754 results about "Vibration acceleration" patented technology

The invention discloses a mechanical transmission system fault trend prediction system based on dynamic feature recognition, and relates to the technical field of mechanical state monitoring. Comprising the following steps: synchronously acquiring a load torque signal and a lubrication state parameter signal of a transmission system and vibration acceleration signals of a plurality of measuring points through a signal acquisition module; the working condition decoupling characteristic generation module carries out time-frequency analysis on the vibration signal, calls a pre-stored load disturbance spectrum template according to a load torque signal to carry out adaptive differential processing so as to eliminate load fluctuation interference, and calls a correction rule set according to a lubrication state parameter signal to carry out form recombination on the signal so as to compensate the lubrication state influence; and finally outputting a working condition decoupling feature representing the health state of the mechanical part. And the trend prediction module calculates and obtains fault development trend and residual life estimation data through a pre-trained fault prediction model. According to the method, the dynamic characteristics representing the essential degradation of the part are effectively extracted, and the accuracy and reliability of fault trend prediction of the mechanical transmission system are improved.

The invention discloses a modularized intelligent tubular column conveying system based on digital twinning and self-adaptive control, and belongs to the technical field of digital twinning. The modularized intelligent tubular column conveying system is characterized in that a digital twinning model integrating a mechanical structure, electrical control and hydraulic power attributes is constructed; acquiring multi-source sensing data including an image sequence, a three-dimensional point cloud and vibration acceleration in real time, driving the digital twin model to perform synchronous simulation, and calculating a deviation value between the digital twin model and internal simulation data; on the basis of the deviation value, a control logic parameter adjusting instruction is generated through a predefined mapping rule, then updated control logic parameters are executed, and a complete pipe column conveying process is simulated to generate a predictive system state sequence; and finally, the safety-related state subsets are issued to a field controller to guide and execute preventive actions. According to the invention, deep fusion and closed-loop control of digital twinning and physical entities are realized, and the debugging efficiency, the environmental adaptability and the operation safety of a tubular column conveying system are effectively improved.

The invention discloses a robot track online compensation and precision control method and system based on multiple sensors, and belongs to the technical field of robot track control. The method comprises the following steps: acquiring target trajectory data and original data of each sensor, and performing time alignment on the original data of each sensor to generate a fusion data set comprising vibration acceleration data and pose data; performing time-frequency feature extraction based on the vibration acceleration data to obtain a dominant vibration frequency spectrum; constructing a mechanical transfer function model of the joint driving torque and the tail end vibration displacement; based on the mechanical transfer function model, constructing an adaptive notch filter to generate a feed-forward compensation sequence; according to the target trajectory data, the pose data and weight parameters, updated in real time, of the sensors, a trajectory fusion deviation value is obtained through calculation; and after the feedforward compensation sequence and the trajectory fusion deviation value are integrated, a joint driving current instruction is generated based on a fuzzy PID control algorithm. The robot trajectory control precision can be improved.

The invention discloses a rotating machine vibration protection method and system of an adaptive resonance neural network, and the method comprises the steps: collecting a millisecond vibration acceleration signal of a rotating machine through a distributed piezoelectric sensor array, obtaining the real-time working condition parameter of equipment, and generating a time-work synchronous vibration signal flow; inputting the time-work synchronous vibration signal flow into a resonance neural network, and dynamically generating a frequency domain-time varying adaptive threshold group in combination with the working condition parameters; extracting a weak impact resonance characteristic tensor by using a multi-scale resonance kernel of the resonance neural network; projecting a weak impact resonance characteristic tensor to an orthogonal fault subspace through a tensor decomposition algorithm, and separating out a decoupling fault characteristic matrix; and generating a real-time protection decision instruction set based on a comparison result of the decoupling fault feature matrix and the equipment operation historical database. According to the embodiment of the invention, the method can improve the detection sensitivity and positioning precision of a weak fault under an unsteady working condition, and achieves the real-time adaptive optimization of the vibration protection of the rotating machine.

The present invention relates to the technical field of intelligent out-of-distribution fault detection for construction machinery, and discloses an out-of-distribution fault detection method and system based on energy propagation and graph learning, and the method includes: acquiring vibration acceleration signals in typical fault states, carrying out similarity calculation to obtain an adjacency matrix composed of the maximum mutual information coefficients, and taking the adjacency matrix as input in a graph neural network; carrying out feature extraction on the adjacency matrix through adopting a GraphSage graph convolution method, and generating each node representation; calculating an energy score of each node, and distinguishing between in-distribution data and out-of-distribution data; and enhancing out-of-distribution data confidence estimation for each node, and carrying out out-of-distribution data identification and out-of-distribution data detection under different working conditions of a rolling bearing.

The invention discloses a bridge service intelligent evaluation and early warning method and system based on dynamic and static load-multi-modal data fusion, and the method comprises the steps: S1, installing a vibration acceleration sensor and a strain gauge at a bridge control part, and collecting data; locally preprocessing data by adopting an edge computing framework, extracting feature values, encrypting and transmitting the feature values to a cloud; identifying and collecting diseases, and combining a deep learning network model to realize pixel-level crack segmentation; s2, establishing a space-time-measuring point-disease three-dimensional correlation model; s3, a three-level early warning threshold system is set, different early warning levels have different requirements for vibration amplitude and crack length indexes, and when a monitoring index exceeds a threshold value, early warning information is pushed to a terminal in real time through 5G; S4, a decision engine is constructed based on a Q-Learning algorithm or a deep reinforcement learning model, early warning levels, residual life and maintenance cost parameters are input, and the early warning level, the residual life and the maintenance cost parameters are calculated. And outputting the optimal maintenance scheme.

Disclosed in the present invention is a rock porosity prediction method based on the inversion of few-shot while-drilling data. While-drilling parameters of a drilling rig during a laboratory or on-site drilling process are collected, wherein the while-drilling parameters comprise parameters such as torque M, thrust force F, rotation speed N, penetration rate V, drill pipe amplitude A and vibration acceleration a; different types of while-drilling parameter data which have been subjected to multiple instances of denoising processing are inputted into a BP-GA model for outlier removal, data augmentation and iterative calculation, so as to form a new while-drilling data set; and while-drilling parameter time-frequency domain feature maps of rocks having different porosities are inputted into a VG-CNN prediction model, an inversion model between while-drilling parameter time-frequency domain features and the porosities is obtained by means of training and learning, and finally inversion prediction is performed on the rock porosities on the basis of on-site real-time while-drilling parameters. The present method enables fast, accurate and quantitative inversion and prediction of rock porosities on the basis of a small amount of borehole while-drilling data.

The invention relates to the technical field of transformer iron core current suppression, and particularly discloses a transformer iron core current closed-loop suppression method and system based on digital twin drive, and the method comprises the following steps: collecting multi-dimensional sensing data of iron core current, oil temperature, environment temperature and vibration acceleration in real time, preprocessing the data, and storing the data according to a time sequence; and constructing and dynamically calibrating a transformer iron core digital twinborn model so as to reflect the coupling dynamic response of the iron core current, the switching resistance, the temperature and the vibration. According to the transformer iron core current closed-loop suppression method based on digital twinborn driving provided by the embodiment of the invention, a digital twinborn model reflecting an electromagnetic-thermal-mechanical multi-physics field coupling effect is constructed and dynamically calibrated by fusing multi-dimensional sensing data such as iron core current, oil temperature, environment temperature, vibration acceleration and the like; and in combination with self-adaptive dynamic safety threshold generation, model prediction control and a closed-loop feedback updating mechanism, the limitation that a traditional method depends on fixed parameters and lacks dynamic adjustment capability is effectively broken through.

The invention discloses a cutter state real-time monitoring and compensating method, system and equipment and a medium, and relates to numerical control precision machining. In the method, a cutting edge optical image, an acoustic emission signal time domain waveform, a vibration acceleration parameter and real-time temperature data of a cutting area of a cutter in the machining process are collected; measuring the maximum width and depth of a wear belt through an image to form a wear characteristic quantized value, and judging a wear over-limit state; judging the damage state based on the continuous ultralimit time of the instantaneous mutation rate of the acoustic emission signal; judging the abnormal vibration grade and state through vibration spectrum analysis; and inputting the vibration spectrum and the temperature data into a prediction model to obtain a position offset prediction quantity. And calculating a size deviation value according to a wear / damage state, calculating a coordinate offset according to vibration abnormity, generating a cutter geometric compensation instruction or a motion trail compensation instruction, and completing real-time compensation operation. According to the scheme, the fine abnormal change of the cutter in the machining process can be captured in time, and compensation is conducted according to the fine abnormal change.

The invention provides a ballastless track joint performance degradation monitoring method and system, and relates to the technical field of track joint performance monitoring, and the method comprises the steps: knocking a ballastless track plate at a track joint through a force hammer to generate excitation, collecting a vertical vibration acceleration response signal through an acceleration sensor, and synchronously recording the knocking time and static parameters. The vertical vibration acceleration response signal is preprocessed, abnormal pulses and baseline drift are removed, a vibration event sample is formed, and joint mechanics characteristic parameters including a vibration frequency parameter reflecting rigidity degradation and a vibration attenuation parameter reflecting damping are extracted. According to the method, the joint performance evaluation index is calculated in combination with the static parameters, the degradation grade is evaluated after the evaluation index is compared with the preset threshold value, an early warning signal is triggered, the joint degradation grade and position information are provided, and monitoring and evaluation of the joint performance are achieved.

The invention discloses a spiral air duct predictive operation and maintenance method and system based on digital twinning, and relates to the technical field of air duct system state monitoring and operation and maintenance, and the method comprises the following steps: deploying a high-frequency acoustic emission AE sensor and a low-frequency vibration acceleration sensor at key nodes of a spiral air duct, and taking a fan start-stop signal and operation power as working condition labels; a high-frequency acquisition mode is automatically triggered, noise reduction processing is carried out, and a multi-mode acoustic vibration original signal data set which is provided with a time sequence and a working condition label and is subjected to preliminary noise reduction is output; the high-frequency acoustic emission AE sensor and the low-frequency vibration acceleration sensor are arranged in the spiral duct, the health state of the thin-wall structure is monitored in real time through the high-frequency acoustic emission technology, weak early-stage fault signals in the duct can be accurately captured and early warning can be given out in the fault germination stage of fretting wear, sealant aging and the like of the spiral duct, and the fault early-warning effect is achieved. Real-time monitoring, early warning and accurate operation and maintenance of the spiral air pipe are achieved.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention discloses a GIS equipment shell vibration fault identification method and system based on a physical information neural network, and relates to the technical field of GIS fault diagnosis, and the method comprises the steps: collecting a vibration acceleration signal of the surface of a GIS shell; establishing a simplified physical model of the vibration system, and determining a control equation, initial conditions and boundary conditions of the vibration system; based on the HFT-MPINN or the RL-PINN, constructing a parameter inversion model; fusing physical constraint loss, experimental data loss and multi-parameter coupling loss of the vibration system to construct a composite loss function; training a parameter inversion model by using the vibration acceleration signal to obtain key physical parameters of the vibration system; time-frequency domain analysis and finite element simulation verification are combined, and the mechanical fault type and degree of the GIS equipment shell are recognized. According to the method, the modeling precision, the noise robustness and the multi-working-condition adaptability of the high-frequency vibration signals are improved, and reliable technical support is provided for intelligent fault diagnosis of GIS equipment.

The invention relates to a bearing fault diagnosis method based on variational mode decomposition and time sequence partitioning cross attention fusion, which comprises the following steps: acquiring an original vibration acceleration signal of a rolling bearing, and constructing a standardized original data set; segmenting the standardized original data into a plurality of data blocks, and generating a time domain embedding feature; based on the time domain embedded features, extracting high-order global time domain features by using a multi-head self-attention mechanism, residual connection and a feedforward neural network; decomposing the standardized original data into a plurality of intrinsic mode functions, and extracting frequency domain distribution features through a convolutional neural network; taking the frequency domain distribution characteristics as query vectors, retrieving and matching related fault context information in the global time sequence characteristics, realizing weighted fusion of time-frequency modes, inputting fused fault representation vectors into a classifier, and calculating a result of a bearing health state; and constructing a loss function containing label smoothing and a dynamic learning rate scheduling strategy, and carrying out iterative optimization on model parameters until the model converges.

The invention relates to the technical field of cable force detection, in particular to a bridge inhaul cable fundamental frequency identification method, device and equipment, a storage medium and a product, vibration acceleration data of a bridge inhaul cable structure to be monitored in the duration time is converted into a power spectrum data set, a noise floor is calculated through the power spectrum data set, the noise estimation precision is improved, and the noise estimation accuracy is improved. Energy peak value screening is carried out on the power spectrum data, part of energy peak value data is screened out, a frequency-energy peak value array is obtained, energy evaluation and scoring are carried out on the frequency-energy peak value array and a predetermined candidate fundamental frequency through a preset fundamental frequency scoring model, and the energy evaluation and scoring precision of the candidate frequency is improved; finally, the actual fundamental frequency of the to-be-detected inhaul cable is determined through the score value of the candidate fundamental frequency, manual screening is not needed, the identification efficiency is improved, and meanwhile high-precision identification of the fundamental frequency of the inhaul cable is achieved.

The invention provides a nuclear fuel grillwork clamping device and a vibration acceleration control method, and relates to the technical field of nuclear fuel grillwork clamping devices.The nuclear fuel grillwork clamping device comprises the steps that a plurality of clamping pieces are slidably installed in a nuclear reaction box body in the first direction and connected with the multiple clamping pieces at the same time through a transmission mechanism, and the clamping pieces are connected with the nuclear reaction box body through the structural characteristics of the transmission mechanism; and the plurality of clamping pieces are enabled to vibrate in two opposite directions of the first direction. Based on the structural design, the mode that the clamping pieces are installed in the nuclear reaction box body is changed into sliding connection from fixed connection, and the multiple clamping pieces are connected at the same time through the transmission mechanism. In the nuclear reaction process, the transmission effect of the transmission mechanism can be utilized to enable the plurality of nuclear fuel grillages to vibrate along opposite directions; therefore, the vibration amplitude of the nuclear fuel grillwork is reduced, and finally the purpose of reducing the failure rate of the nuclear fuel grillwork is achieved.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention relates to the technical field of artificial intelligence, and provides a rail corrugation detection method and device.The method comprises the steps that axle box vibration characteristics and vehicle operation characteristics of the rail vehicle are obtained, and the axle box vibration characteristics at least comprise vibration acceleration signals of axle boxes; the vehicle operation characteristics at least comprise a speed parameter and a load parameter of the rail vehicle and a rail curve radius; based on a corrugation detection model, obtaining a comprehensive feature vector of axle box vibration features and the vehicle operation features; based on the comprehensive feature vector, a first judgment result and a second judgment result are determined, the first judgment result is used for judging the corrugation state of the track, and the second judgment result is used for predicting the corrugation depth of the track. According to the method, the end-to-end corrugation detection model is constructed through multi-modal data fusion based on deep learning, the corrugation condition existing in the track is detected, the corrugation occurrence position and the corrugation occurrence degree can be effectively recognized, and the safety and the operation efficiency of the system are improved.

The invention provides a heavy-duty truck vehicle-mounted safety monitoring method and device, and the method comprises the steps: obtaining the original monitoring data of a wheel-rail force through a wheel-rail force monitoring module which is installed on an axle and has a low power consumption characteristic, and carrying out the edge calculation of the original monitoring data, and obtaining a wheel load shedding rate, a derailment coefficient and a wheel axle transverse force; the framework acceleration and the bearing temperature are obtained through the low-power-consumption vibration acceleration sensing module and the bearing temperature sensing module; performing multi-modal fusion on each index to obtain a multi-modal operation state representation vector; obtaining a dynamic threshold value of each index based on an enhanced feature vector and a multi-modal operation state representation vector obtained by an offline self-learning model; and giving current situation analysis and trend analysis of safety monitoring based on a comparison result of each index and a dynamic threshold value. According to the invention, the limitation of independent analysis of single sensor data in the prior art is overcome, the dynamic adjustment of the safety threshold is realized, and the accuracy and reliability of safety monitoring are improved.

The invention relates to the technical field of oil level monitoring, in particular to a transformer oil level monitoring device and system based on dual-mode ultrasonic sensing. The method comprises the following steps: acquiring transformer oil level and vibration acceleration; threshold value differences are obtained through oil level comparison, and the oil levels are divided; based on different types of oil level fluctuations, quantities and intervals, oil level anomaly comprehensive indexes are obtained; after the over-limit oil level is screened, an oil level abnormal factor is determined, and an oil level abnormal value is obtained in combination with the oil level abnormal comprehensive index; determining a change moment and a reaction time range based on the vibration acceleration; an acceleration oil level influence factor is determined by analyzing correlation, and a fault characteristic value is obtained in combination with an interference factor; and monitoring the oil level through the fault characteristic value. The reliability of the whole monitoring system is improved.

A rock porosity prediction method based on small-sample while-drilling data inversion is provided, including: collecting while-drilling parameters during laboratory or field drilling processes, including parameters such as torque M, propulsion F, rotational speed N, drilling speed V, drill pipe amplitude A, and vibration acceleration a; inputting the different types of multi-stage denoised while-drilling parameter data into a backpropagation regression-genetic algorithm (BP-GA) model for discrete point elimination, data augmentation, and iterative calculation to form a new while-drilling dataset; inputting the time-frequency domain feature graphs of while-drilling parameters for rocks with different porosities into a rock porosity convolutional neural network (VG-CNN) prediction model, obtaining an inversion model between the time-frequency domain features of while-drilling parameters and porosity through training and learning, ultimately achieving inversion prediction of rock porosity according to real-time field while-drilling parameters.

The invention provides a friction stir welding device based on a high-speed motorized spindle and a control method of the friction stir welding device, and belongs to the technical field of friction stir welding devices.The friction stir welding device comprises a high-speed permanent magnet synchronous motor, a coupler and a stirring head, and the high-speed permanent magnet synchronous motor directly drives the stirring head through the flange type rigid coupler; a non-contact eddy current displacement sensor and a three-axis vibration accelerometer are installed on a motor rotating shaft, the deflection and vibration frequency spectrum of a rotor are collected in real time, a rotor stirring head integrated finite element model is arranged, and a critical rotating speed area is recognized. The method adapts to the harsh space constraint smaller than or equal to 150 mm in an aero-engine cabin; whole life cycle verification shows that the fatigue life of the rotor breaks through 1.07 * 10 times of circulation, and leap-type upgrading from vibration instability-heat accumulation defect manufacturing to high-stability zero-defect manufacturing is achieved.

The invention provides an inhaul cable self-vibration fundamental frequency automatic identification method and system based on a harmonic product spectrum, and relates to the technical field of structure monitoring, and the method comprises the steps: receiving vibration acceleration data of an inhaul cable, carrying out the windowing processing of the vibration acceleration data through employing a Hanning window, and generating a windowed signal; carrying out fast Fourier transform on the windowed signal, and improving the frequency resolution by implementing zero filling to obtain an initial frequency spectrum; smoothing the initial frequency spectrum to form a smooth frequency spectrum; performing passband clipping on the smooth frequency spectrum to zero the passband outer amplitude to form a clipped frequency spectrum; calculating the signal-to-noise ratio of the frequency spectrum after cutting, and comparing the signal-to-noise ratio with a preset threshold value; if the signal-to-noise ratio is lower than a preset threshold value, judging that the signal quality is unqualified and terminating the processing flow; and if the signal-to-noise ratio is equal to or higher than the preset threshold value, continuing the normal processing flow. According to the invention, automatic and high-precision identification of the self-vibration fundamental frequency of the inhaul cable can be realized.

The invention relates to the technical field of data processing, in particular to an AI large model multivariate data fusion method and system based on multiple modalities, and the method comprises the steps: collecting multi-source data; performing classification abnormity judgment; abnormal type identification; calculating severity; switching fusion modes; and outputting an alarm. According to the method, parameters such as the spindle temperature, the rotating speed fluctuation rate, the vibration acceleration, the current load, the acoustic emission energy spectrum peak value, the cutting force variance and the cooling liquid metal particle concentration are acquired and fused step by step in multiple dimensions in the machining process of the numerical control machine tool, so that the running state of the machine tool is dynamically sensed from multiple angles; furthermore, the physical essence of tool wear and material peeling is revealed through the coupling relationship between the cutting force variance and the metal particle concentration, and meanwhile, the dynamic stability change of the machine tool structure is captured by combining the spindle rotating speed fluctuation ratio; the problem that state monitoring is not accurate in the numerical control machining process due to data isolation and fusion strategy staticization is effectively solved.

The invention relates to a cable insulation aging state evaluation method based on operation data analysis. The method comprises the following steps: S1, acquiring cable operation data in real time through a heterogeneous sensor array; s2, adopting a layered preprocessing strategy on the edge side; s3, constructing a hybrid deep learning model; s4, outputting by adopting an improved evidence theory fusion model; s5, outputting a fourth-level aging state label and a confidence evidence theory fusion model; electrical parameters and non-electrical parameters of the cable are collected in real time through the heterogeneous sensor array, and multi-dimensional data such as partial discharge signals, three-phase current harmonic distortion rate, distributed optical fiber temperature, mechanical vibration acceleration and environment dew point temperature are covered. The multi-source data fusion mode can comprehensively reflect the operation state of cable insulation, the limitation of single parameter evaluation is avoided, and the accuracy of the evaluation result is greatly improved.

The invention discloses a wind power gear box variable speed fault diagnosis method based on LMSRCT and a medium, and belongs to the field of wind power fault detection.The method comprises the following steps that a vibration acceleration sensor is installed on a wind power gear box, an original vibration signal x (t) of the gear box in the running state is collected at the sampling frequency f s, and meanwhile a rotating speed pulse signal is collected; obtaining accurate rotation frequency f < r > (t) and shaft rotation angle information theta (t); preprocessing the collected original vibration signal x (t) to obtain x pre (t); the x pre (t) is converted to an angle domain through an LMSRCT algorithm, and a one-dimensional angle domain sequence signal s (theta) is generated; the s (theta) is segmented into a sample sequence with a fixed length L for embedded encoding, the sample sequence is input to a four-layer Transform encoder, and probability distribution of different fault types is output; and taking the fault type corresponding to the maximum probability value as a final diagnosis result. According to the method, the LMSRCT algorithm is adopted to convert the vibration signal from the time domain to the angle domain, the problem of diagnosis failure caused by spectrum aliasing is solved, and the accuracy of fault diagnosis is improved by combining the LMSRCT algorithm with a Transform model.

The invention provides a vehicle risk assessment method and a vehicle. The method is applied to the technical field of intelligent driving. The method comprises the following steps: acquiring environmental parameters of a vehicle and target data of a to-be-driven ice surface, wherein the target data comprises a sound wave reflection signal and a vibration acceleration; based on the sound wave reflection signal and the vibration acceleration, obtaining the ice surface thickness; based on the environmental parameters and the ice surface thickness, obtaining a risk level of the ice surface to be driven; and based on the risk level, outputting prompt information which is used for prompting a user of the risk level of the to-be-driven ice surface. The method can improve the accuracy of risk assessment on the ice surface so as to ensure driving safety.

The present invention relates to the technical field of compressor performance testing and testing equipment manufacturing. Disclosed are a system volume-adjustable device and method for evaluating the starting characteristic of a refrigeration compressor. The evaluation device comprises a sample under test, a test refrigeration system and a test control system. By means of controlling a high-pressure-end volume regulator, a low-pressure-end volume regulator and a condensing coil, the evaluation device precisely adjusts a refrigeration system volume, so as to match the test requirements of different compressors for the refrigeration system volume, and provide a volume-adjustable substitute refrigeration system for starting characteristic testing. The present invention synchronously collects transient characteristic parameters such as intake and exhaust pressure, inter-phase voltage, phase line current and housing vibration acceleration at the instant of a compressor start, and non-transient characteristic parameters such as the in-housing temperature of a variable-frequency driver, the temperature of a power module and the temperature of a rectifier bridge, so as to accurately measure and evaluate the starting characteristics of the refrigeration compressor in specific environments.

The invention relates to the technical field of iron tower detection, in particular to a method for detecting hidden defects of an iron tower based on vibration acceleration, and the method comprises the steps: building a finite element simulation model of a power transmission tower, carrying out the comparative analysis of modal and vibration response before and after damage, and determining a preliminary point distribution position; performing iron tower damage and health state vibration detection experiments on the experimental power transmission tower, setting damage points, arranging vibration acceleration sensors according to initial point distribution positions, applying unit load, drawing a frequency response curve according to data acquired by the sensors, and analyzing the frequency response curve; and if the analysis result does not meet the requirement on the accuracy of identifying the hidden defect of the power transmission tower, adjusting the point distribution position to obtain a final point distribution position. According to the method, efficient and accurate recognition of the hidden defects of the iron tower is achieved, remote monitoring can be conducted, safety, economical efficiency and operability are achieved, the use cost is lower, reliable technical support is provided for intelligent monitoring of electric power facilities, and the obvious engineering application value is achieved.

The invention relates to the technical field of radio frequency identification and intelligent logistics management, in particular to a multifunctional cargo tracking method based on RFID, which comprises the following steps: step 1, cargo binding and data synchronous acquisition: acquiring cargo state parameters through an RFID tag integrated with multiple sensors, and when a reader-writer transmits a query instruction, sending a query instruction to the reader-writer; synchronously acquiring a temperature value, a humidity value and a three-axis vibration acceleration, and packaging the temperature value, the humidity value and the three-axis vibration acceleration with a cargo identity code and a timestamp into a combined data unit; 2, dynamic environment interference suppression; step 3, mobile scene routing optimization: acquiring the moving speed of the carrier in real time, dynamically calculating an optimal query interval by combining with an environment interference coefficient, and transmitting a query instruction according to an interval period; and 4, multi-dimensional state fusion verification is carried out. Through innovative measures of multi-sensor integration, dynamic environment compensation, mobile scene optimization and the like, the accuracy, stability and intelligent level of cargo tracking are effectively improved.