3673results about How to "Avoid errors" patented technology

The invention relates to the technical field of bridge structure health monitoring and early warning, and discloses a bridge support static displacement overrun early warning correction method based on a prediction model, and the method comprises the following steps: S1, simulating the dynamic response of a support and an overall structure under various working conditions through building a coupling finite element model of the bridge support and the overall bridge structure; s2, displacement, vibration and temperature data of the support are collected in real time through a multi-source sensor, and data fusion and noise reduction processing are carried out; s3, establishing a multi-level prediction model based on a physical model and a data driving model, and predicting a future displacement trend of the support; and S4, monitoring a prediction error in real time through an error feedback mechanism. Prediction errors are corrected in real time through recursive Kalman filtering and an error feedback mechanism, an early warning threshold value is dynamically adjusted through Bayesian updating, the precision and reliability of early warning are improved in combination with a double triggering mechanism, the method adapts to changes under complex working conditions, and false alarms and missing alarms are reduced.

The invention discloses a trajectory prediction method and device based on a time-space diagram neural network learning model, and the method comprises the steps: carrying out the data cleaning and trajectory compression based on to-be-classified original trajectory data, and outputting a compressed grid sequence; based on the grid sequence, static track information classification is executed, and track static attributes are extracted; dynamic space-time image track classification is executed, and track dynamic space feature attributes are extracted; the method comprises the following steps: pre-constructing a directed weighted network based on a driving road section connectivity attribute feature, and completing training of a trajectory prediction model based on a trajectory static attribute, a dynamic space feature attribute and the directed weighted network; and based on the trajectory prediction model, inputting spatial-temporal characteristic data of a trajectory to be predicted, and obtaining an optimal trajectory path prediction result. According to the method, uncertain track geographical background information on the road network can be effectively utilized, and meanwhile, errors caused by map matching are avoided.

The invention provides a material simulation test method and platform for mold flow analysis, and relates to the technical field of material simulation testing, and the method comprises the steps: carrying out the product demand analysis of a target test material, and obtaining the product demand information; performing injection molding analysis to obtain an injection molding control parameter threshold; obtaining an injection molding simulation test model; carrying out an injection molding simulation test, and carrying out image pre-acquisition to obtain a first pre-acquired image; defect feature analysis is carried out, attention area distribution is carried out, and a target attention area is obtained; an injection molding defect area image is obtained, and defect feature information is obtained; and feedback testing and parameter optimization are carried out, optimized injection molding control parameters are obtained, and injection molding control is carried out. The technical problems that in the prior art, injection molding analysis generally depends on a fixed mathematical model and limited technological parameters, and problems in the molding process cannot be recognized and adjusted in real time, so that the production efficiency and the product quality are low are solved.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention discloses a vehicle illegal parking detection system based on unmanned aerial vehicle inspection shooting, and relates to the technical field of vehicle illegal parking inspection. The vehicle illegal parking detection platform is in communication connection with an unmanned aerial vehicle image acquisition module, an image preprocessing module, a vehicle detection analysis module, an illegal parking judgment module, a license plate recognition module and an early warning notification module, and all the modules are in electric signal connection. According to the method, a large area can be rapidly covered through unmanned aerial vehicle inspection shooting, real-time monitoring and detection of vehicle illegal parking behaviors are achieved, compared with a traditional manual inspection mode, unmanned aerial vehicle inspection greatly shortens the detection period, the detection accuracy is remarkably improved, and the detection efficiency is improved through a high-precision image recognition algorithm. The vehicle illegal parking detection system can accurately recognize the license plate number, the vehicle type and the illegal parking position, and subjectivity and errors of manual judgment are effectively avoided.

The invention discloses a civil engineering cost evaluation method and system based on model optimization, and particularly relates to the technical field of civil engineering cost evaluation. Three-dimensional building information and the actual environment of a construction site are combined by establishing a virtual simulation scene based on a BIM model and combining a virtual reality or augmented reality technology, meanwhile, environment variables related to actual construction are dynamically introduced, the variables are analyzed through a machine learning algorithm, risk assessment is conducted on different construction stages, and the risk assessment efficiency is improved. The BIM model is dynamically adjusted and optimized, and based on the optimized BIM model and the risk assessment result, cost assessment is performed for different construction schemes and a report is output, so that the difference between a traditional virtual environment and a real construction condition is effectively made up, the construction cost and risk can be accurately predicted, project hyperbranched and delay are reduced, and the construction efficiency is improved. And more accurate cost evaluation and construction management are realized.

According to the coastal saline-alkali land underground water level adjusting system based on real-time monitoring and intelligent regulation and control, the system integrates the soil salinity sensor, the underground water level sensor and the central processing unit, future trend prediction is carried out by monitoring key parameters such as soil salinity and underground water level in real time in combination with a deep learning model, and the real-time monitoring and intelligent regulation and control are achieved. And precise intelligent regulation and control are realized. The system can effectively reduce soil salinization, prevent seawater from flowing backwards, improve the utilization efficiency of water resources and reduce the labor management cost at the same time. The method is characterized in that the application of multi-parameter comprehensive monitoring, a deep learning prediction model and a self-adaptive intelligent regulation and control algorithm provides a new technical solution for coastal saline-alkali land treatment.

The invention relates to the technical field of electrocardiogram monitoring, in particular to an electrocardiogram monitoring method and system based on heart rehabilitation data. The method comprises the following steps that electrocardiosignals in different motion states are collected through a multi-lead electrocardiograph and motion monitoring equipment, and fatigue electrocardio mapping is carried out to obtain fatigue signals. Then, heart rate changes of the fatigue electrocardiogram mapping signals are analyzed, the disordered scatter point trend of the heart rate is quantified, dynamic inflection point distribution is fitted, and trend dynamic data are obtained; multi-scale rule intersection analysis is carried out based on the data, a fatigue heart rate change rule is extracted, and rehabilitation deviation quantitative evaluation is further carried out. Finally, the rehabilitation deviation evaluation result is sent to the terminal for real-time electrocardiogram monitoring, and heart rehabilitation management is supported. According to the method, the electrocardiogram monitoring technology is optimized, so that the electrocardiogram monitoring technology is more perfect.

The invention relates to the technical field of image data processing, in particular to a wood composite board furniture surface defect identification method and system. And wood board images are collected from the surface of the wood composite board furniture. Adaptive grid division is carried out on the wood board image according to different scales, texture direction information and high and low frequency texture components are extracted, and a texture feature map is obtained; and extracting structure edge information of the wood board image to obtain a structure information graph. Matching the texture feature map with the structure information map, and calculating local mutual information of the texture feature map and the structure information map in each region; the method comprises the following steps: acquiring resolution images of a wood board image under different scales, and calculating a mutual information matrix of the resolution images; distributing an abnormal score for each matrix element based on the mutual information matrix; and dividing grids according to the abnormal score, calculating a correlation value of adjacent grids, and obtaining a defect area based on the correlation value. And carrying out defect classification on the defect area. According to the invention, interference of natural textures can be reduced, and defect identification of small defects and tiny changes can be improved.

The invention discloses a valve fault data detection method and system, and relates to the technical field of industrial automation control and fault diagnose.The method comprises the steps that historical data of operation of a valve are obtained, the collected historical data are analyzed through a deep learning method, and time sequence features are extracted; a fault scoring model is established according to the extracted time sequence features, the fault of the valve is warned in advance, and meanwhile the state of the valve is monitored in real time through the latest real-time data flow; and the prediction result of the fault scoring model is verified through the real-time data flow, and the valve abnormity scoring standard is dynamically adjusted according to the real-time data and the output of the fault scoring model. According to the method, the fault prediction model is constructed by collecting the time sequence data of the valve rod displacement and combining the deep learning technology, the valve operation state can be monitored in real time, abnormal features can be accurately recognized, and delay and errors of manual detection are avoided.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention relates to a fuel cell test system integrated with an electrochemical alternating current impedance test function, which comprises a fuel cell stack, an air supply unit, a fuel supply unit, a cooling supply unit, a control unit, an acquisition and driving unit and an electrochemical impedance measurement system, the output ends of the air supply unit, the fuel supply unit and the cooling supply unit are connected with the fuel cell stack, the acquisition and driving unit is connected with the fuel supply unit, the control unit is connected with the acquisition and driving unit, and the electrochemical impedance measurement system is connected with the fuel cell stack and the control unit. By adopting the fuel cell test system, method and device integrated with the electrochemical alternating-current impedance test function, the processor and the computer readable storage medium, rapid measurement and impedance calculation of electrochemical impedance in a fuel cell stack test process are realized, the integration level is high, the cost is greatly reduced, the operation is simple, and the test efficiency is high. Errors caused by repeated setting of load parameters are avoided, the measurement efficiency is high, and the measurement precision is high.

The invention relates to the field of cerebrovascular image recognition, in particular to a cerebrovascular lesion positioning method and system based on image recognition. The method comprises the following steps: acquiring cerebrovascular edge pixel points, establishing a preset sliding window by taking the edge pixel points as starting points, and traversing a cerebrovascular region; obtaining a cerebrovascular expansion degree according to pixel point distribution of the cerebrovascular area; obtaining an expansion coefficient by combining pixel point distribution of a cerebrovascular region of the sliding window region; obtaining an abnormal coefficient according to the expansion coefficient difference between the sliding window area and other sliding window areas; screening out an abnormal cerebrovascular region according to the abnormal coefficient; and then cerebrovascular lesion positioning is carried out. According to the method, errors possibly caused by fuzziness and complexity of a cerebrovascular image are avoided, so that related personnel can accurately position a cerebrovascular lesion position.

According to the pile foundation pile sinking monitoring method, device and equipment and the storage medium, the pile foundation pile sinking image is obtained through the collecting equipment arranged at the preset position, the pile foundation pile sinking image comprises the to-be-monitored pile foundation, and the to-be-monitored pile foundation is provided with a pile body scale; calculating the actual size of a unit pixel according to the pile body scale so as to determine a vertical pixel calibration value; according to a preset mark point and the vertical pixel calibration value, pile foundation pile sinking parameters are determined; and the pile sinking parameters of the pile foundation are input into a bearing capacity prediction model, and the predicted bearing capacity of the to-be-monitored pile foundation is obtained. The image acquisition device arranged at the preset position is used for acquiring the pile sinking image of the pile foundation in real time, pixel calibration is carried out in combination with the pile body scale, and key parameters in the pile sinking process of the pile foundation can be accurately calculated, so that a reliable basis is provided for prediction of the bearing capacity of the pile foundation, the monitoring precision and efficiency are greatly improved, and the cost is reduced. And errors possibly caused by manual measurement are avoided.

The invention relates to the technical field of data processing, in particular to a shrimp group health condition assessment method based on deep clustering analysis, and the method comprises the steps: monitoring health parameters; determining a shrimp group dispersion grid; determining a temporary grid; identifying a non-shrimp group abnormal grid; calculating an actual health index; and adjusting the grid step length and evaluating health. According to the method, the health condition of the shrimp group can be accurately monitored and evaluated by combining physiological and behavior data such as real-time density, saturation and activeness, abnormal grids are eliminated step by step, more representative grids are screened out, evaluation deviation caused by environmental fluctuation or measurement errors is avoided, and the evaluation accuracy is improved. The dynamic monitoring of the health condition of the prawn group is realized, and the grid step length is automatically adjusted by comparing the ideal health index with the actual health index, so that the health assessment precision and reliability are improved, and the problem of inaccurate health assessment result caused by unstable environmental parameters is effectively solved.

The invention relates to the technical field of novel power system optimization operation, in particular to a new energy high-uncertainty scene generation method and device based on a CWGAN-GP model, and the method comprises the steps: correcting an initial generative adversarial network through employing a Wasserstein distance and a gradient penalty term, so as to obtain a final generative adversarial network, and the target convolutional neural network is introduced into the final generative adversarial network to obtain a conditional deep convolutional generative adversarial network of improved deep convolution, so that WGAN-GP is introduced into the conditional deep convolutional generative adversarial network of improved deep convolution to obtain a CWGAN-GP model of improved deep convolution. And a target L-ISODATA clustering algorithm is applied to cluster the generated wind and light scenes so as to generate a high-uncertainty new energy power generation output scene. Therefore, the problems that in the related technology, when the GAN processes high-dimensional data, complex space-time characteristics are difficult to effectively capture, and due to lack of prior information, the training direction is prone to deviation, and the scene generation effect and stability are affected are solved.

The invention discloses an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention discloses a lithium battery health state evaluation method based on deep learning, and relates to the technical field of battery evaluation. Comprising the following steps: acquiring a Nyquist curve graph of a lithium battery through an electrochemical impedance spectroscopy test, and mapping the Nyquist curve graph with internal impedance characteristic parameters one by one to generate a sample image set; and establishing a deep learning network model based on the sample image set, and training and generating a prediction model by taking the Nyquist curve graph as input and the internal impedance characteristic parameters as labels. And testing the to-be-evaluated battery, and inputting the target Nyquist curve graph to predict internal impedance characteristic parameters. And calculating a capacity retention ratio index, a cycle efficiency degradation index and a shape expansion influence index in combination with the electric power characteristic parameters. The indexes are comprehensively analyzed to obtain a health state comprehensive coefficient, and the health state comprehensive coefficient is compared with a health threshold value to judge the health state of the battery. According to the method, the evaluation precision is improved through deep learning, and efficient and accurate health state evaluation is realized.

The invention discloses a remote sensing image haze removal processing method and system, and relates to the technical field of image processing. The method comprises the steps of obtaining a to-be-processed remote sensing image; extracting wave band data of a plurality of wave bands in the remote sensing image to be processed, and determining a reference wave band and a plurality of target wave bands based on the wave band data; based on the reference wave band, determining an atmosphere illumination parameter, and calculating a transmittance distribution diagram of each target wave band; for each target wave band, carrying out haze removal processing on the target wave band according to the atmosphere illumination parameters and the transmittance distribution diagram to obtain an initial haze removal image of the target wave band; and performing fusion processing on the initial haze-removed images of the target wavebands to obtain a haze-removed image corresponding to the to-be-processed remote sensing image. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of haze removal processing of the remote sensing image is achieved.

The invention discloses a method for automatically generating a medical report by using multi-modal feature fusion Mamba. The method comprises the following steps: inputting an original medical image into a visual feature extraction module to output visual features; inputting the original medical report into a text feature extraction module, and outputting text features; respectively inputting the visual features and the text features into an SSM-Attention visual text feature integration module, and outputting new visual features and text features; inputting the new visual features into a Transform encoder, enhancing the visual features, and extracting advanced visual features; and inputting the text features and the enhanced advanced visual features into a multi-modal fusion Mama module to output fusion features, and inputting the fusion features into a Transform decoder to automatically generate a medical report. According to the invention, the SSM-Attention and the Multi-modal Mama can be independently developed and improved, newly developed technical modules can be conveniently integrated, and the method is not only limited to fusion of medical image and text features, but also suitable for fusion of any multi-modal features.

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 coarse aggregate ultra-high performance concrete premix lifting, transferring and mixing device comprises a control system, an aggregate batching system, an aggregate lifting system, a dry mixing main machine system, a feeding vehicle, a steel fiber feeding system and a stirring system, the whole stirring process is automatically controlled and comprises key steps of automatic metering, automatic raw material feeding, mixing and the like, so that manual operation is remarkably reduced, errors and inconsistency caused by manual metering are avoided, and the accuracy and repeatability of stirring each time are ensured; step-by-step feeding of each part is coordinated through a control system, so that the stirring efficiency and the uniformity of concrete are improved; by establishing a relational expression between a concrete mixture stirring uniformity index and parameters such as the stirring time, the stirring speed, the number of stirring blades, the vibration frequency of the two-stage vibrating screen and the like, key parameters in the stirring process are more accurately controlled, and it is ensured that the stirring uniformity of each batch of concrete reaches a preset standard.

The invention provides a boiler master control system, a pressure adjusting method, electronic equipment and a storage medium. The system comprises a signal acquisition module, a differential control module, a pressure deviation adjusting loop and an instruction dynamic compensation loop. Compared with the prior art, according to the embodiment of the invention, the differential action intensity is calculated by utilizing the load change signal, the control valve action signal and the main steam pressure signal, so that the first coal quantity adjustment signal is accurately generated. Load change and pressure fluctuation can be responded in time, it is ensured that the coal quantity is adjusted more accurately, and boiler efficiency fluctuation or fuel waste caused by too much or too little coal quantity is avoided. The pressure deviation adjusting loop performs adjustment based on the deviation of the main steam pressure signal, so that the stability of coal quantity control is further improved; the instruction dynamic compensation loop can effectively predict and adjust the coal quantity according to the dynamic change of the boiler load, the parameters of the unit heat energy storage and the boiler capacity input rate so as to adapt to the dynamic change of the boiler load and capacity.

The embodiment of the invention provides a vision-based wood board edge sealing length detection method and system and a medium, and the method comprises the steps: obtaining a wood board image, and carrying out the graying processing of the wood board image, and obtaining a gray image; analyzing the edge area of the wood board based on the gray level image, generating a maximum connected domain, and making a detection surrounding frame according to the maximum connected domain to obtain a detection area; acquiring an image pixel value in the detection area, generating wood board boundary information based on the image pixel value, and generating wood board contour information according to the wood board boundary information; establishing a reference point according to the board contour information, establishing a coordinate system based on the reference point, and generating board boundary coordinates according to the coordinate system; calculating the distance between two coordinates located on the same boundary based on the boundary coordinates of the wood board to obtain a distance value, and obtaining a long edge and a short edge of the sealed edge of the wood board according to the distance value; the measurement method based on vision is not limited by detection point positions, the problem of edge sealing detection position errors is effectively avoided, and the long edge and the short edge of the wood board edge sealing are accurately analyzed.

The invention relates to landslide risk assessment, which is used for solving the problem that landslide risk assessment focuses on single data, neglects the comprehensive influence of related factors, and does not consider the influence of vegetation landslide resistance and rock strength, so that the real-time assessment of landslide risks is inaccurate. The fourth system covering layer slope landslide risk real-time evaluation system based on the Internet of Things comprises a slope monitoring module; according to the method, the vegetation coverage is calculated, the height and crown breadth change rate of the vegetation are analyzed, the influence of the growth condition of the vegetation on the slope stability is evaluated, and the effect of the vegetation on the landslide risk is more accurately judged through the comprehensive vegetation data processing mode; after land types are distinguished, risk judgment is carried out on the rock land based on rock stress related indexes, errors caused by unified standard evaluation are avoided through the distinguishing treatment mode, and the evaluation result better conforms to the actual situation; and the rock strength in the rock land is analyzed in detail, so that the stability of the rock land is mastered more accurately.

The invention discloses an adjustable turnover mold core type mechanical arm and a using method thereof, and relates to the field of mold core mounting, the adjustable turnover mold core type mechanical arm comprises a base and a mechanical arm body mounted at the top of the base, a turnover mechanism is mounted at the output end of the mechanical arm body, and a positioning mechanism is mounted on one side of the turnover mechanism. The air cylinder pushes the connecting plate to move and drives the moving frame and the second rack to slide, the racks apply transverse force to enable the gear to rotate, then the angle of the rotating rod is adjusted, mold core overturning is achieved, meanwhile, the first rack is matched with the gear, and the overturning angle is flexibly adjusted through combined control of the limiting rod and the threaded rod; the limiting rods and the threaded rods ensure that the angle is accurately controlled during small-angle operation, efficient installation can be kept during large-angle overturning, the design meets the installation requirements of different mold cores, and the flexibility, installation precision and efficiency of a production line are improved.

The invention relates to the technical field of hardness detection, in particular to detection equipment for gate valve machining and a detection method thereof. Comprising a base, multiple sets of supporting shafts are fixedly connected to the base, and a top plate is installed at the top ends of the multiple sets of supporting shafts; two groups of vertical plates are mounted on the top plate, and lifting assemblies are mounted on the two groups of vertical plates; two groups of fixing plates are arranged on the base, and two groups of protection mechanisms are arranged between the two groups of fixing plates; a third motor is arranged on one set of fixing plates, the output end of the third motor is in transmission connection with a second threaded rod, the lifting assembly drives the flashboard to move downwards into the two sets of protection mechanisms, the third motor drives the detection mechanism to extrude the flashboard for hardness detection, and the lifting assembly drives the flashboard to descend to different heights. Therefore, the deformation condition of the flashboard bearing the preset pressure value at different descending heights can be detected.

The invention relates to a high-precision variable air volume valve and an air volume control method thereof. The variable air volume valve is composed of a valve body, a front valve block set, a rear valve block, two actuators and a controller, linear adjustment of the air volume can be achieved, the air volume adjustment precision is improved, and adjustment of high and low air volume intervals is supported. The controller collects pressure difference data of an inlet and an outlet through the pressure difference sensor, and the real-time air volume is calculated through a built-in Kolmogorov-Arnold (KAN) neural network model. By comparing the actual air volume with the target air volume in real time, the controller adjusts the actuator to change the opening degree of the front valve plate set and the opening degree of the rear valve plate set, and accurate air volume control is achieved. According to the technical scheme, the accuracy and the adjusting range of air volume measurement are improved, additional resistance in the air pipe is reduced, and the performance and the operation efficiency of an air conditioning system are remarkably optimized.

The invention discloses a sliding bearing-rotor system stability test system and method under an impact load, and relates to the field of rotating machinery test in mechanical engineering. According to the technical scheme, different types of impact loads are applied through a vibration acceleration platform, and starting, stopping or external impact force in actual working conditions is simulated. In the testing process, a high-precision sensor and a multi-channel acquisition board card are used for monitoring the vibration characteristic and the rotating speed of the system in real time, it is guaranteed that signals of different positions and types can be synchronously acquired, and comprehensive system state information is provided. Then, in combination with time domain and frequency domain analysis technologies, key features are extracted from mass data by applying an advanced Fourier transform algorithm, the resonant frequency, modal characteristics and potential fault modes of the system are identified, and accurate evaluation of the stability of the system is realized. Experimental results show that the real-time monitoring and analysis method can effectively improve the stability test precision of the sliding bearing-rotor system under the impact load, and provides powerful technical support for engineering application. The method is used for evaluating the stability of the sliding bearing-rotor system under the impact load.

The invention relates to a positioning method of large-space intelligent glasses, head display equipment and a storage medium, belongs to the technical field of meta-cosmic space calculation, and is suitable for a meta-cosmic positioning function that the head display equipment of players in a multi-user interaction large space can directly interact without learning or reasoning of a central server. A plurality of different texture pictures are arranged on the bottom surface or the ceiling of a large space, any camera on the intelligent glasses is used for collecting images, the texture pictures are recognized from the images collected by the camera T, picture information corresponding to the texture pictures is obtained, the world position of the camera T can be obtained through geometric calculation, and the texture pictures are displayed on the intelligent glasses. The method can be quickly realized on local head-mounted display equipment, so that the learning process of a server or the delay of image reasoning and position information returning and errors caused by a positioning sensor are avoided, and the problem of errors caused by long distance of a large-space site or shielded texture pictures is also avoided.