183 results about "Extreme climate" patented technology

The invention relates to a groundwater safety assessment method under an extreme climate event, which comprises the following steps: collecting multi-source heterogeneous data such as meteorological data, geological data, hydrological data and remote sensing data, and constructing a unified groundwater safety knowledge graph through standardized cleaning, semantic alignment and deletion completion; monitoring an extreme climate event in real time, and updating a node relation weight and sparsifying a transmission path based on knowledge graph dynamic evolution and a time sequence attention mechanism; performing risk propagation path reasoning on the dynamic knowledge graph in combination with an improved graph neural network, identifying key pollution nodes, and outputting a structured risk level and a coping suggestion; the system continuously optimizes atlas and model parameters based on evolution feedback, and high adaptability and reasoning precision of emergency response are achieved. According to the method, the intelligence, the real-time performance and the accuracy of underground water risk assessment are improved. The problems that the underground water pollution propagation path is difficult to dynamically identify and the decision adaptability is insufficient under extreme climate events are solved.

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

The invention discloses a wind power plant micro-siting assessment method based on spatial analysis and climate adaptability, relates to the technical field of wind power plant siting, and solves the problems that in the prior art, spatial analysis depends on static topographic data, climate adaptability assessment uses dynamic meteorological data, and the static topographic data and the dynamic meteorological data lack a space-time synchronization mechanism; and a prediction result has a large deviation. The method comprises the following steps: firstly, acquiring multi-source data and fusing to realize time-space synchronization of the data; then, constructing and solving an objective function to obtain a candidate point set, and screening out an optimized point distribution scheme of which the prevailing wind direction coverage is greater than a set threshold value; and finally, inputting prediction data of various extreme climates into the cost-per-kilowatt-hour model, introducing extreme climate toughness parameters into calculation to carry out climate adaptability data correction, and calculating return on investment cycles under different carbon emission scenes. Through the steps, the limitation that only static wind resources are concerned on traditional site selection is broken through, and combined evaluation of the regional climate adaptability and the complex terrain adaptability is achieved.

The invention provides a method and a system for improving the self-healing capability of a highway micro-grid in extreme weather, and relates to the technical field of power system automation. The method comprises the following steps: deploying a sensor to collect real-time operation data, and combining meteorological data to generate an integrated system state data set through fusion processing; the digital twin engine generates system state prediction data through state estimation and dynamic simulation; the random multi-objective optimization model solves and generates a preventive scheduling instruction according to the above, and controls a power generation unit, an energy storage unit and a load unit to adjust operation points; fault detection and identification are carried out through residual analysis, a self-healing control instruction sequence is generated by using a sequence decision model, the switch equipment is controlled to isolate faults and restore power supply, closed-loop management from prediction and scheduling to self-healing is realized, and the power supply reliability and rapid recovery capability of the highway microgrid in extreme climate are improved.

This invention proposes a method and system for optimizing water resource allocation, which relates to the technical field of water resource optimization. The method includes first obtaining historical hydrological data for a study area, then inputting the data into a preset river runoff prediction model, which then outputs a monthly runoff sequence for a specific year within the region. Based on the monthly runoff sequence, a multi-conditional constrained water resource optimization allocation model based on user allocation is established for the study area under extreme climate conditions. Finally, the multi-conditional constrained water resource optimization allocation model is solved to obtain a solution result, and water resource optimization is performed for users in the study area based on the solution result. This invention effectively achieves accurate and reasonable optimal allocation of water resources to users under multi-objective constraints.

The invention discloses an extreme high-temperature scene multi-energy complementary optimization scheduling method considering uncertainty, which mainly comprises the following steps of: 1, fusing bidirectional time convolution, a bidirectional long-short-term memory network, an attention mechanism and a quantile regression forest, realizing high-precision prediction and uncertainty modeling of wind speed, solar irradiation and load, and constructing a typical day scene set; 2, constructing a two-stage scheduling model fusing epsilon-constraint multi-objective optimization and opportunity constraint mixed integer programming, optimizing and adjusting margin day ahead, and rolling and correcting a scheduling path within the day; 3, three physical correction mechanisms of wind power air density correction, photovoltaic temperature response and hydroelectric evaporation-water level coupling are provided for extreme high-temperature disturbance; and 4, a prediction-optimization-feedback-correction closed-loop process is integrated and constructed, and the stability and response toughness of the system under extreme climate are improved. The method has the beneficial effect that the prediction precision, the scheduling flexibility and the operation toughness of the system under the extreme climate are remarkably improved.

The invention discloses a comprehensive evaluation method for a comprehensive energy system, and the method specifically comprises the following steps: 1, constructing a multi-dimensional index system: employing a hierarchical structure to construct four types of indexes including technology, economy, environment and reliability, and refining each type of indexes into at least three secondary indexes; and 2, dynamic weight adaptive distribution: adopting an improved entropy weight and game theory combined weighting method based on real-time data, such as electricity price fluctuation, weather change and load demand, and preset scenes, such as extreme climate and policy adjustment, and relates to the technical field of comprehensive energy system evaluation. According to the comprehensive evaluation method for the comprehensive energy system, correlation interference among indexes is effectively eliminated through an improved entropy weight and game theory combined weighting method, and the entropy value is dynamically updated by introducing multiple data information, so that weight calculation can adapt to the time sequence change characteristic of the energy system, and the comprehensive evaluation accuracy is improved. And meanwhile, the subjective and objective weight collaborative equalization is realized by constructing a Nash equilibrium game model.

The invention discloses a single-phase immersion battery intelligent thermal management control method and device for an energy storage power station, and relates to the technical field of battery thermal control. The intelligent thermal management control method and device for the single-phase immersed battery for the energy storage power station comprises the steps that S1, battery state data and external environment data in the operation process of the energy storage power station are collected and preprocessed, and a standardized thermal state data set is constructed; s2, performing working condition evaluation on the current working condition state by combining the battery heat flow density, and dynamically adjusting the flow direction of the cooling liquid; s3, the current heat exchange load and the phase state safety boundary are comprehensively analyzed, and the valve opening degree is driven to change; and S4, carrying out stability evaluation on the local thermal stability, and adjusting a power supply loop safety response strategy in real time. The problems that a traditional heat management system lacks the bidirectional regulation and control capacity for coping with wide-temperature-range extreme climate and cannot give consideration to the collaborative requirements of high-temperature heat dissipation and low-temperature heat supply, and consequently system structure redundancy, energy consumption increase and operation and maintenance complexity are caused are solved.

The invention provides a flood control dam surface crack intelligent detection and early warning method based on AI image recognition, and the method comprises the following steps: obtaining multi-dimensional data of a flood control dam, and constructing a basic data set according to the obtained multi-dimensional data; constructing a preprocessing model according to the basic data set, and preprocessing the multi-dimensional data according to the constructed preprocessing model; constructing an intelligent detection and early warning model according to the preprocessed data, and carrying out detection and early warning decision multi-scene adaptive detection on the surface cracks of the flood control dam according to the constructed intelligent detection and early warning model; constructing a region-material-climate adaptive model cluster through federal learning and meta learning; the detection bottleneck of a traditional algorithm on special regions, extreme climates and special material dams is broken through, the cross-scene detection precision is improved by more than 30%, and the risk of missing detection and false detection is reduced.

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 the technical field of cables, and provides a high-weather-resistance power cable. The high-weather-resistant power cable comprises a cable core and an outer sheath, the outer sheath is prepared from the following raw materials in parts by weight: 40 to 50 parts of low-density polyethylene, 25 to 35 parts of ethylene-vinyl acetate copolymer, 20 to 30 parts of ethylene propylene diene monomer, 10 to 12 parts of maleic anhydride grafted ethylene-vinyl acetate copolymer, 16 to 20 parts of ZrO2 coated modified hydrotalcite, 15 to 20 parts of magnesium hydroxide, 4 to 6 parts of silane coupling agent, 2 to 3 parts of dicumyl peroxide and 3 to 5 parts of zinc oxide; 6 to 10 parts of conductive carbon black, 0.8 to 1 part of antioxidant and 1 to 1.5 parts of zinc stearate. According to the cable prepared in the invention, the protection performance, the service life and the safety of an existing high-weather-resistance power cable under extreme weather conditions are improved, and the mechanical property of the cable is also improved.

The invention discloses a regional wind speed meteorological reference station representativeness evaluation method based on a correlation function field, a medium and a program product. The regional wind speed meteorological reference station representativeness evaluation method comprises the following steps: collecting multi-year day-by-day wind speed data of a high-density meteorological observation station in a target region and environment information of the observation point; selecting an independent sample of day-by-day wind speed data in each same time period in the region; establishing a correlation function field between every two stations, and fitting a relation model of correlation functions and distances; estimating a standardized observation random mean square error; calculating the relative standard deviation generated by replacing the target point wind speed truth value data with the reference station wind speed observation data; and evaluating the excellent grade of the spatial distance range represented by the wind speed of the reference station. The method can achieve the good representative evaluation of the reference station in the distance range, and has important practical values for the selection of a reliable meteorological reference station in the climate feasibility demonstration of wind power development site selection, urban and rural construction planning and the like, the evaluation of extreme climate risks and the calculation of the accuracy of engineering meteorological parameters.

The invention discloses an air-ground cooperative photovoltaic panel intelligent snow removal system, and relates to the technical field of intelligent control. A central intelligent scheduling management subsystem is used for receiving operation and maintenance requirements of a photovoltaic power station, planning a routing inspection path, receiving a routing inspection result, analyzing the routing inspection result, and generating an optimal snow removal scheme; the parking station unmanned aerial vehicle subsystem is used for collecting surface data of the photovoltaic panel according to the inspection path and analyzing the surface data of the photovoltaic panel by adopting an accumulated snow automatic identification model to obtain an inspection result; and the ground operation vehicle subsystem is used for cleaning the snow accumulation area according to the optimal snow removal scheme and uploading a cleaning result to the central intelligent scheduling management subsystem. The problems that an existing photovoltaic panel snow removal technology is low in efficiency, poor in adaptability and lack of air-ground cooperative linkage are solved, whole-process intelligentization of routing inspection, diagnosis, sweeping and dispatching is achieved, and the operation and maintenance efficiency and power generation stability of a photovoltaic power station under extreme climate and complex terrain are improved.

The invention is suitable for the crossing field of hydraulic engineering and climate science, and provides a risk evolution-based flood control benefit analysis method for a reservoir group to cope with extreme climate, and the method comprises the following steps: screening extreme climate elements through multi-source data fusion, and constructing a spatial-temporal feature model; extracting extreme climatic elements, inputting the extreme climatic elements into the pre-constructed hydrological model, simulating to obtain an initial flood process, constructing a flood database, training and adjusting hydrological model parameters, and inputting the extreme climatic elements again to obtain an ultimate flood process; flood risk data are collected, a risk assessment index system and a dynamic risk simulation model are constructed, an ultimate flood process is input to calculate flood control risks, and a multi-objective optimization algorithm is adopted to generate an optimal scheduling strategy; according to the method, a reliable basis is provided for scientific scheduling and flood control decision making of the reservoir group through innovative data acquisition and analysis means, advanced model construction and a multi-dimensional evaluation system, and the flood control benefit of the reservoir group under the extreme climate condition is improved to the maximum extent.

The invention discloses a watershed composite extreme climate event comprehensive identification method, device and equipment, and relates to the technical field of climate intelligent identification processing, and the method comprises the steps: obtaining weather and geographic data related to climate event identification; identifying the meteorological and geographic data based on a preset identification algorithm to obtain different types of single extreme climate events; identifying the single extreme climate event according to a preset time-space correlation algorithm to obtain different types of composite extreme climate events; and carrying out spatial-temporal distribution characteristic analysis and long-term change trend analysis on the identified composite extreme climate event, and outputting an analysis result. According to the invention, the accuracy and pertinence of composite extreme climate event identification can be effectively improved.

The invention provides a high-resolution forest ecological safety evaluation method, which comprises the following steps: S1, acquiring and preprocessing multi-source high-resolution spatial data; three criterion layers are systematically calculated: 1) sensitivity, including preparation of a climate sensitivity index for quantifying the response intensity of vegetation to climate fluctuation and preparation of a human footprint response index for quantifying the response intensity of vegetation to a plurality of human footprint subclass indexes; 2) exposure degree, wherein a standardized extreme climate exposure index and a human footprint index are integrated; and 3) toughness: evaluating the recovery capability of the forest ecosystem by adopting a first-order time sequence autocorrelation coefficient based on a critical deceleration theory. According to the method, the weight of each index is determined by adopting a subjective and objective weighting method, and a refined forest ecological safety index spatial distribution diagram is generated through a comprehensive evaluation model, so that refined, dynamic and objective evaluation of forest ecological safety is realized, and scientific decision and support are provided for accurate forest management and risk early warning.

The invention discloses multi-parameter test analysis equipment for a complex environment, which belongs to the technical field of chemical or physical test analysis and comprises a test cabin, a comprehensive measurement and control system, a vacuum pumping system, a field environment simulation system and a climate temperature and humidity simulation system. The vacuum pumping system, the field environment simulation system and the climate temperature and humidity simulation system are sequentially arranged on the outer side of the test cabin body and are communicated with the test cabin body. By changing and adjusting the air pressure, the temperature, the humidity, the wind, the ultraviolet radiation, the space range and the air density in the specified range in the test cabin body, the extreme climate environment of any single parameter can be simulated, the special environment of combination, alternation and intersection of multiple parameters can also be simulated, and different types of test pieces can be made to have different functions. Test detection operation under a traditional atmospheric state, atrocious weather and an extreme environment of an outdoor environment is completed in the same space, so that multi-parameter data of different types of test pieces can be obtained.

According to the reservoir intelligent prediction system and method based on multi-modal space-time fusion, satellite remote sensing (NDVI, LST), radar monitoring (PI) and traditional climate parameters are integrated, and a multi-modal space-time feature fusion system of 15-dimensional feature vectors is constructed; a space-time attention mechanism is introduced, and key element association capture is enhanced based on mutual information and Euclidean distance dynamic weighting features; designing an event-driven extreme climate online learning framework, and shortening a model updating period through an SGD algorithm; and a cross-basin meta-learning migration framework is constructed, and efficient adaptation of a new basin is realized by utilizing meta-training and single-step fine tuning. The system comprises a training stage (feature fusion, network training, online learning and meta-learning migration) and an operation stage (data real-time access, dual-mode prediction and decision support), the prediction error of the water storage capacity can be controlled within 5%, and the intelligent level of scenes such as dynamic regulation and control of water resources, flood control and disaster reduction is remarkably improved.

The invention provides a polar region photovoltaic module snow melting system, a snow melting control method and a polar region photovoltaic module, and belongs to the technical field of photovoltaic equipment. Comprising a snow melting unit arranged on the back surface of the photovoltaic panel, and the snow melting unit is suitable for melting accumulated snow on the photovoltaic panel through heating; the sensing unit is arranged on the photovoltaic panel and is suitable for acquiring a pressure value and a light transmission value of the front surface of the photovoltaic panel; and the signal processing unit is suitable for acquiring the pressure value and the light transmission value, and controlling the snow melting operation of the snow melting unit based on the pressure value and the light transmission value. Efficient and automatic snow melting of the photovoltaic module under the extreme weather condition can be realized, and the stability and continuity of power supply are ensured.

The invention provides a power transmission and transformation project concrete mix proportion optimization method considering drought and cold region constraints, and relates to the technical field of design optimization. Key parameters are randomly allocated, and a time partition concept is adopted, so that the flowability and the hardening performance are respectively optimized in a concrete generation stage and a concrete hardening stage; through double screening of a fluidity index vector and a hardening index vector, the method ensures that the concrete has excellent resistance in a freeze-thaw cycle, and the service life of the structure is prolonged; the time position of the minimum freezing and thawing cycle number is selected through a simulation experiment, the defect that freezing and thawing influences are neglected in a traditional method is effectively overcome, the durability and reliability of concrete under the extreme weather condition are remarkably improved, and the optimization method can effectively reduce material waste and construction cost and is suitable for popularization and application. An economical and efficient solution is provided for concrete construction in the drought and cold areas, and the method has wide application prospects.

The invention discloses a watershed ecological restoration method and a comprehensive improvement method, and relates to the technical field of watershed ecological environment treatment. The method comprises the steps that a drainage basin digital twinborn model is constructed, an ecological scheduling algorithm is embedded, and collaborative distribution of ecological water demand and production water is achieved; a climate self-adaptive composite ecological buffer zone is constructed, and the pollution interception and vegetation survival ability under the extreme climate is improved; biomass and low-pollution bottom mud in the drainage basin are subjected to resourceful treatment, the remediation cost is reduced, and secondary pollution is reduced; local functional florae are screened, and a slow-release microbial inoculum is prepared and added by adopting a composite embedding technology, so that the removal effect of refractory organic matters is enhanced; and multi-technology closed-loop regulation and control are realized through a digital twin model, and the repair process is dynamically optimized. Through multi-technology collaborative integration, the limitation of traditional fragmentation treatment is broken through, the ecological restoration effect and treatment economy of the drainage basin are remarkably improved, and the method is suitable for complex treatment scenes such as agricultural branch drainage basins.

The utility model relates to the technical field of conveying pipeline protection, and discloses a pipeline external protection mechanism in an extreme environment, which comprises a pipeline, two groups of mounting flanges and a protection component. According to the pipeline external protection mechanism in the extreme environment, the outer side of the pipeline is sleeved with a set of sleeves, the two ends of the sleeves achieve sealing of the contact portions of the pipeline and the sleeves through sealing assemblies, temperature loss caused by air convection is reduced, meanwhile, through the multiple sets of annular pipes in the pipeline and argon filled in the annular pipes and the heat preservation cavity, the temperature loss caused by air convection is reduced; meanwhile, through supporting and shaping of the annular pipe, it is guaranteed that the whole sleeve is not prone to deformation, the annular groove formed in the outer side of the sleeve can guide rain and snow accumulated at the top end of the sleeve to flow downwards, the rain and snow are prevented from being accumulated at the top of the pipeline, and the service life of the sleeve is prolonged. And therefore, the safe use performance of the whole pipeline under extreme climate conditions is improved.

The invention discloses a multi-lake system low-carbon-ecology collaborative scheduling method and system adapting to extreme climate. The method comprises the following steps: constructing a future extreme climate scene set; constructing an urban-natural drainage basin coupling hydrological model, and obtaining multiple groups of lake inflow runoff sequences driven by various future extreme climate scenes according to the urban-natural drainage basin coupling hydrological model; taking minimization of annual total diversion carbon emission as a single target, and constructing a low-carbon-ecological collaborative optimization model according to a future extreme climate scene set and an urban-natural drainage basin coupling hydrological model; inputting each group of into-lake runoff sequences into a low-carbon-ecological collaborative optimization model for solving to obtain a plurality of groups of candidate scheduling schemes; and screening the candidate scheduling schemes to obtain an optimal scheduling scheme. The method can fully consider the uncertainty of future climate, improves the reliability of a scheduling scheme, solves the problem of difficulty in decision making of a multi-target model, and can be widely applied to the technical field of water resource scheduling.

The invention relates to the technical field of wind power generation equipment maintenance, in particular to a dynamic deicing system for a fan blade, which comprises an impact device for generating impact waves to an icing area of the fan blade, a monitoring device and a control device. Wherein the control device is used for calculating target impact energy required for deicing according to the ice layer thickness, the ice layer environment temperature and the crack value monitored by the monitoring device, and controlling the impact device to generate impact waves to an icing area for deicing based on the target impact energy; and calculating new impact energy of the impact device when the icing area meets a preset secondary impact condition, and controlling the impact device to perform secondary impact according to the new impact energy. Therefore, the problems that a traditional physical deicing technology is low in energy transmission efficiency, thick ice is difficult to remove, and the icing position positioning precision is insufficient are solved, the ice layer is subjected to chain fracture by accurately controlling the impact position, energy and the time sequence, and the method is suitable for efficient deicing of the large wind generating set under the extreme weather conditions of freezing rain, wet snow and the like.

This invention discloses an adaptive flight control system for unmanned aerial vehicles (UAVs) in complex terrain and extreme weather conditions, belonging to the field of UAV adaptive flight control technology. This adaptive flight control system for UAVs in complex terrain and extreme weather conditions includes a flight data acquisition and preprocessing module, a climate prediction and risk assessment module, an optimal path and task sequence adjustment module, a real-time flight control and path optimization module, and a safety emergency response and recording module. Specifically, the flight data acquisition and preprocessing module acquires weather data; the climate prediction and risk assessment module constructs a neural network model to predict the climate; the optimal path and task sequence adjustment module performs path and task safety assessments; the real-time flight control and path optimization module converts the path and task sequence into aircraft commands; and the safety emergency response and recording module executes emergency strategies and records the data. This system solves the problem in existing technologies where constantly changing weather conditions make it difficult for UAVs to adjust their flight in real time.

This invention discloses a comprehensive energy operation simulation method and system based on multi-objective optimization, belonging to the field of energy technology, aiming to solve the problems of equipment overload and discontinuous power supply caused by supply and demand imbalance under extreme climate conditions. The method establishes a multi-objective collaborative optimization model based on the renewable energy types and seawater desalination load characteristics of the island microgrid. An improved NSGA-III algorithm is used to simulate the model and generate a scheduling scheme, including the start-up and shutdown sequence of the hydrogen production equipment. When a typhoon warning signal is received, the seawater electrolysis hydrogen production equipment is forcibly locked, and the output power of the diesel generator is fixed within a preset range. During the operation of the hydrogen production equipment, the output fluctuation data of the wave energy generator is collected in real time, and the operating power of the reverse osmosis seawater desalination unit is adjusted through a dynamic correction algorithm. When the hydrogen storage capacity of the hydrogen production equipment reaches a threshold, the power lock of the diesel generator is released, switching to a multi-energy coupling control mode, and the charging and discharging strategy of the energy storage equipment is updated synchronously.

This invention discloses an intelligent inverter control method based on an adaptive algorithm, belonging to the field of intelligent inverter control technology. It employs a five-step approach: multi-source temperature acquisition and environmental identification, machine learning-based electro-thermal coupling prediction, adaptive inverter parameter optimization, collaborative thermal management, and fault diagnosis and self-repair. Addressing the risk of high or low temperature failures in motors and inverters under extreme climates, it acquires real-time temperature distribution and load information, predicts temperature rise trends in advance, and proactively adjusts inverter current, voltage, and modulation strategies to achieve safe derating or torque compensation. Furthermore, it performs multi-sensor cross-validation and observer fusion when sensors drift or components age, maintaining stable system operation and efficient energy utilization, significantly improving driving performance and vehicle reliability in extreme environments. During this process, online model updates further refine fault diagnosis and self-repair, enhancing the durability and economy of the electric drive system.

The application discloses a climate self-adaptive vehicle-mounted fuel cell hydrogen management system, which comprises a PLC controller and a first constant temperature device, a second constant temperature device, an expansion tank, a first heat dissipation device and a hydraulic motor which are electrically connected with the PLC controller. The first constant temperature device is arranged on an alternating current motor, and the second constant temperature device is arranged on a hydrogen storage cylinder. The second constant temperature device is connected with the hydrogen storage cylinder through pipelines to form a first working medium one-way circulation pipeline, a second working medium one-way circulation pipeline and a working medium circulation pipeline for the hydrogen storage cylinder. The working medium circulation state of the working medium in different temperature ranges is set in the PLC controller, so that the working state of the hydrogen storage cylinder and the alternating current motor can be accurately controlled and adjusted. The system can stably control the temperature and pressure of an electrochemical reaction in a fuel cell group under extreme climate conditions, thereby improving the working efficiency of the fuel cell and enhancing the endurance of the automobile.

1. Name of the product in this design: Plateau Extreme Climate Environment Test Bench. 2. Purpose of this design: To provide simulated environmental conditions such as temperature, rain, ice accumulation, and high altitude during testing. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1. 5. The bottom surface of this product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted.