500 results about "Stability assessment" patented technology

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

The invention relates to the technical field of aircraft manufacturing, and discloses an aircraft part production quality optimization method based on data analysis. The method comprises the following steps: collecting multi-process real-time processing parameters and quality inspection data of a production line, and generating a dynamic quality weight matrix according to a process parameter coupling degree; extracting process path difference characteristics of qualified products and unqualified products in historical batches, and encoding the process path difference characteristics as a quality evolution chain matched with adjacent process parameter mutation relevance; optimizing process parameters at a quality evaluation node, driving a parameter combination to iterate to minimize quality fluctuation, generating an adjustment amount, updating an evolution chain constraint coefficient, and synchronously constructing a stability evaluation function of a correlation weight matrix and a defect propagation path; triggering process compensation according to an adjustment amount gradient, and verifying the cohesion through a tolerance rule by taking a reference parameter matched with the target evolution chain as compensation data; and generating a feedback matrix by using the quality fluctuation index and the compensation result, and correcting the mapping relation between the weight matrix and the evolution chain.

The invention discloses a tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, and belongs to the field of tunnel-landslide masses, and the method comprises the following steps: S1, extracting a feature parameter set and an abnormal event mark set; s2, constructing a three-dimensional geological-mechanical model; s3, space-time multi-scale modeling of multi-physics field coupling is carried out; s4, outputting a stability prediction level by using the constructed ISSA-LSTM-GARCH combined prediction model; s5, generating an early warning in combination with an association rule engine; and S6, knowledge updating and systematic evolution of closed-loop optimization are carried out. According to the tunnel-landslide mass stability evaluation method based on space-time multi-scale coupling, multi-source monitoring data and a multi-physical field coupling model are fused, landslide mass stability under the influence of tunnel engineering is accurately and comprehensively analyzed through high-precision positioning and parameter inversion optimization, and powerful support is provided for engineering decision making.

The invention belongs to the technical field of plastic part quality detection, and discloses a plastic part gold stamping mark accurate positioning method and system based on visual identification, and the method comprises the steps: collecting environment illumination parameters, equipment operation parameters and image data, and processing the image data to obtain regional image data; performing edge detection and fracture repair operation on the regional image data to obtain identification edge feature data; calculating to obtain a position deviation quantized value according to the identification edge feature data; based on the position deviation quantized value, in combination with preset inclination direction information, adjusting an image visual angle and correcting an angle deviation to obtain corrected image data; executing edge path tracking on the corrected image data to obtain path complete data; obtaining an identifier positioning result based on the path complete data in combination with the environment illumination parameters and the equipment operation parameters; and carrying out stability evaluation on the identification positioning result, and obtaining a final system identification stability result in combination with preset detection fluctuation characteristics.

The invention relates to a geotechnical engineering geological investigation system and an investigation method thereof, which are used for solving the problems of drilling redundancy, repeated model correction and long period caused by traditional uniform hole distribution. The system comprises a geophysical exploration module, a rock-soil body structure feature extraction module, a geological abnormal area identification module, a survey detection point dynamic optimization module, a drilling sampling analysis module and a geotechnical engineering stability evaluation module. Earth material exploration is carried out on representative points to obtain a comprehensive geophysical prospecting data set, inversion is carried out according to the comprehensive geophysical prospecting data set to obtain a three-dimensional rock-soil structure, an abnormal area is recognized, accordingly, the drilling layout is dynamically optimized, and stability evaluation is completed at a time after a small amount of directional drilling sampling is carried out. By adopting the system, a high-precision geologic model and a stable evaluation result can be obtained with fewer drilled holes in a preliminary exploration stage, the cost is remarkably reduced, and the period is shortened.

The invention discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computers and data intellectualization, the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segment set; an average block length is used as a decision quantity, an SBB is optimized to construct a sample space, and an optimal block length is determined in combination with variance consistency and nominal coverage rate consistency criteria; carrying out re-sampling training around short / medium / long scales, and collecting layered indexes such as prediction, parameters and features; stability calculation and empirical coverage rate calibration are completed based on intra-scale statistics and inter-scale weighted mixing, and a pseudo-stationary diagnosis score is output; and finally, engineering judgment and backspacing optimization are carried out according to the coverage rate, the correlation maintenance and the risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has the characteristics of traceability and reverifiability, and is suitable for scenes of production lines, network traffic, finance and the like.

The invention relates to a vehicle stability evaluation and early warning method and device, computer equipment and a medium. The method comprises the following steps: constructing a high-dimensional feature space fusing vehicle lateral, yaw, heeling and tire dynamic states; obtaining a vehicle dynamic state estimation value in the high-dimensional feature space based on the multi-source sensor data; determining a local stable manifold boundary representing the stable motion limit of the vehicle in the high-dimensional feature space according to the current vehicle driving condition; extracting stability risk characteristics of multiple dimensions in parallel according to a geometrical relationship and a dynamic evolution trend between a vehicle dynamic state estimation value and a local stability manifold boundary; generating a comprehensive risk level through a probability fusion model, and identifying a dominant instability mode; and when the comprehensive risk level exceeds a preset threshold value, triggering a corresponding predictive cooperative chassis control strategy according to the dominant instability mode. By adopting the method, multi-dimensional states can be fused in real time, and different instability modes can be recognized and distinguished in advance.

The invention discloses a micro-grid parallel-separation operation switching method, which relates to the technical field of micro-grids and comprises the following steps: S1, collecting real-time state data of a micro-grid, calculating system instability and generating a stability state label; s2, generating an ultra-short-term prediction result, and calculating an energy buffer potential well parameter set and a virtual inertia coefficient in combination with a stability label; s3, triggering a mode switching instruction based on the stability state label, executing the energy buffer potential well parameter set and the virtual inertia coefficient to control the output of the energy storage system, and completing grid-connected and off-grid mode switching; s4, the harmonic content of the power grid is collected, and a protection constant value and reactive power output are dynamically adjusted according to the instability degree of the system; and S5, constructing a system support framework by constructing a hierarchical communication network, deploying a redundancy mechanism and adopting a staged implementation strategy. And through real-time stability evaluation, virtual inertia adjustment and ultra-short-term prediction optimization, the stability, the electric energy quality and the adaptive capacity in the grid-connected and off-grid switching process of the micro-grid are improved, and high reliability and flexibility of the system are ensured.

The invention relates to the field of drug stability evaluation, in particular to an anti-generative drug stability evaluation system and method. Comprising a confrontation sample simulation module, a drug stability test module and an intelligent identification module. The system generates an anti-noise, self-adaptive and stable adversarial sample based on a drug molecular structure, and predicts drug stability under different conditions through environmental physical simulation and a digital twinborn model. And the intelligent identification module executes molecular fragment sensitivity analysis, predicts stability under extreme conditions and outputs an evaluation report. The system solves the problems of efficiency, precision and application range in the prior art, realizes qualitative change evaluation through innovative technology combination, provides a scientific basis for drug research and development, transportation and storage, and remarkably improves the drug stability evaluation level.

The invention relates to the technical field of high-speed rail fault detection, and discloses an intelligent fault detection system for a high-speed rail. A data acquisition module of the system acquires a heterogeneous operation log data set, including vibration, temperature, current time sequence data and control signal data, of a high-speed rail core component in a preset monitoring period; the stability evaluation module performs multi-dimensional stability analysis on the data set to generate vibration, temperature and current stability indexes and signal integrity indexes; the detection configuration optimization module takes a multi-dimensional index as an index, performs self-adaptive search in a pre-constructed high-dimensional fault detection configuration space, and determines a dynamic detection step length; and the fault detection execution module drives the multi-sensor array to execute periodic detection based on the dynamic detection step length, generates a real-time monitoring data stream, and extracts abnormal features through a fault mode recognition engine. The system can comprehensively capture the operation state of the component, adaptively adjust the detection configuration, improve the fault detection accuracy and real-time performance, and guarantee the operation safety of the high-speed rail.

According to the cold region slope multi-field coupling effect simulation test device and method, the device is composed of a freeze-thaw environment test box, a slope model, an anchor rod axial loading system, a crack water injection assembly and a multivariate data monitoring system, and the multi-physical field coupling effect of freeze-thaw cycle, crack water permeation and long-term creep can be simulated. By dynamically adjusting the temperature gradient and graded loading parameters, data of a stress field, a temperature field and a displacement field can be collected in real time, the influence of different freeze-thaw conditions and creep drawing loads on a slope damage mechanism can be analyzed, a key test means is provided for revealing the strain rate effect, the aging deformation characteristics and the instability criterion of the slope in the cold region, and the test efficiency is improved. The method has an important guiding value for stability evaluation and disaster prevention and control of the cold region slope engineering.

The invention relates to the field of geological exploration, in particular to an intelligent analysis method and system for geological exploration data of a coal mining subsidence area. The invention discloses an intelligent analysis system for geological exploration data of a coal mining subsidence area. The intelligent analysis system comprises a data acquisition module, an underground three-dimensional model construction module, a foundation stability evaluation module, a residual deformation prediction module and a suitability judgment module. According to the method, multi-source data such as drilling data, geophysical prospecting data, remote sensing topographic data, monitoring actual measurement data and geotechnical test data are comprehensively analyzed, a unified underground three-dimensional model is constructed, and the limitation of data isolation of a traditional exploration method is broken through; according to the method, through drilling and geophysical prospecting bidirectional complementary correction and combination of a settlement monitoring sequence and static attributes, a more accurate and comprehensive geological exploration result is provided for a coal mining subsidence area, and the accuracy of foundation stability evaluation and deformation prediction is remarkably improved.

The invention discloses a power grid dispatching training method and system based on panoramic digital twinborn simulation, and relates to the technical field of power grid dispatching training, and the method comprises the steps: integrating a power grid topological structure, equipment operation data and historical fault data, constructing a three-level dynamic simulation model of an equipment level, a station level and a region level, and carrying out the simulation of the three levels based on the obtained real-time operation data. After dimensionality reduction and anomaly detection processing are conducted on the data, parameters and states of a three-level dynamic simulation model are updated, simulation is driven through the updated model, power flow analysis, load prediction and stability evaluation are combined, disaster scenes are generated, cognitive process data are collected, quantitative analysis is conducted in combination with preset evaluation indexes, and personalized training feedback is generated. The power grid operation state is highly restored, the data accuracy is improved, the training content is enriched, the training effect is enhanced, the ability of dispatchers to deal with complex conditions is improved, the regional limitation is broken, and the training efficiency and the resource utilization rate are improved.

The invention discloses a big data anti-fraud method based on multi-modal behavior characteristics, and particularly relates to the field of anti-fraud. The method comprises the following steps: constructing a multi-modal behavior characteristic data set by collecting original behavior data of a target user; performing intra-modal stability evaluation and inter-modal consistency evaluation on the multi-modal behavior characteristic data set, and extracting a single-modal behavior mode and inter-modal difference conflict characteristic; carrying out credible behavior mode modeling based on the single-mode behavior mode, and generating credible feature representation of each type of modes; calculating modal influence weights of the potential modal conflict areas based on the inter-modal difference conflict characteristics; performing modal weighted fusion and conflict suppression according to the credible feature representation and the modal influence weight to obtain a multi-modal fusion behavior feature vector; and inputting the multi-modal fusion behavior feature vector into an anti-fraud risk identification model to realize judgment of the behavior fraud risk level of the target user, thereby effectively improving the accuracy and reliability of anti-fraud analysis.

The invention discloses a CKM-CNN-based power system transient stability assessment method, system, equipment and medium, and relates to the technical field of power system assessment, and the method comprises the steps: collecting time sequence electrical quantity data after power system fault disturbance, constructing a transient sample set, carrying out the standardization processing and marking of a sample, and carrying out the calculation of the transient stability of the power system; a transient sample set is divided through a clustering algorithm, based on a sample cluster division result, an adaptive oversampling technology is adopted to carry out directional enhancement on an unstable sample, an equalized sample space is generated, a multilayer convolutional neural network is utilized to carry out hierarchical feature extraction on the equalized sample space, a high-dimensional feature vector is generated, and according to the high-dimensional feature vector, a high-dimensional feature vector is generated. And outputting a transient stable state probability evaluation result through the classification decision-making layer. According to the method, a generalization sample set is constructed through standardization and labeling, the scarce instability sample identification capability is improved by combining two-stage clustering and ADASYN oversampling, and the system dynamic state is accurately captured by utilizing 1D-CNN hierarchical feature extraction, so that efficient and robust evaluation of the transient stability of the power system is realized.

The invention belongs to the field of dam body stability evaluation, and provides a dam body stability evaluation method based on a multi-strategy improved particle swarm optimization algorithm, which comprises the following steps: S1, collecting and preprocessing various monitoring data for dam stability evaluation; s2, establishing a multi-physics field coupling dam model; s3, defining an objective function and constraint conditions of dam body stability evaluation; s4, based on an MSIPSO algorithm, fusing hybrid initialization, adaptive inertia weight and learning factor, dynamic disturbance and re-initialization, multi-subgroup coevolution and a multi-target constraint processing mechanism, and constructing a multi-strategy improved particle swarm optimization algorithm; s5, iteratively optimizing a to-be-evaluated parameter combination in the multi-physics coupling dam model by using the MSIPSO algorithm, inputting optimized parameters into the model for coupling analysis, and calculating a fitness value according to model output feedback so as to update a particle state of the MSIPSO algorithm; and S6, carrying out dam body stability evaluation and risk evaluation based on the optimal parameter combination obtained through optimization of an MSIPSO algorithm and the dam response.

The invention discloses an end slope mining side slope real-time stability testing system and method, and relates to the technical field of open pit coal mine end slope mining safety monitoring and early warning, and the method comprises the steps that a distributed optical fiber sensing network arranged on a side slope surface and a mining chamber top plate collects acoustic emission and strain time sequences; a solid-state laser radar array arranged on the stope platform collects slope surface point cloud data of the stope platform; during data acquisition, the solid-state laser radar array and the distributed optical fiber sensing network are in time alignment; carrying out slope displacement increment extraction on the slope point cloud data of the monitored area by the edge GPU node; the data analysis cloud platform is used for performing microfracture identification according to the acoustic emission and strain time sequence; and inputting the slope displacement increment, the micro-fracture event count and the strain time sequence into the trained stability evaluation model to obtain a stability evaluation result, and capturing the internal micro-fracture, surface micro-displacement and shear-expansion coupling characteristics in real time, and providing high-reliability instability prediction.

The invention relates to the technical field of geophysics, in particular to a multi-source geological disaster information intelligent processing method and system. Unifying multi-source data space-time reference; performing intelligent fusion analysis driven by a physical mechanism, embedding a geomechanical constitutive relation into a multi-source data fusion process, and generating a slope stability dynamic evaluation field; performing disaster chain risk quantitative decision, deducing a secondary disaster chain evolution path based on the stability evaluation field, and outputting a graded prevention and control instruction; compared with the defects of large monitoring blind area and poor space-time continuity due to adoption of local sensor layout or single satellite monitoring in the prior art, the method has the advantages that through space-air-ground cooperative monitoring of the satellite remote sensing platform, the near-surface sensor array and the meteorological and hydrological station, the capability of synchronously sensing wide-area coverage and millimeter-level deformation of a key area is realized; and multi-dimensional three-dimensional capture of the geological disaster precursor is realized.

The invention relates to an embankment slope stability assessment method based on artificial intelligence, and belongs to the technical field of embankment slope monitoring and assessment. The method comprises the following steps: acquiring and marking embankment slope stress sensing data; dividing the data into a plurality of spatio-temporal data blocks; constructing a stability evaluation model; extracting multi-scale convolution features by adopting multi-scale cavity convolution of a high-frequency channel and pooling-deconvolution operation of a low-frequency channel to obtain a fused multi-scale feature matrix; a hidden state sequence is obtained through a space attention mechanism and a double-door-setting mechanism; calculating time interval saliency based on the hidden state vector, then calculating a weighted feature vector, further obtaining a weighted feature matrix, and processing through deep convolution and point-by-point convolution to obtain a pooling feature vector; carrying out stability evaluation grade classification through learnable category prototype and gating feature transformation; and dynamically adjusting sample weight and constraint attention distribution by adopting a total loss function. According to the method, the progressive instability identification capability can be improved.

The invention provides a surrounding rock stability dynamic analysis method and system in the excavation process of an underground cavern group. The method comprises the steps that original surrounding rock monitoring data of the underground cavern group are collected at the self-adaptive sampling frequency through a distributed optical fiber sensor; performing real-time filtering, abnormal value primary screening and data compression processing on the original surrounding rock monitoring data to obtain target surrounding rock monitoring data; performing feature extraction on the target surrounding rock monitoring data to obtain a feature vector corresponding to the target surrounding rock monitoring data; and inputting the feature vector into a pre-constructed surrounding rock stability evaluation model for evaluation to obtain the stability level of the surrounding rock. Therefore, data redundancy can be prevented, key information omission is avoided, and the reliability of a surrounding rock stability evaluation result is improved.

The invention discloses a side slope stability evaluation method based on side slope monitoring equipment, which comprises the following steps: acquiring side slope measurement data, and constructing a reconstruction error, a classification probability and a displacement trend based on the side slope measurement data; three indexes are mapped to the same two-dimensional plane, an equilateral triangle is constructed, and intuitive and explainable joint measurement of heterogeneous information is realized through a geometric fusion strategy of quantifying the overall risk by gravity center shift: normalization and confidence correction are carried out on three types of data including reconstruction errors, state classification probability and displacement trend; the interference of dimensional difference and low-confidence observation is suppressed, and meanwhile, the offset weights corresponding to the reconstruction error, the state classification probability and the displacement trend are calculated, so that the relative dominance of three abnormal factors, namely the reconstruction error, the state classification probability and the displacement trend, on the overall risk can be automatically reflected; and the stability score of the slope stability triangle is calculated by taking the health center as a reference, so that the slope stability evaluation precision is improved.

The invention provides an intelligent management and control method and system for carbon dioxide sequestration, and the method comprises the steps: obtaining multi-source data, carrying out the space-time alignment processing, feature extraction processing and multi-scale hierarchical data fusion processing of the multi-source data, obtaining dynamic parameter field data, carrying out the time sequence feature extraction and spatial feature coding based on the dynamic parameter field data, and carrying out the time sequence feature extraction and spatial feature coding. Generating a spatial-temporal characteristic matrix, inputting the spatial-temporal characteristic matrix into a pre-trained short-term leakage prediction model, and outputting a high-risk area coordinate of which the leakage probability is greater than a preset value in a future first duration, obtaining reservoir pressure, cover layer stress, fault activation index and geomechanical simulation data, inputting the data into a long-term stability evaluation model obtained through pre-training, outputting gas injection adjustment amplitude and recommended dosage of a repairing agent in a second time period in the future, and determining the reservoir pressure gradient and the earth surface deformation rate based on the gas injection adjustment amplitude, the recommended dosage of the repairing agent, the reservoir pressure gradient and the earth surface deformation rate. An executable repair strategy is generated through a multi-objective optimization algorithm, and a repair instruction set is issued according to the repair strategy to execute targeted repair, so that the leakage positioning and risk area identification precision is improved, and a'prediction-decision-repair 'full-process automatic closed loop is formed.

The invention relates to the technical field of water affair state monitoring and management, in particular to a water affair state online monitoring and management method and system for intelligent water affair, which forms a quantifiable flow direction judgment basis by establishing node-based pressure grading and flow difference, so that a flow path and an intersection proportion are constrained and unified in a calculation level, and the flow direction judgment accuracy is improved. A dynamic evolution structure with time-dependent features is formed, the recognition capability for pressure wave propagation and lagging response is enhanced, multi-feature anomaly synchronous recognition is realized through node pressure anomaly frequency, flow difference rate and long and short-term memory network, random fluctuation false alarms are reduced, the accuracy and stability of anomaly elimination are improved, and the reliability of the system is improved. According to the method, the parameter consistency of a constraint coordination system is ensured, a quantitative stable distribution pattern is formed through matrix calculation of a balance index and a neighborhood influence coefficient and an isolated forest, accurate modeling of a dynamic pipe network is realized, and the anomaly detection sensitivity, the flow direction prediction precision and the operation stability evaluation reliability are improved.

The invention relates to the technical field of warehousing of bulk grains in a horizontal warehouse, discloses a warehousing method and system for bulk grains in a horizontal warehouse, and aims to solve the problems of uneven accumulation, potential safety hazards and low efficiency in an existing method. A three-dimensional warehouse body coordinate system is constructed through multi-dimensional analysis, and spatial density distribution is simulated; establishing a dynamic accumulation model based on density data to simulate a spatial-temporal trajectory, calculating acting force among particles through finite element analysis to obtain stress distribution, and constructing a stability evaluation system; the impurity rate is corrected through a coupling model by utilizing environment parameters of the internet of things, and a stability evaluation result is dynamically compensated by adopting a multi-factor matrix; a deep reinforcement learning algorithm is applied to optimize a control strategy, an intelligent model is deployed at an edge node, and a joint control automation system realizes closed-loop management. According to the bulk grain warehousing device, the uniformity, stability and efficiency of bulk grain warehousing operation are improved.

The invention discloses an interface calling control method for a cake electronic coupon API (Application Program Interface) docking system. The method relates to the technical field of interface call control, and comprises the following steps: evaluating high concurrency stability of an interface; high-concurrency stability optimization and circulation are realized; evaluating management and control compliance and problem tracing efficiency; and controlling compliance optimization and iterative closed loop. According to the method, interface calling characteristic parameters and compliance-traceability parameters are collected in real time, a double-layer evaluation optimization system from interface stability to management and control compliance is constructed, bimodal regulation and control of a current-limiting threshold value and cache frequency are intelligently triggered based on stability evaluation, the verification request amount and a positioning mode are dynamically adjusted based on compliance efficiency evaluation, and the reliability of the system is improved. A complete closed loop of monitoring-evaluation-optimization-iteration is formed, the interface calling stable operation capability and compliance control efficiency of the cake electronic coupon API system in a high-concurrency scene are improved, and the problem of low interface calling control stability in the high-concurrency scene in the prior art is solved.

The invention provides a system stability evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-dimensional data of a distributed system, the multi-dimensional data comprising system performance fluctuation data of a time dimension, node connection relation data of a space dimension and service protocol index data of a business dimension; multi-dimensional analysis is carried out according to the system performance fluctuation data, the node connection relation data and the service protocol index data, and multi-dimensional analysis results are obtained and comprise a performance fluctuation prediction result, a topology vulnerability evaluation result and a service influence degree evaluation result; and generating a system stability score according to the plurality of dimension analysis results. Therefore, the accuracy of stability evaluation in the distributed environment is improved.

The invention relates to the technical field of side slope building, in particular to a side slope anchor rod retaining wall structure optimization analysis method, which comprises the following steps: a geological data acquisition step: acquiring geological data of a preset reinforcing area of a side slope according to a multi-source data acquisition strategy, and synchronously acquiring deformation monitoring data of each section of the side slope; a structural model construction step: constructing a three-dimensional coupling model of the side slope and the anchor rod retaining wall according to the geological data; a stability evaluation step of performing analysis according to the three-dimensional coupling model and the deformation monitoring data to determine an anti-sliding stability coefficient, an anti-overturning stability coefficient and a key risk area of each section of the slope, and constructing a three-dimensional safety evaluation space; and an optimization scheme generation step: generating an anchor rod retaining wall optimization parameter combination according to the key risk area and the three-dimensional safety assessment space, and screening out an optimal optimization scheme. The method has the advantages that the design optimization efficiency of the anchor rod retaining wall structure can be improved, and the overall slope retaining wall design construction period is shortened.

The invention discloses a greening tree supporting device for garden design and a stability control method thereof, and belongs to the technical field of garden design. The method comprises the following steps: (1) acquiring tree parameters and position coordinates of a target tree, and establishing a tree stability evaluation model; (2) determining a monitoring priority based on the stability evaluation model, and generating a regional monitoring topology network; (3) collecting stability monitoring data of the target tree through cooperation of multiple sensors; (4) establishing a multivariate data fusion algorithm through the stability monitoring data, and comprehensively evaluating the stability safety coefficient S of the target tree; and (5) executing self-adaptive adjustment control according to the stability safety coefficient S, and maintaining the stability of the tree supporting system.

A stability assessment method of roadway surrounding rock includes: obtaining actual stratum rock parameters; establishing a two-dimensional geological model through numerical simulation based on a drill core columnar diagram and the actual stratum rock parameters; changing influencing factors, and recording amounts and acceleration values of deformation of roadway sidewalls, and whether failure occurs to obtain dynamic response characteristics of roadway surrounding rock, combining the changed influencing factors and the dynamic response characteristics as labels to obtain a dataset, and obtaining multiple datasets including the dataset; dividing the multiple datasets into a training set and a validation set, inputting the training set into a PSO-BP neural network and a GA-SVM deep learning model to obtain a preliminary stability assessment model, and adjusting and validating the preliminary stability assessment model by the validation set to obtain an optimized stability assessment model; and using the optimized stability assessment model to assess roadway stability.

The invention relates to the technical field of power systems, and discloses a multi-level direct current sending end small disturbance stability assessment method, device and equipment and a medium. Carrying out frequency domain impedance modeling on the following network type new energy converter, the constructed network type energy storage converter, the following network type load converter and the modularized multi-level high-voltage direct current converter station, and constructing a frequency domain impedance network of the all-electric system based on an equivalent impedance model of each device; and performing small disturbance stability analysis on the frequency domain impedance network by using a generalized Nyquist stability criterion, and determining the small disturbance stability of the all-electric system in an alternating current and direct current hybrid scene through a Nyquist diagram of impedance ratios of two sides of a voltage source and a current source in the frequency domain impedance network. Therefore, an effective evaluation means is provided for stable operation of the alternating-current and direct-current series-parallel all-electric system.