760 results about "Life evaluation" patented technology

The invention discloses a battery life evaluation system and method based on different decline modes of a battery, and belongs to the technical field of battery management evaluation. Through combination of the data acquisition module and the information management module, battery manufacturing, operation and historical information is comprehensively acquired, accurate management of the whole life cycle of the battery is realized, the data analysis module and the performance evaluation module deeply analyze battery operation data, classify and identify recession modes and key influence factors, and the reliability of the battery is improved. The current performance and the remaining life of the battery are dynamically evaluated by combining a prediction model, decision making is simplified through health scoring, the battery management efficiency is improved, the adaptive performance optimization module adjusts battery parameters and temperature control strategies according to operation scenes, the performance and the life are balanced, active optimization and safety improvement are achieved, and the safety is improved. The carbon footprint evaluation module covers all stages of battery manufacturing, using and recycling, checks full-life-cycle carbon emission, traces the source of high emission links, provides low-carbon optimization suggestions, and assists enterprises in achieving the goal of green development.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention discloses an ACM data analysis method and a data monitoring system, and belongs to the technical field of aircraft maintenance, and the method comprises the steps: constructing an ACM data set, setting a feature engineering processing method, carrying out coupling feature extraction through setting a dynamic window and calculating a dynamic correlation coefficient, dividing working conditions, eliminating fluctuation interference, and generating a structured time series data set; the method comprises the steps of obtaining ACM reference data of aircrafts of the same model, setting a reference fault judgment method, judging latest data in a structured time sequence data set, screening abnormal parameters, judging whether the ACM breaks down or not by combining an actual temperature adjusting effect, reducing single-parameter misjudgment, setting a coupling fault positioning method, diagnosing the faulty ACM, and distinguishing true faults and false anomalies. Meanwhile, a prediction scheduling method is set, fault prediction, health state prediction and service life evaluation are carried out on the fault-free ACM, the probability of sudden faults is reduced, and non-planned flight stopping is reduced.

The invention relates to the technical field of pipeline stress fatigue detection, and discloses a natural gas station elbow tee joint stress fatigue digital intelligent analysis method and system and a product. The method comprises the following steps: establishing a multi-physics field coupling digital twinborn model of the elbow tee joint, integrating a geometric structure, material attributes and a fluid-solid coupling mechanism, and simulating flow-induced vibration stress and corrosion fatigue interaction; collecting real-time multi-source data of the elbow tee joint; real-time multi-source data is utilized to update boundary conditions and parameters of the digital twin model, and material constants are dynamically corrected through machine learning, so that self-calibration of the model is realized; performing stress distribution analysis based on the updated model to obtain stress field data, and performing fatigue crack propagation prediction in combination with real-time multi-source data to generate a prediction result; and based on the prediction result, evaluating fatigue failure risks, including crack growth rate, residual life evaluation and failure probability, and outputting alarm information or optimization decision information.

The invention discloses a lithium iron phosphate battery pack SOE dynamic prediction and life evaluation method and system fused with a P2D model, and relates to the technical field of battery management, and the method comprises the steps: collecting operation data, building a multi-physical field coupled P2D model, carrying out the SOE dynamic prediction based on the P2D model, building a life evaluation model in combination with a performance attenuation mechanism, and optimizing an operation strategy. According to the method, on the basis of historical operation data and real-time state parameters, an SOE dynamic prediction result of the battery pack is generated by using an electrochemical-thermal-mechanical multi-physics field coupled P2D model; and analyzing the prediction result in combination with a long-term performance attenuation mechanism, evaluating the residual life of the battery pack, and outputting an evaluation result. The SOE prediction precision is improved, the performance change rule is comprehensively reflected by using the life evaluation model, and the operation efficiency is remarkably improved and the service life is prolonged through real-time monitoring and optimization. According to the invention, efficient and reliable battery management requirements under complex working conditions can be met.

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention belongs to the field of deep sea corrosion experiment equipment, and particularly relates to a multivariable coupling control deep sea corrosion environment simulation and monitoring device, which comprises an environment control unit, a deep sea corrosion environment simulation unit, a deep sea corrosion environment simulation unit, a deep sea corrosion environment simulation unit and a deep sea corrosion environment monitoring unit, and is characterized in that the environment control unit comprises a seawater environment cabin, a test sample is vertically arranged in the seawater environment cabin, and two ends of the test sample penetrate through and extend to the outer side of the seawater environment cabin; the pressure control system and the temperature control system are connected with the seawater environment cabin through sealing flanges and are respectively used for simulating deep sea hydrostatic pressure and a low-temperature environment; the mechanical loading system is used for bearing the seawater environment cabin and clamping the two ends of the test sample through the sample clamping system so as to apply a multi-axis dynamic mechanical load; the gas regulation and control unit is used for controlling the concentration of dissolved oxygen in the seawater environment cabin; and the multi-source monitoring module is used for collecting detection data and analyzing the detection data to obtain the corrosion rate of the test sample. The single-variable control limitation of a traditional device is broken through, and a high-fidelity experiment platform is provided for deep sea material corrosion mechanism research and equipment life evaluation.

The invention relates to the technical field of bushing detection, and discloses a bushing insulation state detection method based on multi-parameter real-time monitoring. The method comprises the following steps: acquiring multi-dimensional monitoring data such as a partial discharge pulse sequence, dielectric loss angle tangent value fluctuation data and surface leakage current density distribution in a bushing operation environment in real time; carrying out insulation defect feature extraction on the data set, and generating an insulation defect distribution map containing a creeping discharge track, an internal air gap position mark and a carbonization channel density index; high-risk and potential defect areas are divided by adopting a dynamic threshold segmentation algorithm, boundary coordinates are determined, and an insulation state evaluation vector containing parameters such as a surface discharge intensity index and the like is constructed according to the boundary coordinates; and inputting the vector into a pre-trained insulation aging prediction model, and outputting a bushing residual insulation life estimated value and a defect development priority sequence. According to the method, all-around accurate monitoring and pre-judgment of the insulation state of the sleeve are achieved, and scientific support is provided for operation and maintenance decision making of the sleeve.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention relates to a stamping die service life dynamic evaluation system and method based on multi-mode sensing. According to the system, a feature extraction module obtains stress distribution data and crack position information and inputs the data into a trained three-dimensional stress field model to obtain dynamic feature parameters of a stress concentration area under high-frequency cyclic loads; the crack prediction module judges the dynamic characteristic parameters based on a preset threshold value, and once the dynamic characteristic parameters exceed the preset threshold value, analysis is carried out according to the mapping relation between the stress level and the crack rate and the microstructure evolution rule at the same time, and a crack propagation prediction value is generated. And the service life evaluation module inputs the crack propagation prediction value into the trained health state evaluation model, and finally the service life evaluation value of the stamping die is obtained. The service life evaluation value of the stamping die under the existing working condition and the operation condition is accurately estimated, the production efficiency is effectively improved, the production cost is reduced, the potential crack risk of the die is perceived in advance, and accurate and dynamic die service life evaluation is achieved.

The invention discloses a digital twinning method and system for power transmission and transformation equipment, and relates to the technical field of digital twinning of the power transmission and transformation equipment, and the method comprises the following steps: constructing a multi-physical field coupling model of an electromagnetic field, a thermal field and a fluid dynamic field of the power transmission and transformation equipment; collecting multi-dimensional data of the equipment in real time, and preprocessing the multi-dimensional data; constructing and updating a digital twinborn model in real time, and predicting temperature rise and heat dissipation characteristics of the equipment; and obtaining fault prediction and life evaluation of the equipment. Through the innovative multi-physics field coupling and deep learning combined method, the digital twin system can reflect the real-time state of the equipment more accurately and support interaction and feedback of multi-field information, so that the real-time monitoring and service life management capabilities of the equipment are enhanced.

The invention discloses a boiler state monitoring and predicting system based on artificial intelligence, and relates to the technical field of state monitoring. The system comprises a data acquisition part, a combustion stability analysis part, a hydrodynamic instability risk assessment part, a steam drum water level fluctuation prediction part and a fault probability and service life assessment part. The data acquisition part is used for acquiring boiler operation data and water circulation data; the combustion stability evaluation part is used for calculating a thermodynamic efficiency index; the combustion stability analysis part is used for calculating a combustion stability coefficient; the hydrodynamic instability risk assessment part is used for calculating a hydrodynamic instability risk index; the steam drum water level fluctuation prediction part is used for calculating and predicting steam drum water level fluctuation; and the fault probability and service life evaluation part is used for evaluating the fault probability and the residual service life of the boiler. According to the invention, the comprehensiveness, accuracy and predictability of boiler operation state monitoring are obviously improved.

The invention discloses an electric actuating mechanism service life evaluation method and system based on dynamic load monitoring, and particularly relates to the technical field of service life evaluation. For a fretting wear accumulation effect caused by resonance of a load frequency spectrum and a mechanical gap and lubricating oil film fracture reconstruction lag caused by the fact that the load change rate exceeds a critical value, an output shaft torque signal is collected for time-frequency domain conversion, and a load change frequency spectrum and a load change rate are obtained; and in combination with oil film state monitoring, the lubrication state deterioration risk and the wear accumulation effect are evaluated, and the failure critical risk is determined. When the failure critical risk is lower than a safety threshold value, analyzing contact surface stress distribution through a finite element to evaluate the wear degree, predicting the residual life by combining the failure threshold value of the similar electric actuating mechanism in a historical database, and judging whether the electric actuating mechanism needs to be maintained or replaced in advance, so that the actual wear and failure trend of the electric actuating mechanism under multiple working conditions can be accurately reflected; and the scientificity of maintenance decision and the operation safety are improved.

The invention discloses an engine additive component service life prediction method based on modal polycondensation and expansion, and the method specifically comprises the steps: firstly, achieving the grid discretization of an initial defect structure based on a finite element method, and carrying out the local refinement of a defect region; performing modal polycondensation on the overall model to obtain a low-dimensional system model, and performing rapid calculation under the low-dimensional model to obtain modal response; establishing a conversion relation between a displacement vibration mode and a stress vibration mode, and obtaining full-field stress by utilizing modal response and based on modal extension; and solving a stress intensity factor-time history of the crack tip by adopting a stress extrapolation method, obtaining a delta K-R-n three-variable rain flow matrix by utilizing a rain flow counting method, and finally carrying out residual life evaluation through an iLAPS model. According to the method, system dynamics, fracture mechanics and modal order reduction technologies are fused, a new efficient and accurate fatigue life prediction path adapting to complex working conditions is provided for additive manufacturing components with defects, and the method has remarkable engineering application value and popularization potential.

The invention relates to the technical field of circuit breaker service life evaluation, in particular to a high-voltage circuit breaker residual service life intelligent evaluation system, which comprises an operation data acquisition module for acquiring operation data of a high-voltage circuit breaker; the degradation degree feature extraction module is used for extracting degradation degree features from the processed operation data; the residual life calculation module is used for constructing a residual life prediction model by utilizing the degradation degree characteristics and adopting a generalized regression neural network algorithm, and calculating the residual life of the residual life prediction model; the early warning and maintenance strategy module is used for formulating a predictive maintenance strategy of the high-voltage circuit breaker based on the residual life calculation result in combination with the residual life probability density function and the expected loss rate of the circuit breaker, and balancing the economical efficiency and the reliability; according to the method, the degradation process of the circuit breaker is reflected in real time, potential faults of equipment are predicted in time, and delay and inaccuracy caused by traditional experience judgment and regular inspection are avoided.

The invention relates to the technical field of road repair state monitoring, in particular to a road repair state monitoring method and system based on edge calculation. The method comprises the following steps: acquiring planning information of a repaired road, deploying 3D laser scanning vibration meters at a plurality of repairing points, collecting pavement vibration signals in different time periods, and performing disordered distribution time domain feature analysis to obtain a vibration signal disordered distribution time domain graph; then, structure weakening behavior simulation is carried out based on the graph, periodic structure weakening data is quantified, and a pavement water damage cumulative gradient is obtained through water damage cumulative gradient estimation; and finally, in combination with the periodic structure weakening data and the water damage cumulative gradient, carrying out service life evaluation on the restoration state, and sending an evaluation result to the terminal. According to the invention, the road repair state monitoring technology is optimized, so that the road repair state monitoring technology is more perfect.

The invention provides a product storage life evaluation method based on multi-stage success and failure data, which evaluates the storage life of a product by adopting a discrete parameter algorithm according to multi-stage success and failure natural storage test data of the product, and comprises the following steps: S1, performing zero-moment failure probability distribution fitting and mean value estimation, and according to prior information and factory data, calculating a failure probability distribution value; performing fitting on zero-moment failure probability distribution by adopting beta distribution, and calculating expected mean estimation of the zero-moment failure probability distribution; s2, abnormal value screening and order-preserving processing of fusion data are carried out, multi-stage data are combined, an error threshold value is set, abnormal points are eliminated, and order-preserving processing is carried out; s3, discretizing position parameters, and carrying out Weibull parameter estimation; the interval range of the position parameters is determined, equal interval division is carried out, and corresponding translation processing is carried out on the data; and S4, life parameter selection and abnormal point consistency judgment. According to the method, multi-stage success and failure data can be fully utilized, and the product evaluation precision is improved.

The invention discloses a lithium ion battery health life assessment method and system based on a feature image, and the method comprises the steps: pre-training a battery health life assessment model through the sample data of a source domain, and building a mapping relation between the feature image and the health life of a lithium ion battery, and the feature image is generated based on an incremental capacity feature; introducing transfer learning and utilizing sample data of a target domain to optimize a battery health life evaluation model; and extracting incremental capacity features for the to-be-evaluated lithium ion battery in the target domain, generating a feature image of the lithium ion battery, and inputting the feature image into the optimized battery health life evaluation model to obtain the corresponding health life. According to the method, the lithium ion battery health life evaluation model obtained based on source domain data training can better adapt to the battery characteristics and working conditions of the target domain so as to solve the problems of battery aging and differences influenced by various external environment factors, and therefore the accuracy of lithium ion battery health life evaluation on the target domain is improved.

The invention discloses an electric vehicle vehicle-mounted battery health degree prediction method and system based on a neural network, and relates to the technical field of battery health degree prediction, and the method comprises the steps: collecting battery multi-source heterogeneous data, extracting key health factors through preprocessing, room temperature correction and staged feature engineering, and introducing a time window embedding strategy to construct a time sequence sample; a multi-module U-BiLSTM hybrid network is adopted as a core prediction model, and Bayesian optimization and an Adam optimizer are combined to complete hyper-parameter optimization and model training; and a prediction result is optimized through digital twinning correction, a physical compensation mechanism and residual service life label normalization processing. The method solves the problems that a traditional method is insufficient in deep feature extraction, low in time sequence information utilization rate and poor in prediction precision under complex working conditions, the aging state and the remaining service life of the battery can be accurately reflected, and reliable technical support is provided for safety management, operation and maintenance optimization and service life evaluation of the battery of the electric vehicle.

The invention relates to an energy accumulator durability test method and system, and the system comprises an extreme working condition simulation module which can achieve high-voltage, wide-temperature-range and high-frequency dynamic loading; the multi-sensor monitoring network integrates a fiber bragg grating strain sensor, a three-axis acceleration sensor and an acoustic emission sensor to realize collaborative monitoring of strain, vibration and microcracks; the intelligent control and energy recovery module is combined with a fuzzy PID and reinforcement learning algorithm, a magnetorheological fluid generator and a super capacitor bank, so that the control precision and the energy utilization rate are improved; and the data analysis and life evaluation platform accurately predicts the residual life by using an improved Bayesian network model and a real-time health index calculation module. According to the test method, a loading mode is dynamically adjusted through a real-time health index, an energy recovery strategy is optimized, and life early warning is triggered. The invention breaks through the limitation of the traditional test and provides an integrated solution.

The invention relates to the technical field of semiconductor device process reliability testing, and discloses a method, a device and equipment for predicting the time-dependent breakdown life of an insulating dielectric layer, and a medium, and the method comprises the steps: carrying out the ramp voltage testing of the insulating dielectric layer in a plurality of to-be-predicted devices; determining the breakdown electric field intensity of each to-be-predicted device according to the ramp voltage test result, the nominal insulation dielectric layer thickness and the specified voltage; determining the TDDB life of each insulating dielectric layer according to an electric field acceleration model corresponding to the TDDB and the breakdown electric field intensity of each to-be-predicted device; constants in the electric field acceleration model are determined according to TDDB test results obtained by carrying out TDDB tests on historical devices. According to the scheme disclosed by the invention, the slope voltage test is performed on the to-be-predicted device and the TDDB test result of the historical device is combined to predict the TDDB life of the insulating dielectric layer, so that the TDDB life prediction efficiency of the insulating dielectric layer is improved, and the TDDB life evaluation is quickly and effectively performed.

The invention discloses a digital operation management system and method, and relates to the technical field of digital operation management, and the system comprises an operation monitoring module which is used for carrying out the communication with a distributed industrial Internet of Things node through a network interface, collecting production environment parameters and process data, and forming equipment baseline parameters; the knowledge graph engine module is used for constructing an equipment relation network and process flow mapping based on the equipment baseline parameters; the predictive maintenance module is used for carrying out equipment life evaluation and fault early warning according to the relational data of the knowledge graph engine module; the decision support module is used for generating a resource scheduling scheme based on equipment life evaluation and fault early warning in combination with a historical decision effect, and transmitting the resource scheduling scheme to the digital twinning display module; and the digital twinning display module is used for displaying the real-time operation state of the factory by utilizing a three-dimensional visualization technology. The data processing efficiency, the resource utilization rate and the flexibility in the production process are improved, and global monitoring and intelligent optimization of the production process are achieved.

The invention relates to the technical field of wind driven generators, in particular to a wind turbine blade fatigue life evaluation method based on data driving, which comprises the following steps: acquiring blade original data of a wind driven generator; performing feature extraction on the blade original data of the wind driven generator based on the blade fatigue prediction model to obtain a blade fatigue prediction result of the wind driven generator, and solving a stress state accumulated damage value in the blade original data of the wind driven generator; and calculating the final fatigue of the blade of the wind driven generator by combining a blade fatigue prediction result, a stress state accumulated damage value and an environment correction factor of the wind driven generator and taking error minimization of the blade fatigue prediction result and the stress state accumulated damage value as an optimization target. And completing the service life evaluation of the wind driven generator blade based on the final fatigue of the wind driven generator blade. According to the method, accurate matching of fatigue prediction and actually measured damage values can be realized, and a reliable decision basis is provided for maintenance of wind power equipment.

The invention relates to the technical field of life evaluation, in particular to a turbine blade life evaluation method which comprises the following steps: acquiring surface temperature gradient data of a turbine blade of a gas turbine, extracting the temperature change rate of each area of the blade, analyzing the influence of the temperature change rate on the local stress of the blade, and evaluating the life of the turbine blade. And calculating a thermal stress synthetic effect by combining the blade rotation inertia force and the aerodynamic load to obtain blade thermal stress distribution data. According to the method, the thermal stress synthesis effect is calculated by acquiring the temperature gradient data of the surface of the blade and combining the rotary inertia force and the aerodynamic load, the thermal stress distribution characteristics of the blade in different operation states can be accurately reflected, and the life attenuation rate is calculated by combining the current working condition, so that life prediction can be adjusted along with the operation states; the service life offset is calculated by analyzing the influence of the start-stop load of the gas turbine and comparing creep fatigue joint distribution, so that the influence of the running state of the gas turbine on the service life prediction can be dynamically updated in real time.

The invention provides a damp heat-oxygen-internal pressure-load coupled non-metal pipeline aging device and a method capable of evaluating the service life of an in-service non-metal pipeline. The non-metal pipeline aging device comprises six parts, namely an inner cavity, a box body temperature control system, a box body humidity control system, a pipeline internal pressure control system, a pipeline external load loading device and an air supply circulating system, and an accelerated aging test of a non-metal pipeline under different working condition combinations of four factors, namely damp heat, oxygen, internal pressure and load can be carried out by virtue of the aging device. Measuring oxidation induction periods before and after aging of the in-service non-metal pipeline and the non-metal pipeline with the same mark through a differential heat scanning method, substituting the oxidation induction periods into a non-metal pipeline aging life prediction formula coupled with damp heat-oxygen-internal pressure-load factors, and calculating to obtain the service aging life. According to the method, damp heat-oxygen-internal pressure-external load multi-factor coupling is realized, so that an actual service environment is accurately simulated, and comprehensive technical support can be provided for safe operation of the non-metal pipeline.

The invention discloses a multi-axis high-low-cycle composite heat engine fatigue test device and a test method, and relates to the technical field of fatigue test.A clamping swing mechanism and a high-cycle loading mechanism in the device are located on two planes perpendicular to each other, a low-cycle load acts on a test piece through a fatigue testing machine, and a high-cycle load acts on the test piece; the high-cycle load acts on the clamping swing mechanism through the high-cycle loading mechanism, so that high-cycle loading and low-cycle loading of different paths are achieved, and multi-axis loading of the vibration load is achieved due to the asymmetry of the clamping swing mechanism. According to the fatigue test device and test method for the multi-axis high-low-cycle composite heat engine, the real load environment of a blade material is considered, engineering practice is fitted, and a basis is provided for life evaluation.

The invention provides a power transmission line hardware service life evaluation method and system based on an aeolian vibration finite element, and relates to the technical field of power transmission line health evaluation, and the method comprises the steps: carrying out the element division of a fastening hardware finite element model based on a element division standard; applying the aeolian vibration pre-test scheme to the finite element model of the fastening metal, and obtaining a pre-test reference data set; obtaining a pre-test reference result based on the pre-test reference data set; a life diagnosis breeze characteristic limit stored in a database is matched based on a pre-test reference result; aeolian vibration environment parameters of the fastening fitting are obtained, aeolian vibration environment characteristic values are calculated, and a fastening fitting service life diagnosis curve is matched based on the service life diagnosis aeolian characteristic limit and the aeolian vibration environment characteristic values; acquiring stress characteristic parameters of each unit element based on the finite element model of the fastening fitting, acquiring a stress characteristic value of the unit element based on the stress characteristic parameters, and acquiring a service life diagnostic value of the fastening fitting based on the stress characteristic value of the unit element and the service life diagnostic curve of the fastening fitting.

The invention discloses an escalator monitoring operation and maintenance method and system based on multi-source data fusion, and relates to the technical field of escalator operation and maintaining.The method comprises the steps that multi-source parameter collection is conducted on a target escalator, and a multi-source sensing data set is generated; transmitting the multi-source sensing data set to a data fusion processing center in real time; generating a fusion data set; running state monitoring of the target escalator is executed, and an early warning signal is sent out according to the monitoring risk value; and performing performance evaluation and risk grade division on the target escalator, executing a preventive maintenance decision, and sending the preventive maintenance decision to an operation and maintenance management user. The technical problems that in the prior art, monitoring of the running state of the escalator is not accurate, risks are difficult to perceive in time, and consequently the running safety and stability of the escalator are difficult to guarantee are solved, a perfect escalator intelligent operation and maintenance management system is constructed, and the safety and reliability of the escalator are improved. The technical effects of performance evaluation, risk grade division, preventive maintenance, environmental adaptability transformation, emergency response and service life evaluation are achieved.

The invention provides a packaging material intelligent selection method and system based on multi-dimensional data analysis, and relates to the field of material management. Vibration, deformation and temperature and humidity data are collected in real time through an environment sensing module to construct a feature matrix; the data analysis module fuses the historical case library and the logistics task duration to predict the material life, and associates the supply chain inventory to generate residual life evaluation; the decision engine module dynamically distributes cost, safety and environmental protection weights, and combines the supplier credit score to generate a multi-level decision scheme; the execution control module verifies the dimensional tolerance of the material and drives replacement operation, and model calibration is triggered through a 5% deviation threshold value; a humidity sensitive material grading standard and a deformation safety limit value are built in the system, and when the temperature and humidity sudden change exceeds the threshold value, deformation quantity prediction and emergency scheme reconstruction are started; a three-level self-optimization mechanism of environmental perception error, material life error and execution error is established, and closed-loop optimization is realized through sensor calibration, supplier credit recalculation and dynamic adjustment of control parameters.

The invention discloses a fatigue test method for a cabin structural member of an offshore wind power generator. A test piece is subjected to surface treatment, a multi-sensor array is arranged, the test piece is placed in a combined system of a multi-axis dynamic loading unit and a collaborative environment simulation cabin, an intelligent control and analysis platform synchronously applies a multi-axis alternating load, salt spray corrosion and a circulating temperature and humidity environment, and real marine service conditions are simulated; the state of a test piece is evaluated in real time based on criteria such as a strain concentration coefficient and acoustic emission energy, a strengthening test is started for potential damage risks, a load strategy is adjusted to track crack propagation when microcracks are monitored, and after the test is ended, a fatigue life prediction model is established or calibrated by combining failure test piece analysis data. And outputting a life evaluation result and a structure improvement suggestion. Through the multi-factor collaborative simulation and self-adaptive control technology, the authenticity and efficiency of the test are improved, and technical support is provided for design optimization and reliability guarantee of offshore wind power cabin structural parts.