36 results about "Mobile traffic" patented technology

The application discloses the field of flow data processing, and proposes a mobile flow classification method based on a multi-scale pyramid token sliding window. Different scale sliding windows are designed based on the pyramid pooling principle to perform multi-granularity segmentation on token sequences, better capture the distribution characteristics and perform embedded representation, and then convert the fragments into fixed-length vector representation through the multi-scale sliding window mechanism, effectively fuse the multi-scale features, and apply LSTM for deep feature extraction, capture the forward and backward time sequence dependence relationship in each sub-sequence fragment, and introduce the attention mechanism in each time step output, and strengthen the key feature and the feature representation of the difficult classification and easily confused category. The application solves the classification boundary ambiguity and performance bottleneck problem caused by the single scale feature extraction and the lack of effective feature fusion strategy in the existing mobile flow classification method, and improves the classification accuracy.

The utility model provides mobile traffic warning equipment, and relates to the technical field of traffic warning equipment. The mobile traffic warning equipment comprises two square frames and four supporting rods, and the four supporting rods are installed among the four corners of the two square frames to form a frame structure. According to the mobile traffic warning equipment, the warning box capable of sliding up and down is arranged in the frame formed by the square frame and the supporting rods, when the equipment needs to be used, the warning box slides to the top of the equipment, and the position of the warning box is fixed through the limiting assembly, so that people in the distance can better observe the warning box, and the use safety of the equipment is improved. When the equipment needs to be moved, the warning box slides to the bottom of the equipment and is matched with a limiting assembly to fix the position of the equipment, and universal wheels at the bottom of the warning box are used for rapidly moving the whole equipment, so that the equipment is convenient to move, and the using effect of the equipment can be further improved; and practical application and operation are facilitated.

The invention provides a mobile network traffic processing method and device based on a general prediction model, and the method comprises the steps: segmenting target mobile network traffic corresponding to a target prediction task based on a number element segmentation unit of the general prediction model, and obtaining a mobile number element sequence; based on a space-time mask mechanism of the general prediction model and a task type corresponding to the target prediction task, performing mask processing on the mobile number element sequence to obtain a mask sequence; and based on a prediction task processing layer of the general prediction model, performing task processing on the target environment data corresponding to the mask sequence and the moving number element sequence to obtain target prediction data corresponding to the target prediction task. According to the method, the problems of task splitting, insufficient model generalization, high engineering deployment complexity and the like of a current mobile traffic prediction method are solved; multiple types of mobile traffic prediction tasks can be processed at the same time, and excellent migration ability and task adaptation ability are achieved; and the generalization performance and robustness of the model under multiple cities and multiple tasks are improved.

The invention belongs to the field of network traffic classification, and discloses a network traffic classification method and system based on dual position coding and a mixed mask mechanism, and the method comprises the steps: segmenting input network traffic according to time slices, and generating structured traffic representation based on a source address, a destination address, a source port, a destination port and a transmission protocol; inputting the stream data subjected to double-position coding into a Transform-based coding model, and introducing a mixed mask mechanism in an attention calculation process to balance the byte dependency relationship in a packet and between packets; and obtaining a classification result output by the coding model, performing judgment according to a preset confidence threshold, if the confidence of the classification result is within a preset confidence interval, executing stream caching and reconstruction processing, otherwise, directly outputting the classification result. According to the method, the accuracy and robustness of encrypted traffic classification and attack detection can be effectively improved, and the method is suitable for Internet of Things traffic, mobile traffic and cross-platform complex network environments.

A solar intelligent collision early warning mobile signal lamp relates to the field of traffic safety. In order to solve the technical defect that a signal lamp capable of realizing effective monitoring and timely early warning is not disclosed in the prior art, the technical scheme provided by the utility model is as follows: the signal lamp comprises a signal lamp main body; the solar cell panel is used for converting solar energy into electric energy; the LED signal lamp module is used for displaying traffic indication signals; the battery compartment is used for storing electric energy converted by the solar cell panel, and an intelligent signal control machine, a sensor data acquisition module and a communication module are arranged in the electronic control cabin; the sensor module comprises a temperature sensor, an acceleration sensor and a GPS positioning module which are respectively arranged at different positions of the main body; the sensor module sends acquired monitoring data to the sensor data acquisition module. The method is suitable for mobile traffic signal lamp safety control work requiring real-time monitoring and remote management.

The utility model relates to the technical field of mobile vehicles, and particularly discloses an adjustable rear wheel expansion device which comprises a device body. A front case; a rear housing; the connecting assembly is arranged between the front shell and the rear shell, one end of the connecting assembly is slidably connected with the front shell in the front-back direction, and the other end of the connecting assembly is fixedly connected with the rear shell; the first rear wheel assembly and the second rear wheel assembly are arranged on the two opposite sides of the rear shell respectively; the expansion assembly can apply a first acting force to the first rear wheel assembly and the second rear wheel assembly, the first acting force enables the first rear wheel assembly and the second rear wheel assembly to move oppositely, and the expansion assembly can apply a second acting force to the first rear wheel assembly and the second rear wheel assembly; the second acting force enables the first rear wheel assembly and the second rear wheel assembly to move relatively. According to the adjustable rear wheel expansion device, the distance between the rear wheels is adjusted, and the performance of the device in the aspects of the stability and the bearing capacity of the rear wheels is improved.

1. The name of the design product: mobile traffic guidance vehicle. 2. The use of the design product: for traffic monitoring and driving guidance. 3. The design points of the design product: in shape. 4. The picture or photo that best shows the design points: perspective view.

The utility model relates to the technical field of support platforms, in particular to a support platform of traffic meteorological mobile observation equipment, which comprises a fixed connecting rod, a lifting rotating structure is connected onto the end face of the fixed connecting rod, an angle adjusting structure is connected onto the lifting rotating structure, a camera is connected onto the angle adjusting structure, and the camera is connected onto the fixed connecting rod. A controller is connected to the end face of the fixed connecting rod, the lifting rotating structure comprises a connecting vertical rod, the connecting vertical rod is connected to the end face of the fixed connecting rod, a movable box body is connected to the side wall of the connecting vertical rod, and a lifting motor is connected to the side wall of the movable box body through a connecting base; according to the utility model, the angle adjusting structure is arranged in the support platform of the traffic meteorological mobile observation equipment, so that the elevation angle of the camera in use can be adjusted by using the driving motor in the angle adjusting structure through the transmission structure, and the monitoring distance can be wider.

This invention relates to the field of traffic data processing and classification, and proposes a mobile traffic classification method and system based on spatiotemporal hybrid representation learning. Through a preprocessing mechanism of feature hierarchical screening and dynamic noise filtering, the quality of the original traffic feature input is optimized. Subsequently, attention-guided feature interaction is added in the micro-fusion stage to strengthen the nonlinear correlation of spatiotemporal features within a single time step. Furthermore, in the macro-fusion stage, a cross-scale feature alignment and temporal-spatial attention interaction mechanism is designed to avoid the limitations of traditional independent modeling. Finally, the classification module is optimized by combining a class-adaptive loss adjustment strategy to adapt to the sample distribution differences of different traffic types. This invention achieves deep spatiotemporal feature fusion and improves the accuracy of mobile traffic classification.

The application belongs to the technical field of mobile traffic prediction, and particularly relates to a cross-domain transfer spatio-temporal integrated network method, comprising the following steps: S1, constructing an integrated learning model, using integrated learning to initially train a basic learner, and combining a spatio-temporal network to capture spatio-temporal features, which is used to overcome the instability and overfitting defects existing in a multi-dimensional spatio-temporal cross-domain environment, wherein the integrated learning model comprises a traffic data set structure, spectral clustering, a long short-term memory (LSTM) and a convolutional neural network (CNN). The application proposes a new transfer learning algorithm, which trains a mobile traffic prediction model from source domain data rich in data, and then migrates the model to a mobile edge network environment with sparse data, so as to improve the prediction accuracy of the model, and at the same time proposes a new transfer mapping framework, that is, learning a reflection function from experience data and optimizing the reflection function to predict when to migrate or how to transfer to obtain the best prediction performance.

To provide a mobile object traffic control system that controls traffic of a mobile object in real space with high accuracy.SOLUTION: A sensor device acquires image sensor data composed of point clouds in the real space. A server device generates a digital twin based on the image sensor data composed of the point clouds in the real space, and generates a map on which a virtual obstacle for controlling the traffic of the mobile object is placed on the digital twin. The mobile object generates a travel route of the mobile object in the real space based on information related to the map including the virtual obstacle, and controls each drive mechanism for travel.SELECTED DRAWING: Figure 3

1. The name of the design product: mobile traffic signal. 2. The use of the design product: the design product is used for the emergency traffic signal command of the vehicle-road cloud integrated new system test road. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention relates to the technical field of traffic signal control, in particular to a mobile traffic signal lamp control method and system based on photovoltaic power generation. The method comprises the following steps: acquiring data of a mobile traffic signal lamp control area, dividing the data into a plurality of control sub-areas, deploying a multi-parameter acquisition device for the mobile traffic signal lamp in each control sub-area, and acquiring multi-parameter data of the current control sub-area in real time; dividing three-level traffic pressure threshold values of the sub-regions, comparing the multi-parameter data to determine the grade of the current control sub-region, and adjusting signal lamp control data of the current control sub-region; linkage of multiple control sub-regions is carried out, master and slave control nodes are dynamically replaced, and multi-region linkage is carried out according to regional traffic pressure; the system comprises a current area multi-parameter acquisition module, a signal lamp three-level dynamic management module and a multi-area linkage module. Through the mode, multi-area dynamic linkage is realized, so that the traffic management efficiency and the environmental protection property are improved, and diversified traffic scene requirements are met.

The invention provides a flow statistical method and electronic equipment, the method is applied to first electronic equipment, a first SIM card is arranged in the first electronic equipment, the first SIM card is associated with a first phone number, the method comprises the following steps: displaying a first interface, and displaying a first control in the first interface; the first control corresponds to a second SIM card associated with the first phone number; the second SIM card is arranged in second electronic equipment; and a selection operation for the first control is detected, a second interface is displayed, and mobile traffic information corresponding to the second SIM card is displayed in the second interface. The mobile traffic use condition of other electronic devices associated with the same phone number can be checked on one electronic device, the user operation is reduced, and the user experience is improved.

A vehicle detection device of an urban traffic intelligent control system disclosed by the present invention comprises a moving seat, a mounting column is mounted at the upper end of the moving seat, traffic lights are mounted on the four sides of the mounting column, a lifting screw is in threaded connection in a threaded groove, a rotating rod is fixed at the bottom of the lifting screw, and the rotating rod is fixed at the bottom of the rotating rod. And the rotating rod is rotationally connected with the inner bottom of the rectangular groove, a mounting plate is fixed to the upper end of the rectangular column, four cameras with adjustable angles are mounted at the upper end of the mounting plate, and the cameras can detect the vehicle. Flexible moving and stable supporting can be achieved, and different detection requirements can be met by adjusting the height and angle of the camera; according to the invention, illegal vehicles can be detected, the defect that an existing mobile traffic light is single in function is overcome, illegal driving behaviors are effectively reduced, the traffic accident probability is reduced, and the traffic safety level and the road traffic efficiency during temporary traffic control are improved.

The invention relates to the field of traffic data processing and classification, and provides a mobile traffic classification method and system based on space-time mixed representation learning, which optimizes the input quality of original traffic features through a preprocessing mechanism of feature hierarchical screening and dynamic noise filtering, and then adds attention-guided feature interaction in a microscopic fusion stage so as to realize the classification of the traffic data. Non-linear correlation of spatial-temporal features in a single time step is enhanced, a cross-scale feature alignment and time sequence-space attention interaction mechanism is designed in a macroscopic fusion stage, limitation of traditional independent modeling is avoided, finally, a classification module is optimized in combination with a category adaptive loss adjustment strategy, sample distribution differences of different flow types are adapted, and the flow rate is improved. According to the invention, deep fusion of spatial-temporal features is realized, and the accuracy of mobile traffic classification is improved.

The utility model discloses a movable traffic signal lamp, which is applied to the field of signal lamps and comprises a shell, an energy storage battery is bolted in the shell, a barrel is welded at the top of the shell, a hydraulic cylinder is bolted in the barrel in a penetrating manner, a fixing part is mounted at the output end of the hydraulic cylinder, and a movable traffic signal lamp is mounted in the fixing part. Four signal lamp units are installed at the middle end of the fixing piece, a through groove is formed in the top of the cylinder body, a fixing frame is connected to the bottom of the shell in a bolted mode, four fixing blocks are arranged in the fixing frame, and a connecting rod and a threaded rod are welded to one side and the bottom of each fixing block respectively. According to the technical scheme, the storage and protection functions can be achieved, attachment of dust in the idle process can be reduced, the safety in the transportation and carrying process can be improved, practicability is high, meanwhile, the stability of the device can be improved, operation is easy and convenient, the contact range between the bottom and the ground is enlarged, and the wind-resistant and anti-toppling effects are improved.

The utility model relates to a temporary mobile traffic signal lamp, which comprises a signal lamp and a mobile platform, and is characterized in that the mobile platform comprises a driving walking part located at the bottom and a driving assembly; the driving walking part comprises two driving walking wheels which are oppositely arranged, and the driving assembly comprises a driving motor used for outputting torque; a driving steering part is arranged on the front side of the moving platform and comprises a steering driving motor, a steering transmission set, a steering inclined strut and a steering wheel. The signal lamp comprises a lamp holder, and the lamp holder is installed on the moving platform in a lifting mode through a lifting assembly. According to the utility model, the traffic lights can be moved and arranged more easily, the labor cost is reduced through a motor driving mode, the height of the traffic lights can be adjusted, and the working requirements of different scenes can be met.

The invention belongs to the field of intelligent traffic and sight prediction, and discloses a sight prediction method based on head dynamics and a first-person view angle video. The method comprises the specific steps that an inertial measurement unit is used for collecting head dynamic data, a GoPro camera obtains a first person view angle video, and sight line coordinates obtained by a wearable eye tracker serve as training labels; after the resolution of the video is reduced, key object features are extracted by using YOLOv5s, optical flow features are calculated by using an optical flow algorithm, and global gray features are extracted and normalized at the same time; two types of data are respectively processed through a video frame encoder and an IMU encoder, and after features are spliced, a prediction result and real coordinates are visualized in a video frame through network training. According to the invention, through multi-modal feature fusion, sight prediction in a micro-mobile traffic scene is realized, a low-cost and high-performance technical scheme is provided for safety early warning and management of micro-mobile traffic, and the method can be widely applied to scenes of riding safety monitoring, traffic flow optimization and the like.

1. Name of the product in this design: Mobile traffic cone (sheet metal). 2. Purpose of this design: for road warning or isolation. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: 3D view 1.

The application provides a transverse movement flow detection method for detecting whether transverse movement flow exists in a to-be-detected flow sequence generated in a target containerization cluster, and the method comprises a preprocessing stage, a first detection stage, a second detection stage and a detection result output stage; wherein the preprocessing stage is used for obtaining a value range corresponding to a target feature and a topology structure of the target containerization cluster; the first detection stage is used for performing maximum and minimum value detection and topology detection; the second detection stage is used for performing flow detection on the to-be-detected flow sequence by using a pre-trained transverse movement detection model; and the detection result output stage is used for performing deduplication processing to obtain a final detection result of the to-be-detected flow sequence. The technical scheme of the application screens transverse movement flow by setting two-stage detection, thereby improving the detection rate and reducing the false positive rate.

The utility model discloses a portable intelligent traffic signboard, and relates to the technical field of traffic management and intelligent control. In order to solve the technical defect that in the prior art, an existing mobile traffic signboard is restricted in temporary traffic management and emergency scenes, the technical scheme provided by the utility model is as follows: the portable intelligent traffic signboard comprises a signboard main body used for displaying variable traffic information; the lower end of the lifting mechanism is fixedly connected to the base, and the upper end of the lifting mechanism is connected with the signboard body and used for driving the signboard body to ascend and descend. Rollers are arranged at the bottom of the movable base; the solar power supply system comprises a solar cell panel installed on the base and supplies power to the signboard body through an electric control system. The protective shell covers the signboard body, the lifting mechanism and the base and is of a sealed structure. The method is suitable for application scenes of temporary traffic control, construction area guidance, accident warning, large-scale activity traffic dispersion and the like.

The application provides a traffic statistics method and an electronic device. The method is applied to a first electronic device. A first SIM card is arranged in the first electronic device. The first SIM card is associated with a first telephone number. The method comprises the following steps: displaying a first interface. The first interface displays a first control. The first control corresponds to a second SIM card associated with the first telephone number. The second SIM card is arranged in a second electronic device. A selection operation for the first control is detected. A second interface is displayed. The second interface displays mobile traffic information corresponding to the second SIM card. The application can check the mobile traffic usage of other electronic devices associated with the same telephone number on one electronic device, reduces user operation, and improves user experience.

Systems and methods for detecting malicious mobile applications using machine learning in a cloud-based system utilize a traffic forwarding technique and a cloud-based Machine Learning (ML) model to assess the security of apps installed on a user device. This architecture enables the cloud-based system to have visibility of user devices, train the ML model in real-time with a vast amount of mobile app data from multiple users, and enforce security on the user devices from the cloud-based system. Advantageously, the ML model is trained with a vast amount of mobile traffic, leading to better accuracy of prediction. The cloud-based system can be multi-tenant (enterprise), have a large user base, be spread over a large geographic area, etc. This provides a great opportunity for training data. Feedback from live production data can be fed back into the ML model.

The application provides a cross-scale urban population movement behavior generation method and model, and the method comprises the following steps: obtaining real first individual movement trajectories and first group movement flows of urban population; using the first individual movement trajectories and the first group movement flows to generate simulated second individual movement trajectories and aggregate them into simulated second group movement flows; comparing the first individual movement trajectories and the second individual movement trajectories, and the first group movement flows and the second group movement flows respectively, and giving a reward signal based on the comparison results; evaluating the degree of conformity of the generated second individual movement trajectories to individual movement patterns and the degree of conformity of the second group movement flows to group flow rules, and giving an advantage function; and updating the parameters of the generator according to the advantage function. The application overcomes the limitation of the simplification description of the existing model, and can more comprehensively capture the complexity of urban movement, including the heterogeneity of individual movement, the interaction between individuals and the complex relationship between individuals and the environment.

The invention provides a mobile traffic generation method and device oriented to geographic characteristics, and relates to the technical field of mobile communication, and the method comprises the steps: obtaining an interest point vector and an interest orientation vector in a signal coverage range of a target base station, and extracting traffic sequence features from a randomly generated time domain traffic sequence, extracting regional environment features from the point-of-interest vector and the point-of-interest vector; and inputting the regional environment features as keys and values and the traffic sequence features as queries into a cross attention module, calculating to obtain corresponding to-be-processed sequence features, and performing multiple denoising operations on the to-be-processed sequence features to generate a mobile traffic sequence. According to the mobile traffic generation method and device oriented to the geographic characteristics provided by the invention, the dependency relationship between the geographic environment and the mobile traffic is adaptively captured by utilizing a condition generation mechanism through a modeling mode driven by the geographic characteristics, and complex network traffic data can be modeled more accurately.

The present application belongs to the technical field of epidemic infection risk assessment, and provides an early epidemic infection risk quantification method based on regional flow. The method comprises the following steps: 1) constructing a dynamic mobile flow perception map using human flow data to obtain the correlation degree between the flow pattern of each region and other regions constructed by dynamic flow; 2) calculating the epidemic infection risk value caused by the flow within the county based on the spatial interaction function model SIF; 3) quantifying the influence of inter-county flow based on the inflow and outflow quantity of the county, generating a county-level dynamic mobile flow perception map to dynamically quantify the cross-county transmission risk; and 4) calculating the infection risk value of the studied county by integrating the inter-county risk quantification and the intra-county risk quantification. The method of the present application comprehensively considers the flow within the region and the cross-regional flow, and can quickly and accurately quantify the early epidemic infection risk of the region.

The invention relates to the field of traffic data processing, and provides a mobile traffic classification method based on a multi-scale pyramid token sliding window, which comprises the following steps: designing sliding windows with different scales based on a pyramid pooling principle, carrying out multi-granularity segmentation on a token sequence, better capturing distribution characteristics, and carrying out embedded representation, then, the fragments are converted into vector representation with a fixed length through a multi-scale sliding window mechanism, effective fusion of multi-scale features is realized, deep feature extraction is performed by applying LSTM, a forward and backward time sequence dependency relationship in each sub-sequence fragment is captured, an attention mechanism is introduced to each time step output, and the time sequence dependency relationship in each sub-sequence fragment is extracted; and feature representation of key features and difficult-to-classify and easy-to-confuse categories is enhanced. According to the method, the problems of fuzzy classification boundary and performance bottleneck caused by neglect of multi-scale information due to single-scale feature extraction and lack of an effective feature fusion strategy in an existing mobile traffic classification method are solved, and the classification accuracy is improved.

The application belongs to the field of network traffic classification, and discloses a network traffic classification method and system based on double position encoding and a hybrid mask mechanism, which comprises the following steps: input network traffic is divided according to time segments, and structured traffic representation is generated based on source address, destination address, source port, destination port and transmission protocol; double position encoded flow data is input into a coding model based on a Transformer, and a hybrid mask mechanism is introduced in the attention calculation process to balance the dependence relationship between bytes in the packet and between packets; the classification result output by the coding model is obtained, and a preset confidence threshold is used for judgment; if the confidence of the classification result is within the preset confidence interval, flow caching and reconstruction processing are performed, otherwise, the classification result is directly output. The application can effectively improve the accuracy and robustness of encrypted traffic classification and attack detection, and is suitable for Internet of Things traffic, mobile traffic and complex network environments across platforms.