408results about How to "Significant technological progress" patented technology

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention provides a bridge girder erection machine intelligent construction control method and system based on machine vision, and relates to the technical field of intelligent control. Image information of a construction site is collected through a plurality of high-definition cameras arranged on a bridge girder erection machine; inputting the image information into a pre-trained deep learning model, identifying and positioning a target in the construction scene, and constructing a digital twinborn model of the construction scene in combination with a graph structure of a construction scene topological structure subjected to spatial perception graph convolution operation; the intelligent decision agent generates an initial construction strategy according to the current construction scene state, an improved Monte Carlo tree is adopted to search and optimize the initial construction strategy, and an optimal construction strategy is generated through multi-objective solution; and after the construction is completed, based on the digital twin model and the whole process data, analyzing the construction quality and efficiency by using a graph neural network, and generating a multi-dimensional evaluation report and optimization suggestions.

The invention belongs to the field of energy efficiency optimization in industrial production, and discloses a dynamic energy efficiency optimization method and system based on artificial intelligence and machine learning, and the method comprises the steps: collecting energy consumption data and environment parameters in real time through a sensor network and Internet of Things equipment; cleaning, screening and standardizing the collected data; constructing an energy efficiency prediction model and an optimization model based on a machine learning algorithm; training the prediction model and the optimization model by using historical data, and adjusting model parameters; and the trained model is utilized to analyze and predict data acquired in real time, and production parameters are dynamically adjusted to realize optimization of energy efficiency. According to the method, key parameter changes in the production process can be accurately predicted and optimized, dynamic adjustment of production parameters is achieved, an optimal production adjustment strategy can be automatically made, scientificity and efficiency of production decision making are improved, and the method has remarkable technical and economic effects in the field of industrial production.

The invention belongs to the technical field of automobile information safety fuzzy testing, discloses an automobile information safety fuzzy testing method based on a large model, and develops optimization application research of LLMs in an automobile software fuzzy testing scene by utilizing the advantages of an artificial intelligence technology in the aspects of data processing, natural language interaction, generalization ability and the like. The concealment and effectiveness of the test set generated by the fuzz test are optimized, and the fuzz test process efficiency is improved. Through the implementation of the invention, related key technologies are applied to the fuzz test of the automobile, and other related products and enterprises in China are radiated by writing technical standards, so that the fuzz test system can be used as an application demonstration and sample plate of the fuzz test of the intelligent driving automobile. According to market analysis, fuzzy test calculation is carried out on 100 thousand automobiles every year according to the method, and the data safety and the system safety are improved.

The invention belongs to the technical field of welding, and discloses an L-shaped laser-MIG hybrid welding device for a narrow space, and the L-shaped laser-MIG hybrid welding device for the narrow space comprises a welding gun assembly, a laser welding head assembly and a space adjusting assembly. The laser and the electric arc are combined, a laser and electric arc composite heat source is obtained, and the advantages of laser and electric arc composite welding are fully exerted; laser-MIG hybrid welding has the advantages of being high in welding speed, low in heat input, large in equipment clearance tolerance degree and high in welding quality. A welding gun wire feeding route is optimized, the precision of a wire guide assembly is improved, the straightness of a welding wire is guaranteed by combining a three-wheel straightener, and the purpose of stable wire feeding is achieved. The overall connecting piece structure is simplified, the reflecting copper mirror integrating reflection, collimation and focusing is designed, the overall size space is saved, the overall load is reduced, and more functions can be added compared with equipment of the same type under the condition that the load is limited.

The invention belongs to the field of time sequence anomaly detection, and discloses a time sequence anomaly detection method and system based on frequency enhanced convolution Transform, and the method comprises the steps: carrying out the sequence-level preprocessing of an input sequence, including normalization and block generation; encoding the input sequence by adopting a multi-head self-attention mechanism; extracting interrelationships in the sequence by a full convolution module having a plurality of stacked 1D convolution layers of different kernel sizes; s2 and S3 are repeated, and multi-layer coding is carried out on the sequence; converting the encoded embedding into a frequency signal by using a Fourier transform method; the KL divergence and the absolute error are respectively used for calculating the consistency loss of the time domain and the frequency domain, calculating the point-by-point anomaly score, and identifying whether the point is abnormal or not. Wide experiments on five common data sets show that the method is superior to an existing method in the aspect of abnormal recognition.

The invention belongs to but not limited to the technical field of text extraction, and particularly relates to an automatic construction method and system for a process industry text knowledge extraction dataset. Firstly, based on a word segmentation tool and a TF-IDF algorithm, an unlabeled text in the process industry field is converted into a large language model fine tuning dataset, and a large language model is subjected to fine tuning; the system has specific knowledge of the field; then, generating an initial knowledge extraction sample set with confidence through multi-stage exploration-utilization ratio adjustment based on the fine-tuned large language model; and finally, through a confidence-based large-scale sample set error detection and correction method, obtaining a sample data set which is finally used for domain knowledge extraction model training. Through the low-cost and high-efficiency industrial text knowledge extraction sample set construction and application method, the input-output ratio of resources is increased to the maximum extent, and the construction and application efficiency of an industrial safety knowledge extraction algorithm is improved.

The invention belongs to the technical field of anchoring strength detection, and discloses an underground anchor rod anchoring strength prediction method and system based on a support vector machine method. The method comprises the steps of cleaning data, processing missing values and standardizing features; using correlation analysis or other feature selection methods to select features with high correlation with anchoring strength; performing model training by using a support vector machine method SVM, and optimizing hyper-parameters by using grid search; completing the construction of a support vector machine model; and carrying out model evaluation and result visualization. Key parameters in the mixing process are monitored in real time through the sensor network, the system can obtain anchoring strength data in real time, the detection period is greatly shortened, and the construction efficiency is improved. The support vector machine algorithm is adopted to analyze and predict the collected data, personal errors can be eliminated to a greater extent, and the accuracy and reliability of anchoring strength detection are improved.

The invention belongs to the technical field of deep learning, and discloses a drug and protein interaction prediction model construction method based on multi-modal fusion. Due to the lack of protein three-dimensional data, the three-dimensional data of the protein is subjected to high-accuracy complementation by using a prediction model AlphaFold 2, and for the modal fusion problem, the drug molecules are fused at the atomic level with finer granularity, so that the multi-modal fusion effect is further enhanced, and the multi-modal fusion effect is improved. Protein 1D data and protein 3D data are processed by using Cross-Attention, the processed protein 1D data and protein 3D data are subsequently fused by using Cross-Attention, multi-modal fusion is performed by using Cross-Attention, so that the interaction relationship between modals can be carefully captured, and in a word, the DTI task effect is improved by designing a multi-modal fusion mode.

The invention belongs to the field of grid-connected converter control methods, and discloses a torque quantization-based low-frequency oscillation suppression method for a direct-current voltage synchronous inverter, which constructs a grid-connected damping torque model of the inverter, quantifies a damping torque and a synchronous torque of a system and describes oscillation characteristics of the system. When oscillation is unstable and damping or synchronous torque is a negative value, the provided compensation torque stabilizer is introduced through angular frequency deviation, a lag phase of the system at the oscillation frequency is compensated by using a lead-lag corrector, and then the compensation torque stabilizer is fed back to an active power reference value. By designing the compensation torque, it is ensured that the synchronous torque and the damping torque of the system are positive values all the time, and oscillation instability is effectively restrained. The method is suitable for a small-disturbance working condition, the steady-state characteristic of inverter operation is not changed, the grid-connected stability of the grid-forming type photovoltaic inverter is improved, and meanwhile scheduling optimization and energy efficiency evaluation of the system are not affected.

The invention belongs to but is not limited to the technical field of three-dimensional space dose visualization, and discloses a three-dimensional nuclear emergency radiation field calculation and visualization method and system, based on a point nuclear method, a cumulative factor, mass attenuation factor and flux rate-dose rate conversion factor database is established, a mean free path solving algorithm is optimized, and a three-dimensional nuclear emergency radiation field is obtained. The geometric types of radioactive sources are expanded, and the dose calculation efficiency is improved through parallel calculation; plane dose rate drawing at a certain height is realized based on a two-dimensional texture mapping method; three-dimensional space dose rate contour surface drawing is realized based on a moving cube algorithm; and three-dimensional space dose rate volume rendering is realized based on a three-dimensional texture volume rendering technology. Radiation field dose calculation, three-dimensional radiation field dose visualization and GIS are integrated, and the system is used as a subsystem of a nuclear emergency aid decision simulation platform. The dose distribution diagram in the area can be judged more visually, and the method has a good help effect on nuclear emergency command.

The invention discloses a method, a system and equipment for optimizing batching parameters in an industrial silicon smelting process. The method comprises the following steps: firstly, acquiring process parameters in an industrial silicon smelting process, standardizing the process parameters, then converting the process parameters into weight factors influencing a smelting result, and mapping a relation between the smelting result and the weight factors into a parameter equation based on a preset multiple linear regression model, obtaining a parameter set containing a weight factor and a linear regression coefficient; then, based on the parameter set, the initial raw material parameters and the reaction process parameters, an equivalent circuit model is constructed and simulated to generate smelting result data under different parameter combinations, and therefore parameter adjustment variables are obtained; according to the numerical range of the parameter adjustment variables and the initial raw material parameters, an optimal batching parameter combination is obtained; and finally, performing optimization treatment on batching parameters in the industrial silicon smelting process. According to the method, accurate control and rapid optimization adjustment of batching parameters can be stably achieved in the industrial silicon smelting process.

The invention belongs to but not limited to the technical field of new energy, particularly relates to a battery physical and chemical model parameter identification method and system based on different temperature impedances, aims to improve the accuracy of model simulation, considers contact resistance when establishing a one-dimensional P2D physical and chemical model of a battery, and simultaneously solves the problem that the model parameters are more and difficult to identify. In order to solve the problems that in the prior art, parameters with high sensitivity and medium sensitivity and meeting actual conditions are selected by adopting a parameter sensitivity analysis method, finally, the parameters selected under different working conditions are identified by adopting a grey wolf optimization algorithm, simulation EIS data are compared with actual measurement data, and the simulation accuracy of the model is influenced by excessive parameters. The result shows that the grey wolf optimization algorithm realizes accurate identification of the parameters of the one-dimensional P2D physical and chemical model of the battery. The method disclosed by the invention achieves remarkable results in the aspects of battery modeling and parameter identification.

The invention belongs to but is not limited to the technical field of seabed monitoring, and particularly relates to a strain test method and system for subsurface buoy shell strength, and the method comprises the steps: S1, selecting points on the surface of a shell, and pasting strain gauges; s2, putting into a sealed kettle, and injecting water and pressurizing; s3, testing the strain condition of the shell by using a strain tester; and S4, the pressure tolerance degree of the shell is analyzed through calculation. The buoyancy provided by the floating body shell and the overall weight of the subsurface buoy are calculated in detail, and the needed ballast balance weight is accurately measured and calculated. Buoyancy and gravity balance is verified through dynamic simulation, it is ensured that the subsurface buoy can stably suspend or settle in water, and the design feasibility and the actual application effect are improved.

The invention belongs to the technical field of spectral analysis, and discloses a spectral analysis and image recognition combined intelligent stoma evaluation system and method.A three-dimensional scanning module captures the three-dimensional form of a stoma and the three-dimensional form of surrounding tissue of the stoma through the high-precision laser or structured light scanning technology; the spectral analysis module is used for accurately analyzing the color change of the stoma; the tiny color difference of the stoma area is detected to help to identify potential tissue abnormality; the image recognition and processing module is used for analyzing the collected stoma image by using a deep learning algorithm and automatically recognizing morphological characteristics of the stoma; the data analysis and feedback system is used for automatically generating a stoma health assessment report according to the collected multi-dimensional data; through a set standard reference value, an actual measurement value is compared, and a corresponding suggestion is given, for example, whether a nursing scheme needs to be adjusted or further medical intervention needs to be carried out. According to the method, the real-time state of the stoma is accurately evaluated through non-contact scanning and spectrum detection.

The invention belongs to the technical field of aging state evaluation, and discloses a high-voltage dry-type bushing insulation aging state evaluation method based on frequency domain characteristics. According to the method, the FDS is adopted, and the aging state of the insulating material is evaluated according to the change of the complex capacitance, the complex dielectric constant, the dielectric loss factor and the like of a medium along with the frequency under a low-voltage sine alternating electric field. The FDS shows remarkable superiority in a plurality of aspects. The method comprises the following steps: firstly, measuring a dielectric constant and dielectric loss of an insulating medium in a wide frequency range by an FDS method so as to obtain response characteristics of the insulating material under different frequencies; according to the method, the frequency domain dielectric spectroscopy FDS is combined with the advantages of wide frequency range measurement, frequency domain feature recognition, comprehensive reflection of electrical characteristics and the like, so that accurate evaluation of the insulation aging state of the high-voltage dry-type bushing is realized. The safety and the stability of a power system can be improved, and faults and accidents caused by insulation aging can be reduced.

The invention belongs to the field of photovoltaic technology, and discloses a method and system for evaluating different types of roof photovoltaic installation potentials in different climate regions, and the method comprises the steps: shooting different types of aerial images, including orthographic top views and horizontal side views, of buildings with installed roof photovoltaic in different climate regions by using an unmanned aerial vehicle, and combining a roof / photovoltaic identification model to identify the installation potential of different types of roof photovoltaic in different climate regions; and the roof area and the photovoltaic area of each building in the image are counted, and the quotient of the photovoltaic area and the roof area is a roof available coefficient, so that the roof available coefficients of different types of buildings in different climate regions are summarized. The coefficient is closer to an actual value and is more comprehensive and practical compared with 100% utilization or 3D shadow analysis, and in an existing simple coefficient multiplication method, the value of the coefficient is not perfect and scientific. Recognizing the building roof area in the region by combining a roof recognition model; and determining functional attributes of the house building according to the POI data.

The invention belongs to the technical field of satellites, and discloses a centralized photovoltaic power station capacity prediction method and system based on a satellite image and topographic data, and the method comprises the steps: carrying out the recognition of a Sentinel remote sensing image, obtaining a photovoltaic distribution position after photovoltaic automatic recognition according to the spectral characteristics, including the mean value of each spectral band, NDVI, NDWI and other indexes, and carrying out the prediction of the capacity of a photovoltaic power station. Meanwhile, elevation data provided by DEM images are integrated to calculate a topographic slope value, then influence factors such as photovoltaic image area, inclination angle, direction angle and installation spacing are calculated by using parameters such as image resolution and geographic latitude, and finally, parameters such as known photovoltaic scale and photovoltaic materials are combined to obtain a photovoltaic power generation system. And a prediction model of the capacity of the centralized photovoltaic power station is constructed with the installation capacity of a small number of known photovoltaic power stations, so that accurate prediction from the image area of the photovoltaic power station identified by the satellite image to the actual installation capacity is realized. The method comprehensively considers the influence of the direction angle, the inclination angle, the installation spacing and the like of the photovoltaic power station on the actual installation capacity, is suitable for mountainous and hilly areas with complex terrains, improves the problem of inaccuracy of a method for estimating the capacity based on the projection area, and provides a scientific basis for the capacity evaluation of the photovoltaic power station.

The invention discloses a preparation method of a perovskite type high-temperature-resistant CO2 methanation catalyst, which comprises the following steps: firstly, preparing CaZrO3, then dissolving CaZrO3 powder, a nickel source, a soluble organic fuel and a certain amount of ethylene glycol in deionized water according to a molar ratio of 1: (0.2-0.8): (0.1-0.5), stirring to obtain a dispersion liquid, then putting carrier monoclinic ZrO2 powder in the dispersion liquid, and stirring to obtain the perovskite type high-temperature-resistant CO2 methanation catalyst. The preparation method comprises the following steps: uniformly mixing the raw materials, carrying out ultrasonic treatment for 5-12 hours, heating the obtained mixture at 300-550 DEG C until spontaneous combustion, carrying out high-temperature calcination at 800-900 DEG C for 1-2 hours after flame is extinguished, collecting the calcined product, and carrying out ball milling and granulation to obtain the NiO-CaZrO3 / ZrO2 catalyst with a certain mesh number. According to the preparation method of the catalyst, a solution combustion method is introduced in the preparation process, so that active components of the prepared catalyst are fully dispersed, and further high-temperature calcination is performed on the basis, so that the active components are stably anchored on the surface of the ZrO2 carrier, and the catalytic activity and the stability of the catalyst are improved.

The invention belongs to the technical field of welding, and discloses a dissimilar metal power modulation galvanometer laser welding method which comprises the following steps: finely cleaning a 6061 aluminum alloy and a 304 stainless steel plate by using acetone, and then airing for more than 20 minutes; the aluminum steel sheet is assembled, pressed and fixed on a welding test platform in a steel-up-aluminum-down mode; the position of the welding test platform is adjusted, argon serves as welding protection gas, and a protection gas nozzle is arranged to be aligned with a welding pool area; laser welding parameters are set, a laser power modulation mode is set to be a gradient power mode, and the gradient power mode is to regulate and control phase point laser power in a circular scanning track in the advancing direction of a welding seam; a welding test is carried out in the mode that a galvanometer laser head is fixed and a welding test platform is moved, and a laser and the welding test platform are synchronously started and stopped; and the protective gas is started 5 seconds before the laser is started, and after the laser is stopped, the protective gas is stopped lagging for 10 seconds so as to continuously protect the welding seam and prevent oxidation.

The invention belongs to the field of aircraft control, and discloses a composite guidance aircraft guidance control method and system considering flight time falling angle constraint, and the method comprises the steps: inputting target values of flight time and a terminal falling angle according to a specific task requirement; pre-planning a flight path; in the flight process, estimating the current remaining flight time of reaching the target in real time, and performing feedback adjustment on the flight time error; in a flight approaching target stage, calculating a falling angle error in real time according to the attitude and speed of the aircraft, and correcting the flight attitude; through a nonlinear optimal control algorithm, two constraints of flight time and a falling angle are comprehensively balanced; executing mechanisms such as engine thrust, control surface angle and attitude control spray pipes of the aircraft are adjusted in real time according to a composite guidance algorithm instruction; and when the aircraft is about to arrive at the target, final falling angle correction is performed to ensure that the aircraft hits the target at an accurate angle. According to the method, the balance problem between the flight time constraint and the falling angle constraint is effectively solved, and the guidance precision and the task success rate of the aircraft are remarkably improved.

The invention belongs to the technical field of power generation, and discloses a power generation gas turbine grid-connected power allocation technology and device which comprises a power detection module, a data processing module and a power regulation and control module. And the output power of the gas turbine is detected in real time through the power detection module, so that the real-time performance of power allocation is ensured. The power detection has the advantages of single current detection, simplicity, linearity and high efficiency. And accuracy: through the data processing module and a preset algorithm, a power regulation and control instruction can be accurately calculated, and accurate allocation of the grid-connected power of the gas turbine is realized. The power allocation effect is that any proportion can be adopted for adjustment, and the power error is reduced; and stability: the output power of the gas turbine is regulated and controlled through the power regulation and control module, so that the stability of the power grid can be effectively improved. According to the power allocation effect, the problem that power oscillates between the two gas turbines does not occur.

The invention belongs to the technical field of wireless communication, and discloses an optimization method based on active reasoning in an MEC-enabled low-altitude element cosmic system, which realizes low energy consumption and low delay of Internet of Things equipment on the premise of ensuring user service quality through joint optimization of a flight path and a resource allocation strategy of an unmanned aerial vehicle, and improves user experience. And meanwhile, an auxiliary means is provided for improving the economic benefits of operators. Aiming at the dynamic nature of the network, the uncertainty of information acquisition and the incompleteness of data use, an optimization problem is converted into a Markov decision process, an improved deep learning algorithm based on active reasoning is adopted, factors except the reward dimension are additionally considered on the guidance of dynamic learning, and the dynamic learning algorithm is optimized. And a personalized strategy is explored according to the preference of the intelligent agent.

The invention belongs to but is not limited to the technical field of radio detection direction finding, and discloses a double-antenna three-dimensional rotating baseline direction finding method, device and system. Inter-channel phase deviation table compensation is carried out, equal-frequency-interval full-working-frequency-band measurement is carried out on phase deviation between two measurement channels in advance, and a channel phase deviation compensation table AB is formed; the compensation module is used for compensating inherent phase deviation between the channel A and the channel B; after the double-antenna three-dimensional rotating baseline direction finding device starts to work, a baseline formed by the antennas AB rotates anticlockwise at an angular velocity omega, two receiving channels synchronously collect data, and phase difference is calculated for the data corresponding to the baselines AB; based on a two-dimensional joint search method, through combination of coarse search and fine search, an azimuth angle and a pitch angle are estimated. According to the technical scheme provided by the invention, the direction finding range and reliability of a traditional system are greatly expanded, meanwhile, the cost is basically not increased, and the large-scale application of the technical system can be promoted.

The invention belongs to the technical field of power electronic systems, and discloses a distributed photovoltaic cluster modeling method and system based on an improved FCM algorithm, and the method comprises the steps: measuring the outlet voltage Vt of a photovoltaic unit, the voltage VPCC of a PCC point, and the phase angle difference theta between the two, deducing a calculation formula of an equivalent electrical distance from the outlet of the photovoltaic unit to the PCC point under a radiation type structure, and calculating the equivalent electrical distance between the outlet of the photovoltaic unit and the PCC point; calculating an equivalent electrical distance under a chain structure and a complex network topology structure, and calculating a line impedance equivalent coefficient; measuring an active power P, a reactive power Q and a port voltage Vt output by the photovoltaic unit, and proposing an index value representing a photovoltaic voltage support function; an equivalent electrical distance, a photovoltaic capacity and a voltage support function are comprehensively considered, and a comprehensive clustering index accurately representing the characteristics of the distributed photovoltaic power generation system is established; an optimal solution obtained through a particle swarm clustering algorithm is used as an initial clustering center, an optimal clustering result is calculated through an FCM algorithm, and an equivalent model is calculated through parameter equivalence;

The invention belongs to the technical field of energy management of hybrid power systems, and discloses a quantum strategy learning energy management method and system for a multi-stack fuel cell hybrid power system, and the method comprises the steps: building a multi-stack fuel cell hybrid power system model which comprises a fuel cell power model, a lithium battery equivalent circuit model and power balance; constructing a quantum hybrid neural network, interacting with the established hybrid power system environment, distributing output power of different power sources, and constructing a state-action-reward set; the strategy is evaluated and updated on the basis of the collected state-action-reward data set, and then the energy management strategy is optimized. By fusing the variable component sub-circuit and the traditional neural network, efficient operation of the fuel cell system is ensured while the state of charge of the lithium battery is maintained in an ideal interval; and the gradient of the quantum circuit parameters is accurately calculated by using a parameter transfer rule, so that the optimization process of the quantum strategy learning energy management strategy is more accurate and efficient.

The invention belongs to the technical field of stratum recognition, and particularly relates to a shield parameter geological recognition calculation method and system based on an unsupervised clustering algorithm, and the method comprises the steps: selecting a plurality of groups of data containing factors affecting stratum category recognition; pCA converts a group of complex related variables into fewer and irrelevant variables, and the new variables are called as main components and can capture most of information in original data; determining the clustering number of K-means + + by adopting an elbow method; determining the clustering number of a hierarchical clustering method by adopting a hierarchical tree; k-means + + and hierarchical clustering are respectively used for clustering the data, and evaluation indexes such as identification precision or contour coefficients of the data and the data are calculated, so that potential modes in the data are analyzed and mined more comprehensively. The invention aims to deeply explore the internal relevance between shield tunnel parameters and geological types, provides an innovative and robust solution for real-time detection of geological features in shield construction by overcoming the limitation of the prior art, and has wide application potential.

The invention provides a rapid three-dimensional modeling method based on seabed point cloud data, a medium and a system, and belongs to the technical field of seabed depth measurement, and the method comprises the steps: firstly, obtaining low-resolution point cloud data through spatial downsampling, and carrying out the multivariate mixed decomposition of the low-resolution point cloud data, and obtaining a stable component and a variable component; constructing an initial three-dimensional grid model based on the stable component, and then dividing the initial three-dimensional grid model into a plurality of sub-regions by adopting a self-adaptive partitioning strategy; and performing partition processing on the original point cloud data according to sub-regions to obtain a plurality of high-resolution point cloud data blocks. And next, performing anomaly identification and noise reduction processing on each high-resolution point cloud data block, reconstructing a corrected high-resolution point cloud data block, performing grid reconstruction and boundary registration, and constructing a complete high-resolution seabed scene three-dimensional model. And the final high-quality seabed scene three-dimensional model is obtained through processing, so that the technical problem that the existing three-dimensional seabed model cannot fully reflect the detail characteristics of the submarine topography is solved.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention belongs to the field of new energy automobile power batteries and the related field of laser welding, and discloses a technological parameter optimization method and system for spot ring light spot laser welding spatter suppression, and the method comprises the steps that the relation between the preheating temperature of an outer ring light beam to a weld joint and welding technological parameters is obtained; establishing optimization constraint conditions of the preheating temperature, and deducing optimization constraint conditions of process parameters; acquiring a preset range of the process parameters; and substituting the technological parameter values in the preset range into optimization constraint conditions of the technological parameters, and combining the technological parameters meeting the preheating temperature into optimized technological parameters. According to the method, by optimizing the preheating temperature of the annular light beam to the welding seam, regulation and control are directly carried out according to key factors for forming spatter, and generation of spatter is effectively restrained; by optimizing technological parameters, the stability of the welding process is improved, and therefore welding defects are reduced.