384 results about "Soil temperature" patented technology

The invention discloses a mine reclamation effect evaluation method based on a multi-temporal remote sensing image, and belongs to the field of mine reclamation effect evaluation, and the method comprises the steps: obtaining and preprocessing multi-source remote sensing data, and obtaining a standardized multi-temporal data set; performing image registration and radiation correction on the standardized multi-temporal data set to obtain a correction data set with consistent space and consistent radiation; constructing a multi-dimensional feature data set based on the correction data set, wherein the multi-dimensional feature data set comprises surface morphological features, vegetation health features, soil temperature features and underground water level change features; extracting a surface form feature vector according to the multi-dimensional feature data set, and generating a vegetation health distribution map, a soil temperature feature map and an underground water level change distribution map; and fusing the surface form feature vector, the vegetation health distribution map, the soil temperature feature map and the underground water level change distribution map, constructing a comprehensive index system, and outputting a mine reclamation effect evaluation result. According to the method, the accuracy and comprehensiveness of reclamation effect evaluation are remarkably improved.

An unmanned aerial vehicle (UAV) agent for forming a two-tier hybrid smart agriculture network architecture is provided. The UAV agent is configured to support two communication protocols, a short-range communication protocol and a long-range communication protocol. The agriculture sensor gathers agriculture data such as soil temperature and participates in a first-tier short range wireless communication network to send the collected data to at least one UAV agent. A UAV agent participates in one or more first-tier short-range communication networks to pick up sensor data from agriculture sensors in one or more clusters and also participates in a second-tier long-range communication network to route the collected sensor data to at least one cloud server. The tasks in both tier networks are formulated as optimization problems to achieve optimal data delivery and solved by using multi-agent reinforcement learning (MARL), which is implemented by the invented Focus Coordination Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG) algorithm.

The invention discloses a landscaping maintenance monitoring and early warning system, and belongs to the technical field of landscaping maintenance monitoring. Comprising a distributed sensor network, an edge computing processing unit, a cloud intelligent decision center and an intelligent execution terminal, the distributed sensing network comprises a soil sensor array which is deployed according to an elastic density grid and is used for dynamically adjusting the distance between sensors according to a plant value coefficient and monitoring the temperature, the humidity, the pH value and the ion concentration of soil in real time; and the autonomous mobile inspection robot carrying a multispectral imaging module executes dynamic path planning based on the abnormal probability thermodynamic diagram.

The invention discloses a tower grounding grid multi-frequency composite wave electric field imaging and soil environment medium diagnosis method, which comprises the following steps of: generating a composite excitation signal through superposition of a low-frequency sine wave and a high-frequency CHIRP square wave, and collecting multi-dimensional data such as voltage / current response, soil temperature and humidity, electric field intensity and the like in combination with a distributed sensor array; separating signal features by using wavelet transform and extracting impedance information of a target frequency point; a multi-grid homotopy algorithm is adopted to realize grounding grid three-dimensional resistivity distribution reconstruction, corrosion spots and abnormal areas are marked, and high-precision positioning of underground conductors is completed through UWB radar or a capacitance detector; meanwhile, based on the information entropy maximization optimization frequency band combination, the grounding grid state is comprehensively evaluated in combination with a multi-parameter fusion model. The method has the non-contact patrol capability, the unmanned aerial vehicle carries equipment to scan the grounding grid, the detection efficiency and convenience are remarkably improved, and an efficient and accurate solution is provided for guaranteeing safe operation of the power grid.

The invention discloses a rock-soil temperature field rapid solving method suitable for a ground source heat pump system, and the method comprises the steps: firstly building a three-dimensional unsteady-state heat transfer model in consideration of a buried pipe coaxial structure, a porous arrangement form and the characteristics of a multilayer heterogeneous soil body in the ground source heat pump system; then simulating a thermal response process by using the model, constructing a multi-domain coupled thermal conduction augmentation matrix, performing POD on the matrix, extracting a dominant mode function of a temperature field, and constructing a low-dimensional POD-based mode space; and finally, constructing a POD-Galerkin low-order model, simulating a buried pipe group soil temperature field, performing inversion of a heat source function, and outputting stratum thermophysical parameters. According to the method, the calculation efficiency and the simulation precision of the porous coupling and long-period unsteady state heat transfer process are remarkably improved, the problem that traditional finite element and finite difference methods are high in time consumption in porous strong coupling modeling is solved, and rapid prediction of the soil temperature and robust recognition of the heat source function under a complex stratum and a heat exchange structure can be achieved.

The invention discloses an ecological restoration area carbon sink increment real-time prediction method and system based on artificial intelligence, and the method comprises the following steps: multi-source data collection: obtaining a monthly vegetation coverage image through a satellite remote sensing platform, collecting the sensor data of soil temperature and humidity, air CO2 concentration and the like through a laid ground sensor network, and carrying out the real-time prediction of the carbon sink increment of an ecological restoration area; adopting an unmanned aerial vehicle laser radar to obtain vegetation canopy point cloud data according to a preset period, and collecting biomass actual measurement data of a restoration area in a historical preset age limit; preprocessing data, performing radiometric calibration, atmospheric correction and cutting splicing on a monthly vegetation coverage image acquired by a satellite remote sensing platform, and extracting a vegetation coverage and vegetation index time sequence; denoising, ground point separation and single tree canopy segmentation are carried out on vegetation canopy point cloud data acquired by the unmanned aerial vehicle laser radar, and the height and crown breadth of single plant vegetation are calculated. According to the invention, carbon sink increment prediction can be realized more accurately.

The invention discloses a buried pipe heat pump system optimization method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining soil temperature data of a current buried pipe heat pump system in an operation cycle; dynamically correcting the heat exchange capacity of the current buried pipe heat pump system according to the soil temperature data to obtain actual heat exchange capacity data; the buried pipe distribution of the current buried pipe heat pump system is optimized and adjusted based on the actual heat exchange capacity data until a target buried pipe layout scheme capable of meeting the real-time heat exchange requirement is obtained; real environment parameter support is provided for dynamically correcting the heat exchange capacity by obtaining soil temperature data of an operation cycle; actual heat exchange capacity data are obtained through correction, and the problem that traditional fixed value evaluation is inaccurate is solved; and then the distribution of the buried pipes is optimized on the basis of the actual heat exchange capacity, so that the layout is accurately matched with the real-time heat exchange requirement, and finally the heat exchange efficiency of the system and the dynamic adaptability to the real-time requirement are improved.

The invention relates to the technical field of ground source heat pump operation control, in particular to an optimal adjustment method of a ground source heat pump system, which comprises the following steps: S1, collecting underground soil temperature and system heat load of the ground source heat pump system; s2, calculating a soil heat balance index based on the underground soil temperature; s3, according to the soil heat balance index and the system heat load, the optimized operation mode of the ground source heat pump system is judged; and S4, according to the optimized operation mode judged in the S3, the operation parameters of the ground source heat pump system are adjusted, and a corresponding execution mechanism is driven. According to the method, the operation mode is judged by introducing the soil heat balance index and combining the system heat load, meanwhile, the flow of the circulating water pump, the frequency of the compressor and the opening degree of the heat exchange valve are subjected to interval adjustment, and unification of energy efficiency optimization and long-term balance of the soil heat environment is achieved.

The invention belongs to the technical field of data transmission processing, and discloses a control system and method for intelligent agriculture multilayer soil temperature and humidity sampling. The system comprises a land data acquisition module, an updated data acquisition module, a data set preprocessing module, a soil humidity prediction module, a decision report generation module and a report transmission module, and is characterized in that a land data set is acquired, an updated data set is acquired, the land data set and the updated data set are preprocessed to obtain comprehensive feature data, and the comprehensive feature data are transmitted to the decision report generation module; analyzing the land data set, the update data set and the comprehensive feature data set to obtain a predicted humidity reference value, analyzing the predicted humidity reference value to obtain a soil decision report, processing the soil decision report, and outputting the processed soil decision report to a terminal. The method has the remarkable advantages of high accuracy of sampled soil data, strong auxiliary decision-making capability and good use economic effect.

The invention discloses a frozen soil temperature feature recognition algorithm and a moisture and electric field state prediction model, and the method comprises the steps: synchronously collecting temperature, moisture, resistivity and meteorological data through arranging a multi-depth sensor array, and constructing a multi-source data set with time-space alignment; utilizing continuous wavelet transform and cross-modal coherence analysis, and combining a hidden semi-Markov model to extract a depth-periodic characteristic profile; a time domain fusion Transform model embedded with physical constraints is adopted to realize frozen soil multi-parameter physical consistency prediction and uncertainty quantification; and finally, outputting a prediction result through a visual interface and setting an automatic alarm. The problem of cycle identification caused by weak and non-stable deep frozen soil signals is solved, the prediction accuracy and the physical rationality are remarkably improved, an end-to-end solution from data quality control to early warning is formed, and the reliability and the practicability of frozen soil engineering monitoring are greatly enhanced.

The application provides a kind of intelligent temperature-regulated planting field soil temperature control system and method, related to soil temperature control technical field, obtains seedling planting field regional information, extracts all soil temperature monitoring sub-block according to seedling planting field regional information;Obtain the block environment data of soil temperature monitoring sub-block, determine soil temperature stress factor according to block environment data, obtain all soil temperature stress risk areas in seedling planting field based on soil temperature stress factor;Obtain the temperature control resource information in seedling planting field, determine resource area mapping matrix through temperature control resource information, determine temperature control response strategy through resource area mapping matrix and soil temperature stress factor;According to the soil temperature regulation of each soil temperature stress risk area according to temperature control response strategy.This application can realize the risk analysis and resource optimization control of soil temperature, to improve the temperature regulation precision and regulation efficiency of nursery stock cultivation environment.

The invention provides a soil moisture content detection method based on unsteady heat conduction and optical fiber temperature measurement, which comprises the following steps: heating a soil body in a target measurement area based on an active heating method, and measuring the temperature value of the soil body at each moment under different radial distances from a heating source in the heating process; substituting the soil body temperature value at any moment in the heating process and the corresponding radial distance into a predetermined soil body moisture content relation model to obtain the soil body moisture content at the position corresponding to the radial distance from the heating source in the target measurement area at the corresponding moment; and determining the soil mass moisture content of the target measurement area according to the soil mass moisture content at each radial distance position from the heating source in the target measurement area at the corresponding moment. According to the method, the relationship between the soil moisture content and the soil temperature value is established, and in-situ monitoring and long-time monitoring of the large-range soil in the target measuring area are achieved through the soil temperature value obtained through the active heating method.

The invention discloses a dynamic prediction method for a deformation joint of an underground comprehensive pipe gallery. The method comprises the following steps: S1, collecting and fusing multi-source monitoring data; s2, data preprocessing and feature engineering; s3, constructing a dynamic prediction model; s4, dynamic prediction and performance evaluation; s5, early warning and decision support are carried out, in the step S1, temperature field data including the air temperature in the pipe gallery, the concrete temperature (inner and outer surfaces) of the pipe gallery structure and the soil temperature outside the pipe gallery are acquired, and the measuring range ranges from-40 DEG C to 80 DEG C; according to the method, a multi-source monitoring data acquisition and dynamic prediction model system is established, continuous monitoring and trend prediction of the deformation joint of the underground comprehensive pipe gallery are achieved, multi-dimensional data such as the temperature field, the soil pressure and the underground water level are integrated, time sequence analysis is combined, and the deformation joint of the underground comprehensive pipe gallery is obtained. The development rule of the deformation joint can be accurately captured, and a prospective evaluation basis is provided for the safety of a pipe gallery structure.

The invention discloses a construction method of an underground heat exchange model of a U-shaped middle-deep layer buried pipe. The construction method comprises the steps of collecting monitoring data of underground heat exchange of a preset buried pipe, preprocessing the monitoring data, and constructing a physical model of the underground heat exchange of the U-shaped middle-deep layer buried pipe based on the monitoring data; soil temperature distribution and wall temperature are obtained based on the physical model according to the heat transfer process outside the drill hole, and the water temperature at the next moment is obtained based on an energy equation of circulating fluid according to the heat transfer process inside the drill hole; performing underground temperature field evolution simulation analysis on the basis of the mathematical model of the soil temperature distribution, the wall temperature and the water temperature at the next moment to obtain simulation data, and constructing a U-shaped medium-deep buried pipe underground heat exchange model on the basis of underground temperature field evolution simulation; time sequence deviation analysis is conducted on the simulation data and the monitoring data to obtain time deviation data, the U-shaped middle-deep layer buried pipe underground heat exchange model is optimized according to the time deviation data, and a target model is output.

The invention discloses a dense-phase carbon dioxide pipeline leakage simulation system and method.The experimental device comprises a carbon dioxide injection device, a pressure-bearing container, a pressure-bearing pipeline, a temperature monitoring device and a temperature collecting device, and the carbon dioxide injection device is provided with a fluid outlet used for outputting dense-phase carbon dioxide; the pressure-bearing container is provided with a pressure-bearing cavity used for being filled with soil, the pressure-bearing pipeline is arranged in the pressure-bearing cavity, the pressure-bearing pipeline is connected with the fluid outlet, the pressure-bearing pipeline is provided with a leakage opening, the temperature monitoring device comprises a plurality of temperature sensors, the temperature sensors are arranged in the pressure-bearing cavity at intervals, and the temperature sensors are all electrically connected with the temperature collecting device. And the temperature acquisition device is used for receiving and processing the soil temperature monitored by the temperature sensor. The experimental device provided by the embodiment of the invention can simulate and verify the influence and diffusion rule of dense-phase carbon dioxide pipeline transportation leakage on soil temperature distribution, and provides a theoretical basis for dense-phase carbon dioxide leakage prevention and control and emergency disposal in practical application.

The invention relates to the technical field of agricultural irrigation, in particular to a precision agricultural drip irrigation system based on intelligent sensing and a control method thereof, and the system comprises an intelligent sensing module which is used for collecting meteorological data, soil humidity data and soil temperature data of different irrigation areas; the model prediction module comprises a moisture diffusion model and a rainfall prediction model which are respectively used for performing soil moisture diffusion prediction and rainfall prediction on different irrigation areas by utilizing the soil humidity data, the environment temperature data and the meteorological data, and outputting prediction results; the drip irrigation control module is used for controlling operation of a drip irrigation pump; the drip irrigation control module is further used for controlling electromagnetic valves in drip irrigation pipes in different irrigation areas to be opened and closed by utilizing an intelligent control mechanism according to the prediction result of the model prediction module so as to adjust the irrigation volumes in the different irrigation areas; the soil moisture diffusion rule can be predicted, repeated irrigation and irrigation blind areas are reduced, and waste of water resources is reduced.

The invention discloses a remote intelligent control system for a drip irrigation head water pump based on the Internet of Things, relates to the technical field of agricultural intelligent irrigation, and is used for solving the problems of low precision and poor management efficiency of a traditional drip irrigation technology. The system comprises a drip irrigation area parameter acquisition module which acquires soil temperature and humidity, equipment operation parameters and meteorological data in a layered manner through a multi-source sensor network; the data processing module performs differential filling, space-time alignment and integration on the data; the intelligent analysis module realizes drip irrigation demand judgment, quantity value calculation, opportunity optimization and equipment health assessment; and the control execution module converts the instruction into equipment operation and supports remote intervention, so that precise management and control of drip irrigation are realized, and the precision and management efficiency of drip irrigation are improved.

The invention provides an intelligent temperature sensing regulation and control planting field soil temperature control system and method, and relates to the technical field of soil temperature control, and the method comprises the steps: obtaining the regional information of a seedling growing planting field, and extracting all soil temperature monitoring sub-blocks according to the regional information of the seedling growing planting field; obtaining block environment data of the soil temperature monitoring sub-block, determining a soil temperature stress factor according to the block environment data, and obtaining all soil temperature stress risk areas in the seedling cultivation field based on the soil temperature stress factor; temperature control resource information in the seedling growing planting field is obtained, a resource area mapping matrix is determined through the temperature control resource information, and a temperature control response strategy is determined through the resource area mapping matrix and the soil temperature stress factor; and performing soil temperature regulation and control on each soil temperature stress risk area according to the temperature control response strategy. According to the invention, risk analysis and resource optimization regulation and control of the soil temperature can be realized, so that the temperature regulation and control precision and regulation and control efficiency of the nursery stock cultivation environment are improved.

The invention discloses an air energy-soil composite heat pump cross-seasonal energy storage and winter combined heating system and an application method, and relates to the technical field of building energy conservation. Comprising the following steps: acquiring quantitative data of heat unbalance; when a heat unbalance phenomenon exists, determining a soil initial temperature and a maximum rising temperature before heat compensation, and calculating a heat compensation amount; determining a heat compensation operation time period and a system type selection of an air source heat pump; determining an initial rising temperature, a heat compensation initial investment and a preset age limit; the operation cost is determined, when the number of times of heat compensation is larger than a preset year limit, the sum of the initial investment of heat compensation and the operation cost within the preset year limit is recorded, and when it is detected that the total soil temperature increasing value is not lower than the maximum temperature increasing value, the air source heat pump system is determined again; and the strategy with the minimum operation cost and the minimum initial heat compensation investment is used as the optimal soil heat compensation strategy. The system obviously improves the utilization rate of renewable energy sources, and has the advantages of high energy efficiency, wide adaptability and low whole life cycle cost.

The invention discloses an alfalfa planting management method based on high-altitude saline-alkali soil, and relates to the technical field of agricultural cultivation, and the method comprises the following steps: S1, an irrigation water preheating step: enabling irrigation water to flow through a deep-color pipeline laid on the ground surface for solar preheating; the preheated irrigation water is used for irrigating the field in a drip irrigation mode, and the irrigation water amount is determined according to the initial salt content of the soil and the target salt leaching depth. According to the alfalfa planting management method based on the high-altitude saline-alkali soil, through combination of irrigation water preheating treatment and precise drip irrigation, the contradiction between salt leaching operation and ground temperature keeping in a high-altitude area is effectively solved. Preheated irrigation water avoids soil temperature sudden drop caused by cold water irrigation, the layered covering technology synergistically inhibits water evaporation and salt upward returning, meanwhile, the soil temperature is kept stable, a proper water temperature and salt environment is created for alfalfa seed germination, and therefore the seedling emergence uniformity and the seedling survival rate are improved.

The invention discloses a self-powered multi-parameter farmland microenvironment monitoring system, and belongs to the technical field of smart agriculture and Internet of Things. The system comprises a distributed self-powered wireless sensing node array, gateway equipment and a cloud data platform. The sensing node adopts a photovoltaic and soil temperature difference synergistic hybrid energy supply module, integrates temperature, humidity, pH and other multi-parameter sensing probes, and realizes long-distance low-power-consumption transmission through an improved LoRa communication module. And the gateway is used for receiving and fusing the data and uploading the data to the cloud platform through 4G or Wi-Fi. The cloud platform can store and visualize data, spatial modeling is realized by using improved Kriging interpolation, a soil moisture content prediction model and an anomaly detection algorithm are introduced, and intelligent irrigation suggestions are generated in combination with a crop water demand model. The system has the advantages of stable energy supply, high sensing precision, strong communication anti-interference capability, intelligent data processing and the like, can operate unattended for a long time, is widely suitable for scenes of field planting, greenhouse agriculture, scientific research experiments and the like, and provides reliable support for precise and intelligent management of agricultural production.

The invention discloses an optimization method of a ground source heat pump, electronic equipment and a program product, and the method comprises the steps: collecting the operation data and environment data of a ground source heat pump system in real time through an edge computing gateway deployed on site; constructing a multi-objective optimization model of the ground source heat pump system on the cloud server; solving the optimization model by adopting a self-adaptive multi-target genetic algorithm, and outputting a Pareto optimal solution set; selecting a final execution scheme from the Pareto optimal solution set according to a preset decision preference weight, generating a control instruction, and issuing the control instruction to the field executor through the edge computing gateway; the soil temperature field evolution is predicted through the buried pipe heat transfer model, and a prediction result is fed back to the optimization model and used for correcting the soil heat balance objective function, and long-term dynamic optimization is achieved. According to the method, collaborative optimization of economy, environmental protection and sustainability can be realized by constructing the multi-objective optimization model fusing the operation cost, the soil heat balance and the carbon emission.

The invention relates to the technical field of soil moisture content monitoring, in particular to a wireless transmission method and system based on soil moisture content evaporation. The system comprises a data acquisition unit, an MCU control unit, a data transmission unit and a data analysis unit, the MCU control unit adopts a hardware module with an STM32L series single-chip microcomputer as a core. The data transmission unit is realized by adopting an NB-IoT wireless communication module, and the data analysis unit is used for acquiring soil moisture information, soil temperature information and soil humidity information and compensating the soil moisture information based on soil temperature data and humidity data. According to the invention, a frequency domain reflection method measurement technology and a low-power-consumption wide-area Internet of Things technology are combined, so that remote accurate monitoring is realized. The binary regression analysis method is adopted to effectively reduce the influence of the temperature on the sensor precision and reduce the sensitivity temperature coefficient. Ultra-low power consumption is realized through STM32L series MCU and a PSM mode of NB-IoT, and the system is suitable for long-term field monitoring.

The invention relates to the technical field of temperature control, in particular to a temperature control method and device for a nursery stock greenhouse cultivation environment and a storage medium. The method comprises the following steps that an in-greenhouse temperature influence model is established, the in-greenhouse temperature influence model can predict the in-greenhouse air temperature according to the in-greenhouse soil temperature, and optimization is carried out by considering the influence of the out-greenhouse air temperature on the in-greenhouse air temperature; acquiring a seedling target soil temperature range and a target air temperature range at the current seedling growth stage, starting an air temperature adjusting strategy when monitoring that the real-time in-greenhouse air temperature is not within the target in-greenhouse air temperature range, and judging whether the in-greenhouse soil temperature is within the target in-greenhouse soil temperature range or not; and if the soil temperature in the greenhouse is not within the range of the target soil temperature, adjusting the soil temperature. According to the method, the response speed and adaptability to environmental changes are greatly improved, and more stable and suitable growth conditions are provided for nursery stocks.

The invention discloses a grounding resistance thermal-electric coupling prediction method and system suitable for various soil scenes. The method comprises the following steps: acquiring an electrode parameter, a soil parameter, a grounding fault parameter and initial temperatures of an electrode and soil; calculating an electrode current and a soil leakage current of the time step based on the electrode and soil resistivity of the time step; based on the electrode current and the soil leakage current of the time step, calculating the electrode temperature, the soil radial temperature distribution and the grounding potential rise of the time step by adopting a finite difference method; based on the electrode temperature and soil radial temperature distribution of the time step, updating the electrode resistivity and the soil resistivity of the time step + 1; if the end of the fault time is not reached, returning to continuously calculate the temperature of the electrode and the soil at the time step + 1; otherwise, outputting the electrode temperature and soil radial temperature distribution of each time step. According to the invention, thermal-electric dynamic coupling is considered, and accurate evaluation of the performance of the grounding system is realized.

The invention provides a method for regulating and controlling phenological periods of outdoor pear trees, and belongs to the technical field of pear tree cultivation. According to the phenological period delaying method suitable for the outdoor cultivation pear orchard in the south, the phenological period of pears can be delayed by 5-7 days through grass growing in winter, grass covering in early spring and irrigation in early spring, soil temperature rising is restrained, root activity recovery is restrained, and the phenological period of pears can be delayed by 5-7 days. Specifically, by maintaining high soil water content and low soil temperature, inhibiting respiration and physiological activity recovery speed of root systems of pear trees in spring and delaying absorption and transfer of moisture and nutrients by the root systems, the metabolism level of the tree bodies is reduced, and the phenological period of the pear trees is delayed by about 7 days. Through the synergistic effect of vegetation covering and moisture regulation, accurate delay of the phenological period of the pear tree is achieved, operation is easy and convenient, the ecological cultivation requirement is met, and the method is suitable for open-air pear cultivation areas in the south.

The invention relates to the technical field of environmental remediation, in particular to an in-situ soil perfluorinated compound remediation method and system.The method comprises the following steps that electrochemical targeted enrichment is conducted, applying a pulse type direct-current electric field to the anode and the cathode to drive anionic PFAS in the soil to migrate and enrich towards an anode area; in the anode area, the temperature of anode soil is increased, composite peroxide and a chemical activator are continuously injected, and perfluorinated compounds enriched in the anode are oxidized and degraded; and microbial polishing and ecological restoration: stopping the electric field, carrying out post-treatment on the anode region, adding a microbial agent and a nutritional agent into the anode region, and carrying out biodegradation on residual perfluorinated compounds or degradation intermediate products thereof. Through systematic design of physical field guidance, targeted energy injection, multiple catalytic oxidation and ecological function reconstruction, in-situ soil treatment is achieved, and the core problem in PFAS in-situ remediation is precisely solved.

The embodiment of the invention discloses a soil nutrient classification method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring soil attribute information of a target area within a preset duration; the soil attribute information comprises soil conductivity, soil temperature and spectral reflectivity; performing data preprocessing on the soil attribute information to obtain preprocessed multi-channel time sequence data; performing feature extraction and fusion on the multi-channel time sequence data based on a target feature extraction model to obtain hidden state features; the target feature extraction model is obtained by performing semi-supervised mutual learning on a preset feature extraction model by using different enhanced samples obtained by performing different data enhancement processing on unlabeled samples; and determining a soil nutrient classification result of the target region based on the target classification model and the hidden state features. Through the technical scheme of the embodiment of the invention, the soil nutrients can be accurately and conveniently analyzed, and efficient soil nutrient classification in a few-sample environment is realized.

The invention provides a mid-deep layer coaxial buried pipe group optimization design method which specifically comprises the following steps: S1, according to the structural design of mid-deep layer coaxial buried pipes, establishing a three-dimensional heat transfer model of a mid-deep layer buried pipe heat exchanger based on a bicontinuum finite element method; s2, setting model parameters; s3, inlet temperature, heat extraction power and buried pipe spacing parameters are set, and middle-deep layer coaxial buried pipe group heat extraction amount, adjacent rock-soil temperature or outlet temperature are output; and S4, selecting inlet temperature, heat extraction power and buried pipe spacing parameters as an optimal design scheme of the middle-deep layer coaxial buried pipe group according to the output heat extraction amount of the middle-deep layer coaxial buried pipe group, the adjacent rock-soil temperature or the outlet temperature. According to the method, the medium-deep buried pipe group three-dimensional heat transfer model is established, the influence degree of different pipe group design parameters on the long-term operation heat extraction performance of the system is quantitatively evaluated, and a theoretical basis is provided for actual engineering.

The invention provides a cold region greenhouse irrigation method and system, and belongs to the technical field of environment control. The method comprises the steps that soil temperature data and humidity data of different depths in all partitions of a greenhouse are acquired in real time; carrying out preprocessing and spatial-temporal feature enhancement on the collected data, inputting the data into a soil temperature-humidity dynamic coupling monitoring model, and calculating to obtain a root retting risk index of the current soil environment; if the root retting risk index exceeds a dynamic risk threshold, generating an irrigation strategy for inhibiting the root retting risk; if the root retting risk index does not exceed a dynamic risk threshold, generating a conventional irrigation strategy based on a multi-objective optimization algorithm; according to the generated irrigation strategy, driving the irrigation execution mechanism of the corresponding subarea to perform independent action, and realizing subarea accurate irrigation; the problem of root retting caused by low temperature and high humidity in a cold region greenhouse environment is solved, and precision, intelligence and high efficiency of irrigation management are achieved.