11526results about "Earth material testing" patented technology

The invention presents a system for geospatial topographical modeling and real-time system control to be used with subterranean slurry injection technology. The system consists of a predictive modeling subsystem, which processes land characteristics to generate optimal site plans for drilling and injection. The real-time control subsystem implements these plans, dynamically adjusting slurry compositions and injection parameters based on sensor feedback. A feedback loop between the subsystems allows continuous refinement of both predictive models and operational controls, enhancing the precision with which the technology shapes the ground and the efficiency with which the technology sequesters carbon.

The invention relates to the technical field of soil pollution detection, in particular to a soil heavy metal pollution intelligent detection method and system.The method comprises the steps that soil multi-source information and regional environment parameters of to-be-detected soil are collected, and soil pollution characteristics are recognized according to the soil multi-source information; establishing a pollution identification analysis model according to the soil pollution characteristics and the regional environment parameters, and analyzing whether the to-be-detected soil contains heavy metal pollutants or not; if the heavy metal pollutants are contained, pollution diffusion paths of the heavy metal pollutants in time and space are predicted according to a pollution identification analysis model; and if the soil does not contain the heavy metal pollutants, performing time sequence analysis on soil pollution characteristics, monitoring and analyzing future concentration distribution of the heavy metal pollutants, and establishing pollution risk early warning. According to the invention, the problems of long sampling analysis time consumption, incapability of dynamically monitoring pollutant concentration change in real time, insufficient diffusion prediction accuracy and difficulty in comprehensively evaluating high-concentration and potential risk areas in a traditional soil heavy metal detection method are effectively solved.

Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

A detection method for infrared thermal image damage area of coal rock includes: step 1: collecting and recording the infrared thermal image of coal rock in the process of loading destruction; step 2: processing a gray-scale transformation on the collected infrared thermal image of coal rock; step 3: denoising an infrared thermal image of coal rock after the gray-scale transformation by the dense residual image denoising algorithm of autocorrelation network; step 4: conducting an area segmentation on the infrared thermal image of coal rock after denoising to extract eigenvalues by using a coal-rock infrared thermal image damage area segmentation algorithm of improved encoder-decoder network; step 5: observing the damage area of coal rock. The detection method improves the denoising effect of the infrared thermal image of coal rock, strengthens the extraction of the characteristics of the damage area, and improves the accuracy of the segmentation.

A road damage detection method and apparatus, and a computer device and a storage medium. The detection method comprises: acquiring sensing data information and image information, which are sent by collection boxes, of a road, and a plurality of different sample damage ranges, and on the basis of the sensing data information of the road, determining the current damage range of the road and a damage sensing map of the road (S101); identifying a target damage area of the road on the basis of the image information of the road and the current damage range of the road, and identifying a damage type of the road on the basis of the damage sensing map of the road (S102); acquiring damage sensing data of the target damage area, and on the basis of the damage type of the road and a sample damage range of the damage type at each degree, calculating a damage level corresponding to the damage sensing data of the target damage area (S103); and using the target damage area, the damage level and a damage type as target damage information of the road (S104).

The invention provides a soil fertility quality analysis method and system based on corn planting cultivated land, and relates to the technical field of soil quality analysis and evaluation.The soil fertility quality analysis method comprises the steps that a visible-near infrared spectrum image of a soil sample is collected, reflectivity data of a key wave band is extracted from the spectrum image, and a soil health index and a nutrient balance index are calculated; calculating a soil fertility quality comprehensive score for comprehensively evaluating the soil fertility according to the soil health index, the nutrient balance index and the soil moisture content; comparing the comprehensive score of the soil fertility quality with a preset threshold value, and evaluating the soil fertility; a machine learning model is introduced, historical soil data is trained and predicted, a quality analysis model is constructed based on a random forest algorithm, the trained quality analysis model is put into use, and new data is continuously learned to dynamically adjust and update, so that the soil detection process is simplified, and the soil fertility change can be monitored in real time. And agricultural managers are helped to make scientific decisions.

Methods of creating and using digital twins of vegetation / crops in relation to their growing environments. Some embodiments include creating a digital twin of the growing environment using sensor data from a subsurface sensor suite, creating a digital twin of the vegetation crop using data from an overhead sensor suite, and using at least one mathematical model to relate the digital twin of the crop to the digital twin of the growing environment. In some embodiments, the mathematical model(s) implement a root cause analysis that uses a limiting factor approach. In some embodiments, the subsurface sensor suite is on a multi-sensor soil probe. In some embodiments, the multi-sensor soil probe comprises a video microscope, a tip stress sensor, a sleeve stress sensor, and a volumetric- water-content sensor. Other related systems, devices, methods, and software are also disclosed.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The present application provides a multi-parameter environmental quality intelligent monitoring system and a control method thereof. The method includes: collecting environmental quality data, the environmental quality data including water quality parameters, air parameters and soil parameters; preprocessing the environmental quality data to obtain target environmental quality data, the preprocessing including data cleaning, standardization and time alignment; constructing a random neural network with forward regularization, and performing feature extraction and association analysis on the target environmental quality data to obtain feature data; based on the feature data, training an environmental quality assessment and early warning model to obtain an environmental quality monitoring model; deploying the environmental quality monitoring model to perform online monitoring and early warning of the environmental quality data. The present application realizes intelligent analysis and real-time early warning of environmental quality data, and improves the accuracy and efficiency of environmental monitoring.

Disclosed are a dynamic disturbance-induced rock burst test device and a test method. The test device comprise a supporting platform, a plurality of loading plates, an electromagnetic pulse emitting system, an acoustic emission monitoring system, a high-speed camera, an electromagnetic radiation detecting system and a confining pressure servo control loading system. The plurality of loading plates are arranged on a side wall of a coal rock sample and are placed inside a square chest, the electromagnetic pulse emitting system and the confining pressure servo control loading system are arranged inside a bar system, and an acoustic emission probe is arranged inside the loading plate. A three-dimensional real stress environment of the coal rock is simulated by simulating a shear stress through friction between the loading plate and the sample, as well as dynamic and static combined loading of multi-axial and multi-directional static confining pressure and stress wave disturbance.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention relates to the technical field of grouting simulation tests, in particular to a sandy gravel stratum catastrophe grouting prevention and control virtual-real blending collaborative test method and system, and the method comprises the steps: carrying out a grouting test on a sandy gravel stratum simulation model, and obtaining grouting parameters and monitoring parameters in the grouting process; based on the resistivity and the radar data, further obtaining a slurry diffusion path and a diffusion range of the area with the higher surrounding rock grade; based on the resistivity and the optical fiber sensing data, the slurry diffusion path and the diffusion range of the area with the low surrounding rock grade are further obtained; inputting the initial grouting parameters into the trained neural network model to obtain simulation data; carrying out preprocessing and alignment operation on the previous data, and carrying out fusion analysis; and according to the fusion analysis result, grouting equipment parameters and a grouting scheme are pre-controlled and adjusted. And real-time regulation and control of a grouting scheme and equipment are realized.

Carbon Capture, Utilization, and Storage (CCUS) is a relatively new technology directed to mitigating climate change by reducing greenhouse gas emissions. Current and new government requirements require proof that carbon dioxide (CO2) is either sequestered in a stable form or safely stored for long periods of time. In instances when the CO2 is sequestered through mineral formation, the need for long-term monitoring can be reduced, as the stability of the sequestered CO2 is inherent based on a chemical change in subterranean rocks. The reactions between CO2 and rock formations are influenced by numerous factors, including temperature, pressure, fluid composition, and the mineralogy of the formation. Furthermore, these reactions occur over large spatial areas and long timescales, making them difficult to monitor directly. Methods and systems of the present disclosure, therefore, may use a combination of laboratory experiments, field monitoring, and modeling to provide convincing evidence of CO2 mineral sequestration.

The invention relates to the field of saline-alkali soil water-fertilizer regulation, in particular to a saline-alkali soil soybean water-fertilizer-salt regulation dynamic decision-making method and system. A saline-alkali soil soybean water, fertilizer and salt regulation dynamic decision-making system comprises a region division module, a data monitoring module, an irrigation initial decision-making module, a fertilization initial decision-making module, an irrigation decision-making acquisition module and a fertilization decision-making acquisition module. By integrating an advanced sensor technology and an intelligent decision-making model, key parameters are accurately monitored, and the total amount of irrigation and fertilization required in a specific time window in the future is calculated in combination with crop growth stages and historical water and fertilizer records; meanwhile, the irrigation and fertilization frequency can be dynamically adjusted according to soil and environment data monitored in real time, waste of water resources and fertilizer is avoided, the possible soil salinization problem is solved, soil health can be maintained, and crop growth is promoted.

The present disclosure relates to the technical field of landslide hazard model tests, and in particular to a landslide model test method and device for realizing multi-variable synchronous monitoring. In the test device, a soil slope simulation unit is configured to pile up a soil slope and simulate different inclinations of the slope. A rainfall simulation unit is configured to simulate rainfall of different intensities and induce a landslide. A runoff-seepage separation unit is configured to realize separation of runoff from seepage of the slope under the effect of rainfall. A monitoring module is configured to monitor runoff and seepage flow rates of the slope, changes in water content and pore water pressure within the slope, and a morphological change of the slope under the effect of rainfall, to obtain a mechanism of rainfall-induced landslide deformation and damage based on changes in the flow rate.

The invention relates to the technical field of remote sensing estimation, in particular to a spatially-varied multi-scale soil organic matter remote sensing estimation method which comprises the following steps: acquiring soil samples at differentiated positions, testing the content of soil organic matters, extracting environmental factors and performing spatial difference analysis; and analyzing the spatial variation relationship between the environmental factors and the soil organic matters, generating a spatial variation mode, and optimizing the spatial weighting relationship between the remote sensing indexes and the environmental factors to obtain an environmental factor spatial weighting result. According to the method, the spatial variation relationship between the environmental factors and the soil organic matters is analyzed, the weighting result is optimized, the estimation precision is improved, the local abnormal value is corrected through bandwidth optimization and a soil organic matter weight adjustment list, the robustness is enhanced, and the result reliability is improved through spatial heterogeneity adjustment and error correction; the limitation of a static model is overcome through dynamic optimization and real-time updating, the timeliness and accuracy of an estimation result are ensured, and more accurate decision support is provided.

The invention discloses a soil micro-plastic pollution monitoring analysis method based on big data analysis, and relates to the technical field of monitoring analysis. Comprising the following steps: arranging a sensor and sampling equipment, and collecting micro-plastic data; the method comprises the following steps: researching migration and diffusion laws of micro-plastic data in soil in different time periods by utilizing a time sequence analysis method, and constructing a dynamic migration model of micro-plastic in combination with meteorological conditions and geographical conditions; the method comprises the following steps: performing spatial analysis on micro-plastic concentration data of a monitoring area, identifying a high-risk area of micro-plastic pollution, and preliminarily determining a gathering area and a pollution source of pollutants; and comparing the position of the pollution source with a historical pollution source record, verifying the accuracy of the pollution source, and generating early warning information of a potential pollution source. According to the invention, meteorological and topographic factors are combined to identify the trend and path of pollutant diffusion, so that the positioning of the pollution risk area is more accurate, and comprehensive monitoring and deep analysis of micro-plastic pollution are realized.

Disclosed in the present invention are a soil organic carbon prediction method and apparatus based on spectrum-guided ensemble learning. The method comprises: obtaining soil samples and true organic carbon content and original soil spectra thereof, preprocessing the original soil spectra to obtain soil spectrum samples, constructing a sample set on the basis of the soil spectrum samples, dividing the sample set into a first training set and a validation set, and labelling with the true organic carbon content; training a partial least squares regression model, a Cubist model and a random forest model on the basis of the first training set and the corresponding label, and obtaining a carbon content prediction value set of the three models; and constructing a second training set on the basis of the carbon content prediction value set of the three models and soil spectrum principal component data, and training a second random forest model by means of the second training set and the corresponding label to obtain a spectrum-guided ensemble model. According to the method, the advantages of different prediction models are comprehensively utilized, and the carbon content of soil can be accurately predicted.

The invention relates to the technical field of seepage monitoring, and discloses a seepage monitoring system based on a hydraulic engineering project. The system collects water level, flow velocity, seepage flow, soil humidity, soil temperature and soil pressure data through a data collection module, a data analysis module calculates standard soil humidity, standard water flow velocity, standard soil pressure, standard soil temperature and standard seepage flow according to the data, establishes a mathematical model, calculates seepage abnormal indexes, and calculates the seepage abnormal indexes according to the standard soil humidity, the standard water flow velocity, the standard soil pressure, the standard soil temperature and the standard seepage flow. The risk assessment and early warning module performs threshold judgment on the seepage abnormal indexes, sends out an overrun signal when an overrun condition occurs, predicts a future seepage trend by using a moving average method at the moment, generates a future seepage trend broken line graph and a seepage risk assessment report, and uniformly transmits the future seepage trend broken line graph and the seepage risk assessment report to the emergency response module; and when the emergency response module receives the overrun signal, an emergency plan is started, and related managers are informed of the emergency plan, the future seepage trend broken line graph and the seepage risk assessment report in a short message mode.

The invention discloses a farmland soil moisture content fine integration inversion method cooperating with unmanned aerial vehicle multispectral and thermal infrared images. The invention relates to the technical field of farmland soil moisture content integrated inversion, and solves the problems of insufficient inversion precision and weak stability of a conventional machine learning inversion algorithm. The method comprises the steps of obtaining and preprocessing unmanned aerial vehicle multispectral and thermal infrared image data, calculating a vegetation index, constructing a multi-modal data feature set, collecting farmland soil moisture content sample points, constructing a farmland soil moisture content integrated inversion model with residual error optimization, performing farmland soil moisture content inversion and performing farmland soil moisture content inversion result precision evaluation. According to the method, the characteristics and advantages of optical and thermal infrared multi-mode remote sensing data are effectively fused, the farmland soil moisture content integrated inversion model with the residual error optimization is constructed, and the precision and stability of high-resolution soil moisture content dynamic inversion are improved.

The invention discloses a regional soil moisture inversion method and system based on remote sensing data and a water cloud model, and relates to the technical field of microwave radar, and the method comprises the steps: obtaining satellite remote sensing image data of a research region; extracting reflectivity information of the satellite remote sensing image data, and acquiring a spectral index by using the reflectivity information; introducing the spectral index as a vegetation description parameter into a water cloud model, performing water cloud model parameter inversion, and selecting an optimal vegetation description parameter and an optimal soil moisture content inversion model; and establishing a lookup table according to the water cloud model under the condition that the vegetation coverage is lower than a threshold value, inputting the backscattering coefficient obtained in the lookup table into the optimal soil moisture content inversion model, and performing inversion to obtain the regional soil moisture content. According to the method, training inversion is carried out on the optimal soil moisture content inversion model through the posterior scattering coefficient of the water cloud model under the condition that the vegetation coverage is lower than the threshold value, and soil moisture inversion under the condition that different vegetation coverage is lower than the threshold value is achieved.

A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.

The invention provides a garden plant environment ecological assessment method and system. According to the method, self-adaptive layered scanning of vegetation canopies is executed through a flight device, multi-spectral reflection data and three-dimensional morphological parameters are synchronously obtained, and a reflection database for space-time calibration is constructed. High-reflection interference is suppressed by adopting a polarization compensation technology, layered reflectivity weighted calculation is performed based on penetration level identification, and a three-dimensional photosynthetic efficiency map is generated by combining vertical section attenuation gradient and photosynthetic parameter mapping. Secondary scanning is started for the abnormal area, the root system absorption delay error is corrected by fusing soil moisture parameters, and finally an accurate ecological evaluation result is output. According to the technical scheme provided by the invention, three-dimensional dynamic monitoring of the photosynthetic efficiency of the plant community is realized through an air-ground cooperative scanning and dynamic compensation algorithm, and the precision and reliability of ecological assessment in a complex canopy environment are effectively improved.

The invention discloses a rock multi-field coupling test system and a damage evaluation and prediction method thereof, belongs to the technical field of geotechnical engineering and geomechanics experiments, and solves the problem that the rock state under the multi-field coupling condition cannot be monitored in real time in the prior art. The system comprises a multi-physics field coupling loading system, a multi-field data monitoring system and a multi-field data acquisition system. The system can simulate and apply axial pressure, confining pressure, pore pressure and temperature of an in-situ environment to a rock sample, integrates an acoustic monitoring system, a deformation monitoring system and a CT scanning system to realize multi-dimensional real-time monitoring of rock sample damage, overcomes the limitation of a single monitoring method, realizes multi-system electrical connection by taking an upper computer as a data acquisition center, and realizes multi-dimensional monitoring of rock sample damage. And a multi-parameter model is constructed by fusing sound waves, deformation and CT data, so that the whole-process quantitative evaluation from microcrack initiation to macroscopic fracture is realized, and the damage evaluation is comprehensively performed.

The invention discloses a water and soil loss telemetry data analysis method and system based on deep learning, and relates to the technical field of data analysis, and the method comprises the steps: extracting soil loss data, surface runoff data and topographic data; fusing the soil loss data, the surface runoff data and the topographic data, and obtaining water and soil loss risk index data and soil erosion rate data through a feature extraction technology; calculating weight values of the water and soil loss risk index data and the soil erosion rate data by using a feature evaluation technology; and based on the water and soil loss risk index data, the soil erosion rate data and the weight value, constructing a water and soil loss prediction model by using a deep learning framework and a space-time analysis algorithm. According to the invention, through telemetry data collection and feature extraction, key water and soil loss information can be rapidly obtained, the data processing flow is simplified, and the accuracy and reliability of data are ensured.

The invention relates to the technical field of design analysis, in particular to a soft soil deep foundation pit earth excavation construction scheme planning method and system.The soft soil deep foundation pit earth excavation construction scheme planning method comprises the following steps that a three-dimensional geological model is built based on geological exploration data, and rheological parameters, shear strength parameters and permeability coefficients of all soil layers are extracted to serve as layering parameters; according to the layering parameters and preset supporting structure rigidity parameters, a soil body rheological rate compensation coefficient is calculated, and an earthwork block excavation scheme with the grading thickness difference is generated; and in the excavation process, the displacement rate and pore water pressure data of the supporting structure are collected in real time, and when a monitoring value exceeds a preset threshold value, a dynamic adjustment instruction of the block excavation scheme is triggered. According to the method, the layering thickness and the blocking form are intelligently controlled according to different sensitivity levels, the matching coping capacity for soft soil instability inducements is improved, and the limitation that a single physical parameter dominates in traditional blocking design is broken through.

The invention discloses a slope stability evaluation and landslide early warning system and method based on strain monitoring, and relates to the technical field of geotechnical engineering safety monitoring, in the slope stability evaluation system, strain monitoring optical cables are arranged in a plurality of drill holes formed in a slope in a penetrating mode and are fully coupled with slope soil; laser is emitted to the strain monitoring optical cable through a Brillouin optical time domain reflectometer, and a Brillouin scattering light signal is received from the strain monitoring optical cable; brillouin frequency shift is determined according to the Brillouin scattered light signals, then transverse shear force is determined according to the Brillouin frequency shift, the slope stability coefficient of the slope is calculated according to the change of the transverse shear force, and whether landslide is likely to happen or not is further judged in combination with soil humidity measured by the humidity monitoring optical cable, and early warning is conducted in time. Compared with an existing slope monitoring technology, the slope monitoring system has the advantages of being high in precision, wide in monitoring range, resistant to electromagnetic interference, resistant to corrosion and the like, and real-time effective monitoring of different deformation stages of a slope can be achieved.

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.