924 results about "Network analyser" patented technology

The invention provides a livestock breeding risk intelligent assessment method and system based on multi-source data fusion, and the method comprises the steps: collecting livestock individual vital sign data, breeding environment parameters, management behavior data and risk-related historical data through Internet of Things equipment, and carrying out the data preprocessing to form a standardized multi-source data set; extracting risk features of individual, group and environment levels based on the data set, and fusing the risk features to form a multi-dimensional risk feature library; utilizing machine learning to construct a differentiated risk assessment model; analyzing the incidence relation between the risk factor and the actual event through the Bayesian network to calibrate the model; realizing livestock risk grade dynamic division and early warning based on the calibrated risk scoring system; and finally, generating intervention suggestions for risk quantitative evaluation, risk prevention and control decision and loss evaluation. According to the method, accurate evaluation of livestock breeding risks is realized, decision support is provided for breeding safety management, and the method has relatively high application value.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention relates to the technical field of data processing, and discloses a data processing method and system for cloud computing and a storage medium. The method comprises the following steps: acquiring preprocessed data through a distributed acquisition node to obtain a structured data packet; performing classification analysis through mixed feature extraction to obtain a safety disaster recovery data set; analyzing the incidence relation through a heterogeneous fusion network to generate a knowledge base; detecting abnormity through a self-adaptive threshold algorithm to form a risk library; determining a recovery scheme through a multi-objective decision to generate an execution log; and a protection strategy is formed through multi-angle comparison and optimization of parameters. According to the method, the data association relationship is mined and analyzed by constructing the multi-level heterogeneous fusion network, the optimal disaster recovery scheme is automatically determined through the multi-target decision process, and meanwhile, a continuous learning optimization mechanism is introduced; the intelligent level of data processing in the cloud computing environment, the timeliness of disaster recovery response and the stability of system operation are remarkably improved.

The invention discloses a memory access optimization method based on intelligent cache management, and relates to the technical field of computer storage. According to the method, spatial-temporal characteristics and semantic association data of memory access requests are collected in real time, a dynamic heat matrix is constructed, and a multi-dimensional access rule is fused to improve modeling precision. And inputting the dynamic popularity matrix into a hybrid prediction model, predicting a future access probability by using a time convolutional network, analyzing a competition relationship between data blocks through a graph attention network, generating a conflict pre-judgment weight and a corrected popularity ranking, and effectively reducing the cache jitter risk. On the basis of popularity ranking and conflict weight, a fragmented reinforcement learning algorithm is adopted to divide logic sub-regions, a differential reward function is designed to dynamically decide cache operation, and performance and energy efficiency requirements are balanced; and finally, through an online learning mechanism, combining real-time feedback to dynamically adjust a prediction model weight and strategy parameters, forming a closed-loop optimization link, and realizing adaptive stability under long-term load fluctuation.

The invention discloses an inquiry model training method and device based on a long thinking chain, and relates to the field of large models, semantic information is extracted through strategy network analysis of a model, and an initial step decision is generated in combination with context information in a historical memory library; sending the initial step decision into a reasoning path generator, and reasoning to generate a primary diagnosis disease source and an intermediate diagnosis step; sending the primary diagnosis source and the intermediate diagnosis step into a verification module, performing pathological logic verification according to a case diagnosis report and a medical knowledge base, and feeding back a verification result; the reasoning path generator updates the historical memory bank based on the feedback result, the preliminary diagnosis disease source and the intermediate diagnosis steps; the strategy network continues reasoning based on user feedback input and the updated context information in the historical memory bank, and finally an inquiry result is output. According to the scheme, technical means such as reinforcement learning, self-adaptive backtracking and memory enhancement are introduced into a long thinking chain reasoning framework, so that a large language model realizes multi-aspect comprehensive improvement in medical question and answer and auxiliary diagnosis scenes.

The invention discloses a failure chain quantitative analysis and risk assessment method and system based on a multi-level security model, and aims to solve the defects that accident cause analysis of a complex social technology system is inaccurate, and a risk assessment result is lack of effective verification. According to the method, a multi-level causal model is systematically constructed, a multi-dimensional failure chain (MDFC) is extracted, multi-dimensional risk quantification is performed on the MDFC, a directed weighted failure propagation network is constructed based on the multi-dimensional risk quantification, and structural features of the directed weighted failure propagation network are analyzed to identify key risk factors. The core innovation of the method is that reverse accident reason tracing and forward risk propagation path analysis based on the weighted network are fused, mutual verification and iterative optimization are realized by comparing analysis results of the two paths, so that the understanding of an accident evolution mechanism is deepened, and the reliability of evaluation is improved. The system vulnerability can be revealed more comprehensively, powerful support is provided for formulating accurate risk control measures, and the overall safety level of a complex system is improved.

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention discloses an error correction system and method for marine environment observation data. The error correction system comprises a data acquisition module, a compensation modeling module, an error analysis module, a data compensation module, a data fusion module and a feedback optimization module. The data acquisition module acquires ADCP flow measurement, GPS, IMU, temperature, pressure and water quality monitoring data. And the error analysis module detects a drift error by using Kalman filtering and a hidden Markov model, and analyzes an environmental error based on a long short-term memory network. The data compensation module optimizes a compensation model and improves data precision. And the data fusion module fuses multi-sensor data by adopting a particle filter and a Bayesian optimization algorithm. The feedback optimization module dynamically adjusts the weight of the compensation model according to the error correction data, and improves the adaptability of the system. According to the invention, the accuracy of ocean current monitoring data is improved, and more reliable data support is provided for underwater environment research.

The invention provides a classroom behavior analysis method and system, electronic equipment and a storage medium, and relates to the technical field of educational informationization, and the method comprises the steps: collecting videos and audios, carrying out image enhancement, noise filtering and frame segmentation on the videos, carrying out noise reduction, sound source positioning and voice segmentation on the audios, and generating standardized images and voice sequences; an improved MTCNN cascade network is combined with a posture estimation technology, facial key points of students are extracted from videos, class arrival states, head postures and facial micro-expressions are recognized, audios are analyzed through a bidirectional LSTM network, and speaking duration and frequency are obtained; constructing individual behavior indexes according to attendance, head actions and expressions of the students; constructing an interaction index according to the teacher and student speaking data; the individual and interaction indexes are summarized to generate the multi-dimensional classroom behavior portrait, and the relation between the portrait and the teaching effect is analyzed, so that the classroom behavior analysis precision can be improved, the data processing capability can be enhanced, and the multi-dimensional classroom behavior comprehensive evaluation can be realized.

The invention discloses a building health state detection system, and relates to the technical field of photogrammetry and image processing, the system firstly obtains four-dimensional space-time coordinate data of a plurality of monitoring points on a target building based on satellite image analysis, constructs a four-dimensional space-time network model containing spatial topology and time sequence evolution information according to the four-dimensional space-time coordinate data, and then obtains a four-dimensional space-time network model containing space topology and time sequence evolution information; the system maps network model nodes to a predefined standard reference network associated with a local gravity direction, then, the system analyzes dynamic relevance in the four-dimensional network model by adopting a graph self-attention network, and meanwhile, calculates relative relation changes of monitoring points relative to the predefined standard reference network through geometric analysis; the deformation interpretation module distinguishes and determines internal relative deformation, overall displacement and absolute posture change (including settlement and inclination) of the building according to the two types of analysis results and by referring to surrounding environment information, and the state evaluation module integrates the information to generate a health state report containing risk levels.

The embodiment of the invention provides a directional training method and device for an enterprise project text processing model, and the method and device achieve the screening of high-quality training samples through the construction of a project text scoring model and the multi-dimensional evaluation of the text coverage degree, the technical correlation degree and the structural integrity. And analyzing a text structure relationship based on the knowledge graph and the graph convolutional network, and constructing a differentiated text template library. The system performs directional training on a pre-training large model in combination with an enterprise portrait vector, and continuously optimizes model parameters and a knowledge base by adopting an incremental learning strategy. According to the method, the defects of a traditional technology in the aspects of training sample screening, knowledge structure analysis and model personalized customization are effectively overcome, and the practicability and adaptability of a project text processing model are remarkably improved.

The invention relates to the technical field of social network analysis and generative artificial intelligence, and discloses a multi-agent-based complex social behavior simulation and public opinion deduction method, which comprises the following steps of: dividing social groups, and dividing users in a social network into key opinion leader communities and common user communities; respectively constructing a key opinion leader agent and a common user agent which are driven by the large language model; constructing a dynamic weighted directed network, taking each key opinion leader agent model as a node, calculating a network node neighborhood through opinion index and node influence weighting, calculating an emotion score based on a text emotion index, dynamically adjusting the network node neighborhood based on the emotion score, and performing loop iteration for several times to complete deduction and simulation of network public opinions; the problem that public opinion propagation modeling is difficult under the cross-domain dynamic background is solved, the difference of public opinion individuals and the complex social interaction relation can be simulated more accurately, and the method is feasible.

The invention relates to the technical field of platform management, and discloses an internet-based financial service platform management method, which comprises the following steps: collecting static identity data, dynamic behavior data, biological characteristics and environmental parameters of a user in real time, and generating a multi-dimensional dynamic identity chain after desensitization aggregation; the attack-defense engine simulates an attack strategy by using a diffusion model, analyzes causal contradictions and topology aggregation coefficients in combination with a space-time diagram convolutional network, marks high-risk nodes, and iteratively optimizes model parameters everyday through reinforcement learning; quantum signature and alliance chain verification are adopted in the transaction link, transactions involving high-risk countries or transactions with the fund abnormal transfer probability exceeding 95% are automatically frozen, and compliance channels are switched; the biological monitoring module analyzes the physiological signal in real time and triggers a multi-mode challenge; and aggregating edge node data through a federated learning framework, and automatically rolling back to a historical optimal version when the false alarm rate of the updated global risk control model exceeds the standard. According to the invention, efficient management of the financial service platform can be realized.

The invention relates to the technical field of fracture network fracture water channel identification, and discloses an identification method for analyzing a dominant flow channel in a three-dimensional fracture network based on a topological network. According to the method, probability distribution parameters such as geometric occurrence, gap width and density of a rock mass multi-scale fracture system in a target area are obtained through field surveying and mapping and three-dimensional scanning, and a spatial topological structure of the three-dimensional fracture system is reconstructed by adopting Monte Carlo method simulation and discrete fracture network modeling technology; secondly, abstracting the fracture network into a three-dimensional topological graph model based on a graph theory principle, defining fracture center points and cross points as graph nodes, and converting fracture sections into weighted edges; the method breaks through the continuous medium hypothesis limitation of traditional seepage analysis, and has important engineering application value in the fields of deep geological energy storage reservoir seepage risk assessment, shale gas fracture network optimization design, rock slope stability analysis and the like; compared with a traditional numerical simulation method, the method has the technical advantages of being high in calculation efficiency, good in prediction precision, high in multi-scale applicability and the like.

The invention discloses a fry culture dynamic monitoring and feeding optimization method based on deep learning. The method comprises the following steps: S1, installing a water quality sensor and a camera to collect water quality data and fry image data; s2, performing feature extraction on the fry image data by using an improved convolutional neural network; s3, analyzing a growth trend and an activity mode of the fry by using a long-short-term memory network to obtain a comprehensive score; s4, obtaining a feeding strategy based on a multi-target adaptive PPO algorithm, and forming a personalized feeding plan; s5, performing detection and early warning on abnormal data through an anomaly detection algorithm, and adjusting a breeding environment; and S6, continuously optimizing the fry culture dynamic monitoring model. According to the method, an efficient and scientific optimization scheme can be provided in dynamic monitoring and feeding of fry culture, and remarkable technical value and economic benefits are brought to practical application.

The invention discloses a discrete element-based three-dimensional fault / fracture network modeling method and device under a backlash pushing and covering system, and relates to the technical field of three-dimensional fault modeling, and the method comprises the steps: building an initialized fault plane model based on the seismic logging information parameters of a target region; extracting stratum parameters of the target area, and determining a deformation process in combination with an equilibrium profile recovery method; the method comprises the following steps: calibrating mesoscopic parameters of discrete elements according to stratum parameters, depositing a first discrete element model, optimizing and generating a second discrete element model, setting boundary conditions to carry out back-flushing pushing-covering simulation, comparing an output result of the second discrete element model with an actual geological section, adjusting model parameters until the output result and the actual geological section are matched, and establishing a three-dimensional fault and fracture network. And analyzing and establishing spatial distribution characteristics of the target area, and carrying out dynamic evaluation and trend prediction on the activity of the three-dimensional fault. According to the method, the geomechanical model is fused, the subjectivity of manual interpretation is overcome, and the problems of fault tip extension and the like in grid reconstruction are effectively avoided.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

The invention relates to the technical field of fault diagnosis, and discloses an intelligent fault diagnosis method and system for a multi-channel gas sensor, and the method comprises the steps: carrying out the random geometric analysis and response limitation region construction of a multi-channel gas sensor array, and obtaining an effective sensor set; performing correlation analysis and sensitivity precision dilution measurement function screening to obtain an optimal sensor combination; performing periodic sampling and feature extraction to obtain a feature data matrix; performing deep Q network analysis to generate a fault diagnosis trigger signal; performing sensor channel block mapping and position coding on the feature data matrix according to the fault diagnosis trigger signal to obtain a space-time separation feature vector; and inputting the space-time separation feature vector into a bidirectional long-short-term memory network to perform fault type determination, and outputting a fault type determination result, thereby realizing accurate identification and classification of different types of faults of the multi-channel gas sensor.

The invention discloses a building quality inspection management system, and the system comprises a distributed intelligent sensor network which comprises a multi-type sensor array disposed at a key node of a building structure, a mobile detection terminal, and an unmanned plane inspection unit; the multi-modal data fusion module is used for performing space-time alignment and feature extraction on the sensor data, the image data and the BIM model data; the digital twinborn visual platform comprises a quality evaluation engine, a defect traceability module and a three-dimensional visual interface; the defect detection rate in a dark light environment is obviously improved, full-coverage detection of floors can be realized in a short time, field sensor data and a BIM model are synchronized in real time through a 5G network, and when a quality problem is found, the system analyzes 12 types of influence factors such as materials, processes and environments through a Bayesian network, so that the quality problem is solved. The system can trace back to specific construction teams, equipment numbers and even meteorological data, and can reduce the requirements of personnel through intelligent detection, thereby reducing the working cost.

The invention provides an EDA software and heterogeneous process design rule collaborative verification method, which relates to the technical field of EDA software, and comprises the following steps of: extracting time sequence characteristics in a dynamic association graph through a bidirectional long-short-term memory network, analyzing node coupling strength by utilizing a multi-head graph attention network to generate a hierarchical importance scoring matrix; and outputting a conflict risk tensor and a propagation path prediction map based on a depth rule conflict prediction model, and generating a correction strategy in combination with a variational auto-encoder and a reinforcement learning network. According to the method, design rule conflicts can be accurately predicted and positioned, an effective correction scheme is provided, and the accuracy and efficiency of heterogeneous process design rule verification are improved.

The invention provides an intelligent urban municipal planning method and system based on geographic information, and relates to the technical field of data processing, and the method comprises the steps: obtaining urban spatial data of different data sources, including terrain, land utilization, building distribution, traffic network, population distribution and environment index data; and analyzing the urban spatial data, including spatial overlay analysis, buffer analysis and network analysis, and identifying key problems and potential risks in urban municipal planning to obtain a data analysis result. The urban space data can be comprehensively and accurately analyzed, the optimized municipal planning scheme is automatically generated and updated and adjusted in real time, and the urban traffic fluency, the environment quality and the public facility utilization efficiency are improved.

The invention provides an energy digitization platform resource scheduling method based on cloud edge cooperative computing, which comprises the following steps: acquiring real-time supply and demand data, an energy price signal and network topology information from a distributed energy management system, and preprocessing to obtain a structured dynamic supply and demand scene data set meeting a unified format requirement; aiming at a dynamic supply and demand scene data set, respectively detecting the fluctuation frequency and amplitude of an energy price on different time scales by adopting a time sequence analysis method, detecting the change condition of a network topology structure in real time by adopting a network analysis technology, and extracting key parameters reflecting scene dynamic characteristics from the change condition; and extracting a scheduling demand of cross-regional energy flow from the adjusted edge node permission configuration, and optimizing a cross-regional energy flow path in combination with real-time inter-regional supply and demand difference data and network state evaluation to obtain a globally optimized cross-regional energy scheduling scheme.

The invention discloses a method for obtaining high-temporal-spatial-resolution port traffic observation data from AIS data, and the method comprises the following steps: S1, carrying out the preprocessing of the historical trajectory of a ship based on AIS data; s2, based on a stay index formula and a K-means algorithm, dividing the preprocessed historical ship trajectory into a stay section and a moving section; using an isolated forest algorithm to extract center point coordinates of the stay section to obtain a ship travel chain; marking a staying type and a port to which the staying type belongs for each staying section based on a space rule and a process logic; s3, key indexes reflecting the port traffic state are constructed through time-space statistics of mooring and anchoring behaviors; a directed shipping network with ports as nodes is constructed, and traffic relation and characteristics between the ports are mined in combination with a network analysis method. According to the method, the timeliness and accuracy of port traffic situation monitoring can be improved, and the problems of slow updating, coarse granularity and high heterogeneity of traditional statistical data are solved.

A multi-node, multi-container cluster system that generates, aggregates, and manages log files from services and components to be used for audit logs and to debug and perform other serviceability tasks provided by a vendor of the cluster system. Logs are collected from all components of the system and aggregated into a consistent format for user analysis and debugging. Embodiments provide a comprehensive way to parse and index vast numbers of log files that can then be packaged and displayed to a user in a way that facilitates analysis and debugging and / or efficient input to appropriate debugging programs.

The invention discloses an intelligent water level monitoring system and a data transmission method thereof, and belongs to the technical field of Internet of Things sensing. The system comprises a distributed water level sensing node, an edge computing gateway and a cloud management platform, wherein the sensing node is integrated with a piezoresistive water level transmitter and an adaptive Kalman filtering module; the data transmission method comprises a dynamic threshold triggering mechanism and a compression algorithm based on improved Huffman coding, and low-power-consumption and high-reliability transmission is realized by optimizing transmission frequency and a data packet structure. The edge computing gateway is internally provided with an LSTM water level prediction model, historical data are analyzed through an LSTM neural network, and an early warning signal is generated in real time. And the cloud management platform stores the key data by adopting a block chain technology. Through multi-sensor data fusion and layered encryption transmission, the water level monitoring error rate is reduced to + / -0.5 cm, the data transmission energy consumption is reduced by 40%, and the monitoring efficiency and the data safety are remarkably improved in flood early warning and reservoir management scenes.

The invention belongs to the technical field of low-altitude flight safety early warning, and provides a multi-modal data fusion method for low-altitude flight risk early warning. Comprising the steps of flight state data and meteorological data alignment processing, missing data filling, spatial feature extraction, spatial feature and flight state data fusion and space-time convolutional neural network and space-time diagram convolutional network analysis. According to the invention, the flight state and the meteorological data are fused, so that the prediction accuracy and reliability are improved; through the space-time convolutional neural network and the space-time diagram convolutional network, the space and time dependency relationship in the flight data and the meteorological data can be captured at the same time; through spatio-temporal data fusion, missing data can be accurately aligned and complemented, and the data quality and the real-time performance of the system are improved.

The invention discloses a rapid fault positioning method, system and device based on three-section protection and FA cooperative protection, and a medium, and the method specifically comprises the steps: carrying out the detection and judgment through employing a preset three-section action rule, and obtaining a real-time protection action signal; acquiring action time sequence data of each protection device of the power distribution network in real time, constructing an FA fault feature matrix according to the action time sequence data, the electrical quantity information and the topological structure, analyzing time sequence association of the FA fault feature matrix by using an LSTM neural network, and generating an FA fault probability distribution diagram; based on the real-time protection action signal and the FA fault probability distribution diagram, a fault positioning algorithm is adopted for collaborative analysis, and a fault positioning result is obtained; and dynamically planning a load transfer path of the power distribution network by adopting a reinforcement learning algorithm according to a fault positioning result, and generating an optimal isolation and recovery scheme. The method is suitable for various complex power distribution network structures and operation modes, and can realize rapid and accurate fault positioning and processing under different fault conditions.

The invention relates to the technical field of BMS and the like, and provides a battery management system based on adaptive digital twinning, a physical layer of the battery management system comprises a battery pack, a sensor network and an edge computing node, and is responsible for data acquisition and preprocessing; the digital twin layer comprises a self-adaptive multi-scale model and a real-time data engine, battery behaviors are dynamically simulated by coupling electrochemical, thermal and aging models, a future state trajectory prediction result is output, and the real-time data engine fuses sensor data, historical data and simulation data to drive model updating; the intelligent decision-making layer comprises a reinforcement learning controller and a fault prediction module which are deployed in a local server, the reinforcement learning controller dynamically optimizes a charging and discharging strategy according to a prediction result of the digital twinborn layer and issues and executes the charging and discharging strategy, and the fault prediction module analyzes multi-source time sequence data based on an LSTM network so as to early warn thermal runaway and short circuit risks in advance. According to the invention, long-term accurate mapping and adaptive adjustment between the battery physical entity and the digital model can be realized.

The invention relates to the field of distribution network fault detection, in particular to a distribution network fault rapid positioning method based on artificial intelligence. According to the method, a double-layer star topology is established, a dispatching center is effectively connected with a transformer substation and a power distribution area, first-level and second-level power distribution network data are acquired by using a multi-modal sensor, and fault prediction is performed through a federated learning framework; analyzing topological relevance in combination with a graph convolutional network, generating a candidate fault transformer area set, and determining a fault type through cosine similarity verification and current waveform correlation analysis; a fault distribution network point is accurately positioned by using a traveling wave distance measurement technology. When an unknown fault is detected, the system triggers an incremental learning mechanism to update global model parameters, and the fault diagnosis capability is continuously optimized. Based on the method, rapid and accurate distribution network fault detection and positioning are finally realized.

The invention discloses a vertical retaining structure displacement automatic monitoring device and a monitoring method thereof, and relates to the technical field of displacement automatic monitoring, and the device comprises a data acquisition unit which is used for configuring monitoring nodes, obtaining sensor data monitored at the top of a retaining wall, and preprocessing the sensor data to obtain effective inclination angle data; the displacement conversion unit is used for recognizing and optimizing the attitude angle by using a dynamic Kalman filtering algorithm, and calculating a displacement value of the vertical soil retaining structure by combining the vertical distance from the bottom to the top of the soil retaining wall; the monitoring and early warning unit is used for uploading the displacement value of the vertical retaining structure to a remote data platform, constructing a monitoring network, analyzing and predicting the deformation trend of the retaining wall and triggering early warning according to an analysis result; and the battery management unit is used for providing battery state monitoring and management. The displacement of the top of the retaining wall can be accurately sensed and monitored through the inclination angle data, and comprehensive and accurate monitoring is ensured by adopting an attitude solver and a dynamic Kalman filtering algorithm.