2522results about How to "Fast convergence" patented technology

The invention discloses a photovoltaic cleaning robot based on automatic path planning and an operation method thereof, relates to the technical field of robots, and solves the problem of cleaning blind spots in an existing cleaning robot and the endless loop risk of a path planning algorithm. According to the invention, the surface information of the photovoltaic panel is accurately captured through the multispectral acquisition module, a high-resolution and high-definition comprehensive image is generated through the data fusion processing module, then dirt is accurately identified on different scales by using a multi-scale pyramid structure and an adaptive feature fusion mechanism, and an attention mechanism and environmental perception data are combined, so that the real-time performance of the system is improved. The recognition threshold is dynamically adjusted, and misrecognition and missing detection are reduced; and finally, enabling the robot to autonomously learn in a complex environment and optimize an obstacle avoidance strategy through a dynamic obstacle avoidance algorithm based on deep reinforcement learning. The intelligent level and the cleaning effect of an existing photovoltaic cleaning robot are greatly improved.

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention discloses an adaptive genetic algorithm multi-satellite cooperative task dynamic scheduling planning method based on fusion and merging observation, and the method comprises the steps: firstly, decomposing regional tasks through equal-ratio sampling, and constructing discrete observation points; secondly, tasks are converted into a weighted meta-task set, and selectable satellites and time windows are calculated in combination with satellite resources, tasks and attitude constraints; further, detecting and combining the meta-tasks meeting conditions, and constructing a combined observation model taking the maximum observation benefit as a target; and finally, solving is optimized by adopting a self-adaptive genetic algorithm, constraint is ensured to be met, and an executable multi-satellite collaborative observation scheduling scheme is generated. In the regional task planning and scheduling process, a regional discrete adaptive sampling method is adopted to quickly decompose regional tasks into point tasks, and the completion rate of regional observation tasks is increased; in addition, an effective meta-task merging observation method is designed, and the multi-satellite dynamic task planning benefit is effectively improved.

The invention provides a coal mining digital twinning method based on multi-level dynamic deviation correction and AI optimization. The method comprises the steps of monitoring data acquisition and dynamic preprocessing, and performing denoising and space consistency detection on data; constructing and pre-processing the coal mine digital twinborn body; generating a nonlinear deviation field by adopting a high nonlinear variable interpolation and model mapping method; performing Kalman filtering error analysis and correction under the continuous analysis step, and marking an optimization model state in combination with a space mutation step; performing staged sliding window long-time-sequence fluctuation analysis, and classifying and marking regions according to an absolute value of an error and fluctuation; performing multi-level weighted error optimization based on a genetic algorithm, and adjusting material parameters and boundary conditions; carrying out material constitutive rectification based on dynamic Bayesian, and gradually enhancing the adaptability and accuracy of the model; and constructing a hybrid network structure AI optimization prediction model based on deep data driving, capturing the dynamic change of the material constitutive and the characteristic difference of the model constitutive, and realizing the precise digital twinborn simulation of the whole coal mining process.

The invention discloses a multi-unmanned aerial vehicle maritime search and rescue path planning method based on multi-target particle swarm optimization. The method comprises the steps that S1, an area to be searched and rescued is converted into a two-dimensional search and rescue area; s2, performing grid decomposition on the two-dimensional search and rescue area; s3, obtaining a probability map through a Gaussian mixture model; s4, establishing an unmanned aerial vehicle search and rescue multi-target collaborative optimization function; s5, solving the function based on a hybrid particle swarm optimization algorithm to obtain an optimal solution; and generating a specific search and rescue path of the unmanned aerial vehicle according to the solved optimal solution. According to the method, an unmanned aerial vehicle search and rescue multi-target collaborative optimization function is established, the unmanned aerial vehicle is guided to execute the search and rescue task by taking resource balanced allocation, maximum path safety, preferential coverage of a high-risk area and minimum energy consumption as targets, task allocation, path planning and energy management are improved, multi-target optimization of multi-unmanned aerial vehicle search and rescue path planning is realized, and the search and rescue efficiency is improved. An accurate flight path is formulated for each unmanned aerial vehicle, comprehensive coverage of search and rescue tasks is ensured, and the search and rescue efficiency and success rate are remarkably improved.

The invention relates to an intelligent water temperature control system and method based on machine learning. The method comprises the steps that water temperature, flow, pressure and environmental parameter data of a constant water temperature control system are collected, and a standardized time sequence data set is generated; constructing a time-varying dynamic model based on the standardized time sequence data set, and performing parameter identification by using a variable forgetting factor generalized subspace tracker to obtain a time-varying pseudo-modal temperature parameter; training a multi-branch long short-term memory network according to the time-varying pseudo-modal temperature parameters and the standardized time sequence data set to obtain an initial water temperature prediction model; performing system operation anomaly detection and prediction based on the initial water temperature prediction model to obtain an anomaly detection and prediction result; solving a control strategy based on the anomaly detection and prediction result to obtain an optimal water temperature control strategy; and executing water temperature control based on the optimal water temperature control strategy, and updating the initial water temperature prediction model to obtain a target water temperature prediction model. According to the intelligent water temperature control system, accurate control over intelligent water temperature is achieved.

The invention discloses an integrated energy system short-term multivariate load prediction method fusing a deep learning combination model, and the method comprises the steps: obtaining the multivariate load historical data and meteorological data of an integrated energy system, and obtaining a correlation factor which is greatly affected by the multivariate load; dividing the multi-element load historical data into two parts, namely a training set and a test set, and performing maximum and minimum normalization processing; extracting multi-level spatial features of the multi-element load historical data by using a series-parallel superposed multi-scale convolutional neural network; load historical data time sequence features are extracted by using a bidirectional gating cycle unit neural network, and a multi-head attention mechanism is introduced to extract important information of a load prediction task, so that a multi-scale convolution bidirectional gating cycle unit neural network fused with the attention mechanism is designed; and a self-adaptive multi-strategy grey wolf optimization algorithm is utilized to realize optimization parameter search of the bidirectional gating circulation unit neural network, an evaluation index is adopted to carry out error analysis on a prediction result, an optimal parameter is obtained, and accurate load prediction is realized.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention discloses a power system false data injection attack detection method and system. Measurement data of a power distribution system are acquired; constructing an attack sequence, and injecting the constructed attack sequence into the obtained measurement data to simulate a false data attack; constructing a false data injection attack detection model based on the expansion causal convolution and the recurrent neural network to analyze the state change of the power distribution system; and judging an attack behavior according to the analyzed state of the power distribution system. According to the method, time sequence data can be processed, the long-term dependency relationship and the complex mode can be captured, and the response capability to the complex attack mode can be improved, so that the false data injection attack can be effectively detected and prevented, and the safety and reliability of the power distribution system can be maintained.

The invention discloses a KAN-Mama-based multi-source remote sensing image semantic segmentation method and device, which is used for improving the accuracy of remote sensing image semantic segmentation, and comprises a KAN-Mama feature extraction network and a UpHead decoder. The KAN-Mama feature extraction network comprises a Stem convolutional neural network and a four-stage KAN-Mama feature extraction module, and staged feature extraction is performed on the fused remote sensing image through the KAN-Mama feature extraction network to obtain multi-scale features; the UpHead decoder comprises a side edge feature construction unit, a pooling unit, a feature fusion unit and a classification layer, side edge feature construction and pooling operation are performed on multi-scale features through the side edge feature construction unit and the pooling unit, step-by-step feature fusion is performed through the feature fusion unit, channel fusion is performed through a BottleNeck layer, and the classification layer is used for classifying the multi-scale features. And finally, obtaining a semantic segmentation label through the classification layer. The semantic segmentation accuracy of the multi-source remote sensing image is higher, the model convergence is faster, and the detail information is more accurate.

The invention provides an intelligent well completion multi-objective optimization method based on a multi-objective genetic algorithm, and the method comprises the steps: determining an optimization objective and a constraint condition according to a real environment of intelligent well completion; and constructing a multi-target genetic algorithm model, and executing optimization calculation of the multi-target genetic algorithm. And evaluating the influence of each ICV configuration on the optimization target by adopting a fitness function. And an initial population is generated for initialization, a current optimal individual is screened out through iterative operation, and the process is ended after data are output. The method has the beneficial effects that the calculation efficiency is improved by establishing the multi-target fitness model and optimizing the iteration strategy, so that the algorithm can quickly converge to the optimal solution under limited calculation resources. The fitness function of the scheme can accurately evaluate the influence of different parameter configurations on the optimization target, and the optimization result is more reliable and accurate. And an optimization method which is more efficient, more comprehensive and more suitable for actual production requirements is provided.

The invention discloses a dynamic carpooling scheduling method for unmanned electric taxis. The method comprises the following steps: firstly, acquiring data and cleaning the data; mapping the data in each hexagonal grid; establishing a travel demand feature matrix and a spatial dependency matrix; constructing a space-time diagram convolutional neural network based on an attention mechanism, and obtaining a travel demand prediction result; setting constraint conditions matched with the car orders in the dynamic car-pooling scene; based on the travel demand prediction result and the vehicle order matching constraint condition, constructing a vehicle order matching decision model based on distributed deep reinforcement learning; and designing a Double DQN solving algorithm, carrying out real-time order matching and idle vehicle scheduling based on a result, notifying the matched vehicle to complete a corresponding order, and notifying the unmatched idle vehicle to carry out regional scheduling or charging energy supplementation. According to the method, in dynamic carpooling of the multi-passenger bicycle, the endurance process constraint of the electric automobile and the charging energy complementation requirement of the bicycle are considered, the actual operation requirement is better met, and the quality is improved.

The invention discloses a terahertz-based curved surface thermal barrier coating nondestructive thickness measurement system and method, and the method comprises the steps: firstly carrying out the coordinate calibration and reverse reconstruction of a sample piece and a mechanical arm through an optical scanning and trinocular vision measurement system, and building a virtual space; then matching the virtual space with the actual space, extracting a normal vector of the curved surface of the sample piece, performing path planning on the mechanical arms to generate a control script, and controlling the double mechanical arms to carry a terahertz measuring head to scan the sample piece in a manner of being vertical to the surface of the sample piece to obtain a terahertz signal; and finally, constructing a terahertz signal analytical model, optimizing terahertz simulation signal parameters through a self-adaptive teaching and learning optimization algorithm, and fitting a detection signal until an optimal solution is obtained, namely the thickness of the thermal barrier coating. According to the invention, through cooperation of the two mechanical arms and path planning, rapid detection of a complex curved surface structure is adapted; by establishing an analytical model and combining with an optimization algorithm, the detection precision is improved, and high-precision nondestructive detection suitable for a complex curved surface structure is realized.

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

The invention discloses a high-speed rail system sliding layer peak displacement envelope curve and rail direction irregularity prediction method considering seismic oscillation and pier height uncertainty and application. The method comprises the following steps: constructing a multi-mode hybrid neural network model TransTCN embedded with field priori knowledge; feature extraction is carried out on multi-modal input features of a time sequence based on seismic oscillation fusion structure features and a space sequence for outputting peak displacement based on an elastic response spectrum method; the pier height features and the seismic oscillation features are subjected to dimension fusion to serve as time sequence features, and a time dependency relationship is processed; processing a spatial dependency relationship by taking an elastic response spectrum analysis result as a spatial feature; and jointly inputting the extracted features into a constructed multi-mode hybrid neural network model TransTCN, and realizing prediction of the peak displacement envelope curve and rail direction irregularity of the sliding layer of the high-speed rail system under the consideration of seismic oscillation and pier height uncertainty. The prediction precision is improved, the training sample demand is reduced, the regional high-speed rail system damage is rapidly evaluated, and the practical value is good.

The invention discloses a regional risk prediction method and system based on toxic gas diffusion analysis, and provides a regional risk prediction method based on toxic gas diffusion analysis on the basis of source item traceability prediction of a leakage source. The content of the invention mainly relates to development and improvement of a gas diffusion model, data acquisition and processing, construction of a risk prediction model, uncertainty analysis, research of an emergency response strategy, case analysis and verification and system integration and demonstration application, and through comprehensive consideration of factors such as gas properties, meteorological conditions and topographic features, prediction accuracy is improved, and the risk of the gas diffusion model is predicted. Scientific basis is provided for risk management, and practicability and popularization of the prediction system are finally realized.

The invention relates to a personalized federal learning method based on self-adaptive local model initialization and double knowledge distillation, and belongs to the technical field of edge computing. The method comprises the following steps: performing local model initialization on each client according to cosine similarity between a global model and a local model to obtain a self-adaptively initialized local model; respectively performing knowledge distillation on each client according to the global model and the historical local model, and updating parameters of the local model through double knowledge distillation; and the central server receives local models uploaded by all clients participating in training, and obtains a final global model based on dual dynamic aggregation weights of data quality and client contribution degrees. According to the method, the precision and convergence speed of the model can be remarkably improved when the data heterogeneity problem is processed, and good adaptability and robustness are shown in various edge calculation scenes.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention discloses an unmanned aerial vehicle path planning method based on global search and meta-heuristic optimization, and the method comprises the steps: firstly building an unmanned aerial vehicle path planning model under complex constraints, determining a fitness function and a constraint condition thereof, setting algorithm initialization parameters, carrying out the chaotic mapping of an initialization population position, and carrying out the optimization of the algorithm; and performing loop iteration solution by using a meta-heuristic optimization algorithm, outputting a global optimal path, finally realizing the global optimal path on the unmanned aerial vehicle, and calculating a flight distance corresponding to the optimal path to complete unmanned aerial vehicle path planning. According to the method, a meta-heuristic algorithm, namely a crown porcupine algorithm, is combined with tabu search, the global search capability is enhanced, and meanwhile, population initialization is optimized in combination with chaotic mapping, so that the randomness and unpredictability are increased, the algorithm can converge more quickly, and the robustness of the algorithm is improved. The method has obvious advantages in solving the problem of path planning with complex environment and complex constraint, and solves the problem of path planning of the unmanned aerial vehicle in the three-dimensional complex mountain environment.

The invention discloses a charging and discharging control system and electronic equipment. The method comprises the following steps: S1, constructing a charging pile multi-target optimization model; s2, initializing a chaotic particle swarm optimization algorithm; s3, each particle in the particle swarm is optimized according to an objective function in the charging pile multi-objective optimization model; s4, carrying out loop iteration until the chaotic particle swarm optimization algorithm reaches a convergence condition; s5, generating a prediction result of the operating environment of the charging pile; s6, updating the current optimal particle and the global optimal particle; and S7, generating a multi-target charging and discharging optimization control strategy of the charging pile according to the optimized global optimal particle, applying the optimization control strategy to a charging pile charging and discharging control execution module, and executing multi-target charging and discharging control of the charging pile. According to the method, the globally optimal solution in a complex scene can be quickly solved, and the efficient operation requirement of the electric vehicle charging pile in a large-scale deployment scene is met.

The embodiment of the invention discloses a salinity time sequence prediction method and device, and the method comprises the steps: obtaining a historical observation data set, carrying out the preprocessing of the historical observation data set, and carrying out the normalization processing of the preprocessed historical observation data set; constructing a prediction model architecture, and inputting the historical observation data set into the prediction model architecture for training and testing to generate a prediction model; and collecting real-time salinity observation data, and inputting the real-time salinity observation data into the prediction model to output a salinity prediction result. A large amount of historical observation data is collected to construct and test the model, salinity observation data is collected in real time and input into the model to output a prediction result in real time, the problems that data sets are insufficient and hydrological elements are lacked in time sequence prediction in industrial application are solved, the prediction model is higher in precision, and the prediction efficiency is improved. Due to the data quality control process of preprocessing the historical observation data, the data quality is ensured, the problem of low data quality is solved, and reliable salinity time sequence prediction is provided.

The invention discloses a packaging tin material proportioning optimization method based on a particle swarm optimization algorithm, and the method comprises the following steps: initializing a particle swarm, dynamically adjusting the positions and speeds of particles through a fuzzy logic control system, and generating an initial material proportioning scheme; a fitness function is constructed based on a non-Euclidean geometric method, multiple indexes such as strength, toughness, corrosion resistance and cost are comprehensively considered, and fitness values of all the schemes are calculated; gradually optimizing a material proportioning scheme by using a biologically inspired particle swarm optimization algorithm; a dynamic clustering mechanism is introduced, and different material matching schemes are analyzed and optimized; dynamically switching between global search and local search through a hybrid search strategy, and further optimizing a selected scheme; and finally, outputting an optimized optimal material proportioning scheme, and evaluating the reliability of the optimal material proportioning scheme through experimental verification and simulation. According to the invention, the optimization efficiency and performance of the packaging tin material proportioning are improved, and the method is suitable for the requirements of production automation and high efficiency.

The invention provides a remote sensing image road segmentation method combining bidirectional multi-level road feature dynamic fusion and double-context dynamic extraction, and belongs to the field of road segmentation. The problems of shielding and difficulty in distinguishing road levels existing in the current road segmentation network are solved; comprising the following steps of data set production, model construction, model training and result prediction. The invention designs a bidirectional multi-level road feature dynamic fusion and double-context dynamic extraction module. The bidirectional multi-level road feature dynamic fusion module effectively compensates the problem of spatial information and edge detail loss caused by continuous down-sampling through bidirectional interactive transmission of multi-level feature information, and enhances the understanding of the model for complex scenes and multi-level road structures. The double-context dynamic extraction module realizes dynamic fusion of long-distance and local multi-scale context information, is helpful for suppressing background interference, shielding effect and confusion of similar road structures, and ensures the integrity and continuity of extracted roads.

The invention relates to the field of robot target path planning, and discloses a multi-target path planning method and device based on an improved mantis optimization algorithm, and the method comprises the steps: improving the mantis optimization algorithm, and building a grid environment map and a multi-target fitness function, an obstacle avoidance function and a smooth path function corresponding to path planning; the population position optimization corresponds to path nodes in the grid environment map, iteration is performed on all individuals of the population, and fitness values are formed; calculating comprehensive fitness by using a multi-target fitness function to obtain an updated global optimal position and a corresponding optimal fitness value; completing path planning of a multi-target fitness function; and observing the convergence curve graph and the shortest path graph, and verifying the feasibility of multi-target path planning. According to the method, the convergence speed of the algorithm is improved, the global and local search capabilities are balanced, the shortest path length is effectively reduced, path redundant nodes and path inflection points are reduced, and the path quality and efficiency are improved.

The invention relates to a chilled water system of a central air conditioner and an energy-saving optimization control method. The energy-saving optimization method comprises the following steps: S1, establishing an energy consumption model of the chilled water system of the central air conditioner; s2, firstly performing correlation analysis on the selected cooling load influence factors by adopting a grey correlation analysis method, screening out strong correlation influence factors, and establishing a central air conditioner cooling load prediction model based on PSO-LSSVM (Particle Swarm Optimization-Least Square Support Vector Machine); s3, optimizing the energy consumption model parameters of the chilled water system of the central air conditioner while predicting the cooling load value of the next point by using an improved white whale algorithm, and enabling the output result of the energy consumption model to reach the minimum value; the system comprises an energy consumption modeling module, a cooling load prediction module and an energy-saving optimization module. According to the method, the energy consumption model and the cooling load prediction model are established, the cooling load value of the next point is predicted, parameter optimization is performed, and an optimization structure is output; the energy consumption model and the cold load prediction model can be updated periodically, and the problem of performance reduction caused by long-time equipment operation is avoided.

The invention discloses an automatic model tuning system based on an AI training intelligent workbench, and relates to the technical field of artificial intelligence, and the system comprises the steps: setting a data collection instrument and an environment monitoring point, collecting model real-time data in a model training process, and carrying out the preprocessing; performing model performance analysis according to the model real-time data and the model original data to obtain final parameter configuration; and performing model training by utilizing the final parameter configuration, dividing the model training process into different stages, dynamically adjusting an intelligent control strategy based on model performance requirements, and performing optimization on the different stages of the model. According to the method, through multi-dimensional performance evaluation, parameter configuration is optimized, and the robustness and generalization ability of the model are improved. Through dynamic adjustment of an intelligent control strategy, fine management of a training process is realized, efficiency and resource utilization are improved, model convergence is accelerated, and finally an efficient, stable and high-adaptability model training effect is achieved.

The invention relates to the field of short-term load prediction and power dispatching control of a power system, in particular to an energy output optimization dispatching method and system considering a random source load virtual power plant, and designs a virtual power plant double-layer optimization dispatching model. The lower-layer model constructs a user load prediction model of an improved Transform, and then a renewable energy output model is established by using a scene generation method; the upper layer model takes the power generation cost minimization of the wind-light storage and combustion virtual power plant as a target, considers constraint conditions such as gas turbine output power, ramp rate, energy storage charging and discharging power, electricity storage quantity and power balance, and proposes a single guide target function comparison algorithm considering random source load to carry out optimization solution on a virtual power plant optimization scheduling target function; and planning and deciding the output of each energy source in the next 24 hours. Experiments show that the new energy consumption level can be remarkably improved under the random source load condition, and better economic benefits are obtained while stable operation of a power distribution system is promoted.

The invention discloses a pedestrian and vehicle detection method based on an improved YOLOv10n. The pedestrian and vehicle detection method comprises the following steps: on the basis of an original model YOLOv10n, replacing a first layer and a third layer in Backbone and a seventeenth layer Conv in Neck with a space-to-depth convolution SPDConv; a focused spatial pyramid pooling module ASPPF is constructed, and semantic fusion of cross-level features is optimized; designing a global grouping coordinate attention module GGCA, fusing the GGCA into a Neck layer, generating attention maps by using global information of the feature maps in the spatial dimension, and weighting the input feature maps through the attention maps to enhance the feature expression ability; a WIou loss function is introduced, the bounding box loss of the network is optimized, and the convergence speed and regression precision of the model are improved. And training a model by using a data set which is made by autonomous labeling to obtain a training result, and completing a pedestrian and vehicle detection task by using an optimal training weight. According to the method, a good detection effect is achieved with a small number of model parameters, and the pedestrian and vehicle recognition accuracy is well improved.

The invention discloses a remote sensing image target detection method based on an accelerated projection recurrent neural network. The method comprises the following steps: acquiring an original remote sensing image and calculating a remote sensing image autocorrelation matrix; constructing a linear constraint optimization mathematical model of filtering output based on the remote sensing image autocorrelation matrix, and obtaining a linear equation mathematical model through conversion; and solving the linear equation mathematical model by adopting an accelerated projection recurrent neural network algorithm, carrying out vector decomposition on a solving result to obtain a filtering coefficient vector, obtaining a remote sensing image after filtering output based on the filtering coefficient vector, and completing remote sensing image target detection. According to the remote sensing image target detection method based on the accelerated projection recurrent neural network, the convergence speed is higher, the calculation time is shorter, the image threshold classification capability is higher and the target detection precision is higher when the optimization problem is solved.

The invention relates to the technical field of model lightweight processing, and discloses a digital twin model lightweight processing method, and the method comprises the steps: obtaining a digital twin model, constructing a multi-dimensional twin model parameter importance evaluation objective function, and evaluating the digital twin model parameters obtained through training solving; carrying out lightweight processing on the digital twinborn model parameters based on an importance evaluation result to obtain a lightweight digital twinborn model; and carrying out fine tuning on the lightweight digital twinborn model to obtain the lightweight digital twinborn model after fine tuning. Gradient evaluation and channel gain evaluation are carried out according to the gradient of the digital twin model parameters in the training process and the final training result, the digital twin model parameters with high contribution degree to the output result are selected, redundant parameters are filtered, lightweight processing of the digital twin model is achieved, and the training efficiency is improved. And performing fine tuning processing to retain the performance of the original model to obtain the digital twinborn model which reduces the consumption of computing resources and the storage cost.