27results about How to "Minimize consumption" patented technology

The invention provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

The invention discloses an intelligent ventilation system and method for a cage for livestock breeding, and belongs to the technical field of livestock breeding, and the system comprises an acquisition processing module, an environment modeling module, a change prediction module, an optimization decision module, a strategy simulation module, a storage safety module, an equipment control module, a monitoring feedback module, a user interaction module and an emergency response module. According to the invention, environmental conditions can be managed more actively, environmental parameters are prevented from deviating from an ideal state due to a hysteresis effect, the stress influence on animals is reduced, specific requirements of different livestock animals are met, the air quality and animal health level in an animal house are improved, the decision precision of the system is improved, and the economic benefit is increased. It is ensured that the cage environment is always kept in the most ideal state, improvement of air quality in a short term and minimization of long-term energy consumption are balanced, and the overall efficiency of the system is effectively optimized.

The invention discloses an underwater robot self-adaptive control system and method based on data analysis, and relates to the technical field of automatic control. Environment data and underwater robot data in the operation process of an underwater robot are collected, an operation data set S is constructed, feature extraction is performed, and the operation data set S is obtained; the method comprises the following steps: acquiring an acceleration fluctuation coefficient # imgabs0 # and an attitude angular rate fluctuation coefficient # imgabs1 #, summarizing and calculating to obtain a stability score WD, evaluating the operation state of the underwater robot, predicting water flow data at a future time point t + k when the operation state of the underwater robot is abnormal, acquiring an underwater robot adjustment thrust # imgabs2 # in combination with a robot dynamic model, and using a genetic algorithm to calculate the operation state of the underwater robot. According to the method, the optimal thrust distribution scheme of the underwater robot is obtained, the underwater robot is controlled to execute the operation task, strong turbulence and water flow reversing can be effectively handled, the operation failure and damage risk of the underwater robot are reduced, and the operation stability and safety of the underwater robot are improved.

The invention discloses an optimization system for intelligent operation and maintenance of a new energy generator set. The optimization system comprises a data acquisition module for acquiring operation state data and external environment data of the generator set through a sensor network; the data processing and storage module is used for carrying out preliminary data processing by utilizing edge computing equipment; the algorithm model module comprises a maintenance optimization model and a dynamic energy efficiency optimization model based on reinforcement learning; the invention discloses an optimization method for intelligent operation and maintenance of a new energy generator set. The optimization method comprises the following steps: S1, data acquisition and preprocessing; s2, constructing a maintenance optimization model based on reinforcement learning; s3, constructing a dynamic energy efficiency optimization model; s4, fault prediction and maintenance strategy optimization; and S5, system integration and feedback execution. According to the method, the operation parameters of the generator set are adjusted in real time, it is guaranteed that the generator set is always in the optimal operation state under different external environment conditions, and the dual purposes of maximum utilization of resources and minimum energy consumption are achieved.

The invention discloses an urban short-term water supply optimal scheduling method and device based on water-energy balance and a medium, and relates to the technical field of water supply scheduling optimizing.The method comprises the steps that a dynamic topological combination technology is utilized to construct a to-be-scheduled urban water supply system network diagram; extracting a water use sequence sample from the historical water use data of the urban water supply system to be dispatched as an input feature; using the water consumption prediction model and the water consumption prediction error determination model to obtain a water consumption prediction value of the next day; and inputting precipitation forecast information of the current day into the reservoir inflow prediction model to obtain a reservoir inflow prediction value of the next day, determining reservoir water storage change data of the next day, and finishing water consumption scheduling of the to-be-scheduled urban water supply system in combination with the scheduling model. The efficiency and rationality of urban daily water supply scheduling can be improved.

The invention discloses an intelligent solar unmanned logistics distribution control method and system, and relates to the technical field of renewable energy sources, and the method comprises the steps: obtaining real-time weather data and a current geographic position through a vehicle-mounted meteorological sensor and a GPS positioning system; the intelligent energy management system analyzes weather and geographic position data and calculates expected energy output of the solar panel; based on the distribution task list and the current geographic position, a distribution route is planned, the feasibility of the route is evaluated, and an optimal distribution route is generated; according to the optimal distribution route and the current position of the sun, the vehicle speed and the battery use strategy are dynamically adjusted; and after the distribution task is completed, summarizing the distributed energy consumption data and the solar panel efficiency report, and optimizing a dynamic path planning algorithm. Through a series of intelligent control methods, the energy utilization efficiency and the task execution reliability of the unmanned logistics distribution system are improved, and the distribution service quality and the customer satisfaction are further improved through continuously optimizing the path planning algorithm.

The invention provides a self-adaptive model prediction control method and system for a building heating ventilation air-conditioning system, and belongs to the technical field of intelligent building self-adaptive control, and the method comprises the steps: obtaining operation data of the heating ventilation air-conditioning system under different working conditions, and carrying out the preprocessing of the obtained data; for the preprocessed data, calculating the marginal fitness measurement of each data point to each class, calculating the fitness measurement of the data point to each class by combining the marginal fitness measurement of all descriptors, and obtaining the global fitness measurement; setting a global fitness measurement threshold, and classifying the current operation state of the system according to a classification rule; after classification, model parameters of each class are fixed, so that adaptive control strategies are adopted for different operation states.

The invention relates to the technical field of intelligent regulation and control, in particular to a constant-temperature intelligent valve control system and a control method thereof. The method comprises the following steps that a DDC analog quantity control signal is received in real time through a constant-temperature intelligent valve, standby mode vibration-free judgment and valve adjustment control analysis are conducted, and a valve position opening degree adjustment control mode and a valve flow adjustment control mode are generated; performing supply and return water temperature real-time monitoring and valve temperature difference calculation on the constant-temperature intelligent valve to obtain the actually measured temperature difference of the intelligent valve; performing valve regulation control logic response analysis on the constant-temperature intelligent valve to generate an intelligent valve basic regulation mode and an intelligent valve constant regulation mode; and the set temperature difference of the intelligent valve and the set return water temperature of the intelligent valve are obtained, and basic valve regulation and control processing and constant valve regulation and control processing are conducted on the constant-temperature intelligent valve, so that corresponding intelligent valve regulation and control work is executed. According to the invention, the control mode can be automatically adjusted according to actual requirements, and the method has high adaptability, accuracy and energy-saving performance.

The invention relates to a green preparation process of an ezetimibe bulk drug, and belongs to the technical field of synthesis of drug intermediates. The invention provides a green preparation process of an ezetimibe bulk drug, which comprises the following steps: S1, mixing a compound as shown in a formula III, ZTM, TMSCl and a reaction solvent I at the temperature of below 0 DEG C, then sequentially adding diisopropylethylamine and titanium tetrachloride into a catalytic reaction system until a substrate is used up, quenching the reaction system, extracting the reaction system, washing an organic phase, and drying to obtain a compound as shown in a formula III; after 3 / 4 of the solvent is removed, N, O-bis (trimethylsilyl) acetamide is added for continuous reflux reaction, the solution is removed again after completion of the reflux reaction, cooling crystallization is performed to obtain a compound shown in a formula II, the compound shown in the formula II, a reaction solvent II and tetrabutylammonium fluoride are subjected to primary reaction, the solution of a reaction system is removed, then the compound is dissolved in isopropanol, diluted hydrochloric acid is added for reaction, crystallization is performed to obtain a crude product, and the compound shown in the formula II is obtained. And recrystallizing to obtain the ezetimibe bulk drug. The method integrally has the advantages of high product yield, mild reaction conditions, less by-products and environmental protection.

The present invention proposes an adaptive model predictive control method and system for a building HVAC system, which belongs to the technical field of intelligent building adaptive control, and includes: obtaining operation data of a HVAC system under different working conditions and preprocessing the obtained data; calculating the marginal fitness metric of each data point for each class for the preprocessed data, calculating the fitness metric of the data point for each class by combining the marginal fitness metrics of all descriptors, and obtaining a global fitness metric; setting a global fitness metric threshold, and classifying the current operating state of the system according to a classification rule; after classification, the model parameters of each class are fixed, so as to adopt an adaptive control strategy for different operating states.

The invention discloses an emergency communication optimization method for a low earth orbit satellite heterogeneous network. The method comprises the following steps: calculating propagation loss of a communication link in the low earth orbit satellite heterogeneous network and gain in a linear array direction, and calculating a received signal of a ground terminal and an intensity ratio of the signal to interference plus noise; the energy efficiency of the low-orbit satellite and the ground network is maximized by optimizing the transmitting power of a ground base station, then the optimization problem of the transmitting power is solved through a continuous convex approximation algorithm, and regularization factors are introduced into the optimization problem of the transmitting power and limiting conditions of the optimization problem. According to the method, the antenna array radiation gains of the satellite base station and the ground base station and the signal transmission path length are calculated respectively, and the energy efficiency of the whole network is maximized by adjusting the transmitting power, the beam forming strategy and the resource allocation based on a channel recoil response function expression and path loss characteristics through an optimization algorithm.

This invention relates to the field of byte processing technology, providing a method and apparatus for accelerating byte encryption and decryption. The invention adds operation bits to bitwise operation instructions; uses corresponding operation bits to perform byte selection, endianness conversion, single-input multiple shift processing, and / or efficient table lookup operations. Specifically, byte selection avoids invalid bit calculations, improving instruction utilization. Endianness conversion ensures correct data order, minimizing duplicate data requests due to order errors, thus reducing the need for bitwise operations and improving GPU bandwidth utilization. Single-input multiple shift processing reduces the number of shift instructions, increasing throughput and avoiding repeated loading of the same data; efficient table lookup operations improve bitwise computation throughput, significantly reducing bandwidth pressure. This invention optimizes the granularity of bitwise operations, enabling encryption and decryption algorithms to efficiently utilize computing resources on the GPU while minimizing bandwidth consumption.

The invention relates to a method for calculating the probability of a domain being malicious based on an input data set for processing in a computer system, the method comprising, dataset extraction, comprising extraction of two types of input data, i.e. network data and claimable data. The method further comprises data preprocessing including transforming network data from dense to sparse as well as data preprocessing including transforming claimable data into vectorial representation. Finally, the method comprises data processing including processing the data through a trained tree-based neural network to determine the probability of a domain being malicious.

A system and method for determining a runway configuration for an airport. The method includes: retrieving (210) recorded surveillance data (214) including instances of aircraft positions (216) at an airport; determining (220) a plurality of three-dimensional surveillance cells (302) at each end (310a, 310b) of one or more runways (102) at an airport; calculating (230) a count of aircraft positions (216) within each surveillance cell (302); and determining (240) a current configuration of each runway (102) based on the counts calculated for the surveillance cells (302) for the runways (102). The predicted runway configuration for the airport can be used to update a flight plan for an aircraft to reduce overall flight duration and minimize fuel consumption.

The present invention relates to a novel and efficient process for refining or purifying a raw material.

The invention discloses an aerocar hybrid propulsion system space-time joint rotating speed optimization method, which comprises the following steps of: constructing a space factor analysis method: firstly, acquiring position information, gradient, road conditions, air resistance data and current speed through a sensor and a navigation system; time factor analysis is further carried out, and driving time, weather forecast and traffic condition data are collected in real time; after independent analysis of space and time factors is completed, a space-time joint genetic optimization algorithm is designed; an optimization result is applied to an actual hybrid propulsion system, the rotating speed of the hybrid propulsion system is adjusted in real time through an adaptive learning algorithm, a prediction model and a strategy are optimized, and therefore the long-term performance of the system is further improved. According to the method, a space-time joint optimization strategy is introduced, the overall performance of the system is improved, efficient and low-consumption propulsion control is achieved, and the performance bottleneck caused by fixed time point and single-dimension optimization in an existing method is overcome.

The present invention provides a process for recovering substantially dry ethanol from a feedstock comprising ethyl acetate, ethanol and water, the process comprising: (a) passing the feedstock to a first separation column comprising a first separation column reboiler, and maintained under distillation conditions to form an intermediate bottoms stream comprising ethanol and water and having an ethanol concentration higher than that in the feedstock; (b) passing the intermediate bottoms stream to an azeotropic column maintained under distillation conditions to form an azeotropic column bottoms stream and an azeotropic column overhead stream, the azeotropic column overhead stream comprising ethanol and water and having a higher ethanol concentration than the ethanol concentration in the intermediate bottoms stream; (c) feeding an ethanol make-up stream to the azeotropic column; (d) passing the azeotropic column overhead stream through a water removal section to separate water from ethanol, forming a substantially dry ethanol stream; (e) providing heat in the first separation column reboiler using the substantially dry ethanol stream.

The present invention relates to providing a method for optimizing fuel consumption during the cruising phase of an aircraft, wherein the flight parameters and meteorological data of the aircraft are collected in real time through airborne sensors, and the flight parameters and meteorological data include flight altitude, flight speed, outside temperature, wind speed, wind direction and aircraft weight; the collected flight data are transmitted to a ground server at intervals through an air-to-ground data transmission system; the ground server performs data cleaning, data normalization and feature selection on the flight data; a deep learning neural network model is trained based on historical flight data to predict the fuel flow and ground speed under current flight conditions; the unit distance fuel cost is calculated through a mathematical model; an optimization algorithm is used to find the optimal cruising speed with the lowest fuel consumption; and the optimal cruising speed is fed back to the aircraft through the air-to-ground data transmission system to adjust the flight state. The present invention can dynamically adjust the cruising speed according to real-time flight data, significantly improve fuel economy, and has strong adaptability and is easy to operate.

This invention relates to a pheromone-inspired multi-UAV autonomous coverage method, comprising the following steps: Step S1, constructing a coverage scene map of multi-UAV autonomously collecting data on natural disaster areas; Step S2, constructing a multi-UAV path pheromone map based on a pheromone aggregation model and marking the multi-UAV paths; Step S3, defining a state function, action function, and reward function according to the multi-UAV autonomous coverage task, establishing a neural network-based multi-UAV autonomous coverage model, and training it; Step S4, performing multi-UAV autonomous coverage using the trained multi-UAV autonomous coverage model. Compared with existing technologies, this invention has the advantages of high efficiency and energy saving, high coverage, and wide applicability.

The application discloses a kind of mobile multi-view picture data acquisition methods based on graph neural network, comprising the following steps: S1, collect multi-view picture data and carry out repair preprocessing, construct preliminary image dataset;S2, the image is divided into several image blocks, and input in the improved ViT model and carry out preliminary processing, according to the processing weight of each image block dynamic allocation according to task priority;S3, the arrangement order of image block is optimized in combination with bird swarm algorithm, maximizes labeling accuracy and minimizes the consumption of computing resources;S4, the optimized image block arrangement order is input into the improved ViT model again, and the labeled image block is obtained;S5, the relationship between the labeled image block is modeled using graph neural network, and the image block relationship graph is generated;S6, according to the labeled image block and image block relationship graph, generate final image dataset.The application combines graph neural network, improved ViT model and bird swarm algorithm, etc., and optimizes multi-view image data acquisition and labeling.

The present disclosure relates to a 5th generation (5G) or a pre-5G communication system for supporting a higher data transmission rate after a 4th generation (4G) communication system such as long-term evolution (LTE). According to various embodiments, a Doherty power amplifier of a wireless communication system, the Doherty power amplifier includes a first power amplifier, a second power amplifier, a first transmission line connected to an output end of the first power amplifier, a second transmission line connected to an input end of the second power amplifier, a first network, and a second network, the first network may interconnect a first node connected with one end of the first transmission line and a second node connected with an output end of the second power amplifier, the one end of the first transmission line may be positioned on an opposite side with respect to the output end of the first power amplifier, and the second network may connect the first node, the second node, and a third node which is an output end of the Doherty power amplifier.

The invention discloses an intelligent power plant production period energy auditing and optimizing method and system, and belongs to the technical field of energy management, and the method comprises the steps: collecting energy data from an intelligent power plant in real time through a sensor, carrying out the preprocessing, carrying out the multi-dimensional analysis, and generating an energy consumption analysis report; performing dynamic optimization on an energy use strategy in combination with historical data, and generating an optimal energy distribution and use scheme; and auditing the energy use data after the implementation of the scheme, generating a detailed energy auditing report, and comparing the energy auditing report with historical data to evaluate an optimization effect. According to the method, the regression model and the production objective function are constructed, the energy distribution of each device is dynamically adjusted, the energy efficiency output is maximized, and the energy consumption is minimized. Through a continuous energy auditing and feedback mechanism, a detailed auditing report is generated, the situation that the energy efficiency does not reach the standard can be found and corrected in time, it is ensured that the optimal state of energy use is continuously maintained, and the energy efficiency management level and the overall operation benefits of an intelligent power plant are improved.

The invention discloses a flue gas deep dust removal and SCR integrated ultra-low emission system and process, and the system comprises a wet electric dust remover which is connected to the downstream of a desulfurization system; the flue gas condenser is connected to the downstream of the wet electric dust collector; the flue demister is connected to the downstream of the flue gas condenser; the GGH heat exchanger-cold end is connected to the downstream of the flue demister; the flue gas heating system is connected to the downstream of the cold end of the GGH heat exchanger; the SCR denitration reactor is connected to the downstream of the flue gas heating system; and the hot end of the GGH heat exchanger is connected to the downstream of the SCR denitration reactor. By integrating multiple efficient technologies, comprehensive ultralow emission of particulate matter, SOs and NOx in industrial flue gas is achieved, meanwhile, energy consumption and maintenance are optimized, the long-term stability and operation convenience of the system are improved, and the system has the excellent pollutant removal effect and energy-saving and environment-friendly performance and is widely applied to various complex working conditions.

A wireless scanner according to the present invention comprises: a scanner body having a plurality of components arranged on the inside; and a scanner tip coupled to one end of the scanner body and having a passage therein through which light passes. The scanner body includes: an optical heating unit including at least one camera and a light projector; a power heating unit including a battery for supplying power to at least one among the camera and the light projector; and at least one charging module for charging the battery by generating a current in a reception coil formed inside the scanner body, the current in the reception coil being generated by a magnetic field generated from a transmission coil included in a cradle for holding the scanner body.

The invention discloses a solar energy coupling medium-deep layer-shallow layer geothermal energy storage and supply system, particularly relates to the technical field of clean energy utilization, and comprises a PVT solar panel, a deep layer geothermal well, a shallow layer geothermal well, a heat pump system, a heat storage water tank, a storage battery, a control unit, and pipelines, valves and a circulating water pump which are used for connecting all the components, the multifunctional adjusting valve is connected with the two-way variable-frequency circulating water pump, the stop valve body and the preheater. According to the solar energy coupling medium-deep layer-shallow layer geothermal energy storage and supply system, the PVT solar panel capable of synchronously completing photoelectric conversion and heat energy collection, the medium-deep layer geothermal well capable of obtaining high-temperature heat energy and the shallow layer geothermal well capable of utilizing shallow layer heat resources are coupled, and diversified utilization of clean energy and self-consistency of internal energy are achieved; carbon emission is reduced from the source; meanwhile, photovoltaic green electricity generated by the PVT solar panel is stored in the storage battery, energy supply interruption when solar power generation is insufficient is avoided, and a self-sufficiency and standby reliable energy supply mode is formed.

The invention relates to an energy-saving mode control method of a vehicle-mounted air conditioner, a vehicle and a vehicle-mounted system.The energy-saving mode control method of the vehicle-mounted air conditioner comprises the steps that remaining travel information of the vehicle is obtained; when the remaining travel information is lower than a preset travel threshold value, whether the comfort evaluation index of the physical environment in the compartment of the vehicle is within a preset physiological comfort range or not under the operation of the vehicle-mounted air conditioner is judged; if yes, the energy-saving mode of the vehicle-mounted air conditioner is controlled to be started. According to the method, the starting time of the energy-saving mode can be determined on the basis of the remaining travel information in combination with judgment of the comfort state of the vehicle-mounted air conditioner, so that the energy consumption of the vehicle-mounted air conditioner can be minimized while the comfort of passengers is ensured to be maximized, and the comfort of the passengers and the energy efficiency utilization efficiency of the vehicle-mounted air conditioner are both considered.

The invention discloses an intelligent weeding method and system based on a deep learning algorithm, and relates to the technical field of laser weeding, and the method comprises the steps: data collection, analysis, laser adjustment, synchronous collection of multispectral images, thermal imaging images and surface temperature data, and fusion of spectral reflection characteristics and thermal radiation characteristics of vegetation. Through analysis of the environment average temperature and the vegetation spectral index, according to real-time illumination and temperature conditions, the contribution proportion of the spectral features and the thermal features in the fusion process is adjusted, the sensing blind area of a single sensor under strong light, shadow or dust fog is overcome, and meanwhile in combination with laser irradiation parameters generated based on weed information and land surface temperature data, the detection accuracy is improved. And the minimum and sufficient inactivation energy of each weed is analyzed through an ecological energy analysis formula, the balanced energy release is realized according to whether the soil temperature exceeds an ecological protection threshold value or not, and the energy consumption is minimized and the potential thermal shock to the soil ecology is reduced on the premise of ensuring the weeding effect.