480 results about "Field analysis" patented technology

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention relates to the technical field of intermediate frequency furnace sintering temperature control, and discloses a sintering temperature control system of an intermediate frequency furnace. The system comprises a multi-source data acquisition module which is used for acquiring multi-dimensional thermodynamic data, covering temperature distribution, sintering power and heat flow track data in a sintering process in real time; and the temperature field analysis module is used for carrying out dynamic compensation correction processing on the multi-dimensional thermodynamic data, inputting the multi-dimensional thermodynamic data into a sintering analysis processing layer for characteristic modeling, and generating a sintering temperature regulation and control instruction. The sintering analysis processing layer comprises a data preprocessing module and a thermodynamic modeling module, the data preprocessing module performs operation such as temperature interval division and abnormal value elimination on original data, and the thermodynamic modeling module is obtained based on historical data cooperative training. According to the system, through multi-dimensional data acquisition and analysis processing, the sintering temperature can be accurately regulated and controlled, the quality of sintered products and the production efficiency are effectively improved, and the system has good universality and adaptability.

The invention provides a short message issuing processing method and system based on scene self-adaption. The method comprises the steps that a three-dimensional communication characteristic spectrum is constructed through a building information model and infrared thermal imaging data, short message request distribution is analyzed and predicted in combination with a signal attenuation field, cross-operator network quality data is integrated to form a converged communication quality field, and finally short message issuing is intelligently scheduled according to the network state and service requirements. According to the method, accurate short message routing optimization based on real-time communication quality and crowd distribution in a complex building environment is realized. According to the technical scheme provided by the invention, the short message delivery rate and timeliness in a complex building environment can be remarkably improved, and particularly, the communication reliability in a crowded area and a signal blind area is remarkably improved.

The invention discloses a power distribution equipment on-line monitoring system based on multi-modal data fusion, and relates to the field of power distribution equipment management, and the system comprises a sensing module which is used for collecting an electrical signal, a thermal infrared signal, a mechanical vibration signal and an acoustic signal generated in the operation process of power distribution equipment through a multi-type sensing channel, converting the collected various signals into processable equipment state original data to form an equipment operation state original data set; according to the invention, through accurate acquisition of multi-dimensional signals, combination of space mapping and time delay compensation, data quality is optimized, the reliability of original information is ensured, key features are extracted according to working conditions, subtle state changes are captured by means of temperature field analysis and a variable-resolution spectrum technology, the comprehensiveness, accuracy and response timeliness of equipment operation monitoring are effectively improved, and the real-time performance of equipment operation monitoring is improved. Misjudgment and missed judgment are reduced, and fault risks are avoided in advance.

The invention relates to the technical field of data processing, and discloses an employee performance assessment-oriented dynamic data desensitization system, which comprises a field analysis module, a query analysis module, a desensitization strategy module and a desensitization execution module, wherein the field analysis module is used for identifying a risk field combination between fields of object data; the query analysis module is used for calculating the sensitivity of each query party to each field in the risk field combination; a desensitization strategy module synchronously generates a joint desensitization strategy of each query party for the risk field combination based on the sensitivity and desensitization constraint condition of each query party for each field in the risk field combination; and the desensitization execution module is used for executing the joint desensitization strategy and performing joint desensitization of the risk field combination for each query party. According to the method, the desensitization strategy is optimized, and the efficiency and safety of employee performance assessment data management are improved.

The invention provides an XLPE cable aging-based mixed operation system impedance mismatch and imbalance characteristic analysis method, equipment and a medium, and belongs to the technical field of cable aging analysis. Equivalence simplification is performed on a power grid in a simulation system, switching stations and transformer substations are reserved, and a cable structure model is constructed; cable structure parameters are obtained, three-phase unbalanced current-carrying simulation conditions are set, operation parameters are obtained, a cable finite element thermal field analysis model is constructed, cable thermal field analysis data are input, and cable burying depth and surrounding parameters are set; comparing and analyzing the temperature distribution difference, acquiring temperature data of the inner and outer surfaces of the insulating layer, and calculating an average temperature difference value of the insulating layer under the two working conditions; based on the average temperature difference value, the influence degree of the single-phase power-on working condition on the cable insulation life compared with the symmetrical operation working condition is quantified. And according to an evaluation result, a more reasonable cable maintenance plan and replacement period are formulated, faults caused by insulation aging are prevented in advance, and safe and reliable operation of a power system is guaranteed.

The invention provides a micro-flow field measurement method of a mechanical biological net port flowmeter, and belongs to the technical field of micro-flow field measurement, and the micro-flow field measurement method comprises the following steps: firstly, constructing a net port cavity digital model, and realizing micro-flow field high-resolution characterization by using fluorescent particle tracing liquid and a laser confocal system; and recording a fluid movement track and processing original data by applying a track optimization function under the condition that the microfluidic driving system generates a constant pressure gradient. And solving fluid pressure distribution by adopting an orthogonal grid system and a Navier-Stokes equation, and processing speed field and pressure distribution data by utilizing a pre-trained micro-flow field analysis neural network model. Finally, the flow and pressure relation curve is obtained by calculating the flow flux of the cross section of the network port, a micro-flow field measurement correction coefficient table is generated, and the technical problems that in the prior art, the accurate measurement difficulty of the biological network port micro-flow field is large, and multi-scale flow characteristic representation is insufficient are solved.

A multi-physics computation method and system for digital twin online simulation, relating to the technical field of physics simulation. A multi-physics coupling simulation computation model of a simulated object is established, and the multi-physics coupling simulation computation model is simplified, and the order of a temperature field simulation model is reduced, thereby greatly reducing the amount of computation, and improving computational efficiency; a low-precision dataset is obtained by means of temperature field analysis, so that the data size required for a model is reduced by means of low-precision data, thus reducing modeling costs; and a basic data-driven model is constructed by means of the low-precision dataset and a sample space corresponding to the low-precision dataset, so that a temperature field distribution result can be rapidly outputted, further improving computational efficiency and reducing computational errors.

The invention discloses a thermal management control method and device for a new energy automobile battery, and relates to the field of battery thermal management control, the device comprises an entropy change module, a fault module, a decision module, a phase change module and a scheduling module, entropy change intensity and nuclear heat flow field analysis is carried out on collected current and voltage signals to obtain an aging thermal topology, and the aging thermal topology is subjected to thermal management control. The method comprises the following steps: constructing an anisotropic diffusion equation based on aging thermal topology in combination with a fault diffusion tensor, discretizing a sub-strategy set for a safety margin field, constructing a cooling Nash game, analyzing through a payment matrix and a replicated dynamic equation, screening a stable strategy, mapping, and constructing an implicit phase change criterion model for the surface temperature of a battery to obtain a phase change cumulant; the phase change cumulant latent heat is analyzed and then cooperatively arbitrated with the cooling membership degree to obtain arbitration cooling capacity, the water pump flow is obtained through limiting operation based on the arbitration cooling capacity and the temperature deviation, then cubic root mapping is conducted on the water pump flow and the arbitration cooling capacity to obtain the fan rotating speed, and accurate control over battery thermal management is achieved.

The invention discloses an underground anti-seepage structure leakage monitoring method and system based on discrete time, and relates to leakage detection. The method comprises the following steps: collecting transient electromagnetic response data generated by an excitation signal; according to the initial conductivity parameter and the electrode position parameter of the monitoring area, establishing a three-dimensional geophysical model of the monitoring area through a full waveform inversion algorithm; obtaining electromagnetic abnormal data by using a finite element forward modeling algorithm; according to the temperature data, a distributed temperature field analysis method is adopted to calculate the space gradient and the time change rate of the temperature, and temperature abnormal data are obtained; according to the pressure data and time sequence change characteristics of the pressure data, determining pressure abnormal data; fusing the electromagnetic abnormal data, the temperature abnormal data and the pressure abnormal data by using a Bayesian algorithm to obtain fused data; and inputting the fusion data into a pre-trained machine learning model to obtain a leakage detection result. Aiming at the low dynamic leakage monitoring precision of the underground anti-seepage structure, the dynamic monitoring precision is improved.

The invention relates to the technical field of data processing, and discloses a high-temperature early warning method and system for a welding device. The method comprises the following steps: carrying out multi-point temperature measurement by using a K-type thermocouple and an infrared thermal imager; fourier heat conduction calculation is conducted on the welding heat field; extracting temperature anomaly region features through thermal gradient analysis; carrying out multi-parameter risk assessment by combining material melting point parameters; a multi-channel alarm instruction is output through the industrial control bus; and current-voltage curve real-time adjustment is conducted on the welding power source, and closed-loop self-adaptive control is achieved. According to the method, a complete closed-loop system from multi-sensor temperature collection, thermal field analysis, abnormal feature extraction and risk assessment to multi-stage alarm and self-adaptive control is established, so that early recognition and accurate control of thermal abnormality in the welding process are realized, and the welding quality and the production safety are remarkably improved.

The invention provides a grain pile form dynamic monitoring method based on an intelligent visual system, and the method comprises the steps: extracting a speed field and a direction field of grain flow motion from video data in a grain flow falling process through employing an optical flow field analysis method, and obtaining the initial information of a grain flow track; according to the track change information, the initial diffusion range of the grain flow at the bin bottom is analyzed, whether the diffusion mode is uniform or not is judged, and if diffusion is not uniform, the corresponding area is marked as an irregular diffusion area; a dynamic diffusion model is established based on the track change information, the dynamic diffusion model is adopted to process an irregular diffusion area, and the accumulation speed and the form change trend of the grain pile are predicted in combination with the speed field and the direction field of grain flow movement; and according to the predicted grain pile stacking speed and form change trend, operation parameters of the conveying equipment are adjusted, and the operation parameters comprise the speed and angle of a conveying belt.

The invention relates to the technical field of automatic focusing, and discloses a narrow space imaging automatic focusing method and system based on depth-of-field analysis, and the method comprises the steps: obtaining original image data, and carrying out the refraction correction according to the original image data, and obtaining an initial depth-of-field distribution diagram; carrying out scattering interference analysis according to the initial depth-of-field distribution diagram to obtain interference intensity distribution; performing focus analysis according to the interference intensity distribution to obtain a candidate focus set; performing exposure optimization according to the candidate focus set to obtain an optimized focus set; performing locking threshold calculation according to the optimized focus set to obtain a locking threshold of the optimal imaging position; and when the distance between the target position and the optimal imaging position is smaller than the locking threshold value, triggering focus locking, performing scattering compensation, and outputting a high-precision imaging result. The method has the following effect that the accuracy of automatic focusing of imaging in a narrow space can be improved.

The invention discloses a power transformation equipment infrared image analysis and diagnosis method and related equipment, and the method achieves the precise analysis and intelligent diagnosis of the operation state of power transformation equipment through the steps of multi-modal data collection, data preprocessing, equipment target recognition, temperature anomaly detection, intelligent fault diagnosis and equipment health trend prediction. And by adopting the improved target detection network, the identification precision of the equipment target is improved, the segmentation effect of key parts of the equipment is optimized in combination with multi-modal data, and the false detection rate in a complex environment is reduced. Based on a dynamic hot area enhancement algorithm and a temperature gradient field analysis method, an abnormal temperature area is effectively extracted, and the accuracy of abnormal temperature detection is improved in combination with an environment compensation mechanism. The equipment state is intelligently analyzed through a deep learning model, fault types such as poor contact, insulation aging, overload heating and cooling abnormity are accurately classified, dependence on a fixed temperature threshold value is reduced, and diagnosis adaptability is enhanced.

The invention belongs to the technical field of multimedia, and particularly relates to a short drama translation and explanation method based on a multi-mode neural network, which realizes full-process automatic processing from subtitle extraction to culture adaptation translation by integrating advanced technical means such as an OCR (optical character recognition) model, optical flow field analysis, an LSTM (long short term memory) network and a graph neural network. Particularly, the subtitles in a complex background can be accurately recognized and processed, subtitle traces are effectively removed, plot conflicts are understood through a deep learning algorithm, and then translation content conforming to target culture is generated. The method not only improves the accuracy of subtitle extraction, but also enhances the cultural adaptability of the translated content, so that the multimedia content can be more naturally and smoothly understood and accepted under different cultural backgrounds, and the watching experience of global users is greatly improved.

The invention provides a jelly foreign matter deep learning detection method based on multi-mode intelligent sensing data driving, and the method comprises the steps: obtaining an original image of jelly, and generating a cross-view light field matrix through a polarized light compensation algorithm and synchronous triggering time sequence control; a cross-view light field matrix is obtained, the attenuation path of each light ray is reversely tracked in the jelly volume direction, and the foreign matter shielding probability is calculated in combination with a sugar degree-absorbance curve in a material library; foreign matter existence probability thermodynamic diagrams are generated in the three physical levels respectively, and a three-dimensional centroid probability field is obtained; and dynamically compressing a three-dimensional centroid probability field analysis time window according to the passing speed of the current production line jelly, comparing misjudgment cases of the same kind of jelly in a historical library, and adjusting a judgment threshold value of a corresponding space level to obtain a real-time judgment instruction set with production line parameter adaptability. Through deep cooperation of multi-mode light field perception and cross-level probability modeling, the technical breakthrough of full-dimensional foreign matter detection of jelly products is realized.

A micron-sized speckle preparation method for quasi-in-situ OIM-DIC mesoscopic strain field analysis comprises the following steps: (1) polishing the surface of a sample to be detected to obtain a bright and scratch-free mirror surface; (2) preparing Si powder turbid liquid; (3) dipping speckles of a sample to be detected in the Si powder turbid liquid to obtain micron-sized speckles; and (4) observing and shooting speckle morphology, and if Si particles are uniformly distributed and dispersed, applying the Si particles to quasi-in-situ OIM-DIC mesoscopic strain field analysis. The method is simple, convenient and rapid, low in operation and construction cost, high in practicability, good in particle dispersity, high in precision, more suitable for calculation of a quasi-in-situ OIM-DIC mesoscopic strain field, controllable in preparation process, low in cost and free of damage to the surface of a sample, the definition and stability of speckle characteristics are remarkably improved, DIC mesoscopic micron-scale operation under a quasi-in-situ OIM mechanical test can be effectively achieved, and the method is suitable for large-scale industrial production. And the grain and mesoscopic strain field information is successfully coupled.

The invention provides an automatic equipment anomaly monitoring method and system based on image processing, and relates to the technical field of automatic equipment anomaly monitoring, and the method comprises the steps: converting an equipment operation video into a multi-dimensional physical field feature: in a motion field dimension, based on optical flow field analysis, extracting a full-period displacement statistical histogram and a space thermodynamic diagram, the motion instability characteristic of the mechanical transmission system is accurately quantified; in a vibration field dimension, an energy spectrum and a vibration thermodynamic diagram are generated innovatively through time-frequency transformation of a displacement signal of a frequency spectrum monitoring point, and frequency domain feature visualization of a hidden vibration fault is achieved; in the dimension of a structure field, edge gradient analysis and texture feature extraction technologies are fused, a time sequence structure thermodynamic diagram sequence is constructed to capture a progressive damage evolution rule, a three-field abnormal index dynamic weighting fusion mechanism overcomes the limitation of traditional single-point monitoring, connected domain analysis of a fused thermodynamic diagram is combined with an LBP texture and morphological feature decision tree, and the defect of the prior art is overcome. Automatic equipment abnormity monitoring based on image processing is realized.

The invention relates to the technical field of graphene electric heating floor heating, and discloses a graphene electric heating floor heating intelligent temperature control system and method.The system comprises an area temperature recognition module, an instruction analysis module, an energy consumption division module and the like; the thermal field monitoring module collects basic temperature data, a real-time power curve and thermal imaging data, the energy consumption division module judges the temperature and energy consumption grade, the temperature control strategy module generates a dynamic adjustment strategy according to the grade, and the temperature difference judgment module and the path correction module achieve power correction and path optimization. And the thermal field analysis module is combined with the user terminal to early warn abnormity and visualize thermal imaging data. According to the scheme, regional refined temperature control, dynamic energy consumption management and intelligent path planning are achieved, the heating comfort level and the energy utilization rate are improved, and the method is suitable for intelligent temperature control scenes of graphene electric floor heating.

The invention belongs to the technical field of flow field visualization, discloses a quick flow field visualization data loading method, and provides a quick flow field visualization data loading method for effectively utilizing processor resources through a multi-thread method in order to solve the problems that the flow field loading speed is low and grid and flow field rendering efficiency is low. Comprising the following steps: importing and initializing flow field grid data, and normalizing the flow field grid data; flow field features are extracted, physical features of the flow fields are extracted through a multi-thread programming technology according to the properties of all the flow fields, and the extracted physical features are stored; decoupling the graphical user interface and flow field loading, and carrying out data loading and separation of the graphical user interface GUI through a multi-thread programming technology; grid and flow field drawing and visualization are carried out, and grid data are visually displayed through a VTK system; interaction between a user and a 3D visual scene is realized through a VTK system; and performing flow field analysis according to a visualization result.

The invention relates to the technical field of computer modeling, in particular to an earthquake resistance and disaster reduction simulation system for a liquefiable foundation. The system comprises a cyclic stress ratio field analysis unit, an excess pore pressure ratio evolution analysis unit and a stress coupling correction unit, the cyclic stress ratio field analysis unit is used for generating a cyclic stress ratio field by using a depth reduction relation; the excess pore pressure ratio evolution analysis unit is used for deducing an excess pore pressure ratio and establishing an evolution relation of the excess pore pressure ratio along with time and depth; and the stress coupling correction unit is used for updating the effective vertical stress value based on the excess pore pressure ratio, synchronously correcting the shear modulus according to the coupling relationship between the shear wave velocity and the excess pore pressure ratio, and feeding back the corrected shear modulus to the excess pore pressure ratio evolution analysis unit. According to the method, the scientificity and engineering applicability of earthquake resistance and disaster reduction of the liquefiable foundation are effectively improved.

The invention relates to the technical field of aero-engines, and discloses a combustion chamber outlet radial temperature distribution design method and system.A turbine blade temperature field analysis model and a stress field analysis model based on a combustion chamber outlet temperature field distribution curve are constructed, and the influence of multiple fatigue failure modes on the service life of a turbine blade is comprehensively considered; constructing a comprehensive strength coefficient analysis model which simultaneously considers high-cycle fatigue failure, lasting creep failure and low-cycle fatigue failure game mechanisms on the basis of arithmetic mean and geometric mean methods; therefore, a three-dimensional response surface of the comprehensive strength coefficient changing along with the radial temperature distribution coefficient of the combustion chamber outlet and the radial height of the highest temperature position of the combustion chamber outlet is obtained, and an optimal solution in a design interval is obtained by solving an extreme value through the response surface. According to the method, the design iteration efficiency can be improved, meanwhile, comprehensive consideration of various failure mechanisms is achieved, and it is ensured that the radial temperature distribution of the outlet of the combustion chamber better meets the service life design requirement of turbine blades.

The invention provides a flow field analysis method capable of optimizing an anti-blocking adjusting stop valve structure, and belongs to the technical field of stop valves, and the method comprises the following steps: S1, based on an energy minimum multi-scale model, dividing each phase medium into a dense phase and a dilute phase through calculation of each phase distribution in each region in a valve; s2, correcting an inter-phase momentum transfer resistance relationship of the liquid-solid two-phase quasi-fluid model, and establishing a mass transfer and drag action relationship between a dilute phase and a dense phase to form a first-stage model; s3, performing medium serialization on the physical image of the first-stage model to form a single dilute phase and a single concentrated phase, establishing a mass exchange and drag action relationship between the dilute phase and the concentrated phase, ensuring that the concentrated phase and the dilute phase meet mass conservation, and constructing a second-stage model; according to the method, the defects of homogeneous hypothesis are corrected through the multi-stage model, the influence of particle aggregation on flow can be reflected, liquid-solid two-phase flow simulation is closer to the actual working condition, and the prediction error of the erosion risk area is greatly reduced.

The invention discloses a fire temperature field analysis and prediction method based on artificial intelligence, and the method comprises the steps: 1, deploying a distributed heterogeneous sensor network, and outputting a heterogeneous original data set; 2, constructing a space-time diagram convolutional recursive network based on the heterogeneous original data set in the step 1; 3, constructing a heat conduction constraint generative adversarial network by using the spatial-temporal characteristic matrix and the dynamic graph structure in the step 2; 4, constructing a multi-modal fusion prediction model; 5, carrying out self-feedback closed-loop dynamic calibration; the method has the beneficial effects that a fire temperature field prediction system with physical interpretability and environmental adaptability is constructed through a heterogeneous data collaborative closed loop, a physical constraint conduction closed loop and a model self-evolution closed loop, and accurate and reliable technical support is provided for early fire early warning and emergency decision making.

The invention discloses an unstructured mesh generation method applied to casing heat flow field numerical calculation, which relates to the technical field of engineering, and comprises the following steps: obtaining the structural characteristics of a casing, and constructing a casing finite element analysis model; preprocessing the finite element analysis model of the casing pipe, identifying geometric defects and carrying out geometric shape division; performing grid drawing based on the fluid area and the solid area of the casing finite element analysis model to obtain a first grid and a second grid; and based on the first grid and the second grid, casing pipe swept grid data are obtained, and a three-dimensional swept grid is formed. According to the unstructured grid drawing method for sleeve heat flow field numerical calculation based on the sweeping method, the grid subdivision efficiency is improved, the calculation complexity of sleeve heat flow field analysis is effectively reduced, and powerful technical support is provided for design, optimization and safe operation of power equipment.

The invention relates to the technical field of alignment precision of LDI exposure machines, and particularly discloses an LDI exposure machine light path deformation monitoring method based on multi-mode deformation field analysis, which comprises the following steps: acquiring multi-mode monitoring data of a substrate, the multi-modal monitoring data of the substrate comprises substrate datum point array data, a composite calibration pattern, a near-infrared image, grating temperature field distribution data and vibration spectrum data; obtaining deformation characteristic data of a plurality of substrate areas according to the near-infrared image, the substrate reference point array data and the composite calibration pattern; based on each piece of deformation characteristic data, the grating temperature field distribution data and the vibration spectrum data, obtaining fused deformation characteristic data; and generating a light path continuous deformation field corresponding to each substrate area according to the fused deformation feature data. According to the invention, by acquiring the multi-modal monitoring data of the substrate, such as the reference point array data of the substrate, the composite calibration pattern, the near-infrared image and the like, the state of the substrate is comprehensively reflected from multiple dimensions, and the defects of traditional single data monitoring are overcome.

The invention relates to the technical field of structural design, and discloses a method and platform for designing the internal structure of medical and edible plant low-temperature airflow nano crushing equipment, and the method comprises the following steps: firstly, obtaining characteristic parameters of a target crushing object, and generating a first design scheme; then optimizing the first design scheme to obtain a second design scheme; iteratively optimizing the second design scheme to obtain a final equipment internal structure design scheme; and finally, on the basis of the final scheme, an electrostatic monitoring and dynamic adjustment mechanism is integrated, real-time adaptive control is performed on the equipment, and an equipment protection mechanism is established. According to the method provided by the invention, the internal structure of the equipment is optimized by means of airflow field analysis, electrostatic aggregation prediction and the like, and an electrostatic monitoring and dynamic adjusting mechanism is introduced to realize intelligent control, so that the crushing efficiency and the powder quality of the medicinal and edible plants are improved, and meanwhile, the safety and the reliability of equipment operation are enhanced.

The invention provides a shape function KL expansion random field discretization method considering a slope soil body irregular domain, and belongs to the technical field of soil body parameter random field analysis. Comprising the following steps: establishing a slope numerical model and dividing a discrete grid and a form function grid; establishing a probability distribution model of a soil parameter random field; calculating an element stiffness submatrix ke and an element mass matrix Ee; calculating an autocorrelation matrix rho and a global stiffness matrix B; assembling a global matrix; calculating a characteristic value lambda k, a characteristic function phi k and an expansion term number M; and random field discretization of the slope numerical model is realized. The random field discrete technology provided by the invention is used for efficiently and accurately analyzing the random distribution characteristics of the soil parameters in the rock-soil structure. The discrete random field model can be efficiently constructed in a complex geological structure, the calculation cost is remarkably reduced, the method can be conveniently combined with numerical simulation tools such as a finite element method, and the requirement of actual engineering for efficiency is met while the precision is improved.

The invention relates to the technical field of highway engineering safety monitoring, in particular to a highway subgrade three-dimensional deformation monitoring system based on unmanned aerial vehicle oblique photography, and the system comprises an unmanned aerial vehicle oblique photography module which adopts a five-lens synchronous shooting strategy to realize the omni-directional coverage of a subgrade; the three-dimensional point cloud generation engine generates a high-precision three-dimensional point cloud model through feature extraction, registration and triangulation strategies; the deformation field analysis platform models a roadbed surface into a parameterized Riemannian manifold based on a differential geometry theory, captures deformation dynamic characteristics through multi-scale curvature flow analysis, and identifies potential risks by using topological evolution characteristics of deformation singular points; and the risk evaluator evaluates the risk level based on the singular point density, the change rate and the topological stability and generates early warning information. According to the invention, millimeter-level accurate monitoring of roadbed deformation is realized, various deformation modes from micro-cracks to large-scale settlement can be captured, the potential landslide risk is early warned 30-45 days in advance, and the false alarm rate is significantly reduced.