74results about How to "Increase initiative" patented technology

The invention discloses an electric power system vulnerability dynamic risk assessment method and system. The method comprises the steps of obtaining power grid assets, business functions borne by the power grid assets and vulnerability information; calculating asset physical importance based on the real-time data of the power grid; for each vulnerability, respectively calculating a physical risk evaluation value and a business influence evaluation value of the vulnerability; and finally, carrying out weighted fusion on the two to obtain dynamic comprehensive risk scores and sorting. By fusing vulnerability static attributes, asset dynamic physical importance and business criticality, a scene-adaptive multi-dimensional risk assessment model is constructed, and the transformation of the vulnerability risk of the power system from'static pervasive assessment 'to'dynamic accurate portrait' is realized. According to the method, the integrating degree and decision guidance value of the risk assessment result and the actual security situation of the power grid are improved, the problem that the assessment result is one-sided and rigid due to separation from a business scene and a physical operation state in the prior art is solved, and the initiative and accuracy of network security protection of the power dispatching system are remarkably improved.

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention relates to the technical field of flood control dispatching, and discloses a reservoir flood control water level dynamic control and flood recycling method and system, and the method comprises the steps: obtaining a surface rainfall forecast value of a reservoir basin based on real-time rainwater condition data and numerical weather forecast; a hydrodynamics and hydrology coupled drainage basin runoff model is driven, and an in-reservoir flood hydrograph in a predicted period is generated; judging the flood scale and grade based on the hydrograph, and when the flood is medium and small flood, calculating the dynamic flood control storage capacity of a downstream flood control object by adopting an equivalent flood control effect algorithm taking into account the pre-discharge scheduling capability; according to the dynamic flood control storage capacity, a dynamic flood control water level control value is obtained through backstepping of a water level-storage capacity relation curve of the reservoir; and finally, generating and executing a reservoir dispatching instruction based on the control value, and storing the water level of the reservoir to not exceed the dynamic value. The problems that a fixed flood control water level method is low in water resource utilization rate and inflexible in dispatching are solved, and flood resource efficient utilization on the premise of flood control safety is achieved.

The invention discloses a tunneling face circulating air early warning method based on wind resistance inversion. Comprising the following steps: S1, abstracting a mine ventilation network into a topological structure, and determining a target branch; s2, air volume and static pressure data of the key nodes are collected through a sensor; s3, constructing a ventilation network nonlinear equation set taking the dynamic wind resistance of the target branch as a key unknown quantity, and forming a wind resistance inversion problem; s4, solving and outputting a current wind resistance estimated value in real time by adopting an iterative inversion algorithm embedded into a physical model; s5, extracting a time sequence change characteristic of the estimated value, and inputting a preset nonlinear risk judgment function to calculate a circulating air risk score; and S6, according to whether the score exceeds a preset threshold, generating a graded early warning signal. The early warning object is converted into the instant ventilation power state from the lagged gas concentration, advanced pre-judgment of the circulating air risk is achieved, and the active early warning and prevention and control capacity of mine ventilation safety is improved.

The application discloses a lining cloth dyeing control method and system based on artificial intelligence, and is used for the control field, and the method comprises the following steps: real-time monitoring of multi-source data in the lining cloth dye vat, capturing the video stream inside the lining cloth dye vat; using a Gaussian mixture variational autoencoder model to extract features from the multi-source data, obtaining multi-source feature data; obtaining the changing moment of the fabric state in the video stream, and extracting the fabric state change feature data according to the observed fabric color change and the heterogeneity of the dye distribution in the video stream; fusion of multi-source feature data and fabric state change feature data; introducing a model predictive control layer, predicting the result to adjust the dyeing parameters in real time; establishing an intelligent feedback mechanism. The Gaussian mixture variational autoencoder model is trained and optimized through the small-batch stochastic gradient descent and back propagation algorithm, important features are learned and captured from the data.

This invention discloses a method for high-density deformation monitoring and stress field prediction of tunnel surrounding rock, belonging to the field of tunnel engineering monitoring technology. The invention comprises three main modules: preliminary preparation, on-site high-density data acquisition, and data analysis and stress field prediction. Preliminary preparation involves defining monitoring parameters and debugging equipment. On-site monitoring utilizes deep boreholes, movable slideways, and reusable laser displacement gauges to achieve high-density, multi-angle deformation monitoring with a minimum spacing of 10cm, calculating corrected radial strain. In the data analysis phase, numerical simulation, database construction, and an improved BP neural network are used to train a stress field prediction model, predict radial stress around the borehole, construct a spatiotemporal evolution curve of surrounding rock stability, and implement graded early warning. This invention offers high monitoring accuracy, strong adaptability, and precise prediction of stress field evolution, reducing monitoring costs and providing reliable support for tunnel construction safety.

This invention relates to the field of Internet of Things (IoT) technology, specifically to a multi-factor dynamic authentication IoT network security access platform. In this invention, a device behavior acquisition module acquires the real-time behavior characteristics of IoT devices requesting access; a behavior feature analysis and matching module performs feature transformation on the real-time behavior characteristics to generate behavior feature authentication factors, and calculates their matching degree with a pre-set legal behavior feature model for first-level authentication judgment; a multi-factor strategy dynamic decision-making module, based on this result, uses the matching degree as a dynamic weight adjustment parameter to adjust the verification strategy strength of other authentication factors in real time and generate a multi-factor authentication strategy; a comprehensive authentication and access control module performs comprehensive authentication based on this multi-factor authentication strategy and combines the behavior feature authentication factors for second-level authentication judgment, thereby controlling network access; this process achieves dynamic adjustment of authentication strategy strength and continuous verification of device behavior.

The invention provides an underground coal mine methane spontaneous combustion risk early warning method, device, equipment and medium, and belongs to the technical field of methane monitoring. The method comprises the following steps: acquiring methane concentration data through multiple areas, multiple monitoring points and multiple acquisition modes; evaluating the confidence coefficient of each acquisition mode, and determining the weight of each mode and each monitoring point according to the confidence coefficient; and weighting the concentration under the acquisition mode based on the weight, and calculating the concentration of each monitoring point. Weighting the concentration of each point by using the weight of the monitoring point to obtain an early warning value of each region; performing weighted summation on the regional early warning value to obtain an underground overall early warning value; and executing a grading early warning strategy according to the overall early warning value. According to the method, more accurate and sensitive early stage grading early warning of coal mine methane spontaneous combustion is realized through a differential weighting and dynamic multi-stage aggregation method based on data confidence.

The application discloses a drinking water taste score method and device, equipment and a storage medium, and relates to the field of water quality detection. Water quality index data and taste score data are acquired, the water quality data is formed into an initial water quality index matrix, the taste score data is formed into a taste score vector, and a taste regression model is established; principal components of water quality are extracted based on the taste regression model, and principal component indexes are screened from the initial water quality index matrix and the taste score vector according to a mapping relationship therebetween; the principal component indexes are screened with reference to a variable projection importance and a correlation coefficient reference standard, and key water quality factors are extracted; the key water quality factor set is taken as new independent variables, the water quality index matrix and the taste score vector are subjected to regression modeling again, a taste score prediction model is obtained, and taste score detection is performed.

The invention belongs to the technical field of power grid disaster prevention and reduction, and particularly relates to a risk assessment method and device for deicing jump of a power transmission line, electronic equipment and a computer readable storage medium. The method comprises the following steps: determining risk influence factors influencing the deicing jump of the power transmission line, and constructing an original parameter data set containing a plurality of deicing working conditions; establishing a conductor deicing model under a plurality of deicing working conditions and carrying out analogue simulation to obtain a risk consequence index; constructing a standardized decision matrix, respectively calculating the comparison intensity and conflict of each risk influence factor, and calculating the comprehensive information amount and objective weight of each risk influence factor to obtain an objective weight vector; and identifying the key dominant factors based on the objective weight vector and carrying out working condition optimization in combination with the risk consequence indexes under each deicing working condition to obtain the optimal deicing working condition with the lowest comprehensive risk. According to the method, the CRITIC method is applied to power transmission line deicing jump risk assessment, and the deicing working condition with small risk can be selected.

The application discloses a computer network security situation intelligent prediction method based on big data analysis, and comprises the following steps: obtaining a standardized security data set; performing feature extraction to obtain a multi-dimensional security feature set; constructing a network asset graph and determining a target priority sequence; constructing an attack target set, performing probability modeling, and obtaining an attack intention probability distribution model; generating an attack path set; constructing an attack path evolution sequence, screening and sorting the attack path evolution sequence by using MCDM decision, and obtaining a target attack path set; performing risk assessment processing to obtain a risk value corresponding to each attack path, and performing aggregation processing to generate a network security situation assessment result; generating a network security situation prediction result within a preset time range, and outputting risk change trend information, so that global modeling of the network security situation and early prediction of the future risk change trend are realized.

The invention relates to the technical field of artificial intelligence, in particular to an animal carcass hydrolysis process optimization control method and system based on artificial intelligence. The method comprises the following steps: when a Maillard reaction risk index reaches or exceeds a first preset threshold value, triggering three-stage intervention operation according to an intervention logic chain; after each level of intervention operation is executed, monitoring the decline rate of the risk index; if the descending rate is lower than the preset minimum effective rate, triggering the next-level intervention operation in advance; if the risk index is reduced to be below a preset safety threshold value, intervention is terminated; otherwise, triggering the next-level intervention operation; if the risk index is still not reduced to be below the preset safety threshold value within the preset time after the third-level intervention operation is completed, intervention chain failure emergency operation is executed. According to the method, the conversion of the Maillard reaction from being invisible to being clearly visible is realized, and a three-stage targeted intervention logic chain is designed, so that accurate intervention can be automatically triggered at the initial stage when the predicted value of the reaction intensity exceeds a threshold value.

This invention relates to the field of power equipment safety monitoring technology, and addresses the problems of existing technologies such as reliance on physical tags, insufficient accuracy in key point positioning, unstable depth estimation, and difficulty in end-side deployment. The invention constructs a database of key points and depth estimation in substations, designs a lightweight key point detector, and achieves accurate positioning of key points in the risky areas of workers and energized parts of equipment. A stereo perception module outputs dense depth information, mapping two-dimensional key points to three-dimensional space, calculating the minimum safe distance between human key points and energized parts, and combining threshold values ​​to achieve graded early warning and visualization. This invention eliminates the need for additional physical tags, achieving significant improvements in detection accuracy, real-time performance, lightweight deployment, and engineering reliability through key point detection and stereo vision collaborative perception. It effectively solves the problems of insufficient accuracy and difficult deployment in safety distance monitoring in complex substation scenarios.

The application discloses an intelligent security management method based on artificial intelligence and relates to the technical field of security management.The intelligent security management method based on artificial intelligence comprises the following steps: S1, traffic congestion evaluation;S2, traffic signal fault evaluation;S3, traffic management evaluation; and S4, emergency security feedback.The traffic congestion index obtained through traffic data is used to determine whether to perform traffic signal fault evaluation, then the traffic signal fault index obtained based on traffic signal data is used to determine whether to perform traffic signal management, then the traffic management effective index obtained based on management data is used to determine whether to implement an emergency security strategy, and finally, the traffic management effective index obtained after the implementation of the emergency security strategy is used to determine whether to perform feedback, so that the effect of improving the security management efficiency of road traffic is achieved, and the problem that the state change of a traffic signal is not fully considered in the security management of road traffic in the prior art is solved.

The application provides an interactive method and device based on a virtual role, equipment, a medium and a product, applied to the field of artificial intelligence technology, and the method comprises the following steps: when it is detected that a client plays video data, a target video emotion vector corresponding to a current playing time point of the video data is acquired from a video emotion vector sequence generated offline; a user emotion vector collected in real time is acquired; a preset AI language model is called, interactive information is generated according to the target video emotion vector, the user emotion vector and a prompt word template for a video watching scene, and the interactive information is sent to the client so as to feed back the interactive information through a virtual role in the client. Through the embodiment of the application, the virtual role is combined with the offline generated video emotion and the real-time collected user emotion to interact with the user, a companion watching scheme is provided for the user, and the immersive watching experience of the user is improved.

The invention relates to a special operation whole process dynamic risk grading early warning method, system and device, and the method comprises the steps: dividing the whole process of a single special operation into a plurality of continuous operation stages, and recognizing the operation stage of a current operation task; for a current operation task, acquiring and quantifying multi-dimensional risk indexes in real time to form a multi-dimensional risk index value set; according to the operation stage of the current operation task, calling a corresponding weight set from a preset weight configuration library to carry out dynamic risk index calculation so as to obtain a dynamic risk index; and performing graded early warning and strategy matching on the current operation task according to the dynamic risk index. According to the method, a'operation life cycle 'is taken as a time sequence main axis, a dynamic risk index is output through stage adaptive weighting and fusion calculation, and risk management and control of a hierarchical intervention strategy are triggered according to the dynamic risk index, so that risk management is fundamentally converted from static state and single state to dynamic state and comprehensive state, and stage accurate early warning is realized.

The invention discloses an endangered plant phoebe hainanensis identification and systematic monitoring method based on multi-source data fusion, and belongs to the technical field of endangered plant protection. According to the method, a potential distribution area is delineated through a species distribution model, individual accurate identification and filing are achieved through a multi-branch feature fusion model, multi-scale growth and microenvironment data are collected through Internet of Things nodes, and finally a'discovery-identification-monitoring-protection 'whole-process closed loop is formed through a unified data management platform. According to the method, phenological feature extraction, transfer learning modeling, man-machine collaborative verification and low-power-consumption Internet of Things monitoring technologies are adopted, and the problems that a traditional method is low in efficiency, insufficient in recognition precision and fragmented in monitoring are solved. According to the method, efficient discovery, accurate identification and long-term dynamic monitoring of the wild population of the phoebe hainanensis are achieved, scientific support is provided for protection decision making, and the method is convenient to operate and high in practicability and has protection value and application prospects.

The application discloses a kind of based on brain basic cognitive function multidimensional evaluation child reading ability promotion training method and system, method includes: obtaining the multidimensional brain function portrait data of child to be trained in multiple preset cognitive dimensions;According to multidimensional brain function portrait data, the weak dimension and its deviation degree that child to be trained exists in multiple preset cognitive dimensions are identified;According to each weak dimension and its deviation degree, the individualized training scheme of child to be trained is constructed and executed;Wherein, individualized training scheme contains the training task corresponding to each weak dimension;During the execution of training task, according to the real-time performance data of child to be trained, the difficulty parameter of training task is dynamically adjusted.Therefore, in the training process, the system can dynamically adjust the training difficulty according to the real-time performance of the child, so that the child is always in a state of moderate challenge, thereby effectively improving the pertinence, interest, efficiency and long-term compliance of child reading ability training.

The application relates to the technical field of traffic management and deep learning, and discloses a method and system for identifying and managing illegal behaviors of key vehicles based on deep learning. Historical trajectory data, vehicle-mounted sensor data and driver behavior video sequences of key vehicles are acquired, three-dimensional convolutional neural networks and graph convolutional neural networks are used to extract spatiotemporal features and generate a behavior node embedding vector set; a Granger causality test algorithm is used to analyze the time series dependence relationship to generate a causal adjacency matrix; a time series causal graph is constructed to explicitly model the causal chain of driving behavior evolution; after real-time behavior features are mapped to graph nodes, a multi-hop reasoning is performed on the graph attention propagation algorithm, the activation probability distribution of each illegal behavior node in the future time window is calculated, a high-probability illegal behavior node is extracted to generate a pre-judgment result set, and the pre-judgment result set is output to a disposal management system. The application realizes active pre-judgment before illegal behavior occurs, and improves the initiative and prevention effect of disposal management.

This invention relates to the field of intelligent control technology, and more particularly to an AI-based torque prediction and precise feed control system for a centrifugal dewatering machine. The system includes: an acquisition module, an anomaly identification module, a first analysis module, a second analysis module, a prediction module, and a control module. This invention achieves precise detection of deviations from operating conditions by calculating the torque-flow rate slope in real time and comparing it with the statistical distribution of historical normal periods. Through a dynamic judgment mechanism based on historical statistical benchmarks, the system can issue early warnings before an anomaly develops into a torque over-limit stage, gaining a time window for subsequent targeted prediction and control. This significantly improves the dewatering system's proactiveness and operational stability in handling complex operating conditions, effectively solving the problems of low torque prediction accuracy and slow feed flow control response speed caused by the lag response based on a fixed pressure threshold.

The application relates to the technical field of data security, and discloses a data security risk quantitative dynamic evaluation method and system. The method comprises the following steps: collecting interactive data between nodes in real time, constructing a preliminary risk distribution graph, analyzing the graph to identify mutation nodes and defining a target area, evaluating the risk diffusion range of the target area, marking a high-risk transmission chain, extracting a key path, calculating the risk cumulative effect to generate a monitoring frequency parameter, adjusting a monitoring strategy based on the parameter and updating a risk evaluation matrix, then analyzing the interactive intensity synchronous demand between nodes to determine a multi-node collaborative control scheme, finally simulating and verifying the scheme by constructing an interactive scene, judging whether the risk fluctuation frequency reaches a stable state, and outputting a final control scheme. The application improves the accuracy of multi-node data security risk early warning.

The invention relates to the technical field of digital management, and discloses a cloud-side collaborative digital platform based on regional production and operation integrated management and control, which comprises an infrastructure layer used for constructing a unified hardware resource pool and an automatic operation and maintenance base for supporting a cloud-side collaborative architecture, global unified management and automatic operation and maintenance of a bottom server, storage and network hardware equipment are realized; the platform layer is used for converging global data and integrating an application development module and an AI module to form a common tool chain and data sharing service covering low-code development, micro-service and model full-life-cycle management; and the application layer is used for calling the common technical capability and the data sharing service provided by the platform layer, and constructing and deploying a business application system serving for integrated management and control of regional production and operation. According to the invention, a single-point operation and maintenance mode is converted into a new integrated intelligent management and control mode with cross-domain cooperation, active early warning and accurate decision making, and the safety, operation efficiency and risk resistance of regional production and operation are improved.

The invention relates to the technical field of augmented reality, and particularly discloses a 3D model display system based on AR and multimedia content fusion. The system comprises an environment perception and registration module used for establishing a real space coordinate system; the three-dimensional model dynamic loading and rendering module is used for loading and rendering the three-dimensional model in the coordinate system; the multimedia content intelligent association and scheduling module is used for storing and dynamically scheduling the multimedia content associated with the model; the interactive space anchoring and narrating engine is used for driving content scheduling and model state change based on user space behaviors; and the fusion presentation and self-adaptive interface module is used for synthesizing a final picture and dynamically adjusting an interface. According to the invention, deep organic fusion of the three-dimensional model, the multimedia content and the real scene is realized, and immersive and interactive-driven nonlinear narrative experience is provided.

The invention relates to the technical field of financial risk prediction, and discloses a financial transaction risk dynamic prediction system based on artificial intelligence. A financial data acquisition unit of the system obtains real-time market information flow and associated entity information flow of global multi-market transaction objects, a financial space-time cube is formed through space-time alignment processing, and the data flow and the information flow comprise multiple types of information such as an object asset price fluctuation sequence and a market depth change sequence; the risk factor separation unit executes deep tensor decomposition on the financial space-time cube, and extracts a core factor tensor and an abnormal mode indication tensor; a risk strategy generation unit generates a multi-market collaborative real-time risk prevention and control strategy containing subject asset position adjustment and market open control instructions based on the core factor tensor and the mechanism risk preference map; the dynamic strategy adjustment unit identifies an emergency risk event according to the abnormal mode indication tensor, revises the strategy in real time to generate a new prevention and control strategy, and can effectively deal with the dynamic risk of the financial market.

The invention relates to the technical field of industrial internet network security risk assessment, and discloses an industrial internet network security risk assessment method and system. Physical position information, network connection information and network communication behavior information of a mobile terminal are acquired and gathered; and the real-time network state and behavior characteristics of the mobile terminal can be comprehensively mastered. According to the method, the potential risk factors of the mobile terminal can be identified based on the converged network information, an attacker is simulated to take the mobile terminal as an attack path of a springboard, and risk quantification is carried out on the attack path. The dynamic and real-time risk assessment mechanism effectively solves the problems that in the prior art, assessment blind areas exist in high-mobility and intermittent network connection terminals, and evolution threats cannot be tracked.

This invention discloses a fire detection and emergency response system, comprising a data support layer, a physical sensing layer, a digital twin layer, an intelligent decision-making layer, and an emergency response layer. The data support layer provides support for the storage and management of detection data. The physical sensing layer acquires fire detection data and environmental and equipment status data within enclosed spaces. The digital twin layer constructs a full-element virtual mapping model of the enclosed space, achieving real-time synchronization, data fusion, and visualization between the physical entity and the virtual mapping model. The intelligent decision-making layer, based on the detection data and the status of the virtual mapping model, achieves early fire identification, risk assessment, and generates an emergency response plan. The emergency response layer executes the emergency response plan output by the intelligent decision-making layer. This fire detection and emergency response system enables extremely early and accurate detection, real-time synchronization between virtual and physical data, intelligent dynamic decision-making, and rapid closed-loop response, improving the initiative and effectiveness of fire prevention and control in enclosed spaces.

This invention relates to a multi-dimensional collaborative intelligent energy management and control system. The system includes: a data acquisition unit, a data processing unit, a digital twin and risk assessment unit, and a multi-objective collaborative optimization and visualization unit. It acquires multi-dimensional raw data and integrates it into a multi-source dataset; processes the multi-source dataset to generate a standardized dataset and a multi-dimensional feature set; reconstructs the physical topology and equipment parameters of the energy system, constructs a real-time synchronous digital twin, and combines the multi-dimensional feature set to obtain the operating status, analyze risk factors, and output early warning results, integrating and generating an operating status assessment report; obtains optimization benchmarks based on the assessment report, constructs a multi-agent reinforcement learning system using the multi-dimensional feature set to obtain energy dispatching strategies, decomposes them into executable control commands and obtains execution results, and simultaneously builds a multi-zone monitoring interface to visualize the entire energy management and control process data. This system achieves energy management and control using a multi-dimensional collaborative mechanism.

The application provides a storage array-based traffic control method, device, equipment, medium and product. The method comprises: obtaining target characteristic data of a target path, the target path comprising a path between a host and a storage array and / or a path between a takeover storage array and a heterogeneous storage array; predicting, by using a preset delay prediction model, a transmission delay of the target path in a future target time period according to the target characteristic data of the target path, and outputting a target delay time, so as to predict the transmission delay time of the target path in the future target time period, and determine a target input / output parameter of the target path in the future target time period based on the target delay time, so that the system can pre-configure the target input / output parameter to adapt to an upcoming load change according to the target delay time, effectively avoiding performance fluctuations and service interruptions caused by remediation after input / output congestion occurs, and reducing the average input / output delay of the system.

This invention discloses a warehouse data analysis system and method for intelligent monitoring of agricultural product warehouses, relating to the field of agricultural product monitoring technology. The analysis method includes the following steps: based on the warehouse layout, several nodes of the warehouse are obtained, and a warehouse network model is generated; warehouse data of each node in the warehouse network model is periodically collected; correlation analysis is performed on the warehouse data between each node to obtain the causal relationship between different nodes, and several influence paths are generated in the warehouse network model; abnormal nodes in any unit period are identified, several traceability paths are generated, and the optimal traceability path for each abnormal node is obtained; abnormal nodes in the warehouse network model are identified in real time, and the expected abnormal situation of the warehouse is analyzed; breaking through the limitations of traditional threshold alarms, it realizes pre-event prediction and in-event traceability, greatly improving the initiative and accuracy of warehouse management.

The power transmission tower vibration deformation measuring and early warning method based on the ground-based synthetic aperture radar comprises the steps that scanning imaging is conducted on a power transmission tower through the ground-based synthetic aperture radar, and a plurality of radar image sequences which cover a target tower body and are arranged according to the time sequence are obtained; processing the plurality of radar image sequences by adopting a permanent scatterer interference measurement method, extracting a plurality of permanent scatterer points attached to the key points of the power transmission tower, and obtaining a displacement time sequence of each permanent scatterer point along the sight line direction of the radar; decomposing the displacement time sequence along the radar sight line direction into deformation component time sequences in three orthogonal directions under a power transmission tower structure coordinate system; joint time-frequency analysis and modal parameter identification are carried out on the deformation component time sequence in each direction, and modal parameters of the power transmission tower are extracted; establishing a multi-level early warning mechanism, calculating the change indexes of the current modal parameter and the deformation component relative to the reference value, and automatically triggering different levels of power transmission tower structure state early warning.