58 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 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 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.

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 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.

The invention discloses a road surface monitoring method and system based on multi-source data fusion, and relates to the field of intelligent traffic infrastructure safety monitoring, and the method comprises the steps: carrying out the preprocessing of road surface monitoring data, carrying out the structural feature extraction and normalization processing based on the preprocessed road surface monitoring data, and obtaining the health index data of a road surface structure; converting the acquired historical pavement monitoring data into a pavement structure probability distribution function, determining an initial pavement structure weight, and dynamically optimizing the initial pavement structure weight; and calculating a road surface health index according to the road surface structure health index data and the optimized road surface structure weight, carrying out road surface risk early warning, and carrying out damage factor identification based on a road surface risk early warning result to obtain a dominant damage factor. According to the invention, the road health index can be visualized, the risk and wear degree of the road can be accurately fed back, the risk caused by road damage can be reduced, the main damage factors of the road health can be autonomously judged, and the potential safety hazard can be identified.

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.