82 results about "Heat wave" patented technology

The invention relates to the field of disaster prediction, and particularly discloses a prediction system and method for high-temperature heat wave drought composite disasters, and the system comprises a multi-source data collection module which is used for collecting meteorological data, topographic data, vegetation and soil data and human activity data, and constructing a multi-dimensional input feature set; the data processing module is used for carrying out space-time alignment, normalization processing and abnormal value elimination on the acquired meteorological data, topographic data, vegetation and soil data and human activity data, and carrying out dimensionality reduction through a principal component analysis method to extract key features; the deep learning model training module adopts a CNN-RNN fusion architecture; and the prediction output module is used for generating a composite disaster prediction result through the trained deep learning model training module. By adopting the technical scheme of the invention, multi-dimensional data can be considered, a multi-element coupling model is constructed, and prediction of spatial and temporal distribution, evolution rate and coupling strength of high-temperature heat wave drought composite disasters in complex geographic units and ecologically fragile areas is realized.

The invention provides an atmosphere data assimilation method and device, equipment and a storage medium. Relates to the technical field of computer science and atmospheric science fusion. The method comprises the following steps: adding a cost function, priori estimation and the gradient of the cost function into a gradient-based descent algorithm, adding posteriori estimation, and constructing a four-dimensional variation and ensemble Kalman filtering combined model based on Bayesian priori information; and carrying out data assimilation on atmosphere data based on the four-dimensional variation and ensemble Kalman filtering combined model. According to the method, an artificial intelligence technology is combined, especially deep learning and a Bayesian optimization adaptive algorithm are adopted, a traditional data assimilation algorithm is enhanced and optimized, and the method can be widely applied to prediction of extreme weather events such as high temperature and heat waves. Through assimilation of historical data and real-time observation data, the prediction precision of the future climate change trend is significantly improved, and more powerful support is provided for extreme weather early warning and decision making.

The invention discloses a vegetation vulnerability constraint estimation method and system under dry-heat composite stress. The vegetation vulnerability constraint estimation method comprises the following steps: 1, acquiring observed vegetation total primary productivity data, meteorological data and similar meteorological data of each lattice point in a global climate mode; 2, determining a standardized rainfall evapotranspiration index time scale of each lattice point and determining dominant stress types of different lattice points in growing seasons; 3, respectively constructing two-dimensional Copula models of corresponding dominant stress and total primary productivity, and determining a stress threshold value corresponding to induced vegetation loss; 4, calculating future estimation of dominant stress based on a climate mode, and constructing an emergence constraint model; 5, correcting a future estimated mean value and uncertainty of the dry heat stress index based on the emergence constraint relation; and 6, inputting the corrected mean value and uncertainty range of the future dry heat stress indexes into a Copula model, and calculating a corresponding loss probability. According to the method, the response difference of different vegetation ecosystems to dry heat stress is considered while the prediction accuracy of the drought heat waves and the compound events thereof is improved.

The invention provides a community structure detection method and system for spatial concurrent extreme climate events, and the method comprises the steps: collecting lattice point temperature and rainfall data of a research region, extracting a binary time sequence of heat waves and extreme rainfall events, and carrying out the synchronous judgment calculation and significance test of the events; according to the interlayer adjacent matrix, a double-layer coupling heat wave-extreme precipitation network is constructed; based on the double-layer coupling network, a community detection method combining a Leiden algorithm and consensus clustering is adopted, and a stable community classification result is obtained; and carrying out lead-lag correlation analysis on all community pairs, identifying key community pairs with strong correlation and lag time thereof, and further discussing time-space propagation characteristics of climate events. According to the method, the advantages of double-layer coupling climate network analysis are fully utilized, the Leiden algorithm and the consensus clustering method are combined, the accuracy and stability of community detection are improved, and the spatial concurrency characteristics and the space-time propagation law of global extreme climate events can be effectively revealed.

The invention provides a sub-season heat wave prediction method, which comprises the following steps of: determining all optimal prediction factors associated with any air temperature main mode time coefficient under preset prediction time efficiency according to a pre-obtained air temperature main mode of a target area and an air temperature main mode time coefficient-prediction time efficiency-optimal prediction factor relation table; and acquiring data of all the optimal forecasting factors through real-time non-band-pass filtering, inputting a pre-trained sub-season air temperature forecasting model, and acquiring a predicted air temperature abnormal field in the target area under preset time efficiency. And predicting the sub-season heat wave event in the target area according to the predicted air temperature abnormal field. According to the method, the interaction between multiple scales and multiple variables is considered by using the strong data mining capability of the convolutional neural network, the influence of the interannual scale signal on the large-scale sub-seasonal variation rate is fully considered, and the potential correlation between the early-stage multi-scale signal and the future air temperature is mined.

The invention provides a satellite remote sensing high-temperature heat wave risk early warning method, device and equipment and a medium. The method comprises the following steps: acquiring satellite remote sensing data, numerical mode forecast data, cloud coverage data and land utilization type analysis data corresponding to a research area; according to a model dynamic allocation mechanism, dynamically allocating a corresponding high-temperature heat wave risk early-warning model for each grid in the research area according to the cloud coverage data, and determining a high-temperature heat wave early-warning result corresponding to each grid based on satellite remote sensing data and numerical mode forecasting data through the high-temperature heat wave risk early-warning model corresponding to each grid; and determining a susceptibility weight corresponding to each grid according to the land utilization type analysis data, and determining a high-temperature heat wave risk level corresponding to each grid in combination with the high-temperature heat wave early warning result corresponding to each grid. According to the method, the high-temperature heat wave prediction precision can be remarkably improved, and meanwhile risk early warning is more regional and targeted.

The invention relates to the technical field of data processing, and discloses a numerical simulation method for a synergistic effect of an urban heat island and high-temperature heat waves. The method comprises the following steps: collecting urban building form data to construct a data set, and combining remote sensing data to carry out local climate zoning; extracting a high-temperature heat wave process and performing grading by utilizing machine learning; analyzing the urban heat island intensity time sequence change based on the remote sensing data; constructing an urban canopy mode coupling hydrology and an artificial heat source; and inputting different levels of heat wave data to simulate a synergistic effect. The technical problem that the urban heat island effect and high-temperature heat wave synergistic effect mechanism cannot be accurately simulated in the prior art is solved, and the numerical simulation method for quantitatively evaluating the urban heat island intensity amplification coefficient under the condition of different grades of high-temperature heat waves is established.

The invention relates to the technical field of composite heat wave ozone event evaluation, in particular to a composite heat wave ozone event refined identification method, which comprises the following steps: acquiring two types of preprocessed observation daily scale data sequences; performing percentile conversion on the preprocessed observation daily scale data sequence to obtain a percentile sequence; calculating the rising rate of the observation data in the development stage according to the percentile sequence; determining a pollution event of the to-be-identified area and a plurality of characteristic values of the pollution event through the rising rate; and if the pollution event is a non-independent composite heat wave ozone event, combining the non-independent composite heat wave ozone event by adopting an IC method to obtain a new combined composite heat wave ozone event. According to the invention, the accuracy of composite heat wave ozone event identification is improved by determining the rising rate and combining the non-independent composite heat wave ozone event.

The invention provides a lightning fire occurrence probability prediction method fusing extreme climate factors, and relates to the technical field of forest fire prediction and risk assessment. The method comprises the following steps: collecting multi-source data such as thunder and lightning, extreme climate, weather, vegetation, soil and terrain, and constructing a balanced sample set; training an ignition probability model by adopting a random forest, XGBoost, a convolutional neural network and other models, and evaluating the performance difference before and after the extreme climate index is included; explaining variable contribution by using an SHAP method, and identifying a key driving factor; a risk grading grid map is generated according to the prediction probability, and the fire danger grade is automatically improved under the composite condition of heat waves and drought. The method has high precision and strong interpretability, and is suitable for fine prediction and prevention and control decision support of the occurrence probability of the lightning fire under the extreme climate background.

The invention relates to an ocean heat wave prediction method, and the method comprises the steps: obtaining the physical variable historical time series data of each region of a target sea area, generating a physical variable prediction sequence containing the physical variable prediction value of a future time point through a composite model obtained through training, and carrying out the mining to obtain the physical variable relation data. Then, the physical variable prediction sequence, the physical variable relation data and the preset task instruction are input into a heat wave analysis module, the input data are accurately analyzed through the heat wave analysis module, and conclusion deviation caused by non-uniform standards during manual interpretation is avoided. Through automatic analysis, direct manual participation is reduced, and the analysis efficiency and the standardization degree are improved. And finally, obtaining ocean heat wave information output by the heat wave analysis module and obtaining a prediction result.

The invention provides an ocean heat wave interpretability machine learning prediction method based on multi-source data, and the method comprises the steps: collecting simulation data and observation data of a plurality of climate mode members of CMIP6 in a preset time period, carrying out the preprocessing of the simulation data and observation data, and enabling the preprocessed simulation data and observation data to serve as a training set and a test set; constructing a machine learning model for predicting ocean heat waves, and training the machine learning model based on the training set to obtain a trained machine learning model; testing the trained machine learning model by using the test set to obtain a prediction result of the ocean heat wave, and identifying a physical factor with the highest influence degree on the prediction of the ocean heat wave through the interpretability of the trained machine learning model; the method can effectively overcome the problem of insufficient observation data samples of a traditional method, and significantly improves the accuracy of heat wave prediction. Meanwhile, the key functions of different meteorological variables in the prediction process can be determined, so that the scientific interpretability and practicability of the model are improved.

The present invention provides a sub-seasonal heat wave prediction method, comprising: determining all optimal prediction factors associated with any temperature main mode time coefficient under a preset forecast time according to a pre-obtained temperature main mode of the target area and a temperature main mode time coefficient-forecast time effect-optimal prediction factor relationship table. Data of all optimal prediction factors are obtained by real-time non-bandpass filtering, input into a pre-trained sub-seasonal temperature forecast model, and a predicted temperature anomaly field under a preset time effect in the target area. Sub-seasonal heat wave events in the target area are predicted based on the predicted temperature anomaly field. The present invention utilizes the powerful data mining capabilities of convolutional neural networks to consider the interactions between multi-scale and multi-variable variables, fully considers the impact of interannual-scale signals on large-scale sub-seasonal variability, and explores the potential correlation between previous multi-scale signals and future temperatures.

The application provides a city surface area classification method and related equipment. The city surface area classification method comprises: obtaining a first target parameter of a to-be-determined city surface area, the first target parameter comprising a characteristic building height, an average building height, a building height standard deviation, a building coverage rate, an impervious surface ratio, a characteristic vegetation canopy height, an average vegetation canopy height, and a vegetation coverage rate of the to-be-determined city surface area; inputting an electronic map corresponding to the to-be-determined city surface area and the first target parameter into a city surface area classification model to obtain a city surface area type corresponding to the to-be-determined city surface area output by the city surface area classification model, the city surface area type being used for analysis or simulation of heat waves, air quality, and extreme weather of the to-be-determined city surface area. In the application, the fineness and determination accuracy of the city surface area type are improved.

This invention discloses a camera with defogging function, including a mounting housing. A rotating block is fixedly connected to one side of the mounting housing, and a mounting base connected to a wall is rotatably connected to the surface of the rotating block. A camera lens is mounted on the other side of the mounting housing. In low-temperature weather, the cooling fan inside the mounting housing is tilted towards the camera lens, so that when the cooling fan rotates, the heat generated by the circuit board is carried out through the ventilation holes and blown onto the surface of the camera lens through the end face of the protective cover, thereby heating the surface of the lens and preventing a large amount of fog from forming on the lens surface, which would affect the image captured by the camera lens. In hot weather, the cooling fan is tilted to the other side, thereby preventing the hot air generated by heat dissipation in high-temperature weather from blowing onto the surface of the camera lens and forming a heat wave, which would affect the image captured by the camera lens.

To create more coolness and shade, we offer an eco-friendly double fan made by screwing two fans together and unfolding them for use. [Solution] The device includes two overlapping fans: an upper fan 1, a lower fan 2, a small screw rod 3, a wing nut 4 for fastening fan 1 and the screw rod 3, a wing nut for tightening fan 1 and fan 2, a gasket, and a plastic pocket attached to the back of fan 2 for inserting and securing fan 1. By fastening the two overlapping fans together using the screw rod and wing nut and then spreading them out for use, more coolness and shade can be obtained, and it can greatly contribute to countermeasures against the recent heat waves in terms of ease of use and eco-friendliness.

The application discloses an extreme weather prediction and identification method based on multi-model integration and machine learning coupling, and belongs to the field of climate-adaptive building design and building environment engineering. The technical scheme comprises the following steps: taking the GCM data set of CMIP6 and the RCM data set of CORDEX as original data, combining ERA5 reanalysis data to perform deviation correction and time downscaling processing; constructing a prediction model integrating random forest algorithm and SSP-RCP multi-scenario framework, dividing training set and test set according to time sequence, and realizing multi-scale prediction after standardization and super parameter optimization; constructing an extreme weather identification system based on quantile threshold method, dividing heat wave and cold wave threshold, and quantifying the duration, intensity and severity thereof; and finally generating a high-granularity regional future extreme weather data set for 2024-2034. The application can provide high-precision and high-temporal and spatial resolution meteorological data, provide support for building simulation and climate-adaptive building design, and has good regional adaptability and promotion potential.

The invention belongs to the technical field of high-temperature heat wave risk analysis and evaluation, and relates to a high-temperature heat wave risk analysis and evaluation method, medium, equipment and system, and the method comprises the steps: collecting historical day-by-day highest temperature meteorological observation data, historical social economic statistical data, historical geographic space data and historical remote sensing product data of a research area; constructing a high-temperature heat wave disaster risk index system; constructing a high-temperature heat wave risk index model; and generating a high-temperature heat wave risk grade distribution diagram and a risk evolution trend diagram of each year in the research area. According to the method, the multi-dimensional data can be adopted to construct the high-temperature heat wave disaster risk index system, the high-temperature heat wave risk index model is constructed, the data type and the risk assessment dimension are increased, the constructed model is more standardized, the replicability of the assessment system is improved, and meanwhile, the assessment efficiency is improved. And generating a high-temperature heat wave risk grade distribution diagram and a risk evolution trend diagram of each year in the research area through a geographic information system platform, and carrying out visual display on the high-temperature heat wave risk grade distribution diagram and the risk evolution trend diagram.

The invention discloses an electrical load prediction model construction method for a summer high-temperature heat wave scene, and relates to the technical field of artificial intelligence and intelligent power utilization. The method comprises the following steps: acquiring summer electrical load data and meteorological characteristic data of a to-be-predicted area; carrying out data expansion by utilizing a T imeGAN network; training an electrical load prediction model by using the expanded data set, and executing the following operations in the model: inputting a time sequence formed by electrical load data and a plurality of meteorological feature data into a feature attention layer, learning attention weights among input features in the time sequence, and correcting the time sequence by using the attention weights; inputting the corrected time sequence into a Bi LSTM network and a time sequence attention layer, learning attention weights among time steps, and obtaining context features; and predicting the electrical load of the next time step of the time sequence by using the context features.

Disclosed in the present invention is a method for studying the interannual variability of marine heatwaves in the Southern Indian Ocean. The method includes: on the basis of acquired data, exploring the spatial distribution characteristics and linear variation trend characteristics of marine heatwaves in the Southern Indian Ocean; analyzing the relationship between the intensity of marine heatwaves in the Southern Indian Ocean and an El Niño-Southern Oscillation (ENSO) process; analyzing the spatial evolution of marine heatwaves and sea surface temperature anomalies in the Southern Indian Ocean during El Niño events; analyzing the energy contributions of the ocean and atmosphere to marine heatwave events during El Niño events, and exploring the physical mechanism of marine heatwaves in the Southern Indian Ocean; and determining whether marine heatwaves in the Southern Indian Ocean are affected by shortwave radiation and latent heat modulated by the ENSO process. The present invention discovers that during El Niño events, within a study area, low cloud cover decreases while high cloud cover increases, the mixed-layer depth reduces, and shortwave radiation penetrates the sea surface; moreover, northwesterly winds anomalously intensify while climatological southeasterly winds are suppressed, thereby reducing evaporation, decreasing latent heat energy loss, and thus promoting warming.

The application provides a kind of heat wave influence on global navigation satellite system positioning accuracy evaluation method and system, determine the research area and research year of target heat wave event, according to the corresponding climate state of research year;Obtain the temperature data of each meteorological station in the research area, interpolate and supplement the missing data among them;According to the data after interpolation and supplement, determine the research period of heat wave;Analyze the abnormal value spatial distribution of two-meter temperature above ground and daily average soil moisture in the research period of research area;Analyze the change of abnormal value of air temperature and atmospheric precipitable water per hour, and explain the reason of atmospheric precipitable water anomaly through the corresponding relationship of factors;Analyze the abnormal value change of ionospheric total electron content and air temperature per hour;Compare the difference of global navigation satellite system positioning accuracy in heat wave period and non-heat wave period in research period.The application provides a reference for users to carry out high-precision GNSS positioning during heat wave.

This invention provides a method and system for detecting community structure in spatially concurrent extreme climate events, comprising: collecting gridded temperature and precipitation data of the study area, extracting binary time series of heat waves and extreme precipitation events, and performing event synchronization determination calculations and significance tests; constructing a two-layer coupled heat wave-extreme precipitation network based on the inter-layer adjacency matrix; obtaining stable community classification results based on the two-layer coupled network using a community detection method combining the Leiden algorithm and consensus clustering; performing lead-lag correlation analysis on all community pairs to identify key community pairs with strong correlations and their lag times, and further exploring the spatiotemporal propagation characteristics of climate events. This invention fully utilizes the advantages of two-layer coupled climate network analysis, combining the Leiden algorithm and consensus clustering method to improve the accuracy and stability of community detection, and can effectively reveal the spatial concurrency characteristics and spatiotemporal propagation patterns of global extreme climate events.

This invention discloses a method for the quantification and unified identification of joint stress in daily-scale combined drought-heat wave events, comprising: acquiring daily-scale temperature data and land surface moisture data and performing time alignment and spatial consistency preprocessing; then standardizing and determining extreme stresses in the daily temperature data and land surface moisture data to obtain daily-scale single stress intensity indices; constructing daily-scale joint stress intensity indices based on the single stress intensity indices; performing multi-class discrete coding of daily stress states based on the single stress intensity indices; identifying combined drought-heat wave events and single stress events using a continuous run-length scan method; and integrating each identified combined drought-heat wave event and single stress event to construct a structured event record set. Based on this, this invention achieves fine differentiation between concurrent combined drought-heat wave events, continuous combined drought-heat wave events, and single stress events.

The invention discloses a method and system for calculating the influence intensity of high-temperature heat waves on dissolved oxygen in lakes, and belongs to the field of hydro meteorology and ecological environment. Comprising the following steps: integrating to obtain synchronous sample point pairs, and establishing a dissolved oxygen estimation model; calculating a climatic state 90% quantile temperature based on the reanalysis data, creating an air temperature and heat wave event identification condition, and identifying an air temperature and heat wave event by using the air temperature and heat wave event identification condition; acquiring an actual observation temperature in the occurrence time of the air temperature and heat wave event and a climate state average temperature in a corresponding reference year; the dissolved oxygen estimation model is used for calculating the dissolved oxygen concentration at the heat wave temperature and the dissolved oxygen concentration at the average temperature of the climate state, and the influence intensity of the high-temperature heat waves on the dissolved oxygen of the lake is obtained according to analysis of the dissolved oxygen concentration at the average temperature of the climate state and the dissolved oxygen concentration at the heat wave temperature. According to the method, various climate, water quality and geographical factors are comprehensively considered, and the comprehensive influence intensity of the high-temperature heat waves on the dissolved oxygen concentration of the water body is analyzed and evaluated through a systematic model.

The present invention discloses a multilayer material measurement method, device and storage medium, which are applied to the field of thermal property measurement. The multilayer material measurement method includes: obtaining a thermal signal, and controlling the thermal signal to pass through the multilayer material along a first direction, wherein the multilayer material is provided with a plurality of sublayers arranged in a stacked manner; obtaining the temperature-propagation depth change relationship and the temperature-time change relationship of the multilayer material in the first direction; calculating the thermal resistance-propagation depth change relationship according to the thermal signal, the temperature-propagation depth change relationship and the temperature-time change relationship; calculating the thermal wave transfer characteristic-propagation depth change relationship according to the temperature-propagation depth change relationship and the temperature-time change relationship; obtaining the interface thermal resistance of the multilayer material and the transfer characteristics of the thermal wave at the interface of the multilayer material according to the thermal resistance-propagation depth change relationship and the thermal wave transfer characteristic-propagation depth change relationship. The method enables users to better understand the relevant parameters of the multilayer material and facilitates the application of the multilayer material.

The invention relates to a composite heat wave dynamic risk prediction method, system and device and a medium. The method comprises the steps of obtaining time sequence observation data and prediction time lag of a target area, and performing preprocessing to obtain standardized grid point time sequence meteorological data; performing change feature extraction according to the standardized grid point time sequence meteorological data, and determining a dry and wet partition label of the target area according to the dryness index; performing feature splicing on the obtained dry and wet partition labels, daily potential evapotranspiration, daily standardized rainfall evapotranspiration indexes, threshold temperature, a multivariable change feature matrix and grid point static features to obtain feature tensors; inputting the feature tensor into a pre-trained composite heat wave risk prediction model to obtain a prediction result; and on the basis of the physical constraint base line, probability calibration, physical consistency filtering and risk grading are sequentially carried out on the prediction result to obtain a risk probability thermodynamic diagram. By adopting the method, a more accurate dynamic risk prediction result can be generated according to the composite heat wave evolution difference of different dry and wet zones.

The invention relates to the technical field of ocean observation, in particular to an ocean bottom layer heat wave prediction method and system based on a generative adversarial network and computer device.The method comprises the steps that a data set is established, satellite remote sensing data and reanalysis data of the ocean surface are preprocessed based on longitude and latitude grids, and a multi-dimensional data set is constructed; and a bottom layer heat wave prediction step: based on a pre-trained heat wave prediction model, feature extraction is performed on the multi-dimensional data set, the heat wave prediction model outputs a prediction result by using a generator, and the generator is obtained by training confrontation based on historical heat wave phenomenon data of the ocean bottom layer and a discriminator. The method can effectively predict ocean bottom layer heat waves, thus improving deep sea heat wave phenomenon perception capability and response efficiency.

The invention discloses a distributed power supply planning method and device for a power distribution network, terminal equipment and a storage medium, and the method comprises the steps: building a first target function with the minimization of the regional overheating risks of all power distribution regions as a target through considering the regional overheating risks caused by the power failure of the power distribution regions; and solving the first objective function and the second objective function under a first constraint condition that the regional overheating risk of each power distribution region cannot exceed a preset risk threshold value and a second constraint condition used for constraining normal operation of the power distribution network by using the first objective function and the second objective function with the minimization of the distributed power supply planning cost as a target. According to the generated planning scheme, the power utilization experience of users in each power distribution area in extreme hot wave weather is considered, and the installation cost and the power failure cost of the distributed power supply and the operation stability of the power distribution network are also considered.

Methods and systems for determining the AccuWeather HeatWave Counter and Severity Index are discussed. The AccuWeather HeatWave Counter and Severity Index is intended to be used to understand, present, and predict the severity of HeatWaves, issue public safety announcements, and support policy decisions related to climate change. In particular, the methods and systems of the invention are effective for monitoring periods of excessive heat and generating warnings and recommendations during the HeatWaves to reduce temperature-related health risks to people and domestic animals and impacts on temperature-sensitive products.

The invention provides a causal analysis method and system for ocean heat wave events and chlorophyll changes, which are applied to the technical field of ocean environment monitoring, and are characterized in that a multi-dimensional causal feature matrix is constructed through multi-source long-time sequence environmental data in combination with time-space identifiers and abnormal change sequences of the heat wave events, and the causal analysis result of the heat wave events and chlorophyll changes is obtained through joint probability distribution expression. And performing decomposition calculation on the causal information of the heat wave accumulated intensity characteristic and the dynamic environment element characteristic relative to the chlorophyll abnormal change to obtain multiple types of causal information components, judging the dominant action type of the chlorophyll change according to the proportion of the multiple types of causal information components, and outputting a corresponding causal attribution result. According to the causal analysis method and system for the ocean heat wave event and the chlorophyll change, the causal relationship of the ocean heat wave event to the chlorophyll concentration change of the phytoplankton is analyzed through construction of a multi-dimensional causal feature matrix, joint probability distribution mapping and causal information decoupling.

This invention relates to the field of industrial measurement and control, and more particularly to a wireless sensing system for the temperature field of a roasting furnace. The system includes a wireless sensing node module, an adaptive sampling control module, and a temperature field reconstruction module. The wireless sensing node module acquires real-time temperature data with spatial location information; the adaptive sampling control module calculates a heat wave sensitivity factor based on the real-time temperature, determines the adaptive sampling period by combining the heat wave propagation tendency of neighboring nodes, and collects key temperature data according to the adaptive sampling period; the reconstruction module receives the data and generates an isothermal surface cloud map through three-dimensional inverse distance weighted interpolation, completing the three-dimensional visualization of the temperature field and anomaly early warning. This invention adaptively adjusts the sampling period by using the heat wave sensitivity factor and heat wave propagation tendency, and accurately reconstructs the temperature field using a three-dimensional mesh and high-density interpolation, avoiding the distortion and high power consumption problems of fixed sampling, and improving the accuracy of temperature field reconstruction.