132 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 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 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 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 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 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 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 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.

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.

The invention relates to the technical field of image processing, in particular to a hyperspectral image defogging method, which comprises the following steps: acquiring a hyperspectral image, and decomposing the hyperspectral image into a plurality of single-band images; segmenting any single-band image to obtain a single-band low-frequency image, which comprises the following steps of: converting the single-band image from a spatial domain to a frequency domain to obtain a single-band spectrogram; obtaining two annular regions in the spectrogram, calculating an energy gradient between the annular regions, and determining a segmentation threshold according to the energy gradient; segmenting the single-band image based on the segmentation threshold to obtain a single-band low-frequency image; processing the single-band low-frequency image by using an atmospheric scattering model to obtain a single-band defogged image; and combining the plurality of single-band defogged images to obtain a defogged hyperspectral image. Through the segmentation method based on the spectral characteristics, the spectral information of the image can be fully utilized, high-frequency noise and details are removed, effective information is reserved, and the defogging effect is improved.

The present invention relates to the field of automatic control technology, and discloses a method and device for controlling the uniformity of large-area laser annealing. The method includes: obtaining a three-dimensional height distribution map of a substrate, dividing sub-regions using an adaptive segmentation algorithm to obtain height means and boundary coordinates. Calculating a focal length adjustment amount based on the height means to generate a focal point parameter table. Through laser testing and comparison, dynamically adjusting the focus until the focal point offset is less than a threshold to obtain corrected focal point data. Extracting energy transition characteristics and generating a scanning path with a minimum energy gradient constraint. Controlling the laser beam according to the path, collecting energy distribution signals, detecting the initial annealing depth, adjusting the laser parameters of sub-regions that do not reach consistency, and smoothing the annealing characteristics of the edge regions to generate control instructions that meet uniform tolerances. This method realizes uniform control of laser annealing by measuring the height distribution of the entire area of the substrate, dividing different sub-regions of the substrate, and smoothing the energy transition between regions.

The invention relates to the technical field of automatic control, and discloses a uniformity control method and device for large-area laser annealing, and the method comprises the steps: obtaining a three-dimensional height distribution diagram of a substrate, dividing sub-regions through a self-adaptive segmentation algorithm, and obtaining a height mean value and boundary coordinates; and calculating a focal length adjustment amount according to the height mean value to generate a focus parameter table. And through laser test comparison, dynamic focusing is carried out until the focus offset is smaller than a threshold value, and corrected focus data are obtained. Energy transition features are extracted, and a scanning path is generated with minimum energy gradient constraints. And controlling the laser beam according to the path, collecting the energy distribution signal, detecting the initial annealing depth, adjusting the sub-region laser parameters which do not reach the consistency, smoothing the annealing characteristics of the edge region, and generating a control instruction which meets the uniform tolerance. According to the method, the height distribution of the whole area of the substrate is measured, the substrate is divided into different sub-areas, energy transition between the sub-areas is smoothed, and uniform control over laser annealing is achieved.

The invention relates to a laser therapeutic instrument, a laser therapeutic instrument operation control device and a storage medium. The laser therapeutic instrument comprises a processor and a memory. Wherein the memory is used for storing computer programs; the processor executes the following steps when running a computer program: acquiring static characteristics and dynamic characteristics of a target object; based on the static features and the dynamic features, combining a tissue characteristic map and a prediction model to obtain a change trend prediction result of the target object; the tissue characteristic map comprises static characteristic reference values of different tissue types / skin types and a historical change rule curve of dynamic characteristics along with time; the prediction model is obtained based on constitutive equation training of the target object; on the basis of the variation trend prediction result, working parameters during operation on each region of the target object are determined in combination with an energy gradient distribution algorithm; and on the basis of the working parameters, real-time parameters of corresponding components in the laser therapeutic instrument are adjusted, and treatment of the target object is completed. Therefore, the accuracy and the stability of treatment are improved.

The application provides a manufacturing method of glass direct copper clad circuit, relates to the technical field of microelectronic circuit manufacturing, and comprises the following steps: S01, copper foil surface planarization: the copper foil is adhered to a UV photosensitive adhesive film, and the surface of the adhered copper foil is ground; after the surface of the copper foil is ground to reach a target roughness, the copper foil is cleaned and dried; S02, laminated static pressure: a glass substrate, the UV photosensitive adhesive film with the adhered copper foil and a carrier plate are sequentially laminated and fixed, vacuum is drawn and sealed, static pressure is applied to the formed sealed body, and a structure to be welded is obtained; and S03, laser selective welding: an ultrashort pulse laser and a short pulse are combined, and multiple compound scans are performed by using an energy gradient, so that the problem that an interface gap cannot be effectively reduced and the interface is easy to be ablated when a laser is used for welding to manufacture a glass copper clad circuit is solved, and the interface fusion capacity between the copper foil and the glass is improved.

The invention discloses a remediation method and system for soil heavy metal pollution based on a bamboo nano material, bamboo is cut in the axial direction of fibers, ultrasonic cleaning and vacuum drying are carried out, after energy gradient grinding is carried out in sequence, high-pressure homogenization and in-situ dispersion nanocrystallization treatment are carried out, and the porous bamboo nano material is obtained; the method comprises the following steps: inserting an anti-heavy metal target gene into a selected microbial expression vector, introducing the expression vector into microorganisms by using an electroporation method, detecting the expression level of the target gene, inoculating the microorganisms over-expressing the target gene into a culture medium, and determining the heavy metal removal rate based on microbial growth and the heavy metal removal rate. The microorganism with high heavy metal tolerance and biotransformation capability is obtained; the method comprises the following steps: uniformly distributing microorganisms under the assistance of a dispersing aid, carrying out low-temperature standing crosslinking by adopting a cross-linking agent, adsorbing the microorganisms on the surface of a bamboo nano material to obtain a microorganism and bamboo nano material composite repairing agent, and applying the microorganism and bamboo nano material composite repairing agent to polluted soil for heavy metal pollution repairing.

The application discloses a speech detection method and device, a terminal equipment and a computer readable storage medium. The method comprises the following steps: performing frame processing on an original audio signal to obtain each frame signal; calculating an energy gradient factor of each frame signal, wherein the energy gradient factor is the sum of energy gradients between adjacent two frequency points in each frequency point of the frame signal; determining a target energy gradient factor threshold corresponding to a target frame signal in each frame signal; and determining that the target frame signal is a speech signal when the energy gradient factor of the target frame signal is greater than or equal to the target energy gradient factor threshold. The application provides a speech detection scheme using frequency domain gradient information of a signal as a feature to improve the stability of a VAD algorithm in a noise scene.

The invention discloses a continuous three-mass resonance mixing method, a device and a mixing system, which apply resonance mechanical energy to materials, enable the materials to perform mixing motion under the action of the resonance mechanical energy, simultaneously form ellipse-like mixed material flows macroscopically and microcosmically, realize shearing, diffusion and convection actions among the materials and improve the mixing effect of the materials. And the materials are uniformly mixed on the macroscopic and microcosmic mixing levels. According to the invention, uniform mixing can be realized on macroscopic and microscopic mixing levels, the mixing efficiency is high, and the uniformity is good; the continuous three-mass resonance mixing device is low in structure and investment cost, the vibration mechanism is matched with the concave plates and the convex plates which are alternately arranged in the mixing cavity, energy gradient generated in the mechanical vibration process can be provided to form sound waves, then the sound resonance effect is formed, efficient mixing of materials is achieved, and the continuous three-mass resonance mixing device has good popularization prospects.

The present application relates to the field of power system control and power electronic energy storage technology, in particular to a generator set transient power compensation system and method thereof, the method comprising: collecting stator voltage, stator current, rotor angular velocity, power angle and DC bus voltage, and mapping into a generalized state vector; constructing a port-controlled Hamilton system model containing an initial interconnection matrix and an initial damping matrix based on preset physical parameters and obtaining a Hamilton energy function; taking partial derivative of the Hamilton energy function with respect to the generalized state vector, calculating an energy gradient vector, dynamically reconstructing a virtual interconnection matrix and a virtual damping matrix, analyzing control input and converting into a duty cycle command to send to an energy storage converter to perform transient power compensation; the system comprises a data acquisition module, a Hamilton energy controller and an energy storage converter; faster transient recovery, lower mechanical stress and more stable closed-loop response can be achieved.

The invention relates to the technical field of computational chemistry and drug screening, and discloses a fluorinated derivative screening method based on computer-aided design, which comprises the following steps: acquiring an initial binding conformation of fluorine-containing molecules and a target system, and constructing a follow-up local orthogonal coordinate system by taking the axial direction of a fluorocarbon bond as a reference; projection coordinates of the environmental particles relative to the fluorocarbon bonds are calculated and mapped as a spherical coordinate index, a preset anisotropic potential energy surface grid is called, and a potential energy value is calculated through interpolation; the method comprises the following steps: generating an anisotropic correction term, monitoring the modulus length of the energy gradient in real time, and when the modulus length of the energy gradient exceeds a sensitive threshold value, adaptively and nonlinearly reducing a sampling step length and executing high-frequency local sampling in a sensitive area. The problem of false negative activity prediction caused by insufficient sampling when a traditional equal-step algorithm is used for processing anisotropic distribution of fluorine atom electron clouds is solved, and the recognition precision and screening efficiency for weak directional interaction such as halogen bonds are improved.