69 results about "Hill climbing" patented technology

The invention discloses a corrugated carton production management system and method, and relates to the technical field of corrugated carton production, the system comprises a production scheduling module, a material tracing module, an equipment cooperation module, a multi-mode detection module and a cost control module, and solves the problem that supply and demand relationships, production efficiency and quality management and control are not coordinated; according to the technical key points, an order ID is coded into an integer chromosome, sequential crossover and reverse mutation operation are applied, a fuzzy comprehensive evaluation model is constructed by combining Hash chain table and local hill climbing method optimization, and factors are quantified through a trapezoidal membership function, so that the order delivery punctuality rate and the equipment utilization rate are improved, and the mold replacement cost is reduced; the real-time production cost is collected and calculated through Kafka and Flink, an operation cost method model is constructed to maintain a standard cost database, abnormity is detected through a CEP engine, the cost hyper-branched risk is predicted through an LSTM-Transform hybrid neural network, a thermodynamic diagram is generated through a cache result to assist decision making, the cost accounting delay time is shortened, and the hyper-branched processing efficiency is improved.

The invention provides an earthquake landslide disaster chain risk assessment method, medium and equipment, and relates to the technical field of geological disaster chain risk assessment, and the method comprises the steps: obtaining earthquake-induced disaster chain data, and constructing an earthquake landslide disaster chain knowledge graph; according to the knowledge graph, acquiring disaster influence factors, determining association among the disaster influence factors, and determining key disaster influence factors with relatively high association degree; key disaster influence factors are used as Bayesian network nodes, node variable data are discretized, a maximum-minimum hill climbing algorithm is used for structural learning, and a directed acyclic graph structure of the Bayesian network is determined; performing Bayesian network parameter learning by using an expectation maximization algorithm to obtain a prior probability of Bayesian network nodes; if the prediction result of the model does not reach the expectation, the association between the nodes is adjusted, and if the prediction result reaches the expectation, the model is used for earthquake landslide disaster chain risk assessment. According to the scheme, the reliability of the prediction result can be improved.

The invention discloses a multi-target optimization unit adaptive combustion and control real-time optimization method and system. The method comprises the following steps: acquiring unit operation and decision parameters; constructing a target function with net power; an online Gaussian mixture process regression and uncertainty quantization network (OH-GPR-UQN) is adopted to online identify a dynamic relationship between a decision parameter and a net power objective function, a gradient is estimated, and the uncertainty of the gradient is quantized; on the basis of the gradient and the uncertainty thereof, an adaptive exploration and utilization constraint hill climbing algorithm (BOA-CEHC) based on Bayesian optimization guidance is adopted, and the adjustment step length and direction disturbance of decision parameters are determined by maximizing an acquisition function which considers the utilization gradient and the exploration uncertainty at the same time; processing operation constraints by combining gradient projection and a penalty function to obtain optimized parameters; the optimized parameters are used for closed-loop control and operation data feedback so as to update the OH-GPR-UQN online. According to the method, accurate identification, intelligent optimization and robust processing constraint can be realized, and the comprehensive benefits of the unit are remarkably improved.

A power load prediction method, system and device based on GHOA-CNN-iTransform-LSTM, and the method comprises the steps: firstly obtaining the multi-source data of a power load and analyzing the causal relationship, then constructing an analysis model based on scene injection, constructing an improved hiking optimization algorithm GHOA based on a hierarchical strategy evolution framework, optimizing the hyper-parameters of the analysis model, and finally obtaining the multi-source data of the power load. And finally, after the analysis model is trained, power load prediction of the smart power grid is realized. By acquiring the multi-source data, analyzing the causal relationship and adaptively transforming the kernel of the analysis model, the analysis model can integrate the time and the causal logic for dynamic allocation, accurate perception of external events is realized, a stable deduction result is obtained from complex data, and the accuracy of the deduction result is improved. According to the method, the self-adaptive optimization of the optimal parameters is realized through the hierarchical strategy evolution framework, the search strategy is ensured to dynamically adapt to the high dimension and complexity of the parameter space, and the precision and adaptability of power load prediction are ensured.

The invention provides a method for adjusting a variable-speed pumped storage unit under a working condition of a water turbine, which comprises the following steps of: arranging a multi-source sensor array in a flow channel to collect parameters such as water head pressure, rotating speed fluctuation and active power, generating operation state data through space-time alignment and self-adaptive weighted fusion, and triggering re-calibration when a threshold value is exceeded; an efficiency optimization index is calculated, fluctuation is smoothed through moving average filtering, water head fluctuation is predicted, and early warning is conducted when the water head deviates from the optimal working condition; according to the efficiency index and the water head amplitude variation factor, a maintenance / early warning / emergency adjustment grade is divided, and a guide vane instruction is generated during high deviation; determining unit importance, evaluating resource requirements by using a multi-objective optimization algorithm, and dynamically monitoring deviation; and optimizing a rotating speed regulation scheme by adopting a self-adaptive hill climbing algorithm, evaluating an iterative optimization regulation path, and triggering parameter re-calibration when the deviation exceeds the limit. The operation efficiency, the adjusting precision and the stability of the variable-speed pumped storage unit under the working condition of the water turbine can be improved, and the energy consumption and the instability risk are reduced.

The invention discloses an online car-hailing order dispatching method considering driver characteristics based on artificial intelligence, and relates to the technical field of intelligent scheduling of online car-hailing, and the method comprises the steps: associating vehicle track data preprocessed based on a large language model with order data, and obtaining driver order receiving data; in consideration of driver characteristics, a driver order receiving preference prediction model is constructed by utilizing machine learning, parameter calibration is completed, a fuzzy control model among driver age, working duration and order receiving reaction time is established in combination with a fuzzy reasoning technology, and an influence factor of the driver order receiving reaction time is calculated; building a supply-demand relationship evaluation index, judging a peak period and a flat period according to a supply-demand relationship, flexibly converting an order sending mode, and calculating order receiving reaction time of a driver under different order sending strategies; a utility function is constructed by taking minimization of a vehicle scheduling distance and order transaction time as targets, and an order dispatching scheme is optimized by using a hill-climbing algorithm HC. Based on the artificial intelligence technology, the platform order sending efficiency and the user satisfaction degree are remarkably improved.

The invention relates to the technical field of equipment control, in particular to an intelligent control method and system for chemical production equipment. The method comprises the following steps: constructing a forward analysis data segment of a newest data point in a historical rotating speed data time sequence of the newest data point of the stirrer; calculating a primary change factor of the latest data point; obtaining a motor power data segment corresponding to the forward analysis data segment of the latest data point, and obtaining the motor power data segment according to the primary change factor of the latest data point and the difference between the cumulative sum of the collection moments of the extreme points in the forward analysis data segment of the latest data point and the cumulative sum of the collection moments of the extreme points in the corresponding motor power data segment. Calculating a target change factor of the latest data point; and the preset maximum restart frequency in the random restart hill climbing algorithm is adjusted based on the target change factor of the latest data point, so that intelligent control of the stirrer is realized, and the accuracy of intelligent control of the stirrer is effectively improved.

The invention discloses an intelligent electromagnetic scale removal and prevention method based on a hill climbing method and a scale removal and prevention device, and relates to the technical field of electromagnetic scale removal and prevention. Based on step length self-adaptive adjustment and direction switching of a conductivity change trend, electromagnetic field frequency tracks water quality fluctuation in real time, and sudden reduction of scale inhibition efficiency caused by resonance mismatch is avoided; guiding multi-coil phase difference iterative adjustment through a field intensity distribution data table pre-stored by finite element simulation; in addition, the dynamic weight distribution algorithm adjusts the fusion weight in real time according to the deviation degree of sensor data, the misjudgment risk in an oil-containing / high-turbidity scene is inhibited, and errors are reduced. Dynamic frequency adjustment and phase cooperative control reduce an invalid electromagnetic field coverage area, and redundant energy consumption is reduced in combination with a closed-loop updating mechanism.

The application discloses a kind of model construction method and device of wind power generation system, the method wind power generation system, at least including wind turbine and permanent magnet synchronous generator, the method includes: the mechanical characteristics of wind turbine is modeled according to momentum theory data to obtain wind turbine model;Maximum power output point of wind turbine is tracked by analyzing wind turbine model and using hill climbing search method;Ideal conditions met by permanent magnet synchronous generator are obtained;Under ideal conditions, three-phase voltage equation under natural coordinate system, flux linkage equation under natural coordinate system, electromagnetic torque equation of motor, mechanical motion equation of permanent magnet synchronous generator are established to obtain permanent magnet synchronous motor model.The application can reduce the loss generated by mechanical rotation in power generation process, so that the power generation efficiency of system is higher.

The invention relates to the technical field of factory production scheduling, and provides a factory production scheduling plan automatic generation method and device and electronic equipment, task information of a to-be-scheduled task and production elements of a production factory are obtained, and the production factory is used for producing the to-be-scheduled task; and based on the task information of the to-be-scheduled task and the production elements of the production factory, determining an optimal scheduling plan of the production factory for the to-be-scheduled task through mixed integer programming and a hill climbing algorithm. By combining the accuracy of the mixed integer programming and the high efficiency of the hill climbing algorithm, a high-quality production scheduling scheme can be found, and calculation can be completed within acceptable time, so that automatic and efficient production scheduling is realized, and the production efficiency and the management level of a factory are improved.

The present invention discloses a multi-level supply chain distribution optimization method based on an improved hill climbing method. The method includes the steps of: (1) establishing a mathematical model for supply chain distribution optimization; (2) solving the optimization mathematical model in step (1) using a greedy algorithm, and optimizing the initial solution using the improved hill climbing method. The present invention uses an improved hill climbing method with an increased memory function to solve the problem that the selection of the starting point of the hill climbing method has a great influence on the search result, so as to effectively solve the multi-level supply chain distribution optimization with large-scale and multi-period demands and the problem of dynamic selection of nodes, and provide certain decision-making references for the optimization of the multi-level supply chain distribution network.

The present invention provides a zoom tracking method for a zoom lens in a live broadcast scene, which can ensure that the current scene is clear during the zooming process, and the zooming is smooth during the zooming process without any jamming. The present invention includes the following steps: step 1, defining various control parameters of the zoom lens; step 2, obtaining a basic focus curve for slow correction; step 3, obtaining a plurality of object distance focus curves at different object distances; step 4, obtaining a focus difference curve at multiple object distances; step 5, fitting a high-order polynomial; step 6, obtaining a basic focus curve for rapid correction; step 7, searching for a focus motor point value FP corresponding to the quasi-focus of the current scene image at the current magnification based on a hill climbing algorithm; step 8, obtaining the upper and lower limit values ​​of the focus motor position; step 9, obtaining a zoom tracking curve at the current focusing object distance.

A multi-agent path planning method based on an adaptive meme algorithm addresses the immaturity and slow convergence speed of current multi-agent path planning methods. This method employs a structure combining global optimization and local search, utilizing chromosomes as generated paths in a two-dimensional grid map and optimizing their fitness values ​​through strategies such as "selection" and "crossing." A hill-climbing algorithm is used as a sub-algorithm for local optimization to prevent generated solutions from getting trapped in local optima, thereby improving the accuracy of multi-agent motion control and enhancing the algorithm's convergence speed and optimization efficiency. This solves the problems of slow convergence speed and low path optimization accuracy that are difficult to address in current multi-agent path planning methods. Experimental results show that this method can accurately optimize multi-objective paths for multiple agents, and the average length of each path converges to the globally optimal path length with population iterations, achieving the expected results.

The application discloses a kind of climbing auxiliary service demand price curve drawing method, device and storage medium, wherein method includes: obtaining the system net load value of preset scheduling interval, system net load value includes system net load forecast value and system net load measured value;System net load value is classified as the typical day data set of different time periods;Calculate the system net load error of each scheduling interval, obtain the system net load error set corresponding to different time periods;According to system net load error set, the frequency of system net load prediction error in different error intervals is counted, and the error frequency of frequency relative to the data quantity in the system net load error set of current period is calculated;According to error frequency, probability density distribution histogram is drawn, and the climbing auxiliary service demand price curve of each period is drawn.The present application can comprehensively reflect electric energy cost and climbing auxiliary service benefit, and provide effective data support for building climbing auxiliary service market.

The invention provides an optical lens focusing processing method and device, equipment and a medium, and relates to the technical field of automatic focusing, and the method comprises the steps: obtaining the environment brightness information of a scene where imaging equipment is located at present, so as to determine a focusing strategy corresponding to the environment brightness information; collecting a target definition sequence according to the search resource configuration information in the focusing search process of the imaging equipment; under the condition that the definition trend feature corresponding to the target definition sequence does not meet the trend judgment condition, continuing to collect a new target definition sequence until the definition trend feature corresponding to the new target definition sequence meets the trend judgment condition; and controlling the imaging device to focus based on the new target definition sequence. The method can get rid of the dependence of a hill climbing algorithm on the definition value, and further improves the focusing speed and focusing accuracy in a low-light and low-contrast scene.

The application provides a communication interference strategy generation method based on reinforcement learning, comprising the following steps: step one, constructing a system model of a communication party and a communication interference party; step two, based on the system model of the communication party and the communication interference party constructed in step one, learning an anti-interference scheme of the communication party by using a win-or-learn policy hill climbing algorithm, and designing a corresponding interference decision model; step three, using the interference decision model obtained in step two, learning an anti-interference strategy of a communication target and implementing interference according to a decision process of 'observation-adjustment-decision-action'. The application comprehensively considers interference basic principles and behavior changes of the communication target, combines a jam-to-noise ratio and anti-interference behaviors such as frequency change and transmission power increase of the communication target after being interfered, and uses the two as measurement indexes of interference effects, so that the purpose of real-time and rapid interference is achieved.

This invention discloses an automatic image focusing method for an imaging device, comprising the following steps: obtaining several target images; randomly generating a parameter set characterizing the lens physical parameters of the imaging device's lens; based on the parameter set as the initial position of the lens, using a neighborhood generation rule to perform operations on a preset step size at the initial position to generate a neighborhood, each neighborhood corresponding to a lens position; evaluating the focusing effect by comparing the focusing effect evaluation values ​​corresponding to different lens positions; obtaining a locally optimal lens position based on a hill-climbing algorithm; and processing the locally optimal solution based on a simulated annealing algorithm to obtain the optimal solution. This invention can effectively find the globally optimal focusing parameters, ensuring the clearest image is captured under this focusing condition, and combines the hill-climbing algorithm and the simulated annealing algorithm to achieve automatic focusing.

The invention relates to a photovoltaic cluster climbing event prediction method based on a satellite cloud picture, in particular to the technical field of photovoltaic power generation power climbing prediction, and the method comprises the steps: obtaining corresponding photovoltaic power data, weather forecast data and satellite cloud picture data of a photovoltaic power station in a photovoltaic cluster within a preset collection time, the preset acquisition time is a time period before the prediction time of the photovoltaic cluster climbing event; determining a forecast meteorological factor corresponding to the photovoltaic cluster from the weather forecast data according to the association strength between the weather forecast data and the photovoltaic power of the photovoltaic power station; determining the extreme weather of the photovoltaic cluster corresponding to the prediction time according to the satellite remote sensing value of the photovoltaic cluster geographic area corresponding to the photovoltaic cluster in the satellite cloud picture data; determining the predicted power of the photovoltaic cluster corresponding to the prediction time based on the extreme weather and the forecast meteorological factor; and determining a photovoltaic cluster climbing event corresponding to the prediction time according to the climbing characteristic quantity corresponding to the prediction power.

The invention discloses a wind tunnel fluctuating wind pressure vibration load regression analysis method. The method comprises the following steps: acquiring experimental data, and processing the experimental data according to a hill climbing method to obtain fluctuating wind pressure; acquiring a frequency response function formula; a fitting wind pressure function is obtained, the fitting wind pressure function is # imgabs0 #, # imgabs1 # is a peak valley factor, and # imgabs2 # is a transfer function amplitude-frequency characteristic; and analyzing the wind pressure load of the wind tunnel according to the fitting wind pressure function. According to the invention, the problem that the analysis of the safety and durability of the wind tunnel structure in the prior art is complicated is solved.

The invention discloses a combination test method and device based on a time sequence input space model. The method comprises the following steps: firstly, constructing a time sequence input space model, and adding description of a safety key software input time sequence in the model; processing the input space model based on an improved particle swarm algorithm to generate a test case; and finally, sorting the cases in the test case set through a safety factor concept provided by the invention and a sorting algorithm fusing a hill climbing algorithm and a greedy algorithm, and giving an optimal test sequence. The case generation method provided by the invention consists of a model and an algorithm, realizes automation of the whole process, and solves the problem of automation of safety-critical software combination testing.

The application provides a flexible workshop scheduling optimization method based on a meta-heuristic algorithm, data sets of flexible workshop scheduling are acquired; a differential evolution algorithm based on a trigonometric function mutation is adopted to process the data sets of flexible workshop scheduling and preliminarily form a scheduling scheme by introducing a nonlinear disturbance and a dynamic adjustment mechanism of a companion population; the adaptive hill climbing algorithm is adopted to optimize a current optimal solution and process again to form an optimal scheduling scheme by combining a multiple disturbance mechanism and a disturbance intensity adjustment strategy; and the optimal scheduling scheme achieving the maximum completion time is visually presented; the flexible workshop scheduling optimization method based on the meta-heuristic algorithm can improve the ability to jump out of a local optimum, can significantly reduce the maximum completion time and can improve the solution efficiency of the flexible workshop scheduling problem.

The invention discloses a curvature method-based method for determining an optimal temperature regulation interval in a whole growth stage of greenhouse peppers, which comprises the following steps of: measuring photosynthetic rates of the peppers in different growth stages under different environmental conditions through a nested experiment, and constructing a data set; the environmental conditions comprise temperature, CO2 concentration and photosynthetic photon flux density; taking the growth stage, the CO2 concentration, the temperature and the photosynthetic photon flux density as input vectors, constructing a photosynthetic rate prediction model by adopting a back propagation neural network optimized by a genetic algorithm, and generating a corresponding photosynthetic rate response surface; dispersing the photosynthetic rate response surface into discrete points, calculating the Gaussian curvature of each point, and constructing a Gaussian curvature response surface; and discretizing the Gaussian curvature response curved surface into a temperature-Gaussian curvature response curve, taking a u chord value as a measure, determining a critical boundary of the response curve by adopting a random restart hill climbing method, and finally obtaining an optimal temperature regulation and control interval.

The present application belongs to the field of quantum physics and quantum technology, and particularly relates to an autofocus method for atomic imaging, comprising the following steps: acquiring a fluorescence or absorption image of single atoms in a region to be imaged to obtain two-dimensional gray image data; determining the position of single atoms in the two-dimensional gray image data, and intercepting a region of interest (ROI) of a preset size centered on the position of single atoms; calculating the centroid coordinates of a light spot in the ROI, and taking the centroid as a reference center of a subsequent sharpness evaluation index; calculating a sharpness evaluation index based on the ROI, and combining the sharpness evaluation index according to a preset weight to obtain a sharpness score S; performing a focus search strategy, including an initial scanning stage and a hill climbing search stage, to determine an optimal focus position z best ; and controlling an electric displacement table to adjust the camera to the optimal focus position z best . The present application can improve imaging resolution and signal-to-noise ratio, reduce manual intervention, quickly adapt to different experimental modes, and improve experimental efficiency.

This invention discloses an intelligent fire-fighting patrol vehicle that integrates hazard inspection, fire identification, path planning, and initial fire suppression. The system combines lidar, visual recognition, infrared, and ultrasonic sensors with a YOLO visual model, an improved A* algorithm, and PID control to achieve environmental mapping, fire source location, autonomous navigation, and precise fire suppression. The vehicle uses a Mecanum wheel chassis, enabling omnidirectional movement, obstacle crossing, and hill climbing, making it suitable for complex environments such as factories, shopping malls, hospitals, and schools. The fire suppression module dynamically adjusts the extinguishing agent and spray parameters according to the fire situation, improving fire suppression efficiency and reducing water consumption. This invention effectively enhances the intelligence level of fire inspection, reduces manual intervention, and strengthens emergency response capabilities and safety.

The present invention relates to a prefetch control strategy based on an improved hill-climbing method under an asymmetric multi-core architecture, belonging to the fields of cache prefetching and asymmetric multi-cores. This method takes into account the different performances among cores under the asymmetric multi-core architecture. Through the improved hill-climbing method, it controls the prefetch aggressiveness for different cores to improve the IPC of the cores. The prefetch aggressiveness is characterized by the prefetch degree and the prefetch distance. Considering that the hill-climbing method may fall into a local optimal solution, it jumps out of the local optimum by combining with the simulated annealing strategy. At the same time, considering that the hill-climbing method is not suitable for solving two-dimensional search problems, this method uses the method of controlling variables and adjusts the prefetch strategy through the IPC feedback in the sampling stage to enhance the IPC of the cores. This method uses the hill-climbing method to adjust the prefetch aggressiveness of different cores according to different stages of program operation, so as to achieve the IPC growth of different cores.

The invention discloses a power distribution network three-phase load balance optimal distribution method, and relates to the field of power distribution network optimization in a power system. The problem that the loss of a power distribution network line is increased due to three-phase imbalance of an existing power distribution network is solved. According to the method, a user clustering-based load three-phase balanced distribution model is adopted, and a mountain climbing algorithm is adopted to optimize a moth flame algorithm, so that the local optimization capability of the moth flame optimization algorithm is enhanced, a three-phase balanced optimal distribution strategy is obtained, the three-phase load is balanced to the greatest extent from the source, and accurate loss reduction of the load side of the power distribution network is realized. The method is suitable for obtaining the three-phase load balance optimal distribution strategy.

The present invention discloses a method for locating multi-point error parameters of a power flow calculation model based on random hill climbing, which can accurately calibrate the dynamic parameters of a generator, thereby providing a reliable reference quantity for real-time fault detection and optimal control of the generator to ensure the normal and safe operation of the power grid. It is very simple to operate and can be completed in a short time.

The present invention relates to the technical field of optical technologies, and particularly relates to a focusing method and device for a maskless lithography machine, including the following steps: S1: Determine the initial position of the displacement stage and collect the light intensity at the initial position; S2: Control the displacement platform to move along the z-axis and collect the light intensity to determine the position when the light intensity reaches the maximum value; S3: Drive the displacement stage to move to a position that is separated from the maximum light intensity by a coarse step size; S4: Use the hill climbing method for rough positioning to determine the approximate position of the focal plane; S5: Fine positioning of the focal plane: Set a fine step size, within ± one coarse step size near the position in step S4, drive the displacement stage to move within this interval according to the length of the fine step size, and obtain the maximum light intensity as P1; S6: Determine the optimal positive focus position: Continue to use the hill climbing method to search for the optimal focus position near the focus, calculate the evaluation function value each time, compare the current calculated value with the database, determine the search direction and step size based on the change of the function value, and efficiently find the positive focus position.

The invention discloses a shipyard total group tray site layout optimization method based on simulated annealing in combination with a local hill climbing algorithm, which comprises the following steps: acquiring basic data of a shipyard total group tray site, constructing a multi-target optimization model, and optimizing the multi-target optimization model by taking the maximum space utilization rate, the minimum transportation cost and the minimum process conflict as optimization targets. And solving the multi-objective optimization model by adopting a simulated annealing-local hill climbing hybrid algorithm to obtain an optimal layout scheme, performing constraint verification and effect evaluation on the optimal layout scheme, and outputting a final layout scheme. Compared with a traditional empirical layout, the space utilization rate is improved by 15%-25%, the transportation cost is reduced by 20%-30%, the secondary transfer rate is reduced to 5% or below, and compared with a single simulated annealing algorithm, the convergence rate is improved by 40%-50%; compared with a single local hill climbing algorithm, the method has the advantages that the target function value is reduced by 10%-15%, parameters can be adjusted according to the actual condition of a shipyard, an output scheme can be directly connected with an MES system, and intelligent production decision making is supported.

The application discloses a neural network deployment method of a ReRAM memory-computing integrated array, comprising the following steps: (1) adopting a Reset-Set double sequence verification combined with a non-disturbance conductance reading, subdividing device states of the ReRAM array into three categories of easy-to-use states, low-impact states and high-impact states; configuring differentiated scores, and generating a state matrix and a score matrix consistent with the dimension of the ReRAM array; (2) based on the state matrix and the score matrix, taking the maximum normalized score rate of a subarray as the target, and through the combination of a greedy algorithm, a hill climbing algorithm and a genetic algorithm, a target subarray matched with a target weight matrix is screened out; (3) the target weight matrix is mapped to the target subarray according to rows and columns, the weight is converted into a device conductance through an adaptive programming pulse, and solidification deployment is completed. By using the application, the hardware utilization rate of the ReRAM memory-computing integrated array and the neural network weight deployment efficiency can be significantly improved.