342 results about "Life span" patented technology

The invention relates to the technical field of distributed resource management and optimization, in particular to a distributed resource life cycle evaluation and dynamic updating method, which comprises the following steps: data acquisition: collecting multi-modal data of distributed resources, including resource state data, use behavior data, environment data and historical maintenance data; feature extraction: carrying out feature engineering processing on the multi-modal data, and extracting key indexes such as a resource health state, a degradation rate and environmental sensitivity; building a life cycle evaluation model, building the model by using the time sequence data of the resources based on a multi-task learning framework, and predicting the health state and the residual life of the resources; and optimizing a dynamic updating strategy, and monitoring and dynamically adjusting in real time. The method provided by the invention overcomes the problems of insufficient life cycle dynamics and rigid updating strategy in a traditional method, has the characteristics of high efficiency, intelligence and accuracy, and is suitable for intelligent management and optimization of new energy equipment, industrial assets and other distributed resources.

The invention discloses a digital twinborn model construction method, a life prediction method, a model construction device and equipment of a crane, and relates to the technical field of industrial Internet of Things, the method comprises the following steps: constructing a multi-dimensional digital twinborn model, the multi-dimensional digital twinborn model comprises a physical entity layer, a connection layer, a virtual model layer, a twinborn data layer and a service function layer, the virtual model layer comprises a target twin model, and the service function layer corrects key components of the crane; transmitting the physical parameters of the crane to a connection layer for preprocessing to obtain a physical data set; the physical data set is transmitted to a virtual model layer, and an initial twin model is obtained through kinetic analysis and knowledge graph processing; and transmitting the virtual data set to a twin data layer, calculating target correction parameters according to the physical data set and the virtual data set, and correcting model parameters of the initial twin model to obtain a target twin model. According to the method, the problem of insufficient physical mapping precision of the existing virtual model is solved.

The invention provides a method and device for constructing a hydraulic pump service life prediction model. The method provided by the invention comprises the following steps: acquiring segmented life test data and continuous life test data of the hydraulic pump; performing degradation feature analysis on the segmented life test data and the continuous life test data to obtain a first fusion feature and a second fusion feature; wherein the first fusion feature is a feature corresponding to the segmented life test data, and the second fusion feature is a feature corresponding to the continuous life test data; constructing a first life prediction model according to the first fusion feature; constructing a second life prediction model according to the second fusion feature; and fusing the first life prediction model and the second life prediction model to obtain a life prediction model. According to the method and device for constructing the hydraulic pump life prediction model, the problems that degradation data of the whole life cycle of the hydraulic pump are incomplete, and the hydraulic pump life prediction model is difficult to construct can be solved.

The invention discloses a concrete bridge reinforcement carbon emission calculation method based on a fiber reinforced composite material, and the method comprises the steps: obtaining the carbon emission data and material attribute data of the fiber reinforced composite material, i.e., an FRP material, in a whole life cycle, and building an FRP material database; establishing a bridge building reinforcement information model according to the design model of the concrete bridge and the acquired bridge data, performing static data-driven open-loop semi-entity simulation on the bridge building reinforcement information model, and continuously optimizing and updating the selected FRP material; constructing a carbon emission calculation model of the full life cycle of the bridge reinforcement project based on the FRP material database; and determining the total carbon emission of the concrete bridge reinforcement construction based on a carbon emission calculation model and the optimally selected FRP material. According to the method, the carbon emission of the FRP material in the bridge reinforcement process can be accurately evaluated, the material selection is optimized, the influence on the environment is reduced, the sustainable development is promoted, and the scientificity and accuracy of decision making are improved.

The invention discloses a tensile total strain energy density-based life prediction method considering a stress relaxation effect, which comprises the following steps of: firstly, performing tests in a strain control mode at the same temperature to obtain mechanical response parameters and test life of a material in each test in a half-life cycle, and then calculating the tensile total strain energy density based on the mechanical response in a load holding stage. Fitting a stress relaxation model, representing the inelastic strain energy density caused by various loads by taking the area under each region of a half-life cycle hysteresis loop as a damage parameter, meanwhile, considering the complex effect of a material, correcting the influence of average stress on the life, and finally, establishing a life prediction model based on a strain energy density-frequency separation method. And the fatigue-creep life prediction is realized. The model of the method can comprehensively consider the influence of various important factors such as stress relaxation, loading waveform, loading frequency, average stress and the like on the service life, and the area of the hysteresis loop serves as a damage parameter, so that the model has certain physical significance and has high universality in practical engineering application.

The invention provides a humanoid robot joint life monitoring method, and belongs to the technical field of life monitoring. The method comprises the steps that 1, a multi-mode sensor array is constructed to collect original joint motion data of a target robot; 2, standardizing the original joint movement data according to feature types to obtain standard feature data of all parameter types; 3, establishing a joint degradation model of the target robot based on the standard feature data; 4, inputting real-time motion data of the target robot into the joint degradation model, and outputting a joint residual life index; and 5, when the residual life index of the joint is lower than a preset threshold value, a self-adaptive maintenance strategy is triggered. Through real-time monitoring of joint health, early warning and optimization maintenance, the reliability of the robot is improved.

The invention discloses a charging pile part life cycle prediction and management method, and relates to the technical field of charging piles, and the method comprises the steps: constructing a multi-source data collection system, and obtaining the operation state, environment parameters and user behavior data; key characteristic variables are extracted through principal component analysis, and health state evaluation indexes are generated through a coupling aging model; an improved Transform prediction network is designed to realize accurate prediction of the residual life; and making a dynamic operation and maintenance strategy based on a prediction result and optimizing a maintenance period. The prediction precision can be improved through the environment adaptation factor and the nonlinear mapping layer, the maintenance cost is reduced, and the equipment availability rate is improved.

The invention discloses an inner mold vibration mechanism for a concrete component vibration table, including a vibrator body, the vibrator body is supported by a flexible mounting frame, the eccentric vibrator of the vibrator rotates to generate horizontal vibration force, and the outer wall of the vibrator body is provided with a vibration transmission mechanism for contacting and connecting with the inner mold, and there is at least one group of vibration transmission mechanisms, each group of vibration transmission mechanisms includes at least one fixed contact piece and at least one telescopic contact piece, or includes at least two telescopic contact pieces. The advantages of the vibrator are: it can be applied to the molding of concrete components of different sizes, and the hydraulic vibration transmission connection structure can ensure a tight connection and strong transmission capacity; the horizontal vibration mode is adopted, which consumes less energy, has higher stability and longer service life; the flexible installation is adopted, and the transmission shaft is vertically retractable, which can eliminate unnecessary vertical stress generated during the rotation of the eccentric vibrator, and ensure the quality of the concrete component; it is convenient to be sleeved with the vibrator during the hoisting process of the molding mold.

The invention relates to a bearing life prediction method and system fusing depth separable cavity convolution and an attention mechanism, and the method comprises the steps: firstly collecting the whole life cycle vibration signal of a bearing, and constructing a training sample through standardization processing and sliding window reconstruction; secondly, establishing a DSDC-SE-BiGRU-ATT prediction model which adopts depth separable cavity convolution to enlarge a receptive field while reducing a parameter quantity, adaptively calibrating a feature channel weight through an SE channel attention mechanism, capturing bidirectional long-term time sequence dependence by using a BiGRU network, and introducing a zoom dot product attention mechanism to focus a key degradation stage; and finally, taking a mean square error as a loss function, adopting an Adam optimizer to train the model, inputting online monitoring data into the trained model, and outputting a life prediction value. The problems that in an existing method, calculation complexity is high, long-time-sequence feature capture is insufficient, and noise interference is serious are effectively solved, and the precision and efficiency of bearing service life prediction are remarkably improved.

The invention relates to the technical field of data processing, in particular to a device and a system for predicting the service life of an automobile universal joint, and aims to realize personalized prediction of the service life of the universal joint according to different driving habits of users. The device comprises a driving style analysis model which analyzes driving habits in combination with historical data and identifies the driving style of a user; the personalized service life prediction module is used for constructing a personalized service life prediction model and adaptively selecting the service life prediction model according to the driving style to perform service life prediction on the universal joint; wherein the personalized life prediction model comprises a first prediction model, a second prediction model and a third prediction model; and the abnormal mode detection module is used for monitoring the running state of the universal joint, optimizing the personalized life prediction model when an abnormal condition is detected, and dynamically correcting a life prediction result.

The invention discloses an intelligent prediction method and system for the life cycle of a building material, and the method comprises the steps: obtaining the failure data and state data of the building material, fitting the failure data to obtain the basic predicted life of the building material, calculating the comprehensive damage degree according to the state data of the building material, calculating the environmental impact factor according to the environmental parameter, and predicting the life cycle of the building material. And calculating a first predicted life, a life deviation coefficient, an environmental damage degree, a loss life and a second predicted life, constructing a building material life cycle intelligent prediction model according to a calculation result, and inputting failure data and state data of a to-be-predicted building material into the building material life cycle intelligent prediction model to obtain a predicted life and a risk factor. The method not only can improve the efficiency and accuracy of building material life cycle intelligent prediction, but also has good interpretability, and can be directly applied to a building material life cycle intelligent prediction system.

The invention discloses a ship engine component cross-working-condition life prediction method and system, and the method comprises the steps: collecting the operation data of key components of a ship engine in real time, and carrying out the preprocessing of the operation data of each component, so as to guarantee the data synchronism and data validity of the operation data; inputting the preprocessed operation data into the corresponding trained life prediction network model, and outputting a life prediction result corresponding to each component; the life prediction network model comprises a feature extraction module used for extracting time sequence features under different scales, splicing all the time sequence features on a feature dimension, performing feature enhancement on a splicing result based on a Transform model, and outputting a global feature vector after compression of a global average pooling layer; the life prediction module is used for outputting a life prediction result according to the global feature vector; the domain discrimination module is used for outputting a domain discrimination result according to the global feature vector; in training, the feature extraction module and the domain discrimination module carry out adversarial training.

The invention discloses a source load impact and system bearing life evaluation method and system, equipment and a medium, and relates to the technical field of traction power supply, and the method comprises the steps: collecting data to recognize impact, building a source load model, analyzing an impact diffusion path, evaluating system bearing, fusing multi-dimensional damage, calculating the residual life of the equipment, and automatically updating parameters. And generating a closed-loop regulation and control strategy. The system comprises a data analysis and dynamic modeling module, a bearing evaluation and conduction analysis module, a damage coupling and life prediction module and a parameter correction and strategy generation module. The traction impact time domain-frequency domain-topology integrated quantization is realized, and the problem of impact time-space diffusion assessment is solved. A life model is constructed, and multi-dimensional stress damage is accurately mapped. The micro-simulation and on-line filtering technology is utilized to ensure that model parameters are adaptively updated along with working conditions, and the problem of disjunction between evaluation and actual conditions is solved. The method supports side cloud cooperation and closed-loop pre-control, effectively improves the safety margin of the power grid, and delays the aging of equipment.

The invention discloses a multi-dimensional feature fusion-based high-speed optical module life prediction and fault early warning method. The method comprises the steps of constructing a physical-signal double-layer high-frequency feature space containing bottom-layer physical state data and upper-layer signal quality data; a full-life-cycle self-adaptive physical constraint network model is constructed and trained, a degradation stage self-recognition module is arranged in the model, and the weights of physical constraint items such as monotonicity and curvature in a loss function can be dynamically adjusted according to the running-in period, the stable period or the aging acceleration period of the optical module; and finally, performing graded early warning based on the quantized prediction uncertainty. According to the invention, the physical constraint can be adaptively applied according to the life cycle stage of the optical module, the problem of inaccurate prediction caused by poor adaptability of the traditional static constraint in different stages is solved, and the accuracy and robustness of life prediction are remarkably improved.

The invention discloses a service life prediction system and an air conditioning system, and the system comprises a data collection module which is used for collecting the operation data of a to-be-predicted device; the data analysis module is used for calculating the life loss of the to-be-predicted device according to the operation data; and the life prediction module is used for estimating the residual life of the to-be-predicted device according to the life loss. Compared with the prior art, the service life of the to-be-predicted device can be accurately predicted according to the environment parameters and the operation parameters of the to-be-predicted device, so that the to-be-predicted device is maintained and replaced in advance, the service life of the whole system is effectively prolonged, and fault risks and resource waste are reduced.

The present application relates to a shield cutter health status assessment method based on Seq2Seq. After collecting data, the score of the time domain features is calculated for feature extraction, and the first 5% of the data is selected as a training set. All collected data is used as a test set, and the model is trained with the training set. The test set is then input into the GRU-based Seq2Seq model to output the difference, and a full life cycle degradation curve is drawn based on the difference graph. The wear state of the cutter disc is judged based on the full life cycle degradation curve. The present application can evaluate and predict the life cycle of the tool based on the tool wear mechanism to accurately control the timing of tool change.

The invention relates to the technical field of rubber materials, and relates to a rubber multi-stress aging life prediction method based on an Eyring model and a PSO algorithm, and the method comprises the steps of multi-stress aging experiment data collection, single degradation track curve modeling, reference condition and acceleration factor definition, Eyring model establishment and parameter identification, and extrapolation of life under natural use conditions. According to the method, the stability of parameter estimation is remarkably improved by introducing a two-stage parameter identification method, a parameter initial value is obtained through individual curve fitting, then a shape parameter alpha and a position parameter gamma are fixed, a rate parameter beta is identified again, and the parameter standard deviation can be reduced; the ratio of the reaction rates is introduced as an acceleration factor for parameter identification instead of directly identifying the reaction rates, so that the dimensional influence and experimental system errors are effectively eliminated, data of different batches of materials under different experimental conditions have comparability, and the engineering applicability of the model is remarkably improved; the PSO algorithm is adopted to carry out global parameter optimization, so that the prediction precision and the calculation efficiency can be effectively improved.

The invention relates to a supply chain management method, in particular to a circulation box full life cycle management method and system based on a block chain and a program product. According to the method, logistics event information is collected and layered splitting is carried out, three parallel block chains including a logistics main chain, a carbon evaluation side chain and a prediction side chain are constructed, cross-chain atomic writing is realized in combination with two-stage submission and BLS aggregation signature, the residual life and path of a circulation box are predicted by using a digital twin model, and a carbon quota is dynamically allocated through deep reinforcement learning, so that the residual life and path of the circulation box are predicted. And finally, the position information privacy is protected by means of zero-knowledge proof. The circulation efficiency of the circulation box, the carbon emission reduction transparency and the system safety are remarkably improved.

The invention relates to the technical field of fan blade life prediction, and discloses a large fan blade life prediction method based on a simulation model. The method comprises the following steps: acquiring a stress-strain data stream and an operating environment data stream of a blade, and analyzing the stress-strain data stream by means of a preset life constraint index to obtain a blade life distribution characteristic; combining a prediction decision rule base and a life fitness condition, integrating the life distribution characteristics and the operation environment data flow to generate a preliminary life prediction scheme, and pre-judging the leaf life distribution characteristics and environment data change by adopting a trend analysis technology to obtain predicted life distribution characteristics and predicted environment data; and optimizing and correcting the initial scheme according to the prediction data to form an optimized life prediction scheme, thereby realizing the life prediction of the large-scale fan blade. According to the method, multi-source data are integrated, trend analysis and scheme optimization are combined, prediction reasonability and accuracy are improved, and reference is provided for blade operation and maintenance.

The invention belongs to the technical field of semiconductor testing, and discloses a WAT test probe card life cycle management method and system. The method comprises the following steps: collecting full-life-cycle multi-source data of a probe card, performing unified management, and constructing a probe card digital twin data model; carrying out change point detection and segmentation on monitoring time sequence data, and constructing a stable fragment library; extracting a unified feature vector including electrical contact, test distribution, space structure and life load; calculating a needle card health index based on a machine learning regression model; predicting the remaining service life and the failure probability by using a proportional risk survival model; according to the health index, the remaining life and the failure probability, generating a maintenance decision suggestion and recommending execution time; the model is updated through maintenance result feedback, and closed-loop optimization is achieved. By means of the mode, accurate evaluation and predictive maintenance of the health state of the probe card are achieved.

The invention relates to the technical field of life prediction, and discloses a life cycle evaluation system and method for a construction device, and the system comprises a historical database module; the acquisition module is used for calculating the predicted residual life cycle of the to-be-monitored construction device based on the analysis result; obtaining a data fluctuation value according to the operation data set; wherein the operation data set comprises the operation data of the to-be-monitored construction device at the current moment and the operation data of the to-be-monitored construction device at the previous moment; the judgment module is configured to compare the data fluctuation value with a fluctuation threshold value and judge abnormal data according to a comparison result; judging whether to adjust the predicted residual life cycle or not according to the life influence factor; and the adjusting module is used for adjusting the predicted residual life cycle according to the adjusting coefficient to obtain an adjusted residual life cycle. Maintenance and replacement of the construction device can be more scientifically and reasonably arranged, accidental shutdown and production loss caused by equipment faults are avoided, and construction efficiency and economic benefits are improved.

The invention discloses a life prediction method, electronic equipment and a readable storage medium, and relates to the technical field of equipment health management, and the method comprises the steps: obtaining a monitoring index curve for a target part, and determining a health index value of the target part according to the monitoring index curve; the health index value serves as an observation value to be input into a Kalman filter, a degradation state estimation value is obtained through output, and the degradation state estimation value is used for representing the degradation degree of the target part; and determining a residual life prediction value of the target component based on the degradation state estimation value and a preset failure threshold value. According to the invention, the robustness of equipment component life prediction is improved.

The invention relates to a life evaluation model establishment method, device and equipment and a storage medium, and is applied to the field of integrated circuits, and the method comprises the steps: determining the electric stress condition of a power device, and measuring the failure time of the power device under the electric stress condition; calculating a Webster cumulative distribution function of the power device in the failure time; fitting and calculating a characteristic life value of the power device under the electric stress condition according to the Webster cumulative distribution function and a preset global shape factor; and constructing a deep neural network model, training the deep neural network model by using the characteristic life value obtained by fitting calculation, and generating an accelerated life evaluation model. The method has the technical effects that failure analysis and service life evaluation are carried out on devices with complex processes and structures, and the evaluation accuracy is improved.

The invention relates to the technical field of engineering management, in particular to a steel structure engineering full-life-cycle management method and system based on digital twinning, which can comprehensively and accurately grasp the condition of a steel structure in the full life cycle, and can monitor the dynamic evolution of the connection condition, the shape change and the design standard of the steel structure in real time. According to the method, potential safety hazards can be found in time and early warning can be carried out in advance, dynamic human-structure interaction and surface damage caused by equipment use are fully considered in aging analysis, the aging process of the steel structure can be predicted more accurately in combination with a quantitative model of environmental factors, and meanwhile, the accuracy of aging analysis is improved. According to the method, the data acquisition period can be dynamically adjusted according to safety risk conditions of different stages, resource waste is avoided, flexibility and pertinence of data acquisition are achieved, the scientific, comprehensive and efficient management method greatly improves the quality and efficiency of steel structure engineering management, and safe and stable operation of a steel structure in the whole life cycle is powerfully guaranteed.

The invention relates to the technical field of life cycle traceability, in particular to a full life cycle traceability system and method based on intelligent washing. The method comprises the following steps: acquiring washing clothes and user information, generating an RFID identification code, binding the RFID identification code with the user information, creating a user-clothes binding relationship, performing digital mirror image copying on the binding relationship, generating a clothes data file in combination with a database chain technology, and predicting the service life of the clothes by analyzing clothes state data and predicting the service life of the clothes. And making a personalized washing plan. And executing the washing plan and monitoring the washing result, and finally updating the clothes data file according to the washing effect to generate a washing traceability file. According to the invention, safer and more accurate full-life-cycle traceability operation is realized.

The invention belongs to the technical field of mechanical life prediction, and discloses a life migration prediction method based on an improved compression aggregation excitation network, and the specific technical scheme is as follows: constructing health indexes of a rolling bearing by using a deep convolution auto-encoder, and measuring the DTW distance between the health indexes to divide the degradation type of the rolling bearing; dividing the degeneration type of the bearing into gradual change and mutability; an improved compression aggregation excitation network combined with the maximum mean value difference is built, a service life prediction strategy under different degradation types is provided, and the service life of the rolling bearing is predicted under multiple working conditions; a life migration prediction model based on degradation type classification and an improved compression aggregation excitation network is provided, the model divides degradation types of the bearing under different working conditions and realizes residual life prediction, and the model has higher prediction precision and better robustness than other methods under three working conditions of a PHM2012 data set.

The invention relates to a bearing life prediction method based on deep learning of CBAM attention. The method comprises the steps of collecting a to-be-predicted bearing vibration signal; inputting the to-be-predicted bearing vibration signal into a preset bearing life prediction model, and predicting the service life of the bearing; wherein the bearing life prediction model is a CBAM attention mechanism-based parallel time convolutional network and long and short term memory network model; the training set comprises the bearing vibration signals of the whole life cycle of the normal operation stage and the fault stage of the bearing. According to the technical scheme, richer features can be extracted, and the service life of the bearing can be predicted more accurately and reliably.

The invention discloses a life bearing capacity degradation prediction method for a port and wharf caisson structure, which comprises a data acquisition module, a coupling analysis processor and a dynamic early warning output module which are arranged, and is characterized in that the data acquisition module comprises a machine vision unit, a distributed optical fiber sensing unit and a parameterized seismic oscillation input module which are arranged; through cooperative work of multiple modules, accurate prediction of the caisson structure is realized. According to the life bearing capacity degradation prediction method of the port and wharf caisson structure, machine vision and optical fiber sensing are combined, surface-interior synchronous three-dimensional quantification of caisson corrosion damage is achieved, the problem of data discreteness of a traditional method is solved, physical mechanism constraints are provided through a finite element model, model errors are corrected through machine learning, and the service life bearing capacity degradation prediction method is achieved. The bearing capacity prediction precision under near-fault pulse type seismic oscillation is improved, a lightweight parallel computing architecture is designed, dynamic coupling calculation of corrosion monitoring data and seismic oscillation input is supported, and minute-level bearing capacity threshold value updating is achieved.

The present invention belongs to the technical field of rotary joints, and discloses a rotary joint life test bench for fluid transportation, including a base, a base is fixedly installed at a position near the middle of the top of the base, a frame is fixedly installed on the left side of the top of the base, a longitudinal guide rail is fixedly installed on the right side of the top of the base, a guide block is movably connected to the inner part of the longitudinal guide rail, the guide block is displaced up and down relative to the longitudinal guide rail, and a water delivery component is fixedly sleeved in the middle of the base. The present invention directly drives the rotary joint to complete the rotation test by utilizing the rotation process of the power component, and converts the rotation action into up and down linear motion through the cooperation of the power component and the transmission component, so that each rotation can complete an upward movement process, and the number of rotations can be quickly obtained by counting each upward movement process. The whole process can be completed automatically, and the number of rotations can be calculated in real time until leakage occurs, which significantly improves the detection efficiency.

The invention discloses application of rapamycin combined with a vascular endothelial growth factor A in improvement of an ovarian tissue function. The improvement of the ovarian tissue function comprises any one or more than two of the following aspects: increasing the survival rate of follicles, prolonging the functional life of the ovarian tissue and improving the quality of oocytes. The effective concentration of the rapamycin is 500nM, and the effective concentration of the vascular endothelial growth factor A is 5ng / mL. The follicle loss dynamic after OTCT is tracked through a mouse model, it is found that rapid follicle loss is related to tip cell damage and primordial follicle activation, then a rapamycin and vascular endothelial growth factor A combination strategy is provided, follicle loss can be remarkably reduced, the reproductive function period can be prolonged, and the oocyte quality can be improved; according to the invention, a systematic solution is provided for OTCT technology optimization.