43 results about "Infrastructure security" patented technology

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 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 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.

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.

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 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 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.

This invention relates to the fields of remote sensing data processing and engineering safety monitoring, specifically to a method for spatiotemporal completion and uncertainty generation of SAR deformation field data. First, the raw data undergoes quality screening and anomaly removal to identify time-missing segments and spatial void regions. Second, a temporal evolution model is constructed for each time-missing location to generate time-completed results. Then, based on spatial correlations, spatial void regions are completed point-by-point. Next, iterative spatiotemporal joint constraints ensure consistency between the time-completed and spatial-completed results in the spatiotemporal dimensions. Finally, based on model output, data quality, and historical error information, multi-level uncertainty indicators corresponding to deformation values ​​are generated synchronously. This invention achieves spatiotemporal consistency completion and reliable quantification of SAR deformation field data, resulting in completion results that better conform to actual deformation patterns, providing a complete and reliable data foundation for geological disaster monitoring and infrastructure safety assessment.

The invention discloses a hydrogen refueling station multi-mode data acquisition method based on an equipment failure mode and terminal equipment, and belongs to the technical field of hydrogen energy infrastructure safety monitoring. The multi-level image intelligent detection algorithm comprises three levels of bottom-layer multi-mode feature extraction, middle-layer failure feature classification and high-layer multi-mode fusion decision; (2) analyzing a risk level corresponding to the failure mode in combination with a cloud equipment knowledge graph, wherein the knowledge graph cloud equipment knowledge graph comprises equipment manufacturing, installation, process, maintenance and detection data; and (3) dynamically scheduling the collection frequency and range of the terminal data collector according to the risk level and failure mechanism sorting result. The terminal device comprises an image acquisition module, a sensor module and a communication module. The problems that an existing hydrogen refueling station data acquisition method is fixed in strategy, low in failure recognition precision and insufficient in risk association are solved.

This invention discloses a method for constructing a road cavity detection model based on 3D ground-penetrating radar (GPR). The method includes constructing a hybrid spatiotemporal detection model, which comprises a feature preprocessing module, a soft clipping module, a spatiotemporal feature extraction module, and an output module. The feature preprocessing module performs feature pre-extraction on preprocessed and labeled data, outputting a pre-extracted feature map. The soft clipping module performs spatiotemporal perception on the pre-extracted feature map, outputting a clipped feature map. The spatiotemporal feature extraction module analyzes the clipped feature map, outputting feature information with global spatiotemporal information. The output module integrates the feature information and predicts the existence of cavities and the range of their occurrence channels. By combining the correlations between data in the spatiotemporal dimension, it accurately detects underground cavities, solving the problems of high computational load and difficulty in adjusting model parameters in existing 3D GPR high-resolution data interpretation. This provides an efficient and reliable technical solution for intelligent road cavity detection and infrastructure safety maintenance.

A computing system enables retrieval of a device primary account number (dPAN) via open banking, utilizing an open banking system, a token requester server system, and a token service system. The process begins with a financial application on a cardholder's device requesting financial account information and prompting the open banking system to launch a login interface for authentication of the cardholder by the financial account issuer. Upon successful authentication, an authorization code is issued, converted into an authorization token, and sent with account details to the token requester server system. The token requester server system then submits a payment token request to the issuer, which returns a token account receipt. The token service system subsequently processes the receipt to generate the device primary account number (dPAN) and returns the dPAN to the token requester server system.

The application discloses a kind of multi-source data fusion's road surface monitoring method and system, it is related to wisdom traffic infrastructure safety monitoring field, the method includes: to road surface monitoring data is preprocessed, based on the road surface monitoring data after preprocessing is carried out structure feature extraction and normalization processing, obtain road surface structure health index data;The historical road surface monitoring data obtained is converted into road surface structure probability distribution function, determines initial road surface structure weight, and the initial road surface weight is dynamically optimized;According to road surface structure health index data and the road surface structure weight after optimization calculates road surface health index, and carries out road surface risk early warning, based on road surface risk early warning result carries out damage factor identification, obtains leading damage factor.The application can visualize road surface health index, accurately feedback the risk and wear degree of road, reduce the risk brought by road damage, and can independently judge the main damage factor of road surface health, identify potential safety hazard.

This invention relates to the field of infrastructure safety monitoring technology, specifically to a method and system for infrastructure risk assessment based on spatiotemporal state characteristics. The method includes: acquiring spatiotemporal state characteristics containing state vectors and their evolution indicators, representing the operational state; reorganizing the spatiotemporal state characteristics to form a risk assessment feature set containing temporal evolution and spatial correlation characteristic components; assessing and outputting the risk level and risk type of the infrastructure, respectively representing the severity and formation mechanism of the risk; extracting key spatiotemporal state characteristics affecting the results and generating explanatory information. The system includes: a spatiotemporal state characteristic acquisition module, a risk assessment feature construction module, a risk assessment module, and a assessment result output module. Through the above methods, the phased development characteristics and spatial coupling characteristics of risks are characterized, accurately capturing the actual risk state of infrastructure, thereby meeting the needs of refined and standardized infrastructure risk management.

The present invention relates to an infrastructure safety inspection system using an artificial intelligence-based unmanned aerial vehicle for autonomous flight in a GPS shaded area. The infrastructure safety inspection system using an artificial intelligence-based unmanned aerial vehicle for autonomous flight in a GPS shaded area, according to the present invention, comprises: a replaceable variable rotor; an optical camera; a thermal imaging camera; a movable lidar sensor; a gyro sensor; an acceleration sensor; a temperature sensor; a three-axis laser range finder; an infrared sensor; an ultrasonic sensor; an RF sensor; an assembly frame; a remote controller capable of controlling a UAV; a controller for performing wired / wireless remote control of equipment and a photographing device; a control system; and a control unit in which an algorithm for controlling the artificial intelligence-based unmanned aerial vehicle for autonomous flight in a GPS shaded area is installed.

AI-Driven Infrastructure Security Management Computer Device

This invention discloses a verification method for an early warning model platform that supports parallel verification of online and offline dual-source data. Belonging to the field of infrastructure safety monitoring technology, it is applicable to the early warning assessment and analysis of monitoring data for structures such as geological disasters, slopes, and bridges. Addressing the problems of existing technologies, such as single data sources, high verification costs, low efficiency, and poor universality, this method acquires offline heterogeneous data and online historical data, processes them through standardization, configures unified test parameters, and establishes a dual-channel system to achieve parallel verification of dual-source data. It automatically calculates indicators such as false alarm rate and missed alarm rate, and compares the results with traditional threshold verification. Furthermore, it constructs a standardized verification system and functional modules to achieve quantitative presentation of verification results, full-process traceability, and adaptation to multiple types of early warning models. This invention overcomes the limitations of a single data source, reduces verification costs, improves model adaptability and iterative optimization efficiency, and ensures the reliability of the early warning model.

The invention relates to the technical field of traffic infrastructure safety risk monitoring, and discloses a traffic infrastructure safety risk monitoring system and method based on a distributed neural network, and the system comprises a hierarchical sensing node, a distributed data transmission network, a central data processing device, a state data transmission control module, and a video rechecking unit. According to the system, continuous physical quantity monitoring is converted into discrete risk level state monitoring through hierarchical sensing nodes; the state data transmission control module dynamically adjusts the data reporting frequency according to the state; and the central data processing device receives the state data, combines the state data into a state input vector, performs pattern recognition through a built-in lightweight neural network model, and outputs a structured risk event description. By performing data dimension reduction and event-driven transmission at the front end and combining centralized lightweight intelligent analysis and video review, low-cost and high-reliability security risk monitoring is realized.

To provide a system for quickly collecting and analyzing information in a disaster-stricken area, optimizing a rescue route and securing a communication infrastructure, and for achieving effective disaster countermeasures and rescue activities.SOLUTION: The system includes means for collecting video data in real time by causing a plurality of autonomous flying devices to fly to a designated disaster-stricken area when a disaster occurs, generative artificial intelligence means for analyzing the collected video data, means for calculating a rescue point and a safe rescue route on the basis of an analysis result and providing the rescue point and the safe rescue route to a rescue organization and a medical institution, means for temporarily securing a communication infrastructure of the disaster-stricken area by the autonomous flying devices, and means for specifying an unrescued point, maintaining public security, checking the health condition of a victim, and guiding the victim to an evacuation site.SELECTED DRAWING: Figure 1

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 invention provides a temperature and pressure synchronous sensing method and device for monitoring parameters of a bridge ship collision prevention system, and relates to the technical field of optical fiber sensing. The method comprises a sensor calibration module and a real-time demodulation module. The sensor calibration module comprises a sensor calibration unit, a spectral feature extraction unit, a database unit, a data preprocessing and standardization unit, a model construction and training unit and a hyper-parameter optimization unit. The model building and training unit is used for building a multi-output support vector regression model, specifically, regression functions are built for temperature and pressure respectively, and meanwhile a first SVR model and a second SVR model are included. According to the invention, synchronous and high-precision measurement of temperature and pressure can be realized by using a single sensing unit and a single demodulation model; therefore, the reliability, the compactness and the long-term stability of the system are remarkably improved while the measurement precision is ensured, and the harsh requirements for safety monitoring of major infrastructures such as a ship colliding with a bridge and the like are met.