156 results about "Energy gradient" patented technology

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention provides a self-adaptive power supply energy-saving configuration method and system based on energy gradient utilization, and relates to the technical field of power supply management, and the method comprises the steps: collecting the operation parameters of electric equipment, building a power utilization model, and carrying out the self-adaptive correction; constructing a multi-stage energy distribution matrix according to the corrected power utilization model, and performing energy efficiency grading on the power utilization equipment; performing spatial-temporal feature extraction by adopting a bidirectional attention mechanism, generating an electric equipment priority strategy in combination with a self-calibration network, and further calculating an energy distribution proportion of each level of electric equipment; and finally, generating a control signal according to the energy distribution proportion, adjusting the power supply power of each stage of electric equipment, distributing the exceeded power to the electric equipment with the secondary high energy conversion efficiency when the power value exceeds a target power threshold value, and outputting a self-adaptive energy balance control instruction according to the prediction control model to adjust the operation state of the electric equipment.

The invention discloses a zinc coating shedding performance detection method and system, particularly relates to the technical field of metal surface treatment quality detection, and is used for solving the problems of high misjudgment rate and insufficient detection precision caused by coating surface pseudo defect interference in the existing method. The method comprises the following steps: synchronously acquiring a multispectral reflection image and three-dimensional morphology data of the surface of a zinc coating through an industrial vision imaging device, extracting grain boundary distribution information based on multispectral data, and identifying radial pseudo defects in combination with a curvature manifold geodesic line energy gradient direction of the three-dimensional morphology; and further performing micro-crack topology analysis and grain boundary spatial correlation verification on the candidate region, eliminating texture interference and grain boundary overlapping regions, and finally outputting a high-confidence fall-off defect detection result, so that the identification capability of real defects under a complex galvanized surface is remarkably improved, and the method is suitable for automatic detection requirements of a high-speed continuous production line.

The invention discloses a gas concentration prediction method based on micro-seismic monitoring data, and belongs to the technical field of coal mine safety monitoring. Comprising the steps that micro-seismic sensors and gas sensors which are distributed in a net shape are arranged in a mining area, and data are collected in real time and subjected to denoising and standardization processing; the method comprises the following steps of: constructing a gas concentration prediction model fusing a long-short-term memory network and a convolutional neural network by extracting multi-dimensional characteristic parameters such as a microseismic energy gradient, a seismic source aggregation degree and a gas concentration change rate, and dynamically distributing spatial-temporal characteristic weights by utilizing an attention mechanism; a safety threshold value is dynamically adjusted based on coal seam permeability, mining depth and geological parameters, score calculation of comprehensive danger evaluation indexes is combined, and three-level early warning of underground sound-light alarm, ventilation regulation and control and remote expert cooperation is triggered; and meanwhile, through periodic model calibration and parameter retraining, the prediction precision is improved. According to the invention, space-time collaborative perception and self-adaptive early warning of the gas risk are realized, and the real-time performance and reliability of coal mine safety prevention and control are obviously improved.

The invention discloses a method for preparing a copper-chromium-niobium alloy high-thermal-conductivity part based on green laser SLM, and relates to the technical field of copper-chromium-niobium alloys. Comprising the following steps that copper-chromium-niobium alloy powder is pretreated; the pretreated copper-chromium-niobium alloy powder is laid on a base plate, green laser is adopted for selective laser melting printing, at least two layers of printing parameters with different energy densities are set in a layered energy gradient control mode, layered printing is carried out, a pore structure with the porosity in continuous gradient distribution is formed, and a pre-machined part is obtained; wherein the wavelength of the green laser is 500 nm to 550 nm, and the spot diameter of the green laser is 15 [mu] m to 30 [mu] m; and the pre-machined part is subjected to heat treatment, and the high-heat-conduction part is obtained. The copper-chromium-niobium alloy part is prepared through the green laser SLM technology, the layered energy density gradient control technology is adopted, and the problems that in copper-based alloy additive manufacturing, the reflectivity is high, the structure is limited, and the performance is insufficient are systematically solved.

The invention relates to the technical field of temperature control, in particular to a mass concrete water cooling water temperature control method and system. The method comprises the following steps: collecting concrete key parameters through a multi-source sensor, constructing a microenvironment model based on information geometry, and generating a thermosensitive characteristic spectrum; forming a distributed thermodynamic model by utilizing a mutual information routing algorithm and heat-fluid-solid coupling analysis, and calculating an optimal cooling strategy; water flow path and flow optimization is realized in combination with fractional order particle swarm optimization; a pump set and a valve are adjusted through fuzzy self-adaptive control; and a potential instability region is identified through the spectrum energy gradient, and risk early warning is triggered. The precision and reliability of mass concrete cooling temperature control are remarkably improved, and intelligent, efficient and safe temperature control adjustment in the whole process is achieved.

The invention discloses a vehicle body flexible welding process and related equipment. The process comprises five steps of parameter space real-time partitioning, initial control strategy generation, key moment dynamic correction, group collaborative optimization and adaptive parameter adjustment. Identifying a core control parameter and a compensation parameter through scientific partitioning of a multi-dimensional parameter space; retrieving an optimal control strategy based on the physical feature vector; identifying a key control moment by utilizing energy gradient analysis to implement accurate intervention; considering a welding spot group stress transfer relation to optimize an execution sequence; and performing closed-loop correction on the whole control link according to the real-time feedback. According to the technical scheme, the problem of complex coupling of the welding parameters in the multi-vehicle-type high-frequency switching environment can be effectively solved, dynamic optimization and rapid convergence of the nonlinear relation between the parameters are achieved, and the welding quality consistency and the resource utilization efficiency are remarkably improved.

The invention relates to the technical field of big data, and discloses a big data-based smelting furnace system fault early warning method, which comprises the following steps of: acquiring multi-source sensor time sequence data when a smelting furnace runs; extracting a temperature signal gradient characteristic of the smelting temperature signal, and extracting a signal mean value characteristic of the electrode current signal; decomposing the crucible vibration signal into preset frequency bands, calculating the product of the standard deviation of the energy value of each frequency band and the energy ratio of the adjacent frequency bands, and generating a frequency domain energy gradient feature vector; generating a multi-dimensional feature matrix according to the signal mean value feature, the temperature signal gradient feature and the frequency domain energy gradient feature vector; inputting the multi-dimensional feature matrix into a pre-trained lightweight gradient boosting tree model, and outputting a fault boundary of the smelting furnace; and when the fault boundary reaches a preset triggering condition, triggering an early warning instruction corresponding to the grading early warning signal of the smelting furnace, and performing fault early warning on the smelting furnace by using the early warning instruction. According to the invention, the accuracy of fault early warning of the smelting furnace system can be improved.

The invention relates to the technical field of path analysis, and discloses a path planning simulation method and system for operation of a reach stacker in a container yard. The method comprises the following steps: performing multi-scale path topology analysis on a dynamic cost map, a density cost map and a safety cost map to obtain potential path manifold data; non-stationary state quantitative analysis is carried out on the potential path manifold data to obtain path dynamic characteristics, and the path dynamic characteristics comprise path differential geometric characteristics, space-time entropy characteristics and energy gradient characteristics; according to a preset path performance standard, performing multi-modal feature fusion and hierarchical weight distribution on the path dynamic features to obtain an optimal path score and dynamic performance parameters of the operation of the front lifting machine with space-time adaptability; and generating a self-correcting path planning scheme according to the optimal path score and the dynamic performance parameters of the operation of the reach stacker. According to the invention, the accuracy of the path planning simulation of the operation of the reach stacker in the container yard is improved.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source geological data processing method and system of a three-dimensional geological model.The method comprises the following steps that mountain landform and river valley images are obtained, gray frequency characteristics are extracted, a frequency energy gradient layer is constructed, a frequency continuous response area is screened to generate a structure boundary set, and a structure boundary set is constructed; the method comprises the following steps of: extracting a boundary normal vector by utilizing principal component analysis, identifying boundary sections with consistent directions, estimating a physical property parameter gradient direction, judging an included angle screening blocking region, generating a space attribute limiting layer, carrying out space alignment analysis on an overlapping region vector included angle, updating a boundary label, and generating an available attribute path structure set in three-dimensional geological modeling through a Dijkstra algorithm. According to the method, a conduction model is constructed through frequency domain decomposition and logarithmic transformation enhanced recognition, frequency window analysis noise reduction, principal component extraction vector analysis direction and center difference estimation, dynamic matching is promoted through alignment, a Dijkstra algorithm optimizes a path, and the geological model bedding characterization and conduction simulation precision is improved through cooperation of a multi-dimensional technology.

The invention discloses an intelligent power grid optimization scheduling method based on digital twinning, and relates to the technical field of power grid optimization scheduling, and the method comprises the steps: building a dynamic power grid mirror image model according to a standardized power grid data matrix, and generating an evolution trajectory prediction report through a quantum magnetic coupling mechanism; establishing a variable association network based on the evolution trajectory prediction report, and generating an optimization scheduling instruction set in combination with the standardized data matrix; executing an optimization scheduling instruction set to obtain magnetic potential gradient distribution, and generating a topology reconstruction scheme by identifying a magnetic potential change trend; and inputting the topology reconstruction scheme into the dynamic power grid mirror image model to generate an electromagnetic field evolution trajectory, and generating a safety scheduling report in combination with magnetic potential energy gradient distribution. According to the method, the power grid magnetic field data is coded into an evolution trajectory prediction report through a quantum-magnetic coupling mechanism, and high-precision risk pre-judgment is realized; a topology reconstruction scheme is dynamically generated according to the electromagnetic energy gathering trend, and the power grid optimization scheduling efficiency is improved.

The invention discloses a computing device parameter adjustment method, a product, a device, a server and a medium, relates to the field of servers, and aims to solve the problem that energy efficiency and delay of a computing device cannot be balanced. The method comprises the following steps: acquiring a current energy efficiency gradient map of the computing device; determining a target position point on the current energy efficiency gradient map according to the current batch processing size and the current kernel frequency of the computing device, and executing current adjustment operation on the computing device based on the energy efficiency and delay comprehensive performance value corresponding to the target position point; and in response to the condition that the new batch processing size and the new kernel frequency meet the delay and energy efficiency balance condition after the current adjustment operation is executed, controlling the computing equipment to continuously run according to the new batch processing size and the new kernel frequency. According to the method, the configuration can be automatically adjusted according to the actual operation condition of the computing equipment, different workloads and operation environments are adapted, the dynamic balance between the energy efficiency and the delay is realized, and the optimal operation state of the computing equipment between the performance and the energy efficiency is ensured.

The invention relates to the technical field of electric power facility anomaly monitoring, in particular to a computer monitoring system anomaly monitoring method for large-scale electric power construction engineering, which comprises the following steps of: acquiring an insulator leakage current signal, a temperature signal and an ultrasonic signal, and calculating a corresponding energy gradient; calibrating the correlation coefficient of the sensor based on the physical characteristics of an insulator material, and calculating the dynamic weight according to the energy gradient, thereby achieving the dynamic weighting of a fault sensitive signal; generating a fusion reference signal through weighted summation; constructing a dynamic threshold by combining the thermal physical parameters of the equipment, the real-time signal characteristics and the environmental parameters; calculating a signal difference quantity between the original signal and the fusion signal, and if the signal difference quantity exceeds a dynamic threshold value, triggering abnormal early warning; noise influence on a single sensor is suppressed, the signal-to-noise ratio of a fusion signal is effectively improved, the problems of abnormal missing report and false report easily caused by a fixed threshold value are avoided, and the insulator abnormal detection precision is improved.

The invention discloses a fluorescence in-situ hybridization image contour enhancement method based on illumination fusion and local binary, relates to the field of image processing, and solves the problems of different dye concentrations and the like due to image defects such as non-uniform local illumination and relatively large noise in an acquired image during existing tissue cell fluorescence image preprocessing. The method comprises the following steps: removing noise in an image by adopting a median filtering method, calculating a linear equation to obtain a texture weight of the image, performing analysis processing by utilizing the texture weight and a smoothing function to obtain a brightness image and a smooth image, and performing gray processing to obtain gray distribution information; adopting a local binary algorithm to select a coefficient, and performing enhancement processing on the image to obtain an illumination enhanced image; the contour feature image and the illumination enhancement image are subjected to weighted addition, eight neighborhoods around eight maximum pixel values are analyzed, an area with a high energy gradient is recognized through a piecewise linear function and a thermodynamic diagram, and a high-frequency signal area is the contour area of the fluorescence in-situ hybridization tissue cell image.

The invention discloses a method and a system for identifying a micro-seismic signal mode of a surrounding rock rockburst risk, particularly relates to the technical field of deep underground engineering rockburst monitoring, and is used for solving the problem that an existing micro-seismic early warning model lags in identifying a rock mass damage evolution critical state. The method comprises the following steps: collecting a surrounding rock micro-seismic event signal in real time, intercepting an event sequence based on a sliding time window, and calculating a space-time fractal dimension; extracting an event space density field in the window to calculate an energy gradient, and marking a strong interaction cluster of which the energy gradient exceeds a threshold value; storing the fractal dimension change sequence in real time; performing characteristic frequency band filtering on the signals in the window, calculating a ratio of an energy ratio entropy descent rate to a fractal dimension descent rate, and determining a damage acceleration state when the ratio is a supercritical coefficient; generating a rockburst high-risk instruction when the strong interaction cluster and the damage acceleration state coexist and the fractal dimension descent rate is supercritical threshold; the multi-dimensional collaborative identification of the rockburst precursor is realized, and the early warning accuracy and timeliness are remarkably improved.

The invention discloses a flexible light ring lamp control method and system, a storage medium and a program product, and the method comprises the steps: constructing a multi-dimensional interaction feature space, and mapping state change information into track points; calculating a state transition vector between adjacent track points, and determining a state transition threshold value; dividing a plurality of state stable intervals; generating an interaction strength curve; calculating an energy distribution function of a stable state interval, and determining a key interaction segment; extracting characteristic parameters of the key interaction segments and obtaining energy gradient values of adjacent segments; generating a lamp effect rendering strategy according to the characteristic parameters and the energy gradient value; and adjusting the display state of the flexible light ring lamp according to the lamp effect rendering strategy so as to enable the display state of the flexible light ring lamp and the fluctuation trend of the energy distribution function to keep synchronous change. When frequent state changes and interaction events are carried out on the tablet computer, the matching degree of collaborative operation of the flexible light ring lamp is improved, and then the adaptability of providing visual feedback for users is improved.

The invention discloses a galvanometer scanning-based curved surface self-adaptive dynamic light spot laser quenching method, which has the following beneficial effects: (1) dynamic regulation and control of a light spot energy field: a traditional uniform light spot is reconstructed into a self-defined light spot with an adjustable energy gradient according to a curvature value of a workpiece, so that the energy of each section of the light spot is dynamically distributed according to the curvature; and the energy density deviation caused by geometric deformation is compensated. And (2) scanning-energy cooperative control: through coupling optimization of a light spot form and a scanning path, galvanometer high-frequency galvanometer micro-swing scanning and laser output energy adjustment are adopted in a curvature sudden change area, so that accurate regulation and control of heat input are realized, a heat accumulation effect is inhibited, and depth uniformity (fluctuation is less than or equal to 10%) of a hardening layer is ensured. And (3) online feedback and closed-loop correction: an infrared thermal imager and a surface topography sensor are integrated, a quenching point temperature field and a hardened layer topography are monitored in real time, a light spot energy ratio and a scanning strategy are dynamically corrected through an AI algorithm, and the dynamic characteristics of material phase change in the machining process are adapted.

The invention discloses a gas insulation cabinet on-line monitoring method and device, and relates to the technical field of insulation cabinets, and the method comprises the steps: building a discharge signal category library based on a gas insulation cabinet off-line test; a multi-channel sensing unit is optimally arranged in the key area to collect partial discharge signals; performing time-sharing superposition on the multi-channel signals, performing fast Fourier transform to obtain a frequency domain amplitude spectrum, and further extracting voltage frequency domain envelope of the insulation cabinet and voltage energy distribution of the insulation cabinet; calculating an energy distribution difference value with a category library by dynamically adjusting a frequency weight, calculating a frequency domain envelope similarity value by using a dynamic time warping method, obtaining a discharge signal category matching value by combining the two values, and identifying a discharge type. Based on the discharge type and the dielectric characteristics, an energy gradient estimation method is adopted to realize discharge source positioning; and collecting the time sequence characteristics of the positioning points for trend analysis to obtain a partial discharge signal evolution value, dividing the discharge risk level according to the partial discharge signal evolution value, and completing online monitoring and early warning.

The invention discloses an LNG cold energy gradient utilization general modeling and optimizing method and system for multi-energy micro-grid energy management, and the method comprises the steps: considering the transfer coupling and loss of cold energy based on the gradient utilization purpose and equipment parameters, and building a utilization general model for each stage of the cold energy gradient utilization process; the method comprises the following steps: establishing a multi-energy micro-grid energy optimization model containing cold energy gradient utilization, and carrying out energy management cooperation on a cold energy gradient utilization link and a multi-energy micro-grid based on a multi-energy micro-grid architecture and distributed energy characteristics by taking system economic carbon reduction operation as an optimization target; and converting the energy optimization model into a two-stage stochastic optimization model based on multiple scenes and solving the two-stage stochastic optimization model according to the uncertainty of the source load and the operation characteristics of the multi-energy micro-grid equipment to obtain a multi-energy micro-grid energy management decision of cold energy gradient utilization. According to the invention, integration and collaboration are carried out on a universal model for gradient utilization of cold energy and energy consumption management of the multi-energy micro-grid, and an energy management decision with economic low-carbon property and high energy efficiency property is obtained through optimization.

The invention belongs to the technical field of gas storage building, and particularly relates to an aquifer gas storage building displacement front identification method. According to the method, a water-bearing stratum gas storage model grid system is constructed, the displacement front edge of the water-bearing stratum gas storage model is positioned on the basis of interface energy gradient analysis, and the dynamic front edge boundary is calibrated with the maximum gradient 10% threshold value by calculating the interface energy and the three-dimensional gradient of each grid; and further quantizing the front edge complexity by adopting a coarse-fine grid fractal dimension method, and dividing the front edge into a stable region, a transition region and a risk region through quartile. According to the method, the dynamic evolution characteristics of the gas-water interface are directly represented through an interface energy gradient mechanism, the high-risk gas channeling region is quantitatively recognized in combination with the fractal dimension, and technical support is provided for reservoir building optimization and gas channeling prevention and control of the heterogeneous aquifer gas storage.

The invention relates to the technical field of vehicle trajectory analysis, in particular to a vehicle trajectory anomaly detection method and system based on deep learning, and the method comprises the following steps: obtaining trajectory coordinates and boundary distance to generate symbol offset, constructing a road offset continuous field through interpolation and spatial embedding, calculating the ratio of instantaneous speed to reference speed, and obtaining a vehicle trajectory anomaly detection result. And generating an unbalance degree parameter, executing vector dimension stretching, extracting a trajectory embedding vector and an abnormal score by using a long short-term memory network, executing square summation and difference operation, and generating a trajectory energy gradient value. According to the method, migration characteristics are mapped to a continuous field to achieve track and road geometric constraint association, unbalance degree parameters are used for remarking directional quantity scales to enhance speed dynamic perception, energy gradient modulation scoring is cooperated, a confidence probability curved surface is constructed by using trilinear interpolation, and a curved surface peak value is positioned to output a detection result. Positioning noise is suppressed; and abnormal precision under sparse sampling is improved.

The invention discloses a virtual power plant scheduling method for evolutionary deep reinforcement learning, and belongs to the technical field of power control. According to the method, a hydrogen-containing virtual power plant low-carbon economic dispatching model is constructed, hydrogen energy gradient utilization is realized through electric hydrogen production, electric-to-gas and hydrogen-to-ammonia technologies, a stepped carbon trading model is improved, a hydrogen energy green certificate trading model is introduced, and an evolutionary soft action device-evaluator (ESAC) algorithm is adopted to carry out optimized dispatching. According to the algorithm, population optimization and deep reinforcement learning of an evolutionary strategy are fused, and the mutation rate is dynamically adjusted through an automatic mutation adjustment mechanism. According to the method, the optimization precision and robustness of virtual power plant scheduling are remarkably improved, the operation cost is effectively reduced, and low-carbon operation of a multi-energy system is promoted.

The invention discloses a building electrical circuit arc fault multi-dimensional detection and positioning method and system. The method comprises the following steps: injecting a low-amplitude broadband excitation signal into a key node of a power distribution loop through an electromagnetic excitation and sensing device, collecting a line response waveform and establishing a steady-state electromagnetic response baseline; performing self-correlation operation and spectral analysis based on the baseline, extracting formant frequency, energy distribution and bandwidth characteristics, and generating an electromagnetic modal characteristic fingerprint; through constructing a time-frequency coupling resonance matrix, formant frequency shift amount and energy gradient change are monitored in real time, and an abnormal resonance state of a line is identified. After abnormality is detected, establishing a spatial positioning equation according to multi-node phase distribution, and determining the position of an arc or a breakdown source by adopting a phase inversion algorithm; calculating a line stability coefficient by combining a positioning result and time sequence resonance data, and predicting an arc risk level; when the risk level exceeds a threshold value, an electromagnetic compensation module is activated, an opposite-phase suppression signal is generated and injected into an abnormal area, and active protection closed-loop control is achieved.

The invention relates to the technical field of concrete detection, in particular to a concrete structure crack damage evaluation system based on acoustic emission sensing, which comprises a signal acquisition module, a characteristic discrete evolution module, a probability inference module, an energy gradient analysis module and a boundary defining module. According to the method, a multi-dimensional evolution set is constructed by extracting the amplitude and energy standard deviation of an acoustic emission signal in a continuous time window, the synchronous change trend of the multi-parameter standard deviation is analyzed by using a Bayesian probability model, and the non-uniform expansion posterior probability is calculated to lock a key signal set of dominant damage expansion. An energy fluctuation coefficient is calculated and a space sequence is generated by combining sensor space coordinates, and a fluctuation coefficient stable interval is identified according to a gradient attenuation rule of energy along with a distance, so that a physical boundary of a crack damage dynamic active region is quantitatively defined, and accurate evaluation of a non-uniform expansion state and an active range of a concrete crack is realized.

The invention discloses a diesel engine cylinder cover vibration signal inversion virtual key phase pulse and whole period feature extraction method, and belongs to the field of mechanical signal processing. Collecting a diesel engine cylinder cover vibration signal; based on the autocorrelation function of the signal smooth envelope energy gradient, identifying the whole-cycle signal length of the diesel engine; judging whether a reference signal exists or not, if yes, identifying a key phase triggering position based on interpolation alignment and a cross-correlation function, and if not, intercepting a single full-period signal and storing the single full-period signal as the reference signal; inverting the virtual key phase pulse signal so as to intercept a required full-period signal; typical signal features are extracted, and a whole-period signal feature parameter set is constructed. The method overcomes the problem of difficult key phase pulse signal acquisition caused by difficult installation of a key phase sensor on a high-power-density diesel engine, fully considers the quasi-periodic characteristics of the working cycle of the diesel engine, can accurately identify the whole cycle length, efficiently inverts the virtual key phase pulse signal, and improves the accuracy of the key phase pulse signal. And full-period feature extraction of the vibration signal of the diesel engine under the keyless phase sensor is realized.

The invention relates to a reconstruction optimal fractional order estimation method for an off-axis digital hologram, and the method comprises the following steps: operating an off-axis digital holographic optical path system, and obtaining a to-be-processed off-axis hologram; performing fractional order Fourier transform on the off-axis hologram to obtain time-frequency domain information of a positive first order of the off-axis hologram, and then performing filtering processing on the time-frequency domain information of the positive first order; setting a search range of a particle swarm optimization algorithm by using rotation addition of fractional Fourier, then setting parameters of the particle swarm optimization algorithm, and calculating an energy gradient of a reconstructed amplitude image through a fitness function; and estimating a fractional order corresponding to the maximum energy gradient of the reconstructed amplitude image by applying a particle swarm optimization algorithm, wherein the fractional order is the optimal fractional order. Compared with the prior art, the method has the advantages of high accuracy, high calculation speed and the like.

The invention relates to the technical field of crack detection, in particular to a porcelain insulator micro-crack infrared spectrum detection method and device capable of accurately sounding, and the method comprises the following steps: obtaining a temperature change rate sequence, screening a stable interval, establishing a thermal radiation synchronous identifier, generating a time index set, carrying out energy registration mapping based on the index set to form a registration structure body, and carrying out infrared spectrum detection on the registration structure body; analyzing the energy gradient to identify the crack boundary to generate a three-dimensional coordinate set, and reconstructing voxel to analyze the crack morphology to generate a three-dimensional mapping set. According to the method, a crack voxel structure in a three-dimensional space is constructed by constructing an infrared acquisition time index, screening a radiation state stable section, combining multi-channel energy peak-valley comparison and space coordinate mapping and by means of an energy gradient distribution analysis and space clustering recognition mechanism; the synchronous and accurate acquisition of key characteristics such as crack position, shape, direction and depth is realized, and the comprehensive capability of spatial positioning, depth identification and crack penetration trend discrimination of the porcelain insulator microcrack is obviously improved.

To provide a molecule kinetics calculation device or the like which adopts a charge amount calculation method advantageous in calculation load and versatility.SOLUTION: This molecule kinetics calculation device calculates a charge amount of each atom, performs molecule kinetics calculation on the basis of the charge amount of each atom and a descriptor of interaction in each atom pair, and updates position coordinates and kinetic momentum coordinates of each atom. Adopted as the charge amount calculation method is a method which sets a total charge amount of atoms contained in a prescribed region as a prescribed fixed value, connects a charge system composed of charges of each atom to a multilayer hot bath composed of two or more layers to apply a kinetic equation of each charge, and calculates a charge amount of each atom, or a method which sets a total charge amount of atoms contained in a prescribed region as a prescribed fixed value, applies a gradient method based on an energy gradient obtained by differentiating a potential function describing interaction energy into one or more stages by charges to a charge system composed of each charge of each atom, and calculates a charge amount of each atom.SELECTED DRAWING: Figure 9

The invention discloses a roundness detection device and method for a finished aluminum alloy pipe, and relates to the technical field of pipe detection, and the method comprises the steps: constructing a multi-mode sensing array, calibrating and mapping the array to the axis of the pipe, triggering a constant-speed sliding table, and synchronously collecting a section point cloud and a vibration signal, an attractor feature matrix is generated based on phase-space reconstruction and a coding dictionary, a weighted directed graph is constructed to extract pulse energy distribution so as to accurately position a deviation node, a judgment threshold is adaptively corrected in combination with an energy gradient and connectivity change, and enhancement precision, rapid detection and a steady state tracking mode are dynamically switched. Model parameters are continuously optimized by comparing measured values of the high-precision roundness measuring instrument on line, a quality report containing deviation distribution, grade statistics and unique tracing identification is finally generated, and batch tracing and remote query are supported. Through combination of phase-space reconstruction attractor coding, pulse energy density analysis and dynamic mode switching, efficient identification of tiny deviation, precise positioning and balance between precision and efficiency are realized.