548 results about "Soil heavy metals" patented technology

The invention relates to the technical field of soil pollution identification, and discloses a soil heavy metal pollution identification system. A multispectral remote sensing sensing module of the system obtains surface reflectance data and soil in-situ spectral data through a satellite load and a vehicle-mounted mobile platform respectively, and the surface reflectance data and the soil in-situ spectral data are processed by a heterogeneous data fusion gateway to generate multiband spectral response signals. In the pollution risk assessment module, a spatial distribution analysis unit outputs a heavy metal spatial distribution map, a migration risk prediction unit generates a pollution migration probability cloud map in combination with meteorological and hydrological data, and a pollution threshold defining unit outputs a soil remediation safety threshold. In the treatment decision execution module, an in-situ remediation execution unit adjusts passivator injection parameters, a pollution source management and control unit regulates pollution source blocking equipment and collects monitoring signals, and a three-dimensional dynamic early warning platform generates a comprehensive pollution risk index. According to the system, the cooperative operation of soil heavy metal pollution identification, evaluation and treatment is realized.

The invention provides an industrial park soil and groundwater pollution early warning grade evaluation method and system.The method comprises the steps that production raw material components of pollution source enterprises, waste discharge data and soil heavy metal concentration evolution data in historical pollution events are integrated, optical sensing data of earth surface and underground monitoring points are combined, and the early warning grade of soil and groundwater pollution is evaluated; constructing a dynamic characteristic factor library reflecting pollution source characteristics; carrying out spatial concentration gradient calculation on the optical sensing data based on the characteristic factor library, and generating a dynamic heat map containing heavy metal distribution and migration trends at the same time; performing cross-modal analysis on the pollution source characteristics and the dynamic heat map, and establishing a spatio-temporal evolution trajectory of a pollution diffusion path; according to the diffusion rate and direction in the track, differentiated pollution risk early warning intervals are divided, and a targeted grading prevention and control strategy is generated. According to the invention, the timeliness of pollution early warning and the targeting of prevention and control measures are improved.

The invention discloses a field soil heavy metal pollution health risk dynamic assessment and intelligent early warning system, and relates to the technical field of soil environment monitoring. The system comprises a multi-source data acquisition unit, a data preprocessing unit, a risk calculation engine and a visual interaction terminal. The key technical point is that a dynamic field evolution analysis module and an adaptive grid rendering control module are introduced; the dynamic field evolution analysis module constructs a pollution potential energy field matrix representing a pollutant migration trend based on soil heavy metal concentration and hydrogeological parameters, and calculates a space-time gradient change vector of the pollution potential energy field matrix; and the latter dynamically adjusts the grid local density according to the gradient vector module value, and automatically encrypts the computational nodes in the region with severe risk change. In cooperation with a time sequence prediction deduction and feedback correction mechanism, the method can simulate the dynamic evolution of the pollution plume in the porous medium in real time, solves the problems that a migration rule is difficult to capture and the calculation efficiency of a uniform grid is low in traditional static evaluation, and achieves three-dimensional dynamic risk early warning with high precision and low calculation power consumption.

The invention relates to the technical field of in-situ / ex-situ remediation of sites polluted by heavy metals such as cadmium, lead, copper and zinc, and discloses a heavy metal polluted soil remediation system and method based on intelligent management and control. The system comprises an electrode system module, an activator module, a targeted adsorption device module, an automatic monitoring system module, an intelligent decision-making system module and a photovoltaic power supply module. The method comprises the steps of driving heavy metal ions in soil to directionally migrate through an electric field; the activating agent module works, green micromolecule organic acid and other activating agents are injected in a gradient mode, and the migration efficiency of heavy metal is improved; activated heavy metal ions can be captured by an adsorption material in the targeted adsorption device module in cooperation with a selective enrichment device; meanwhile, the automatic monitoring system module collects environmental parameters in real time; the intelligent decision-making system module optimizes the remediation efficiency and energy consumption according to the real-time data, and low-cost and high-efficiency remediation of soil heavy metal pollution is achieved. The remediation efficiency is remarkably improved, and the risk of secondary pollution is reduced.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention relates to acid-resistant sulfate reducing bacteria and application thereof, and belongs to the technical field of microorganisms. The acid-resistant sulfate reducing bacterium disclosed by the invention is Desulfovibrio desulfuricans QY411, and is preserved in the Guangdong Microbial Culture Collection Center on March 14, 2024, and the preservation number of the acid-resistant sulfate reducing bacterium is GDMCC No: 64412. The acid-resistant sulfate reducing bacterium provided by the invention can survive in an environment with the pH value of 4, and can be applied to ecological reconstruction of acid tailings and waste dump soil, the pH values of mining wasteland soil and surface water after ecological reconstruction are remarkably increased, the concentration of SO4 < 2-> in the soil is remarkably reduced, the acidification process in the mining wasteland soil is effectively inhibited, and the acid-resistant sulfate reducing bacterium has the advantages of being safe and reliable. Therefore, the acidification process in the mining wasteland is effectively controlled. Meanwhile, soil heavy metal pollution is reduced, and vegetation growth is recovered.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention relates to the technical field of soil heavy metal pollution detection, and discloses a soil heavy metal pollution detection method and system, and the method comprises the steps: collecting an original signal containing a target heavy metal characteristic signal and a plurality of auxiliary background signals; preprocessing the original signal to obtain a preprocessed multi-dimensional signal; analyzing the multi-dimensional signal to identify interference characteristics of the target heavy metal characteristic signal and the auxiliary background signal; according to the interference characteristics of the auxiliary background signal, evaluating the composition and intensity of background interference, and according to the composition and intensity of the background interference, separating a pure signal of the target heavy metal from the background by using a nonlinear compensation algorithm; the target heavy metal pure signal is converted into the concentration value of the target heavy metal, chemical digestion is not needed, complex background interference can be effectively recognized and compensated for for soil heavy metal pollution detection, the accuracy and reliability of field detection are remarkably improved, and errors caused by inaccurate reagent degradation and background deduction in a traditional method are overcome.

The invention relates to a real-time monitoring method for soil remediation by utilizing multi-spectrum of an unmanned aerial vehicle and sensing coupling of the Internet of Things, and belongs to the technical field of soil environment monitoring and remediation. The method comprises the following steps: constructing a space grid model of a monitoring area; a space-air-ground integrated monitoring network is arranged, image data are obtained through multi-spectral remote sensing of an unmanned aerial vehicle, and soil environment parameters are collected through a ground Internet of Things sensor; preprocessing and fusing the multi-source spatio-temporal data, and establishing a spatio-temporal matching model; constructing an inversion model of the soil heavy metal content, the organic matter content and the pollutant degradation degree based on the fusion data; and the repair efficiency is dynamically calculated and visualized, and real-time evaluation and early warning of the repair process are realized. According to the invention, space-air-ground data collaboration is realized, the real-time performance, accuracy and space coverage of monitoring are remarkably improved, the defects of high cost, low efficiency and limited data dimension of a traditional method are overcome, and whole-process and intelligent decision support is provided for soil remediation.

The invention relates to the technical field of soil pollution analysis, and discloses a soil heavy metal intelligent evaluation method, system and terminal, and the method comprises a multi-source heterogeneous data fusion and preprocessing step, a dynamic spatial interpolation modeling step and an intelligent visualization and decision support step. The system corresponds to the method, and the terminal equipment corresponds to the system. According to the method, comprehensive data support for soil heavy metal pollution is realized through multi-source heterogeneous data fusion and preprocessing; through dynamic spatial interpolation modeling and a dynamic parameter adjustment mechanism, high-precision grid surface layer generation is realized, and the fitting precision of pollution spatial distribution is improved; intelligent pollution grading and multi-factor dynamic calculation of pollution indexes are realized through self-adaptive grading and pollution index calculation, and the accuracy of an analysis result is improved; through intelligent visualization and decision support, a dynamic and visual decision basis is provided for multi-department collaborative governance and risk early warning.

The invention discloses a method and a device for treating soil heavy metal pollutants by an electroosmosis technology, and relates to the technical field of soil pollution treatment and remediation, and the method comprises the following steps: constructing an electroosmosis system, a liquid injection system and a pumping drainage system; liquid injection and pumping drainage are conducted, electroosmosis enhancer liquid medicine is injected into the soil, and meanwhile the liquid is pumped drainage till the concentration of the electroosmosis enhancer in the pumped drainage liquid reaches the preset requirement; soaking and electroosmosis are carried out; sampling detection is conducted, and liquid pumping sampling is conducted on the soil in the to-be-treated area till the pollutant residual concentration meets the preset requirement; water injection and pumping drainage are conducted, clear water is injected into the soil, meanwhile, liquid is pumped drainage, and soil treatment is finished until the concentration of the electroosmosis enhancer in the pumped drainage liquid is smaller than or equal to the preset concentration limit value. According to the method, a proper amount of electroosmosis enhancer liquid medicine is injected, heavy metal pollutants are dissociated through the electroosmosis effect and pumped and discharged to the hazardous waste pool, and heavy metal in soil is efficiently and reliably reduced.

The invention discloses a soil heavy metal content inversion method combining VIS-NIR and XRF spectrums, which comprises the following steps: firstly, collecting the VIS-NIR and XRF spectrums of a soil sample to be detected in a laboratory, and preprocessing the spectral data; then, a competitive self-adaptive reweighted sampling algorithm is adopted to screen characteristic wave bands, and a data set is divided into training data and verification data; then, adopting a partial least square method to respectively construct soil heavy metal content estimation models of different spectrum types and different spectrum preprocessing methods, and further evaluating a dominant single spectrum model; and finally, fusing the characteristic wave bands of the VIS-NIR and XRF spectrums of the dominant model, and constructing a soil heavy metal content inversion model based on spectrum fusion. The precision of a soil heavy metal content inversion model can be improved by combining the VIS-NIR spectrum and the XRF spectrum, the problem that the soil heavy metal content monitoring precision is difficult to meet by independently using the VIS-NIR spectrum and the XRF spectrum is solved, and a technical means is provided for rapidly estimating the heavy metal content based on multi-source spectrum fusion.

The present application relates to the technical field of the treatment of heavy metal pollution, and in particular, to a method for predicting heavy metal accumulation in soil based on an emission inventory and a receptor model. Atmospheric dust fall, irrigation water, a fertilizer, and a pesticide are used as heavy metal input flux sources of farmland soil, and surface runoff and a crop are used as output fluxes. A heavy metal pollutant input-output flux inventory is established to specify a dynamic equilibrium relationship of heavy metal accumulation in soil, and soil samples are collected and monitored continuously, which provides important help for determining a migration equilibrium of heavy metals in a farmland region affected by a nonferrous metal dressing and smelting slag yard and a source of soil heavy metal pollution, thereby providing theoretical guidance for subsequent prevention and accurate control of heavy metals in soil.

The invention discloses a sodium alginate embedded iron-carbon microcapsule material for repairing cadmium and arsenic polluted soil and a preparation method of the sodium alginate embedded iron-carbon microcapsule material, and belongs to the technical field of soil heavy metal pollution repairing materials. The material comprises straw biochar, high-iron steel slag powder, a sulfydryl acetylated chitosan-iron monatomic cluster compound, a boron-doped graphitized carbon coated cobalt ferrite nanosheet, sodium alginate, calcium chloride and sodium carbonate. Wherein the sulfydryl acetylated chitosan is prepared by reacting chitosan with sulfydryl acetic anhydride, dialyzing and drying, and performing solvothermal reaction with ferric nitrate under high pressure. The preparation method comprises the following steps: pyrolyzing and drying the straw biochar, reacting the high-iron steel slag with sulfuric acid, adding the biochar, adjusting the pH value, adding sodium carbonate, mixing to obtain an iron-carbon-based passivation material, mixing with two modified compounds, mixing with a sodium alginate solution, dropwise adding a calcium chloride solution, and stirring and curing. The material can be applied to remediation of cadmium and arsenic polluted soil.

The invention discloses a soil heavy metal form analysis method and system based on multi-modal data fusion and small sample learning, and belongs to the field of agricultural environmental science. The soil heavy metal form analysis model based on multi-modal data fusion and small sample learning is operated for direct output, multi-modal data isomerism and a low-data-volume scene can be considered, the method is suitable for multi-scene and multi-range soil heavy metal form proportion prediction, and a scientific basis is provided for accurate pollution control to output the to-be-detected heavy metal form proportion.

The invention relates to the technical field of soil heavy metal pollution remediation, and particularly discloses a preparation method of iron-modified biomass charcoal, which comprises the following steps: preparing garden waste into biomass charcoal (PBC), and then carrying out iron modification by using FeCl3 to obtain iron-based biomass charcoal (PIBC). The invention further provides a method for repairing the cadmium-arsenic combined polluted soil through the iron-modified garden waste biochar. The method comprises the steps that the iron-modified biomass charcoal prepared through any method is added into the soil; the iron-modified biomass can be applied as a base fertilizer at one time, and is turned over to be fully and uniformly mixed with a plough layer of soil. By adopting the method disclosed by the invention, the bioavailability of Cd / As in the soil can be synchronously reduced, and waste resources are utilized to realize pollution control by waste.

The invention relates to the technical field of environment monitoring, in particular to a soil heavy metal pollution intelligent identification and dynamic early warning system and method, and the system comprises a data collection and preprocessing module which is used for obtaining multi-source satellite hyperspectral data of a to-be-detected region, carrying out the data preprocessing and data fusion of the data, and obtaining the surface reflectance data of the to-be-detected region; the spectral feature enhancement and extraction module is used for carrying out soil pixel purification and spectral feature enhancement on the surface reflectance data and extracting sensitive spectral features; the data analysis and inversion module is used for inputting the sensitive spectral characteristics into a preset intelligent inversion and early warning model library to obtain a soil heavy metal pollution grade identification result and a quantitative concentration inversion result; and the dynamic early warning module is used for carrying out time-space diffusion trend analysis and dynamic risk assessment on soil heavy metal pollution and generating a risk early warning report. The soil heavy metal pollution monitoring method is low in cost, high in efficiency and wide in range, and a soil heavy metal pollution monitoring solution of business operation can be realized.

The invention discloses a multi-source data fusion-based farmland heavy metal multi-medium migration flux simulation method, which comprises the following steps of: firstly, based on a law of conservation of materials and a farmland heavy metal source-sink relationship, determining a farmland heavy metal input and output process, and constructing a multi-medium flux model framework; determining a function relationship between migration processes and influence factors of farmland soil heavy metals, and constructing a multi-medium migration flux model; based on a farmland soil heavy metal multi-medium migration flux model, multi-source data and spatialization key parameters are fused, and each input and output migration process flux is simulated. Compared with the prior art, the method has the advantages that a GIS spatial modeling method is applied, sampling test, statistics, remote sensing and other multi-source data are fused, advantage complementation of existing monitoring technical means and multi-source data is achieved, spatial large-range farmland heavy metal migration flux is simulated, and scientific support is provided for large-range farmland heavy metal pollution abatement in drainage basins and the like.

The invention discloses a seed coating containing an arbuscular mycorrhizal inoculant. The seed coating is prepared from the following components in parts by weight: 25 to 35 parts of the arbuscular mycorrhizal inoculant, 35 to 45 parts of a soil conditioner, 15 to 20 parts of talcum powder and 15 to 20 parts of polyvinyl acetate. The invention further provides a preparation method of the seed coating containing the arbuscular mycorrhizal inoculant and application of the seed coating to reduction of soil heavy metal pollution, increase of disease resistance and stress resistance of crops and promotion of crop growth. The seed coating containing the arbuscular mycorrhizal inoculant wraps the surfaces of plant seeds, provides good nutritional ingredients for growth and development of the seeds, and is beneficial to improving the germination rate of the seeds under heavy metal cadmium stress, reducing soil heavy metal cadmium pollution, improving soil quality, increasing soil fertility and improving disease resistance and stress resistance of crops; growth of seedlings is promoted, and crop quality and yield are improved.

The invention relates to the technical field of image processing, and relates to an unmanned aerial vehicle-based rice field soil heavy metal dynamic monitoring method and system, and the method comprises the steps: obtaining image data of a rice field region, and determining a first heavy metal index of rice field soil in the image data through a preset water body absorption index; dividing the rice field area into a plurality of rice field sub-areas, and obtaining a second heavy metal index corresponding to each rice field sub-area; determining a spatial stability coefficient and a time trend coefficient by using the second heavy metal index; determining a comprehensive quantitative index by using the spatial stability coefficient and the time trend coefficient, and determining a heavy metal monitoring result of the soil in the rice field region according to the comprehensive quantitative index; according to the method, the influence of environmental interference on data accuracy is reduced by inhibiting water body signals, after soil heavy metal indexes of all areas are obtained, time-space associated comprehensive indexes are constructed to quantify the change condition of heavy metal pollution in the areas, and dynamic monitoring of the rice field heavy metal is implemented according to the quantitative result.

The invention relates to the technical field of particulate matter source analysis, and discloses a refined source analysis method and system.The method comprises the steps that a plurality of pollution sources and dust falling particulate matter sampling points are determined according to soil pollution distribution of a target area and an atmospheric pollution source survey result, by collecting single particle mass spectrum data of pollution sources, sampling points and receptors, constructing a mass spectrum library and combining an ART-2a algorithm and a deep learning model, the heavy metal pollution contribution proportion is accurately analyzed and traced to specific discharge ports of enterprises, and the problem that specific pollution enterprises and working sections cannot be positioned in the prior art is solved. According to the method, a pollution source and an environment receptor are sampled and analyzed by utilizing a single-particle mass spectrometry technology aiming at atmospheric dust fall particles in soil, and the heavy metal in the atmospheric dust fall particles in the soil is finely traced to specific enterprises and working sections by analyzing the heavy metal, so that a fine technical support is provided for soil heavy metal pollution management and control.

The invention discloses a soil heavy metal pollution detection and remediation hardware system based on isotopic tracing, which relates to the technical field of soil and comprises an isotopic tracing module, a heavy metal detection module, a remediation execution module, a real-time monitoring module, an intelligent regulation and control module and a data storage and transmission module, the data storage and transmission module is connected with the isotope tracing module, the heavy metal detection module, the repair execution module, the real-time monitoring module and the intelligent regulation and control module, and transmits data to a remote monitoring center in a wired or wireless communication mode, so that further data analysis and remote control are carried out; through multi-source data fusion and intelligent algorithm optimization, precise positioning, dynamic monitoring and efficient repairing of heavy metal pollution are achieved, the isotope tracing technology can position a pollution source and predict a diffusion path, and the plant and microorganism synergistic repairing efficiency is improved; the system is suitable for industrial pollution sites, farmlands and other scenes, and has remarkable environmental and economic benefits.

The application provides an in-situ passivation remediation method for heavy metals in farmland soil, and belongs to the field of heavy metal passivation in soil. The in-situ passivation remediation method for heavy metals in farmland soil is composed of the following steps: premixing, primary fermentation and secondary fermentation. The premixing is to uniformly mix pig manure, chicken manure, a composite passivation agent, a microbial inoculum and farmland soil, to adjust the C / N ratio and the water content of the mixture, to obtain a premix, and then to perform primary fermentation and secondary fermentation in an aerobic environment. The in-situ passivation remediation method for heavy metals in farmland soil can effectively combine the passivation agent with the compost fermentation, avoid the adverse effects of the fermentation components on the passivation agent during the composting process, improve the adaptability to different soil characteristics, realize stable and efficient treatment of different characteristic soils, further improve the in-situ passivation remediation effect on heavy metals in farmland soil, and effectively avoid the situation that the heavy metals in the soil are released again after the soil heavy metals are passivated.

The utility model relates to the technical field of leaching devices, and discloses a leaching device for soil heavy metal remediation, which comprises a shell, first fixing plates are fixedly connected to two sides of the shell, two second fixing plates are fixedly connected to two sides of the shell, a bracket is fixedly connected to one side of the shell, and a motor is fixedly connected to the top of the bracket. Two second limiting rods are fixedly connected to the sides, close to each other, of the two second fixing plates, two first limiting rods are fixedly connected to the side, close to the first fixing plate, of one second fixing plate, the two first limiting rods located at the same end are sleeved with two supporting plates, and the supporting plates are slidably connected with the first limiting rods; the two ends of the first limiting rods are sleeved with first springs correspondingly, the tops of the four supporting plates are fixedly connected with the same supporting plate, and the top of the supporting plate is fixedly connected with a screening box. According to the soil remediation device, dried and blocky soil can be dispersed, it is guaranteed that the soil can be fully leached by a cleaning solution, and the remediation effect can be guaranteed.

The invention relates to the technical field of soil monitoring, and discloses an agricultural soil heavy metal in-situ monitoring device which comprises a main rod, the top of the main rod abuts against a mounting plate, one side of the top of the mounting plate is rotatably connected with a solar panel, the outer wall of the main rod is fixedly connected with a shell, and a dismounting assembly is arranged in the mounting plate. A fixing assembly is arranged in the main rod, a folding assembly is arranged on the outer wall of the main rod and comprises a lifting sleeve and a fixing plate, the outer wall of the lifting sleeve is slidably connected to the outer wall of the main rod, and a lifting assembly is arranged at the bottom of the fixing plate. Fixing of the mounting plate and the main rod can be removed by pushing the push rod, the mounting plate is convenient to disassemble, after the mounting plate is disassembled, the elastic force of the second damper is utilized, the moving rod is reset, the lifting sleeve and the sliding sleeve are released, the supporting rod can be folded, device transportation is facilitated, disassembling steps are simplified, monitoring of different areas is facilitated, and the flexibility of the device is improved.

The invention discloses a soil heavy metal content prediction method and device, a storage medium and electronic equipment, and relates to the technical field of soil heavy metal prediction.The method comprises the steps that soil samples with different heavy metal concentrations and different particle size grades are prepared; acquiring spectral reflectivity data of the soil sample corresponding to multiple observation angles, and performing smooth filtering processing on the spectral reflectivity data by adopting a preset smooth filtering algorithm; on the basis of the spectral reflectivity data of different observation zenith angles under the same observation azimuth angle after smooth filtering processing, same-direction difference spectral features are constructed; and performing cross-particle size robustness verification on the same-direction difference spectral features to screen out target spectral features, and inputting the target spectral features into the trained soil heavy metal content prediction model to obtain a soil heavy metal content prediction result. According to the method, the soil heavy metal content can be stably and accurately predicted.

The invention provides bacillus mycoides Y2 with functions of reducing cadmium content of crops and promoting growth and application of the bacillus mycoides Y2, and relates to the technical field of soil heavy metal pollution bioremediation and agricultural microorganism application. The bacillus mycoides Y2 is preserved in the China General Microbiological Culture Collection Center (CGMCC) on July 14, 2025, and the preservation number is CGMCC 35231. The strain can be applied to cadmium-polluted soil through root application, can reduce the cadmium content of edible parts of crops such as rice, oilseed rape and fast vegetables, and promotes crop growth and rice root surface iron plaque formation. The contaminated soil in Hunan, Henan and Tianjin is verified to be capable of stably colonizing and adapting to different soils and crops. The strain and the matched preparation are green, efficient, high in stability and wide in universality, and provide key technical support for safe utilization of cadmium-polluted cultivated land.

The invention discloses a CA-LSTM-RF soil heavy metal accumulation space-time prediction algorithm under a deep learning framework, and belongs to the technical field of soil heavy metal accumulation space-time prediction algorithms, and the algorithm comprises the steps: obtaining soil heavy metal content data of each sampling point in a plurality of historical periods in a target region and an environment variable data set affecting soil heavy metal accumulation; the method comprises the following steps: performing abnormal value detection, missing data supplementation, alignment processing, quantification processing, normalization processing and time sequence coding on data, performing cellular water flow direction judgment by improving a CA model and designing diffusion process simulation of adaptive time, and calculating heavy metal surface runoff diffusion by using a Manning formula. And taking the diffusion value of the heavy metal along with the surface runoff and the environment variable data as input, extracting a historical data relationship through LSTM, outputting a soil heavy metal accumulation prediction value at the current moment, and taking the output of the LSTM as input to construct an RF model to predict the spatial characteristics of the soil heavy metal.

The invention discloses a microbial soil conditioner and a preparation method thereof, and relates to the technical field of soil improvement. The method comprises the following steps: culturing heavy metal-resistant chlorella and aspergillus niger to obtain a mixed bacterial suspension, combining the mixed bacterial suspension with a composite chelating modifier prepared by reacting chitosan quaternary ammonium salt, ethylenediamine tetraacetic acid and epichlorohydrin, carrying out crosslinking by using genipin to obtain a functional microbial agent, and carrying out pyrolysis and acid oxidation on corn straws as a raw material to obtain the heavy metal-resistant chlorella modified functional microbial agent. The preparation method comprises the following steps: preparing a nano zero-valent iron-biochar composite material, reducing and loading ferrous ions through sodium borohydride, preparing the nano zero-valent iron-biochar composite material, mixing the nano zero-valent iron-biochar composite material with zeolite, bentonite and a silane coupling agent, carrying out spray drying to prepare a porous carrier substance, and finally stirring and mixing an organic fertilizer complex microbial inoculant, the functional microbial inoculant and the carrier substance, and aging to prepare the soil conditioner. The prepared conditioner has a good remediation effect on soil heavy metal pollution.