1932 results about "Environmental simulation" patented technology

The invention relates to the field of simulation environment monitoring, in particular to a remote monitoring and fault diagnosis method and system for an environment simulation system. The method comprises the following steps: identifying all distributed multi-dimensional sensor nodes, carrying out spatial topology distribution analysis, and constructing a plurality of node region models; carrying out omnibearing environment simulation operation state monitoring on the environment simulation sample box, and carrying out environment state nonlinear correlation mining to obtain nonlinear operation state correlation characteristics; performing dynamic state rendering on the plurality of node area models based on the nonlinear operation state association features, and constructing a plurality of environment simulation data twin models; and performing operation simulation monitoring processing on the plurality of environment simulation data twin models, and performing parameter difference analysis between the models to generate parameter difference characteristics of different models. According to the invention, efficient and accurate fault analysis and diagnosis of the environment simulation system are realized.

The invention relates to an aviation turbine blade service damage monitoring method based on digital twinning, belongs to the technical field of aero-engines, and solves the problems that an existing detection algorithm is high in calculation complexity and low in efficiency and precision. Determining a to-be-monitored turbine blade physical entity, and collecting turbine blade environment information; a digital twin is constructed on the basis of a physical entity, the turbine blade three-dimensional model construction module constructs a turbine blade three-dimensional model according to the physical entity, and the turbine blade operation environment simulation module performs environment simulation on the model according to real-time load, rotation speed and environment temperature data of a turbine blade. And the turbine blade defect evolution prediction module identifies blade surface defects of the model at each moment by using an intelligent defect detection algorithm to obtain the category, position information and size information of the defects, and performs evolution simulation and danger coefficient analysis. According to the invention, real-time on-line, high-precision and high-efficiency detection of the surface defects of the aviation turbine blade is realized, and the service life of the turbine blade is prolonged.

The invention belongs to the field of deep sea corrosion experiment equipment, and particularly relates to a multivariable coupling control deep sea corrosion environment simulation and monitoring device, which comprises an environment control unit, a deep sea corrosion environment simulation unit, a deep sea corrosion environment simulation unit, a deep sea corrosion environment simulation unit and a deep sea corrosion environment monitoring unit, and is characterized in that the environment control unit comprises a seawater environment cabin, a test sample is vertically arranged in the seawater environment cabin, and two ends of the test sample penetrate through and extend to the outer side of the seawater environment cabin; the pressure control system and the temperature control system are connected with the seawater environment cabin through sealing flanges and are respectively used for simulating deep sea hydrostatic pressure and a low-temperature environment; the mechanical loading system is used for bearing the seawater environment cabin and clamping the two ends of the test sample through the sample clamping system so as to apply a multi-axis dynamic mechanical load; the gas regulation and control unit is used for controlling the concentration of dissolved oxygen in the seawater environment cabin; and the multi-source monitoring module is used for collecting detection data and analyzing the detection data to obtain the corrosion rate of the test sample. The single-variable control limitation of a traditional device is broken through, and a high-fidelity experiment platform is provided for deep sea material corrosion mechanism research and equipment life evaluation.

The invention discloses an information scanning and penetration test path planning method in a Windows domain based on reinforcement learning. The method comprises the following steps: carrying out information detection on a target Windows domain environment, surveying and mapping a network topology of the target Windows domain environment, establishing connection with a target machine by utilizing protocols such as LDAP (Lightweight Directory Access Point) and the like, and collecting related information in the domain; the method comprises the following steps of: collecting, sorting and abstracting detected related information, solving a multi-stage sequence decision problem by utilizing a Markov decision process, realizing environment simulation, constructing a reinforcement learning agent, generating a penetration test path by performing interactive training with a simulation environment, and performing adjustment according to a reward strategy so as to obtain a penetration test result. And searching an optimal penetration test path by accumulating rewards, constructing a Windows domain penetration test model, and finally generating the optimal penetration test path. According to the method, collection of information in the Windows domain and planning of the penetration test path are realized, and finally, the environmental safety of the Windows domain is evaluated and maintained.

The invention discloses a bridge beam plate load testing device for road and bridge construction, and belongs to the technical field of bridge beam plate detection. The device comprises the following modules: an intelligent multi-point collaborative loading module for realizing multi-point synchronous dynamic loading of a beam plate so as to simulate various traffic load conditions; the multi-modal sensing and data acquisition module is used for acquiring dynamic response data of the beam slab in an omnibearing manner; the central control module is used for coordinating and managing the work of each module, processing dynamic response data in real time and dynamically optimizing a test strategy; the self-adaptive safety protection module monitors the health state of the beam slab in real time, predicts potential risks and takes active prevention measures; the environmental simulation and compensation module simulates the influence of various environmental conditions on the beam slab and compensates the interference of environmental factors on a test result in real time; the artificial intelligence data analysis module evaluates the performance of the beam slab and predicts the service life of the beam slab; and the augmented reality interaction and visualization module provides a visual user interface and visualizes the test process and result in real time.

The invention relates to the technical field of carbon sequestration capacity monitoring, in particular to a carbon sequestration capacity monitoring and evaluating method for an island ecosystem, which comprises the following steps: S1, multi-source data fusion and habitat partitioning: dividing an island habitat type partitioning map; s2, stratified sampling and carbon reserve actual measurement: measuring the carbon content, and constructing a carbon reserve actual measurement database; s3, constructing a dynamic carbon sink model: carrying out a whole growth cycle environment simulation experiment on sea grass and algae in different habitats, and establishing the dynamic carbon sink model taking habitat types as classification units; s4, carbon sequestration capability quantification and bottleneck diagnosis: calculating carbon sequestration contribution values of various habitat types, identifying low-efficiency carbon sequestration areas through contribution value comparison, and analyzing environmental limitation factors of the low-efficiency carbon sequestration areas; and S5, integrating a sink increasing technology and generating a path: designing a habitat specific sink increasing scheme for the identified low-efficiency carbon sink area. According to the invention, high-precision monitoring, intelligent evaluation and system optimization of the island ecosystem carbon sequestration capability are realized.

The invention provides an LCM module aging test method, device and equipment and a storage medium, and the method comprises the steps: obtaining the initial state data and initial environment parameter combination of an LCM module through intelligent environment simulation and initialization processing, and carrying out the multi-mode alternate aging test and real-time monitoring processing of the module through an intelligent environment simulation system, and obtaining performance data in a dynamic environment, and carrying out surface periodic scanning and working sound recording to obtain microstructure data and acoustic data. Through intelligent fault detection and deep analysis processing, fault features and reasons of the module are identified, predictive analysis is performed on the fault in combination with a machine learning algorithm, and a predictive fault trend and an optimized environmental parameter combination are generated. And performing comprehensive performance evaluation on the module according to the fault features, the reason analysis, the prediction trend and the optimization parameters. According to the invention, through combination of intelligent environment simulation, real-time monitoring and deep analysis, accurate performance evaluation and fault prediction of the LCM are realized.

The invention provides an intelligent maintenance method and system for the wall of a squat silo, and the method comprises the steps: applying controllable environment parameters to a concrete test block group with the same proportion through an environment simulation device, and obtaining test block deformation data, moisture content data and stress data; performing time sequence correlation mapping on the test block deformation data, the moisture content data and the stress data and environmental parameters to obtain a maintenance decision data set; acquiring a real-time environment vector through a sensor network deployed on the site of the squat silo; inputting the real-time environment vector into a maintenance decision model, and generating a spraying control instruction by matching the maintenance decision data set; and driving a spraying system to execute operation according to the spraying control instruction, and dynamically correcting spraying parameters based on moisture monitoring data. According to the method, through data-driven intelligent maintenance decision and dynamic closed-loop control, the crack risk of the wall of the squat silo is remarkably reduced, meanwhile, water resource consumption is reduced, and double breakthrough of maintenance quality and resource efficiency is achieved.

The invention discloses an acid mine wasteland ecological restoration plant dynamic optimization configuration method, and relates to the field of ecological restoration, and the method comprises the following steps: 1, collecting soil attribute data in a predetermined range of an acid mine wasteland in a dotted manner, and synchronously obtaining growth attribute data of to-be-selected plants and periodic weather data of a target area; step 2, extracting a deterioration feature set having a deterioration effect on plant growth based on the soil attribute data, and analyzing a bioremediation feature set capable of inhibiting the deterioration feature in the plant attribute data to generate a candidate plant set; 3, constructing a dynamic environment simulation model based on a deep learning algorithm, wherein input dimensions comprise weather data time sequence features, soil deterioration feature vectors and a plant bioremediation feature matrix; the method can accurately predict the restoration effects of plants under different climate and soil conditions, simulate the effects of different intervention measures in real time, output the optimal intervention scheme, and ensure the balance between the resource utilization efficiency and the restoration effects.

The invention relates to an energy-saving enthalpy difference test room, which comprises two adjacent rooms, namely an indoor environment simulation room and an outdoor environment simulation room, and further comprises an indoor air handling unit, an outdoor air handling unit and an indoor and outdoor air exchange unit, the outdoor environment simulation chamber is used for placing an outdoor unit of an air conditioner to be tested, the indoor air processing unit is installed in the indoor environment simulation chamber and can adjust the temperature of the indoor environment simulation chamber, and the outdoor air processing unit is installed in the outdoor environment simulation chamber and can adjust the temperature of the outdoor environment simulation chamber. The indoor and outdoor air exchange unit is communicated with the indoor environment simulation chamber and the outdoor environment simulation chamber, and cold / hot air generated by the indoor unit of the tested machine can be introduced into the outdoor environment simulation chamber.

The invention relates to the field of environment simulation control, in particular to an environment simulation method and system for a high and low temperature test box. The method comprises the following steps: acquiring initial state monitoring parameters of a high and low temperature test box, performing regional environment parameter division and time point gradient change fitting, and constructing a plurality of regional temperature and humidity gradient change curves; identifying a test object in the high and low temperature test box; performing transient thermal change limit evolution excavation on the test object according to the plurality of regional temperature and humidity gradient change curves to generate a transient thermal change limit curve of the current test object; acquiring a preset test box environment simulation log, performing global temperature change cross period optimization according to the transient thermal change limit curve of the current test object, and constructing a global temperature change optimization simulation period; and executing environment simulation control operation according to the global temperature change optimization simulation period, and collecting high and low temperature simulation environment monitoring data. According to the invention, quick-response, accurate and stable environment simulation control is realized.

The invention discloses a real-time simulation platform of a floating production storage and offloading (FPSO) riser support system based on Modelica, and belongs to the field of intelligent simulation. Comprising a demand definition module, a data loading module, an environment simulation module, a riser support modeling module, a system verification module, a simulation calculation module, an optimization scheduling module, an evaluation analysis module, a control suggestion module, a real-time visualization module, a storage backtracking module and a feedback adjustment module. According to the method, a high-dimensional nonlinear space can be globally searched, a better solution can be found, premature convergence is avoided, the stability of an optimization result is improved, it can be ensured that parameter configuration always keeps the optimal performance under variable working conditions, the intelligence and adaptability of an operation strategy can be improved, and it is ensured that a riser supporting system is always in an optimal control state under different working conditions; according to the method, high efficiency of multi-objective optimization is achieved, the best balance point among vibration reduction, abrasion reduction and equipment service life prolonging can be found, and therefore double improvement of economical efficiency and safety is achieved.

The invention relates to an attitude and orbit control engine high-altitude environment simulation ignition test system and a verification method, in particular to a high-altitude simulation test bed active injection verification system and a verification method, and solves the technical problems of high cost, high risk and low verification data validity during verification of a high-altitude simulation test bed in the prior art. The active injection verification system for the high-altitude simulation test bench comprises a vacuum chamber, a diffuser, a water spray cooler, a first corrugated pipe, a first gas collection chamber, a second corrugated pipe, n groups of coolers, a third corrugated pipe, a second gas collection chamber, a steam-jet pump, a pipeline and a data acquisition module, according to the verification method of the active ejection verification system of the high-altitude simulation test stand, ejection capacity verification is carried out in two stages, and the verification effectiveness is greatly improved on the premise that the verification cost is greatly reduced.

The invention relates to an aquatic environment simulation monitoring method and device for evaluating the influence of pollutants, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring environmental parameters and dividing the range of a fine model based on the environmental parameters; constructing a simplified model based on the environmental parameters in the non-fine model range; constructing a fine model based on the environmental parameters in the fine model range; and outputting and displaying a corresponding simulation result based on the simplified model and the fine model. By the adoption of the method, through multi-scale coupling of the simplified model and the fine model, it can be guaranteed that the diffusion trend of pollutants can be rapidly simulated in a large range, detailed analysis can be conducted in the local key area through the high-precision model, and therefore the overall simulation efficiency and precision are improved.

The invention discloses a multi-agent training method and system fusing a knowledge graph. The system comprises a knowledge graph management module, an agent training module and an interaction and feedback module. And the knowledge graph management module is responsible for organizing, updating and the like of the knowledge graph and covers data acquisition, cleaning, storage, construction, updating, query, reasoning and feedback collection. The agent training module comprises an environment simulator, a plurality of learning modules, a knowledge graph interaction module, a strategy generation module and a feedback mechanism, and helps an agent to learn and optimize a decision strategy in a simulation environment. The interaction and feedback module is provided with an interaction interface, a feedback calculation module, a knowledge graph association module and a performance monitoring and visualization module, and is used for realizing communication and performance evaluation between the intelligent agent and the environment; and the interaction and feedback module realizes environment interaction, feedback calculation, knowledge graph association and performance monitoring evaluation and adjustment.

The invention discloses a flight space directional training auxiliary system and method, and the system comprises a flight task simulation module; the meteorological environment simulation module is used for simulating a meteorological scene corresponding to a flight task, and simulating the meteorological scene by establishing a multi-dimensional meteorological model and combining meteorological parameters; the meteorological environment parameter control module comprises a time step length control module which adopts a preset time step length to input meteorological parameter observation data collected in real time to the meteorological parameter setting module; the specific meteorological event control module is used for transmitting meteorological parameters of a specific meteorological event area to the meteorological parameter setting module when the airplane triggers a specific meteorological event; and the training scheme generation module is used for generating a proper training scheme according to the historical training information of the flight personnel. According to the system and the method, a real and personalized simulation training environment can be provided for the flight personnel, and the professional skills and the capability of coping with various flight conditions of the flight personnel are effectively improved.

The invention discloses a water resource demand optimization method based on deep reinforcement learning, and relates to the technical field of water resource management, and the method comprises the steps: obtaining original data and real-time data; preprocessing the original data; extracting parameters of an extreme climate event according to the original data, generating extreme scene data of a plurality of disturbance scenes based on a pre-trained environment simulator according to the historical climate data and the parameters of the extreme climate event, and training a preset DQN-based deep reinforcement learning model according to the optimization training set, the network architecture of the DQN-based deep reinforcement learning model is CNN, optimizing the DQN-based deep reinforcement learning model based on a multi-target reinforcement learning strategy to obtain a trained DQN-based deep reinforcement learning model, inputting real-time climate data into the trained DQN-based deep reinforcement learning model, and outputting real-time optimized regional water resource demand data. According to the invention, the decision-making efficiency and accuracy of water resource demand optimization are significantly improved.

The invention discloses an integrated buoy for ocean monitoring and a buoy testing method.The buoy comprises a floating body, a solar panel support, a sensor support and a data acquisition communication box are fixedly installed on the top of the floating body, and a plurality of solar panels with the bottoms inclined outwards are installed on the solar panel support; a plurality of sensors are installed on the sensor support, and communication equipment and a data collector are installed in the data collection communication box. According to the invention, the laboratory integration test, the coastal water surface test and the offshore laying test are combined, three times of data comparison and verification are carried out, the consistency and precision of the data can be effectively ensured, and in addition, the environmental simulation equipment is added during the offshore laying test to simulate different environments in the marine environment, so that the test efficiency is improved. The floating property and the stability of the buoy under different extreme conditions are tested, remote monitoring and self-diagnosis technologies are introduced into the buoy in a matched mode, personnel can conveniently and visually know the condition of the buoy in the testing process, and the use requirement is met.

The invention discloses a machining control method, device and system based on a five-axis machining machine tool and a medium. The method comprises the steps that geometrical characteristic parameters of a to-be-machined workpiece and tool characteristic information are obtained, a cutting force model is established according to the obtained geometrical characteristic parameters, and cutting force distribution conditions under different paths are calculated; in the cutting force model, based on the cutting force distribution condition and the geometric feature parameters, generating an optimal cutter path corresponding to the cutter feature information by using a path optimization algorithm; according to the generated tool path and tool feature information, establishing a simulation environment of the five-axis machining tool, and simulating motion states of the tool in different paths; in the simulation environment, setting a reward function including path smoothness and cutting force stability; using a reinforcement learning algorithm to optimize the fairness of the tool path, and generating a smooth tool path; verifying the generated smooth tool path in a simulation environment; and triggering a processing execution instruction. The machining precision and the surface quality are improved.

The invention relates to a load quantification method for a floating fan in a typhoon scene, and the method comprises the steps: constructing an environment data simulation frame which comprises an external nesting and an internal nesting, the internal nesting is used for achieving wave flow coupling and generating wave flow scene data, and the external nesting is used for carrying out wave flow coupling and obtaining external environment data of the floating fan; the method comprises the following steps: constructing a multi-time-scale load prediction framework, inputting environmental data into the multi-time-scale load prediction framework, and outputting load distribution of typhoon to a floating fan under a long time scale, the multi-time-scale load prediction framework comprising a short-time-scale dynamic coupling analysis model and a long-time-scale load prediction model, and the long-time-scale dynamic coupling analysis model comprises a short-time-scale dynamic coupling analysis model and a long-time-scale dynamic coupling analysis model. The short-time-scale dynamic coupling analysis model is used for obtaining a load prediction sample, and the long-time-scale load prediction model is used for predicting the load prediction sample and outputting load distribution under a long time scale. Compared with the prior art, the method has the advantages of being more practical in environment simulation, accurate and efficient in load prediction and the like.

The invention discloses a water quality prediction method based on machine learning and an SWAT model, and belongs to the technical field of water environment simulation and water quality early warning. The method comprises the steps that S1, hydrology, water quality, meteorology, land utilization types, pollution sources and spatial elevation multi-temporal data are collected from multiple channels in a drainage basin, and noise reduction, normalization and missing value filling preprocessing methods are adopted for original data; s2, performing sensitivity analysis on the nonlinear relationship between the SWAT model parameters and the output by adopting SVR, and screening out key parameters with remarkable influence; s3, on the basis of the water quality monitoring data, constructing a water quality time sequence prediction model by adopting an LSTM model; and S4, constructing a self-learning mechanism based on the LSTM and the SVR model, and retraining the model by adjusting a learning time window and regularly utilizing latest data. The method is excellent in the aspects of model efficiency, prediction precision and application adaptability, and has remarkable engineering application value and popularization potential.

The invention relates to the technical field of virtual simulation, and particularly discloses a sudden major respiratory infectious disease on-site epidemiological investigation virtual simulation experiment system which comprises a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module and an analysis and evaluation module. The virtual scene management module is used for creating and managing a simulation experiment environment and simulating an epidemic background and a development process of sudden major respiratory infectious diseases; the data management module is used for simulating the process of collecting case information, environment information and social contact data when students perform on-site epidemiological investigation; according to the system, big data analysis, the GIS technology and an AI intelligent analysis tool are integrated, a disease transmission path and a development trend are simulated, and virtual simulation not only helps students to master how to support a decision through scientific data, but also can improve the emergency response capability and the comprehensive analysis capability of the students in a complex epidemic situation.

The invention discloses an intelligent ship steering capability multi-dimensional evaluation system based on virtual-real fusion, and relates to the technical field of intelligent ships and virtual-real fusion. The system comprises a visual platform and an integration module, wherein a simulation environment is constructed through a virtual-real data interaction engine, and ship behaviors under different sea conditions are simulated; a virtual-real data bidirectional calibration module is used for dynamically correcting simulation parameters, and it is ensured that the simulation parameters are consistent with real ship data; based on the multi-dimensional evaluation index system, the intelligent evaluation decision module comprehensively scores the steering performance; and the behavior guiding module outputs an evaluation report and a risk avoidance suggestion. According to the invention, part of real ship tests can be replaced, extreme working conditions can be simulated, the evaluation precision is guaranteed, the test period is remarkably shortened, the cost is reduced, simulation parameters are dynamically corrected through a bidirectional calibration mechanism, comprehensive and multi-dimensional quantitative evaluation on steering capability in a scene close to a real scene is realized, and a credible evaluation report is output.

The invention discloses a bay environment simulation forecasting method and device, relates to the technical field of marine environment forecasting, and aims to solve the problem that in the prior art, forecasting precision and forecasting timeliness cannot be taken into account when environment simulation forecasting is carried out on a bay. The method comprises the following steps: based on coastline data, performing encryption processing on a target sub-region in a target gulf region by adopting a curve orthogonal grid, and generating a variable-resolution refined curve orthogonal grid from an inshore region to an open sea region; based on a refined curve orthogonal grid, according to a model simulation data format requirement, a variable-resolution grid file is manufactured and obtained; determining a model input file based on the water depth topographic data and the variable resolution grid file; and inputting the model input file into a preset three-dimensional oblique pressure ocean numerical model for calculation to obtain simulation forecast data. Therefore, the simulation precision requirements of different bay areas can be met, and the simulation calculation speed and the simulation calculation efficiency of the whole target bay area are ensured.

The invention provides an indoor crustal stress test device and method based on optical strain measurement, the test device comprises a stress simulation device, the stress simulation device can perform real three-dimensional crustal stress environment simulation on a test rock mass as far as possible; the drilling releasing device is provided with a hollow drill bit used for drilling the optical strain measurer installed in the test rock mass, and the drilling hole and the hollow drill bit are coaxially arranged. Thus, according to the method, pressure is applied to the test rock mass from multiple directions through the multiple hydraulic loading columns, the real three-dimensional crustal stress environment can be more accurately simulated, speckle image changes in the speckle coating can be continuously recorded in real time through the optical strain measurer, and hole wall strain data are obtained; and converting the strain data into stress by applying an elastic theoretical formula, and accurately calculating the stress state of the experimental rock mass.

The invention discloses a green low-carbon building simulation analysis method based on a three-dimensional model, and the method comprises the steps: 1, carrying out the pre-analysis, constructing a BIM model of an initial building design, obtaining the surrounding environment information of a building, and inputting the information into building simulation software; 2, environment simulation and design analysis are carried out, building design is optimized, and a first design stage is formed; forming a second construction stage; monitoring the energy consumption of the building in real time to form a third operation stage; step 3, simulation optimization; 4, simulating and analyzing data mining; and a fifth step of information sharing and assistance. According to the method, design decision and optimization are assisted through simulated beforehand analysis and simulation, so that energy consumption and carbon emission of the building in each life cycle are reduced, the green performance of the building is improved, efficient and accurate energy consumption evaluation and optimization of a building design scheme are achieved, and generation of a low-carbon and energy-saving building scheme is effectively promoted.

The invention relates to the technical field of automatic testing, and discloses an automatic testing scheme generation method and device and a medium, and the method comprises the steps: matching a demand action in a test demand with a code entry name corresponding to a code segment, and determining a code risk level of a current demand code block; analyzing the code change frequency and the code dependency degree of the current demand code block according to historical test records in the test domain knowledge base, and determining the test dynamic priority of the current demand code block; constructing a control flow diagram and a data flow diagram of the current demand code block, and detecting risk nodes in the control flow diagram and the data flow diagram; generating a test scene of the current demand code block according to the risk node, and generating a boundary value test rule according to a key constraint condition in the data flow diagram; and querying an environment simulation rule of the current demand code block, and packaging the test scene, the boundary value test rule and the environment simulation rule into an automatic test scheme. According to the invention, the code coverage when the test scheme is generated can be improved.

The invention relates to the technical field of rubber performance evaluation, in particular to a performance data analysis and evaluation method for a rubber material. The method comprises the following steps: acquiring X-ray scanning data of a rubber sample; performing phase contrast imaging on the X-ray scanning data of the rubber sample to obtain projection data of the rubber sample; performing back projection reconstruction on the projection data of the rubber sample to obtain three-dimensional structural data of the rubber material; carrying out multi-parameter environmental simulation loading on the rubber sample to obtain environmental response data of the rubber material; dynamically collecting the environmental response data of the rubber material to obtain deformation process data of the rubber material; synchronously recording and analyzing the deformation process data of the rubber material to obtain a stress-strain characteristic pattern of the rubber material; and performing polarization spectrum excitation on the rubber sample based on the rubber material stress-strain characteristic pattern to obtain rubber molecular chain fluorescence data. According to the invention, the reliability and accuracy of rubber material performance evaluation are improved.

The invention relates to the technical field of desert vegetation recovery planning and monitoring systems, in particular to an intelligent desert vegetation recovery planning and monitoring system and method. And the planning optimization module is used for comparing and analyzing the image data of each time node in the data set to obtain the current ecological situation, carrying out simulation experiments based on different vegetation restoration schemes, and planning and designing vegetation restoration according to simulation experiment results. Obtaining a planning design scheme including a vegetation recovery type, a vegetation recovery region, different plant configuration, regional environment simulation and regional water system simulation; the monitoring module is used for monitoring the planning and design scheme, obtaining a vegetation image through an infrared sensor, and processing the vegetation image to obtain vegetation parameters; the service layer carries an artificial intelligence engine, undertakes core algorithm functions of data processing, change detection, growth prediction and the like, and provides an intelligent brain for scheme generation and problem diagnosis.

The invention discloses an electronic function test system of a high-voltage PTC (Positive Temperature Coefficient) electric heater, which relates to the technical field of PTC electric heaters and is characterized in that extreme temperature, humidity and voltage disturbance data are acquired in a programmable multi-dimensional environment simulation cabin, and the temperature deviation of the high-voltage PTC electric heater is predicted by using a multi-channel fusion and compensation model; according to the method, the high-voltage PTC electric heater is used as a power source, the output power is adjusted in a real-time feedforward and feedback mode in the self-adaptive controller, finally, through dynamic calibration and closed-loop verification, a model and gain parameters are optimized in multiple scenes, a complete system which can deal with large environmental changes and is high in temperature control precision is formed, the stability and safety of the high-voltage PTC electric heater under the severe working condition are remarkably improved, and the high-voltage PTC electric heater is suitable for being used in the field of electric heaters. And overshoot and lag are reduced, the energy utilization efficiency is improved, the system is suitable for industrial heating, vehicle warm air and other scenes needing high reliability and quick response, and the maintenance cost can be effectively reduced.