215 results about "Soil salinity" patented technology

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention relates to a method for improving satellite data inversion soil salinity by using hyperspectral data of an unmanned aerial vehicle. The method comprises the following steps: collecting a soil sample in a test area, and calculating soil salt content; acquiring and preprocessing satellite remote sensing image data of a test area; storing data, outputting a single-band satellite image, and calculating a spectral index; obtaining and preprocessing hyperspectral image data of the unmanned aerial vehicle in the test area; reconstructing and upscaling unmanned aerial vehicle image data pixels to a spatial resolution of 10m; reconstructing the narrow-band spectral data of the unmanned aerial vehicle into broadband spectral data consistent with a satellite image, storing and outputting a single band, and calculating a spectral index; screening a soil salt content characteristic variable, and constructing a soil salt content inversion model by taking the screened characteristic variable as an independent variable and the actually measured soil salt content as a dependent variable; and fitting a function relationship between the upscaling unmanned aerial vehicle soil salt content inversion model and the satellite inversion model by adopting a numerical regression function method to realize correction of the satellite inversion model.

According to the coastal saline-alkali land underground water level adjusting system based on real-time monitoring and intelligent regulation and control, the system integrates the soil salinity sensor, the underground water level sensor and the central processing unit, future trend prediction is carried out by monitoring key parameters such as soil salinity and underground water level in real time in combination with a deep learning model, and the real-time monitoring and intelligent regulation and control are achieved. And precise intelligent regulation and control are realized. The system can effectively reduce soil salinization, prevent seawater from flowing backwards, improve the utilization efficiency of water resources and reduce the labor management cost at the same time. The method is characterized in that the application of multi-parameter comprehensive monitoring, a deep learning prediction model and a self-adaptive intelligent regulation and control algorithm provides a new technical solution for coastal saline-alkali land treatment.

The invention discloses a layered alternate fertilization method suitable for saline-alkali soil improvement. The method comprises the following steps: determining the soil salinization degree and the main salt ion type of target saline-alkali soil; the method comprises the following steps: designing a layered fertilization scheme according to the soil salinization degree, the main salt ion type and the growth demand and salt tolerance of a target crop, and determining the fertilization types, fertilization amounts and fertilization depths of different soil layers; formulating an alternate fertilization strategy, and determining fertilization periods and the sequence and interval time of fertilization of different soil layers in each period; fertilization operation is carried out on the target saline-alkali soil according to a layered fertilization scheme and an alternate fertilization strategy, the dynamic change of soil salinity and the growth condition of target crops are monitored in the fertilization process, and the follow-up fertilization scheme and strategy are adjusted in time according to the monitoring result. Soil conditions are determined, a layered fertilization scheme is designed, an alternate fertilization strategy is formulated, fertilization operation is adjusted according to monitoring results, and the purposes of improving saline-alkali soil and meeting the growth requirements of target crops are achieved.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention discloses a farmland precise irrigation decision-making method based on fusion of a physical process and machine learning, and the method comprises the following steps: S1, data collection: collecting meteorological data, soil data, fertilization and irrigation data and cotton growth data in a cotton growth period, S2, data preprocessing: carrying out the cleaning, standardization and normalization processing of an original data set, and carrying out the calculation of the data set; the method comprises the following steps: S1, preprocessing a crop mechanism model, S3, data enhancement: constructing and calibrating the crop mechanism model by using preprocessed data, S4, selecting key feature variables, and dividing the obtained data set into a training set and a test set by using an original data set and an enhanced data set, S5, model training and optimization, and S6, model verification and evaluation. According to the method, by coupling the crop growth mechanism model and the machine learning algorithm, not only can the growth conditions of crops under different moisture conditions be simulated and predicted, but also the influence of soil salinity and alkalinity on crop growth can be evaluated, so that comprehensive decision support is provided for irrigation management.

The invention discloses a soil salinity inversion method, system and device based on multi-mode remote sensing data fusion and a storage medium, and is applied to the technical field of soil quality monitoring, and the method comprises the steps: obtaining radar and optical remote sensing data of a to-be-inverted region of soil salinity, and carrying out the conductivity measurement, and taking the obtained data as an inversion index of the soil salinity; obtaining multi-modal remote sensing features based on radar and optical remote sensing data fusion, optimizing a remote sensing feature combination by combining the radar and optical remote sensing data and based on a random forest algorithm and utilizing a recursive feature elimination method, and performing model performance evaluation on the remote sensing feature combination by adopting cross validation to obtain an optimal remote sensing feature combination; and based on the optimal remote sensing feature combination, constructing and training a soil conductivity prediction model based on a random forest algorithm, and inputting to-be-measured data to the conductivity prediction model after hyper-parameter adjustment to complete soil salinity inversion. According to the method, more accurate inversion of the soil salinity under the interaction influence of a complex environment and human factors is realized.

The invention discloses a coastal wetland vegetation degradation dynamic monitoring method based on multi-modal transfer learning, and belongs to the technical field of ecological environment monitoring, and the method comprises the steps: data acquisition: collecting coastal wetland region data through a plurality of sensors, feature extraction and fusion: preprocessing multi-source data, extracting space, time series and physical parameter features, and carrying out feature fusion; and fusing the data features of different modalities. According to the method, all-around and high-precision perception of the coastal wetland ecological environment is realized, the spatial-temporal resolution of data acquisition is improved, the response capability to complex environmental factors is enhanced, the generalization capability and prediction precision of the model are effectively improved, key environmental factors such as soil salinity can be incorporated into an evaluation system, and the evaluation accuracy is improved. The scientificity and pertinence of ecological risk identification are further improved, the efficiency and response speed of coastal wetland ecological management are improved, and powerful support is provided for formulating scientific and reasonable ecological protection and restoration measures.

The invention relates to the technical field of saline-alkali soil improvement. The invention provides an extremely severe saline-alkali soil improvement method which comprises the following steps: (1) ploughing soil to be improved, ditching, and spreading a material A into ditches; (2) irrigation is carried out after crops are sown in the ditches, and field management is carried out in the crop growth period; (3) after crops are harvested, crop straw is smashed and subjected to rotary tillage into soil; and (4) repeating the steps (1)-(3), and carrying out continuous soil improvement treatment. According to the technical scheme, the soil structure and the microbial environment are improved through soil preparation and fertilization before sowing, the salt content of soil of a plough layer is reduced through a salt reducing measure, soil osmotic pressure is reduced, and the salt content of the soil is continuously reduced through a crop planting measure. According to the scheme, the soil saline-alkali improvement effect is good, a good soil environment is created for seed budding and seedling emergence stages, and the seed emergence rate is increased.

The invention relates to the technical field of remote sensing monitoring, solves the technical problems that an existing soil salinity inversion method is insufficient in precision and poor in generalization ability, and particularly relates to a bare soil salinity inversion method fusing partial least squares and a random forest. Comprising the following steps: acquiring spectral reflectivity data and salinity spectral index of a multiband range and spatial resolution, and preprocessing to establish a digital orthoimage; removing a water body and a vegetation coverage area in the digital orthoimage by using a normalized vegetation index to obtain bare soil data containing 24-dimensional feature variables; establishing a fusion model and carrying out training evaluation; and performing prediction by taking Sentinel-2 satellite remote sensing data as input of the fusion model to obtain the salt content of the bare soil. According to the method, the precision, reliability and generalization ability of soil salinity inversion can be effectively improved, compared with a traditional single model, the nonlinear relation and noise in remote sensing data can be better processed, and the generalization ability of salinity inversion is remarkably improved.

The invention discloses a performance optimization integrated learning soil salinity prediction precision improvement method, and belongs to the technical field of soil quality monitoring. The method comprises the following steps: acquiring a first spectral image of to-be-predicted soil; performing feature extraction on the first spectral image to obtain a target spectral feature; inputting the target spectral features into a target soil salinity prediction model to obtain a soil salinity prediction result; the target soil salinity prediction model is obtained by the following steps: testing a plurality of first soil salinity prediction models based on a first soil salinity prediction model test set corresponding to sample soil to obtain a test scoring result of each first soil salinity prediction model; determining at least one second soil salinity prediction model from the plurality of first soil salinity prediction models based on the plurality of test scoring results, and taking the at least one second soil salinity prediction model as a target soil salinity prediction model; the input feature of the first soil salinity prediction model test set is the first spectral feature of the sample soil, and the target variable is the salinity of the sample soil.

The invention discloses an intelligent control method and system for saline-alkali soil slow-release fertilizer application, and the method comprises the steps: analyzing the space-time trend of soil salinity change and pH dynamic change through a convolutional neural network, judging the matching demand of the fertilizer release rate, and generating a target release curve; the method comprises the following steps: extracting a high-activity strain adapting to a high-salt environment from a salt-tolerant microbial library through a high-throughput screening algorithm, and determining optimal release time interval distribution of strain function activation by combining with the release characteristic of a dynamic release coated fertilizer; if the soil salinity change exceeds a preset threshold value, sensor monitoring data and a target release curve are integrated through a closed-loop control system, and an optimized fertilization amount scheme is obtained; a deep learning model is adopted to analyze the relation between the optimized fertilization amount scheme and soil deep salt distribution, the parameter requirement of depth accurate regulation and control is judged, and a layered fertilization depth control strategy is generated. Through real-time feedback and optimization iteration, accurate improvement and sustainable nutrient management of the saline-alkali soil are realized.

The invention discloses a soil salinity monitoring method based on multi-source satellite remote sensing and deep learning, and is applied to the technical field of soil salinity monitoring. Comprising the following steps: obtaining a vector graph and a satellite data set of a farmland, carrying out cloud removal screening and median synthesis on the vector graph of the farmland, carrying out resampling on image data of the satellite data set, selecting features in the data set through a feature selection algorithm, and comparing a traditional machine learning model with a deep learning model through an SHAP value. Evaluating the performance of different models, determining the key influence characteristics of the soil salinity, and in the model training process, updating the parameters of each model through a minimization logarithmic loss function, selecting a prediction regression model, and generating a farmland analysis visualization graph. According to the invention, the problems of untimely monitoring, time-consuming and labor-consuming process, complex data processing, unreliable monitoring precision and the like of the farmland in the prior art are solved, and key decision support is provided for soil salinization treatment of the farmland.

The invention provides a saline-alkali soil underground water regulation and control method and device, electronic equipment and a storage medium, and relates to the technical field of land governance. Inputting the first historical monitoring information into a pre-constructed prediction model to obtain a predicted value of soil salinity in a future time period; the predicted value of the soil salinity is input into the analysis model, and the target water level of underground water in the future time period is obtained; wherein the target water level of the underground water is used for restraining the target value of the soil salinity to be smaller than a preset salinity threshold value and the target value of the soil humidity to be larger than a preset humidity threshold value; generating a water level adjusting strategy based on the target water level of the underground water, and controlling the opening or closing of the water pump based on the water level adjusting strategy. According to the method, the soil salinity in the future time period is predicted by acquiring the saline-alkali soil monitoring information, so that the target water level of the underground water is obtained, the water level regulation strategy is generated, and the perspectiveness, dynamic nature and stability of underground water level regulation are ensured.

The invention provides a remote sensing multispectral imaging system for agricultural monitoring, which relates to the technical field of space remote sensing, and realizes accurate selection by indicating an economic crop determination module, analyzing historical soil salinity and water shortage data and selecting sensitive crops; the correlation evaluation module calculates a water shortage index change rate, analyzes correlation between historical data and current data, and improves prediction accuracy; the value range determination module divides soil salinity intervals and standardizes management data in combination with evaluation coefficients; the indication area determination module realizes accurate monitoring by using a multispectral image and enhances representativeness; the humidity evaluation module analyzes local spectral data to quantify the soil humidity; the prediction model construction module predicts the salinity content by using machine learning, and can accurately predict dynamic changes of soil salinity and moisture; the correlation analysis module verifies real-time monitoring data in combination with the prediction model; and the division module optimizes a soil management strategy according to the water shortage index and the humidity coefficient.

The invention discloses a method for regreening gobi deserts by utilizing microalgae crust and application, and belongs to the technical field of ecological restoration. The method mainly adopts a mode of combining microalgae spraying liquid with an engineering remediation method. When the microalgae spraying liquid is prepared, indigenous microalgae and known domesticated desert algae are mixed and co-cultured to prepare a spraying agent, and then soil bacillus is added before spraying. The engineering regulation method mainly comprises the following steps: forming a concave-convex structure by piling up soil mound bags and digging ditches, increasing the specific surface area of the growth of the microalgae spraying liquid by using the uneven land surface layer, and reducing the evaporation speed of the moisture of the microalgae spraying liquid by using the concave land, thereby promoting the growth rate and adaptability of the microalgae and improving the skinning efficiency. In addition, nutritional functional organic fertilizers and / or nano conditioners need to be added into the soil to be remediated in advance. According to the method, the vegetation coverage rate can be increased to about 60% from the initial nearly zero, the gobi desert ecological environment can be obviously improved, the soil salinity and alkalinity can be obviously reduced, and the soil water retention capacity is enhanced.

A comprehensive regulation and control method suitable for improvement of a coastal mud flat severe saline-alkaline dry land comprises the steps that firstly, the area of a field needing to be improved and the soil salinity are measured, the use amount of acid volcanic rock debris and the use amount of high-fiber fermented wood chips are calculated, the application amount of the acid volcanic rock debris is 6-10 cubic meters / mu, and the use amount of the high-fiber fermented wood chips is 85-150 kg / mu; secondly, when the soil salinity is gt; when 0.8%, canal irrigation tube row is combined for concealed tube salt elimination, and the salt is reduced to about 0.4%; thirdly, uniformly scattering acidic volcanic rock debris and high-fiber fermented sawdust into the severe saline-alkali soil, then ploughing the field with the ploughing depth of 5-20 cm, uniformly mixing the field with soil, and then planting and cultivating dry farming crops to complete comprehensive regulation and control.

The invention discloses a coastal mud flat soil salinity assessment method. The method comprises the following steps: step 1, acquiring a multi-source satellite image of a to-be-assessed area and salinity data of sampling points; step 2, carrying out correlation analysis on the salinity data of the sampling points and the corresponding multi-spectral wave band reflectivity, and screening out sensitive wave bands; 3, constructing a plurality of spectral indexes, performing correlation analysis on the plurality of spectral indexes and the salinity data of the sampling points, and selecting the spectral index with the highest correlation degree as an optimal spectral index; 4, constructing an inversion model, taking the optimal spectral index as the input of the inversion model, and taking the salinity data of the sampling points as the output of the inversion model; and 5, performing real-time evaluation on the salinity of the mud flat soil by using the trained inversion model through a real-time multi-source satellite image. Through multi-source data fusion, novel spectral index construction and an optimized model construction mode, high-precision inversion and real-time evaluation of coastal mud flat soil salinity are realized.

The invention relates to a special fertilizer suitable for saline-alkali soil and a preparation method thereof. The fertilizer is granular and has a multi-layer structure which is sequentially arranged from inside to outside: a nutrition core layer, a controlled release film layer, a soil improvement fast exchange layer and a microorganism attaching layer. The nutrition core layer contains nitrogen, phosphorus, potassium and medium trace elements, and meets the basic nutrient requirements of crops; the controlled release film layer is self-assembled layer by layer by adopting alginate / chitosan, a humic acid-ferrous complex and a breakable cross-linking point are inserted into the film, and starch is sprayed on the outer layer, so that an ionic strength / alkalinity dual-response controlled release system is formed; the soil improvement quick exchange layer contains microcrystalline gypsum and ferrous sulfate; the microbial epiphytic layer fixes salt-tolerant phosphate solubilizing bacteria, potassium bacteria and exopolysaccharide-producing bacillus by using a charcoal-zeolite carrier, and the bacteria are distributed on the surfaces of the particles in a dotted manner. The fertilizer realizes organic combination of soil improvement, fertilizer supply and microbial regulation and control, can significantly reduce the soil salinity and alkalinity in a saline-alkali soil environment, and improves the fertilizer utilization rate and the crop growth performance.

The invention provides a high-salinity high-tidal-flat mangrove forest ecological restoration method. The high-salinity high-tidal-flat mangrove forest ecological restoration method comprises the following steps of S1, soil improvement, S2, seedling cultivation, S3, seedling planting, S4, seedling reseeding, S5, bank protection device arrangement and S6, pest control. The soil conditioner is used for improving the soil structure and reducing the soil salinity. Salt-resistant domestication is performed on the seedlings by gradually improving the salinity of irrigation water, so that the salt resistance of the seedlings is improved. Organic fertilizer, ferriporphyrin, abscisic acid and brassinolide are used for providing nutrients for seedlings, osmotic equilibrium in plants is adjusted, opening and closing of air holes are adjusted, plasticity of cell walls is adjusted, and therefore the salt resistance of the plants is enhanced. According to the method, the roots of seedlings are inoculated with the halorhodobryum terbach and the proteobacteria, the halorhodobryum terbach and the proteobacteria terbach form a symbiotic relationship with the roots of plants, a microbial community is formed, and the absorption efficiency of the plants to nutrients in soil is According to the method, the planting survival rate and the plant growth rate are improved through the multi-way overall synergistic effect, and then the biological diversity is improved.

The invention relates to the technical field of soil drainage and salt improvement, in particular to a coastal saline-alkali soil drainage and salt improvement intelligent regulation and control method and system based on artificial intelligence, and the method specifically comprises the following steps: dividing treatment units, arranging sensing equipment, and collecting multi-source heterogeneous data to form a data set; after data cleaning and time-space synchronization are conducted, standardized feature vectors are obtained through sectional physical perception standardization processing; constructing a graph neural network micro-area irrigation volume demand prediction model, and predicting recommended irrigation volume and soil salinity variation; calculating a loss function of the model and performing iterative training on the model; and performing periodic decision regulation and control circulation on the trained model, converting the output of the multi-source heterogeneous data collected at each decision moment through the model into an instruction, issuing the instruction, and synchronously performing drainage regulation and control. According to the invention, water-salt environment optimization regulation and control can be realized, and an intelligent treatment closed loop is formed.

The embodiment of the invention discloses an unmanned aerial vehicle image-based hexagonal grid unit soil salinity assessment method, which comprises the following steps: acquiring multispectral images of a cotton field to be assessed through an unmanned aerial vehicle, including a red light image, a green light image, a blue light image and a near-infrared image; dividing a plurality of to-be-evaluated regular hexagon evaluation units according to the to-be-evaluated cotton field multispectral image; training a cotton field soil salinity evaluation model; and performing soil salinity evaluation on each to-be-evaluated regular hexagonal evaluation unit by adopting the trained cotton field soil salinity evaluation model. Compared with the prior art, the invention provides a method for calculating and evaluating the soil salinity by using an unmanned aerial vehicle image and a regular hexagon evaluation unit, and more image features related to the soil salinity strength can be captured, such as the area ratio of crops in the unit, the physiological and ecological indexes of the crops in the unit and the salt spot condition around the unit. More features are more beneficial to accurate evaluation of soil salinity, irrigation can be optimized, and the cotton yield can be increased.

The invention discloses a saline-alkali soil fertilization method and system based on intelligent recommendation. A map model based on saline-alkali soil is constructed, monitoring areas are divided for preset saline-alkali soil, similarity analysis is carried out on environment characteristic data of every two monitoring areas, and a feasible prediction area group is judged; in a real-time monitoring period, carrying out soil salinity and alkalinity detection on the sampling region, and importing detection data into an LSTM prediction model to carry out salinity and alkalinity content value prediction so as to obtain predicted salinity and alkalinity data of a feasible prediction region group; by predicting saline-alkali data and historical saline-alkali content data, fertilization and irrigation schemes are set for a plurality of monitoring areas, and intelligent scheme recommendation is performed based on different monitoring areas. Through the method, the saline-alkali content of the saline-alkali soil can be effectively predicted, the consumption of manpower and material resources for detecting soil is reduced, and the regulation and control capability of the saline-alkali soil is improved.

The invention provides a conditioner for soil heavy metal pollution remediation and a preparation method thereof, and belongs to the technical field of soil conditioning. The conditioner for soil heavy metal pollution remediation provided by the invention is prepared from the following components in parts by mass: 12 to 25 parts of biochar, 5 to 7 parts of potassium feldspar, 1 to 5 parts of inorganic nano material and 40 to 70 parts of water, the fertilizer is prepared from 40-50 parts of humic acid, 15-25 parts of amino acid, 5-12 parts of citric acid, 4-8 parts of bagasse and 25-40 parts of vinasse. 0.5 to 1 part of aspergillus niger and 0.1 to 1 part of clostridium capsulatum. By utilizing the adsorption of inorganic components on heavy metal elements, the improvement of organic components on saline-alkali soil and the continuous remediation effect of microbial components on the soil, through the synergistic effect of the multiple components, the soil is treated at the same time from multiple aspects of soil fertilization, plant growth, soil loosening, salinity and alkalinity reduction and the like; the soil salinity and alkalinity are reduced, and heavy metals are adsorbed and absorbed.

The invention discloses a restoration method for improving salinized grassland, and relates to the technical field of ecological environment restoration. Physically, a laser land leveling instrument is combined with topographic surveying and mapping to control the land flatness, and an intelligent irrigation and drainage system is constructed; chemically, a novel composite modifier is prepared to regulate soil; biologically, saline-alkaline tolerant grass seeds are treated to construct communities, and microbial agents are applied. And meanwhile, a monitoring network is constructed for precise monitoring and regulation, the cost is reduced by utilizing agricultural wastes and local materials, and sustainable development is realized. The method can effectively reduce the soil salinity, adjust the pH value, improve the vegetation coverage rate, increase the number of soil organic matters and microorganisms, and realize saline-alkali grassland soil improvement, vegetation recovery and ecological sustainable development.

The invention relates to a compound microorganism bacterium agent which comprises halophilous bacillus HMF09 and bacillus subtilis, and the preservation number of the halophilous bacillus HMF09 is CGMCC (China General Microbiological Culture Collection Center) No.30475. The complex microbial inoculant can effectively reduce the pH value of soil, reduce the salt content of the soil, improve the emergence rate and salt and alkali resistance of crops and increase the yield of the crops.

The invention relates to the field of soil improvement, in particular to a saline-alkali soil improvement method which comprises the specific steps of deep ploughing and crushing, drainage system building and pesticide spreading, high-molecular polymer application and microbial agent inoculation. Good soil conditions are created through a physical improvement method, the soil salinity and alkalinity are rapidly reduced through a chemical improvement method, the salt-tolerant microbial agent can decompose organic matter, improve soil micro-ecology, reduce salinity toxicity and promote plant growth, and the saline-alkali soil improvement efficiency and effect are remarkably improved through mutual cooperation of the three methods; the method reduces the excessive use of chemical substances in a single chemical improvement method, realizes the ecological restoration of the saline-alkali soil through a physical method, a chemical method and a microbiological method, reduces the negative influence on the environment, improves the sustainability of improvement, effectively improves the soil structure and the ecological environment, provides good conditions for plant growth, and realizes the efficient utilization of the saline-alkali soil.

The invention belongs to the technical field of transformer substation grounding grid monitoring, and discloses a transformer substation main grounding grid real-time safety monitoring system based on multi-sensor fusion, which adopts a MySQL and InfluxDB mixed architecture through a dynamic association database module, and stores static data such as transformer substation position, grounding grid material and the like, and dynamic data such as soil parameters, corrosion rate and the like respectively; meanwhile, a soil parameter and sampling frequency dynamic mapping table is built in, and when it is recognized that the soil salinity is larger than 15 mS / cm or the pH is smaller than 5.5, a sampling frequency adjusting instruction can be sent to the sensor array module, and the automatic soil sample collecting device is started; the module also establishes a material corrosion digital twinborn model and a climate partition library, provides scene data for the scheme dynamic adaptation and operation and maintenance module, enables the scheme dynamic adaptation and operation and maintenance module to output an adaptive scene corrosion prevention scheme, calculates a grounding electrode replacement period according to a corrosion rate, constructs a dynamic association between a soil working condition and corrosion protection, and effectively improves the durability of a grounding grid.

The invention belongs to the technical field of photovoltaic solar power station park rainfall irrigation, and discloses an arid area grassland photovoltaic system under-panel irrigation monitoring method, system and device. According to the method, a monitoring device is used for monitoring the soil humidity under a current arid area photovoltaic park (grassland photovoltaic system) panel in real time, and when the soil humidity is lower than a threshold value set by a user, the monitoring device starts a water suction pump to work through an internal processor; meanwhile, the monitoring device controls the opening degree and the opening and closing time of an intelligent switch electromagnetic valve additionally arranged on a tee joint or a four-way joint in the transverse pipe and longitudinal pipe connecting device; and the remote arid area grassland soil monitoring end performs comprehensive treatment according to the current soil humidity to obtain an optimal soil humidity and soil salinity and alkalinity combined value. The soil humidity around the drip irrigation opening of the underground drip irrigation pipeline can be monitored, the water suction pump is turned on and turned off through the monitoring center device, and the water utilization efficiency of vegetation of a grassland photovoltaic system in an arid region is improved.

The invention discloses a preparation method and application of a saline-alkali-resistant soil conditioner taking mixed algae as a raw material. The saline-alkali-resistant soil conditioner is specifically prepared from the following raw materials in parts by weight: 50-80 parts of mixed algae powder; 10 to 30 parts of humic acid; and 5-10 parts of a microbial agent. The invention relates to the technical field of soil conditioning, in particular to a preparation method and application of a saline-alkali-resistant soil conditioner taking mixed algae as a raw material, the soil salinity and alkalinity of saline-alkali soil can be obviously reduced, for example, in the later growth period of wheat, the soil salinity and alkalinity can be reduced to 0.2%-0.3% from the initial 0.5%-1.0%, the reduction amplitude reaches 50%-60%, and the soil salinity and alkalinity can be obviously improved. The soil fertility is greatly improved, the conditioner can remarkably increase the content of nutrients such as organic matters, nitrogen, phosphorus and potassium in soil, meanwhile, the pH value of the soil is reduced, the pH value of the soil is improved, the content of the organic matters can be increased by 50%, the content of the nitrogen can be increased by 40%, the content of the phosphorus can be increased by 40%, and the content of the potassium can be increased by 30%. The potassium content is increased by 33.3%, the PH value is reduced by 4.88%, and compared with a commercially available conditioner, the soil fertility index is increased by the conditioner.