800 results about "Cultivated land" patented technology

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

The invention discloses a farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion, and relates to the technical field of agricultural information, and the method comprises the steps: collecting physical and chemical parameters of soil temperature, humidity, pH value, organic matter content, nitrogen phosphorus and potassium concentration and the like in real time through a multi-source sensor network; removing abnormal values by using a data preprocessing algorithm, performing standardization processing, generating a standardized soil parameter set for subsequent climate and management factor correlation analysis, automatically generating a risk early warning signal, pushing the risk early warning signal to an agricultural management decision platform, and completing full-process analysis from data acquisition to decision support; the farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion can provide a scientific basis for agricultural management decision, effectively guide farmland management practice, and improve soil quality and agricultural production efficiency.

The invention relates to the technical field of cultivated land resource management, and discloses a data optimization and intelligent decision support system in cultivated land resource early warning, which comprises a cultivated land data acquisition module, a soil index analysis module, a degradation identification module, a land force deduction module, a threshold adaptation module and a decision generation module. The cultivated land data acquisition module acquires cultivated land multi-dimensional attribute data in real time and partitions the data; the soil index analysis module divides soil fertility units and fuses soil indexes to generate resource state vectors; the degradation identification module extracts a core degradation factor, establishes a risk layering model and obtains a data correction coefficient; the land force deduction module identifies the association rule and the dynamic correction factor and calculates the land force attenuation gradient; the threshold adaptation module derives an optimal early warning threshold and generates a risk difference sequence; and the decision generation module analyzes the sequence and integrates the sequence into a regulation and control decision scheme. According to the system, intelligent analysis of cultivated land data, dynamic early warning of degradation risks and accurate decision making are realized, and scientificity and efficiency of cultivated land resource management are improved.

The invention provides a remote sensing AI intelligent interpretation-based cultivated land change pattern spot intelligent identification method and system, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, comprehensively recording the cultivated land conditions of the target region in different time periods, carrying out the preprocessing operation of the obtained multi-temporal remote sensing image data set, and carrying out the recognition of the cultivated land change pattern spots. Then feature extraction operation is carried out on the preprocessed multi-temporal remote sensing image data set, a spectral feature sequence and a texture feature sequence of a target area are generated respectively, farmland features are described in detail from different angles, and finally, a time sequence analysis method is combined to analyze the farmland features. And performing change detection processing on the generated spectral feature sequence and texture feature sequence to generate a cultivated land change pattern spot data set of the target area, thereby effectively improving accuracy and efficiency of cultivated land change pattern spot identification.

The invention discloses a farmland abandoned land identification method based on multi-source remote sensing data and time sequence correction. The method comprises the following steps: 1, data acquisition and preprocessing; 2, extracting a key phenological period; 3, constructing a feature library; 4, training and predicting a random forest model; and 5, post-processing and precision evaluation are carried out. The method comprises the following steps: firstly, integrating multi-source remote sensing image data, and strictly preprocessing the multi-source remote sensing image data to ensure the time-space consistency of the data; and accurately extracting key phenological period information by deeply analyzing the minimum vegetative period and the vegetative peak period of the vegetation through NDVI and NDSI time sequences. In the feature construction stage, multiple spectral indexes are fused, principal component analysis and feature importance screening are applied, feature dimensions are optimized, and model performance is improved. A random forest model is adopted for training and prediction, and a high-precision abandoned land spatial distribution diagram is finally generated by combining an oversampling technology and a category weight adjustment technology.

The invention relates to the technical field of image processing, in particular to a remote sensing monitoring method and system for cultivated land protection, and the method comprises the following steps: obtaining a remote sensing image sequence of a cultivated land region at a differential time node, extracting pixel gray values of the same land parcel, arranging the pixel gray values according to time, calculating the ratio of a difference value between adjacent frames to an intra-frame gray average value, and obtaining a remote sensing image sequence; and marking positions continuously exceeding the fluctuation reference ratio, and generating a pixel region set. According to the method, a sampling frequency threshold optimization strategy is adopted, abnormal areas are screened in combination with albedo change rate, short-time fluctuation interference is reduced, time sequence consistency is enhanced, a solar incident angle and a terrain slope are introduced to calculate projection influence, a slope surface shielding area is eliminated, and space grid marking and boundary integrity judgment are adopted; the method improves the continuity of cultivated land distribution information, combines time sequence analysis, sampling optimization, physical characteristic compensation and spatial consistency correction, enhances the fine recognition of the dynamic change of cultivated land, and improves the monitoring precision and data reliability.

The invention discloses a remote sensing image cultivated land segmentation method and system fusing context and boundary perception, and belongs to the technical field of remote sensing image processing and agricultural information. Constructing a cultivated land segmentation initial model composed of a backbone network, a feature enhancement module, a multi-scale feature fusion de-wharf module and a mask prediction module; training set data are input into the initial model, a composite loss function value is calculated, back propagation is executed, and a cultivated land segmentation model with boundary sensing ability is obtained through multi-round iterative optimization; and inputting the remote sensing image into the trained cultivated land segmentation model, and outputting a binary segmentation image representing the cultivated land position. Visual state space modeling and large receptive field convolution are combined, deep and shallow layer information is fused through feature injection, boundary perception supervision and composite loss are introduced, cultivated land boundary discrimination is improved, remote sensing image cultivated land high-precision extraction is achieved, and the method is suitable for agricultural interpretation and monitoring.

The invention provides a fine-grained cultivated land protection potential optimization method, medium and system, and belongs to the technical field of cultivated land data processing.The method comprises the steps that firstly, multi-source remote sensing, soil and climate data are obtained, and a basic feature matrix is extracted through multi-dimensional decomposition; then, multi-scale analysis of soil quality features is realized by utilizing a pyramid neural network model, and adaptive fusion is carried out through a feature integration module; and then constructing a contribution degree optimization equation set, comprehensively considering four aspects of benchmark evaluation, space correlation, time sequence evolution and weight balance, realizing accurate evaluation of the cultivated land protection potential, and generating an optimized protection partition scheme. On one hand, the pyramid neural network model overcomes the problem of insufficient feature extraction of a traditional method through multi-scale feature extraction and adaptive fusion; and on the other hand, through multi-dimensional information fusion and dynamic optimization, the contribution degree optimization equation set solves the problem that accurate evaluation of the cultivated land protection potential is difficult to realize in the prior art.

The invention relates to the technical field of cultivated land supervision, in particular to a big data-based cultivated land utilization supervision system, which comprises a pattern spot remote sensing interpretation module, a pixel state aggregation module, a boundary mutation recognition module, a time sequence cultivation fluctuation analysis module and a response priority pattern output module. According to the method, a time sequence label grid set is established through spatial boundary analysis and pixel index extraction of remote sensing image spots, state labels are introduced to classify pixel tillage evolution, an image spot state evolution chain and path structure is constructed on the basis of spatial aggregation, and a continuous fluctuation area is extracted through sliding window analysis of planting behavior factors in the image spots. A co-occurrence block of boundary disturbance and behavior fluctuation is positioned in combination with a space overlapping relation, a response urgency index is constructed after multiple indexes such as state density, disturbance range and duration period are quantified, multi-dimensional judgment and intervention priority ranking of the farmland utilization abnormal area are achieved, and the identification precision and response efficiency of farmland utilization changes are effectively improved.

The invention discloses a fragmented cultivated land remote sensing extraction method based on space-time deep learning, and the method comprises the following steps: 1, data preparation: selecting a high-spatial-resolution remote sensing image, and capturing the space-time change of cultivated land in different growth cycles and environment conditions in combination with multi-temporal data of different times and seasons; a second step of model construction: constructing a deep learning model fusing ConvNeXt and a U-Net framework, namely a ConvNeXt-U model; 3, model training is carried out; 4, crushing cultivated land extraction; and 5, analyzing and outputting a result. Through multi-dimensional technical innovation, remarkable performance breakthrough and application value improvement are realized in the field of fragmented cultivated land remote sensing monitoring, and the method is specifically embodied in the following three aspects: 1, high precision and robustness improvement are realized; 2, optimizing calculation efficiency and generalization ability; and 3, dynamic evolution analysis and decision support capability.

The invention provides a space-spectrum fusion high-standard farmland utilization mode remote sensing monitoring method and system, and relates to the field of remote sensing monitoring, and the method comprises the steps: carrying out the multi-temporal image synthesis and preprocessing of a remote sensing image set, and obtaining a key phenological period synthesis image set, the NDVI value of the research area in each key phenological period is calculated through the key phenological period synthetic image set; phenological characteristic analysis is performed on different farmland utilization modes based on each NDVI value, farmland utilization priori knowledge is combined to construct a cultivated land utilization map, and spectrum information of the cultivated land utilization map is acquired; performing multi-scale segmentation on the remote sensing image set to obtain field images with spatial information; and assigning spectrum information of the farmland utilization map to the field images with spatial information, and judging the farmland utilization mode of the research area. According to the invention, the spectrum information of the cultivated land utilization map and the field images with spatial information are fused, the farmland utilization mode of each field is visually displayed, and remote sensing monitoring of the farmland utilization mode at the land parcel level is realized.

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The invention discloses a cultivated land reserve resource potential grading method based on multi-dimensional cultivated land quality evaluation, particularly relates to the technical field of land resource management, and is used for solving the problems of insufficient cultivated land potential grading precision and lack of ecological constraints caused by single dimension and data splitting of an existing evaluation method. Interpreting land types by verifying and fusing cultivated land quality classification codes and remote sensing, complementing constraint fields such as ecological protection areas and pollution levels, and constructing a dynamic model associated with natural quality parameters and ecological limitation indexes; on the basis of land parcel type matching differentiation screening rules, threshold screening and suitability scoring are carried out on unutilized lands, abandoned garden lands and the like in a classified mode; eliminating ecological sensitive or pollution exceeding land parcels through logic verification, calculating a comprehensive potential score in combination with a multi-dimensional parameter weight and a dynamic threshold value, and dividing the comprehensive potential score into high, medium and low grades according to regional resource requirements; and the cultivated land quality multi-dimensional collaborative evaluation and ecological protection two-way decision is realized.

The invention provides a natural resource dynamic monitoring method and system based on multi-source data fusion, a medium and equipment. The method comprises the following steps: acquiring satellite remote sensing data, aerial remote sensing data and ground monitoring data; performing data fusion on the satellite remote sensing data and the aerial remote sensing data to obtain fused data; integrating the fusion data with the ground monitoring data to obtain comprehensive monitoring data; performing terrain classification on the comprehensive monitoring data based on deep learning to obtain terrain classification results including cultivated land, construction land, water, forest land and grassland; and optimizing the ground feature classification result based on a space-time logic consistency algorithm to obtain an optimized classification result and ground feature change information. According to the method, through fusion of the multi-source data and classification optimization of the natural resources, in combination with deep learning and a spatial-temporal logic consistency algorithm, dynamic monitoring of the natural resources is realized, the monitoring precision is improved, and reliable data support is provided for natural resource management and environmental protection.

The invention discloses a farmland field segmentation method and system based on multispectral SAM model and multitask learning guidance, and belongs to the field of field segmentation. The problem that in the prior art, channel number matching is conducted in a simple channel compression or deep convolution mode, and the challenges that the boundaries of cultivated land parcels are complex, diversified and subtle are difficult to effectively deal with is solved. The method comprises the following steps: performing spectrum compression on a multispectral remote sensing image through a U-Net structure fused with a CBAM attention mechanism to generate a three-channel pseudo visible light image; the image is input into an SAM-ViT encoder subjected to LoRA fine tuning, and global semantic features are extracted; a task token sequence is constructed, explicit interaction between the token and the image features is realized through a bidirectional Transform module, and high-resolution fusion features are generated; calculating cross-task attention, and outputting a multi-task mask through inner product operation; and performing post-processing by using a watershed algorithm to generate a field boundary. The method is used in the field of cultivated land identification.

The invention discloses a photovoltaic auxiliary greenhouse cultivation system which comprises a greenhouse body composed of a framework system and a photovoltaic ceiling, and the photovoltaic ceiling is laid on the top of the framework system, provided with a light splitting type solar thin film cell panel set with adjustable light transmittance and laid in a partitioned mode according to the illumination requirement. The dynamic light compensation system, the temperature balance system and the energy storage system are arranged at the top of the framework system, the energy storage system is used for storing energy and supplying power, the dynamic light compensation system is used for supplementing light for each indoor crop area according to photosynthetically active radiation and sun illumination conditions, and the temperature balance system is used for ventilating and humidifying the greenhouse; and the control machine is arranged on the greenhouse main body and is electrically connected with the dynamic light compensation system, the temperature balance system and the energy storage system so as to receive the monitoring data and control the operation of each electric appliance element. According to the system, lithium battery energy storage and a grid-connected inverter are integrated, the energy self-sufficiency rate is optimized, and the space utilization rate is increased through spectrum customization and three-dimensional planting; the method is suitable for tilled land shortage areas, and gives consideration to clean energy production and agricultural efficient output.

The invention discloses a semantic edge and segmentation-based agricultural plot form local updating method and device. The method comprises the following steps of: preparing a plot boundary vector of a previous period of a specified application area and a later-period target area remote sensing image which finally needs to be locally updated and extracted; selecting and designing a multi-task learning network model capable of simultaneously generating semantic segmentation and semantic edges; using the semantic segmentation and semantic edge multi-task learning model to train a previous-stage plot boundary vector and a later-stage remote sensing image to obtain a multi-task learning model weight, predicting high-resolution remote sensing image data of a target application area, and obtaining a line result and a surface result of agricultural cultivated land of the application area; and performing small pattern spot removal processing on a surface prediction result, comparing results of two-stage semantic segmentation, and selecting updated land parcel elements according to a threshold value. The updated land parcel elements are classified and processed according to the following conditions: 1, common edges do not exist: if the common edges do not exist, an edge vector result generated by a multi-task learning model through later remote sensing is directly adopted; 2, existence of a common edge: in the two-stage remote sensing images, the area of the agricultural plot may change (increase or decrease), the two-stage images are compared according to an edge fusion algorithm, the same edge in the two-stage images is reserved, and the changed edge is updated; according to the invention, local updating of the remote sensing image plot is finally realized.

The invention provides a cultivated land quality monitoring system based on cultivated land protection. The cultivated land quality monitoring system comprises a multi-source sensing module, a heterogeneous transmission module, a space-time database module, a quality degradation prediction module, a dynamic early warning module, a repair decision module and a quality traceability block chain module. The multi-source sensing module collects data such as soil pH value, organic matter content, heavy metal concentration, crop canopy temperature and vegetation index; the heterogeneous transmission module carries out encryption packaging and format standardization on the data; the space-time database module stores the processed data and space-time associated information; the quality degradation prediction module generates multi-dimensional monitoring data and predicts a degradation risk; the dynamic early warning module generates a multi-stage early warning signal according to the risk value; the restoration decision module outputs soil improvement engineering technical parameters and ecological compensation schemes; and the quality traceability block chain module realizes data verifiable evidence storage. The intelligent level of cultivated land protection can be improved, and comprehensive technical support is provided for sustainable utilization of cultivated land quality.

The invention discloses a video geographic positioning method and device based on iron tower monitoring, and the method comprises the steps: constructing a monitoring video homonymy point sample library, collecting the homonymy point data of a monitoring video and a remote sensing orthoimage, and storing a reference image; establishing an initial calibration mapping relation, and realizing coordinate transformation of the remote sensing image and the video image through projection transformation; matching a reference image closest to the real-time video frame from a reference image library through image retrieval; dense matching of inclined images is carried out by using a deep learning model, and automatic alignment of feature points is realized; and resolving a video image coordinate to a geographic coordinate through homography transformation and an initial calibration model to complete high-precision positioning. According to the method, the bidirectional conversion function of finding the land by the video and finding the video by the land is supported, the application efficiency of iron tower monitoring in the fields of cultivated land protection and the like is effectively improved, a good positioning effect is still achieved for complex scenes such as plains, mountainous regions and the like, and corresponding technical support is provided for wide application of iron tower video monitoring.

The invention discloses a cultivated land state monitoring method based on cultivated land boundary identification, and relates to the technical field of image analysis, and the technical scheme comprises the steps: carrying out the preprocessing of a periodically obtained cultivated land real-time image, and carrying out the screening to obtain a gray-scale map corresponding to a set optimal spectral band; based on the grey-scale map and the spatial distribution characteristics and the time sequence difference characteristics of the seedlings, calculating the comprehensive probability that each local peak pixel point represents the existence of the seedlings in the grey-scale map of the current period; based on the comprehensive probability, judging whether each local peak pixel point represents a seedling or not; dividing a cultivated land boundary based on a judgment result, and distinguishing a cultivated area and a non-cultivated area; according to the method, the existence probability of the seedlings is judged by utilizing the spatial distribution characteristics and the time sequence difference characteristics of the seedlings, the accuracy of cultivated land boundary identification can be improved, and the cultivated land state is periodically updated and monitored in real time.

The invention discloses a saline-alkali cultivated land information monitoring method based on spectral analysis. The method comprises the following steps: step 1, data preparation: collecting and processing data covering a target area; step 2, spectral index calculation and fusion: carrying out unary linear regression fusion on the salt index subjected to ground hyperspectral transformation and the salt index subjected to simultaneous Sentinel-2 multispectral calculation to generate a high-multispectral fusion index data set, and constructing a characteristic variable library; 3, feature variable selection: screening out key features from a feature variable library; 4, constructing and training a support vector machine model: training a support vector machine regression model; 5, salinity inversion and precision verification: outputting a soil salinity prediction value, generating a soil salinity spatial distribution diagram of the cultivated land in the Hetao irrigation area according to the soil salinity prediction value, and dividing cultivated land salinization grades by combining an inversion result; and 6, dynamic monitoring: repeating the steps to the step 5, and generating a salinization dynamic change diagram.

According to the agriculture-like cultivated land and land humidity monitoring robot and the monitoring method, structurally, a soil turning device, a soil extraction device and a driving wheel device are connected with a driving motor, the driving motor, a camera device and the soil extraction device are in signal connection with a remote control terminal, and the soil turning device is arranged in a front-back telescopic and up-down movable mode relative to a machine body; the soil extraction device is movably arranged up and down relative to the machine body, the camera device is rotationally arranged relative to the machine body and used for monitoring the movement of the robot in real time, and by optimizing robot hardware configuration and sensor layout and integrating multiple sensors and a core control unit, comprehensive and accurate collection of farmland soil key parameters is achieved. And a wireless data transmission and cloud storage system is deployed to ensure real-time and accurate transmission and analysis of data. Artificial intelligence and computer vision technologies are integrated, soil images are processed, features are extracted and classified, and accurate evaluation and prediction of soil quality are achieved.

The invention discloses a soil acidification improvement method, and relates to the technical field of soil improvement, according to the soil acidification improvement method, evaluation of soil attributes is completed through multi-level diagnosis and calculation of the acidification coefficient, the physical obstacle coefficient and the buffer capacity coefficient of soil, and comprehensiveness of soil acidification analysis is ensured. The problem of limitation existing in the current soil acidification analysis process is solved, meanwhile, a threshold value is used for driving decision control flow branches, an improvement scheme is flexibly upgraded based on real-time monitoring data, the soil improvement effect is improved, closed-loop learning double feedback and S6, data are fed back to S1, self-optimization of the system is achieved, the system adapts to soil changes, and the soil acidification analysis efficiency is improved. An abnormal state after improvement is warned in time, so that the soil safety is guaranteed; the method is suitable for the fields of land restoration and the like, the soil improvement efficiency is improved through data-driven intelligent decision, resource waste is reduced, soil acidification is improved and upgraded, and a technical engine is provided for agricultural sustainable development and cultivated land protection.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

The invention relates to the technical field of soil data analysis, and discloses a farmland soil cadmium content distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining historical data of multi-dimensional indexes of farmland soil in a target region and known soil point cadmium content data; calculating characteristic factors according to historical data of the multi-dimensional indexes; constructing a data set according to the historical data of the multi-dimensional indexes, the characteristic factors and the known soil point cadmium content data; training the random forest model by using the data set to obtain a farmland soil cadmium content distribution prediction model; and obtaining multi-dimensional index data of the target time sequence, calculating a characteristic factor, and inputting the characteristic factor into the cultivated land soil cadmium content distribution prediction model to obtain a cultivated land soil cadmium content distribution prediction result. The scheme can consider the influence of the soil cadmium element input and output process on the soil cadmium content, and improves the accuracy of the model prediction result.

The embodiment of the invention provides a method and a device for dynamically monitoring arable soil desertification based on multi-source satellite remote sensing. The method is applied to the technical field of environmental protection, and comprises the following steps: training a machine learning model by using a constructed inversion data set to obtain a soil sand content inversion model; based on the constructed long-time-sequence cultivated land bare soil data set and the corresponding topographic data, performing inversion by using a soil sand content inversion model to obtain cultivated land soil sand content data of the research area; on the basis of the cultivated land soil sand content data, cultivated land soil desertification early-warning factors are screened from climate data, topographic data, social economic data and soil data of the research area, weights corresponding to the cultivated land soil desertification early-warning factors are calculated, and a cultivated land soil desertification early-warning model is established on the basis of the cultivated land soil desertification early-warning factors and the weights corresponding to the cultivated land soil desertification early-warning factors; and on the basis, arable soil desertification early warning is carried out on the research area. In this way, arable soil desertification dynamic monitoring and risk early warning can be achieved.

The invention discloses a degraded cultivated land soil organic matter three-dimensional mapping method fusing remote sensing, near-earth sensing and historical data, and relates to the technical field of digital soil mapping, the method is based on an INLA-SPDE three-dimensional model framework, remote sensing, near-earth sensing and historical data are fused, and high-precision three-dimensional mapping is achieved. The method comprises the following steps: collecting organic matter data and near-sensing visible near-infrared reflection spectrum data of different depths of a soil profile at each sampling point in a research area, then screening visible near-infrared reflection spectrum characteristic wavebands significantly related to organic matters, collecting and integrating historical data, and completing standardization processing on remote sensing and near-earth sensing data; dividing a modeling set and a verification set, calculating a range value based on a semi-variable function, determining triangular mesh parameters, and constructing an SPDE model; the near sensing data and the remote sensing data serve as covariables and are fused into the INLA-SPDE three-dimensional model, and a soil organic matter three-dimensional space distribution diagram is generated after model fitting and uncertainty is quantified. According to the method, the problem that the vertical change of the soil attribute cannot be accurately simulated by a traditional method is solved, and high-precision and high-efficiency three-dimensional mapping of the regional scale soil organic matter in the horizontal and vertical dimensions is realized.

The invention belongs to the technical field of crop planting, and discloses a Yellow River Delta winter wheat salt-tolerant variety screening and cultivated land quality improving method, which comprises the following steps: step 1, indoor culture test, namely influence of different salinity concentrations on winter wheat budding, step 2, field positioning test, and step 3, field positioning test. The invention relates to screening of salt-resistant suitable-growth high-yield varieties of winter wheat in mild saline-alkali cultivated land, and influences of organic materials with different carbon sources on soil quality and crop yield. According to the invention, the physiological status and agronomic traits of the current Yellow River Delta slight saline-alkali soil winter wheat variety in different growth periods are explored, and a theoretical basis is provided for screening the salt-resistant, suitable-to-grow and high-yield winter wheat variety; the influence of different carbon source materials on the physicochemical properties of the saline-alkali soil is analyzed, the microbial enhancement action mechanism in the saline-alkali soil is explored, 4-5 salt-tolerant winter wheat varieties are screened for the theoretical basis for improving the quality and productivity of the saline-alkali soil of the Yellow River delta, the physicochemical properties of the saline-alkali soil are improved, and the soil microbial community structure and diversity are enhanced.

The invention discloses a power system energy management method and equipment based on hydrogen energy hybrid power, belongs to the technical field of energy management, and is used for solving the technical problems that the random load characteristic of field operation of an existing tractor is complex, and a hybrid power tractor is uneven in ploughing and sowing under the complex terrain operation working condition and the tillage state. The method comprises the following steps: collecting multi-modal data of a farmland environment; performing vehicle speed prediction processing on the tractor under related environment characterization to obtain a dynamic prediction vehicle speed of the tractor; carrying out rolling optimization processing on the dynamic prediction vehicle speed under related output power to obtain the driving demand power of the tractor; and superposing the driving required power and the pre-obtained tillage tool required power to obtain the total required power of the power system, decomposing the required power in a frequency domain according to a formulated variational mode decomposition rule, and endowing the required power to a driving power generation system formed by a fuel cell, a power cell, a hydrogen internal combustion engine and a generator.

The invention provides a cultivated land identification method, system and device based on modal fusion and a medium, and belongs to the technical field of cultivated land identification. The method comprises the following steps: acquiring image data of to-be-identified cultivated land through an unmanned aerial vehicle, and acquiring GIS vector data and historical time sequence images of the to-be-identified cultivated land to form a basic data set; performing correction, splicing and segmentation processing on image data in the basic data set, performing unified coordinate system processing on GIS vector data in the basic data set, and calculating field piece regularity; processing the basic data set by using a single-mode visual perception model, outputting a preliminary detection result containing a probability graph and boundary information, correcting the preliminary detection result by using a multi-mode logical reasoning module in combination with auxiliary data, and generating an identification result; and carrying out visualization and vectorization processing on the identification result to generate visual data, and displaying the visual data. According to the invention, through multi-modal fusion and multi-dimensional processing, the precision and reliability of cultivated land identification are significantly improved.