187 results about "Soil component" patented technology

The invention relates to the technical field of cross-modal data fusion analysis of soil component detection and improvement agent preparation, in particular to a preparation method of a soil improvement agent based on component detection, which comprises the following steps: acquiring soil component data through a near infrared spectrometer, a high performance liquid chromatograph and an inductively coupled plasma mass spectrometer; an incremental principal component analysis algorithm is combined with an oscillation suppression function to update a covariance matrix in real time, a dynamic defect factor priority list is generated, a multi-objective optimization model improved based on NSGA-II is guided to integrate the soil pH value, humidity and organic matter content to construct a dose response curved surface, and a Pareto optimal solution set is solved. A sensor array collects environment feedback data, drives a transfer learning algorithm to calibrate parameters, a dynamic incidence matrix attenuation rate and optimal weight adaptive adjustment, iteratively outputs a modifier synergistic effect solution set through a closed-loop control mechanism, and solves the problems of difficult data fusion, principal component weight offset and component synergistic deviation. And the proportioning accuracy and stability are improved.

The invention discloses a method for predicting the content of heavy metals in different occurrence forms in soil based on machine learning, and relates to the technical field of soil component content analysis, and the method comprises the following steps: (1) collecting soil physicochemical properties, the total amount of soil heavy metals, the types of the heavy metals in the soil and the content of the heavy metals in different occurrence forms in the soil from a literature library, constructing a sample database; (2) preprocessing the sample database to obtain a preprocessed sample database; (3) for each kind of heavy metal, extracting corresponding sample data from the preprocessing sample database, dividing the sample data into a training set and a test set, selecting a machine learning algorithm, establishing a prediction model training model of different occurrence form contents of the heavy metal in the soil, and optimizing hyper-parameters in the training process; and (4) predicting the content of heavy metals in different occurrence forms in the soil to be detected by using the optimized prediction model. The method has a good application prospect in the aspects of heavy metal pollution risk evaluation and the like.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

The invention relates to the technical field of soil component analysis, in particular to a five-pin soil multi-parameter sensor, which comprises a soil parameter measurement and calibration module, a measurement data integrity guarantee module, a measurement frequency dynamic adjustment module, a soil state change monitoring module and a regional state distribution identification module, and the measurement frequency is dynamically adjusted. According to the method, energy consumption of data is reduced when the environment is stable, meanwhile, quick response is achieved when conditions change, the real-time performance and accuracy of monitoring are improved, data integrity is enhanced through dual storage and a re-measurement mechanism when necessary, information completeness is guaranteed, and in addition, through accurate time point and regional anomaly analysis, the accuracy of data analysis is improved. According to the scheme, the risk area is effectively identified and positioned, decision support is optimized, and soil monitoring is more scientific and efficient.

The invention relates to the technical field of data processing, and provides a regional soil composition detection method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a hyperspectral image of a to-be-detected region; acquiring LIBS spectral data of spatial point locations in the to-be-detected area based on a laser-induced breakdown spectroscopy (LIBS) technology; based on the spatial position information, matching the hyperspectral image with the LIBS spectral data, and performing spatial point location soil component labeling on the hyperspectral image based on a matching result to obtain a labeled hyperspectral image; and detecting the soil components of the to-be-detected area based on the labeled hyperspectral image to obtain a soil component detection result. According to the regional soil component detection method and device, the electronic equipment and the storage medium, low-cost and high-efficiency automatic labeling of the hyperspectral image is realized, and the method and the device have important significance in promoting development of the field of remote sensing image field large-area environment detection.

The invention discloses a farmland soil heavy metal inversion method fusing spectrum layering and machine learning, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a to-be-measured spectral variable of soil in a survey area; and inputting the to-be-detected spectrum variable into the soil heavy metal inversion model, generating a heavy metal prediction concentration spatial distribution diagram of the investigation area output by the soil heavy metal inversion model, and constructing the soil heavy metal inversion model based on the calibration sample set and the verification sample set. The calibration sample set and the verification sample set perform stratified sampling determination on a plurality of training samples based on soil component information of sample soil, and the training samples comprise important spectral variables and heavy metal concentrations of the sample soil; and determining a heavy metal concentration spatial distribution diagram of the farmland in the investigation area according to the heavy metal predicted concentration spatial distribution diagram. According to the method, the accuracy of soil heavy metal concentration monitoring is remarkably improved, and the monitoring cost and time are effectively reduced.

The invention provides a regional soil component detection method based on a remote sensing image, and belongs to the technical field of soil detection. Aiming at the problems that a traditional detection method depends on a single optical remote sensing image, is interfered by weather and vegetation, and is low in detection precision, low in efficiency, unguaranteed in data credibility and the like, radar and thermal infrared remote sensing data are introduced to be fused with an optical image, features are extracted through principal component analysis, a generative adversarial network is adopted to carry out hyperspectral image super-resolution reconstruction, and a high-resolution hyperspectral image is obtained. A genetic algorithm is utilized to realize adaptive wave band selection, a real-time atmospheric correction model is constructed, a recurrent neural network and a spatial convolutional neural network are utilized to mine spatio-temporal context information, a quantum computing acceleration algorithm is introduced, block chain management data is utilized, and unmanned aerial vehicle cooperative operation is combined. According to the method, the detection precision, efficiency and data credibility are remarkably improved, and the regional soil component detection process is effectively optimized.

The invention relates to the technical field of geotechnical engineering investigation and artificial intelligence crossing, in particular to a marine fine-grained soil content prediction method based on multi-source CPTU data and machine learning, comprising: acquiring CPTU data and corresponding geotechnical test component content data; constructing a CPTU prototype vector of the geotechnical test point, and calculating a cosine similarity weight of the data point and the prototype vector and a Gaussian distance weight of the data point and a window center; generating a plurality of sliding windows by taking each test point as a center, and extracting a high-dimensional feature vector by using double weights; training a random forest regression model by using the feature vectors; moving a window of a to-be-predicted drill hole according to the step length, extracting features, inputting the features into the trained model, and generating a continuous depth profile of the fine-grained soil component content. According to the method, an indirect qualitative judgment mode of a traditional experience method is broken through, direct, quantitative and continuous prediction from CPTU data to the soil body component content is achieved, and the technical problems that a traditional method is high in cost, long in period, discrete in data and incapable of achieving quantification are solved.

The invention discloses a rice seedling raising nutrient soil taking and screening all-in-one machine which comprises a main body assembly, the main body assembly comprises a tractor, a tractor frame of the tractor is fixedly provided with a traction machine frame, and the traction machine frame is connected with a soil taking machine frame in a lifting mode; the soil taking assembly comprises a rotary tillage cutter shaft rotationally connected to the front end of a soil taking rack, a plurality of rotary tillage cutters are distributed on the rotary tillage cutter shaft, a soil conveying cavity is formed in the portion, behind the rotary tillage cutter shaft, of the soil taking rack, a soil conveying shaft is rotationally connected to the soil taking rack, and a soil throwing opening is formed in the portion, behind the soil conveying shaft, of the soil taking rack; a left soil conveying blade and a right soil conveying blade which are opposite in spiral direction are arranged on the soil conveying shaft on the left side and the right side of the soil throwing opening respectively, a plurality of soil throwing plates are arranged on the soil conveying shaft in front of the soil throwing opening, and the soil throwing plates can throw soil backwards through the soil throwing opening; the soil screening assembly comprises a soil screening roller rotationally connected to the upper end of the traction rack, and the soil thrown backwards can be conveyed into the soil screening roller; rotary tillage, soil taking and soil screening are integrated, and the operation efficiency is high.

The invention relates to the technical field of cultivation, in particular to a peach orchard cultivation method which comprises the following steps: based on peach tree growth area characteristics, plot slope measurement and soil component collection are performed, and soil pH value and organic matter content comparison is performed through inclination angle reading and soil sampling record numerical values. Through comprehensive analysis of plot slope measurement and soil component collection, plot distribution parameters are generated according to regional characteristics, a more scientific planting zoning management mode is constructed, and the land utilization efficiency is improved. Dynamic detection of soil humidity, temperature and seedling root activity is introduced into planting planning, so that the suitability of a seedling growth environment is ensured, and the growth problem caused by improper plot selection is reduced. The survival rate tracking link is combined with dynamic data monitoring and survival plant number statistics, the hysteresis problem of traditional judgment depending on experience is solved, and real-time optimization of water and fertilizer supply is achieved.

The invention discloses an artificial soil for cultivation of zoysia matrella based on synergistic utilization of organic and inorganic wastes. The artificial soil is a compound product of a sludge compost product and a waste resource material containing phosphorite industry waste, rice hull biochar and the like. The method is characterized in that phosphorus-containing ore industry waste components are introduced into an artificial soil component structure, phosphorus-containing ore industry waste is used for adsorbing and fixing free potassium, sodium, calcium, magnesium and heavy metal ions brought by compost products and other components, the conductivity of the artificial soil is reduced, and the adverse effect of high salinity and high heavy metal on plant growth and development is reduced. The phosphorus-containing ore industry waste component can also provide phosphorus nutrients for plants, thereby promoting the growth of zoysia matrella. The invention provides a novel artificial soil formula for soilless culture of zoysia matrella, and provides technical support for urban landscaping engineering.

The invention discloses a plastic film mulching and soil covering all-in-one machine for agricultural planting, which comprises a rack, furrow openers capable of lifting up and down are arranged on both sides of the front end of the rack, a film mulching roller is mounted at the rear end of the rack, and a film pressing and ridging structure and a seeding structure corresponding to the furrow openers in position are mounted through a turnover frame; according to the invention, a rotary sleeve with a plurality of digging assemblies is rotatably mounted outside a seed loading cylinder, and each digging assembly consists of a digging shovel with a seed discharging bin and a film breaking hot knife; when the all-in-one machine works, a power machine is used for driving the all-in-one machine to move forwards at a constant speed, a digging shovel is communicated with a seed falling opening of a seed containing cylinder in the process of screwing into soil, so that seeds can be quantitatively warehoused conveniently, the warehoused seeds are uniformly dispersed in a seeding pit through sieve pores, and soil is covered by a soil covering rod at the rear, and the seeding quality is improved; and meanwhile, the heated film breaking hot knife is in contact with the film when being screwed into soil, a regular notch is formed in the film through high-temperature ironing, and the film covering quality is improved.

The invention discloses a shield muck heavy metal pollution intelligent sorting system and method based on multi-parameter online detection, and relates to the field of shield muck sorting, the shield muck heavy metal pollution intelligent sorting system comprises a muck sample pretreatment module, an online detection module, a pollution evaluation analysis module, a sorting execution module and a linkage control feedback module; the muck sample pretreatment module is used for separating coarse particles with the particle size larger than 50 mm and impurities in shield muck through a vibrating screen. According to the method, the initial value of the fine-grained soil content is detected in real time, data is automatically corrected based on a preset intelligent densimeter and a traditional densimeter difference correction model, rapid measurement is achieved, the detection efficiency is improved, a dynamic correlation model of fine-grained soil component changes is constructed by combining auxiliary parameters such as the muck weight, the water content and coarse grain grading, and the detection accuracy is improved. And the working condition adaptation module is linked to automatically improve the fine-grained soil detection frequency when the stratum mutation is identified by the shield PLC parameters, so that the data is ensured to closely follow the stratum change.

The invention relates to a road bougainvillea speetabilis intelligent fertilization supervision method and system, and the method comprises the steps: enabling collected multispectral image data and thermal imaging image data to be precisely matched with GPS coordinates, enabling soil component parameters to be aligned with the corresponding multispectral image data and thermal imaging image data, and forming a spatiotemporal data set, performing feature extraction on the multispectral image by adopting a convolutional neural network, constructing a growth attitude comprehensive analysis model, comparing a result output by the growth attitude comprehensive analysis model, namely a current growth state quantized value, with a preset standard based on historical growth data and a bougainvillea speetabilis growth rule, and calculating the missing amount of related fertilizers, a precise fertilization formula and a fertilization time node are determined by combining the current fertility condition of the soil, a control instruction is generated according to the fertilization formula and the time node, and the fertilization robot executes fertilization operation based on the control instruction and a preset path. The method has the effects of improving the fertilizer utilization rate, reducing the labor cost and improving the maintenance efficiency.

The invention provides a soil sampling device for soil analysis, and relates to the technical field of soil analysis. A depth sensing module, an intelligent sampling rod, an analysis and feedback module and a reinforcement learning module are integrated. The optimal sampling depth is predicted through the ultrasonic detection and near infrared scanning technology and the deep learning model. The intelligent sampling rod controls the sampling depth, the sampling amount, the vertical rotating force and the vertical rotating direction according to set parameters. The analysis and feedback module integrates various types of microsensors, monitors the pH value, conductivity and moisture of soil in real time, pretreats a sample, and obtains a soil component overview through spectral analysis. And the reinforcement learning module continuously updates and optimizes a sampling strategy through real-time data feedback and cloud computing. The display module visually displays real-time data, supports mode switching, planning and recording, and is simple and convenient to operate. According to the method, multiple methods such as deep learning and reinforcement learning are combined, the accuracy and efficiency of soil sampling are improved, and powerful technical support is provided for soil analysis.

The invention relates to the technical field of soil detection, and particularly discloses a soil component detection device and method for ramie planting, the detection device comprises a base, the base is arranged on the ground to be detected through supporting columns on the periphery, a layered detection mechanism is arranged at the lower part of the base, and the layered detection mechanism comprises a detection rod; the detection rod comprises a vertically arranged guide probe tube, and the top end of the guide probe tube is connected with the base through a pushing mechanism; a plurality of detection windows are formed in the exterior of the guide probe tube, a shielding substrate is attached to the positions, corresponding to the detection windows, of the inner wall of the guide probe tube, the shielding substrate is rotationally matched with the guide probe tube, and a flexible shielding part and a detection sensor array part which are matched with the detection windows are arranged on the outer surface of the shielding substrate; at the beginning, the shielding substrate shields the detection window through the flexible shielding part on the outer surface; the device can carry out layered accurate detection on soil with different depths, and the using effect of the device is improved.

The utility model discloses intelligent soil fertility monitoring equipment for planting, relates to the technical field of soil monitoring, aims to realize intelligent, automatic and integrated monitoring of soil fertility and improve the yield and quality of crops, and adopts the technical scheme that the intelligent soil fertility monitoring equipment for planting comprises an outer box and a monitoring part, the outer box comprises a box body, a support and a handle, the monitoring part comprises an intelligent monitoring system, a display control system and a soil monitoring comprehensive sensor, and the box body is provided with a sensor connecting port, a printing paper replacing port, a printing port, heat dissipation holes and a USB interface. The intelligent monitoring system comprises an A / D module, an acquisition control module, a data storage module, a data transmission module, a GPS module, an interaction module and a lithium battery pack, and the display control system comprises a touch display screen, a processor and a miniature printer. Data support is provided for planting precision of agricultural production, and a scientific and reasonable agricultural fertilization plan is analyzed.

The invention relates to the technical field of soil component detection, in particular to a soil component detection method and system based on spectral analysis, and the system comprises an intelligent mobile platform and a multifunctional integrated spectrum acquisition module. In the advancing process, soil is continuously scanned through the spectrum collection module, spectrum information is obtained in real time, data are processed through the spectrum feature extraction unit, the soil component type and distribution proportion are obtained, and a soil component distribution diagram and quantitative data are generated. According to the method, the detection efficiency can be remarkably improved, the problem of data missing of a traditional fixed-point sampling mode is avoided, meanwhile, the adaptability to complex terrains and diversified soil types is enhanced, and high precision and wide applicability of a detection result are ensured.

The utility model relates to the field of vegetable drip irrigation equipment, in particular to a vegetable water and fertilizer integrated drip irrigation device which comprises a liquid storage tank, a fertilizer tank, a flow guide pipe, a first meter, a water inlet pipe, a second meter, a drip irrigation pipe, a hydraulic controller, a master control module and a soil tacheometer, according to the vegetable water and fertilizer integrated drip irrigation device, the first meter and the second meter are arranged on the outer side of the joint of the fertilizer tank and the liquid storage tank as well as the outer side of the joint of the water inlet pipe and the liquid storage tank of the existing vegetable water and fertilizer integrated drip irrigation device respectively, so that the amount of water and fertilizer can be accurately controlled; detection information can be transmitted to the master control module for comprehensive analysis, then the master control module is triggered to automatically control the water and fertilizer proportion at the first meter and the second meter, accurate proportioning of water and fertilizer is achieved, the quality and growth stability of vegetables are guaranteed, drip irrigation is carried out in an accurate proportioning mode, the situation of excessive fertilization is not likely to be caused, and the work efficiency is improved. And more resources are saved.

The invention discloses soil component detection equipment for digital intelligent forestry planting, and relates to the technical field of soil component detection, the soil component detection equipment comprises a lower shell and a mounting block, one side of the lower shell is rotatably connected with an upper shell, the other side of the lower shell and the top of the upper shell are respectively provided with a first handle, and the lower shell is internally provided with a printer and a plurality of detection holes; and a detection box is inserted into the detection hole. According to the soil component detection equipment for digital intelligent forestry planting disclosed by the invention, a fixed plate and a mounting seat are rotated clockwise, a sampling rod is driven by a thread to drill into soil, the mounting seat is rotated clockwise, a scraping plate is rotated through a linkage rod, and the mounting seat is rotated to drive the sampling rod to rotate anticlockwise for three circles; a soil sample is scraped into a rotating cylinder through a scraping plate, then a mounting seat and a sampling rod are rotated anticlockwise, so that an abutting block slides into a sliding groove, the interior and the exterior of the rotating cylinder are isolated, the sampling rod is pulled out of soil, and the effect of extracting multiple layers of soil samples at a time is achieved.

The invention discloses solidified soil prepared from solid waste and a preparation method of the solidified soil, and relates to the technical field of constructional engineering materials. According to the solidified soil prepared from the solid waste, the self-made anti-dispersing agent and the composite curing agent are added, the self-made anti-dispersing agent is prepared by introducing graphene oxide to the surface of pretreated aramid fiber and then generating nano silicon dioxide on the surface in situ, the self-made anti-dispersing agent enters solidified soil components, dispersion is reduced, and therefore the strength of the solidified soil is kept; the composite curing agent comprises modified glass fiber reinforced plastic, sulphoaluminate cement, Portland cement and gypsum powder, the modified glass fiber reinforced plastic is prepared by carboxylating glass fiber reinforced plastic and then coating the carboxylated glass fiber reinforced plastic with a sulfydryl cross-linked polymer, and the modified glass fiber reinforced plastic enters the structure of the cured soil, so that shrinkage deformation is prevented, and the curing strength is enhanced.

The invention discloses soil vertical batch sampling equipment and method for environmental geological survey. The equipment comprises a main body supporting mechanism and a distributed sampling mechanism arranged on the main body supporting mechanism, the main body supporting mechanism comprises a main body supporting plate which is horizontally arranged; the distributed sampling mechanism comprises a sampling mechanism supporting shell arranged at the sampling lifting through groove, a sampling outer drill rod extending vertically is fixed to the lower end of the sampling mechanism supporting shell, and a sampling inner drill rod is rotationally connected into the sampling outer drill rod; the side wall of the sampling outer drill rod is provided with a plurality of outer drill rod side surface sampling holes which are communicated inside and outside, and the side wall of the sampling inner drill rod is provided with a plurality of inner drill rod side surface sampling holes which are communicated inside and outside; the depth of the sampling outer drill rod and the sampling inner drill rod inserted into the soil can be accurately controlled by utilizing the sampling static pressure driving mechanism, which is crucial for researching the characteristic distribution of the soil at different depths, and the distribution condition of the soil components in the vertical direction can be determined by accurately controlling the sampling depth.

The invention provides a soil pollutant intelligent detection method and system. The method comprises the following steps: removing soil matter creation conditions in example soil acquisition component data; obtaining soil component attribute data of the influencing soil matters according to the types of the influencing soil matters, and obtaining the influencing soil matters according to the sample soil collection component data, the multiple soil pollutant information sets and the soil component attribute data of the influencing soil matters through a deep learning-based influencing soil matter pollutant detection network. According to the method, regression analysis information about the range of pollutant data in sample soil acquisition component data is obtained, and the soil matter pollutant detection network is optimized according to the difference between the regression analysis information and annotation information, so that the trained soil matter pollutant detection network can be used for detecting soil matter pollutants in the sample soil acquisition component data. The method has the capability of detecting the matters influencing the soil based on the multi-dimensional information, and the pollutant data can be accurately detected by the pollutant detection network influencing the soil matters.

The invention relates to the technical field of shield construction, in particular to a shield muck fine-grained soil content real-time detection system and deduction method based on multi-parameter fusion, and the system comprises a multi-parameter sensor unit, a data acquisition and transmission unit, a data processing and analysis unit and a display unit. According to the system, the content of fine-grained soil in muddy water separated from the muck is rapidly detected by using an intelligent densimeter after the muddy water is precipitated, a correction algorithm based on a data difference rule obtained by an indoor test is introduced, fine-grained soil content data detected by the intelligent densimeter is corrected, and a multi-parameter fusion algorithm is applied to determine the content of the fine-grained soil in the muddy water. Various processed shield muck data are comprehensively analyzed, rapid detection and analysis of fine-grained soil component changes in the shield muck are achieved, the problems that a traditional densimeter method is long in time consumption, complex in operation and poor in adaptability are solved through the system, real-time monitoring of the fine-grained soil content is achieved through the detection system, and the detection accuracy of the fine-grained soil component changes in the shield muck is improved. And a basis is provided for resource utilization of the muck.

The invention relates to the technical field of physical detection of deep soil, in particular to soil detection equipment for determining components of deep soil, which comprises a deep soil detection mobile rack; a bearing top frame is fixedly assembled on the surface, and a vertical groove is formed in the inner wall of the front side of the bearing top frame; a deep soil sampling device is arranged on the bearing top frame; an outer gear ring drives a drilling bit to rotate into the ground, along with rotation of the drilling bit, a first servo motor is started, a lifting frame box drives a deep soil detection structure, a soil depth extension assembly and a deep soil drilling assembly to descend, and the drilling bit continuously opens a hole along an adjusting hole; according to the deep soil sampling structure, the deep soil drilling assembly and the soil depth extension assembly penetrate into the ground, the deep sampling applicability is high, the soil depth extension assembly and the deep soil drilling assembly in the deep soil sampling structure are modularly arranged, and the number of the soil depth extension assemblies can be increased according to needs so as to meet the sampling requirements of deep soil at different depths; the flexibility is high.

To provide a detergent composition for soils having excellent detergency even for cleaning in an aqueous system and a cleaning method of soils.SOLUTION: A detergent composition for soils includes the following (A) component and (B) component (excluding those falling under the (A) component), wherein (A) component is a polyoxyalkylene alkyl or alkenyl ether and (B) component is a block copolymer having polyoxyethylene parts and polyoxypropylene parts.SELECTED DRAWING: None

Film bag concrete has wide application scenes in protection projects such as dam slope protection and channel bank protection, but under the scouring erosion action of surge load, the film bag concrete cracks to easily cause loss of bank slope soil components and reduction of strength, and finally cause instability damage of a bank slope. Therefore, main and secondary factors influencing the long-term operation stability of the film bag concrete slope protection are necessary to be fully researched under laboratory conditions, and a scientific basis is provided for further application of the film bag concrete in water transportation engineering. Therefore, the test device for simulating the long-term operation stability of the membrane bag concrete slope protection is provided, the test device is composed of a transverse-longitudinal bidirectional wave making device, a membrane bag concrete ballasting-cracking simulation device, a sensor monitoring analysis system and a model box, and the invention further provides a test method based on the test device. According to the invention, the long-term operation stability of the film bag concrete slope protection under the real complex wave load condition can be simulated under the indoor laboratory condition, and a scientific basis is provided for the reinforcement design and construction organization of the film bag concrete slope protection.

The invention discloses an agricultural planting soil parameter analysis system, which relates to the technical field of soil intelligent analysis, and comprises a data acquisition module, a data determination module, a quality evaluation module and a dynamic optimization module, the data acquisition module is used for acquiring soil parameters and farming operation record data of each planting area; the data determination module is used for analyzing the soil component difference value of each planting area to generate a first influence difference; the quality evaluation module is used for analyzing the spatial correlation of the first influence difference by using the interaction correlation to obtain a soil spatial distribution prediction map; and the dynamic optimization module is used for dynamically regulating and controlling the soil content according to the soil spatial distribution prediction map. The farmland ecological environment is protected, agricultural sustainable development is promoted, and the intelligent level of soil parameter adjustment is improved.

The invention provides a waste potato-derived fulvic acid slow-release full-degradable mulching film as well as a preparation method and application thereof, and belongs to the technical field of agricultural environment. Waste potatoes are prepared into a solution, then the solution is compounded with potassium sulfate, tartaric acid and fulvic acid according to a certain proportion to form a mixed solution, and the waste potato source fulvic acid slow-release type fully-degradable mulching film is prepared through uniform spreading and microwave irradiation. According to the invention, resource utilization of the waste potatoes is realized; meanwhile, the prepared mulching film is good in mechanical performance, and the actual application requirement is met. The prepared mulching film has good heat preservation and soil moisture conservation effects, fulvic acid is slowly released, and the film-fertilizer dual-function effect is achieved. In addition, after the mulching film is degraded, soil components are improved.

This application relates to an IoT-based intelligent land remediation monitoring method and system. The monitoring method includes: acquiring satellite remote sensing elevation data and historical soil composition data of the land area to be monitored; dividing the land area to be monitored into multiple grid units and assigning grid identifiers; deploying fixed soil sensor nodes and mobile drone nodes to generate a sensor network topology; collecting real-time soil parameters of each grid unit and performing noise filtering and anomaly detection to generate a soil composition dataset; inputting the soil composition dataset and historical soil composition data into a cloud-based prediction model to output a land degradation risk score and remediation strategy; controlling the execution equipment to perform land remediation operations and monitoring changes in soil parameters after execution in real time; updating the parameters of the cloud-based prediction model and optimizing the sensor network topology based on the deviation between the changes in soil parameters after execution and the expected values. This application improves agricultural production efficiency and soil health.