1315 results about "Area network" patented technology

The invention provides an enterprise employee behavior analysis and safety risk early warning monitoring method and system, and relates to the technical field of enterprise risk management, and the method comprises the steps: collecting employee terminal operation behavior data, building an activity thermal distribution diagram based on office area grid behavior association intensity, and training a behavior evaluation model. And inputting the behavior scoring matrix into a deep neural network to extract target behavior characteristics, calculating an abnormal behavior risk weight coefficient in combination with a department security policy, performing classification and analyzing a risk diffusion probability, and generating a risk situation index to determine an early warning level. Starting a response strategy according to the early warning level, blocking high-risk early warning in real time, tracking associated accounts, establishing a risk association map, identifying potential risk propagation nodes and performing active protection, finally generating an early warning report and feeding back effective protection rules to a behavior baseline model, and realizing continuous optimization of a risk early warning mechanism. And the accuracy and effectiveness of safety risk early warning in the enterprise are improved.

The invention discloses a nuclear radiation index early warning system based on multi-algorithm fusion, and the system comprises an environment data collection module which is used for collecting gamma-ray dose rate data, wind speed data, wind direction data and geographic position information, and constructing a nuclear radiation environment data set; the dose rate prediction module is used for predicting a future change trend of gamma ray dose rate data based on a sparse recurrent neural network model; the anomaly detection module is used for identifying an abnormal mutation point and positioning the position of a high-risk sensor; the diffusion region modeling module is used for calculating a nuclear radiation diffusion influence region; the grid risk evolution module is used for simulating time sequence evolution of a regular grid risk state based on the multi-state cellular automaton; and the fusion judgment module is used for fusing output results and generating a final early warning grade and a spatial risk distribution result. According to the invention, accurate prediction of the nuclear radiation risk and dynamic space early warning are realized, and the early warning accuracy and response efficiency are significantly improved.

A diagnostic system and method for identifying and resolving vehicle Controller Area Network (CAN) bus faults is disclosed. The system connects to a vehicle's data link connector (DLC) and automatically retrieves diagnostic trouble codes (DTCs) from multiple electronic control units (ECUs). It filters the DTCs to identify those related to CAN communication errors, groups repeated DTCs across ECUs to highlight systemic faults, and assigns repair priority based on status, scope, and criticality. A user interface displays prioritized DTCs with definitions, affected systems, possible causes, and repair guidance. Upon technician input, the system re-scans the network to confirm resolution and updates the display accordingly. The invention supports semantic and conceptual recognition of communication faults, enabling robust, language-agnostic analysis. This system reduces diagnostic time, improves repair accuracy, and supports multilingual or manufacturer-specific DTC definitions without requiring external infrastructure or advanced user expertise.

The invention relates to the technical field of RFID smart tags, and discloses an RFID smart tag positioning and tracking printing method and system, and the method comprises the steps: inputting an RFID tag radio frequency signal into a conditional variation automatic encoder for convolution processing and attention weighting, and obtaining an RFID tag global feature vector; establishing a layered sensor node network to perform multi-dimensional acquisition on environmental parameters to obtain an environmental spatio-temporal data set; inputting the global feature vector of the RFID tag and the environment spatio-temporal data set into a deep fusion network for feature fusion to obtain a multi-modal fusion feature; performing sequence modeling based on the multi-modal fusion features to obtain an article trajectory prediction model; the object track prediction model is applied to a target area grid coordinate system for density distribution calculation, a real-time object positioning heat map and a track database are obtained, an efficient object positioning heat map generation and track data management mechanism is established, and real-time position visualization and historical track query are facilitated for the system.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention provides an electric power monitoring system network space surveying and mapping method and device, and the method comprises the steps: initializing a plurality of agents based on the structural features and surveying and mapping demands of a target electric power monitoring system network, and distributing surveying and mapping tasks covering different network regions for each agent; based on the surveying and mapping task, each agent collects network dynamic information of different areas of the target power monitoring system network, and obtains a global dynamic network topology based on the network dynamic information of the different areas; based on the global dynamic network topology, the threat knowledge base and the attack mode base, analyzing potential safety hazards and potential threats of the target power monitoring system network through a deep learning algorithm, and generating a threat positioning result and a vulnerability repair suggestion; and formulating a linkage defense strategy based on the threat positioning result and the vulnerability repair suggestion in combination with the state data of the plurality of regional networks and a cooperation mechanism of the plurality of agents. According to the invention, the efficiency and accuracy of the power monitoring system to deal with complex network threats are improved.

Various implementations include a method of training a road noise cancelation (RNC) system for a vehicle, including: providing inputs to RNC system, the inputs obtained from: a set of ear-mounted microphones on a user, at least one transducer, an accelerometer, a set of cabin microphones in the vehicle, and a controller area network (CAN) bus, the inputs from the set of ear-mounted microphones on the user approximating a signal detected by the ears of the user; adapting a set of parameters in the RNC system defining an estimated signal detected at respective ears of the user based on the inputs; and generating at least one of the following for input during an operating mode of the RNC system: estimated ear microphone signals based on the adapted set of parameters, or a set of projection filters for use in determining an estimated ear signal at the respective ears of the user.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

The invention relates to the technical field of electric power communication, and discloses a district physical topology identification method based on dual-mode communication signal strength, comprising the following steps: S1, building a district network; s2, topology identification is started, self initialization is completed, and necessary environment preparation before topology identification is carried out; s3, adding STA operation participation data one by one until all STA data are added; s4, identification calculation is started, branch calculation is carried out through HPLC signals of neighbor STAs by adopting a mode of selecting a rated threshold value, meter box calculation is carried out through HRF signals of the neighbor STAs, and relevancy ranking is determined; and S5, obtaining a topology result, and ending identification. According to the method, identification of the physical topology of the transformer area can be completed without additional equipment and manual intervention, and the method is safe, convenient and high in practicability. In the recognition process, the method is not affected by load voltage and current changes and the length of a power line, and compared with an existing impedance calculation and time delay calculation scheme, the recognition accuracy is high.

The invention discloses a method and a system for processing route planning data for electric power line inspection. The method comprises the following steps: acquiring line distribution and topographic feature data; gridding the power network region, determining the patrol priority, and generating a region priority sequence; the overall patrol task is decomposed, a plurality of task subsets are generated, one task is allocated to each unmanned aerial vehicle, and each task subset comprises an initial position and a target line section; generating an initial route path based on the topographic features; comprehensively analyzing all initial paths, identifying potential airspace intersections and performing conflict prediction, and generating a conflict-free route path set by performing height hierarchical adjustment on related paths; and calculating predicted flight time and energy consumption of each unmanned aerial vehicle, redistributing boundary tasks according to a load balancing principle, and finally generating a balanced multi-unmanned aerial vehicle cooperative route scheme. According to the method, the safe and efficient multi-machine collaborative inspection route can be automatically generated through systematic task decomposition, conflict prediction and load balancing optimization.

The invention discloses a charging pile load prediction method based on deep learning. The method comprises the following steps: S1, constructing a regional grid and initializing a cellular basic attribute vector; s2, collecting historical load data and external influence factors, and unifying the historical load data and the external influence factors into an input tensor; s3, generating a time continuous evolution state by using an ordinary differential equation modeling module; s4, constructing a state vector at the current moment; s5, the differentiable cellular automaton module executes primary spatial state propagation; s6, performing multiple rounds of spatial propagation iteration, and outputting enhanced state representation; s7, the load prediction module outputs a future multi-moment prediction value; s8, errors are calculated, and end-to-end training optimization model parameters are executed; and S9, outputting a final optimization model as a load prediction method. According to the method, the response capability of the model to sudden disturbance and the stability of multi-region collaborative prediction are remarkably improved, and the method can be widely applied to multiple application scenes such as smart energy management, electric traffic scheduling and urban public infrastructure intelligent optimization.

The invention relates to the technical field related to automatic driving, in particular to a trajectory prediction method based on adaptive interaction and dynamic intention, which comprises the following steps: firstly, constructing a heterogeneous interaction map, dividing a traffic scene into a vehicle grid, an environment grid and a non-driving area grid, and embedding multi-dimensional dynamic features; then dynamically adjusting a region of interest based on the behavior intention of the target vehicle, and extracting a high-correlation interaction subnet; modeling an interaction relationship by adopting a heterogeneous graph convolutional network, and processing the motion characteristics of the target vehicle and the neighbor vehicle through a sub-channel coding strategy; further realizing dynamic intention perception through a double-branch parallel attention architecture, and fusing macroscopic intention and dynamic intention information; and finally, iteratively generating a future trajectory prediction result based on a decoding architecture of a message passing mechanism. The method can effectively improve the long-term prediction performance in a lane changing scene, adaptively captures a dynamic interaction relationship, and improves the adaptability of a prediction system to the behavior intention change of a driver.

The invention provides a vehicle identity anonymous authentication method in a space-air-ground fusion vehicle-mounted network. The method comprises the following steps: carrying out multi-source data acquisition; generating a dynamic area identifier based on the vehicle real-time position data, and performing dynamic updating according to a vehicle moving threshold value; vehicle equipment identification information is acquired, a salted hash value is generated in combination with the dynamic region identifier, and an anonymous certificate containing dynamic effective duration is generated; extracting spatio-temporal data fluctuation parameters according to the environmental perception data, and extracting delay trend parameters and channel stability parameters according to the regional network state data; the weight is dynamically adjusted according to the link type and the time period type, and the dynamic trust quantized value of the vehicle is calculated through weighted summation; differential authentication is executed based on the dynamic trust quantized value of the vehicle and the anonymous voucher, and the differential authentication comprises the steps that lightweight verification is adopted when the vehicle is highly trusted, two-factor authentication is started when the vehicle is low trusted, and link timeout redundancy authentication is combined. According to the method, the trust evaluation accuracy can be improved, and the dynamism of a privacy protection mechanism is enhanced.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention provides an intelligent agricultural management method and system based on image processing, and the method comprises the steps: carrying out the rice-fish symbiotic region network deployment of a rice-fish symbiotic region, carrying out the multi-source data collection of rice, fish and environment based on the rice-fish symbiotic region network, obtaining the rice-fish symbiotic ecological data, carrying out the data preprocessing of the rice-fish symbiotic ecological data, and carrying out the data processing of the rice-fish symbiotic ecological data. Constructing a rice-fish data heterogeneous graph based on the rice-fish symbiotic ecological data after data preprocessing, constructing a rice-fish attention network model, importing the rice-fish data heterogeneous graph into the rice-fish attention network model, performing cross-domain feature fusion on the rice-fish symbiotic ecological data based on the rice-fish attention network model, and obtaining rice-fish associated data. And then performing rice-fish causal reasoning based on the rice-fish association data, generating a rice-fish association knowledge graph, establishing a rice-fish decision model, importing the rice-fish association knowledge graph into the rice-fish decision model, and generating a rice-fish regulation and control decision. Through the method, an accurate and efficient intelligent dynamic regulation and control decision is provided for a user.

The invention discloses an intelligent networked passenger car network system architecture design method based on link redundancy, which comprises the following steps of: constructing a CAN (Controller Area Network) line network module and a vehicle-mounted Ethernet module, and connecting a plurality of control nodes in a passenger car to obtain a connection link; judging connection link connectivity and transmission quality of the plurality of control nodes by utilizing a heartbeat packet mechanism to obtain link quality; and carrying out link switching according to the link quality to obtain the network system architecture of the intelligent networked passenger car. The invention relates to the technical field of intelligent traffic, and solves the technical problems of low error-tolerant rate, insufficient reliability and easy threat to driving safety of the existing single-chain design or double-chain design during link fault.

The invention discloses a simulation platform and a calculation method for computational fluid mechanics, and belongs to the technical field of computational fluid mechanics. In order to solve the problems that precision is insufficient, numerical dissipation is too large and calculation efficiency is low when a traditional method is used for processing the high Reynolds number turbulence or multi-physics coupling problem, a model and boundary conditions are rapidly generated, a grid generation module automatically optimizes a flowing important area grid through an adaptive algorithm, the solving process is accelerated, and the calculation efficiency is improved. Through parallel computing and efficient time integration methods, simulation efficiency of complex transient and steady problems is greatly improved, accurate calculation of critical areas such as a boundary layer and a separation area is ensured through grid encryption and optimization, consistency verification is combined with physical parameters and a control equation, numerical errors and parameter mismatching are avoided, and the method adapts to multi-physical-field complex scenes and is high in practicability. Meanwhile, optimization is carried out by automatically iteratively adjusting input parameters, and accurate and reliable design and research support is provided.

The invention discloses an Ia type super-novelty star spectral parameter rapid inversion method based on a KAN network, and the method comprises the following steps: S1, carrying out data preprocessing, generating simulation spectral data, including spectral lines and parameters, and carrying out the normalization; s2, building a KAN (Kernel Area Network) network model by using a python framework and a pytorch framework; s3, designing a loss function; s4, training the model, and testing the error of each parameter in the stage; s5, performing learning rate adjustment by using an Adam optimizer and a StepLR scheduler in the training process, and optimizing the model; s6, performing Gaussian sampling on an actually measured spectrum, and inputting the actually measured spectrum into the trained KAN model for prediction; s7, analyzing the result, and calculating the standard deviation of the output parameter data; by using the deep learning technology of the KAN network, key physical and chemical parameters are efficiently predicted from the celestial body spectrum, the problems that in the prior art, the calculation amount is too large, and an accurate calculation result cannot be efficiently obtained are solved, and the method has remarkable advantages in the aspects of improving the calculation efficiency and the processing capacity and is suitable for spectrum data analysis in the field of astronomical observation.

The invention relates to the technical field of data processing, and discloses a geographic space object indexing method and device, a storage medium and a computer device.The method comprises the steps that a geographic space object corresponding to a minimum enclosing rectangle located in a single initial regional grid is stored in a regional exclusive R tree corresponding to the initial regional grid, and the geographic space object corresponding to the minimum enclosing rectangle located in the single initial regional grid is obtained; storing the geographic space object crossing the initial region grid in a region sharing R tree; when any one of the initial regional grids is in a dense state, re-dividing the initial regional grids until the divided regional grids are in a sparse state; and in the current latest geographic space grid chart, determining a target area grid according to an index box selection area corresponding to the geographic space object index instruction, and querying and returning a target geographic space object corresponding to the index box selection area by using the area sharing R tree and the current latest area exclusive R tree corresponding to the target area grid in parallel. By reducing the density of the grid and then storing the object by using the R tree, the subsequent indexing efficiency can be improved.

The invention discloses a security check door security check process optimization scheduling system and method based on big data, and belongs to the technical field of intelligent security check, a whole security check area is divided into a plurality of functional areas and mapped into network nodes according to security check process links and physical space distribution, directed edges among the nodes represent passenger flow paths, and the flow paths of passengers are distributed in the network nodes. A weight matrix of edges is calculated through historical flow data, and a security check area network topology model is constructed; fusing ticket business, historical security check and real-time sensor data, constructing a load evaluation model, and calculating a node load index and an influence conduction coefficient; based on passenger ticket business time and historical behavior records, establishing a time urgency index and risk level model, and forming a two-dimensional decision matrix to dynamically divide priorities; constructing and training a congestion propagation model, predicting a future congestion path in combination with priority distribution, and identifying bottleneck nodes; and according to a prediction result, cooperatively implementing a grooming strategy from four dimensions of personnel, channels, equipment and processes.

The invention discloses a field plant data visual management system based on a grid storage architecture, relates to the technical field of data visualization, and improves the field plant inspection efficiency. The method comprises the following steps: establishing a plant data relation tree according to a plurality of pre-stored plant multi-source information data set, establishing a regional visual grid map according to various real-time data, traversing a target index condition in the plant data relation tree according to basic index information, randomly setting a plurality of initial detection grids in the regional visual grid map, and determining the target index condition according to the initial detection grids. The target index condition is matched with each initial detection grid, each initial detection grid is recorded as a target area grid according to a matching result, the basic index information is matched with an adjacent area grid of each target area grid, and then a shortest exploration driving route is divided in the area visualization grid map according to the matching result.

This application relates to the field of international trade, and discloses a remote dynamic monitoring system for import and export commodities based on the Internet of Things, including: a perception layer, which is a multimodal sensor network used to collect environmental parameters and location information of import and export commodities; an edge computing layer, which includes sensor nodes, local gateways and regional gateways, and performs data preprocessing, aggregation, feature extraction and distributed collaborative computing; an intelligent prediction and anomaly analysis layer, which predicts environmental parameter trends and detects anomalies through deep learning models; a communication and data transmission layer, which provides event-driven data transmission strategies and dynamic channel selection functions. By deploying a multi-level collaborative computing framework at the edge computing layer, the upload of redundant data is reduced. At the same time, the regional gateway uses a lightweight machine learning model to reduce the processing burden of the central server, and cooperates with the dynamic threshold adjustment mechanism to further improve the accuracy of data analysis and the adaptability of the system.

A hierarchical-context area network includes a first virtual private network infrastructure context area network for a first virtual private network infrastructure context area in a first virtual private network infrastructure context level that includes a first virtual private network server, a second virtual private network infrastructure context area network for a second virtual private network infrastructure context area in the second virtual private network infrastructure context level that includes a second virtual private network server, wherein the first virtual private network server receives a protocol data unit from a client device and identifies the second virtual private network server, in accordance with a routing control policy defined by the hierarchical-context area network, as a current point of egress for transporting the protocol data unit through the hierarchical-context area network to send to an external device.

The invention belongs to the technical field of video processing, and particularly discloses a video mouth shape matching correction method for cross-language dubbing, and the method comprises the steps: voice and mouth shape data collection, voice recognition and translation, face key point detection, and mouth shape matching and correction. According to the scheme, a phoneme-mouth shape dynamic mapping technology based on multi-modal deep learning is adopted, a facial key point detection technology is utilized to accurately position pixels of a lip region, grid vertexes of the lip region are separated through a dynamic mask generation technology, and accurate mapping from cross-language audio features to target mouth shape actions is realized; facial dynamic parameters are extracted through a 3D facial grid predictor, a moving average potential smoothing technology is combined, a gradual change process of mouth opening amplitude is automatically learned according to weight coefficients of adjacent frames, so that generated lip actions can be fused with facial structural features and historical frame action modes, and meanwhile, a synchronous regularization technology is introduced; and forcibly constraining the synchronization of the generated video and audio.

The invention provides a mutual supply and mutual aid hybrid control method based on a honeycomb-shaped transverse topological framework, and the method comprises the following steps: carrying out the abstract representation of the transverse topological framework of a honeycomb-shaped power distribution network through a graph theory, and enabling each vertex to represent a main transformer substation of each regional grid in the honeycomb-shaped power distribution network, the regional grids are connected through a flexible interconnection device; constructing an effective node set through a screening method based on honeycomb-shaped effective ring topology; and representing the hybrid discrete-continuous action space model through a conditional variation auto-encoder and an embedded table, and embedding an approximate tidal current diagram neural network into a reward function of a parameterized action Markov decision process to realize optimal cooperative mutual supply and mutual aid of the honeycomb transverse topology. According to the method, the overall load balancing rate of the honeycomb-shaped power distribution network can be greatly improved on the basis of meeting the power flow constraint, the safe and optimized operation of the power grid is ensured, and the application of the deep reinforcement learning method in the power grid mixing problem is expanded.

The present disclosure relates to a Controller Area Network, CAN, controller, comprising: an input interface, a transmit data, TXD, interface, and a processing unit, wherein the processing unit is configured to receive via the input interface a data packet comprising a packet priority field, a packet payload field, the processing unit is configured to generate a first CAN frame based on the data packet, such that a first payload field of the first CAN frame represents at least the packet payload field and a first identifier field of the first CAN frame includes, a first identifier part representing predefined data for identifying the CAN controller and a second identifier part representing the packet priority field and / or includes a queue field representing a queue priority for the first CAN frame, and the processing unit is configured to send the first CAN frame via the TXD interface.

The invention relates to the technical field of automatic driving vehicle control, in particular to a drive-by-wire chassis whole vehicle test device and method, which considers the delay compensation of a vehicle steering actuator and comprises an upper computer, a real-time controller, a drive-by-wire chassis vehicle, a combined navigation system and a USB-CAN (Universal Serial Bus-Controller Area Network) acquisition module. The method comprises the following steps that a vehicle in-loop system is built on a rack, and the delay characteristic of a steering actuator is identified through step and sine mixed excitation signals; establishing a vehicle trajectory tracking error model containing delay compensation based on an identification result; deploying a delay compensation model in a real vehicle environment, and verifying trajectory tracking performance; comparing the transverse error and the course angle error before and after compensation, and evaluating the compensation effect. The invention provides a drive-by-wire chassis whole vehicle test device and method integrating delay identification, model injection and real vehicle verification, and an engineering scheme is provided for dynamic rapid calibration and algorithm migration of an automatic driving drive-by-wire chassis actuator.

The invention discloses a large-particle pneumatic conveying pipeline optimal wind speed calculation method based on digital twinning and machine learning, and the method comprises the steps: S1, carrying out the digital twinning modeling of a gas fluid region and large-volume particles, including an overall pipeline fluid model, fluid region grid division, a large-particle model, and a large-particle transportation volume fraction algorithm; s2, a gas-solid coupling numerical simulation experiment is carried out to calculate simulation data, and the optimal conveying wind speed for conveying spherical particles with different volumes and masses is obtained; and S3, fitting simulation data by adopting multiple linear regression analysis in machine learning, establishing a pipeline optimal wind speed calculation model, and determining a high-order term order of an independent variable. According to the method, digital twinborn modeling is conducted on the pneumatic conveying pipeline and the large-size particles, the optimal conveying wind speed for conveying the spherical particles of different sizes and masses is obtained through a gas-solid coupling numerical simulation experiment, and it is avoided that data are obtained through complex, tedious and even difficult-to-execute actual experiments.

The invention is suitable for the technical field of inverters, and provides a parallel control method for inverters, which comprises the following steps: after a plurality of inverters are powered on and a parallel operation task is started, acquiring a time base counting register EPWM count value and a real-time clock RTC time value in the plurality of inverters; and then, based on the EPWM count value and the RTC time value, determining a controller area network (CAN) communication expansion frame of each inverter. And then, according to the CAN communication expansion frame of each inverter, determining a host inverter and a plurality of slave inverters from the plurality of inverters, and allocating CAN communication addresses to the plurality of slave inverters through the host inverter. According to the method, the master-slave relationship of the inverter is determined, and the CAN communication address is automatically allocated, so that the complexity of manually allocating the addresses of the master and the slave is avoided, and the conflict of the communication address is effectively reduced.

An example system including a vehicle having a multi-zone network with a plurality of network zones, each one of the network zones including at least one zone manager, and wherein one of the at least one zone managers includes a central zone manager; and a network management controller, including: a zone implementation circuit structured to interpret a zoned architecture communication scheme; a zone manager command circuit structured to interpret a zone manager command description; and a zone execution circuit structured to provide the corresponding zone manager command description to each of the at least one zone managers; and wherein each of the at least one zone managers is responsive to the corresponding zone manager command description to control communications between end points of the corresponding network zone and end points of at least one other zone of the plurality of network zones.