1977results about How to "Avoid misjudgment" patented technology

The invention provides a petrochemical production process anomaly diagnosis and optimization method and system integrated with a knowledge graph, and relates to the technical field of petrochemical production, and the method comprises the steps: collecting technological parameters and equipment state data, carrying out the preprocessing and feature extraction through adaptive wavelet threshold denoising and depth variational mode decomposition, and carrying out the feature extraction; a heterogeneous graph neural network is constructed, a causal relationship graph is constructed in combination with a neural causal discovery network, the causal relationship graph is optimized through knowledge distillation, missing relationships are complemented to obtain a knowledge graph, real-time features are compared with normal feature distribution of a pre-trained contrast diffusion model, and after anomaly detection is triggered, the real-time features are extracted. A causal inference engine is combined with a knowledge graph to analyze an anomaly propagation path, an anomaly root and a prediction trend are determined based on multi-agent reinforcement learning, a multi-objective optimization problem is constructed according to the knowledge graph, an optimization strategy is generated and dynamically adjusted, and an optimization result is evaluated by combining a knowledge distillation hybrid model. And finally, progressive optimization is realized and the knowledge graph is updated.

The invention provides a communication network operation and maintenance fault positioning and tracking method and system, relates to the technical field, and belongs to the technical field of communication network fault diagnosis. According to the method, historical operation data, including network flow data, performance index data and alarm log data, of communication network equipment are collected, fault feature vectors are generated through training of a deep learning model, and a fault feature knowledge base is constructed; receiving operation data reported by equipment in real time to perform feature extraction, and performing similarity matching based on the fault feature knowledge base to determine a fault type; and analyzing a fault propagation path by adopting a graph traversal algorithm, determining fault root cause equipment by calculating a fault probability score of an equipment node, and generating a fault tracking report. According to the invention, accurate positioning and rapid diagnosis of communication network faults are realized, the fault processing efficiency is improved, and the network operation and maintenance cost is reduced.

The invention provides an intelligent task alarm rule self-learning method and system based on support priority, and relates to the technical field of intellectualization, and the method comprises the steps: constructing a bidirectional feature extraction network to collect task operation data, and combining historical alarm data to generate a task exception association model; and carrying out migration mapping on the model and a business scene knowledge base by utilizing a migration learning algorithm, generating a task priority quantitative index, and constructing a self-adaptive priority scoring system. And training an alarm rule generator based on the model and a scoring system, generating a multi-level alarm strategy tree, and realizing differential alarm. And the incremental learning engine monitors a task state in real time, dynamically adjusts an alarm response strategy and feeds back data to optimize a rule. A federal learning mechanism is adopted to cooperatively train a model, and the generalization ability and adaptability of alarm rules are improved, so that more accurate and efficient task alarm is realized, the false alarm rate is reduced, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of data processing, and discloses a coal and gas outburst dynamic monitoring and intelligent early warning method and device. The method comprises the following steps: collecting and preprocessing multi-source sensor data arranged in a target area to obtain a multi-dimensional data set; performing multivariable time sequence analysis on the multi-dimensional data set to obtain coal seam gas outburst risk prediction data; based on coal seam gas outburst risk prediction data, performing dynamic risk assessment and spatial interpolation processing on the multi-dimensional data set to obtain real-time risk distribution data of the target area; performing multi-dimensional threshold analysis on the real-time risk distribution data to obtain preliminary early warning data; performing multi-modal information fusion on the preliminary early warning data to obtain target early warning data; and performing intelligent decision tree analysis on the target early warning data to obtain a target early warning scheme. According to the invention, the accuracy and reliability of dynamic monitoring and intelligent early warning of coal and gas outburst are improved.

The invention discloses a visual inspection system for appearance defects of a pressure sensor, and relates to the technical field of visual inspection of defects. Comprising a light source control module, an image acquisition module, an image preprocessing module, a feature extraction module, a defect identification module, an evaluation grading module, a result display module and a man-machine interaction module which are integrated on the appearance defect AOI equipment, and all the modules are connected through electric signals. According to the visual inspection system for the appearance defects of the pressure sensor, through the high-precision industrial camera and the advanced image processing technology, precise recognition of tiny defects on the surface of a product is achieved, compared with a traditional manual inspection mode, higher inspection precision is achieved, tiny scratches and sunken defects which are difficult to perceive by human eyes can be found, and meanwhile, the visual inspection system is convenient to use. The automatic detection process greatly shortens the detection time and improves the production efficiency, and the system can detect a large number of products in a short time.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention provides a braid quality detection system and method based on industrial camera detection, and relates to the technical field of data processing. A multi-angle acquisition module; an image fusion module; an optical correction module; a preprocessing module; a preliminary defect detection module; a fine defect analysis module; a self-adaptive filtering module; the dynamic coordinate mapping module is used for mapping the physical position of the defect to obtain actual coordinate data; the data storage and analysis module is used for storing and analyzing historical actual coordinate data and related detection data and generating a quality report and a trend analysis report; the alarm and control module is used for automatically sending out an alarm signal and adjusting production parameters or pausing production when the defect number or the trend change rate in the trend analysis report reaches a preset threshold value; according to the invention, the accuracy and efficiency of braid quality detection are improved.

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention discloses a coal mine dust diffusion intelligent monitoring method based on a distributed sensing network, and relates to the technical field of coal mine dust diffusion intelligent monitoring, and the method comprises the following steps: deploying a plurality of sensing nodes in a coal mine, each node having a function of collecting environmental data, a distributed sensing network is formed through a wireless or wired communication module, and when all nodes are started, a unique node identifier is issued through a central controller, and self-inspection is carried out. Through bidirectional delay calculation and a progressive fine tuning strategy, high-precision time synchronization is achieved, and it is ensured that timestamps of data such as dust concentration and airflow are accurate and reliable. And data jump caused by large correction is avoided through gradual fine adjustment, data continuity is guaranteed, and the precision of dust diffusion analysis and an early warning model is improved. A redundant node management and automatic switching mechanism improves the fault processing capability of the system, avoids a monitoring blind area, accelerates maintenance response through fault information reporting, and ensures stable operation of a monitoring network.

The invention relates to the technical field of data processing, in particular to an intelligent logistics platform management system and method based on multi-source data. The method comprises the following steps: acquiring cold-chain logistics order data; performing multi-type cargo temperature control demand processing on the cold-chain logistics order data to generate cargo temperature control demand data; performing multi-node temperature control task decomposition on the cold-chain logistics order data through the cargo temperature control demand data to generate multi-node fine temperature control task data; performing cold chain abnormal feature analysis according to the multi-node fine temperature control task data to generate abnormal temperature control processing scheme data; and carrying out transportation-junction point temperature control correction according to the abnormal temperature control processing scheme data, and carrying out cold chain quality weighted scoring to generate logistics quality scoring data. According to the invention, through cold chain task decomposition and cooperation of multiple transportation nodes, intelligent cold chain logistics management is realized, and the temperature control management efficiency and cargo quality guarantee of cold chain logistics are improved.

The invention belongs to the technical field of data detection, and particularly relates to an efficient data anomaly automatic detection and alarm system. The detection and alarm system comprises a sensor and data acquisition device, a data processing and analysis module, an alarm module, a monitoring center and visual interface, a communication and network module, a storage and backup module, an intelligent decision support module and a user feedback and adjustment module. According to the system, data can be monitored and analyzed in real time, abnormity can be quickly found, an alarm is given, manual monitoring requirements are reduced, data misjudgment is avoided, data accuracy is ensured through data cleaning and processing, an intelligent decision support module provides optimization suggestions based on AI, and a user can adjust rules to adapt to requirements. The system has the functions of data processing, anomaly recognition and trend prediction, the monitoring center visually displays the state, the storage and backup module ensures long-term storage of data, the automatic mechanism improves the response speed and the operation benefits, and the adaptability is high.

The invention belongs to the technical field of collaborative robots, and particularly discloses and provides an anti-explosion mechanical arm positive pressure control system and method.The system comprises a mechanical arm motion tracking module, an environment data monitoring module, a configuration adjustment analysis module, a pressure adjustment analysis module, a database and a pressure adjustment execution terminal. The mechanical arm motion tracking module is designed to track the motion state of the mechanical arm and record motion data, a key part pressure sensor is started to monitor pressure data, and the correlation change of motion and pressure can be captured. The environment data monitoring module monitors environment parameters of a working area in real time, and environment changes are comprehensively mastered. The all-dimensional monitoring capability of the system is improved, factors influencing positive pressure control can be obtained more comprehensively, meanwhile, configuration adjustment judgment is carried out in combination with environment, movement and pressure data, adjustment values are dynamically determined, and intelligent decision making and self-adaptive adjustment of mechanical arm positive pressure control are achieved.

The invention provides a distributed service intelligent master selection method and system based on double-engine cooperation, and relates to the technical field of intelligent master selection, and the method comprises the steps: collecting the processing capability index, the network throughput and the resource utilization rate of a service node to construct a performance data matrix, and generating an intelligent identification code based on a node scoring function and a physical clock value; nacos and Redis signals are monitored through a double-layer event monitor, the optimal incremental step length is determined according to the score sequence distribution trend, and a target score is generated to construct a dynamic priority sequence; and periodically selecting a main node and a preheating standby node, and when the main node fails, starting increment state synchronization based on Redis affairs after voting arbitration confirmation, so as to realize main and standby non-inductive switching. According to the method, the election accuracy of the main node and the switching reliability are improved, and the system interruption risk is reduced.

The invention discloses a battery production defect inspection method, system, medium and equipment, and relates to the technical field of battery defect inspection. The method comprises the following steps: acquiring a video stream corresponding to each battery production line, and extracting a key frame sequence corresponding to each video stream; performing fusion analysis on spatial features and time sequence features of the key frame sequences to obtain abnormal state features; performing multi-stage screening on each key frame sequence based on the abnormal state features to obtain a target key frame with defects; performing space-time mapping on the target key frame, and determining an associated station corresponding to the abnormal production line; and according to the process parameters of the associated stations, carrying out grading evaluation on the abnormal state features, and generating abnormal early warning information. By implementing the technical scheme provided by the invention, misjudgment or missed judgment of abnormal conditions on the battery production line can be effectively avoided, so that the production quality of battery products is improved.

The invention discloses a solid state disk testing method and system, a storage medium and a program product, and relates to the field of computer storage testing, and the method comprises the following steps: dividing a storage space of a solid state disk to be tested into a plurality of physical areas; determining the number of read-write tests of the to-be-tested solid state disk within a preset duration at a preset temperature; performing read-write test operation on the plurality of physical areas in sequence within a preset duration to obtain read-write error rates respectively corresponding to the plurality of physical areas, the number of read-write times of the read-write test operation being the number of read-write test times; if the read-write error rates corresponding to the physical areas exceeding the preset number are higher than a preset error rate threshold value, determining that the to-be-tested solid state disk is unqualified; and if the read-write error rates corresponding to the physical areas lower than the preset number are lower than a preset error rate threshold, determining that the to-be-tested solid state disk is qualified. By implementing the method, the specific problem area in the solid state disk can be positioned and diagnosed more accurately, so that the quality evaluation standard and the maintenance efficiency of the whole solid state disk are improved.

The invention provides a reflux valve supporting remote monitoring of the Internet of Things. The reflux valve comprises a valve body provided with a fluid inlet, a fluid outlet and an internal flow channel, and an electric actuating mechanism is configured for controlling the opening degree of the valve; a microprocessor and a memory are arranged in the control unit, and the control unit processes data of the sensor, calculates a fluid density correction coefficient and a pressure correction coefficient, corrects original flow data collected by the flow sensor and generates an actual flow value; the control unit further identifies the current fluid type and adaptively adjusts the countercurrent detection threshold value of the upstream and downstream pressure difference based on the relationship between the flow and the pressure data; when the pressure difference exceeds the adjusted threshold value, the electric executing mechanism executes emergency turn-off operation; meanwhile, a wireless communication unit is arranged, real-time data transmission is supported, control parameters issued by external monitoring equipment are received, and intelligent control and remote management are achieved; the intelligence and the operation efficiency of the reflux valve can be effectively improved, and the reflux valve is suitable for safety protection and operation optimization under the complex fluid working condition.

The invention discloses a fault monitoring method and system for a new energy photovoltaic power station power distribution network, and relates to the technical field of power distribution network fault monitoring. Analyzing and identifying abnormal areas based on the monitoring data of the edge monitoring point of each area, and updating the final monitoring level of each abnormal area; according to the fault detection sequence, the new energy photovoltaic power station power distribution network evaluation information of the monitoring points in all the abnormal areas and the difference information between the adjacent monitoring points are analyzed in sequence, the monitoring points in all the abnormal areas are checked, suspected monitoring points in all the abnormal areas are identified and marked, and a detection and maintenance path is generated. The problems of delayed detection, unreasonable resource allocation, low troubleshooting efficiency, insufficient intelligence, single maintenance strategy and insufficient data utilization in traditional new energy photovoltaic power station power distribution network fault monitoring are solved.

The invention relates to the technical field of obstacle avoidance early warning, in particular to an obstacle avoidance early warning method and system for a robot. The method comprises the following steps: acquiring motion data and moving target point data of a robot; robot motion mode classification is conducted on the robot motion data, and a robot motion mode and a robot static mode are generated; performing environment detection on the moving target point data by using a built-in laser radar of the robot according to the static mode to obtain standard static environment detection data; and planning the path according to the standard static environment detection data, and generating initial running path planning data of the robot. According to the invention, the obstacle avoidance early warning precision and real-time performance of the robot in a complex environment are improved by accurately distinguishing static and dynamic objects, reconstructing the motion trail and planning and adjusting the path.

The invention provides a multi-core heterogeneous system-oriented startup deployment and cooperative processing method, which comprises the following steps of: when the system runs, obtaining startup codes after a master core and a slave core are electrified, respectively obtaining mirror image files of Linux and RTOS (Real Time Operating System) on the basis of synchronous running and storage partition, and running the mirror image files on the master core and the slave core after moving the mirror image files; and after the heterogeneous operating system is loaded, generating an interrupt signal based on inter-core interrupt and a corresponding register through a CLINT module in the main core, and realizing data communication between the main core and each slave core based on the interrupt signal and the shared memory. By means of the method, after the system is powered on, the multiple cores can synchronously run the starting codes, and time delay and uncertainty possibly caused by one-by-one starting of the single cores are avoided; communication and data transmission among multiple cores are realized through interruption and memory sharing, the synchronism and consistency of data are ensured, and misjudgment and decision errors caused by data delay and inconsistency are avoided, so that the real-time performance of the system is remarkably improved.

The invention discloses an air conditioner fault detection method and system, electronic equipment and a storage medium, and relates to the technical field of air conditioner fault detection. The method comprises the steps that parameter values corresponding to all dimensions during operation of the air conditioner are obtained, the operation state of the air conditioner is determined based on the parameter values corresponding to all the dimensions, and all the dimensions are at least one of pressure, temperature, current or rotating speed; when the operation state is a preliminary abnormality, determining an abnormal dimension and a fluctuation feature corresponding to each abnormal dimension based on a parameter value corresponding to each dimension; performing fusion calculation on the fluctuation characteristics to obtain a fault probability value of the air conditioner; and when the fault probability value is larger than a probability threshold value, fault positioning is conducted on the basis of all the fluctuation characteristics, and a fault detection result of the air conditioner is obtained. By implementing the technical scheme provided by the invention, the accuracy of air conditioner fault detection is improved.

The invention relates to the technical field of image data processing, in particular to a wood composite board furniture surface defect identification method and system. And wood board images are collected from the surface of the wood composite board furniture. Adaptive grid division is carried out on the wood board image according to different scales, texture direction information and high and low frequency texture components are extracted, and a texture feature map is obtained; and extracting structure edge information of the wood board image to obtain a structure information graph. Matching the texture feature map with the structure information map, and calculating local mutual information of the texture feature map and the structure information map in each region; the method comprises the following steps: acquiring resolution images of a wood board image under different scales, and calculating a mutual information matrix of the resolution images; distributing an abnormal score for each matrix element based on the mutual information matrix; and dividing grids according to the abnormal score, calculating a correlation value of adjacent grids, and obtaining a defect area based on the correlation value. And carrying out defect classification on the defect area. According to the invention, interference of natural textures can be reduced, and defect identification of small defects and tiny changes can be improved.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention discloses an intelligent driving environment sensing method and system based on multi-modal data fusion, and relates to the technical field of automatic driving, and the method comprises the steps: firstly obtaining the position information of a vehicle through a GPS, and starting an auxiliary positioning mechanism if the GPS signal is weak; multi-modal data acquisition and preprocessing: after an auxiliary positioning mechanism is started, a vehicle acquires surrounding environment information through an RGB camera, a laser radar and an IMU sensor; performing feature extraction on the RGB image by using an improved deep convolutional neural network, and generating a high-precision 3D bounding box through a region suggestion network; and multi-modal information is fused through a constructed efficient image reasoning framework. Based on multi-modal data fusion and an environment perception result, the method not only can carry out accurate target identification on the surrounding environment in the automatic driving process, but also can carry out global and local path planning.

The invention discloses pre-hospital emergency medical aid emergency help integrated equipment and system. The method comprises the following steps: S1, acquiring key vital sign data of a patient by using medical equipment; s2, calling basic information and medical history of the patient through equipment; s3, transmitting the collected data to a server of a remote data processing center in real time; s4, performing comprehensive analysis on the received vital sign data and the medical history information of the patient by applying a pre-trained deep learning model on the server, and generating a prediction report; s5, displaying a prediction result on a portable device or a remote monitoring station in real time by using a data visualization technology; s6, based on a visual prediction result and medical history analysis, guiding first-aid personnel to take targeted emergency medical measures; and S7, continuously monitoring the vital sign response of the patient and any health change related to the medical history, and updating the treatment strategy according to the further analysis of the deep learning model.

The invention relates to the technical field of carrier communication, in particular to a multichannel power line carrier communication system which comprises a data acquisition module, a first determination module, a second determination module, a judgment module, a selection module, a sending module and a correction module. According to the invention, through an accurate data acquisition and real-time determination mechanism, complex environment changes in a power network can be effectively dealt with, stability and high efficiency of communication are ensured, channels with optimal parameters such as signal strength, noise, delay and the like are optimized through multi-stage screening and determination, channels with relatively large interference and noise are effectively avoided, and the communication efficiency is improved. Meanwhile, the correction module can adjust the intensity threshold value in real time, it is guaranteed that the system still maintains excellent communication performance when the number of channels fluctuates and the network load changes, and the problems that due to the fact that the attention degree of the influence of real-time environment changes on signal quality is low, channel selection is inaccurate, and verification results are deviated are effectively solved.

The invention relates to the technical field of data processing, in particular to a shrimp group health condition assessment method based on deep clustering analysis, and the method comprises the steps: monitoring health parameters; determining a shrimp group dispersion grid; determining a temporary grid; identifying a non-shrimp group abnormal grid; calculating an actual health index; and adjusting the grid step length and evaluating health. According to the method, the health condition of the shrimp group can be accurately monitored and evaluated by combining physiological and behavior data such as real-time density, saturation and activeness, abnormal grids are eliminated step by step, more representative grids are screened out, evaluation deviation caused by environmental fluctuation or measurement errors is avoided, and the evaluation accuracy is improved. The dynamic monitoring of the health condition of the prawn group is realized, and the grid step length is automatically adjusted by comparing the ideal health index with the actual health index, so that the health assessment precision and reliability are improved, and the problem of inaccurate health assessment result caused by unstable environmental parameters is effectively solved.

The invention provides a turbine cooler fault diagnosis method and device under a variable rotating speed, and belongs to the field of aviation electromechanics. The method provided by the invention comprises the steps that a monitored fault signal is determined based on historical faults of the turbine cooler under the variable rotating speed, and the fault signal changes in each historical fault; determining a critical value of the fault signal based on the historical fault, wherein the critical value is a value of the fault signal when the historical fault is generated; calculating a first relationship between the rotating speed of the turbine cooler and a historical fault judgment threshold value based on the critical value; correcting the first relation based on the rotating speed change rate of the turbine cooler to obtain a second relation; real-time information of the turbine cooler under the variable rotating speed is obtained; a fault of the turbine cooler is identified based on the real-time information and a second relationship. According to the fault diagnosis method and device for the turbine cooler under the variable rotating speed, rapid fault positioning of the turbine cooler can be supported under the complex variable rotating speed working condition.

The invention relates to the technical field of network security, in particular to a method and device for detecting illegal external connection of a terminal and a storage medium, and the method comprises the steps: obtaining all terminal equipment connected with a local area network, distinguishing authorized terminal equipment and unauthorized terminal equipment through authorization detection, and carrying out the risk grading of the authorized terminal equipment; the method comprises the following steps: generating an external connection behavior data set by combining client monitoring data, network layer monitoring data and intranet detection data, extracting key information such as an external connection target, access time, a protocol type and a port number, and dividing into pure intranet connection equipment, normal external connection equipment and unknown external connection equipment according to external connection permissions; inputting the log data of the unknown external connection equipment into the log anomaly detection model based on the Transform architecture, identifying an abnormal event, and calculating a log risk score; and if the log risk score exceeds a preset threshold, determining that the device is an illegal external connection device. According to the method, accurate detection of illegal external connection behaviors is realized, and the safety performance of a monitoring system is remarkably improved.

The invention provides a tunnel engineering advanced geological forecasting method and system based on multi-source information fusion, and the method comprises the steps: obtaining multi-source geological data, carrying out the preprocessing and data fusion of the multi-source geological data, generating comprehensive geological data, carrying out the deep learning model based on a convolutional neural network architecture, extracting the features of the comprehensive geological data, and generating geological data. And the geological data are dynamically updated through a feedback optimization mechanism according to new address data obtained in the construction process. According to the method, the influence of complex geological conditions can be overcome, missed judgment or misjudgment of unfavorable geologic bodies is avoided, the accuracy, real-time performance and reliability of advanced geological forecast are improved, the construction safety of tunnel engineering is fundamentally ensured, and a guarantee is provided for the construction progress and cost control.

The invention discloses a transmission tower foundation differential settlement monitoring method and system based on LSTM, and belongs to the technical field of transmission tower monitoring. The system comprises a monitoring unit, a data processing unit, an operation unit, a judgment unit and an early warning unit. The method comprises the steps that monitoring data of differential settlement of the transmission tower and monitoring data of the surrounding environment of the transmission tower are collected through a monitoring unit; a data processing unit is used for preprocessing the monitoring data to form model data; an LSTM long short-term memory network algorithm is applied through an arithmetic unit, and a prediction model for differential settlement of the transmission tower foundation is constructed through model data training; the displacement data of the transmission tower in the future time are predicted according to the prediction model, the predicted displacement data are compared with standard displacement data through the judgment unit, and early warning is conducted through the early warning unit according to the displacement offset. According to the invention, manual monitoring cost is reduced by means of artificial intelligence, huge monitoring data can be processed, and monitoring timeliness and accuracy are improved.