325 results about "Simulating annealing" patented technology

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 a national secret algorithm data encryption transmission method and system. The method comprises the steps of obtaining to-be-encrypted data and network parameters, establishing a Bayesian network probability ablation model, performing Monte Carlo sampling ablation national secret encryption and evaluating attack risks, and generating a probability security encryption strategy; and extracting a Brinell feature set, constructing a long and short-term memory network time prediction model, and optimizing by using a simulated annealing algorithm to obtain an optimal encryption parameter configuration sequence. Generating a key pair according to the sequence and SM2, establishing a shared key by means of an elliptic curve Diffie-Hellman protocol, deriving an SM4 session key through SM3, and establishing a hybrid encryption key system; constructing a teacher and student network model, optimizing multi-thread scheduling through adversarial distillation training and a retrieval enhancement technology, and generating a multi-thread parallel encryption architecture; and network parameters are monitored in real time, a reinforcement learning adaptive decision engine is constructed, a strategy is dynamically adjusted, and adaptive encryption transmission is completed. According to the invention, the optimal balance between the security and the efficiency in the data encryption transmission process is realized.

The invention relates to the field of thermal fault early warning of machine room inspection robots, in particular to a multi-modal sensing thermal fault early warning method of a machine room inspection robot, which comprises the following steps: acquiring infrared thermal image, acoustics, vibration and gas concentration data through a multi-modal sensor array, and generating voxelization temperature distribution; according to the method, temperature features are extracted through wavelet transform and a fractional Brownian motion algorithm and are fused into an equipment global temperature feature set; constructing noise, vibration and gas concentration tower extraction auxiliary features, and combining non-extensive entropy and a graph attention model to be fused into a high-dimensional fault feature pool; building a fiber bundle model by taking the machine room topology as a base space, and locating a thermal fault after optimizing parameters by a simulated annealing algorithm; and finally, a dynamic thermodynamic diagram and the like are generated through digital twinning, so that accurate early warning is realized.

The invention discloses a hardware-in-the-loop test method and device and electronic equipment, relates to the technical field of automotive electronics, and adopts a simulated annealing algorithm in an optimization stage, adjusts parameters according to characteristics of an electronic stability control system, optimizes data distribution, and improves test data quality and fault detection capability. In the verification stage, the generated test data is subjected to actual operation test, the data problem is fed back by comparing actual output with an expected result, and the optimized test data which does not pass verification is sent to the target hardware-in-loop test model for optimization adjustment, so that a closed-loop optimization verification process is formed. The iterative closed-loop process comprehensively covers all possible working conditions and fault modes, key test points are prevented from being omitted, the reliability of the electronic stability control system is enhanced, the accuracy, effectiveness, comprehensiveness and sufficiency of hardware-in-the-loop test of the electronic stability control system are ensured, and the reliability of the electronic stability control system is improved. And the limitation of hardware-in-the-loop test of the electronic stability control system is reduced.

The invention discloses a workshop dynamic scheduling method considering equipment occupation under emergency order insertion, which comprises the following steps: receiving a trigger event in a production system, and obtaining workshop production real-time state data according to the trigger event; according to the real-time state data, a dynamic scheduling decision engine is activated, and a global scheduling problem is decomposed into deterministic sub-problems at the current decision moment; according to the deterministic sub-problem, identifying the state of a work-in-process and generating an unfinished process set, and re-bringing the unfinished process set into a rearrangement resource pool; selecting a rearrangement strategy based on a preset influence threshold according to the unfinished process set; according to the rearrangement strategy, a planning model is constructed, and the planning model aims at minimizing the maximum completion time and minimizing the order insertion change degree; according to the planning model, an improved genetic simulated annealing algorithm is adopted for solving, and a dynamic scheduling scheme is output and issued to a production execution layer. According to the invention, the disturbance of order insertion on the original production system can be minimized.

The invention provides a design optimization method and system for a grouting screw anchor foundation structure in gravelly soil, and relates to the technical field of anchoring and civil constructions.The design optimization method includes the steps that related physical parameters of the gravelly soil are obtained, a plurality of combinations are randomly generated in a value interval of the foundation structure size, and the anchoring bearing capacity of each combination is calculated; then, a combination with the maximum anchoring bearing capacity is sought through a mapping relation in combination with an optimization algorithm to serve as an optimization combination, a finite element model under the optimization combination is constructed to obtain mechanical parameters and calculate corresponding energy values, finally, the energy values of the optimization combination serve as initial solutions, iterative optimization is carried out based on a simulated annealing algorithm, and the optimal anchoring bearing capacity is obtained. And establishing a self-adaptive cooling rate adjusting mechanism, and setting an energy value threshold value, so as to obtain an optimal combination of the sizes of the target grouting screw anchor foundation structure. According to the method, the finite element analysis and the simulated annealing algorithm are combined, and efficient optimization of the size of the target grouting screw anchor foundation structure is achieved.

The invention discloses a tight sandstone gas reservoir logging scale fracture development evaluation method, and relates to the field of earth exploration physics, and the method comprises the steps: carrying out the inversion based on a complex pore structure high-angle fracture type rock physical model and actual logging data through employing a simulated annealing particle swarm algorithm, and obtaining two groups of fracture density parameters in a well; respectively calculating crack density identification factors under different rotation angles; based on a correlation coefficient between the Poisson impedance and the fracture density under different rotation angles, determining an optimal rotation angle when the correlation coefficient reaches a maximum value; calculating a crack density identification factor under the optimal rotation angle by combining longitudinal wave impedance and transverse wave impedance data; and applying the fracture density identification factor under the optimal rotation angle to actual logging data, and representing fracture development. The method can effectively distinguish and quantify multiple groups of high-angle fractures developed in the tight sandstone reservoir, significantly improve the prediction precision and reliability of the fractured reservoir, and provide a key basis for reservoir sweet spot prediction and well location deployment.

The invention provides a wire harness diameter simulation calculation method based on a simulated annealing algorithm, and relates to the field of wire harness design, initial layouts are randomly generated for wires and wire harnesses, the wires are small circles, and the wire harnesses are large circles; obtaining a local optimal layout and a given illegal layout by using a continuous optimization method; judging whether the dynamic adjustment amount of the large circle is smaller than set precision or not, if yes, outputting a layout result, meeting constraint conditions, of each circle in the current layout, and if yes, reducing the radius of the large circle by adopting a dichotomy method, and obtaining a new layout from the layout with the reduced diameter of the large circle through a simulated annealing algorithm; new layouts obtained through the simulated annealing algorithm are divided into legal layouts and illegal layouts, the legal layouts are processed according to local optimal layouts, the illegal layouts output through simulated annealing adopt a dichotomy method to increase the radius of a large circle, and iteration is conducted again through the simulated annealing algorithm. The method is high in accuracy and wide in applicability, and the wire arrangement condition of the cross section of the wire harness can be obtained.

The invention discloses a home appliance dual-network hub site selection planning method based on a hybrid heuristic algorithm. The method comprises the following steps: firstly, modeling a home appliance dual-network hub site selection planning problem into a mathematical model taking the minimum comprehensive cost as an optimization target; and then, solving by using a hybrid heuristic algorithm. The algorithm is a priority competition allocation algorithm based on a simulated annealing framework. Two attribute indexes of a comprehensive load coefficient and a distance sensitivity coefficient of a site are defined, the competition degree of the site for hub resources and hub distance sensitivity of the site are quantified, a reasonable distribution sequence is determined, resource conflicts are reduced, efficient configuration of hub resources of a sales service network is achieved, and the service efficiency of the sales service network is improved. The problem that a traditional clustering method is low in solving quality is effectively solved.

The invention relates to the technical field of intelligent medical monitoring, and discloses a critical patient blood glucose monitoring and risk intervention system based on artificial intelligence. The system comprises a multi-source physiological data integration module, a risk map construction module, an intervention decision generation module and an intervention execution control module. The system integrates and standardizes physiological data from the continuous blood glucose monitor and the multi-parameter monitoring equipment; the physiological parameters are mapped into pathological nodes based on the medical knowledge map, and the activation probability of the pathological nodes is calculated to construct an individualized risk map, so that systematic risk assessment is realized. Further, in combination with individual information of the patient, performing multi-objective optimization on the intervention strategy by applying a simulated annealing algorithm to generate an optimal decision; and analyzing the decision into a control instruction to drive the insulin pump to execute precise intervention. According to the invention, the crossing from passive alarm to active risk prediction and personalized intervention is realized, and the safety and accuracy of blood glucose management of critical patients are improved.

The invention provides a circuit board, a layout optimization method and device thereof and prefabricated vegetable quick-freezing refrigeration equipment, and relates to the technical field of computers.The layout optimization method of the circuit board comprises the steps that grid division is conducted on the circuit board based on a finite difference method, and a temperature field model of a heat transfer equation containing a plurality of position nodes is established; taking the temperature data of all electronic components on the circuit board in the temperature field model as an objective function of layout optimization; and performing iterative optimization on the target function by using a simulated annealing algorithm, and outputting a corresponding electronic component layout and a temperature condition of the electronic component layout. The problems of inaccurate temperature field modeling, insufficient vibration heat source quantization and lack of local hot spot optimization in a traditional method are solved, and the method has the advantages of improving the temperature field modeling precision and optimizing the heat management effect in the vibration environment, so that the stability and durability of a circuit board are improved, and efficient operation of quick-freezing refrigeration equipment is ensured.

The invention provides a shale fracture attribute inversion method based on clay matrix dominant azimuth distribution correction, which utilizes an azimuth distribution function to describe the influence of directional arrangement of a clay matrix part on rock anisotropy, and meanwhile, utilizes an improved anisotropic matrix Eshelby-Cheng model to simulate the influence of a horizontal fracture. According to the method, a shale rock physical model considering directional arrangement and horizontal cracks of a clay matrix part at the same time is constructed, shale reservoir crack attributes and anisotropy parameters are estimated in combination with logging information, and in the inversion process, a simulated annealing particle swarm optimization (SA-PSO) is used for improving the solving capacity of a non-linear objective function. According to the method, the influence of anisotropy caused by directional arrangement of the clay matrix on fracture attribute inversion can be reduced, the accuracy of shale reservoir fracture attribute estimation and anisotropy parameter inversion is improved, and development of a shale gas resource exploration and development technology is assisted.

The invention discloses a cooperative control method for stay cable force and main beam line shape of an extradosed cable-stayed bridge. The cooperative control method comprises the steps that cable force data, main beam line shape data and main beam section internal force monitoring data in the construction stage are collected; based on a preset cable force adjustment judgment criterion, whether cable force adjustment needs to be started or not is judged; when triggering conditions are met, a geometric nonlinear analysis model considering cable-beam-tower cooperative deformation is established, and a cable force-main beam line shape sensitivity influence matrix is generated; screening K stay cables as key adjustment objects by adopting an influence matrix column vector L2 norm sorting method, and performing global optimization based on a simulated annealing algorithm to obtain an optimal displacement mean square error and an optimal cable force adjustment amount; and according to the key adjustment object and the cable force adjustment amount, multi-cable synchronous grading tensioning work is carried out. The invention provides a systematic and intelligent solution for the construction control of the extradosed cable-stayed bridge, and has remarkable economic benefits and social benefits.

An unmanned aerial vehicle routing inspection path planning method under a same-tower multi-loop power transmission line comprises the following steps: modeling a routing inspection target, and setting a hovering point; establishing a three-dimensional path optimization model; performing preliminary path planning by adopting a genetic algorithm to obtain a preliminary optimal path; carrying out local optimization by fusing a simulated annealing algorithm, and outputting a globally better inspection path; and polling path output and visual display are carried out. According to the method, firstly, a high-altitude hovering point is set based on a tower structure, an electromagnetic simulation result and a shooting visual angle, and then a three-dimensional path optimization model including constraints such as a safety distance and obstacle avoidance is constructed. A genetic algorithm is adopted to perform preliminary path search, and a simulated annealing algorithm is fused to perform local optimization, so that the globality and stability of a path solution are improved. The result shows that compared with a single algorithm, the method can effectively shorten the path length and improve the inspection efficiency, and is suitable for unmanned aerial vehicle intelligent inspection application of the multi-loop high-voltage transmission line.

The invention discloses a tight sandstone shear wave prediction method based on a fluid saturation dual fracture coupling model, and relates to the technical field of earth exploration physics. According to the method, a saturated rock physical model containing two groups of vertical orthogonal fracture fluids is constructed based on a Chapman multi-scale fracture medium theory framework, and an inter-fracture fluid mass distribution function is introduced to construct a dual-fracture coupling model based on fluid saturation. According to the method, conventional logging information is used as prior information, a rock physical model is used as a drive, an actual longitudinal wave speed and a simulated transverse wave speed are used as nested constraint conditions, a transverse wave speed prediction objective function is constructed, the tight sandstone transverse wave speed and the reservoir multi-group fracture density are estimated, a simulated annealing particle swarm optimization algorithm is introduced, and the reservoir multi-group fracture density is predicted. And the global optimization capability and the local convergence efficiency are enhanced. According to the method, the shear wave velocity prediction error can be effectively reduced, meanwhile, reservoir microstructure parameters are provided, and effective support is provided for reservoir fluid property judgment, lithology accurate recognition and underground fracture system quantitative description.

The invention provides a dynamic multi-modal biological sequence staged pre-training system for precise breeding, which comprises a multi-modal data organization unit used for collecting a single-modal sequence, generating a two-modal pairing sequence, constructing a three-modal interpenetrating sequence and simulating biological information transmission; the unified sequence representation unit is used for performing unified word segmentation on each modal sequence and adding modal marks; the progressive pre-training strategy unit is used for training in three stages and dynamically adjusting the modal mixing proportion in combination with a simulated annealing strategy; the cross-modal autoregression unit realizes prediction conversion between modals; the sequence feature extraction and prediction unit is used for obtaining a quantitative prediction value. The invention further provides a dynamic multi-mode biological sequence staged pre-training method for precise breeding. Therefore, the biological sequence feature prediction precision can be remarkably improved, the training convergence speed is increased, the performance equivalent to that of a large model is achieved, the cost is low, flexible deployment in various environments can be achieved, and efficient, accurate and low-cost technical support is provided for biological sequence analysis.

The invention provides a digital twinborn enhanced unknown environment unmanned aerial vehicle three-dimensional trajectory optimization method, which relates to the field of wireless communication, and comprises the following steps: constructing a system model and a joint optimization problem; a digital twinning driven trajectory framework is constructed, the framework comprising a physical entity layer, a digital twinning layer and a connection between the physical entity layer and the digital twinning layer, the digital twinning layer running on a digital twinning server, the digital twinning layer running on the digital twinning server during task execution, and the digital twinning layer running on the digital twinning server. The digital twin server uses the constructed virtual environment to continuously monitor actions of the unmanned aerial vehicle and assess potential safety risks; user scheduling based on simulated annealing is designed, a joint optimization problem is modeled as a Markov decision process, and an unmanned aerial vehicle is used as an intelligent agent to maximize accumulated rewards related to task completion time and collision avoidance; and training the path optimization model by using a TD3 algorithm. According to the invention, the unmanned aerial vehicle can efficiently and safely provide communication service for ground users in an unknown environment.

The invention provides an unmanned aerial vehicle terminal distribution method and system based on a public transport network, and belongs to the technical field of logistics distribution, and the method comprises the steps: obtaining customer information, public transport network information and unmanned aerial vehicle information; establishing a multi-target optimization model for cooperative distribution of the bus and the unmanned aerial vehicle by using the customer information, the public transportation network information and the unmanned aerial vehicle information; the target optimization module takes the minimum cost as a target function and considers time difference constraint, route constraint, load constraint and energy constraint; adopting a non-dominated transformer neighborhood search algorithm of a simulated annealing algorithm acceptance criterion to solve the multi-objective optimization model to obtain an approximate Pareto optimal solution set; the optimal solution set is an unmanned aerial vehicle tail end distribution scheme considering cost, time and risk. Based on the method, the invention further provides an unmanned aerial vehicle tail end distribution system based on the public transport network. According to the invention, through combination of unmanned aerial vehicle electric driving and public transportation network cooperation, multi-objective optimized logistics distribution is realized.

The invention provides a 3D curved surface flexible photovoltaic module / array dynamic topology optimization method and system, and the method comprises the steps: S1, building a curved surface photovoltaic module geometric model, carrying out the division of curved surface photovoltaic micro-elements, and obtaining the irradiance and temperature of each micro-surface through a radiation distribution model; s2, calculating electrical performance parameters of each battery through the photovoltaic battery model; s3, constructing an m * n-dimensional photovoltaic module current matrix, and constructing a target function by taking a current mismatch index CM I as a target; and S4, based on an improved simulated annealing algorithm I SAA, performing dynamic topology reconstruction optimization on the current matrix of the 3D curved-surface photovoltaic module, and based on the optimized current matrix, establishing measurement indexes of output characteristics of the reconstructed curved-surface photovoltaic module, including output power, efficiency and daily generating capacity of the photovoltaic module. According to the method, on the basis of the irradiance equilibrium principle, the simulated annealing algorithm is combined, and the current mismatch is reduced by dynamically optimizing the circuit topological structure of the photovoltaic module, so that the curved-surface photovoltaic module obtains the maximum daily generating capacity.

The invention relates to a method and a system for comprehensively evaluating and quantifying an aging transformation effect of an old community, and particularly belongs to the technical field of aging transformation evaluation. A three-dimensional model is constructed by fusing spatial, physical and environmental multi-source data through AR scanning and an algorithm, resident demands are extracted through natural language processing and a Delphi method, tags are matched and transformed, a multi-target scheme is optimized by adopting a simulated annealing algorithm, and a comprehensive index is calculated in combination with basic evaluation, investment benefits and property management coefficients. The system comprises five functional modules, achieves the precision and scientization of evaluation, effectively improves the rationality of a reconstruction scheme, and promotes the standardized development of the aging-suitable reconstruction industry of old communities.

The invention discloses a microphone array layout optimization method based on IGSA, and belongs to the field of sound source localization. The method comprises the following steps: firstly, constructing a topological structure of a microphone array as a rectangular array; then, the directivity and the directional diagram of the microphone array are adopted to express the performance of the microphone array; constructing a fitness function through the width of a main lobe, the level of a side lobe and the number of microphones; then, on the basis of a genetic algorithm, improving crossover and mutation steps, adopting a hierarchical crossover strategy and a similarity regulation and control mutation strategy, and simultaneously optimizing an array structure by a simulated annealing operator in an interspersed manner; and finally, outputting the optimal microphone array arrangement to meet the requirements of low sidelobe performance and high resolution. The IGSA algorithm is adopted to optimize the microphone array layout, so that more accurate global optimization, faster convergence speed and better multi-target balance are realized, and particularly, the number of microphones and the hardware cost are remarkably reduced while the sound source localization performance is ensured.

The invention relates to the technical field of microbial fermentation, in particular to a biological fermentation monitoring method and system based on multi-source data fusion. The method comprises the following steps: acquiring multi-source monitoring data in a fermentation tank and constructing a multi-dimensional state vector; on the basis of data of the multi-dimensional state vector in a preset time window, constructing a metabolic entropy of the current fermentation process; a simulated annealing algorithm is adopted to optimize control parameters, the temperature of the simulated annealing algorithm is positively correlated with the metabolic entropy, and minimization of a second energy function is taken as a target; a catastrophe reignition mechanism is set in the optimization process, fermentation abnormity is diagnosed based on triggering of the catastrophe reignition mechanism, and abnormal state characteristics are matched with a preset abnormity database through iterative search of an optimal weight distribution scheme so as to determine the abnormity type with the highest matching degree. By introducing the metabolic entropy and the catastrophe reignition mechanism, the sensitivity, the accuracy and the robustness of monitoring the biological fermentation process are remarkably improved.

The invention discloses an intelligent vibration sensor layout method based on cooperation of physical field modeling and hybrid optimization. The method comprises the steps that a geometric model of a target mechanical structure is acquired, and a material parameter set is extracted; the curvature and stress of the surface of the mechanical structure are calculated, and a high-sensitivity area is recognized in combination with a vibration transmission efficiency matrix; curvature self-adaptive sampling is executed in the high-sensitivity area, a candidate point set is generated based on an improved greedy algorithm, and redundant candidate points are eliminated to obtain an effective candidate point set; calculating coverage weights of the effective candidate points; adding the candidate point with the maximum coverage weight selected through bubble sorting into a sensor set, and calculating the total coverage rate once when each effective candidate point is added until the coverage rate reaches a preset value; performing global optimization based on a simulated annealing algorithm, and performing crossover and mutation operation on the selected sensor individuals; calculating the contribution degree of each sensor, and removing low-efficiency sensors until the maximum iteration value is reached. According to the invention, efficient calculation and resource configuration optimization are realized.

The invention provides a trajectory tracking control method and system for a high-load omni-directional AGV, the omni-directional AGV is driven by a plurality of steering wheel mechanisms which are independently steered and driven, and the trajectory tracking control method is characterized by comprising the following steps: acquiring vehicle state information and expected trajectory information of the omni-directional AGV in real time; on the basis of the vehicle state information and the expected track information, core parameters of a model prediction controller are optimized online through a chaos simulated annealing algorithm, and the core parameters comprise a state weight matrix, a control increment weight matrix, a prediction time domain and a control time domain; wherein the model prediction controller performs state prediction based on a longitudinal-transverse decoupling dynamic model adaptive to a high-load working condition; performing rolling horizon optimization through the model prediction controller by utilizing the optimized core parameters, and solving to obtain an optimal control increment; and converting the optimal control increment into a driving instruction for a multi-steering-wheel mechanism, and controlling the omnidirectional AGV to track an expected trajectory.

The invention provides a building public electric equipment positioning and early warning system, and relates to the technical field of electric equipment positioning and early warning. A coding-decoding model for realizing line power feature-power feature-equipment power feature-line power feature conversion in a normal state and calculating a reconstruction error; when the reconstruction error is smaller than a threshold value, the feature adjustment module obtains the optimal adjustment value of the power feature of the equipment based on a particle swarm algorithm and a simulated annealing algorithm; and a positioning early warning module which is used for analyzing the difference degree between the power characteristics of the equipment and the optimal adjustment value so as to determine the abnormal public electric equipment. When the abnormity is judged through the reconstruction error, the closest optimal adjustment value of the power characteristics of the equipment is found based on the particle swarm optimization and the simulated annealing algorithm; and determining which public electric equipment is abnormal by analyzing the difference degree between the public electric equipment and the electric power characteristics of the equipment, so as to realize the positioning and early warning of the abnormal public electric equipment.

Determining a probability distribution for a solution is disclosed. A diagonal of a QUBO matrix is evaluated. When the diagonal is positive semidefinite, the QUBO may be solved using a simulated annealer and a solution can be sampled to determine a probability distribution. If the diagonal is not positive semidefinite, the QUBO may be sampled using a simulated annealer in an intermediate step. If a variance of a dispersion measure is too large, the QUBO is solved and sampled using a quantum annealer in order to determine the probability distribution.

The invention relates to the technical field of educational administration management, in particular to an intelligent educational administration management and automatic course arrangement system based on a simulated annealing algorithm, which comprises a data and knowledge layer module, a constraint and target engine module, a simulated annealing optimization engine module, a business collaboration and interface module and a visualization module. The data and knowledge layer stores and manages cultivation schemes, teachers, classrooms, semester time models and historical class schedule data, and generates a teaching task quintuple including courses, teachers, classes, classrooms and time; defining and calculating hard and soft constraints by the constraint and target engine, and supporting scenarized configuration of constraint weights; the simulated annealing optimization engine optimizes the class schedule through initial solution construction, multi-neighborhood search and other units; the business collaboratively responds to the business change and triggers global or incremental optimization; and an interface and visual output course arrangement result and visual conflict are provided, and an API integrated external system is provided. The curriculum schedule is ensured to be feasible, and the curriculum arrangement quality and efficiency are improved.

The application discloses a kind of multi-camera automatic layout method in multi-vision measurement system, it includes: step S1: initialization camera layout parameter;According to the CAD model information of workpiece to be detected, the initial position of camera is obtained, and then the initial parameters of all cameras are obtained;Step S2: optimization camera initial layout based on simulated annealing algorithm;After obtaining the initial parameters of all cameras, iterative optimization is carried out using simulated annealing algorithm, and the optimal solution satisfying the constraint condition is obtained;Step S3: based on the differentiable adjustment promotion, the measurement accuracy and robustness of three-dimensional reconstruction under the above simulated annealing camera layout are improved.Based on the optimal layout obtained based on simulated annealing, the error back propagation algorithm based on stochastic gradient descent is used, and the Euclidean distance between reconstructed three-dimensional coordinates and CAD model coordinates is used as a loss function to optimize, to obtain a camera layout with better reconstruction robustness.The application has the advantages of high automation, strong layout practicality, good accuracy and robustness of three-dimensional reconstruction, etc.

The application relates to a highway ramp traffic control method based on a UAV-LLM-FCS, and belongs to the technical field of intelligent traffic. The method comprises data acquisition and processing, collection of ramp traffic flow data by using a UAV, data analysis and processing by using an LLM; a two-type fuzzy control system is constructed, a fuzzy reasoning mechanism is constructed based on traffic flow and vehicle speed data, input and output domain scaling factors are dynamically adjusted in combination with an LLM dynamic rule generation technology; a two-type fuzzy controller is optimized, the input and output domain scaling factors are optimized by using a simulated annealing genetic algorithm, and an adaptive domain adjustment strategy is designed; simulation experiment and actual application, a simulation software VISSIM is used, a decision recommendation is generated by using an LLM scene automatic configuration function to carry out deployment, and the control effect is dynamically analyzed in real time, and algorithm parameters are iteratively optimized. The application dynamically adjusts and optimizes a ramp control strategy through multi-modal data integration, improves ramp traffic efficiency, and promotes the deep integration of low-altitude economy and intelligent traffic.

The invention discloses an official vehicle scheduling and optimizing method and system for mixed type tasks, and belongs to the field of official vehicle intelligent scheduling. The method comprises the following steps: acquiring basic information of official business tasks, drivers and vehicles, constructing a task distance matrix, then establishing a scheduling model which takes the minimum total fuel consumption as a target and contains constraints such as grade matching and passenger-carrying upper limit, and setting genetic and simulated annealing algorithm parameters; constructing an initial scheme population through chromosome codes matched with task levels, and calculating the fitness value of each scheme; and taking the chromosome as a simulated annealing initial solution to complete local optimization, transmitting the solution into a genetic algorithm to carry out crossover and mutation iteration, and finally outputting an optimal scheduling scheme. According to the method, a hybrid algorithm framework is adopted, global search and local optimization capabilities are considered, the compliance, low oil consumption and high resource utilization rate of a scheduling scheme are realized, the problems of level mismatching, resource waste and algorithm limitation of traditional scheduling are solved, and the method is suitable for official car scheduling scenes of various organizations, enterprises and public institutions.