2414 results about "Network data" patented technology

The invention provides a dynamic routing selection method and system, electronic equipment and a storage medium, and aims to solve the problem that a routing strategy is difficult to adapt to a dynamically changing network, the method comprises the following steps: a terminal layer collects the state of a terminal and network data, and performs lightweight feature extraction; the edge node layer receives the data of the terminal layer, carries out space-time-semantic feature aggregation, and generates a region-level resource scheduling and routing decision strategy based on fragmented reinforcement learning; the central cloud service layer gathers whole network data, generates a global optimization strategy and issues the global optimization strategy; the edge node layer fuses global optimization and a region-level strategy, and executes dynamic routing selection; and security and privacy protection are provided through the trusted chain layer. According to the invention, adaptive path selection can be realized, the network resource utilization rate is improved, and the network stability is improved.

The invention relates to the technical field of network flow analysis, in particular to a non-intrusive network flow real-time analysis method and system based on an eBPF, and the method comprises the steps: deploying an eBPF program in a kernel mode, and capturing a network data packet entering a kernel protocol stack in real time; identifying an application layer protocol type and analyzing a key field; the structured data is transmitted to the user mode through the annular buffer area; and the user state carries out TCP stream recombination and session-level aggregation statistics on the structured data. According to the invention, on the premise that application codes are not modified, real-time and structured protocol analysis can be carried out on various common application layer protocols in a kernel layer; end-to-end delay is accurately disassembled through a full-link delay disassembling mechanism, and a performance bottleneck link is positioned; the analysis result is output in the form of structured data, second-level performance statistics and abnormal behavior detection are supported, and the operation and maintenance analysis efficiency and the system observability are remarkably improved.

The invention discloses a viscous coarse-grained soil permeability coefficient prediction method based on a physical constraint neural network, and the method comprises the following steps: carrying out an indoor viscous coarse-grained soil seepage test, and establishing a viscous coarse-grained soil permeability coefficient formula considering porosity and grain composition characteristics, further constructing a mixed model containing a physical driving item and a neural network data driving item, forming a complete data set through a numerical simulation technology and literature investigation on the basis of a seepage test, complementarily collecting porosity, grain composition characteristics and corresponding permeability coefficient data of the viscous coarse-grained soil sample, and dividing the complete data set into a training set and a test set; according to the method, optimal hyper-parameters are dynamically searched in combination with Bayesian optimization for model training, a loss function curve and permeability coefficients of the viscous coarse-grained soil under different porosity and grading characteristics are obtained, tests show that high-precision prediction of the permeability coefficients of the viscous coarse-grained soil is achieved, and the problems that a traditional method is insufficient in physical constraint and low in prediction precision are solved.

The invention discloses a crop disease diffusion prediction method and system based on multi-modal fusion, and the method comprises the following steps: S1, collecting and preprocessing an RGB image sequence and a sensor data sequence of a crop growth environment, and generating an RGB image time sequence difference result and a sensor difference result through time difference processing; s2, mapping the RGB image time sequence difference result and the sensor difference result to a shared time sequence space through a time alignment algorithm, and generating a sensor alignment result and an RGB alignment result; s3, an FD-ViT prediction model is constructed; inputting the sensor alignment result and the RGB alignment result into an FD-ViT prediction model for prediction, and generating a prediction result; and S4, generating a disease diffusion thermodynamic diagram and early warning information according to a prediction result. According to the method, RGB image data and sensor network data are fused, a Transform-based time sequence prediction model is constructed, and early recognition and diffusion trend prediction of crop diseases are realized.

The invention relates to the field of intelligent operation and maintenance, and discloses a multi-source network data operation and maintenance system based on micro-service architecture and AI collaboration, comprising the steps of collecting multi-source data of a micro-service system, and performing cleaning, format unification and time alignment on the collected data; based on a data result of the data acquisition module, constructing a micro-service call chain and a dependency graph, embedding a real-time performance index and a log feature in each node, and dynamically updating a service relation graph; carrying out real-time anomaly detection on the multi-source data, and judging the alarm effectiveness in combination with a dynamic threshold and an AI alarm confidence self-learning mechanism; the AI conducts reasoning along the call chain anomaly map, causal relationship reasoning is added, and the anomaly propagation influence range is predicted; and feeding back a root cause positioning result and the optimized alarm information to an operation and maintenance system, optimizing an alarm threshold and decision parameters in combination with historical records, and outputting an updated operation and maintenance decision scheme. The method has the advantage of improving the operation stability of the system.

The invention belongs to the technical field of network communication, and particularly relates to an operation and maintenance network fault solving system and method.The operation and maintenance network fault solving method comprises the steps that a data collection module collects multi-source original network data of target network equipment and preprocesses the multi-source original network data to obtain multi-source standard network data; a semantic reconstruction module identifies non-business purpose fields in the multi-source standard network data and performs semantic reconstruction processing on the non-business purpose fields to obtain a target state set; the fault diagnosis module carries out fault analysis on the target state set and determines a fault type and a root position; and the execution module generates and executes a predictive isolation strategy according to the fault type and the root position, records a fault processing process and updates a dynamic coding rule and a diagnosis weight distribution strategy. According to the invention, the problems of low efficiency and insufficient accuracy caused by dependence on artificial experience and rule analysis in a network fault diagnosis method can be solved.

The invention discloses a cooperative scheduling method for a zero-carbon park complementary energy storage system, and the method comprises the steps: enabling an electric energy quality index to be explicitly incorporated into an optimization target through multi-source resource dynamic modeling and scene prediction, and building a strong coupling relation between a physical constraint and a scheduling decision; the hierarchical execution mechanism gives consideration to global optimization and local quick response, realizes undisturbed switching under abnormal working conditions, forms a prediction-optimization-execution-feedback closed-loop control system, and can accurately describe physical connection and electrical characteristics of a park power grid by establishing a power distribution network equivalent model and acquiring topological parameters, thereby realizing the optimal control of the park power grid. And basic network data is provided for subsequent optimization. By determining the controllable resource set and completing topological mapping, the position and the regulation and control range of each device in the power grid can be determined, and mistaken sending or conflict of instructions can be avoided. An apparent power upper limit constraint and SOC dynamic model is established, overload operation of equipment can be avoided, the energy storage charging and discharging capacity can be accurately represented, and the performability of a scheduling scheme is ensured.

The invention provides a switch security protocol interaction method and device, equipment and a storage medium, and the method comprises the steps: carrying out the protocol type recognition of a received network data frame, and generating first intermediate data according to the recognized protocol type; according to the protocol type identifier of the first intermediate data, converting the format of the network data frame through a protocol mapping rule to generate second intermediate data; determining a security processing strategy corresponding to a preset strategy rule set according to the strategy identification field of the second intermediate data; reconstructing the original content of the second intermediate data into a target data frame conforming to a target protocol format according to a security processing strategy; and according to the security level field and the communication direction of the target data frame, performing path screening through the trusted path list, and determining a target interaction path. According to the scheme, the trusted interaction path can be dynamically screened based on the protocol type and the security level field of the target data frame, and controllable data transmission between switches under security protocol cooperation is realized.

The invention discloses an Internet of Things edge computing task unloading method based on a comprehensive gateway, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring tasks, equipment and network data of Internet of Things equipment, and fusing to generate a unified information view; determining task priorities based on the view by applying a dynamic priority evaluation mechanism, and generating a dynamic priority list; applying the time sequence analysis model to generate short-term prediction data; in combination with the dynamic priority list and the short-term prediction data, multi-objective optimization is performed through a collaborative decision model, and a task unloading strategy is generated; generating feedback data according to the task unloading strategy; and generating an adjustment signal by using the feedback data, and updating the dynamic priority evaluation mechanism and the collaborative decision model. According to the method, the unified information view is generated by fusing the multi-source data, collaborative decision making is carried out in combination with dynamic priority evaluation and short-term prediction data, and the model is continuously optimized by utilizing feedback data, so that self-adaptive, forward-looking and efficient task unloading can be realized.

The embodiment of the invention provides a cross-network data exchange method, electronic equipment, a storage medium and a program product, and the method comprises the steps: receiving transmission data, determining the identification information of the transmission data according to the service identification, timestamp and segment sequence number of the transmission data, and determining the verification value according to the performance requirement, the security requirement and the data integrity; the hash algorithm is obtained by performing hash operation on the segmented data; according to the identification information, judging whether the transmission data is duplicated data or not; judging whether the transmission data is complete data or not according to the verification value; whether the transmission data is lost data or not is judged according to the segment serial number, if yes, a retransmission instruction is sent to the sending end equipment, the data segments which are repeatedly transmitted can be accurately recognized through the unique identification information, redundant storage and processing are avoided, the data repetition problem is solved from the source, then the integrity of the data is verified according to the verification value, and the data transmission efficiency is improved. And the lost data end is judged according to the segment sequence number, so that the problem of data loss is solved.

Systems and methods for generating network data structures from local peripheral signals are disclosed. A system can receive image data from a capture device, where the image data includes a plurality of pixels. The system can determine that the plurality of pixels depict a visual representation of a first data record satisfying a predetermined criterion, the first data record corresponding to a set of data record parameters. The system can generate a set of data from the first data record using a subset of the plurality of pixels of the image data, and can transmit the set of data to at least one server, causing the server to identify a second data record based on the set of data record parameters. The system can present, via a graphical user interface, a content item representative of the second data record received from the server.

The invention relates to the technical field of satellite communication networks and on-board buses, in particular to a data deterministic transmission scheduling method for a satellite-borne time-sensitive network. The method comprises the following steps: constructing a satellite-borne network architecture, connecting each terminal and a bus gateway by a TSN management node to form a unified data bus, distributing priority and monitoring link state; performing feature modeling on the data frames to define six-tuple parameters; designing a hierarchical queue architecture comprising a key task queue, a load data queue and a background traffic queue; a dynamic shaping mechanism is adopted, corresponding shaping devices are arranged for different queues, and parameters are set; the invention further provides an anti-disturbance scheduling strategy which comprises dynamic priority improvement and elastic bandwidth allocation. And finally, performing output scheduling according to a strict priority and a weighted fair queue rule. According to the method, by constructing a unified TSN bus and integrating heterogeneous subsystems, low-delay and low-jitter transmission of key task data is realized, the bandwidth utilization rate of scientific load data is improved, the network reconstruction time is shortened, the anti-interference capability is enhanced, and satellite-borne mixed data stream transmission in a high-dynamic and multi-task scene is effectively optimized.

The invention relates to an underwater wireless sensor network path sensing routing method based on deep reinforcement learning, which comprises the following steps that: firstly, a node constructs and periodically updates a transmission preference model based on local and neighbor node interaction information; secondly, deploying a deep reinforcement learning model at each underwater sensor node to perform distributed routing strategy learning; and finally, generating a global guide vector by the sink node according to the routing path information of the received data packet, reversely spreading the global guide vector to the source node, fusing the global guide vector with a local transmission preference vector of the node to generate a guide reward, optimizing the deep reinforcement learning model, and updating a routing strategy. According to the method, the problems of difference and complexity of underwater transmission tasks can be solved, and the network data transmission efficiency and the overall service quality are improved in combination with local preference and global guidance while the node online learning is kept to adapt to the dynamic underwater environment.

A processing system including at least one processor may obtain a natural language request for a network topology visualization associated with a communication network. The processing system may next generate a prompt based upon the natural language request in accordance with a prompt mapping function, apply the prompt as an input to a generative model to generate a query, and apply the query to a communication network database system to obtain a query result. The processing system may generate the network topology visualization from the query result. The processing system may then present the network topology visualization via at least one display device.

Systems and automated processes are described to provide collection of wireless network status data, such as a 5G network, and to automatically respond to queries regarding the performance of the network. Systems and automated processes may, in response to a request for network performance information, obtain performance and static data from network data sources, analyze the static data to generate validated site data, apply a KPI formula to the performance data to generate KPI data, and generate a network performance report based on the validated site data and KPI data. In addition, the systems and automated processes may use an appropriate machine learning model from a library of models to determine recommended actions to increase network performance and may automatically implement such actions. A top offender system may be implemented to analyze the status data to determine the network elements having the largest negative impact on network performance.

The invention provides a chip interconnection line delay prediction method and device, equipment and a storage medium, and the method comprises the steps: combining geometric design parameters generated in a chip wiring stage with electrical attribute parameters, and generating a distributed RC network model; carrying out electromagnetic field solving on interconnection lines of the distributed RC network model, extracting coupling capacitance parameters of interconnection line segments, and generating a parasitic parameter file; correcting the resistance value and the capacitance value of the parasitic parameter file according to the PVT environment data of the chip interconnection line, and generating target RC network data; inputting the target RC network data and the interconnection line delay value of the parasitic parameter file into a preset deep neural network for training to obtain a first delay prediction result; and fusing the first delay prediction result with a second delay prediction result calculated by the distributed RC network model to generate a delay prediction value. According to the scheme, the accurate delay prediction value can be generated in a complex interconnection wiring environment, and the accuracy of delay prediction is improved.

The invention discloses a method for predicting an abnormal behavior state of an elderly living at home based on digital twinning, and the method comprises the steps: obtaining a social relation network data flow and a non-intrusive energy consumption data flow of the elderly living at home, and constructing a time sequence dynamic social relation graph based on the social relation network data flow, meanwhile, a semantic activity event chain is generated based on energy consumption data flow overload decomposition, cross-modal fusion processing is carried out on the social relation graph and the activity event chain, a composite state vector is generated, and based on a time sequence of the composite state vector, the social relation graph and the activity event chain are subjected to cross-modal fusion processing; and predicting the probability of entering a preset abnormal behavior state (such as social isolation, depression tendency or decline of self-care ability) of the home elderly. According to the invention, the privacy of the user is protected through non-intrusive data acquisition, and the social communication and exclusive activity information of the elderly are fused, so that the transformation from post-event alarm to pre-event early warning is realized, a precious time window is won for guardians or service institutions to take preventive intervention measures, and remarkable social and humanistic values are achieved.

The invention belongs to the field of network security, and relates to a method and device for constructing a network security management and control platform, electronic equipment and a storage medium, and the method comprises the steps: constructing a P-POT-PDRR security system model; constructing an intelligent multi-agent system consisting of a sensing agent, an analysis agent, an execution agent and a collaborative scheduling agent; through multi-source network data acquisition, cleaning, fusion and asset map construction, perception of a network environment is realized. Constructing an AI intelligent analysis and threat identification engine; based on an analysis result and a predefined strategy, risk assessment, attack path prediction and automatic response decision are realized, and the threat disposal time is shortened; and iterating parameters of the P-POT-PDRR security system model by continuously collecting execution feedback and optimizing strategy rules. The collaboration of a safety system is improved; the intelligent level is higher; the threat processing time is shortened, the loss caused by attacks is reduced, and the response efficiency is better; and the method has good expandability and compatibility.

The invention relates to the technical field of network security, and discloses a data desensitization and access control method based on data consanguinity and sensitivity identification, which comprises the following steps of: constructing a data consanguinity graph for describing derivation, circulation and dependency relationships among fields; identifying a sensitivity level of each field based on a field consanguinity context provided by the graph; in response to a data access request of a user, dynamically generating an access control strategy for the target field in combination with a service scene, a user role and the sensitivity level of the target field, the strategy specifying access permission, access denial or access after desensitization; and executing the strategy through a data interceptor deployed in a data access key layer, releasing and shielding the data or outputting the data after real-time desensitization by calling a specified algorithm. According to the invention, accurate identification and dynamic and consistent fine-grained access control of the sensitivity of the network data are realized, and the security protection capability of the network data is improved.

The invention relates to the technical field of earthquake monitoring and early warning, in particular to an earthquake early warning analysis system and method based on intelligent equipment. The system comprises a terminal sensing and initial judgment module which collects data through a sensor array, identifies P-wave features by using a dynamic weight adjustment model and generates an encrypted initial alarm; the adaptive network transmission module is used for establishing a dynamic self-organizing network based on a D2D protocol and realizing multi-hop relay transmission of alarms by electing relay nodes; the edge aggregation and verification module is used for executing space-time matching and cross verification on a plurality of alarms in a specific area, judging the authenticity of an event and generating graded early warning information; and the cloud control fusion and actuarial module is used for fusing the regional event data and the professional network data, executing high-precision positioning and earthquake magnitude calculation, and issuing a final early warning instruction. According to the invention, a high-density sensing network is constructed by using massive intelligent devices, the early warning blind area is effectively eliminated, and faster, more accurate and more robust earthquake early warning is realized.

The invention discloses a water purification method and system for urban and rural integrated water supply, and relates to the technical field of water treatment. The water purification method for urban and rural integrated water supply solves the problems that an existing system is poor in process adaptability, inaccurate in control and low in energy efficiency. Water quality and pipe network data are collected in real time through a sensor; an adjustable folded plate flocculation device is adopted, and the flocculation effect is optimized in combination with turbidity dynamic adjustment; constructing a multi-parameter dosing model, and dynamically dosing according to flow, turbidity and the like; the LSTM neural network is used for predicting the state of a filter layer, and air-water pulse backwashing is intelligently controlled; and adding a composite flocculant to enhance pollutant removal, and realizing water quality prediction and system collaborative optimization by virtue of a random forest algorithm and a digital twin platform. The flocculation efficiency is improved, dosing is accurately controlled, energy consumption and water resource waste are reduced, and an efficient and stable scheme is provided for urban and rural water supply.

Systems and automated processes are described to provide collection of wireless network status data, such as a 5G network, and to automatically respond to queries regarding the performance of the network. Systems and automated processes may, in response to a request for network performance information, obtain performance and static data from network data sources, analyze the static data to generate validated site data, apply a KPI formula to the performance data to generate KPI data, and generate a network performance report based on the validated site data and KPI data. In addition, the systems and automated processes may use an appropriate machine learning model from a library of models to determine recommended actions to increase network performance and may automatically implement such actions. A top offender system may be implemented to analyze the status data to determine the network elements having the largest negative impact on network performance.

The invention provides a computer network data information identification system, which relates to the technical field of computer network security and data processing and comprises an edge cloud collaborative data acquisition module, a multi-modal feature fusion module, a federated learning dynamic model training module and an intelligent decision and response module. The method has the advantages that the edge cloud collaborative architecture is adopted, data preprocessing and feature extraction are conducted on the network edge, the transmission quantity and delay are reduced, and the real-time performance and the processing efficiency are improved; a self-attention mechanism is used for fusing multi-modal features, so that the recognition accuracy is improved; the data privacy security is protected based on a federated learning framework training model; a reinforcement learning algorithm is introduced to optimize federated learning and decision strategies, and the adaptability and intelligence of the system are enhanced; and response abnormal data can be intelligently dispatched according to an identification result to ensure safe and stable operation of the network.

The invention relates to the technical field of fault detection, in particular to a network fault real-time predicting and positioning method and system based on a multi-modal AI small model, and the method comprises the following steps: obtaining node multi-modal behavior data, extracting a delay and state sequence, recognizing a reversal behavior and a consistent trend segment, and tracking behavior continuity. And identifying a path offset structure and a response difference, and screening jump nodes to obtain a network fault prediction and positioning result set. According to the method, a node behavior input set is constructed through combination and association of multi-modal network data in a time dimension and a path structure, the node behavior input set is constructed through time and path sequence fusion of the multi-modal network data, and node fragments consistent in change direction are extracted to focus an abnormal path section. A node behavior sequence is generated by combining behavior continuity tracking, a path offset structure in a fluctuation trend is identified, state asymmetry of topological response is analyzed, offset nodes are screened, and the identification capability of abnormal links in path evolution is enhanced.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention discloses a coal storage and transportation intelligent management method and system, relates to the technical field of intelligent management systems, and aims to solve the problems of lack of environment monitoring, scheduling decision lag, extensive safety response and serious data island in the prior art. The method comprises the following steps: collecting the physical state, environmental parameters and operation behavior data of a coal storage yard in real time through a distributed sensor network; constructing a three-dimensional dynamic digital twinborn model and inputting the model into a time sequence neural network to predict the risk of spontaneous combustion, collapse or dust explosion in the future; and generating a dynamic scheduling instruction in combination with the task priority and the equipment state, and triggering a multi-level security response mechanism. The system comprises a sensor network module, a data fusion modeling module, a risk prediction module, a scheduling optimization module, and a closed-loop control and safety response module. According to the scheme, safety early warning preposition, scheduling decision intelligent optimization and resource energy efficiency collaborative improvement in the storage and transportation process are achieved, and the response capability and operation reliability of the coal supply chain are remarkably enhanced.

The invention discloses a method and device for identifying telecommunication fraud, equipment and a storage medium. The method comprises: in response to a received target user number, obtaining multi-dimensional communication data corresponding to the target user number through a plurality of communication data interfaces, the plurality of communication data interfaces including an operation support system interface, a service support system interface and a network data platform interface; extracting cross-modal fraud features from the multi-dimensional communication data by using a feature recognition model; analyzing the cross-modal fraud features by using a risk assessment model, and determining a risk score of the target user number; and displaying an identification result of the target user number on a front-end interface according to the risk score. According to the method, the big language model is used for carrying out feature extraction analysis on the multi-dimensional communication data, the fraud number can be accurately recognized, and the method has high applicability.

A computing system and method are disclosed for converting network configurations across different platforms using a language model. This system preprocesses a source network configuration from a first network platform, generates a prompt based on the preprocessed source network configuration and additional network data for input to the language model. In response to the prompt, the language model converts the source network configuration into a target network configuration for a second platform, ensuring that common configuration elements are preserved while unsupported protocols are adapted to supported alternatives on the second network platform. The system outputs the converted configuration, facilitating seamless network migration with minimal, or no, manual intervention. Additional features include the ability to receive and process active device states and network diagrams, suggesting alternative solutions for unsupported protocols, validating the target configuration against the second platform's specifications, and interacting with users to refine the conversion based on their input.

The invention discloses a low-carbon scheduling method, system, equipment and medium for an integrated energy system, and belongs to the technical field of low-carbon scheduling, and the method comprises the steps: obtaining power network data, thermal network data and natural gas network data of the integrated energy system, establishing a multi-energy-flow carbon flow dynamic model according to the power network data, the thermal network data and the natural gas network data; calculating the time-varying node carbon intensity of each node based on the multi-energy-flow carbon flow dynamic model; calculating carbon flow betweenness centrality according to the time-varying node carbon intensity and a network topology structure, and identifying a carbon flow propagation key node through the carbon flow betweenness centrality; and establishing a low-carbon optimal scheduling model based on the time-varying node carbon intensity, and determining a low-carbon scheduling scheme of the integrated energy system in combination with the low-carbon optimal scheduling model and the carbon flow propagation key node. The method has the beneficial effects that the carbon emission of the comprehensive energy system can be effectively reduced, and meanwhile, the increase amplitude of the operation cost is reduced.

The embodiment of the invention discloses a water heater constant temperature control method, device and equipment based on environmental parameters and a medium, and relates to the technical field of water heaters. The method comprises the following steps: sending an environment parameter acquisition request to a cloud server; receiving the geographical environment parameters returned by the cloud server; and adjusting parameters used for constant temperature control are generated based on the geographical environment parameters, and the gas water heater is controlled to operate based on the adjusting parameters. The cloud server analyzes the geographic position according to the IP address of the water heater, obtains the environmental parameters and issues the environmental parameters to the local equipment to generate the adjustment parameters. According to the method, the traditional hardware sensor is replaced by the data service, so that the cost and the maintenance difficulty are remarkably reduced, and the system reliability is improved. By integrating real-time weather and pipe network data, the system can deal with environment sudden change in a prospective manner, the crossing from static presetting to dynamic self-adaptive control is realized, and finally a high-precision and high-stability constant-temperature control effect is achieved.