557 results about "Hybrid algorithm" patented technology

The invention relates to the technical field of computer networks, and discloses a computing power network real-time performance evaluation method based on an analytic hierarchy process, and the method comprises the steps: collecting a node operation state and task demand data through a sensor, and generating a local performance index in combination with an edge quantum algorithm; simulating a future network state by using digital twinning, and fusing to generate a multi-dimensional performance data set; the AHP weight is dynamically adjusted based on resource deviation and a geological classification model, high-frequency updating is started for high load / fault, and the weight range is expanded for low load; introducing a risk assessment algorithm to quantify a performance-cost-carbon effect conflict level, and triggering resource recovery, optimization prompt or single index suggestion; scheduling strategies are triggered in a grading mode according to evaluation results, and active intervention is started in combination with anomaly detection; the AHP weight is dynamically updated through reinforcement learning, and quantum-classical hybrid algorithm parameters and block chain verification weight are automatically optimized. The real-time response efficiency and the resource utilization rate of the computing power network can be improved.

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

The invention relates to a deep integrated electric drive assembly design method and system, and the method comprises the steps: obtaining the parametric modeling data of an electric drive assembly, and carrying out the digital twin synchronous motor modeling, and obtaining a drive three-dimensional model; topological structure data of the electric drive assembly are obtained, and mixed algorithm optimization calculation is carried out on the topological structure data and the drive three-dimensional model to obtain a system optimization solution set; performing model prediction control construction based on the system optimization solution set, and performing gear contour line modification processing on the electric drive assembly to obtain a speed reduction transmission strategy; performing heat dissipation management extraction optimization on the system optimization solution set according to a preset variable cross-section micro-channel heat dissipation algorithm to obtain a dynamic heat dissipation strategy; preliminarily integrating the speed reduction transmission strategy and the dynamic heat dissipation strategy to obtain an initial design scheme; eMI filtering optimization is carried out on the initial design scheme, multi-stage electromagnetic shielding construction is carried out, and a system performance optimization scheme is obtained. The comprehensive performance of the system can be improved through integral design, regulation and control.

The invention discloses a WMS warehouse task dynamic scheduling method and system based on multi-objective optimization, and the method comprises the steps: collecting scheduling parameters, such as task priority, equipment load and goods location distance, through a RidgeOS intelligent warehouse scheduling platform, and inputting a dynamic scheduling task uncertainty perception model to generate an uncertainty feature vector; constructing a scheduling decision space by adopting an elastic time slice-reinforcement learning hybrid algorithm, dividing an elastic time slice interval, and iteratively updating a scheduling strategy in the interval; sorting the to-be-executed tasks of the warehouse according to the updated strategy, wherein the uncertainty feature vectors are used as constraints during sorting; a scheduling instruction is sent to the equipment through the platform, and meanwhile parameter change data in the equipment execution process is collected and fed back to the sensing model. The system comprises six units which are connected. According to the method, the task execution uncertainty can be accurately perceived, the scheduling strategy flexibility and iteration efficiency are improved, and the intelligent storage multi-target optimization scheduling requirement is met.

The invention discloses a charging pile intelligent heat dissipation method and device based on heat conduction line optimization and a medium, belongs to the technical field of charging pile heat dissipation, and aims to solve the technical problem of how to avoid the defects of high thermal resistance, response lag and large energy consumption in a traditional charging pile heat dissipation scheme and improve the heat dissipation efficiency and operation reliability of a charging pile. According to the technical scheme, a heat conduction structure is optimized; deploying a thin film thermocouple and constructing a three-dimensional temperature field model through infrared thermal imaging; establishing a thermal resistance-flow-power transfer function model by adopting a fuzzy PID (Proportion Integration Differentiation) and model predictive control hybrid algorithm; historical charging data are analyzed based on an LSTM neural network, a power peak value is predicted 300 ms ahead of time, and heat dissipation equipment is pre-started.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention relates to the technical field of cables, and particularly discloses a cable terminal simulation and state evaluation method and system in a humid environment, multiple types of sensor arrays are deployed, distributed temperature and humidity sensors are installed at key parts of a cable terminal, and aiming at the technical problems of one-sided single-dimensional monitoring, simulation model solidification and insufficient static weight adaptation, the cable terminal simulation and state evaluation method and system are provided. Cooperative acquisition of multiple parameters such as temperature and humidity, partial discharge and corrosion current is achieved through deployment of multiple types of sensor arrays, the nonlinear interaction relation between humidity permeation and electric field distortion is dynamically reflected in combination with a multi-physics field coupling simulation model, the one-sidedness of single parameter monitoring is solved, and the risk misjudgment rate is reduced by 40% or above; a dynamic water film thickness model is adopted to update interface parameters of the sealing ring in real time, a simulation result is updated per hour in combination with a finite element-boundary element hybrid algorithm, an electric field distortion simulation error is controlled within 12%, and the simulation precision is improved by more than one time compared with a fixed boundary condition.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

The invention discloses an SSL VPN security gateway communication method fused with a post quantum cryptography technology. Comprising the following steps: S1, a client and a server respectively configure a serial hybrid signature digital certificate containing an SM2 algorithm and a serial hybrid encryption digital certificate containing a PQC algorithm, and a corresponding PQC encryption key pair private key, a PQC signature key pair private key, an SM2 encryption key pair private key and an SM2 signature key pair private key; s2, newly adding a hybrid algorithm password suite using an SM2 algorithm and a PQC algorithm in a password suite list of the client, and forcibly jacking the priority of the hybrid algorithm password suite; and S3, a message structure in a handshake protocol is transformed to use a series hybrid encrypted digital certificate to realize that PQC algorithm processing is added on the basis of an original national cryptographic algorithm to realize quantum key negotiation resistance. According to the method, the PQC algorithm is added on the basis of the original national secret algorithm, and the handshake protocol is transformed, so that anti-quantum-attack key agreement and identity authentication can be realized, the communication security is remarkably improved, and meanwhile, the interoperability with the standard SSL VPN is kept.

The invention relates to the technical field of die parameter optimization, and discloses a process optimization method and system for a multi-part adaptive self-adjusting stamping die, and the method comprises the following steps: generating a structured process input parameter set through input parameters, and storing the structured process input parameter set in a central database; searching related cases and rules from the knowledge graph according to the input parameters; and calculating the similarity between the current parameter and a historical case, using a hybrid algorithm, carrying out weighted averaging, explaining the weight of each parameter, processing special constraints, and finally carrying out normalization to obtain a comprehensive similarity index. Real-time monitoring and dynamic adjustment are achieved through an intelligent control system, self-adaptive mold design optimization and centralized material characteristic database management are combined, an integrated quality detection system automatically recognizes and classifies defects through image recognition and machine learning technologies, and the problems that key parameters cannot be adjusted in time according to actual working conditions, and the working efficiency is high are effectively solved. The production efficiency is low; and the product quality is unstable.

The invention relates to the technical field of intelligent warehousing monitoring, and discloses an intelligent warehousing monitoring system based on digital twinning, which comprises a data acquisition layer, a digital twinning modeling layer, an intelligent analysis layer and an execution control layer, and is characterized in that three-dimensional coordinates and temperature distribution data of goods are acquired through a laser radar array and an infrared thermal imager; constructing a dynamic point cloud data set in combination with RFID positioning; an improved YOLOv7 model is adopted to be embedded into a CBAM attention mechanism to recognize the cargo form risk, and an LSTM time sequence model is fused to predict a temperature anomaly trajectory; an AGV obstacle avoidance path is optimized based on a genetic-ant colony hybrid algorithm, and a temperature control system and a security device are driven to perform linkage execution through an OPC UA protocol. And finally, millisecond-level synchronous mapping of the physical warehouse and the digital twinborn body, real-time deviation correction regulation and control of an equipment running track and cross-system collaborative response of an emergency strategy are realized, and the safety early warning accuracy and dynamic protection robustness of the storage environment are improved.

The invention discloses a dynamic short-term and temporary intelligent forecasting system for mine time-delay rockburst, and belongs to the field of short-term and temporary forecasting of rockburst disasters. The system comprises a deep well three-dimensional micro-seismic field global sensing module, a micro-seismic characteristic parameter extraction module, a rockburst catastrophe evolution data set construction module, a data preprocessing module, a deep learning prediction module, an interpretability analysis module and a dynamic short-term and temporary intelligent forecasting module. A dynamic tracking type micro-seismic sensing network is constructed through a three-level annular nested layout strategy, micro-seismic event data are collected in real time, spatial-temporal characteristic parameters are extracted, a rockburst time sequence data set is constructed in combination with a sliding time window method, and the occurrence probability and intensity level of rockburst are predicted. The system adopts a grey wolf-simulated annealing hybrid algorithm to optimize model parameters, and improves the credibility of a prediction result through SHAP value analysis. The system can realize dynamic short-term and temporary early warning of rockburst disasters, and provides scientific decision support for mine safety management.

The invention relates to the technical field of process data analysis and processing, and discloses a coal-based solid waste recycling full-process intelligent management and control platform and a digital twin system. The platform is characterized in that an identification module identifies a coal-based solid waste mineral phase through a ray diffractometer to obtain mineral composition classification data; the construction module constructs a digital twinborn model based on the classification data, simulates a dissolution reaction, and obtains weathering soil formation process data; the establishment module establishes a pollutant migration prediction model according to the process data to obtain environmental risk data; the optimization module optimizes the risk data through an improved particle swarm-genetic hybrid algorithm to obtain a resource path scheme; and the adjusting module adaptively adjusts the process parameters based on the path scheme to obtain a whole-process management and control instruction. The problems that accurate mineral phase recognition and intelligent classification are lacked in the coal-based solid waste recycling process, and a dynamic prediction model cannot be constructed and a weathering soil forming process cannot be simulated in real time based on mineral composition data are solved.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention belongs to the technical field of unmanned aerial vehicle technology and intelligent operation and maintenance management, and discloses an unmanned aerial vehicle maintenance task scheduling and resource management platform and method, and the method comprises the steps that a data collection and fusion module obtains and fuses multi-source data; the fault diagnosis and prediction analysis module uses a deep learning model and a CNN-LSTM hybrid model to diagnose faults, predict life and generate a maintenance task list; an optional task dynamic priority evaluation module performs priority ranking on the maintenance task list; the intelligent scheduling decision module uses an improved NSGA-II and VNS mixed algorithm to generate an optimal allocation scheme according to priority and resource constraints; the resource dynamic management and cooperation module realizes dynamic allocation and cooperation of personnel, spare parts, tools and sites, and can integrate AR guidance; and the optional visual monitoring and feedback optimization module displays the state, feeds back data and realizes closed-loop optimization. The maintenance efficiency of the unmanned aerial vehicle can be remarkably improved, the operation cost is reduced, and the flight safety is guaranteed.

The invention discloses an offshore wind power plant micro-siting optimization method considering regular layout. The method comprises the following steps: firstly, obtaining wind data, wind power plant parameters, fan parameters and wake flow parameters; performing multi-angle rotation on the wind power plant based on an equivalent rotation coordinate method, constructing a gridding mixed integer quadratic programming model, and performing rotation rule layout optimization (RRLO-1) to obtain an initial fan layout; on the basis of the initial layout, a hybrid algorithm (GA-ISSA) of a genetic algorithm and a sparrow search algorithm is adopted, the position of a draught fan and the rotation angle of the wind power plant are finely adjusted, and fine rule layout optimization (RRLO-2) is completed; and finally, the wake flow influence is calculated through a Cosine wake flow model and a quadratic sum superposition model, a fan power curve is processed in a piecewise linearization mode, and the final layout is output by taking the maximization of the total generating capacity as the target. According to the method, a fan position configuration scheme meeting the engineering rule layout requirement is provided for early-stage planning of the offshore wind plant, the wake effect is reduced, the power generation efficiency is improved, and development of the offshore wind power industry and low-carbon target implementation are facilitated.

The invention discloses a generative adversarial network architecture search method and system based on a GA-PSO hybrid algorithm, and belongs to the technical field of deep neural networks. The method constructs a generative adversarial network super-network architecture, globally explores an architecture population by using a genetic algorithm, and performs local parameter adjustment on an elite architecture in combination with particle swarm optimization; the inertia weight and the learning factor are dynamically balanced, and framework compliance is ensured through discretization coding constraint; and finally, based on Pareto optimality, integrating a multi-target evaluation result and a primary and secondary collaborative proportion loop optimization solution set. Compared with traditional architecture search, the method has the advantages that the premature convergence problem is solved through a hybrid optimization mechanism, global and local collaboration is achieved in combination with weight sharing evaluation and hierarchical coding, and the search period is remarkably shortened while the generation quality is improved.

The invention discloses a ship steel plate digital cutting and typesetting optimization method and system, and the method comprises the steps: collecting the morphology data of a to-be-cut steel plate, and generating a machining region model; based on the machining area model and the specification data of the target part, a deep reinforcement learning-genetic hybrid algorithm is adopted as a solving strategy, and an optimal typesetting scheme is output; generating a numerical control cutting path and a corresponding numerical control code according to the preferable typesetting scheme, and carrying out kerf gap constraint and thermal deformation compensation on the numerical control cutting path; based on the deviation between the ideal coordinates of the reference mark points in the machining area and the real coordinates of the reference mark points projected on the to-be-cut steel plate, the to-be-cut steel plate is positioned and calibrated; and executing a cutting instruction, evaluating a cutting result, and feeding back deviation data to adjust and optimize strategy parameters. On the premise of ensuring the process feasibility, limit utilization of the steel plate is achieved, the cost is reduced, the cutting quality is improved, and the method is suitable for shipbuilding scenes with various complex parts and high cutting precision requirements.

The invention discloses a regional fan virtual inertia support scheduling method considering a wind power plant group synergistic effect, and aims to solve the problems of non-uniform inertia distribution, response conflict and economy in wind power plant group scheduling. By introducing a hybrid multi-level cooperative control architecture, the inertia vacancy of the power grid is calculated in real time, the inertia demand of each wind power plant is allocated, and the scheduling strategy of the wind power plant group is optimized. And a time-varying inertia demand curve is generated by adopting a hybrid algorithm, the future inertia demand is accurately predicted, and the fan rotating speed recovery cost, the equipment loss and the wind curtailment amount are minimized in combination with a multi-target optimization model. A model predictive control framework and a Nash equilibrium theory are introduced, the game problem between wind power plants is solved, collaborative scheduling of a wind power plant group is optimized, and timeliness and stability of a scheduling instruction are ensured by embedding a time delay compensation algorithm. According to the method, the inertia supporting efficiency of the power grid can be remarkably improved, the equipment loss and the wind curtailment amount are reduced, the robustness of the system is improved, and the method has high application value and prospect.

According to the industrial solid waste multi-target collaborative management method and system based on the digital twinborn and hybrid optimization algorithm, industrial solid waste data are collected in real time through an Internet of Things sensor, a dynamic digital twinborn model is constructed to simulate a physical process, and the problems of data dispersion and lag in a traditional method are solved; establishing a multi-objective optimization function by combining three major objectives of economy, environment and society, and generating a Pareto optimal strategy set by adopting an improved NSGA-II and particle swarm optimization (PSO) hybrid algorithm; the method combines historical and real-time data through a twin model, employs an LSTM algorithm to synchronously predict the type and quantity of solid wastes, the equipment operation state and the environment change, achieves the dynamic simulation and prediction of the whole process of solid waste generation, transportation and processing, remarkably improves the data integration efficiency, provides precise real-time support for decision making, and improves the real-time performance of the system. The treatment cost is reduced, the carbon emission is reduced, and the public satisfaction is improved; the strategy response time is shortened through dynamic model correction, an intelligent solution is provided for industrial solid waste management, and the function of multi-dimensional collaborative optimization of industrial solid waste management is achieved.

The invention discloses a system and a method for multi-parameter cooperative control in PVC steel wire hose production, and relates to the technical field of plastic hose production, the system comprises a parameter monitoring module, a central processing module, a parameter adjusting module and a quality detection module, the parameter monitoring module is a high-precision intelligent sensor and has a self-adaptive filtering function, and key parameters are measured and transmitted to the central processing module; the central processing module uses an improved multi-objective optimization algorithm to calculate an optimal cooperation value, a simulated annealing-genetic hybrid algorithm is used for iteration, the parameter adjustment module uses intelligent PID, adaptive variable structure control and model prediction control adjustment equipment, and the quality detection module uses multi-modal fusion deep learning and high-precision physical performance detection equipment. And if not, recalculating the adjustment parameters. The production quality and efficiency of the PVC steel wire hose are improved, the cost is reduced, parameters are precisely and cooperatively controlled, the hose is stable in physical performance and few in surface defect, the raw material and equipment maintenance and energy consumption cost is reduced, and the enterprise revenue and market competitiveness are improved.

The invention relates to the technical field of artificial intelligence, and discloses a precise targeted AI recommendation system. The method comprises the following steps: firstly, acquiring multi-source heterogeneous data, constructing a global space-time database through space-time alignment, complementing and repairing the data by using a generative adversarial network, and cleaning and verifying; and constructing a three-dimensional topological model based on the improved graph neural network, and fusing multiple features to generate a multi-resolution model. A scene simulation platform is established by adopting a multi-objective reinforcement learning algorithm, multi-objective optimization is set, and a strategy library is constructed. And screening key factors by using a sparse Bayesian theory to construct a lightweight model. And designing a layered dynamic optimization framework, solving an optimal solution of each sub-layer by using a hybrid algorithm, realizing cross-layer constraint synchronous decoupling through a block chain smart contract, and outputting a global optimal three-dimensional design scheme. The method improves the application level of the digital achievement of the power grid design, gives consideration to stability, economy and environmental protection, and has important application value.

The invention discloses an urban traffic signal space-time adaptive flexible regulation and control method and system. The method comprises the steps of collecting traffic data such as the number of vehicles, the vehicle speed, the number of pedestrians and the pedestrian speed at an intersection in real time; performing cleaning, normalization and feature extraction on the acquired traffic data to generate standardized traffic parameters; based on the standardized traffic parameters, a signal lamp timing scheme is generated through a space-time diagram convolutional network and lightweight deep reinforcement learning hybrid algorithm; adjusting the traffic signal lamp in real time according to the signal lamp timing scheme; the method comprises the steps of storing traffic data and remotely monitored intersection states, integrating information of adjacent intersections to perform regional strategy optimization, integrating strategy data of each intersection through federal learning, and generating a global model to optimize a collaborative penalty coefficient; the method can quickly respond to the dynamic change of the traffic flow, optimizes the signal lamp timing scheme, and remarkably improves the data processing speed and decision-making efficiency.

The invention discloses a source-grid-load-storage cooperative scheduling method and system based on multi-time scale cost optimization, and the method comprises the steps: outputting a dynamic coupling map containing short-term and long-term cost evolution paths according to the source-side power generation cost, the grid-side transmission loss, the load-side load demand and the storage-side life attenuation data of an energy system; based on the dynamic coupling map, outputting an uncertainty quantization parameter containing probability distribution; according to the uncertainty quantization parameters, outputting a collaborative scheduling framework containing space-time correlation constraints; and based on the collaborative scheduling framework, performing multi-objective optimization solution by adopting a hybrid algorithm, and outputting a global optimal scheduling strategy. By utilizing the embodiment of the invention, the global optimal decision can be realized to balance the economy and safety of the system and the service life of the equipment.

The invention discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

The invention provides an intelligent household electrical appliance type identification method and system based on a neural network, and relates to the technical field of intelligent household electrical appliance identification. The method comprises the following steps: acquiring electrical parameters of the household appliance in stable operation; cleaning the electrical parameters by using a mixed algorithm based on statistics and signal processing, and performing normalization processing on the cleaned voltage data and the cleaned current data; constructing voltage and current track curves, and combining the voltage and current track curves in the same two-dimensional plane coordinate system to form a track image; establishing a mapping relation between the power factor and an image pixel gray value, and based on the track image and the mapping relation, constructing a two-dimensional track image containing gray information corresponding to the operation state of the electric appliance; carrying out feature vector extraction on the two-dimensional track image containing the gray scale information; and performing electric appliance classification and identification based on the convolutional neural network model. According to the invention, the accuracy of electrical identification and classification can be improved.

The invention relates to the technical field of artificial intelligence, in particular to an injection molding process parameter optimization method and system based on hybrid algorithm and model fusion, and the method comprises the steps: optimizing a parameter combination of a support vector regression model through a simulated annealing algorithm, building a weighted fusion model based on the optimized support vector regression model and a random forest, and optimizing the model; constructing a hybrid model of an adaptive selection weighted fusion model and an optimized support vector regression model; constructing a three-objective optimization model including buckling deformation, volume shrinkage and production energy consumption, and searching a Pareto optimal solution set in a process parameter space by adopting a multi-objective genetic algorithm by taking the hybrid model as a target value evaluation tool; carrying out local correction on the key process parameters by adopting a gradient descent method until the deviation falls back to be within a preset threshold value, and obtaining optimized process parameters; the defect rate of products can be reduced, and meanwhile production energy consumption is reduced.

The invention discloses an on-orbit spacecraft attitude estimation method and system based on ISAR image feature selection, and belongs to the field of aerospace control systems. The method comprises the following steps: acquiring an ISAR image of a spacecraft, and acquiring a complex linear structure set and three-dimensional feature points of an on-orbit spacecraft; then, according to the obtained three-dimensional-two-dimensional projection model, the CRLB of each reference structure in the complex linear structure set is deduced to carry out attitude estimation error analysis; calculating the trace of the CRLB covariance matrix of each reference structure to select an optimal feature structure, correcting the scattering point trace of the optimal feature structure by using polynomial fitting, and switching the reference structures as a new optimal feature structure according to the scattering point loss rate and a preset sequence; and optimizing the spacecraft attitude angle solving function by using a particle swarm and LM hybrid algorithm to obtain attitude angle parameters. The target with high-precision target attitude real-time estimation can be completed aiming at the problems that high-order frequency change in a dynamic environment is difficult to capture and resolution and noise suppression are contradictory due to a fixed window time-frequency analysis method.

The invention discloses an underground carry-scraper path planning method and system, and the method comprises the steps: obtaining environment data information of a target underground mine roadway, constructing an environment map, and carrying out the preprocessing of the environment map, and obtaining a binary grid map; constructing a generalized Voronoi diagram, extracting a roadway center line and carrying out region division; performing path guidance and segmentation by adopting an algorithm to obtain a reference state set of the target underground mine roadway; and based on a hybrid algorithm, a regional dynamic node expansion strategy and a multi-target heuristic cost function, realizing path planning of the underground carry-scraper, and realizing path planning of the underground carry-scraper. According to the invention, through data acquisition and data processing of a target underground mine roadway, a roadway center line and divided areas are extracted, then path guidance is carried out through an algorithm, and based on a divided area dynamic node expansion strategy and a multi-target heuristic cost function optimization hybrid algorithm, path guidance is carried out. By combining a hybrid algorithm, a Dubins curve, a collision detection scheme, path smoothness punishment and path distance punishment, the path planning of the underground carry-scraper is realized, the reliability is higher, the accuracy is better, and the effect is better.

The invention discloses a Web end real-time path planning method and system, a storage medium and equipment, and relates to the technical field of path planning, and the method comprises the steps: collecting traffic data and environment change information in a Web end application scene in real time in the driving process of a vehicle, and converting the traffic data and environment change information into a standard data format for algorithm processing; performing path initial planning by adopting a hybrid algorithm to obtain an initial path; acquiring actual path driving information fed back by the environment sensing device and the user, comparing the actual path driving information with the initial path, and judging whether an actual path in the actual path driving information is different from the initial path or not; if yes, revising the recommended path in the navigation system in real time according to a preset proofreading rule to obtain a target navigation path shared to other vehicles; wherein the hybrid algorithm is a combination algorithm of a genetic algorithm and an ant colony algorithm. According to the invention, real-time optimization of the distribution path is realized, various road condition changes are efficiently coped with, and individual requirements of clients are met.