25912results about How to "Reduce energy consumption" patented technology

The invention discloses an unmanned aerial vehicle route planning and dynamic management and control method for an urban low-altitude airspace, and relates to the technical field of unmanned aerial vehicle route planning and dynamic management and control, and the method comprises the steps: dividing the low-altitude airspace into a basic flight layer, a buffer anti-collision layer and an emergency guarantee layer in the vertical direction; setting different air route grades and limitation rules in different areas in the horizontal direction; generating a basic airway network skeleton and performing structure optimization; obtaining the operation state and environment updating data of the unmanned aerial vehicle; semantic annotation is carried out on each leg and node in the basic airway network skeleton, and airspace regulations, supervision rules, flight limiting areas, temporary control areas and recommended flight corridor information are embedded into a semantic layer of the airway network; and inputting the basic airway network skeleton, the semantic annotation information and the real-time monitoring data into an urban low-altitude airspace management and decision-making platform. According to the invention, cooperative scheduling and avoidance among multiple unmanned aerial vehicles are realized, and the operation efficiency of an air route network and the cooperative work capability of the unmanned aerial vehicles are improved.

The invention provides a machine room energy consumption optimization method and system based on temperature monitoring, and relates to the technical field of energy consumption optimization, and the method comprises the steps: collecting the temperature data of a machine room in real time, continuously monitoring the temperature gradient of each region, carrying out the fusion processing, and constructing a three-dimensional dynamic temperature distribution model; a hot spot area, an airflow dead angle and a cooling capacity waste area are identified through a deep learning algorithm, a flowing track and a mixing process of cold and hot air in a machine room are predicted through a track regression neural network in combination with machine room layout and equipment distribution, and a machine room temperature control strategy set is generated through adaptive neural fuzzy reasoning, correction and updating of a prediction result; and by taking minimum energy consumption and maximum refrigeration efficiency as optimization objectives and taking a safe operation temperature range of equipment in the machine room as a constraint condition, a plurality of parameters are jointly adjusted through a temperature control optimization algorithm to obtain an optimal temperature control curve, and the optimal temperature control curve is converted into a refrigeration system control instruction, so that collaborative optimization of the temperature and the energy consumption of the machine room is realized.

The invention discloses an expressway service area sewage treatment system based on the Internet of Things, and relates to the technical field of sewage treatment.The expressway service area sewage treatment system is provided with a multi-modal sensor and an edge computing unit, real-time monitoring and data processing of sewage flow and water quality parameters are achieved, the water quality change trend and the parameter change rate are rapidly recognized, and the sewage treatment efficiency is improved. The sensing capacity of the system to load changes is remarkably improved, the system conducts real-time adjustment according to the water quality changes, fluctuation of the treatment effect and waste of energy consumption are avoided, an LSTM model is adopted for conducting dynamic prediction on water quality parameters, in combination with real-time monitoring data, the aeration strategy is optimized through intelligent self-adaptive adjustment and closed-loop fine control, and the aeration efficiency is improved. The LSTM model can predict the future sewage load and ammonia nitrogen concentration change, the aeration intensity and time are adjusted in advance, the fluctuation of the ammonia nitrogen concentration is quickly responded through PID control and closed-loop control of a feedback mechanism, and the ammonia nitrogen removal efficiency and the aeration accuracy are improved.

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention discloses a coal mine dust diffusion simulation and control method based on artificial intelligence, and relates to the technical field of coal mine dust diffusion simulation and control, and the method comprises the following steps: collecting environmental data such as dust concentration, ventilation flow velocity, temperature, humidity and equipment operation state through a monitoring system in a coal mine; and cleaning and formatting processing are carried out in combination with historical working condition data. According to the method, reinforcement learning and virtual extreme scene simulation are utilized, the recognition and emergency response capability of the system to edge working conditions is improved, dust diffusion and explosion risks are effectively reduced, and mine safety is guaranteed. By dynamically adjusting the ventilation and water spraying system, efficient utilization of resources is achieved, energy consumption and operation cost are reduced, and the service life of equipment is prolonged. The system has an adaptive learning capability, can optimize a control strategy according to monitoring data and historical events, can automatically adjust even if the environment changes, improves robustness and flexibility, and reduces manual intervention.

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention provides an energy-saving control method and device for a central air-conditioning system and a medium, and the method comprises the steps: constructing a cooling load prediction model of the central air-conditioning system, and predicting the cooling load per hour of the next day; under the conditions that the hourly cooling load of the next day is met and the power utilization cost is the lowest, the cooling capacity in a fixed time period is distributed; and an energy efficiency model and a function model of the central air-conditioning system are constructed, and the operation strategy of the central air-conditioning system corresponding to the given cooling capacity is optimized with the purpose of minimizing the power consumption cost, so that the comprehensive energy consumption of the central air-conditioning system is minimized. According to the method, the cooling load prediction result is improved by comprehensively considering various cooling load influence factors and effectively capturing the long-term dependency relationship in historical cooling load data, the comprehensive energy consumption minimization and the power consumption cost minimization of the central air-conditioning system serve as targets, the operation strategy is optimized through simulation combination and energy efficiency comparison, and the energy efficiency of the central air-conditioning system is improved. Therefore, the requirement of a target environment for an efficient and energy-saving central air conditioning system is met.

The invention discloses a photovoltaic panel automatic cleaning system based on an unmanned aerial vehicle and a method thereof, and relates to the technical field of solar photovoltaic. The system comprises an unmanned aerial vehicle, a pollutant cleaning device, a pollutant detection device and a remote control center, comprising the steps of planning a flight path of an unmanned aerial vehicle and obtaining a photovoltaic panel image to determine the type, position and cleaning time of pollutants; a cleaning mode is selected to carry out cleaning operation, and secondary detection is carried out on the cleaned photovoltaic panel; and recording each cleaning task and process and establishing a self-adaptive model. The unmanned aerial vehicle is used for monitoring the photovoltaic panel in real time, image analysis and pollutant identification are carried out through the remote control center, the cleaning device is used for cleaning the photovoltaic panel, and the pollutant detection device is used for carrying out secondary detection after cleaning; according to the cleaning device, multiple cleaning modes are combined, flexible selection is achieved according to the types of pollutants, the cleaning effect of the photovoltaic panel is guaranteed, the cleaning efficiency is improved, and meanwhile the energy-saving effect is achieved.

The invention discloses a self-adaptive variable-frequency speed regulation device based on a scraper conveyor and a control method, and relates to the technical field of automatic control, and the method comprises the following steps: collecting real-time operation data of the scraper conveyor through a data sensing module, and determining operation demand information of equipment; the evaluation calculation module sets an operation parameter interval and generates an evaluation index. And the rotating speed correlation analysis module performs rotating speed correlation analysis on the real-time data according to the evaluation indexes, constructs a rotating speed correlation list and generates a correlation learning result. And the dynamic matching module calls the frequency converter for dynamic matching to generate an output frequency parameter group. And the linkage control strategy making module executes the intelligent speed regulation control strategy and adjusts the belt speed of the scraper conveyor. The technical problems that in the prior art, due to the fact that the speed of a scraper conveyor cannot be intelligently adjusted under different load conditions, overload shutdown is caused, the energy efficiency is low, and equipment abrasion is aggravated are solved, and the technical effects of reducing energy consumption, reducing equipment abrasion and prolonging the service life are achieved.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention relates to a temperature control intelligent optimization method and system for adhesive tape production. The method comprises the following steps that S1, production data in the drying process are collected through a multi-mode sensor; s2, constructing a state prediction model based on GNN and LSTM, inputting the collected production data into the prediction model, and dynamically predicting the state of each temperature control area; s3, modeling each temperature control area into an intelligent agent based on the predicted state between the temperature control areas, and realizing dynamic distribution of heat resources of each heating interval by training a cooperative control strategy between the intelligent agents; s4, a fuzzy control optimization scheme incorporating environment compensation: for short-term changes of environment temperature and humidity, dynamically adjusting temperature zone gradient distribution between drying sections by using fuzzy control, and optimizing energy efficiency; and S5, the temperature control module adjusts the temperature and the wind speed of the temperature control area in real time according to the optimization schemes obtained in the steps S3 and S4. The intelligent level of the temperature control system in the adhesive tape production drying process is comprehensively improved.

The invention discloses an unmanned aerial vehicle path planning method and system based on digital twinning. The method comprises the following steps: constructing a three-dimensional environment model based on first target environment data in first target multi-source environment perception data; constructing a digital twin based on the geometric model, the physical model, the control model, the target physical data hybrid dynamic model and the three-dimensional environment model; inputting the second target multi-source environment sensing data into the digital twin, and dynamically simulating the flight behavior of the physical unmanned aerial vehicle based on the second target multi-source environment sensing data to obtain simulated flight environment state data; and based on the simulated flight environment state data, performing path planning on the physical unmanned aerial vehicle in combination with a prediction control algorithm and a path planning algorithm to obtain a target flight path, transmitting the target flight path to the physical unmanned aerial vehicle, and controlling the flight of the physical unmanned aerial vehicle. The method can dynamically simulate the real-time change of the physical unmanned aerial vehicle, plan the optimal flight path and effectively avoid obstacles, and can be widely applied to the technical field of unmanned aerial vehicle path control.

The invention provides an unmanned aerial vehicle multi-target tracking method and system based on visual and millimeter-wave radar information fusion, and the method comprises the steps: obtaining an association result through employing a space-time consistency model, constructing a self-adaptive joint probability data association frame according to the association result, so as to obtain an initial target state estimation value, and carrying out the recognition of the initial target state estimation value. A prospective path planning algorithm is adopted to obtain an optimal observation angle flight path, a Bayesian network is utilized to obtain initial observation data, based on the initial observation data, an optimal target state estimation value is generated, a distributed cooperative sensing mechanism is used to generate an initial tracking task allocation scheme, and an intelligent resource allocation strategy is implemented. Generating an initial resource allocation scheme, starting a quick response protocol to obtain an optimal tracking task allocation scheme, and generating an optimal resource allocation scheme based on the optimal tracking task allocation scheme; according to the technical scheme provided by the invention, the accuracy of target identification is improved, the environmental adaptability is enhanced, the tracking efficiency is improved, and the working time is prolonged.

The invention provides an oil smoke real-time concentration monitoring and intelligent adjusting method and system, and relates to the technical field of intelligent control, and the method comprises the steps: collecting real-time air quality data of a catering area through a plurality of environment sensors, including oil smoke concentration, PM2.5 concentration, carbon monoxide concentration, temperature and humidity; and the control center calculates an oil smoke concentration spatial and temporal distribution field based on real-time data in combination with spatial interpolation, considers smoke exhaust energy consumption, smoke exhaust efficiency and regional oil smoke concentration, and determines an optimal cooperative control strategy by utilizing a multi-target optimization algorithm, and the optimal cooperative control strategy comprises the fan rotating speed of each range hood, the smoke exhaust direction and the air supply parameters of the fresh air system. And the control center issues the control strategy to a range hood controller to adjust operating parameters of the range hood. Dynamic optimization control over the air quality of the catering area can be achieved, the oil smoke concentration is reduced, energy is saved, and the smoke exhaust efficiency is improved.

The invention discloses an energy storage cluster dynamic optimization control system based on edge computing, which relates to the technical field of energy storage clusters and comprises the following steps: acquiring historical data of an energy storage cluster and local data of an edge node; and performing time sequence prediction by using an ARIMA model, performing nonlinear error identification and correction by using a BP neural network, and outputting a final power prediction value. According to the method, through short-term trend prediction of the ARIMA and error correction of the BP neural network, power fluctuation can be responded more quickly, power distribution can be optimized, stable operation of the energy storage system in the power grid is ensured, local BP neural network model training is performed on each edge node, distributed computing resources can be fully utilized, and the calculation efficiency is improved. Under the synergistic effect of the double-layer optimization model, a final power distribution scheme and a final transmission path optimization result are output, and efficient operation, energy consumption reduction and maximum utilization of renewable energy sources can be ensured under the fluctuation of load requirements and power supply.

The invention discloses a multi-target intelligent optimization air conditioner control method and system based on multi-dimensional machine learning, and aims to acquire outdoor environment data by acquiring personnel flow and environment data inside and outside a building in real time and combining weather forecast information and the like, and predict future personnel flow trend and environment change conditions by utilizing a machine learning algorithm, so as to realize intelligent optimization of the air conditioner. And data support is provided for an artificial intelligence control strategy of the air conditioning system. Based on the prediction results, a multi-target optimization control algorithm is used for conducting multi-dimensional parameter regression on the air conditioner operation effect, and operation parameters such as the temperature, the humidity and the wind speed of the air conditioner system are dynamically adjusted. The air conditioning system is suitable for various large buildings such as office buildings, shopping malls and airports, the energy utilization efficiency of the air conditioning system is remarkably improved and the energy consumption is greatly reduced on the premise that the indoor environment comfort is ensured, and the air conditioning system has remarkable energy-saving and carbon-reducing effects, wide application prospects and remarkable economic and environmental protection benefits.

The invention belongs to the technical field of engineering supervision, and discloses a concrete strength measurement system and method for civil construction engineering, and the method comprises the following steps: a test point planning module determines the number and position of detection points; the route planning module plans a running route of the detection mechanism; the detection mechanism sequentially moves to each detection point position according to the planned route; the positioning module analyzes the distance from the ultrasonic probe to the laser; the ultrasonic probe A is aligned with the ultrasonic probe B; the ultrasonic probe A and the ultrasonic probe B scan a concrete building; detecting the strength of the concrete building by using a rebound apparatus; the humidity acquisition module acquires humidity data of concrete; the temperature acquisition module acquires environment temperature data; and the ultrasonic analysis module comprehensively calculates the concrete strength by combining the rebound value, the concrete humidity, the environment temperature and the ultrasonic detection sound velocity. According to the invention, concrete strength on-site detection can be carried out on a high-rise building; by integrating various data, the strength of the concrete can be evaluated more accurately.

The invention relates to the technical field of drying equipment, in particular to clothing cloth drying equipment. The clothing cloth drying equipment comprises a box body, a discharging shaft for discharging cloths, a drying module, an ironing module, an air-drying module, a rolling shaft for rolling the cloths and a controller. The top of the left side of the box body is provided with a storinghole for storing the cloths, a damping piece mounted at the bottom of the box body is arranged under the storing hole, and the discharging shaft is vertically mounted on the damping piece. The dryingmodule is used for drying the cloths, the ironing module is used for ironing the cloths, and the air-drying module is used for air-drying the ironed cloths. The rolling shaft is located on the rightside of the air-drying module, a material taking hole for taking out the cloths is formed in the top of the box body over the rolling shaft, the rolling shaft is driven through a rolling motor, and the rolling motor is electrically connected with the controller. The clothing cloth drying equipment is mainly suitable for being used in a clothing manufacturing shop and has the characteristics that the size is small, the weight is small, the price is low, and energy consumption is little.

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

The invention discloses a remote regulation and control LED lighting system based on the Internet of Things, and relates to the technical field of LED lighting systems, comprising an LED lighting equipment module which comprises an efficient lamp, a microcontroller, an Internet of Things communication module, a remote control terminal module supporting multi-protocol and encryption, having Mesh networking, a multi-terminal control, having multiple functions and innovation formulas, and a sensor module; the intelligent LED lighting control system has the advantages that environment information is collected, the data management and analysis module stores and analyzes data, energy-saving management and the like are achieved, all the modules work cooperatively, and the functions of remote intelligent regulation and control, fault self-inspection, butt joint with other systems and the like of LED lighting are achieved. Through collaborative innovation of multiple modules, remote intelligent regulation and control of LED lighting are realized, the lighting quality and user experience are improved, energy is saved, consumption is reduced, the system has the functions of fault self-checking and alarming and the like, the reliability and compatibility of the system are improved, and the diversified requirements of modern lighting systems are met.

The invention discloses an Internet of Vehicles multi-parameter monitoring method and system based on 5G multi-access edge computing. The method comprises the following steps: collecting multi-source heterogeneous data in real time by using a vehicle-mounted sensor; transmitting the collected multi-source heterogeneous data to the nearest edge computing node in real time by using a 5G network; preprocessing the multi-source heterogeneous data at an edge computing node; inputting the fused data into a local model deployed on an edge node, and carrying out real-time monitoring and real-time evaluation on a vehicle state and a driver state; the obtained evaluation result is uploaded to a cloud platform, and the cloud platform dynamically adjusts a driving assistance decision and a control instruction according to the vehicle driving working condition and the environment data to generate an optimal decision result; and issuing the optimal decision result to a vehicle-mounted execution unit through a 5G network. According to the method, key parameters such as the vehicle speed and the physiological state of a driver are monitored, and the real-time performance and accuracy of a monitoring system are improved.

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention discloses an exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention belongs to the technical field of polyester recycling, and relates to a method for efficiently producing terephthalate by depolymerizing waste polyester through carbonic ester, which comprises the following steps: depolymerizing waste polyester, carbonic ester and a catalyst under the protection of nitrogen or inert gas and solvent-free conditions, a is more than one of zinc acetate, sodium acetate, potassium chloride, lithium chloride, potassium carbonate, sodium carbonate, ethylene glycol antimony, antimony trioxide, antimony acetate, ethylene glycol titanium and titanium dioxide, and B is sodium ethoxide. The method solves the problems of limited conversion of the waste polyester chemical recovery monomer and long production process in the prior art, obviously improves the catalytic efficiency and the depolymerization speed through the compound catalyst, simplifies the depolymerization process, reduces the energy consumption, and is beneficial to industrial application.

The invention relates to the technical field of cable production automation control, and discloses a control method, system and device of cable extrusion equipment and the extrusion equipment, the method comprises the steps that sensor parameters are acquired, and the sensor parameters comprise the cable extrusion temperature and the extrusion speed; performing time delay correction on the sensor parameter to obtain a corrected parameter value; inputting the correction parameter value into a pre-trained extrusion speed and extrusion temperature dynamic compensation model to generate an adjustment instruction; and sending the adjustment instruction to equipment to adjust the heating temperature and the extrusion speed of the cable. Compared with the prior art, the method has the following effects that the heating temperature and the extrusion speed of the cable can be accurately adjusted in real time through combined use of time delay correction and an extrusion speed and extrusion temperature dynamic compensation model, and the waiting time and the adjustment time in the production process are reduced, so that the control precision and the production efficiency of cable extrusion equipment are improved.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The invention relates to the technical field of unmanned aerial vehicle management, in particular to an unmanned aerial vehicle low-altitude flight record management system. The invention aims to solve the problems that unmanned aerial vehicle low-altitude flight records are mainly used for flight data storage and simple playback in an existing unmanned aerial vehicle management system, and deep analysis and judgment of the unmanned aerial vehicle low-altitude flight records are lacked. According to the invention, the task evaluation module evaluates the feasibility of the low-altitude flight task of the unmanned aerial vehicle and generates an initial flight scheme, the path optimization module optimizes the initial flight scheme to generate a flight path adapted to a low-altitude environment, and the real-time monitoring module evaluates the flight state of the unmanned aerial vehicle in real time. The recording and judging module evaluates the flight state of the unmanned aerial vehicle after the low-altitude flight task of the unmanned aerial vehicle is completed; and the recording and playback module provides whole-course playback of the low-altitude flight process of the unmanned aerial vehicle according to low-altitude flight records of the unmanned aerial vehicle. Through mutual cooperation among the modules, comprehensive data support and application support are provided for low-altitude flight management of the unmanned aerial vehicle.

The invention relates to the technical field of gas energy monitoring, and discloses a gas energy monitoring method based on Internet of Things, comprising the following steps: S1, multi-source data acquisition: using an intelligent sensor to acquire gas related data, and sending the data to an edge device or a cloud platform through a wireless network; s2, data preprocessing: carrying out data cleaning, denoising and feature extraction on edge equipment, executing multi-source data fusion, carrying out anomaly detection and processing, and filtering abnormal data; and S3, data transmission: performing data transmission by adopting an encrypted communication protocol, and using a data buffering and retransmission mechanism in the transmission process, the data buffering and retransmission mechanism being used for ensuring data integrity. By using a multi-source data fusion technology and an advanced data preprocessing algorithm, sensor noise and data errors are effectively reduced, the quality and consistency of gas data are remarkably improved, and the problem that the analysis accuracy is reduced due to data source diversity and complexity is solved.

The invention discloses a power station photovoltaic field area automatic inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection, and the unmanned aerial vehicle comprises the following components: a meteorological self-adaptive intelligent inspection flight system, a power supply module, a rotor wing device and a communication module; the power supply module is used for providing power support for each component of the unmanned aerial vehicle; the communication module is used for realizing data interaction between the unmanned aerial vehicle and the ground control station and between the unmanned aerial vehicle and the photovoltaic field area monitoring equipment; by carrying the meteorological self-adaptive intelligent inspection flight integrated system, meteorological conditions of a photovoltaic field area can be perceived and analyzed in real time, and an inspection path and a flight attitude are dynamically optimized according to the meteorological conditions, so that the unmanned aerial vehicle can preferentially inspect an area where a problem is most likely to occur, and the inspection efficiency is remarkably improved; meanwhile, the unmanned aerial vehicle can keep stable flight under complex meteorological conditions such as strong wind, flight accidents are avoided, and the accuracy of inspection data is ensured.

The invention relates to an adaptive control method and device for a supplementary light source of an industrial camera, and the method comprises the steps: obtaining scene perception data of the industrial camera, carrying out the real-time classification and environment parameter analysis of a shooting scene based on the scene perception data, and obtaining scene feature information; performing image quality evaluation on the scene feature information, and comparing with a preset image quality standard to obtain an image quality matching degree; performing control parameter analysis based on the image quality matching degree and the scene feature information to obtain an initial light supplementing control parameter; acquiring image data of the industrial camera, and performing brightness distribution and detail feature analysis on the image data to obtain an image analysis result; performing dynamic light source simulation on the scene feature information based on an image analysis result to obtain a corresponding dynamic supplementary light source parameter; and performing comprehensive processing on the initial light supplement control parameter, the image analysis result and the dynamic light supplement source parameter to obtain a light supplement source adjustment strategy. The problem that a traditional fixed light supplementing method is difficult to adapt to a complex environment can be effectively solved.