66 results about "Sensible heat" patented technology

The invention discloses a total primary productivity estimation method and system based on multi-model coupling deep learning, and the method comprises the steps: obtaining the multi-source data of meteorological data, remote sensing images, latent heat flux, sensible heat flux and solar radiation, carrying out the quality control, missing value processing and nearest neighbor interpolation of different data sources, and carrying out the prediction of the total primary productivity. Unifying to a target spatial resolution and a time resolution; in a light energy utilization rate (LUE) model family, a solar radiation phase factor is introduced into a photosynthetically active radiation absorption ratio (FPAR) to obtain a phase modulation type FPAR, drought duration is introduced into a water stress function f (W) to obtain an exponential decay type f (W), and a GPP time sequence of a plurality of improved mechanism models is calculated according to the exponential decay type f (W); extracting spatial texture features from a remote sensing image stack by using a convolutional neural network (CNN), and performing cross-modal fusion on the spatial features and the GPP estimated by the plurality of improved mechanism models in a gating mode to form fusion representation; carrying out learning and collaborative optimization on the fusion representation of the GPP and CNN spatial features estimated by the plurality of improved mechanism models by adopting a gradient lifting tree model; and high-precision estimation of the GPP is realized through multi-model collaborative optimization. The method aims at solving the problem that a traditional light energy utilization rate model is insufficient in response under the extreme environment conditions of drought and intense radiation, the adaptability limitation of a traditional single model under the complex environment is broken through, and therefore high-precision GPP estimation under the complex environment is achieved.

The invention provides an optimization method of a magnesium slag sensible heat recovery system, which comprises the following steps: acquiring particle size distribution data of magnesium slag particles, and judging whether the problem of non-uniform particle size distribution exists or not according to a preset particle size threshold value; aiming at the problem of non-uniform particle size distribution, magnesium slag particles are classified by adopting a clustering algorithm, and the distribution areas of large particles and small particles are determined; according to the distribution area of the large particles, the rotating speed of the cooling roller is dynamically adjusted, and the accumulation probability of the large particles in the roller is reduced; the temperature change trend of the inner wall of the cooling roller is monitored in real time, and if it is detected that the local temperature abnormally rises, it is judged that local hot spots exist; when a local hot spot is detected, a spraying cooling system in the roller is started, and intensified cooling is conducted on a local area; pressure and temperature data of high-pressure and high-temperature water are collected in real time through a pressure sensor and a temperature sensor, and whether fluctuation exists or not is judged.

The invention provides a surface water heat flux integrated estimation method, system and equipment, and relates to the technical field of hydro meteorological remote sensing, and the method comprises the steps: according to the target surface temperature data, target net radiation data, target air temperature data, target vegetation coverage data and target soil moisture data of a target region, calculating the target surface temperature data; constructing to obtain target surface environment key parameters corresponding to the target region; and inputting the target earth surface environment key parameters into the surface water heat flux integrated estimation model to obtain target earth surface sensible heat, latent heat and soil heat flux estimation values which are simultaneously output by the surface water heat flux integrated estimation model and correspond to the intra-day time scale of the target region, the surface water heat flux integrated estimation model is obtained by training a physical information neural network machine learning model based on the sample surface water heat flux observation data corresponding to the sample surface water heat flux key parameters and the sample surface water heat flux key parameters. According to the method, the surface water heat flux estimation accuracy is improved.

The invention discloses a radiation air conditioner heat and humidity decoupling cooperative control method based on dynamic dew point prediction, and the method comprises the steps: building a multi-dimensional environment state sensing system, collecting indoor environment parameters in real time, including indoor temperature and humidity and capillary network water supply temperature, calculating indoor air dew point temperature and moisture content change rate, and forming a dynamic anti-condensation safety margin. And the minimum water supply temperature constraint allowed by the capillary radiation system is generated. Based on the constraint, the system achieves sensible heat and latent heat decoupling of indoor cold loads, sensible heat loads are preferentially borne by the capillary radiation system, wet load disturbance of latent heat loads is eliminated by the fresh air system through feedback compound control, and stable adjustment of indoor temperature and humidity is achieved. And under the working condition of insufficient sensible heat, the system automatically performs self-adaptive redistribution of sensible heat load, and the fresh air system assists in supplementing cold energy, so that comfort and high-energy-efficiency operation are ensured to be maintained within an anti-condensation safety boundary. According to the method, indoor air condensation can be effectively prevented, cooperative operation of the capillary radiation system and the fresh air system is optimized, and accurate heat and humidity control is achieved.

The invention provides an intelligent energy-saving regulation and control system and method based on building air conditioner energy consumption prediction. A basic sensible heat load sequence of each temperature control area in a prediction time period is generated through outdoor weather prediction parameters of a target building in the prediction time period and historical operation data of each temperature control area; according to the basic sensible heat load sequence and a preset indoor comfort degree reference range, time-related comfort degree analysis is carried out on the interior of the target building, and a time-related comfort degree allowable range is generated; minimizing the total energy consumption of the air conditioning equipment in the target building is taken as an optimization target, and an energy-saving regulation and control instruction sequence of the air conditioning equipment in each temperature control area is generated by taking meeting the comfort allowable range as a constraint condition; and the energy-saving regulation and control instruction in the current time period in the energy-saving regulation and control instruction sequence is issued to the corresponding air conditioner equipment to execute energy-saving regulation and control. According to the technical scheme, optimization regulation and control of building air conditioner energy saving can be achieved in the full prediction time period.

The invention discloses a contribution decomposition calculation method for lake expansion. The contribution decomposition calculation method comprises the following steps: extracting a lake area through a remote sensing image and a digital elevation model, inverting water volume change, judging a rainfall phase state by using a temperature threshold method, estimating ice and snow ablation amount in combination with a degree-day factor method, and calculating a glacier runoff total amount; constructing a single-layer evaporation model, calculating flux of net radiation, sensible heat, latent heat, water body heat storage and the like based on a lake surface energy balance equation, and obtaining daily lake surface evaporation capacity; quantile mapping and Bayesian fusion are combined, and lake surface rainfall is corrected; non-glacial runoff is calculated according to a lake water balance equation; dividing a stable period and an expansion period according to the lake area trend, calculating an abnormal value of the expansion period by taking the hydrological mean value of the stable period as a reference, and finally determining the expansion contribution of each hydrological factor according to the increment proportion of the abnormal value in the total water quantity. Through the method, the contribution intensity of different driving factors is determined, scientific support is provided for lake change simulation and evolution prediction, and ecological protection and management strategies are more targeted.

The application discloses a lake expansion contribution decomposition calculation method, extracts lake area and reverses water quantity change through remote sensing image and digital elevation model, discriminates precipitation phase state by using a temperature threshold method, estimates ice and snow ablation amount by combining a degree-day factor method, and calculates total glacier runoff; a single-layer evaporation model is constructed, net radiation, sensible heat, latent heat and water body heat storage and other fluxes are calculated based on a lake surface energy balance equation, and daily lake surface evaporation is obtained; quantile mapping and Bayesian fusion are combined to correct lake surface precipitation; non-glacier runoff is calculated according to a lake water balance equation; the stable period and the expansion period are divided according to the lake area trend, the stable period hydrological mean value is taken as a benchmark to calculate the expansion period abnormal value, and finally, the expansion contribution of each hydrological factor is determined according to the proportion of the abnormal value in the total water quantity increment. Through the method, the contribution intensity of different driving factors is clear, scientific support is provided for simulating lake change and predicting evolution, and ecological protection and management strategies are more targeted.

The invention provides a novel system for efficiently carrying out heat-work conversion by taking liquid ammonia / water as a circulating working medium, low-grade sensible heat can be recovered, latent heat is recovered by using an ammonia compressor to compress and heat a circulating working medium gasification heat source, and the heat-work or thermoelectric conversion efficiency is greatly improved. According to the invention, the liquefied latent heat of the working medium is effectively utilized as an internal heat source for circulating gasification, the economic and technical bottleneck of low efficiency of implementing low-grade thermoelectricity or heat-work conversion in the existing combined heat and power generation or ammonia working medium is well broken, the energy consumption and the working medium circulation amount can be greatly reduced, and the method has wide popularization and application values and is worthy of popularization and application. And greater economic and social environmental protection benefits can be created, and energy conservation and emission reduction are greatly realized.

According to the cold and hot microclimate adjusting system for air-cooled heat pump cluster installation and the control method, through the triple synergistic effect of the closed ventilation pipeline, the draught fan and the high-pressure micro-mist spraying system, the overall operation performance is effectively improved, and energy consumption is reduced; the sealing pipeline and the directional fan thoroughly isolate cross back suction of exhaust air and inlet air between units, and cold air and hot air mixing is eliminated from the source. In the summer mode, the high-pressure micro-mist nozzle efficiently reduces the air inlet temperature through a sensible heat and latent heat dual heat exchange mechanism, and the heat island effect is relieved; in winter, the cold island effect is relieved through pure physical isolation; and the dynamic adjusting mechanism ensures that the air inlet temperature is always in a target interval by optimizing the fan rotating speed, the nozzle spacing and the water flow in real time. According to the scheme, the technical contradiction that physical isolation and temperature regulation are difficult to consider at the same time is overcome, and the operation energy efficiency of the unit is further optimized.

The present application relates to the field of air conditioning, and particularly relates to a kind of dehumidifier unit runner performance monitoring method and system, the temperature and humidity sensor is collected the temperature and humidity data of the entrance and exit of runner processing side and regeneration side, and the air volume data of processing side and regeneration side are obtained by the fan of runner processing side and regeneration side;According to the temperature and humidity data of processing side entrance and exit, the air moisture content of processing side entrance and exit is calculated;According to the air volume data of processing side and regeneration side and the air moisture content of processing side entrance and exit, the runner dehumidification amount is calculated, and the latent heat variation corresponding to the runner dehumidification amount is further calculated;According to the temperature and humidity data of regeneration side entrance and exit, the sensible heat variation of regeneration side is calculated;According to the latent heat variation of processing side and the sensible heat variation of regeneration side, the heat transfer efficiency of runner is calculated, whether the performance of runner is abnormal is judged according to the heat transfer efficiency, the performance of runner can be effectively monitored abnormal, and runner is replaced in time.

The invention discloses a machine room equipment monitoring system and method based on the Internet of Things. The method comprises the following steps: synchronously acquiring thermal physical parameters in a micro module; and deriving sensible heat taken away by the air side and cooling capacity provided by the cold source side according to the thermal physical parameters, and outputting a heat exchange capacity evaluation result based on the sensible heat and the cooling capacity. And constructing a time evolution trajectory based on a heat exchange capability evaluation result in a set time period, and recognizing a cold source change working condition in the micro-module in combination with a pre-constructed reference model. And when the cold source capacity corresponding to the cold source change working condition has an attenuation precursor, the cold source capacity compensation amount is calculated, and a refrigeration control strategy is generated according to the cold source capacity compensation amount. And a refrigeration control strategy is executed to adjust the corresponding temperature control equipment according to the cold source capacity compensation amount, the adjusted thermophysical parameters are monitored in real time, it can be detected that the temperature fluctuation lags behind the working condition change of the upstream cold source, and then the risk that the local adjustment action and the actual capacity of the cold source are misplaced and the temperature overshoot is triggered is avoided.

The invention discloses a method for calculating the hydrothermal flux of an ecological system based on daily scale flux variance, and belongs to the technical field of calculation of the hydrothermal flux of the ecological system, and the method comprises the following steps: collecting multi-station flux and meteorological data; the method comprises the following steps of: respectively establishing linear relations between products of daytime and nighttime temperature standard deviations and friction wind speeds and heat-sensitive flux of vortex correlation observation by utilizing multi-site data, respectively acquiring daytime and nighttime heat-sensitive flux of a single site by combining linear parameters, the temperature standard deviations and the friction wind speeds, and then calculating to obtain daily scale heat-sensitive flux # imgabs0 #; or the linear relation between the product of the temperature standard deviation and the friction wind speed and the heat-sensitive flux is directly established through multi-site data, and the daily scale heat-sensitive flux # imgabs1 # of the single site is obtained through direct calculation in combination with the linear parameter, the temperature standard deviation and the friction wind speed; and finally calculating the latent heat flux. By adopting the method, atmospheric stability parameters can be prevented from being used, flux variance universality parameters can be obtained based on multi-site data, and the method is suitable for large-scale popularization.

The invention discloses an intelligent temperature control system based on Internet of Things and cloud computing, and relates to the technical field of intelligent temperature control. The method comprises the following steps: constructing a three-dimensional twinborn model in a building, deploying a data acquisition end, obtaining a sunshade data set and a ventilation data set, obtaining a photo-thermal influence quantity in the building according to the sunshade data set, and obtaining a sensible heat influence quantity and a latent heat influence quantity in the building according to the ventilation data set; temperature fluctuation values under different influence quantities are obtained in the three-dimensional twinborn model, a fluctuation evaluation model is constructed, a first-class fluctuation value and a second-class fluctuation value are obtained through the fluctuation evaluation model, first-class adjustment is conducted on equipment according to the first-class fluctuation value to obtain a first-class temperature control scheme, and a second-class temperature control scheme is obtained; performing second-class adjustment on the equipment according to the second-class fluctuation value to obtain a second-class temperature control scheme; the stability of the indoor temperature can be guaranteed, the energy consumption of the temperature control equipment can be effectively reduced, and an intelligent temperature control scheme can be achieved.

The present application belongs to the technical field of surface water heat flux inversion, in particular to a multi-temporal surface water heat flux collaborative inversion method, device and medium. The method comprises: calculating the change rate of surface radiation with vegetation coverage by using multi-temporal remote sensing surface temperature; realizing multi-temporal remote sensing surface temperature collaborative decomposition; solving the joint balance equation of multi-temporal water heat flux and its components; and inverting multi-temporal surface sensible heat flux, water heat flux and its components. The present application realizes multi-temporal surface temperature joint decomposition based on surface temperature and vegetation coverage time series information, thereby proposing a new idea of multi-temporal surface water heat flux joint cutting sharing reference temperature, establishing multi-temporal balance equation considering surface water heat flux and its components, overcoming the deficiency that current remote sensing model is only suitable for single temporal surface water heat flux inversion, improving the efficiency of surface water heat flux remote sensing estimation, and providing technical services for climate change, agricultural production, ecological construction, water resources management and the like.

The invention belongs to the technical field of machine learning, and particularly relates to an emulsifier production process temperature control method and system based on machine learning, and the method comprises the steps: obtaining the operation data of an emulsification kettle, and removing the no-load loss to construct a shearing power density sequence; obtaining an embedded vector representing viscosity and heat retardation through the rheological state mapping network; the embedded vector is injected into a gate control circulation unit updating gate, and a temperature prediction sequence is output; calculating a consistency index based on the thermodynamic entropy yield, and combining sensible heat temperature rise power residual error to construct a correction term to rectify the prediction sequence; and inputting the consistency temperature into a model prediction controller to generate a valve instruction. According to the method, deep learning is constrained through a physical mechanism, and the problems of temperature control lag and overshoot of the non-Newtonian fluid caused by shear heat are solved.

The application discloses a kind of multi-component greenhouse gas flux observation data adaptive correction and completion method, its steps include: using the edge computing node deployed in vorticity correlation observation tower acquires data and uploads to cloud platform;Cloud platform calculates Richardson number according to received data, constructs the weight function that changes dynamically with Richardson number Weighted adjustment of sensible heat flux correction term and latent heat flux correction term in WPL density correction formula is carried out, and the correction formula is obtained;The current received data is corrected using the correction formula.The application solves the technical problems that cold event interference is difficult to eliminate in existing alpine greenhouse gas flux data observation, density correction error is large under non-steady state, freeze-thaw period emission is difficult to complete, etc., so that carbon budget estimation is more in line with biological objective laws, and the credibility of scientific conclusion is improved.

The application discloses a high-rise building outer facade fire three-dimensional spread blocking and fire extinguishing control system, relates to the technical field of building fire protection, and is used for building fire spread behavior analysis and fire prevention and control under different outer facade structures. The system comprises a multi-source heterogeneous sensing module, a cross-modal evidence fusion research and judgment module, a linkage execution module, a self-adaptive dynamic quantitative blocking decision module and an intelligent targeted fire extinguishing cooperative decision module. Through cross-modal evidence fusion and dynamic trust punishment, the application effectively suppresses false fire alarms (false alarms) caused by environmental interference such as strong light reflection and steam. Based on dynamic quantitative calculation of the activation floor number and the extension distance of the smoke blocking vertical wall plate according to the heat release rate and the flame height of the fire scene, the application overcomes the failure defects of the static fireproof isolation belt. The application adopts a heat balance equation containing sensible heat and latent heat to calculate the water demand, combines with GA-BPNN jet trajectory wind deflection compensation, realizes precise targeted fire extinguishing under high-altitude strong wind, and thus improves the fire prevention and control capability and the intelligent level.

The invention discloses a freezing-rich soil thawing settlement prediction method, and relates to the technical field of cold region geotechnical engineering and disaster prevention and reduction. The method comprises the steps that a frozen soil layering parameter model is constructed, a temperature field is solved in a vectorization mode based on the Fourier law and a sensible heat capacity method, and the type of a soil body is automatically judged according to an initial void ratio threshold value; calculating settlement by adopting a large-strain consolidation mode aiming at common frozen soil, and directly converting the volume of melted ice into settlement volume by adopting a volume collapse mode aiming at an ice-rich layer; node coordinates and physical parameters are updated in real time in combination with a Lagrange dynamic grid technology, bidirectional coupling of thermal parameters is realized, and adaptive time step control is introduced to improve the calculation efficiency. By coupling the consolidation and collapse mechanisms, the method solves the problems of high frozen soil settlement prediction distortion and low calculation efficiency of a traditional method, can accurately simulate settlement deformation of complex stratums such as ice wedges and ice lens bodies under the long-term thermal action, and is suitable for stability evaluation and disaster prevention and control of cold region engineering.

The application discloses a kind of based on satellite data's sensible heat latent heat influence under photovoltaic power prediction model's construction method, by obtaining photovoltaic field station's stationary satellite band and era5 meteorological assimilation data and carry out space-time alignment processing, calculate surface net radiation flux and surface soil heat flux, latent heat flux is calculated in combination with sensible heat flux, construct sensible heat flux and latent heat flux training set, carry out sensible heat flux and latent heat flux time series analysis by multiscale diffusion prediction network, output sensible heat flux and the short-term forecast data of latent heat flux, obtain historical near weather data and physical constraint data, jointly short-term forecast data carries out photovoltaic power prediction, constructs photovoltaic power prediction model, obtains the actual photovoltaic power measurement value of photovoltaic field station, combined with photovoltaic power prediction result carries out prediction accuracy evaluation to photovoltaic power prediction model, carries out model optimization based on evaluation result. With the effect of improving photovoltaic field station to respond to atmospheric sensible heat latent heat under photovoltaic power prediction ability.

The invention discloses a near-surface air temperature data downscaling method driven by multi-source collaborative variables, which relates to the technical field of air temperature data processing and comprises the steps of data preprocessing, sample division, downscaling model construction and air temperature data processing. The method comprises the following steps: firstly, introducing cloud cover, long / short wave radiation, display / latent heat flux and other elements as collaborative variables based on a mode output statistical thought, generating a multi-dimensional multi-source variable data set, and performing preprocessing such as resampling to form a complete data set; carrying out sample division and balance by adopting K-fold cross validation and a first-time adaptive decline algorithm to obtain a total training set and an independent validation set; stable and effective features are screened, and then three types of models including an elastic network, a Bayesian optimization random forest and a neural architecture search-based long and short term memory network are constructed; the to-be-processed data is input into the model to generate high-resolution air temperature data, the data precision is ensured through multi-dimensional verification, systematic deviation is effectively restrained, nonlinear correlation and extreme air temperature correction capacity are enhanced, the space-time stability is improved, and the method is suitable for climatic analysis, ecological environment evaluation and other scenes.

The invention relates to the technical field of building energy conservation, in particular to a heat preservation wall based on building energy conservation, which comprises a multifunctional heat preservation layer, an adjusting unit, a sensor unit and a control unit, the multifunctional heat preservation layer comprises an aerogel heat insulation layer and a phase change material layer, and the adjusting unit comprises a ventilation cavity and an electrochromic reflection layer; the control unit sets an indoor target temperature range according to the current season, the day and night time period and the room type; the phase state stage and the heat flow direction of the phase change material layer are determined according to the phase change material temperature data; and based on the stage of the phase change material, the sensible heat exchange process and the latent heat storage and release process are promoted or inhibited by adjusting the ventilation cavity and the electrochromic reflecting layer, so that the indoor temperature approaches the indoor target temperature range. The thermal performance of the wall is automatically adjusted according to seasonal changes, day and night time periods and indoor temperature, the energy utilization efficiency is improved, and building energy consumption is reduced.

The present invention discloses a method for calculating ecosystem water and heat flux based on daily-scale flux variance, belonging to the technical field of ecosystem water and heat flux calculation. The method comprises the following steps: collecting multi-station flux and meteorological data; using the multi-station data to establish a linear relationship between the product of the daytime and nighttime temperature standard deviations and friction wind speeds and the sensible heat flux observed by eddy covariance; combining the linear parameters, temperature standard deviation, and friction wind speed to obtain the daytime and nighttime sensible heat fluxes of a single station, and then calculating the daily-scale sensible heat flux; or directly establishing a linear relationship between the product of the temperature standard deviation and friction wind speed and the sensible heat flux using the multi-station data; combining the linear parameters, temperature standard deviation, and friction wind speed to directly calculate the daily-scale sensible heat flux of a single station; and finally calculating the latent heat flux. The present invention avoids the use of atmospheric stability parameters and can obtain universal flux variance parameters based on multi-station data, making it suitable for large-scale promotion.

The present invention discloses a flame arrester, comprising: an outer tube, an inner tube, and a flame arrester core. The outer tube wraps around the inner tube, and the outer tube is connected to the inner tube. The flame arrester core is housed in the inner tube. The outer tube is partially arranged indoors and partially arranged outdoors. The inner tube is partially arranged indoors and partially arranged outdoors. The tail gas inlet of the outer tube is located indoors. The flame arrester of the present invention is a double-layer structure comprising an outer tube and an inner tube, and is partially arranged indoors and partially arranged outdoors. The indoor tail gas passes through the outer cavity to preheat the inner cavity, while condensing the moisture in the tail gas, thereby preventing ice from forming from two perspectives: reducing the water content in the tail gas and preheating the inner cavity. Therefore, the design of the double-layer structure allows the temperature of the inner cavity where the flame arrester core is located to be maintained above a certain temperature, and the water content of the tail gas passing through is relatively low. The present invention utilizes the sensible heat of the tail gas itself, does not add other heat sources, does not introduce redundant equipment or complex structures, has low cost, and is safer.

This application provides a method for segmenting surface available energy, which can be applied to the field of climate and hydrological remote sensing technology. The method includes: acquiring surface environmental observation data of a target area at multiple times within a target duration; based on the surface environmental observation data at multiple times, determining target surface environmental variables corresponding to the target duration, including vegetation variables characterizing surface vegetation status, and average and fluctuating state variables of surface temperature, net surface radiation, and air temperature within the target duration; based on the target surface environmental variables, using an evaporation ratio expression to characterize the change of evaporation ratio with surface environmental variables, determining the target evaporation ratio corresponding to the target duration; and based on the target evaporation ratio, performing energy segmentation processing on the target surface available energy of the target area within the target duration to obtain the target latent heat flux and target sensible heat flux corresponding to the target duration.

An air conditioner including an outdoor unit; at least one indoor unit configured to cool and heat an indoor space while repeating operation (Thermo-ON) and operation stop (Thermo-OFF); a sensor unit configured to measure a temperature and humidity of each indoor space in which the at least one indoor unit is located; and a controller. The controller calculates a sensible heat load and a latent heat load of each indoor space, based on a time when the operation stop occurs, a time when the operation starts again after the operation stop occurs, and temperature and humidity information of each indoor space, derives target sensible heat and target latent heat based on the calculated sensible heat load and latent heat load, and derives a target refrigerant temperature and an air volume of the at least one indoor unit based on the target sensible heat and the target latent heat.

The invention relates to the technical field of energy and environmental protection, and particularly discloses a dynamic regulation and control method and system fused with multispectral monitoring time sequence characteristics, and the method comprises the steps: building a characteristic migration model based on comparative learning through continuously collecting furnace bottom slag falling visible light sequence images, infrared temperature field dynamic distribution and 3D material level change data, and obtaining a characteristic migration model; the method comprises the following steps: extracting falling slag thermal state reference characteristics under different load conditions, establishing a coupling time sequence prediction model of hot slag sensible heat and wastewater evaporation by combining dynamic response characteristics of wastewater atomization evaporation in a falling cloud bed, and generating a real-time regulation and control strategy by adopting a deep reinforcement learning algorithm; according to the method, the limitation of traditional static parameter setting is broken through, the unit variable load working condition is adapted through transfer learning of the multispectral time sequence characteristics, the contradiction between insufficient heat utilization and equipment scaling in high-salinity wastewater drying is solved, and the evaporation efficiency of the desulfurization wastewater is improved.

The invention discloses a phase change heat sink-oriented heat exchanger universal chained calculation method, which comprises the following steps of: dispersing a heat exchange process into a plurality of heat exchange micro-sections along the flow direction of a cold source working medium, and updating the temperature, thermophysical parameters and flow state of a cold and hot working medium in a chained manner in each micro-section on the basis of the principles of energy conservation, heat transfer theory and fluid mechanics; unified analytical calculation of sensible heat transfer, phase-change heat transfer and overheating processes is achieved, overall design parameters such as the length required by the heat exchanger, the effective heat transfer area, the volume and the mass are obtained, and the influence of thermophysical property differences of different working media on heat exchange structure design is quantitatively analyzed. According to the method, the problem that the on-way heat transfer process is difficult to accurately describe under the working conditions of phase change and large temperature difference by adopting a black box model or a normal physical property hypothesis is solved, and full coupling modeling of physical property change-phase state transition-on-way analysis is realized; and the engineering applicability and the design reliability of the universal chain type calculation method of the phase change heat sink heat exchanger in an aircraft consumable heat management system are improved.

The invention discloses a power transmission line icing simulation method and system based on meteorological data and observation data. The method comprises the following steps: acquiring meteorological data and icing observation data; matching the acquired meteorological data with the icing observation data to obtain an observation sample data set; constructing a liquid water content estimation function by introducing a linear temperature correction model, reconstructing a freezing coefficient by introducing a kinetic energy impact factor and a water drop sensible heat item in an energy driving item, and correcting a Makkonen model; and performing joint optimization on multiple groups of parameters contained in the linear temperature correction model, the liquid water content estimation function and the reconstructed freezing coefficient structure to obtain an optimal parameter set, and applying the obtained optimal parameter set to the corrected Makkonen model to perform icing simulation. The ice coating growth process of the wire can be accurately simulated under the condition of lack of actual measurement of liquid water content (LWC).

The invention provides a sintering flue gas pollutant collaborative removal and energy recycling method, which comprises the following steps that coal and a desulfurizing agent are introduced into a hearth of a circulating fluidized bed boiler, primary air and secondary air are introduced, the source of the primary air is air, the source of the secondary air is sintering flue gas, and the temperature of the hearth is 800-900 DEG C; smoke dust formed after combustion in the hearth is subjected to afterburning and SNCR denitration and then is introduced into a cyclone separator to be separated, separated smoke gas enters a tail flue, and separated bed materials return to the hearth; flue gas in the tail flue is discharged to the atmosphere after being subjected to wet desulphurization, SCR denitration, heat exchange and dust removal. The sintering flue gas is introduced into the hearth for combustion, boiler fuel consumption is reduced through sensible heat and chemical energy of the sintering flue gas, and waste heat recovery of the sintering flue gas and cooperative treatment of pollutants are achieved.

This invention discloses a method for estimating the actual evapotranspiration of different irrigation zones of farmland based on temperature. The method first measures meteorological parameters of a reference irrigation zone and calculates the sensible heat flux and evapotranspiration of the reference irrigation zone. Characteristic parameters of the irrigation zone to be tested and the reference irrigation zone are then obtained. Based on the difference in the characteristic parameters between the irrigation zone to be tested and the reference irrigation zone, the sensible heat flux and evapotranspiration of the irrigation zone to be tested are estimated. This method can effectively calculate the actual evapotranspiration of different irrigation zones of farmland.