dynamically predicting the multi-moment blocking risk level; a dynamic cleaning path is generated in combination with a quantum annealing algorithm, a genetic algorithm parameter library is called to match scene gene data, and cleaning priorities of a fourth pipe body and a seventh pipe body are determined; the control module generates an air cylinder driving instruction according to the cleaning path and the priority, and drives the spiral brush head to execute high-frequency inching unblocking operation, dynamic sensing and accurate cleaning of the blocking risk are achieved, and the data reliability and operation and maintenance safety of thermal power generation pressure measuring points are improved.

The invention relates to the technical field of image processing, in particular to a remote sensing monitoring method and system for cultivated land protection, and the method comprises the following steps: obtaining a remote sensing image sequence of a cultivated land region at a differential time node, extracting pixel gray values of the same land parcel, arranging the pixel gray values according to time, calculating the ratio of a difference value between adjacent frames to an intra-frame gray average value, and obtaining a remote sensing image sequence; and marking positions continuously exceeding the fluctuation reference ratio, and generating a pixel region set. According to the method, a sampling frequency threshold optimization strategy is adopted, abnormal areas are screened in combination with albedo change rate, short-time fluctuation interference is reduced, time sequence consistency is enhanced, a solar incident angle and a terrain slope are introduced to calculate projection influence, a slope surface shielding area is eliminated, and space grid marking and boundary integrity judgment are adopted; the method improves the continuity of cultivated land distribution information, combines time sequence analysis, sampling optimization, physical characteristic compensation and spatial consistency correction, enhances the fine recognition of the dynamic change of cultivated land, and improves the monitoring precision and data reliability.

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

The invention relates to the technical field of data analysis, in particular to a road traffic intelligent operation and maintenance method and system based on multi-source data fusion, and the method comprises the steps: constructing a three-dimensional model through a credibility calculation module by integrating three types of factors, namely a road environment, an equipment state and historical verification, generating a dynamically updated credibility index, and correcting a weight in real time based on feedback; the problem that the reliability of the multi-source data fluctuates dynamically due to the influence of environmental interference, equipment aging and historical deviation is solved; the road feature conflict resolution module maps to feature dimensions corresponding to road topology conflict features through dynamic credibility indexes, constructs a road conflict decision matrix, and fuses and generates a road anomaly decision set with credible labels according to the feature dimensions; the resource scheduling module is used for dividing three-level response areas based on credible indexes, executing accurate scheduling of triple constraints in combination with device priorities and credible labels and generating a resource-decision association graph, and self-adaptive optimal configuration of limited maintenance resources according to data reliability is achieved.

The invention discloses an address mapping method in DPU local storage virtualization, which comprises the following steps: a virtual machine calls a system call interface provided by an address mapping configuration management module on a host side to perform mapping configuration of a virtual machine physical address and a host physical address, and issues the virtual machine physical address and length to a virtual address mapping module of a DPU; the virtual address mapping module manages global direct memory access address resources, firstly allocates a global direct memory access address on a host side, then maps a virtual machine physical address to the allocated global direct memory access address in a mapping page table of the DPU, and returns the virtual machine physical address to the address mapping configuration management module; and the address mapping configuration management module obtains the distributed global direct memory access address, and maps the distributed global direct memory access address to a host physical address in the IOMMU. According to the method, by optimizing address mapping, the efficient and flexible local storage virtualization service can be realized while the data reliability is ensured.

The invention discloses an Internet of Things intelligent gas meter leakage detection and early warning system and method, and relates to the technical field of gas leakage detection, and the method comprises the following steps: S1, collecting data through multiple sensors; s2, identifying an equipment operation state based on the data set and outputting a state confidence coefficient; s3, a stable monitoring window period is judged, a corresponding strategy is selected to compensate pressure data, and reliability is marked; s4, dynamically generating a detection threshold in combination with the historical mode, the real-time parameters and the data reliability; s5, dynamically adjusting a risk assessment weight according to the confidence coefficient and the reliability, calculating a risk score and determining an early warning level; s6, safety operation is executed according to grades; and S7, updating the model by using process data to realize self-optimization. According to the invention, by deploying a multi-sensor array and adopting a multi-modal signal fusion algorithm, the system can accurately identify the running state of the gas appliance, provides reliable preposition information for subsequent analysis, and overcomes the defect that data of a traditional single sensor is easily interfered.

The invention discloses a data filtering and noise reduction method based on laser point cloud. According to the method, the multi-scale features are intelligently screened through the random forest algorithm, and the system can automatically generate the optimal filtering rule according to the local geometric characteristics of the point cloud without depending on manual parameter presetting. In a complex urban scene, a fine structure of a high-rise vertical face can be accurately reserved through curvature features, and sparse noise of a vegetation area is rapidly eliminated through a density threshold value. The data-driven adaptive mechanism not only reduces the manual intervention cost, but also greatly improves the scene generalization ability of the filtering strategy, so that the filtering strategy can still keep stable output in a dynamic environment. The dynamic interference is accurately positioned by combining the consistency verification of the motion trails of the multi-frame point clouds and utilizing the time sequence correlation analysis, so that the data reliability in a complex dynamic scene is remarkably improved, and a solid foundation is provided for high-precision three-dimensional reconstruction and real-time perception.

The invention relates to the technical field of polishing, and discloses an intelligent steel rail polishing control method which comprises the following steps: acquiring surface and internal defect data of a steel rail through laser and ultrasonic fusion detection, discretizing the data into multi-dimensional nodes, and constructing a dynamic graph model comprising a microscopic-macroscopic dual-cluster neural network to predict a defect evolution trend; based on fractal dimensions and information entropy, multi-source data are dynamically fused, abnormities are eliminated, chaotic optimization and wear prediction are adopted to generate global optimal polishing parameters, and finally parameter dynamic adjustment is achieved through closed-loop feedback of pulse frequency and control errors. Steel rail defect collaborative perception is achieved through a multi-scale dynamic graph model, fractal-entropy weight dynamic fusion is combined to enhance data reliability, global optimal parameters are generated through chaos optimization and abrasion prediction, accurate grinding is achieved through pulse-error closed-loop control, and the method is simple and convenient to implement. The problems of data fusion failure, defect response lag, parameter local optimization and insufficient control precision in the prior art are solved.

The invention relates to a land utilization type intelligent identification method and system based on remote sensing data. The method comprises the following steps: acquiring multi-source remote sensing data; based on a preset land utilization characteristic model, analyzing and processing the multi-source remote sensing data to obtain corresponding land utilization characteristics in the land utilization characteristic model; based on a land utilization characteristic knowledge base, matching and analyzing the land utilization characteristics to obtain a matching result; and according to a matching result, determining a land utilization type corresponding to the matching result, and outputting the land utilization type as an identification result. By adopting the method, multi-dimensional information complementation can be realized, space-time coverage is enhanced, and data reliability is improved; a high-precision classification result is provided, dynamic monitoring is supported, and an application scene is expanded; the defect of a single data source is overcome, manual dependence is reduced, the operation period is shortened, the cost is reduced, the complex scene recognition capability is improved, and space-time continuous dynamic monitoring and fine management support is provided for the fields of ecological assessment and the like.

The invention discloses a time-space dynamic weight-based building monitoring data accurate registration method, which comprises the following steps of: establishing a response model considering nonlinear behaviors of materials by analyzing deformation characteristics in multi-source monitoring data; constructing a dynamic weight function based on a structural stress state, and realizing self-adaptive distribution of sensor data reliability weight; geometric deformation correction is carried out by using the material nonlinear response model, and geometric distortion caused by material nonlinear behaviors is corrected; space-time registration of multi-source data is realized by adopting a weight fusion and deformation gradient constraint method; and parameter adaptive optimization is carried out through an online learning mechanism. According to the method, the technical problems of low geometric correction precision of a high-stress area, dynamic change of sensor reliability and the like are solved, and submillimeter-level precision building monitoring data registration is realized.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention discloses a water conservancy facility monitoring method and system based on digital twinning, and relates to the technical field of water conservancy monitoring. Historical operation data of a water conservancy facility are obtained based on a digital twinning model for analogue simulation, and full life cycle data of the water conservancy facility are obtained; and importing the standard operation data into the digital twin model, carrying out data comparison and data compensation on the standard operation data and the full life cycle data, verifying the accuracy of the standard operation data, and generating output verification data after comparison is completed. The verification data is generated by comparing the operation data with the data simulated by the digital twin model and combining cross verification of multiple operation data in the same area, so that data accuracy confirmation and data compensation can be carried out on the data distortion problem caused by abnormal data acquisition of the water conservancy facility operation data acquisition equipment; the reliability of the collected operation data is ensured, and the accuracy of evaluation and decision-making of the subsequent water conservancy facility monitoring operation state is guaranteed.

The invention belongs to the technical field of product inventory management, and discloses an e-commerce product inventory management method based on machine vision, which realizes a full-automatic process of product information from acquisition, recognition to statistics by means of a machine vision technology, acquires images in real time from multiple angles by a high-definition industrial camera, inputs the images into a CNN model to extract features after preprocessing, and realizes product inventory management. Through accurate identification classification and quantity statistics, manual intervention of inventory and data entry is not needed; through the automatic inventory management process, the manpower cost expenditure of manual checking, data processing and the like is greatly reduced, and the personnel employment and training cost is reduced. And on the other hand, historical data is deeply mined by using algorithms such as time sequence analysis, the sales trend and the inventory demand are accurately predicted, the replenishment strategy is optimized, capital and storage space occupation caused by inventory overstock is avoided, meanwhile, order loss caused by stockout is prevented, and in addition, a distributed database storage technology ensures data reliability, and the efficiency is improved. And extra cost caused by data errors is reduced.

The invention discloses a battery capacity performance testing method and device and a storage medium, and relates to the technical field of battery capacity performance testing. By constructing a dynamic load model and a multi-load test protocol and combining a high-precision data acquisition and filtering algorithm, adaptive data analysis and a machine learning regression model, the problems of difficulty in balancing precision and efficiency, large data acquisition error, fixed test protocol and the like in the traditional battery test are solved; by dynamically adjusting test parameters and environmental conditions, complex working conditions of the battery in actual use are accurately simulated, meanwhile, dynamic updating and optimization of a test protocol are achieved, the test precision, the data reliability and the test efficiency are remarkably improved, and a comprehensive, efficient and high-adaptability solution is provided for battery performance evaluation and health management.

The invention relates to the technical field of carbon emission data processing, in particular to a carbon emission data processing method, device and equipment and a readable storage medium. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, abnormal data identification and correction based on a hybrid machine learning model, energy optimization configuration assistance and real-time monitoring and feedback adjustment, and data acquisition and preprocessing: constructing a multivariate data acquisition network and preprocessing the acquired data; therefore, standard carbon emission data for subsequent analysis can be obtained. Through a multi-element acquisition network and fine preprocessing, the comprehensiveness, accuracy and timeliness of data are greatly improved, a foundation is built for accurate analysis, abnormity is accurately recognized and corrected through a hybrid machine learning model, the accuracy is improved, data reliability is guaranteed, association between carbon emission and energy is mined, enterprises are assisted in optimizing energy configuration, and the energy utilization efficiency is improved. The carbon emission is reduced, and the system stability is enhanced by real-time monitoring feedback.

The invention belongs to the technical field of data processing, and particularly relates to a rutile type titanium ore prospecting model construction method. Comprising the following steps: collecting remote sensing time sequence, real-time geophysical prospecting and geochemical data, carrying out geological causal feature analysis after standardization so as to construct a metallogenic causal graph, and meanwhile, collecting anti-fact features to generate virtual mineralization feature vectors; a prospecting model comprising an input layer, a weight optimization layer (comprising a causal attention module and a particle swarm anti-fact comparison module) and a mineralization probability output layer is constructed, and a high-frequency data acquisition mechanism is triggered with assistance of a metallogenic event threshold. According to the method, the data reliability and the model generalization ability are improved, the mineralization probability calculation precision and the potential mineralization target region judgment accuracy are remarkably improved, and a scientific support is provided for efficient prospecting of rutile type titanium ore.

The invention relates to the technical field of automobile automatic transmission, and discloses an AMT bearing operation state detection system and method under an active and passive switching working condition, and the method comprises the steps: collecting real-time data through a vibration acceleration sensor, a current sensor, a temperature sensor and a rotating speed encoder, carrying out the preprocessing of envelope demodulation, Kalman filtering and the like, and carrying out the detection of the operation state of an AMT bearing; and generating a time-frequency characteristic matrix by using variational mode decomposition and Hilbert transform. And inputting the matrix into a probabilistic neural network model adopting a sliding time window mechanism, and outputting a bearing health state probability value. A multi-parameter state evaluation model optimized by a quantum genetic algorithm is constructed, an optimal feature combination is obtained, and early warning levels and maintenance suggestions are output through a belief rule base inference device in combination with a hierarchical diagnosis control model (including an acquisition layer, an analysis layer and an execution layer). The system is further provided with a signal verification module to ensure data reliability. According to the invention, multi-source data fusion and dynamic adaptive diagnosis are realized, and the state detection precision and real-time performance of the AMT bearing under complex working conditions are improved.

Systems and methods for dynamically evaluating metadata consistency and data reliability in a data management system are disclosed herein. The system may retrieve first metadata and second metadata. The system may retrieve a metadata ruleset. Based on the metadata ruleset, the system may generate a first metadata consistency metric indicating a first measure of consistency. The system may determine to process each record of the first metadata as a batch. The system may generate a second metadata consistency metric indicating a second measure of consistency. The system may determine to process each record of the second metadata independently.

The invention discloses a construction monitoring system for a horizontal warehouse arched roof based on multi-modal data fusion, relates to the technical field of construction monitoring, and is used for solving the problem of poor structural construction anomaly recognition. The method comprises the steps of collecting multi-modal data and completing synchronous preprocessing, setting a sampling frequency according to modal characteristics, dividing construction stages, constructing a stage response function, dynamically updating during stage switching, fitting an actual observation value and a prediction value, extracting a modal response deviation degree, constructing a credibility function and evaluating data reliability. Further analyzing a residual trend, adjusting an abnormal score in combination with credibility, identifying a collaborative abnormal behavior between modals, systematically constructing a modal causal graph, identifying an abnormal propagation path, extracting modal conduction characteristic information, and triggering a risk response strategy according to the modal causal graph, so that dynamic tracking of the structural abnormality in the construction process of the horizontal warehouse arched roof is realized; and the accuracy of construction process monitoring identification is improved.

The invention discloses a mechanical property testing method for an inflatable wing, and belongs to the technical field of aerospace testing. The method comprises the following steps: S1, wing installation: clamping an inflatable wing on a test board, and enabling a chord plane of the inflatable wing to be perpendicular to a loading direction; s2, applying a load: driving a loading mechanism to apply a vertical load to the inflatable wing through an external universal tester; s3, data acquisition: synchronously acquiring a deflection curve and stress distribution data by using a displacement measurement unit and a strain detection unit; and S4, failure judgment: when monitoring that the shape of the deflection curve suddenly changes or the strain data exceeds a threshold value, judging that the inflatable wing is subjected to buckling failure, stopping loading and recording the maximum load. The test bench serves as an expansion platform of a universal tester, can be adapted to a large-size inflatable wing, does not need special equipment, reduces the cost, realizes cooperative monitoring of displacement and strain data, realizes closed-loop control of inflation air pressure-deformation-load, and remarkably improves the test precision and the data reliability.

The invention relates to the technical field of soil remediation management, in particular to a method and system for soil remediation and digital management, and the method comprises the steps: data construction: fusing multi-source monitoring soil environment parameters with space-time background data such as a GIS layer and a meteorological time sequence to form a data set; state quantification: outputting a comprehensive state index for quantifying restoration urgency and complexity through the soil health assessment model; scheme generation: matching the restoration target library and the resource library, and generating a digital restoration scheme containing an expected state trajectory through an optimization algorithm; closed-loop control is carried out, feedback data comparison tracks are continuously collected, if the deviation exceeds a threshold value, the index is recalculated, the scheme is dynamically adjusted, and the system comprises a data construction module, a state quantification module, a scheme generation module and a closed-loop control module and executes the method. According to the method, the data reliability and evaluation scientificity are improved, the scheme adaptability is guaranteed, dynamic and controllable remediation is realized, and digital and efficient management of soil remediation is promoted.

The invention discloses a gear performance accurate evaluation method based on voiceprint analysis, and relates to the field of gear performance evaluation. Determining the optimal arrangement position of the acoustic sensor according to different gears, and adjusting the sampling frequency and duration in real time according to the operation state change of the gears to collect voiceprint data; de-noising by using a composite de-noising network based on deep learning, and processing data by using a new normalization method; extracting a plurality of innovative features to construct a feature vector, and combining a deep forest with model fusion to establish an evaluation model; and inputting a to-be-evaluated gear feature vector into the model to evaluate the performance, verifying an evaluation result through a virtual verification mode and the like, and optimizing the model based on feedback. The gear performance is accurately evaluated, voiceprint data are intelligently collected, and the evaluation accuracy is improved through feature extraction and model construction; real-time monitoring and early warning are achieved, and different gears are adapted; the block chain ensures data reliability, the evaluation model evolves continuously, the maintenance cost is reduced, and stable operation of equipment is guaranteed.

The invention provides a method and a device for evaluating and measuring microscopic mechanical properties of TC4 alloy, relates to the technical field of microscopic mechanical properties, and realizes accurate evaluation of material properties through combination of multi-scale characterization and numerical simulation. Firstly, a standard sample is prepared through selective laser melting, and residual stress field distribution is obtained through X-ray diffraction; dividing a stress field into sub-regions, and establishing a quantitative relationship between microscopic structure parameters and residual stress by combining SEM and EBSD analysis; the method comprises the following steps: acquiring local mechanical properties through a nanoindentation test, and constructing a stress-performance correlation model; and finally, establishing a finite element model, transmitting stress and tissue parameters by adopting trilinear interpolation and an inverse distance weighting method, verifying data reliability through a stress difference criterion, and calculating mechanical properties of each unit. According to the method, multi-scale correlation from microstructure characteristics to macroscopic mechanical properties is innovatively realized, so that the mechanical property distribution of the material is accurately evaluated.