41 results about "Real-time web" patented technology

The invention relates to the technical field of multimedia data processing, and particularly discloses an interactive media intelligent preloading and seamless switching method and system based on a dynamic narrative engine, and the method comprises the steps: carrying out multi-step logic deduction according to a predefined narrative logic structure and real-time playing context data of interactive media, calculating the selected dynamic probability of the media segment corresponding to each subsequent narrative node, outputting a high-probability target media segment list arranged in a descending order according to the probability, and deciding the pre-loading content and the loading priority according to the high-probability target media segment list, the attributes of the segments and the real-time network state; the high-probability target media segment is preloaded to a local cache; in response to a user selection instruction, obtaining a target media segment from a local cache or a network; performing decoder management and / or timestamp alignment and / or visual transition processing on the target media segment to obtain a seamless playing switching result; and the narrative logic can be deeply understood, the user behavior can be intelligently predicted, and zero-perception switching can be realized.

The invention provides a false threat intelligence detection method and system based on dynamic graph comparative learning. Belongs to the technical field of network security. The method comprises the following steps: acquiring a CTI report to construct a CTI heterogeneous subgraph with semantic features; acquiring a communication network log to construct a real-time network behavior graph; fusing the two into a global heterogeneous time sequence diagram based on an IoC anchor point; respectively constructing feature codes of the CTI semantic view and the network behavior view by utilizing the GAT and the time sequence GNN; minimizing comparison loss through a graph contrast learning (GCL) framework, and learning a unified embedding space; and finally, the embedding distance of the CTI to be verified under the double views is calculated to serve as an abnormal score for detection. Through a semantic-behavior cross validation mechanism, the problems that in the prior art, external reputation is relied on, semantic forgery cannot be recognized and local fact validation is lacked are solved, the accuracy and robustness of threat intelligence detection are remarkably improved, and the method is particularly suitable for scenes such as communication operators with high requirements for data privacy.

An IoT-based weighing system to increase baby safety, consisting of: an intelligent cradle that has been specially designed and configured to meet the needs of an infant; a microcontroller configured to receive and process data from a variety of sensors and components integrated into the smart charging station; a variety of sensors integrated into the smart cradle and operationally connected to the microcontroller, the variety of sensors including: an infrared temperature sensor for non-contact detection of the infant's body temperature, a microphone sensor for detecting sounds from the infant, a moisture sensor for detecting wetness in a diaper, a gas sensor for detecting the presence of gases and smoke in the environment, and a pulse sensor for monitoring the infant's heart rate; a Wi-Fi module integrated into the microcontroller, configured to transmit the processed data to the cloud server via the internet; a servo motor that is operationally connected to the microcontroller and configured to generate a rocking motion in the cradle, the servo motor being activated by the microcontroller when the microphone sensor detects crying sounds from the infant; a buzzer that is functionally connected to the microcontroller and configured to trigger an audible alarm; a camera module with a webcam configured to capture a live video image of the infant and transmit this live video image via the WLAN module, wherein the camera module is further configured to enable facial recognition and facial identification for the purpose of identifying unauthorized persons, wherein the live webcam feed is retrieved by the microcontroller to perform facial recognition and facial identification, and the microcontroller sends a notification via the cloud server when the webcam detects an unknown face; and a cloud server configured to store the processed data and send notifications to a caregiver's mobile device.

The invention discloses a method and device for achieving screen projection control based on a WIFI-Display protocol, and relates to the technical field of wireless communication and multimedia transmission, and the method comprises the steps: firstly, building a WIFI-Display connection session between a source device and a receiving end; secondly, a dynamic updating area of the screen is intelligently captured by monitoring a graphic buffer area and using a difference block detection algorithm, and an original screen data stream is only generated for a changing area so as to improve the efficiency; then, dynamically generating coding control parameters by analyzing image motion complexity and real-time network state parameters; and finally, carrying out real-time coding and adaptive network packaging on the data stream based on the parameter to form a screen projection data stream, and sending the screen projection data stream to a receiving end. Through the above mode, the method effectively reduces the bandwidth consumption and the coding calculation amount, and remarkably improves the transmission fluency and the image quality stability of the dynamic screen content in a complex network environment through the adaptive code rate and the packaging strategy.

The invention discloses a real-time network security protection system based on big data analysis. The real-time network security protection system comprises the following steps: collecting real-time network data and constructing a dynamic network diagram data set; constructing a graph state space model based on the dynamic network graph and completing state space initialization; a parallel selective state scheduling mechanism is introduced into the state updating path, parallel analysis is carried out on network node states, and state importance parameters are generated; reconstructing a state updating process according to the state importance parameters, and only executing state updating on the network nodes meeting conditions; performing security judgment on the corresponding communication behavior based on the updated state space representation, and generating a security risk identifier; and generating and issuing a network security protection control instruction according to the security risk identifier, and updating a state scheduling parameter based on execution feedback. According to the invention, through parallel selective state scheduling and state updating reconstruction, the real-time performance and scheduling efficiency of security protection in a large-scale network environment are improved.

The application provides a multi-protocol file transmission state visualization method and system of an intelligent conference terminal, relates to the technical field of data transmission, and through innovative design of protocol identification and priority ordering, in combination with an electrostatic fluctuation correlation coefficient and a buffer influence factor adjustment strategy, can not only dynamically optimize a transmission protocol path, improve transmission efficiency and stability, but also effectively reduce the negative influence of electrostatic disturbance on the transmission process, guarantee the transmission stability of the system in a complex environment; a distributed buffer adjustment strategy is adopted, the optimal file transmission protocol path is selected according to real-time network environment evaluation, the efficient and stable transmission process is ensured, and a visualization interface convenient for users to quickly obtain information is provided, so that the user experience and the actual operation effect of the conference are significantly improved.

The application discloses a CC attack intelligent defense method, device and equipment based on a large language model, comprising: performing traffic semantic translation on captured real-time network traffic to generate a semantic description object including a time sequence behavior sequence and context features; inputting the semantic description object into a domain fine-tuned large language model to perform attack intention reasoning to obtain a threat judgment result, generating a corresponding dynamic defense strategy description based on the threat judgment result; compiling the dynamic defense strategy description into an executable instruction sequence, and issuing the executable instruction sequence to a downstream security device to drive execution to obtain an execution result; based on the execution result, monitoring network traffic situation and business indicators after the downstream security device executes the strategy to generate strategy effect evaluation data; and optimizing the large language model based on whole-link data including the semantic description object, the threat judgment result, the dynamic defense strategy description and the strategy effect evaluation data.

To provide an environment in which a user can safely perform online activities by providing a real-time phishing fraud detection and warning system using a generative model.SOLUTION: The phishing fraud detection system includes means for analyzing the received digital information using a generative model trained using knowledge of security experts and training data obtained from specialized information sources to detect characteristics of the fraudulent activity, means for generating an alert based on the analysis, and means for providing specific safety action advice to the user.SELECTED DRAWING: Figure 1

The application discloses a self-adaptive video stream coding optimization method and device, equipment and product, and relates to the technical field of multimedia communication. The method comprises the following steps: inputting image basic data of a video stream into a video feature extraction model; calculating image feature differences between video frames by the video feature extraction model to obtain target image features; determining user viewing preferences by using a pre-trained binary classifier according to the image basic data and the target image features; and performing coding optimization on a current video by a video stream coding model based on a current network state and the user viewing preferences. The pre-trained binary classifier is used to determine the user viewing preferences, the viewing preferences of different users for different video contents are determined, the viewing preferences of the users and real-time network conditions are combined, the optimization points are considered from multiple angles, the best coding configuration is selected for real-time interactive video streams, and the self-adaptability of the interactive video streams in different scenes is improved, and the user experience is improved.

This invention proposes a method and system for detecting false threat intelligence based on dynamic graph contrastive learning, belonging to the field of network security technology. The method includes: acquiring CTI reports to construct a heterogeneous CTI subgraph with semantic features; acquiring communication network logs to construct a real-time network behavior graph; fusing the two into a global heterogeneous temporal graph based on IoC anchors; constructing feature encodings for the CTI semantic view and network behavior view using GAT and temporal GNN respectively; minimizing the contrastive loss through a graph contrastive learning (GCL) framework to learn a unified embedding space; and finally calculating the embedding distance of the CTI to be verified under the dual views as an anomaly score for detection. This invention, through a "semantic-behavioral" cross-validation mechanism, solves the problems of existing technologies relying on external reputation, being unable to identify semantic forgery, and lacking local fact verification, significantly improving the accuracy and robustness of threat intelligence detection, and is particularly suitable for scenarios with high data privacy requirements, such as telecommunications operators.

The invention relates to the field of network security, in particular to a network anomaly detection method, system and device based on two-dimensional space-time prediction and behavior flow compression and a storage medium. The method comprises the following steps: collecting and preprocessing historical access data in a network to obtain a historical behavior data sequence; based on the historical behavior data sequence, constructing a two-dimensional space-time behavior model including space path prediction and time dynamics prediction; performing behavior flow compression on the collected real-time network access data to generate a real-time behavior fingerprint; extracting an abnormal mode from the historical behavior data based on a normal behavior benchmark defined by the two-dimensional space-time behavior model, and generating an abnormal feature library; constructing and maintaining a dynamic behavior baseline based on a sliding window mechanism by taking the two-dimensional space-time behavior model as a reference; comparing the real-time behavior fingerprint with the dynamic behavior baseline, and matching the real-time behavior fingerprint with an abnormal feature library to obtain a deviation degree; according to the method and the device, the accuracy of a detection result can be improved.

The invention relates to the technical field of video transmission, in particular to a video secure transmission method, device and system in a low-bandwidth scene, and aims to improve the video secure transmission efficiency by defining the influence weight of a historical frame image on a current frame image and combining the characteristic that each motion area is in a continuous high ROI state in the historical frame image. Determining a code rate increasing factor of each motion area of the current frame image; constructing the weight of each motion area of the current frame image in a smooth matrix by using the code rate increasing factor and the space center position of each motion area, constructing the smooth matrix in combination with a Gaussian kernel function, and performing smooth processing on the code rate increasing factor, thereby effectively inhibiting the visual discontinuity phenomenon between the frame images; determining a code rate relative weight of each motion region of the current frame image by jumping maximization of the visual quality of the key region and minimization of the quality between the regions into a target design target function; and in combination with the real-time network bandwidth and the dynamic scaling code rate allocation result, the optimal balance of quality and smoothness is realized under the bandwidth constraint.

This invention discloses a real-time data security monitoring system based on online data modeling, specifically relating to the field of network security technology. The system includes a data acquisition module for collecting real-time network stream data and extracting feature sequences of behavioral patterns; a feature space module for constructing a feature space and generating feature drift mapping sequences and abnormal behavior recognition sensitivity change trend sequences; a shift analysis module for identifying feature variation trends caused by low-intensity probing attack samples; a decay detection module for identifying the degradation characteristics of the real-time security monitoring classifier's ability to recognize real attack events; a risk assessment module for evaluating the risk level of induced drift in the real-time security monitoring classifier based on feature variation trends and recognition ability degradation characteristics; and a reconstruction trigger module for determining the risk level of the real-time data security monitoring system based on the risk level and triggering a security reconstruction mechanism for the real-time security monitoring classifier, thereby improving the stability of the security monitoring system in recognizing persistent induced attack behaviors.

The invention relates to the technical field of multi-screen display, in particular to an information data processing method based on the industrial internet. According to the technical scheme, efficient information data processing is achieved through the industrial internet, the data acquisition module automatically identifies and converts heterogeneous protocols such as Modibus, OPC UA and MQTT into a unified JSON or XML format, and data compatibility and consistency are improved. Data processing is carried out in a layered mode, the edge nodes rapidly process real-time data, anomaly detection and filtering are included, and low-delay response is ensured; the cloud computing platform carries out deep analysis and trend prediction, and a machine learning model is used to enhance insight. The communication module adopts a dynamic path optimization algorithm, adaptively selects an optimal transmission path based on real-time network parameters, improves the reliability and efficiency of data transmission, and supports a standby mode to cope with network faults. The modular interface design allows hot plug and standardized interaction, and the expandability and maintainability of the system are improved.

The embodiment of the invention relates to a processing method and device for planning a short message retransmission strategy based on a network awareness state. The method comprises the following steps: carding a state type set S, defining a cost index C, and configuring a strategy parameter set and a strategy parameter value domain DP corresponding to the strategy parameter set; constructing an index prediction model and a strategy prediction model; constructing a first data set; training an index prediction model based on the first data set; optimizing the optimal strategy configuration corresponding to each state sample by taking the minimum cost index as an optimization target, and constructing a second data set based on all the state samples and the corresponding optimal strategies; training a strategy prediction model based on the second data set; and the strategy prediction model and the index prediction model dynamically plan the short message retransmission strategy of the server according to the real-time network state of the short message sending server and predict the cost index of the planning strategy. According to the invention, the configuration flexibility of the short message retransmission strategy can be improved, and the cost control can be optimized.

The invention relates to the technical field of network security, in particular to a self-adaptive network intrusion detection and response method and system, and the method comprises the steps: collecting real-time network flow data, and collecting the number of login failures of a user and user behavior data; calculating a traffic abnormity threat value according to the real-time network traffic data; calculating an abnormal behavior threat value according to the number of login failures of the user and the user behavior data; and calculating a comprehensive threat value according to the traffic anomaly threat value and the behavior anomaly threat value, and generating a response priority in combination with the asset value. And the comprehensiveness of threat detection is improved. Through multi-source data fusion, a dynamic weight mechanism and asset value association, the accuracy of intrusion detection and the rationality of a response strategy are significantly improved, and the method is suitable for a complex and changeable network security environment.

The application relates to the technical field of Internet of Things, in particular to an Internet of Things device asynchronous reporting method and system for guaranteeing data integrity. The method comprises the following steps: obtaining a hydrological buoy group information set, balancing the contradiction between the timeliness and the integrity of hydrological data collected by the buoy based on the hydrological buoy group information set, and obtaining a data criticality evaluation information set; based on the data criticality evaluation information set, dynamically sorting the hydrological reporting data according to the real-time network state and the data context correlation, and obtaining a reporting priority information set; based on the reporting priority information set, generating an asynchronous reporting scheduling strategy, executing a reporting decision instruction, and outputting a hydrological buoy data reporting log. The method effectively solves the resource competition problem in the hydrological high-priority data reporting process, ensures the timely reporting of abnormal data and complementary data, and improves the integrity and timeliness of the hydrological data.

The application relates to a network traffic dynamic control method and system based on priority scheduling, which comprises the following steps: collecting real-time network traffic data packets and extracting real-time traffic feature vectors, generating a queue based on initial priority weight matching of a historical database; obtaining a basic dynamic adjustment factor by calculating a real-time load state, and generating a timeout risk compensation value according to the proportion of the queuing waiting time and the protocol timeout threshold of low-priority data packets, and superimposing the two to obtain a target priority weight; generating a scheduling instruction set according to the weight proportion and distributing the scheduling instruction set to network equipment for execution; finally, extracting an actual transmission delay according to the returned performance log, calculating the deviation value of the actual transmission delay and an expected delay to generate a weight update parameter, and feeding back the weight update parameter to the historical database to update the matching logic. The application takes into account the global link load balancing and the timeout retransmission risk prevention and control of low-priority traffic, and realizes the precision and adaptive dynamic optimization of the network traffic scheduling strategy.

Embodiments of the present application disclose a file fragment uploading method and device, electronic equipment, computer program and storage medium; the embodiments of the present application obtain the file to be uploaded and file information, and the file information at least includes file size;According to the initial network speed of current network, the file to be uploaded is cut, and a plurality of initial file fragments are obtained;Initial file fragments are sequentially uploaded to the server, and the upload time consumption and upload rate corresponding to the initial file fragments are recorded;According to the upload time consumption and upload rate of the uploaded initial file fragments, the size of the next initial file fragment is adjusted, and the target file fragment is obtained;The target file fragment is uploaded to the server. Therefore, by using the mode of "dynamic fragment uploading+network adaptation", in the file uploading process, the real-time network state is determined according to the uploading situation of the uploaded fragment, and the fragment size is dynamically adjusted accordingly, so as to improve the file uploading success rate and user experience, and be flexibly applied to various large file uploading scenes.

The invention relates to the field of data processing, in particular to a cloud computing data acquisition and transmission method and system of a distributed architecture, and the method comprises the steps: obtaining real-time state information of each data source in a cloud computing environment in response to an acquisition instruction; based on the real-time state information, analyzing the acquisition instruction into a plurality of acquisition sub-tasks, and distributing the acquisition sub-tasks to corresponding acquisition nodes according to the real-time state information; executing the collection subtask through the collection node to obtain original data, and locally processing the original data to generate protocol data; identifying attribute information and a real-time network state of the protocol data, and determining a transmission strategy for each acquisition node according to the attribute information and the real-time network state of the protocol data; and based on the transmission strategy, sending the protocol data to the central node through the acquisition node. The transmission strategy is intelligently determined by integrating the internal attribute of the protocol data and the external real-time network state, and the adaptive process of optimal balance can be made according to the specific situation.

The invention discloses a network traffic abnormity identification method and related equipment, and relates to the technical field of network security, and the method comprises the steps: obtaining a structured traffic feature of real-time network traffic data of a target system; determining an initial anomaly score of the structured traffic feature based on the first baseline threshold and the second baseline threshold; in response to the condition that the initial abnormal score is greater than a preset score threshold value, performing influence propagation simulation in the network resource topology model based on the real-time network flow data to obtain a predicted resource pressure value of the at least one resource node; determining a target anomaly score according to the initial anomaly score and the predicted resource pressure value; and comparing the target anomaly score with a decision score threshold to determine whether the real-time network traffic data is abnormal. According to the method and the device, through a decision-making mechanism of multi-baseline anomaly scoring, resource topology influence simulation and global anomaly rate driving, the network traffic anomaly can be identified more accurately, comprehensively and stably while the processing efficiency is ensured.

This invention discloses a socket-based remote control method and system, relating to the field of remote protocol control technology. The method includes: firstly, acquiring multiple control terminals of the remote protocol control system to obtain multiple user request information, then collecting module call information to obtain corresponding multiple module call information, and then sending it to the server. The server receives the module call information, locates the called module, generates multiple module call instructions, obtains real-time network environment information to generate a network load balancing coefficient, and uses it along with the multiple module call instructions as input information. This is then input into the remote control configuration module for remote call configuration, generating an instruction call sequence to control the multiple module call instructions to respond. This invention solves the problem that existing systems cannot adopt a multi-user mechanism and do not support multiple control terminals and controlled terminals being online simultaneously. It achieves the goal of remote control and file transfer through multiple clients, as well as remote control, management, and maintenance.

This disclosure relates to a method, apparatus, and medium for identifying abnormal network traffic, belonging to the field of network security technology. The method includes: collecting real-time network data packets; processing the real-time network data packets to obtain semantic features and time-series feature indicators; encoding the semantic features using an improved BERT model to obtain high-dimensional semantic vectors, wherein the improvement to the BERT model includes: designing corresponding Tokenizer and Embedding layers for the semantic features; modeling and encoding the time-series feature indicators using a Prophet model to obtain time-series feature vectors; generating multimodal feature vectors; and using the model to detect the multimodal feature vectors to obtain identification results. The reinforcement learning network model uses the DDQN algorithm, and the model includes states, actions, and rewards; the parameters of the network model are updated based on the final reward for the action execution. This solution can accurately identify abnormal network traffic and can continuously adapt to new and unknown attack patterns.

Described herein are systems and methods for providing wager recommendations and wager comparisons based on a player prompt by preparing the input context of a large language model. A system can maintain an indexed data structure comprising wager scenarios for live events, each associated with a respective odds value. The system can receive, from a client device during a session identifying a first wager scenario, a prompt requesting a comparison to the first wager scenario. An input context can be generated using the prompt, data of the first wager scenario, and second wager scenarios retrieved by searching multiple indexes of the data structure. Using the language model and the input context, the system can generate an output message indicating the comparison between the first and second wager scenarios, and provide the output message to the client device in response to the prompt.

The invention relates to the technical field of software updating, in particular to an intelligent door lock overall online upgrading method and system.The intelligent door lock overall online upgrading method comprises the following steps that firmware and chip codes are obtained to construct an upgrading package and an abstract value, task increment difference is compared to allocate a transmission bandwidth block, the upgrading package is issued, and an attenuation rate parameter is calculated based on the time migration rate and a write-in delay difference value; adjusting the upper limit of packet sending interval and retransmission times, decrypting and verifying firmware, distributing decrypted data through an interface, writing the decrypted data into a non-running partition to verify integrity, and generating an upgrade audit log. According to the method, bandwidth optimization scheduling is allocated according to task increment difference, an attenuation rate is calculated by utilizing an offset rate and a delay difference, a packet sending interval is flexibly adjusted to adapt to a real-time network, data security is ensured by combining algorithm decryption and abstract value verification, target interface accurate distribution is identified based on codes, and firmware is written into a non-running partition to realize non-inductive upgrading. The collaborative updating efficiency is improved by summarizing data to generate logs.

The present disclosure relates to a method (300) and a provisioning unit (200) for providing real-time network slice usage metrics. The provisioning unit (200) is commutatively coupled to at least one network slice admission control function (NSACF) server (145). The provisioning unit (200) is configured to receive a request, from a computing device, for providing network slice usage metrics. The provisioning unit (200) is configured to establish a connection with the NSACF server (145), and a at least one data source (150) associated with the NSACF server (145) and extract network slice usage data from the at least one data source (150). The provisioning unit (200) is further configured to process the extracted network slice usage data to generate a plurality of network slice usage metrics. The provisioning unit (200) is configured to categorize the generated plurality of network slice usage metrics slices based on distinct access types.

A malicious traffic identification method and a related apparatus are provided. The malicious traffic identification method may include: determining a receiving time of first alarm traffic; obtaining, according to a preset policy, a plurality of pieces of second alarm traffic corresponding to the first alarm traffic within a target time period, where the target time period is a time period determined based on the receiving time, and a similarity between each of the plurality of pieces of second alarm traffic and the first alarm traffic is greater than a preset threshold; performing feature extraction on the plurality of pieces of second alarm traffic to obtain first feature information; and determining, based on the first feature information, whether the first alarm traffic is malicious traffic. According to this present disclosure, accuracy of malicious traffic identification on a live network can be improved by using a multi-flow traceback method.

The invention relates to a unified management method and device for edge home applications and electronic equipment. According to the method, a natural language instruction of a user can be obtained, network state information of a current home network can be obtained, semantic understanding is performed on the natural language instruction and the network state information through an edge home application management model deployed on home edge equipment, and a task instruction is generated. Wherein the task instruction is used for indicating the target home application executing the task instruction and the to-be-executed task content. And controlling the target family application to execute task content according to the task instruction. Through the setting, unified intelligent driving of the natural language instruction to the home application is realized, and dynamic decision making is performed by fusing the real-time network state, so that the technical problems of application control splitting, non-intelligent interaction and incapability of adaptive scheduling according to the environment in a traditional scheme are solved, and the convenience of home application management is remarkably improved.

A baseline multicast traffic is derived for an SSID from the network traffic statistics using unsupervised machine learning. Responsive to detecting a deterioration in the real-time network traffic statistics for the SSID in relation to the baseline throughput and the baseline multicast traffic, the multicast data rate can be adjusted to match the lowest unicast data rate for the SSID.

The present disclosure discloses an apparatus and method for real-time web-based urban flood simulation. An apparatus for urban flood simulation according to the present disclosure includes a hardware processor and a storage unit connected to the processor to store a digital elevation model of a city, city design information and precipitation information, and at least one computer program configured to perform the method for urban flood simulation. The method for urban flood simulation includes (a) generating simulation zone information and water flow information using the digital elevation model of the city and the city design information, (b) generating flood-related information using the simulation zone information, the water flow information, and the precipitation information, and (c) visualizing and displaying the flood-related information.