73 results about "Infrastructure security" patented technology

The invention provides an infrastructure security analysis method and system based on a large model, and the method comprises the following steps: collecting multi-source heterogeneous data of an infrastructure, including physical equipment sensor data, network flow data, operating system logs and external threat intelligence; converting the multi-source data into standardized feature vectors of a unified time axis through a space-time alignment module; and inputting the standardized feature vector into the fine-tuned large language model for semantic understanding, and generating an equipment behavior semantic description and threat intention analysis result. According to the method provided by the invention, a large language model and reinforcement learning collaborative architecture is provided, and the large language model is improved, so that end-to-end mapping from multi-source data to threat semantics is realized, and the attack detection rate is improved.

The invention discloses a ground surface settlement measurement data real-time fitting method based on an evolutionary algorithm. The method comprises the following steps: S1, acquiring a ground surface settlement measurement data set; s2, preprocessing the ground surface settlement measurement data set; s3, generating a ground surface settlement measurement data feature matrix; s4, constructing a radial basis function neural network model for ground surface settlement measurement data fitting; s5, optimizing parameters of the initial ground surface settlement measurement data fitting function by adopting an alpha evolutionary algorithm; and S6, adopting the optimized ground surface settlement measurement data fitting function to predict the input ground surface settlement measurement data. According to the method, the calculation efficiency, the nonlinear expression capability and the global optimization capability of ground surface settlement measurement data fitting are effectively improved, and the method can be widely applied to multiple scenes of ground surface settlement monitoring, infrastructure safety evaluation and mining area collapse prediction.

The invention provides a distributed quantum security encryption method, system and device. The method comprises the following steps: monitoring the quality of a quantum key distribution link in real time, and dynamically selecting a quantum key, a post-quantum session key or a fusion key of the quantum key and the post-quantum session key as a working key; and when the node cannot be directly reached, the trusted relay node generates an end-to-end key seed by using a quantum key and a post-quantum cryptography shared key which are respectively established with the two ends, the end-to-end key seed is encrypted and distributed by a public key and then a session key is independently derived by the two communication parties, and the relay node cannot decrypt. The system adopts a distributed Mesh network architecture supporting a terminal / relay dual mode. The device integrates a quantum random number generator, a post quantum cryptography coprocessor and a mixed key engine. The method integrates the advantages of quantum physical security and post quantum cryptography, has high security and availability, and is suitable for constructing a key infrastructure security communication network resisting quantum computing attack.

The invention discloses a signal reconstruction and noise suppression method and system for a distributed Raman temperature measurement sensing system, and the method comprises the steps: obtaining original anti-Stokes light and Stokes light time domain signals of the distributed Raman temperature measurement sensing system, and constructing a space-time signal matrix; performing preprocessing and data enhancement on the space-time signal matrix; constructing a dual-path feature fusion network for Raman signal reconstruction and noise suppression, and initializing the network; defining a loss function fusing temperature physical constraints, and training the dual-path feature fusion network; and inputting an original Raman signal to be processed into the trained network, outputting the reconstructed anti-Stokes and Stokes signals with the high signal-to-noise ratio, and carrying out temperature demodulation according to the anti-Stokes and Stokes signals with the high signal-to-noise ratio. While the spatial resolution is maintained or improved, the noise of the Raman temperature measurement system is effectively suppressed, especially the real signal of a temperature abrupt change point can be recovered, the temperature demodulation precision, stability and reliability are improved, and reliable data are provided for infrastructure safety monitoring.

The invention relates to the technical field of infrastructure data analysis, and discloses a real-time analysis method and system for safety monitoring of urban infrastructures, and the method comprises the steps: firstly obtaining the type and distribution data of the infrastructures, carrying out the matching of a corresponding IOT sensor from a sensing database, carrying out the scientific arrangement, and generating a digital twinborn model based on an IOT sensing network of various types of facilities; then acquiring Internet of Things networks corresponding to first and second types of facilities with a first Internet of Things relation by means of the model, respectively extracting sensing data, identifying abnormal data through a preset identification strategy, and judging and triggering a corresponding abnormal prompt in combination with a data volume ratio; and finally, based on the two types of abnormal prompts, performing linkage early warning on other facility types related to the first Internet of Things. According to the invention, accurate adaptation of facility monitoring, reliable identification of abnormal data and linkage prevention and control of associated facilities are realized, and the real-time performance, pertinence and comprehensiveness of urban infrastructure safety monitoring are improved.

The disclosure provides a system / method / scheme to securely send data from a cloud, or cloud service provider, to users via a secure connectionless system, referred to herein as a C-VPN communication infrastructure (C-VPN CI). In one example a method of communicating from a cloud service provider to a user via a C-VPN CI includes: (1) obtaining, by a cloud service provider, security parameters from a SDE Cloud server operating on a computing system of the cloud service provider, wherein the security parameters include a set of mathematical rules and values for converting plain text to ciphertext, (2) creating a secure communication using the security parameters received from the SDE Cloud server, wherein the secure communication includes a secure header and secure data, and (3) sending the secure communication to the user via a generic electronic message delivery system.

The present application relates to the technical field of intelligent city infrastructure safety monitoring, and discloses a real-time response system for safety events of intelligent pedestrian tiles. The system comprises a real-time state monitoring module, a dynamic event detection module, a multi-dimensional event difference analysis module, a safety area correlation positioning module and a self-adaptive response generation module. The sensor array collects tile pressure distribution, surface humidity and temperature fluctuation data, and combines historical event data to construct a dynamic prediction model to output potential safety event probability values. A multi-dimensional difference analysis generates an event-level difference feature matrix, and a spatial topology network and environmental parameters are used to generate a safety event heat distribution map and locate high-risk areas. Finally, response parameters are adaptively configured. The system realizes real-time sensing, accurate positioning and dynamic response of safety events in urban pedestrian areas.

The invention discloses a soft soil area earth surface deformation identification method and system based on Beidou short message and InSAR cooperation, and the method comprises the steps: collecting high-frequency deformation data through a ground sensor array, obtaining a low-frequency wide-area deformation image through combining with an InSAR satellite, and constructing a multi-source data set through space-time matching; and data missing is filled by adopting an interpolation algorithm, and the data quality is ensured through consistency verification. And a support vector machine classifier is used to identify an abnormal settlement area, and a filtering algorithm is used to predict a deformation trend based on a historical sequence. And when the predicted value exceeds a threshold value, an alarm signal is automatically generated, a thermodynamic diagram is visually rendered through a GIS system, and the high-risk infrastructure is positioned. And finally, the data set is circularly updated through real-time sensor feedback, fusion parameters are iteratively optimized, and hidden deformation dynamic accurate sensing is realized. According to the method, the hidden deformation identification precision is remarkably improved, and efficient and accurate monitoring and early warning support is provided for the safety of infrastructures in the soft soil area.

The invention discloses a landslide detection deep learning method fusing geometry, context and frequency domain multi-source features, and belongs to the technical field of geological disaster recognition and landslide remote sensing information processing. The problem that a traditional landslide detection method in the prior art is low in recognition precision is solved. The method comprises the following steps: constructing a landslide detection model based on a GCF-Net architecture, and inputting a to-be-detected image into the landslide detection model; capturing feature information of different scales in the to-be-detected image through a ghost pyramid feature fusion module; an output characteristic pattern is input to a decoder through a GCFmodule; and an EdgeECI module in the decoder is used to process the output feature map of the GCFI module, and a final output feature map after fusion is obtained. The landslide detection accuracy is effectively improved, and the method can be applied to geological disaster monitoring and early warning, mountain traffic and infrastructure safety, water conservancy and energy engineering operation monitoring, remote sensing big data processing platforms and other scenes.

The invention discloses a roadbed settlement time sequence prediction and three-dimensional deformation monitoring system and method, and belongs to the technical field of earth surface deformation monitoring and prediction.The system comprises a data acquisition module, a spatial-temporal feature construction module, a prediction model fusion module, a deformation field reconstruction module, a synthetic data generation module and a monitoring and early warning module; according to the method, an InSAR satellite remote sensing image and foundation micro-deformation sensor data are fused, a time-space coupled Transform-GRU prediction model is constructed, a pixel-level settlement rate field algorithm is created for the first time, the earth surface deformation monitoring precision is improved to 0.5 mm every year, historical settlement images are synthesized through a generative adversarial network, training data are enhanced, and the earth surface deformation monitoring precision is improved to 0.5 mm every year. Accurate reconstruction and long-term prediction of the three-dimensional deformation field of the roadbed are realized, the problem of insufficient generalization ability under the conditions of insufficient three-dimensional deformation reconstruction, long-term prediction precision and data in the prior art is effectively solved, and powerful support is provided for safe operation of traffic infrastructures.

Safety test results generated in a safety testing system 31 are displayed to an operator for input, by receiving the results at a server 34; using a high integrity safety test reporting system 32 hosted thereon to pre-filter the results into a log file; passing the log file to a low integrity report viewer (e.g. commercial off-the-shelf (COTS) viewer or browser 35) embedded in the server; and operating the reporting system and viewer with a display device 33 to display the log file to an operator for input. The server may be cloud-based. The test results may relate to a rail infrastructure safety system 39,40. The safety test reporting system may meet EN 50129 SIL4 requirements whereas the report viewer need not meet any SIL requirements.

The invention discloses an Internet infrastructure security risk assessment index system method, and the method comprises the steps: firstly constructing a security risk assessment index system of Internet infrastructure; wherein the security risk assessment index system comprises a plurality of security risk assessment indexes; calculating a weight vector of each security risk assessment index; then, according to the weight vector of each security risk assessment index, a fuzzy comprehensive evaluation method is used for calculation to obtain a comprehensive evaluation vector of the Internet infrastructure; and finally, according to the comprehensive evaluation vector, determining a comprehensive security evaluation result of the Internet infrastructure. According to the technical scheme, the Internet infrastructure security risk assessment index system is constructed, and the Internet infrastructure security risk assessment index system comprises a plurality of security risk assessment indexes of different security categories. Omnibearing, multi-dimensional and quantifiable accurate evaluation of the security risk of the Internet infrastructure in a complex network environment can be realized.

The invention discloses an infrastructure safety detection system and method based on moire fringes, and relates to the technical field of infrastructure safety monitoring, the system comprises a sensing node, a cloud server and an intelligent early warning module, the sensing node is composed of a double-grating structure, a monochromatic light source and the like, and the reference grating and the sensing grating are respectively fixed at a stable part and a displacement sensitive part of the infrastructure. During detection, the monochromatic light source irradiates the double gratings to generate moire fringes, the moire fringes are collected by the camera, displacement data are calculated by the processor and then uploaded to the cloud, the cloud analyzes structural performance by combining a finite element model, and the intelligent early warning module triggers an alarm according to a preset rule. The system achieves high-precision non-contact measurement, has the advantages of being waterproof, dustproof, high in weather resistance and the like, can be matched with various infrastructures such as bridges and ancient buildings, supports long-term automatic monitoring, and effectively overcomes the defects of a traditional measurement method.

Systems, computer program products, and methods are described herein for dynamically encrypting network-based interactions based on evaluated infrastructure security. The present disclosure is configured to evaluate a set of infrastructure security within a network-based interaction. The system includes evaluating a set of infrastructure security within a network-based interaction. The set of infrastructure security for the network-based interaction may comprise at least one node and a layer of security protocols. The system may further include assessing the set of infrastructure security for the network-based interaction, wherein the set of infrastructure security is assessed on a set of predetermined criteria. The system may include encrypting the network-based interaction based on assessment of the infrastructure security for the network-based interaction, with encryption complexity correlating to assessment of the set of infrastructure security for the network-based interaction.

The present invention discloses a rail infrastructure safety assurance method, system, device, and storage medium, relating to the field of intelligent safety monitoring and control technology for rail transit. The method comprises: collecting raw track data through a multi-source perception module; processing the raw track data using a bilateral filter and region growing method to obtain track data containing semantic tags; extracting features from the track data through a data fusion hub to construct a spatiotemporal correlation feature matrix; performing thermal-mechanical coupling simulation based on the track data through a digital twin decision platform to obtain simulation results; and generating optimized control instructions and alarm information through an adaptive control module based on the spatiotemporal correlation feature matrix and the simulation results. The present invention solves the problems of perception system fragmentation, insufficient intelligent diagnosis and decision-making, and passive risk management in related technologies.

Embodiments of the present application provide a pipeline detection database logical detection method and device, electronic equipment and medium, and relate to the field of data processing. The sewer pipeline detection data in the pipeline detection database is respectively standardized to obtain standardized data with unified format; the detection categories of each standardized data are obtained, and the conventional logical check items corresponding to the detection categories are selected; additional logical check items are selected; the standardized data are detected according to the conventional logical check items and the additional logical check items, and the basic detection results and the additional detection results are obtained; and the basic detection results and the additional detection results are determined as combined detection results. Through the logical check on the underground sewer pipeline detection database, the accuracy of the data can be improved, which is beneficial to the analysis, mining and application of the sewer pipeline data in the later stage, and can also be used for finding the hidden dangers of the sewer pipeline and further applied to the safety warning of urban underground municipal infrastructure and the like.

Security system for IT infrastructures, comprising: a) at least one initial processing component (10) operating at a system level below the network layer and analyzing data packets before processing by higher system layers; b) at least one monitoring component (20) integrated into the data flow of the IT infrastructure (100) and continuously analyzing it for anomalies; c) at least one decision component (30) configured to classify security threats using multi-stage validation; d) at least one response component (40) configured to automatically neutralize detected threats; wherein the security system operates as an internal monitoring and defense system within the IT infrastructure (100) and remains functional even after overcoming external protection layers.

The invention discloses an intelligent capital construction safety management and control system and method based on cloud edge human-computer interaction, and relates to the field of capital construction safety management. The system comprises an edge sensing layer, a cloud processing layer and a man-machine interaction layer. The edge sensing layer collects and preprocesses environment, equipment operation and personnel behavior data; after the cloud processing layer receives the data, a risk assessment result is generated through a random forest algorithm and convolutional neural network analysis, and a control instruction is generated by a decision generation module; and the man-machine interaction layer realizes information interaction through a mobile terminal and a fixed terminal. According to the method, the real-time performance of data processing is improved through cloud edge cooperation, the risk identification accuracy is improved in combination with an intelligent algorithm, the executive performance is enhanced by means of a dynamic management and control instruction and multi-element interaction, the security accident rate is effectively reduced, and the infrastructure security management and control intelligent level is improved.

The invention relates to a slope full-life-cycle intelligent repairing method and system based on the Internet of Things. The method comprises the following steps: acquiring multi-source monitoring data of a slope; performing standardization processing on the multi-source monitoring data to obtain standardized multi-source monitoring data; obtaining slope state data based on a preset slope digital twinborn model and the standardized multi-source monitoring data; according to the slope state data, a slope restoration scheme is generated; the slope repair scheme comprises repair time, a repair position, a repair mode and repair resource allocation; according to the slope restoration scheme, a slope restoration instruction is generated; the side slope repairing instruction is used for indicating preset equipment to carry out corresponding side slope repairing operation according to a side slope repairing scheme; the preset equipment is used for executing slope repairing operation. By adopting the method, the integrity of slope monitoring, the scientificity of restoration decision and the effectiveness of full-cycle management can be improved, the slope stability and the infrastructure safety are powerfully guaranteed, and precise and intelligent restoration of the slope in the full life cycle is realized.

The invention discloses a data element driven AI native infrastructure safety operation method, and belongs to the technical field of artificial intelligence and data safety. Aiming at the technical problems of extensive authority management, difficulty in safety traceability and low compliance verification efficiency in the existing AI infrastructure safety operation, the intelligent agent authority dynamic binding is realized by establishing a data element safety label system; constructing a full-link safety traceability map, and combining an AI abnormal behavior recognition and fusing mechanism; a compliance intelligent verification module is integrated, and multi-industry supervision requirements are automatically met. According to the method, refinement of safety management and control, automation of operation and intelligentization of compliance are realized, and the safety operation and compliance cost of an enterprise is remarkably reduced. The method is mainly used for safety operation and data management of the AI native infrastructure.

The present application relates to the field of distributed optical fiber sensing, and discloses a kind of infrastructure security monitoring oriented calibration-free Raman temperature sensing device and method.The device comprises: pulse laser, the pulse laser emitted by pulse laser is sequentially incident to first calibration fiber ring, second calibration fiber ring, third calibration fiber ring and sensing fiber after wavelength division multiplexer, the Raman backward stokes scattering light and Raman backward anti-stokes scattering light generated in sensing fiber are respectively detected by two ports of avalanche photodetector, and the output end of avalanche photodetector is connected with computer through data acquisition card;The present application directly fits out calibration curve by measuring the average intensity of Raman backward anti-stokes light signal and Raman backward stokes light signal at first calibration fiber ring, second calibration fiber ring and third calibration fiber ring, realizes temperature demodulation, avoids calibration process, and improves signal-to-noise ratio and temperature measurement accuracy.

The invention relates to the technical field of municipal safety, discloses a telescopic embedded displacement monitoring sensor, and realizes comprehensive improvement of the performance of the displacement monitoring sensor through innovative design. The elastic force adjustable mechanism prevents the sensing string wire from being stressed for a long time, and the graphite clamping piece and the protective corrugated pipe are combined, so that the long-term stability and durability are remarkably improved. The integrated excitation-sensing design and the displacement amplification mechanism greatly improve the monitoring sensitivity and the signal-to-noise ratio. The sensor has an initial spacing adjusting capability, supports rapid replacement of the string wire, and is good in installation adaptability and simple and convenient to maintain. And meanwhile, intelligent monitoring and state self-diagnosis are realized through remote controllable excitation and self-adaptive tension maintenance. The whole structure is firm and reliable, and an embedded monitoring solution which is high in precision, high in reliability and easy to maintain is provided for the safety of municipal infrastructures.

The invention discloses an urban highway tunnel operation period toughness safety evaluation method, and relates to the field of traffic infrastructure safety evaluation, and the method comprises the steps: constructing a standardized toughness safety database of an urban highway tunnel operation period; finely adjusting the large language model based on domain ontology knowledge to form a semantic analysis tool; building a knowledge graph through entity relationship mining; establishing a multi-scene index system in combination with a text mining technology; and dynamic weight calculation and safety scoring are realized by using an analytic hierarchy process. The method has the advantages that the objectivity of an evaluation index system is improved, the multi-scene adaptive capacity is enhanced, and the operation process is simplified.

The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station coal shed color steel plate abnormality monitoring system based on unmanned aerial vehicle remote sensing, and relates to the technical field of infrastructure safety monitoring. The application discloses a heavy-load railway loading station

Security system for IT infrastructures, comprising: a) at least one initial processing component (10) operating at a system level below the network layer and analyzing data packets before processing by higher system layers; b) at least one monitoring component (20) integrated into the data flow of the IT infrastructure (100) and continuously analyzing it for anomalies; c) at least one decision component (30) configured to classify security threats using multi-stage validation; d) at least one response component (40) configured to automatically neutralize detected threats; wherein the security system operates as an internal monitoring and defense system within the IT infrastructure (100) and remains functional even after overcoming external protection layers.

The invention relates to the field of power transformation infrastructure security and protection, in particular to a power transformation infrastructure risk identification method and system and a medium, and the method comprises the steps: collecting multi-modal data of a construction site, the multi-modal data comprising video monitoring data, operation monitoring data, equipment state data and environment monitoring data; a BIM model is adopted as a three-dimensional digital base map of a construction site, fusion modeling is carried out on the multi-modal data and the BIM model, and the BIM model after fusion modeling is obtained; inputting the multi-modal data into a pre-trained AI large model, extracting safety risk characteristics and violation behavior modes of the construction site through the AI large model, and identifying safety risks and violation behaviors in real time by the AI large model based on the extracted safety risk characteristics and violation behavior modes; and in response to the AI large model, identifying security risks and violation behaviors in real time, generating alarm information, and positioning and displaying the alarm information through the BIM model after fusion modeling.

The application discloses a kind of surface subsidence measurement data real-time fitting method based on evolutionary algorithm, S1.Acquire surface subsidence measurement data set;S2.The surface subsidence measurement data set is preprocessed;S3.Surface subsidence measurement data feature matrix is generated;S4.Radial basis function neural network model for surface subsidence measurement data fitting is constructed;S5.The parameters of initial surface subsidence measurement data fitting function are optimized using alpha evolutionary algorithm;S6.The input surface subsidence measurement data is predicted using the optimized surface subsidence measurement data fitting function.The application effectively improves the calculation efficiency, nonlinear expression capacity and global optimization capacity of surface subsidence measurement data fitting, and can be widely applied to surface subsidence monitoring, infrastructure safety assessment and mine subsidence prediction multiple scenes.

The invention discloses a railway multi-temporal change detection method and system based on a cross-domain generated image. The method comprises the following steps: obtaining an unmanned aerial vehicle single-temporal abnormal image of a road scene; obtaining a front time phase image set of the railway scene based on the image generation model; obtaining a time phase image set after the railway scene based on the image editing model; style migration is adopted to simulate diversity and construct a change detection data set; constructing and training a change detection model based on state space perception; and inputting a real railway double-time-phase image into the model for prediction to obtain a change information binary image. According to the method, the railway scene double-time-phase image set is generated by using the easily acquired road abnormal image, so that the problem of scarcity of the abnormal data of the railway scene is effectively solved, and the detection precision of the railway surrounding environment risk hidden danger change is improved through a structure perception and state space updating mechanism; the method is widely suitable for railway surrounding environment inspection, infrastructure safety detection, traffic scene data enhancement and intelligent analysis and other scenes.

This invention relates to the interdisciplinary field of next-generation information technology and intelligent transportation infrastructure safety monitoring, and discloses a bridge fire risk status assessment method and system based on a large model. The key technical points are: system initialization and physical device-twin base mapping construction; collaborative perception of multimodal physical quantities and spatial visual features; transient thermal resistance network deduction and global synchronization deviation quantification; dimension-aligned graph network feature fusion; large model state reasoning with embedded physical knowledge constraints; safety margin quantification and three-dimensional coordinate-level hardware linkage. It employs digital twin thermal resistance network deduction, graph topology feature fusion, and large model physical knowledge constraint collaboration technology. By transforming the underlying objective thermodynamic mechanism into a dynamic penalty term and injecting it into the large model reasoning layer for decision correction, it eliminates the problem of insufficient physical consistency in purely data-driven models and constructs a hardware-level control closed loop for the entire system from multimodal perception to three-dimensional spatial targeted electromechanical linkage.

This invention relates to the field of infrastructure safety monitoring technology, specifically to a ground monitoring system and method for verifying the risk status of infrastructure. The method includes: acquiring the risk level, risk type, and status confidence level of the infrastructure, and integrating them into risk status information; dividing key risk areas and collecting infrastructure monitoring data in these areas in real time; mapping the infrastructure monitoring data to real-time operating status, performing consistency analysis between the real-time operating status and the risk status information, generating risk status verification results, and dynamically adjusting the monitoring strategy. The system includes: a risk status information acquisition module, a ground monitoring data acquisition module, a risk status verification module, and a monitoring strategy dynamic adjustment module. Through the above methods, the degree of matching between the risk status information and the actual operating status reflected by the on-site monitoring data is quantitatively judged from multiple dimensions, thereby improving the scientific nature, accuracy, and effectiveness of infrastructure safety risk management.