19 results about "Climate system" patented technology

The application discloses a filtering method in marine climate research and application thereof, and comprises the following steps: (1) Fourier transform is conducted on time or space data x to obtain results in a frequency domain or a wave number domain; (2) a function is used to conduct a truncation processing on the frequency domain or the wave number domain, and low-pass and high-pass filtering frequency domain results are obtained respectively; (3) energy of the low-pass and high-pass filtering results is calculated and analyzed; (4) inverse Fourier transform is conducted on the low-pass filtering frequency domain or wave number domain result, and a large-scale characteristic distribution in the time or space domain can be obtained; (5) when the filtering result meets a filtering quality index in the frequency or wave number domain obtained in the step (3) by 20%, the function parameter in the step (2) belongs to a range of function parameters suitable for scale division; and (6) the filtering result is brought into a marine climate system of the earth to improve accuracy. The application aims to provide a filtering method and application for judging and improving scale division accuracy in marine climate research.

Carbon dioxide air conditioning system comprising a cooling device (2) with a refrigerant circuit (3), wherein carbon dioxide (K) is incorporated as a refrigerant in the refrigerant circuit (3) and wherein the refrigerant circuit (3) comprises means for evaporating and condensing the carbon dioxide (K).

A climate-controlled freight container (10) comprises a casing (12), enclosing a cargo compartment (20) and a control compartment (26). The container further comprises a climate system (30) that has at least two climate modules (40) and an air distribution arrangement (21), configured to distribute air from the climate system into the cargo compartment and. A central climate-control unit (52) is configured for collection of measurements associated with climate conditions of the cargo compartment and for controlling the climate system. Each of the climate modules is configured for adapting climate properties of air flowing through the climate modules from a return air plenum (36) to a supply air plenum (34). Each of the at least two climate modules comprises a local climate-control unit (46), configured for controlling an operation of the climate module. The central climate-control unit is configured for providing operational instructions to the local climate-control units.

The invention discloses a method for analyzing long-term response of ozone in the Antarctic stratosphere to high-energy particle sedimentation, which comprises the following specific steps: firstly, acquiring vertical distribution characteristics of ozone from the Antarctic stratosphere to the middle layer from January to December in 30 years, and drawing a historical vertical distribution diagram; compiling a special method technical scheme for analyzing the long-term response of the ozone in the Antarctic stratosphere to the sedimentation of the high-energy particles, and carrying out technical disclosure to workers; then, the geomagnetic index is adopted as an agent index of high-energy particle sedimentation, a unary linear regression model of the southern hemisphere winter atmospheric variable is established, and the regression model is shown in the specification. The process that high-energy particles are settled and generated and conveyed to the stratosphere through polar region vortex and the ozone loss chemical process of the high-energy particles can be quantitatively analyzed, and the potential influence of extreme space weather events on an earth climate system can be conveniently evaluated.

The application relates to the technical field of data processing, and discloses a hydrological climate element history sequence reconstruction method based on multi-layer nested coupling, which comprises the following steps: unifying the time resolution of multi-source substitute data and observation sequences; performing adaptive noise complete ensemble empirical mode decomposition, recombining to generate high-frequency layer and low-frequency layer control sequences; calculating cross wavelet power spectrum, extracting instantaneous phase difference sequences and wavelet square coherence sequences; constructing a nested coupling regression benchmark model, calculating dynamic high-low frequency regression coefficients and generating a two-dimensional system lookup table; using the two-dimensional system lookup table to obtain regression coefficients of a history period, combining with coherent logic gate control sequences generated by the wavelet square coherence sequences to process distorted data, and outputting reconstructed sequences. The application isolates the interference of signals of different frequency bands, adapts to the time-varying properties of climate system parameters, and improves the reconstruction quality of long-term trends and short-term fluctuations.

This invention discloses a climate prediction method and apparatus based on observation constraints and a multi-time-dependent sliding window. The method includes: acquiring the first climate data for the current and historical years predicted by a specified climate system model; for each month in the historical year: using the first climate data of that month and a predetermined number of previous months as input features, and the actual climate data of that month as labels, generating sample pairs for that month and adding them to a training sample library; training a pre-constructed climate prediction model using the training sample library with the objective of minimizing the joint loss, the joint loss including the root mean square error, spatial anomaly correlation coefficient, and prediction score characterizing the model's prediction performance; for any month to be predicted in the current year: inputting the first climate data of that month and a predetermined number of previous months into the trained climate prediction model, and outputting the climate prediction result for that month. This invention improves the reliability and fine spatiotemporal resolution of climate prediction.

The invention relates to the technical field of photovoltaic generating capacity prediction. The invention provides a regional photovoltaic annual generating capacity prediction method and system fused with a climate change trend. The method comprises the following steps: constructing and preprocessing a multi-source data set including historical power generation, historical meteorological observation, climate system mode output and photovoltaic module performance parameters; extracting trend components and volatility components of climate key factors in a future year, and generating a multi-path climate change scene; establishing a component performance attenuation-climatic factor dynamic response model, and outputting a photovoltaic component equivalent power generation efficiency sequence considering a climatic change cumulative effect; based on photovoltaic power station space distribution and landform data, a region-level power generation capacity space-time aggregation model is constructed, and through scale conversion and probability aggregation, a prediction result and a confidence interval of photovoltaic annual power generation capacity of a target region are output. The problem that the long-term prediction deviation is large due to the fact that an existing photovoltaic generating capacity prediction method does not fully consider the future climate change trend influence is solved.

This invention, based on spatiotemporal analysis and deep learning, proposes a joint downscaling method for multiple oceanic elements, belonging to the field of meteorological and climate prediction and data processing technology. It includes: S1, data preparation and preprocessing; S2, multivariate empirical mode decomposition (MEOF); ​​S3, constructing an SRGAN model capable of learning spatial mode mapping relationships at different resolutions; and S4, generation and reconstruction of high-resolution ocean fields. This invention obtains joint spatial modes and time series through multivariate empirical mode decomposition. The model's learning of the joint spatial modes maintains the inherent spatiotemporal coupling relationships between multiple variables. It significantly improves the detail restoration capability of the downscaling results. In adversarial training, the model can generate more realistic and detailed high-resolution spatial structures, effectively capturing complex nonlinear processes in the climate system. It is highly efficient and flexible; after model training, it can quickly downscale long-term, multi-scenario low-resolution data, greatly saving computational resources.

Disclosed are systems and methods that provide a novel framework for a secure, geofenced-based control and management of a thermostat and / or an associated HV AC / baseboard climate system. Activities related to a status, cunent operation mode, temperature control and / or operation / temperature changes can be based on detected user presence / occupancy within a geofenced area that corresponds to the climate system. The disclosed framework can further or alternatively base control and / or management of the thermostat based on whether operational values (e.g., temperatures, for example) conelate with particular deviations from operational settings (e.g., setpoints, for example). Thus, the disclosed framework can enable an automated and / or dynamic operational control of a climate system based on geofenced and current and / or requested operational values.

The application provides a system modeling method and device based on multi-time scale characteristics, equipment and medium, and relates to the field of data processing. The method comprises the following steps: determining a target system to be modeled and a target parameter in the target system, the target system (for example, a climate system) being used to predict the state value of the target parameter at any time; obtaining sample trajectory data of the target parameter changing over time; obtaining feature representations of the target parameter on multiple different time scales according to the sample trajectory data; determining initial values of slow variables and initial values of fast variables corresponding to the target parameter according to the multiple feature representations; and training the target system according to the initial values of the slow variables, the initial values of the fast variables, a neural ordinary differential equation corresponding to the slow variables, and a neural ordinary differential equation corresponding to the fast variables. The application can model a complex system (the target system) with significant multi-time scale characteristics, so that the target system can accurately predict the state of the system.

1. The name of the design product: climate system simulation comprehensive test bench. 2. The use of the design product: for temperature, humidity, low pressure, vibration coupling environment reliability test system. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view. 5. The bottom of the design product is not easy to see or not visible during use, omit the top view.

A closed-loop climate system including a a water tank; cooling pads positioned above the water tank, a thermoelectric unit (TEU) connectable to a power supply, and configured to cool / heat water received from the water tank, a sprinkler tube positioned above the cooling pads and configured to sprinkle the water heated by the TEU onto the cooling pads.

A closed-loop climate system including a water tank; cooling pads positioned above the water tank, a thermoelectric unit (TEU) connectable to a power supply, and configured to cool / heat water received from the water tank, a sprinkler tube positioned above the cooling pads and configured to sprinkle the water heated by the TEU onto the cooling pads.

The invention discloses a dynamic game modeling method for a regional environment-climate-economy system, and relates to the technical field of environment-climate-economy coupling modeling. According to the method, a dynamic game model is constructed, Nash equilibrium is solved, the emission reduction effects of regional cooperation and non-cooperation games are quantitatively compared, the optimal path of carbon emission reduction and pollution emission reduction of each region is described, an economic module, a climate module and an environment module are included, and the pollution reduction and carbon reduction synergistic effect and the like are considered. Under the cooperation strategy, all regions cooperate, and the welfare maximization of the whole society is pursued. And under the non-cooperative strategy, the regions do not cooperate, and the social welfare maximization of each region is pursued. The model comprises five sub-models which interact with each other. CO2 and pollutants are generated at the same time through economic activities in all regions; cO2 is discharged to a climate system, and global temperature rise and income loss are caused; pollutants are discharged to an environmental system for regional diffusion and transfer, heterogeneity influence is caused on health of people, and then labor loss is caused; meanwhile, economic cost is generated by carbon emission reduction and pollution emission reduction measures in each region; finally, climate damage, health damage, carbon emission reduction cost and pollution emission reduction cost jointly act on an economic system; and emission synergy and cost synergy of CO2 and pollutants are also considered, so that global carbon emission and pollutant emission of each region under a cooperative situation and a non-cooperative situation can be accurately predicted.

The invention discloses a climate prediction method and equipment based on observation constraint and a multi-aging sliding window. The method comprises the following steps: acquiring first climate data of current and historical years predicted by a specified climate system model; for each month in a historical year, taking the first climate data of the month and a preset number of months as an input feature, taking the actual climate data of the month as a label, generating a sample pair of the month, and putting the sample pair into a training sample library; using the training sample library to train a pre-constructed climate prediction model with minimum joint loss as a target, the joint loss including a root-mean-square error representing model prediction performance, a spatial anomaly correlation coefficient and a prediction score; and for any to-be-predicted month in the current year, inputting the first climate data of the to-be-predicted month and a set number of previous months into the trained climate prediction model, and outputting a climate prediction result of the to-be-predicted month. According to the invention, the reliability and fine temporal-spatial resolution of climate prediction are improved.

The invention discloses a rainfall dynamic distribution modeling method and system. The method comprises the following steps: S1, grading rainfall according to magnitude; s2, respectively identifying climate driving factors for different grades of rainfall; s3, establishing a time-varying cumulative distribution function of different levels of rainfall and climate driving factor sequences; and S4, based on the time-varying cumulative distribution function, establishing a deep learning fusion model, and predicting future rainfall. According to the method, the credibility of climate system prediction factors can be improved, different levels of rainfall climate driving factors and response rules thereof are disclosed, and the rainfall prediction capability and accuracy are greatly improved.

A closed-loop climate system including a water tank; cooling pads positioned above the water tank, a thermoelectric unit (TEU) connectable to a power supply, and configured to cool / heat water received from the water tank, a sprinkler tube positioned above the cooling pads and configured to sprinkle the water heated by the TEU onto the cooling pads.

Disclosed are systems and methods that provide a novel framework for automatically and dynamically controlling and managing a climate system at a location based on past, present and / or predicted spatial, temporal, logical and / or user / device data at the location. The disclosed framework can maximize the capabilities of an implemented control system (e.g., climate and / or security) to leverage AI / ML and / or LLM predictions via novel mechanisms to understand the current and / or future needs of a user(s) within such location. The framework can enable automated notifications and / or responses to control a current environment of a location, as well as the desired environment of a user at and / or within the location, such that framework can provide, in concert with the control system at the location, a dynamically adaptive, automated system that can leverage generative software to control how climate and / or security systems control an environment tied to the comfort and protection of a locations' occupants.

Climate control system for a vehicle (1, 1'), comprising: - a climate bus (41); - an air conditioning system (30), in particular a heater (30); - a control element (2) for setting a target temperature value of the air conditioning system (30); - a gateway device (60) which is connected to the climate control device (30) via the climate bus (41), where The gateway device (60) is configured to communicate as a master on the climate bus (41) and includes a control device (66) for controlling the climate control device (30), and the gateway device (60) generates control commands that are sent via the climate bus (41) to the climate control device (30) in order to control the climate control device (30) in such a way that the temperature in the vehicle interior corresponds to the set temperature value.