138 results about "Surface water body" patented technology

The invention provides a mine water disaster monitoring and early warning method and system based on multi-source heterogeneous data fusion, and the method comprises the steps: collecting multi-source data of a mining area, carrying out the time-space alignment of the multi-source data, and generating a data set, the multi-source data comprising remote sensing data; preprocessing the data, inputting the preprocessed data into a multi-modal fusion network, and extracting surface water body distribution, lithologic permeability, underground water level and structural fracture characteristics to obtain a three-dimensional hydrogeological static model; the hydrological numerical model based on physical driving is coupled with the static model, and the dynamic model is used for simulating the dynamic change of an underground water flow field and a pollutant diffusion path; based on the dynamic model updated in real time, multi-target collaborative evaluation is carried out to evaluate the mining area water resource, ecological and social collaborative effect; and calling an unmanned aerial vehicle to inspect a leakage position or a settlement position in the hydrogeological risk map based on a multi-target collaborative evaluation result. According to the method, the prediction accuracy is improved through the multi-source data.

The invention discloses a water quality prediction method based on a Transform-LSTM fusion model, and belongs to the technical field of water quality time series data prediction and artificial intelligence. Comprising the following steps: (1) acquiring water quality data from a water quality monitoring station; (2) carrying out pretreatment; (3) screening out water quality characteristic data; (4) dividing into a training set, a verification set and a test set; (5) inputting the data into a Transform-LSTM (Long Short Term Memory) fusion model; (6) embedding water quality data time sequence information by a Transformer encoder through position coding, extracting a global dependency relationship among features by utilizing a multi-head attention mechanism, and optimizing gradient propagation by combining residual connection and layer normalization; (7) the LSTM layer receives the high-order features after Transform coding, and captures a local time sequence dynamic mode; and (8) mapping the extracted water quality time sequence characteristics to a specific prediction result by a regression output layer by adopting a linear activation function, and calculating an evaluation index. According to the method, the water quality change trend of the surface water body can be effectively predicted, and powerful support is provided for water ecological protection and sustainable development.

The invention discloses a multisource remote sensing image surface water body extraction method based on a graph neural network. The SAR image is used as a main part, and the optical image, the digital elevation model and the local incident angle information are fused. Firstly, radiation correction, geometric correction and noise suppression are carried out on SAR data, and multi-source data spatial resolution matching is carried out. And then converting the image into a graph structure by adopting a self-adaptive Bayesian superpixel segmentation method. Then, geometric, texture and physical features are constructed for the superpixel nodes, and feature dimensions are optimized through a feature selection algorithm; and finally, constructing a classification aggregation graph convolutional neural network, wherein the core lies in a self-adaptive microaggregation mechanism of the classification aggregation graph convolutional neural network. According to the mechanism, neighborhood superpixels can be dynamically divided according to categories and feature fusion is carried out, and the recognition capability of the model on water body boundaries and regional heterogeneity is remarkably improved. According to the method, multi-source remote sensing data, superpixel graph structure conversion and an optimized graph convolutional network are comprehensively utilized, and the precision and robustness of water body extraction in the complex surface environment are effectively improved.

The invention discloses an urban flood mapping and driving factor analysis method based on multi-source remote sensing data, and the method comprises the steps: respectively extracting an optical semi-permanent water body and an SAR semi-permanent water body based on a global surface water body data set and a Sentinel-1 image, and taking the intersection of the optical semi-permanent water body and the SAR semi-permanent water body to generate an enhanced semi-permanent water body mask; extracting a water body in a flood period by using the Sentinel-1 image to obtain flood water body distribution, and performing water body extraction precision verification in combination with the Sentinel-2 image; the precision of the SRTM DEM is improved based on a DEM error prediction model of a Transform network, and a corrected DEM is generated; and constructing a multi-dimensional spatial analysis model based on the elevation, gradient and surface coverage type of the corrected DEM, and identifying spatial distribution characteristics and a terrain driving mechanism of the flood water body. According to the method, comprehensive analysis is performed in combination with terrain factors, and the spatial precision and timeliness of flood risk assessment can be improved.

The invention belongs to the technical field of environment monitoring, and particularly relates to a quantitative recognition method for contribution of autogenous source organic carbon of a surface water body. Based on the stable isotope ratio and the three-dimensional fluorescence spectrum, the water DOM source analysis method is constructed, the source and characteristics of DOM are comprehensively analyzed, and the characteristic information and contribution of autogenous source organic carbon are specifically recognized. The three-dimensional fluorescence spectrum has the advantages of being rapid in detection, small in sample use amount and the like, DOM sources can be rapidly and qualitatively identified, different DOM sources can be quantitatively calculated through stable isotope ratio analysis, and meanwhile the research area land utilization, hydrological water environment and water quality parameters are combined, so that the DOM sources can be rapidly and qualitatively identified. And the source and migration rule of the DOM in the water body can be determined more comprehensively. The quantitative identification method provided by the invention has the advantages of more comprehensive water DOM research, more complete identification system and more reliable traceability result.

The invention discloses a culture pond monitoring method based on Otsuu-MNDWI collaborative screening and machine learning, and the method is characterized in that the method comprises the following steps: S1, collecting multi-source remote sensing data in a to-be-detected region; s2, carrying out image preprocessing on the surface reflectance data; s3, MNDWI calculation is carried out on the preprocessed surface reflectance data, and a corresponding MNDWI index map is obtained; s4, dividing the MNDWI index map into a water body region and a non-water body region by adopting an Otsu threshold segmentation method; s5, performing water body boundary extraction on the water body area obtained in the step S4 through a Canny algorithm; s6, taking radar VH polarization data, digital elevation data and global surface water body frequency data as auxiliary features, and performing multi-source data fusion and filtering on the auxiliary features and the water body region features obtained in the step S5; and S7, extracting the multi-dimensional feature of each pixel in the water body region processed in the step S6 as an input feature, inputting the input feature into the integrated learning model and training, and outputting a spatial distribution result of the culture pond.

The invention belongs to the technical field of lake and reservoir water source risk prediction, and provides a lake and reservoir water source algae bloom risk early warning system based on a 16S / 18SrRNA gene expression quantity threshold, and the method comprises the following steps: S1, collecting a sample from a lake and reservoir water source surface layer water body; s2, using 7-gate water bloom algae specific primers for blue-green algae, green algae, diatom, euglena, dinoflagellate, chrysophyta and cryptoalga; s3, based on the qPCR standard curve, calculating the copy number of the 16SrRNA / 18SrRNA gene of the seven water bloom algae; s4, when the gene expression quantity of a certain algal bloom algae continuously reaches 106-7 copy number / mL for 7-12 days, determining that the algal bloom algae is in a window phase; according to the threshold value of the gene expression quantity of the water bloom algae 16SrRNA / 18SrRNA, the window period of water bloom algae cells can be accurately recognized, so that early warning of the algae bloom risk in the lake and reservoir water source is achieved, and early warning of algae bloom outbreak in the lake and reservoir water source is achieved one week or above in advance.

The utility model discloses an aeration cyclone offshore algae control system, which belongs to the technical field of offshore prevention and control of blue-green algae, and comprises an air compressor, a water tank, a heat exchanger, a cooling circulation device and a plurality of aeration cyclone mechanisms, each aeration cyclone mechanism comprises an aeration main pipe, a horizontal aeration branch pipe and a plurality of vertical aeration branch pipes, according to the utility model, active offshore algae control is realized in the time period of stronger illumination and higher air temperature, and the'gas stripping 'effect generated by the rising of bubbles is utilized, so that the Reynolds number is increased, the turbulence degree of a water body is increased, and the aeration effect of the water body is improved. Meanwhile, the normal migration rule of the blue-green algae can be destroyed due to the occurrence of the rotational flow, so that the blue-green algae cannot normally perform photosynthesis, and water layers at different temperatures can be sufficiently mixed due to the rotational flow, so that the water temperature of the surface water body is greatly reduced, and the growth of the blue-green algae is effectively controlled.

The invention discloses a surface water body classification data quality evaluation method based on a remote sensing image, and the method comprises the steps: grouping to-be-classified remote sensing image samples, and distributing different groups to different persons for classification; establishing a square grid; selecting a cross-group grid and a cross-group sample; calculating the percentage of various surface water bodies of each group in the cross-group grid; calculating correlation coefficients of percentages of various surface water bodies among each group in the cross-group grid; and judging whether the groups are abnormal or not according to the correlation coefficient. And based on the similarity principle in the first law of geography, carrying out anomaly detection and quality evaluation on massive classification results by utilizing the classification result correlation coefficients of different classification personnel.

The invention provides a hydrological big data real-time monitoring method and device based on quantum computing and a medium, and belongs to the technical field of hydrological data processing. Hydrological data and change data of a surface water body are collected; determining river course roughness and infiltration coefficient parameters as a hydrological change actual data set by using a quantum annealing process; predicting hydrological change data in the drainage basin based on a random forest regression model; performing comparative analysis on the hydrological change data and a prediction result based on the real-time rainfall data to generate a parameter adjustment instruction; setting a water level change threshold value and a precipitation threshold value; and receiving the precipitation in real time, triggering a historical similar flood event case library retrieval function on the basis of a threshold exceeding condition, and outputting an early warning response record corresponding to a historical event. A quantum walking algorithm is used for simulating flood diffusion, a submerging depth thermodynamic diagram is generated in combination with a digital elevation model, the flood submerging range and depth distribution are rapidly and accurately displayed, and visual and visual support is provided.

The invention belongs to the technical field of water quality monitoring methods, and particularly discloses a stacking integrated machine learning water quality remote sensing inversion method and system based on model contribution degree self-optimization weighting, and the method comprises the following steps: extracting a surface water body range and corresponding water quality monitoring data, integrating the water quality monitoring data into a daily average water quality data set, and calculating the daily average water quality data set; selecting wave bands B2, B3, B4, B5, B6, B7, B8 and B8A, and matching the in-situ water quality monitoring data with the reflectivity of the Sentinel-2 wave band; combining the B2-B8A in pairs to obtain seven mathematical changes, and generating waveband combination features; screening an optimal wave band combination, and carrying out model training to obtain a base model prediction result; and selecting the base model with the optimal determination coefficient R2 as a meta-model, constructing a new feature data set, inputting the new feature data set into the meta-model for final training, and outputting a water quality remote sensing inversion result. By adopting the technical scheme, the high-precision prediction of the water quality parameters is realized by dynamically evaluating the prediction performance of the base model and automatically optimizing the contribution weight of the base model.

The invention discloses an industrial park-oriented pollution accident traceability and risk intelligent prediction system, and relates to the technical field of park pollution prediction, and the system comprises a pollution data collection module which is used for arranging a water quality monitoring terminal in an industrial park factory area, collecting pollution data, and forming structured pollution data; the hydrological structure modeling module is used for constructing a hydrological network diagram structure based on the physical connection relationship between the park underground drainage system and the surface water body and outputting a directed graph; and the pollution path calculation module is used for calculating propagation paths of pollutants on the possible paths and theoretical propagation time corresponding to the paths on the basis of the directed graph structure output by the hydrological structure modeling module. According to the method, a full-path modeling process of pollutants from a discharge source point to a water body diffusion terminal is broken through by constructing a double-drive coupling model, and the model supports dynamic prediction of pollution paths and influence areas under different hydrological situations and process characteristics.

According to the coastal saline-alkali soil water storage system device and the application thereof, on the basis of the characteristics that water and salt in coastal saline-alkali soil are actively transported, and the salinity change amplitude in an irrigation canal is large, ground surface reservoirs of different sizes are laid between field parcels, and the water flow direction of the reservoirs is judged through real-time conductivity data of pond water, canal water and irrigation water; the salt gradient controllable water storage system is formed and controlled according to salt tolerance threshold values of nearby crops, so that the irrigation and salt leaching requirements of different crops in different seasons are met. The water storage device can input the capacities of different reservoirs, crop salt tolerance thresholds, irrigation water demand and the like, estimates the field covered by the water storage device, and optimizes the cultivation mode planning. The device can accumulate irrigation water in different salinity ranges under the condition that the salinity of a surface water body changes drastically, can fully utilize different water sources such as surface water and rainwater, and is very suitable for coastal salinization farmland with relatively rich water resources.

The invention relates to the technical field of surface water body treatment, and discloses a multifunctional unmanned ship, which comprises a ship body, a power system, a control system, a sensor module, an execution module and a data communication module, the ship body adopts a streamline design, the load capacity is 55kg, and the anti-storm grade reaches above level 3; the power system is driven by hybrid energy and comprises a solar cell panel and a lithium battery pack. The control system supports autonomous navigation, path planning and obstacle avoidance functions based on an artificial intelligence algorithm; the sensor module comprises a water quality sensor probe, a visual identification system and a sonar system; the execution module comprises a garbage collection device and a rescue device; the data communication module comprises a wireless communication antenna, supports wireless network transmission and uploads collected data to a cloud platform. Water quality monitoring, water surface cleaning, fish and bird recognition, underwater topographic mapping and emergency rescue functions are integrated on a single platform, so that the comprehensive efficiency of water area management is remarkably improved.

The invention relates to the technical field of underground water state evaluation, and discloses an underground water level dynamic evaluation method based on multi-source data mapping and fluid model correction. The invention aims to solve the technical problems of insufficient underground water level monitoring precision and lack of space details caused by low spatial resolution of existing satellite gravity detection data and difficulty in accurately separating a deep underground water change signal from a land total water reserve signal. According to the method, through space-time reference unification of multi-source heterogeneous data, high-resolution surface texture features are utilized to guide a low-resolution gravitational field to carry out space downscaling reconstruction; surface water body and soil water reserve signals are removed layer by layer in combination with spectral characteristics and a thermal inertia principle; and introducing measured data to construct a spatial error correction curved surface of specific yield parameters, and identifying the underground water funnel by using an image connected domain analysis technology.

The invention provides a soil pollution source analysis method and related equipment, and is applied to the technical field of data processing. The method comprises the following steps: acquiring soil profile sample information, environment end member sample information and a preset fractionation model; the environmental end member sample information is preprocessed, and the ratio information of the initial cadmium isotope is generated and comprises the ratio information of the cadmium isotope in the surface water body, the ratio information of the cadmium isotope in the atmospheric total sedimentation sample and the ratio information of the cadmium isotope in the soil forming parent material sample; performing correction processing on the ratio information of the initial isotope based on a preset fractionation model to generate ratio information of the target cadmium isotope; performing correction processing on the soil profile sample information to generate an isotope source analysis result; processing the isotope source analysis result to generate isotope source verification information; and processing the isotope source verification information to generate soil pollution source information.

The invention relates to the technical field of water resource investigation, in particular to a method and system for calculating dynamic surface water reserves in combination with an SWOT satellite.The method comprises the steps that water surface height data are collected, water surface area calculation is conducted, dynamic weight distribution sensitive to environmental factors and multi-source fusion are conducted, and the water surface area of a target surface water body is obtained; performing water level measurement; fusing and correcting all the water level measurement data obtained by calculation to obtain a corrected water level elevation; acquiring underwater topographic data, and performing feature-level fusion and error minimization optimization on the underwater topographic data by adopting a multi-modal deep learning framework to obtain the underwater topographic data; and calculating the water reserves of the target surface water body. The method solves the problems of single observation data source and susceptibility to environmental interference in the prior art, and has the characteristics of high robustness and all-weather adaptability.

The invention discloses an oil and gas site leakage pollution prediction method based on an HGS, and relates to the field of oil and gas site pollution prediction, and the method comprises the following steps: constructing an integrated three-dimensional geological grid comprising a surface water body, an aeration zone and a water-bearing layer through an HGS platform; on the basis of an integrated three-dimensional geological grid, pollution migration of pollutants in the shaft leakage process is simulated through a three-dimensional underground water flow model, leakage pollution plume changes of the pollutants in underground water are depicted through a solute migration function, and spatial and temporal distribution data of pollution plume migration are output; and obtaining a pollution prediction result based on the spatial and temporal distribution data of pollution plume migration. According to the method, the spatio-temporal evolution law of underground water pollution of the oil and gas field can be accurately displayed, and the problem that in the prior art, only simplified simulation can be conducted on solute transport in an underground water system, oil and gas field leakage pollution scenes under multiple working conditions cannot be accurately simulated, and consequently the oil and gas field leakage pollution prediction accuracy is low is solved.

The invention discloses a pyrite coupled corncob mixed nutritional denitrification vertical flow artificial wetland system, relates to the field of sewage treatment and water ecological restoration, and relates to an artificial wetland. The invention aims to solve the problem that nitrate nitrogen pollutants in farmland recession enter peripheral surface water and underground water due to excessive application of nitrogen fertilizer. The artificial wetland system consists of wetland plants, a coarse sand layer, a functional layer and a gravel layer from top to bottom. According to the iron ore coupled corncob mixed nutritional denitrification vertical flow constructed wetland system, full supply of autotrophic and heterotrophic electron donors is realized in the integrated vertical flow constructed wetland, and high-efficiency treatment of wastewater with different types of carbon-nitrogen ratios is facilitated. The nitrate nitrogen removal rate and the TN removal rate can exceed 98% and 90% respectively.

The invention provides a method for sequencing new organic silicon pollutants in a surface water body according to toxicology priority indexes, and belongs to the technical field of environmental toxicology and ecological risk assessment. The new organic silicon pollutants in the surface water body are sequenced through comprehensive concentration, detection rate, degradation half-life period, toxicity parameters and multi-dimensional indexes such as binding energy and migration ability obtained through molecular docking analysis; according to the method, a molecular docking technology is introduced into an ecological risk evaluation system for the first time, and key risk factors are highlighted by quantifying binding energy of a target substance and a biological receptor and combining differentiated weight distribution. The method has the advantages that 1) the limitation of a single toxicity index is broken through, and multi-factor comprehensive risk quantification is realized; 2) optimizing environment monitoring and governance resource allocation based on the scientific sorting of ToxPi; and 3) scientificity and accuracy of evaluation are improved, and efficient decision support is provided for prevention and control of organosilicon pollution of surface water.

The invention discloses a method for estimating the burial depth distribution of regional underground water based on Bayesian deep learning, and the method comprises the steps: employing discrete observation well position data, and combining the multi-source data, such as rainfall, evapotranspiration, temperature, gradient, distance to a nearest surface water body, soil type, land utilization type, and underground water reserve, as input factors; and establishing a nonlinear mapping relation between the underground water burial depth and the input factors through a Bayesian deep learning model. According to the method, the model is trained and verified by using the measured data at the observation points, and then the regional groundwater burial depth distribution is estimated based on the input factor data of the research area, so that the problem of insufficient coverage of a traditional groundwater monitoring network can be effectively solved, and the regional groundwater burial depth distribution characteristics can be described with relatively high spatial resolution; and scientific support is provided for sustainable utilization and ecological protection of regional underground water resources. The method has universality and is suitable for underground water burial depth prediction in different areas and under various data conditions.

The invention discloses a'breathable 'ecological plastic steel sheet pile, and relates to the technical field of plastic steel sheet piles, the'breathable' ecological plastic steel sheet pile comprises a main body, a plurality of holes are formed in the main body, the front surface of the main body is used for facing a surface water body, and a plurality of convex structures are fixedly arranged on the front surface of the main body; the number of the protruding structures is the same as that of the holes, the protruding structures are in a concave shell shape, one protruding structure buckles one hole, and the bottom edge and the two side edges of each protruding structure are all fixedly connected with the body. A cavity with a top opening or a bottom opening is defined by each protruding structure on the front side of the body. According to the scheme provided by the invention, the structural strength of the hole of the main body is enhanced through the convex structure, and the seepage direction of underground water flow is changed by forming the cavity with the top opening, so that water and soil loss is prevented.

The invention provides a surface water body dynamic monitoring method based on multi-source remote sensing and spatial similarity reconstruction, and relates to the technical field of remote sensing detection. The method comprises the following steps: acquiring a to-be-reconstructed water body distribution diagram sequence collected by an optical remote sensor; searching a reference image with the smallest spatial structure difference with the to-be-reconstructed water body distribution map in the reference image set, and taking the reference image as an optimal reference water body map of the to-be-reconstructed water body distribution map; and carrying out pixel replacement on a missing region in each to-be-reconstructed water body distribution diagram by utilizing a pixel category in the optimal reference water body diagram, and finally obtaining a reconstructed water body distribution diagram sequence. According to the method, pixel-level completion is carried out on a to-be-reconstructed water body distribution diagram acquired by an optical remote sensor by using a pre-constructed missing-free reference image set, so that the integrity of a time sequence water body information image acquired by the optical remote sensor within a period of time is ensured, and the continuity of water body information in time sequence is further ensured; the monitoring requirements of large-range and high-dynamic change water bodies can be met.

The invention discloses an emergency compound agent for coping with short-term total phosphorus increase of a surface water body, and belongs to the field of emergency compound agents for coping with short-term total phosphorus increase of the surface water body. A preparation process comprises the steps of preparing a PAC solution, preparing a diatomite modifier, preparing modified diatomite, preparing the emergency compound agent and the like. The steps of ultrasonic loading, calcining, washing and the like are added in the process, so that the adsorption precipitation capacity of the modified diatomite is improved; and compounding agents with different mass ratios are prepared according to the phosphorus content of the treated water body, so that the application range and the capability are improved. The method is simple to operate, green and environment-friendly, and by modifying the diatomite raw material and increasing the loading degree of the modified diatomite, the adsorption and precipitation capacity of the prepared modified diatomite can be remarkably improved, and the stability after adsorption and precipitation is promoted, so that the use performance and the application range of the prepared emergency compound medicament are effectively improved.

The present invention discloses a quantitative calculation method for the hydrogen and oxygen isotopes in surface water during the evaporation fractionation process in the field of isotope tracing technology, aiming to solve the problems of low calculation accuracy and large uncertainty in the existing calculation methods. It includes: analyzing the kinetic effect of the evaporation fractionation process; constructing the probability mass function of the isotope body and decomposing the ratio of the heavy / light isotope body; quantitatively describing the dissipation rate of the heavy / light isotope body in the evaporation fractionation; combining the water evaporation rate to obtain the change efficiency of the heavy / light isotope body during the evaporation fractionation process; and calculating the isotope body content in the remaining surface water. The present invention enhances the application adaptability of the calculation method to various conventional and extreme situations, reveals the isotope fractionation change law in surface water during the evaporation process, and improves the reliability of the results.

The application discloses a kind of spatiotemporal continuous land surface water body generation methods based on multi-source geospatial data, belong to remote sensing information processing and geographic information technical field.The method includes: obtaining the multi-source geospatial data related to water body distribution, including climate, hydrology and topographic data;It is spatiotemporal resolution and coordinate system, constructs pixel level characteristic variable;Based on existing water body data constructs training sample library;Using random forest or gradient boosting tree and other machine learning methods trains water body distribution reconstruction model;The model is applied to historical or missing time node, and water body distribution prediction result is generated every time phase;Finally form long time sequence, spatially continuous, resolution consistent land surface water body grid data set.The application can effectively fill in remote sensing data missing, improve the spatiotemporal continuity and consistency of water body data, suitable for wide range, long time sequence water body dynamic monitoring and analysis.

The invention relates to the technical field of surface water quality evaluation, in particular to a water quality sampling and evaluating method and device, electronic equipment and a storage medium. Secondly, for each section, constructing a detection data matrix by using a plurality of corresponding detection data vectors according to a time node sequence, and performing dimension transformation on the detection data matrix to obtain section vectors; finding a plurality of similar vectors from the plurality of water quality classes according to each section vector, and constructing the plurality of similar vectors into a reference vector group; and finally, determining the water quality grade of each section according to the reference vector group, and determining the water quality grade of the water body according to the water quality grades of the plurality of sections. According to the method, detection data of a plurality of detection items, a plurality of time nodes and a plurality of sections of the water body are integrated, the water quality of the water body is reflected objectively, few samples are needed, the construction process is simple, and the output result is more accurate and reliable.

The invention provides a nitrogen pollution regulation and control method and device, a storage medium and equipment, and the method comprises the steps: obtaining nitrogen emission data in the production process of industrial and agricultural products, and the total nitrogen of surface water and atmospheric nitrogen oxides in a target region; the method comprises the following steps: acquiring a mapping relation between nitrogen emission of industrial and agricultural products and total nitrogen of surface water and atmospheric nitrogen oxides in a target area, and constructing an economic constraint function and a nitrogen emission constraint function based on an economic development target and an environmental protection target of the target area; and based on second environment data, an economic constraint function, the nitrogen emission constraint function and the mapping relationship, obtaining yield recommendation information of the industrial and agricultural products by using a multi-objective optimization model, and performing production distribution of the industrial and agricultural products according to the yield recommendation information. The production management and control of industrial and agricultural products can be realized by combining economic development and environmental protection, and the local environmental quality is improved while the economic development is guaranteed.

The present invention discloses a tunnel surrounding rock control technology and construction method for supporting a soft rock tunnel before excavation, comprising the following construction steps: S1, designing the spiral radius of an elastic ring support (1) according to the tunnel cross-sectional dimensions, surrounding rock properties, ground stress, elastic-plastic influence zone of the tunnel surrounding rock, and mechanical characteristics of the elastic support, determining the length of the elastic ring support (1) along the preset tunnel strike, whether grouting treatment is required, fixing the anchor length, and arranging the position; S2, performing spiral drilling in a predetermined direction on site using a drilling rig (11) and navigation equipment, and reaching a designated bottom plate; the tunnel surrounding rock control technology and construction method for supporting a soft rock tunnel before excavation of the present invention can be applied not only to soft rock tunnel support, but also to reinforced support of mine tunnels under deep complex geological conditions and high stress environments, as well as reinforced support of deep-sea tunnels and deep underground tunnels, reinforced support of tunnels under surface water bodies, and reinforced support of military underground tunnels.

The invention provides a micro-disturbance under-ice surface water in-situ layered collection monitoring device and method, the device comprises a layered collection device and a monitoring device, the layered collection device comprises a water taking hose, a steering shaft, a support frame, a sliding push rod, an under-ice support frame, an under-ice sampling frame and a fixed support; the steering shaft is installed at the upper end of the supporting frame, the sliding push rod is installed on the outer side wall of the supporting frame, the lower end of the supporting frame is connected with the under-ice sampling frame, the lower end of the under-ice supporting frame is connected with the tail end of the under-ice sampling frame, and the upper end of the under-ice supporting frame is connected with the sliding push rod. The lower end of the water taking hose is inserted into the supporting frame from the upper end of the supporting frame and connected with the water taking hard pipe, and the upper end of the water taking hose is connected to monitoring equipment. The device is simple in structure, convenient to carry and operate and capable of completing high-precision layered fidelity sampling of the water area under ice. And the gas concentration and related environmental parameters of the surface water body under ice can be monitored on site.