50 results about "Port security" patented technology

The invention relates to a ship berthing behavior prediction and violation early warning method and system based on multi-source data fusion. The method comprises the steps that a multi-source data set is acquired; constructing a multi-source feature set based on the multi-source data set; inputting the multi-source feature set into a behavior simulation model; obtaining port scheduling information and port safety area information of the target port, and performing analysis and comparison based on a risk assessment strategy; and when any risk probability in the risk assessment strategy is greater than a corresponding threshold value, generating a corresponding graded early warning signal. The problem that a traditional single-source monitoring system is large in error in a severe environment is solved through multi-source data collaborative perception, the limitation of fixed threshold judgment is overcome by introducing a dynamic risk assessment model, and intelligent collaboration of berthing operation is achieved by fusing port scheduling information; the problems that in the prior art, ship berthing monitoring is not comprehensive, early warning is not accurate, and adaptability is poor are effectively solved, and the effects of improving port safety and operation efficiency are achieved.

The invention provides a BIM-based port structure safety real-time monitoring method and system, and relates to the technical field of port intelligent monitoring. According to the invention, through construction of the high-precision digital twinborn model and a multi-source data fusion technology, full-process closed-loop management from data acquisition to risk early warning is realized. Firstly, virtual-reality data integration of a BIM model, real-time sensor data and GIS environment data can accurately simulate the influence of external factors such as tide and wind speed on the structure, and the prediction accuracy is improved; and secondly, an AI-driven dynamic prediction model is combined with expert knowledge base verification, so that the risk early warning accuracy is remarkably improved. The method provides an intelligent and digital complete solution for port safety management, and has wide application value and popularization prospect.

The invention relates to a port water area ship navigation risk distribution calculation and visualization method and device based on AIS and GIS data fusion, and the method comprises the following steps: 110, obtaining ship AIS data, and carrying out the preprocessing of the ship AIS data, thereby obtaining the preprocessed ship AIS data, and the preprocessing comprises the standardization, feature extraction and abnormal point detection; step 120, carrying out port water area grid construction, and carrying out grid mapping and legality screening on the preprocessed ship AIS data; step 130, performing port water area static risk assessment, port water area dynamic risk assessment and port water area propagation risk assessment on the port water area, and performing port water area comprehensive risk assessment on the basis to obtain an assessment result; and step 140, drawing a port water area risk thermodynamic diagram based on the evaluation result to realize risk distribution visualization. According to the invention, efficient and scientific decision support can be provided for port safety management.

The invention relates to a multi-unmanned aerial vehicle path collaborative planning method based on an improved sparrow search algorithm. The method comprises the following steps: establishing a space-time dynamic model and a three-dimensional environment model of multiple unmanned aerial vehicles; respectively constructing cost functions of energy consumption, path length, time error and obstacle avoidance capability of the unmanned aerial vehicle, and endowing the constructed cost functions with different weights to construct a target function; and taking the constructed target function as a fitness function of an improved sparrow search algorithm, adding a time difference penalty term into the target function, and realizing three-dimensional path planning of multiple unmanned aerial vehicles from different starting point positions to the same target point at the same time. Compared with the prior art, the method has the advantages that the multiple unmanned aerial vehicles can reach the same target point from different starting point positions at the same time, and the capacity of port safety defense for resisting invasion targets and executing complex tasks is improved.

The invention relates to the technical field of fault prediction, in particular to a port safety real-time evaluation system based on the AI technology, which comprises the following steps: step 1, acquiring data of the operation state of energy equipment to obtain various monitoring parameters for predicting equipment faults, step 2, analyzing the various monitoring parameters acquired in the step 1, and determining the fault of the equipment according to the analyzed monitoring parameters. 3, comparing the characteristic parameters of the predicted equipment fault with corresponding threshold values based on the characteristic parameters of the predicted equipment fault in the step 2, and inputting a comparison result into a fault prediction model to obtain a prediction result; step 4, comparing the prediction result with the actual fault condition of the energy equipment to obtain a fault prediction evaluation result, and step 5, transmitting the fault prediction evaluation result in the step 4 to an administrator information terminal for man-machine interaction. According to the method, potential fault hidden dangers can be found in advance, and the accuracy and timeliness of fault prediction are improved.

The invention relates to the technical field of port safety management, and discloses a safety management system and method suitable for a port complex environment, and the system comprises a positioning collection module which employs UWB and RTK technologies to carry out the positioning of the dynamic positions of indoor and outdoor personnel; the spatial modeling module is used for constructing a three-dimensional model of the belt conveyor system based on laser point cloud scanning and a BIM technology, and marking equipment coordinates; the hierarchical management and control module is used for dividing an electronic fence area based on the three-dimensional model; a data fusion module; an intelligent early warning module; and a scheduling execution module. According to the method, the UWB is used for inhibiting steel structure signal attenuation to guarantee indoor continuous tracking, the RTK is used for achieving outdoor centimeter-level positioning, outputting three-dimensional coordinates and mapping the three-dimensional coordinates to a transfer tower floor, the three-dimensional coordinates serve as a core base to be fused into a space-time engine, an electronic fence is driven in real time to trigger dynamic equipment emergency stop control, and a digital human system is linked to automatically dispatch recent security officers to execute closed-loop protection. And linkage regulation and control of personnel and equipment resources are realized.

The invention provides a YOLOv5s-based safety equipment wearing target detection method and system, and the method comprises the steps: firstly obtaining a port safety operation image, carrying out the safety equipment wearing compliance marking according to a preset marking criterion, introducing an SIoU loss function on the basis of a YOLOv5s network model to replace a GIoU loss function in the YOLOv5s network model, and obtaining a new YOLOv5s network model; a new YOLOv5s network model neck network is constructed, a dynamic hierarchy adjustment strategy is combined with an adaptive feature fusion mechanism, the number of neural network layers of an FPN structure in the new YOLOv5s network model neck network is automatically and dynamically adjusted, an improved FPNX structure is obtained, an improved YOLOv5s network model is further obtained, then first-stage small target screening detection and second-stage large target screening detection are sequentially carried out, and a new YOLOv5s network model is obtained. And obtaining the second target detection result containing all the safety equipment in the target image to complete the wearing detection of the safety equipment, so that the detection capability of small targets and large targets can be remarkably improved to adapt to different detection scenes.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent risk hidden danger prediction method for port safety. The intelligent risk hidden danger prediction method for port safety comprises the steps of collecting heterogeneous data of a plurality of data sources from a port, and performing time and space marking on the heterogeneous data; performing feature extraction including time features and space features on the heterogeneous data, and constructing a dynamic space-time knowledge graph based on the extracted features; and inputting the dynamic space-time knowledge graph into a graph neural network to obtain a node feature vector of each node based on space-time knowledge graph topology, and performing risk and hidden danger prediction based on the node feature vectors. In this way, the dynamic space-time knowledge graph constructed based on the time information and the space information of heterogeneous data is combined with the knowledge association of the graph neural network, so that the effective intelligent prediction of the harbor security risk hidden danger can be carried out.

The invention provides a network port security protection system and protection method, the network port security protection system comprises a protection device and a verification assembly, the protection device is provided with a terminal jack and a protection plug, the terminal jack is used for connecting a peripheral terminal, and the terminal jack has the same jack structure as a communication network port and is used for plugging the peripheral terminal; the protection device is provided with a control module, and the control module is connected with the verification assembly and used for controlling the switching state of the protection plug and can obtain equipment information of the peripheral terminal; and the verification component is used for receiving verification equipment information and executing corresponding operation. According to the network port security protection system provided by the invention, the external terminal can be prevented from easily accessing the internal communication network, the abnormal behavior of the external invasion terminal can be quickly identified, and the security protection capability of the internal communication network is improved. The invention also provides a protection method of the network port security protection system.

The invention provides a port security alarm processing method and device, and relates to the technical field of port intelligent security based on computer vision, and the method comprises the steps: inputting a to-be-detected image into an image processing model, and determining a first output result through the target detection processing of the image processing model; performing background modeling on the target detection frame in the first output result and the corresponding image data to determine a contained moving target; obtaining target video data containing a moving target; performing optical flow analysis on the video data to determine a moving track of each moving target; inputting the target video data and the moving track of each moving target into a multi-modal large model, and determining a second output result through the processing of the multi-modal large model; and according to the second output result, determining an alarm processing result of the port security event, thereby effectively reducing the occurrence of misjudgment alarm conditions through dual judgment processing, and improving the accuracy of port security alarm processing.

The invention provides a linkage control method and system for multiple optoelectronic devices. The method comprises the steps that a monitoring area of the optoelectronic devices is divided into a plurality of monitoring sub-areas; acquiring position information of the monitored object; listing the photoelectric devices, containing the position information of the monitored object, in the monitoring area as selectable photoelectric devices; turning the selectable photoelectric equipment to the monitored object, and displaying the monitored object at a monitoring end of the selectable photoelectric equipment; performing parameter calibration on the monitoring area of the photoelectric equipment by using a reference object, and storing a calibration result of the monitoring area in a calibration database; and carrying out parameter calibration on the monitoring sub-region of the photoelectric equipment by using a reference object, and storing a calibration result of the monitoring sub-region in the calibration database. The technical scheme of the invention can be widely applied to the fields of maritime supervision, port security and protection and the like.

The invention discloses a switch port security management method, device and equipment and a storage medium, and relates to the technical field of network management, and the switch port security management method comprises the steps: obtaining port operation data and traffic data of a switch; generating a security policy of the port according to the port operation data and the traffic data; after the security policy is executed, carrying out real-time state monitoring on the port of the switch and determining an abnormal event; and executing a safety response operation according to the type and the severity level of the abnormal event. According to the invention, the security management accuracy of the switch port can be improved.

The invention discloses a port storage tank risk prediction method and system based on machine learning, and the method comprises the steps: obtaining the storage tank data and regional data of a preset port, carrying out the logistics transportation analysis and energy density simulation according to the regional data, carrying out the statistics of historical risk factors of a risk region based on the simulation energy density fluctuation range, and obtaining a risk prediction result; and constructing a criticality matrix according to the historical risk factors and the logistics grid point locations, performing risk assessment on the risk region by using the criticality matrix, inputting a risk assessment result and risk factor items of the storage tank data into a random forest for training, and obtaining a storage tank risk prediction model. And outputting a risk prediction result of the storage area according to the storage tank risk prediction model. According to the method, risk prediction is carried out by utilizing logistics grid division, risk area irregular splitting, energy density simulation and quality detection data of the storage tank, a multi-dimensional decision basis is provided for safe operation of a port, and meanwhile, the method has relatively good interpretability.

The invention discloses a radar-sonar-video fusion association method and system based on cross-modal semantic mapping. The method comprises the steps that modal specific feature vectors are extracted from three heterogeneous sensors including a radar sensor, a sonar sensor and an optical video sensor respectively; constructing a three-branch deep metric learning network, and mapping the heterogeneous features to a unified d-dimensional semantic embedding space; a tripartite graph model is constructed, and semantic similarity and kinematics consistency constraints are comprehensively considered for edge weights; an improved Kuhn-Munkres algorithm is applied to solve the global optimal matching of the tripartite graph; and outputting a fusion target state and matching confidence evaluation. Cross-physical-domain invariant feature representation is automatically extracted through deep metric learning, three-mode global optimal association is achieved in combination with a graph optimization algorithm, the technical problem that cross-physical-domain data fusion is difficult to process through a traditional method is solved, and the method has high accuracy, strong generalization ability and real-time performance in application scenes such as ocean monitoring and port security and protection and is suitable for being used in the field of ocean monitoring and port security and protection. And an effective technical scheme is provided for multi-sensor heterogeneous data fusion.

The invention relates to the technical field of port safety management, in particular to a port loading and unloading production dynamic closed process operation system based on deep learning and an image recognition algorithm, which comprises a port machinery surrounding environment monitoring safety management and control system, a port mobile machinery anti-fatigue driving system and an intelligent scheduling system, the harbor machinery surrounding environment monitoring safety management and control system comprises an electronic fence, an environment information acquisition camera, an edge computing terminal, a display device and an electronic alarm. The anti-fatigue driving system comprises a state monitoring camera, a behavior recognition module and a reminding module. The intelligent scheduling system comprises a cargo identification camera, an edge calculation chip, a cargo identification sensor, a PLC controller, a transportation device and an industrial robot. Through the system, through deep fusion of deep learning and an image recognition algorithm, the safety and management efficiency of port machinery night operation and the reliability and efficiency of anti-intrusion control are improved.

The application relates to a ship berthing behavior prediction and violation early warning method and system based on multi-source data fusion, which comprises the following steps: acquiring a multi-source data set; constructing a multi-source feature set based on the multi-source data set; inputting the multi-source feature set into a behavior simulation model; acquiring port scheduling information and port safety area information of a target port, and performing analysis and comparison based on a risk assessment strategy; when any risk probability in the risk assessment strategy is greater than a corresponding threshold value, a corresponding graded early warning signal is generated. The application solves the problem of large errors of a traditional single-source monitoring system in a harsh environment through multi-source data collaborative sensing, overcomes the limitations of fixed threshold value judgment by introducing a dynamic risk assessment model, realizes intelligent collaboration of berthing operations by fusing port scheduling information, effectively solves the problems of incomplete ship berthing monitoring, inaccurate early warning and poor adaptability in the prior art, and has the effects of improving port safety and operation efficiency.

The invention is a method for enabling security hardening settings to be applied on device ports and VLANs by applying port security controls on network devices in batches for multiple ports when necessary, in order to detect and prevent OSI Layer 2 attacks before they occur.

Access point network equipment has communication ports that allow communications with other network components. Communications cables coupled to ports can be easily removed and replaced with unauthorized devices that have access to the network creating a security breach. Covers placed over the authorized cable connectors can create a physical barrier preventing unauthorized connections to the access point network equipment. When an authorized cable is removed from a port, a link down event can be detected by tamper detection by tamper detection software running on the access point network equipment. When a link down events is detected the tamper detection software can perform various possible security actions to protect the network.

The application discloses a port safety monitoring edge computing task unloading scheduling method based on deep learning, relates to the technical field of port safety monitoring, and comprises the following steps: performing deep learning inference on a port safety monitoring video to extract abnormal behavior semantic features, quantifying a task processing process to extract task data scale features, and respectively constructing semantic risk potential energy values and load scale potential energy values based on the abnormal behavior semantic features and the task data scale features; constructing a potential energy confrontation domain based on the direction difference of the semantic risk potential energy values and the load scale potential energy values in a unified reference space, and judging whether the abnormal behavior semantic features and the task data scale features conflict according to the potential energy confrontation domain. The application solves the problem that the scheduling emphasis direction cannot be adjusted based on the semantic risk and the task scale conflict intensity, and realizes the effects of high-risk task priority response and dynamic optimization of a scheduling strategy.

Access point network equipment has communication ports that allow communications with other network components. Communications cables coupled to ports can be easily removed and replaced with unauthorized devices that have access to the network creating a security breach. When an authorized cable or plug is removed from a port, a link down event can be detected by tamper detection by tamper detection software running on the access point network equipment. When a link down events is detected the tamper detection software can perform various possible security actions to protect the network.

This invention discloses a safety early warning method and system based on port monitoring data analysis, relating to the field of port safety monitoring technology. The method identifies human figures and mobile work equipment in the land-based work area from port monitoring videos and maps them onto a land-based work area plan. Simultaneously, it acquires data on ship entry and exit and cargo loading / unloading in the port's water-based work area, determining the tonnage and cargo volume of each berthed ship to form a ship / cargo parameter group. The land-based work area plans corresponding to ship / cargo parameter groups with a similarity greater than or equal to preset values ​​are integrated and categorized, and the performance characteristics of the human figure and mobile equipment mapping points are analyzed to derive characteristic parameters. These characteristic parameters are then mapped back to the land-based work area plan to form a plan template for analyzing real-time port monitoring data and issuing safety early warnings based on the analysis results. The above technical solution of this invention achieves port safety early warning and improves the safety of port operations.

The application discloses a switch port security management method and device, equipment and a storage medium, and relates to the technical field of network management. The switch port security management method comprises the following steps: obtaining port operation data and traffic data of a switch; generating a security policy of a port according to the port operation data and the traffic data; performing real-time state monitoring on the port of the switch and determining an abnormal event after the security policy is executed; and performing a security response operation according to the type and severity level of the abnormal event. The application can improve the accuracy of switch port security management.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent evaluation method for a harbor potential safety hazard treatment effect, and the method comprises the steps: constructing a harbor safety knowledge graph based on harbor static data and harbor potential safety hazard treatment dynamic data; performing text analysis based on semantic understanding of a port potential safety hazard treatment scheme and treatment records, and converting the text into a structured action sequence; creating a copy of the port safety knowledge graph, and mapping the structured action sequence to the copy of the port safety knowledge graph to obtain a simulation evaluation initial state graph; performing simulation deduction on the simulation evaluation initial state diagram by using Monte Carlo tree search to obtain probability distribution of a deduction result; and determining an evaluation score of the harbor potential safety hazard treatment effect based on each deduction result included in the probability distribution of the deduction results and the probability score thereof. Therefore, the accuracy of the evaluation score is improved.

The present invention discloses an automatic monitoring method and system for port safety hazards and insurance rate adjustment, including collecting safety monitoring data and image data of the port, and preprocessing the safety monitoring data and the image data; performing a safety assessment on the safety monitoring data to obtain a risk score, and correcting the risk score by using the image data to obtain a safety risk value; adjusting the protection rate according to the safety risk value to obtain an adjustment ratio, and constructing a port safety monitoring insurance rate adjustment model according to the adjustment ratio; optimizing the port safety monitoring insurance rate adjustment model, inputting the data to be adjusted into the port safety monitoring insurance rate adjustment model, and outputting an adjustment result. This method can not only improve the accuracy of automatic monitoring of port safety hazards and insurance rate adjustment, but also has good interpretability and can be directly applied to the insurance rate adjustment system.

The present application provides a kind of port structure deformation monitoring method and system, belong to port safety technical field.Utilize GNSS sensor to the position information of port terminal is monitored to obtain the spatial coordinate information of port terminal;According to wharf horizontal coordinate information and wharf vertical coordinate information in spatial coordinate information, based on the first prediction method and the second prediction method, the lateral displacement and settlement displacement prediction of port terminal is carried out;Finally, based on lateral displacement prediction curve and settlement displacement prediction curve, port structure deformation early warning is carried out.The monitoring method of the present application only needs to utilize the spatial coordinate information of port terminal, and the required input data amount is small;Different prediction methods are used to predict the lateral displacement and settlement displacement of port terminal, which can take into account the relationship between algorithm complexity and prediction accuracy;When setting the prediction coefficient of the prediction method, the fluctuation of the data observation value is considered, and the stability and prediction sensitivity of the prediction method are considered.

This invention discloses a comprehensive prevention method for hazardous chemical safety accidents in ports based on WGT (Warranty-Gross Theory), relating to the field of port safety technology. It collects risk influencing factors for different types of hazardous chemical safety accidents occurring in different ports; classifies these risk influencing factors using WSR theory; further filters and grades them using GRA (Gross Response Analysis); calculates the probability of occurrence of risk influencing factors for different types of hazardous chemical safety accidents in ports; and couples the selected risk influencing factors using TSS (Treatise on Risk-Gross Theory) to calculate the probability of occurrence of risk influencing factors for different types of hazardous chemical safety accidents in ports under a three-dimensional risk scenario; then identifies the risk influencing factor index corresponding to the maximum probability of coupled risk for each port in different types of hazardous chemical safety accidents; and formulates comprehensive preventive measures to avoid and reduce the risk of the same type of accident occurring in ports, preventing the same type of accident from recurring in other ports.

The invention relates to the technical field of port safety management, in particular to a ship navigation risk alarm information intelligent identification method and system. According to the method, when entities and relationships are extracted, iterative updating of a graph attention network of directional entity perception is performed on an initial node feature matrix, and a final high-order node feature matrix is obtained; inputting the final high-order node feature matrix into a classifier, and performing entity recognition and relationship classification; in an iterative updating link, a brand-new final high-order node feature matrix is output, and each row in the matrix is a depth feature which is rich in global context information and directional entity perception and customized for entity recognition and relationship classification. And the depth feature is directly input into a subsequent classifier to finish a final extraction task, so that the extraction precision and robustness of the complex alarm statement are remarkably improved.

The disclosure provides an example method for implementing a network policy in a software defined networking environment. The method generally includes receiving a manifest defining a plurality of pods, wherein: for a first pod, the manifest defines a first environment value, a first port number for a first container of the first pod, and a name for the first port number; for a second pod, the manifest defines the first environment value, a second port number for a second container of the second pod, and the name for the second port number; and the manifest defines a security policy applied to a third pod which defines a first egress policy indicating the first environment value and the name; and creating, based on the manifest indicating different port numbers, but the same name, for the different containers of the different pods, separate egress firewall rules for the first and second pods.

The invention discloses a server port security configuration method and device and a server, and relates to the technical field of network services. The method comprises the steps that an open port and a rule port are obtained through configuration, the open port is a port which can be actively accessed, the open port comprises a user login identity verification interface, and the rule port is a port which cannot be actively accessed; obtaining a white list of the rule port based on the white list rule configuration of the rule port, wherein the white list rule of the rule port comprises that the request end passing the verification of the user login identity verification interface of the open port is allowed to access the rule port; the device is a computer readable storage medium in which a program is stored, and the steps in the method are implemented when the program is executed by a processor. The server is obtained according to the configuration of the method; and for the request end which does not pass verification, the rule port is hidden, so that the security of the server port is improved.

The present application provides a kind of breakwater wave force real-time calculation method and system, it is related to port safety monitoring technical field.The method is by wave monitoring sensor array and structural response monitoring sensor network synchronous acquisition wave parameter and structural response data;Based on wave parameter and initial wave force calculation model, obtain initial wave force value;Utilize structural response data through structural mechanics model conversion and numerical difference analysis, obtain error signal;Combined with error signal and environmental state monitoring data, using reinforcement learning algorithm dynamically adjusts model parameter;Finally, based on the model parameter of optimization, recalculate to obtain real-time wave force value.The present application realizes the dynamic correction and self-adaptive optimization of wave force calculation, effectively improves the accuracy and real-time of breakwater safety monitoring.