1747 results about "Multiple node" patented technology

The invention relates to the field of supply chain scheduling decisions, in particular to a supply chain intelligent scheduling decision optimization method and system based on big data driving. The method comprises the following steps: collecting supply chain heterogeneous data streams, extracting abnormal disturbance events, carrying out nonlinear disturbance response mining, and constructing a supply chain disturbance response field; identifying dynamic state parameters of all nodes of the supply chain, analyzing correlation characteristics among the parameters, performing multi-node logistics state transfer evolution, and generating logistics state transfer characteristics of each node; and multi-node delay evaluation is carried out based on the supply chain heterogeneous data flow, global distribution mapping is carried out, and an upstream and downstream logistics state delay characteristic spectrum is constructed. According to the invention, adaptive optimization collaboration between multiple nodes and multiple targets is realized, so that the stability, flexibility and economic benefit of the whole supply chain system are effectively improved.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling method and system based on dynamic load balancing. The method comprises the following steps: acquiring real-time resource state data of a plurality of nodes in the distributed system and demand characteristics of a task to be allocated, generating a dynamic task allocation strategy, allocating the task to a target node for execution, monitoring a resource state of the target node in real time, and triggering task reallocation when the resource state deviates from a preset threshold value. The system comprises a monitoring acquisition module, a task analysis module, a strategy generation module, a scheduling execution module and a resource abstraction module. According to the method, tasks and node resources can be accurately matched, the resource utilization rate is improved, the real-time performance and reliability of the tasks are guaranteed, the overall performance of a distributed system is optimized through heterogeneous resource unified scheduling and a resource reserving and recycling mechanism, and the method is suitable for various distributed computing scenes.

A wireless communication method and system enabling dynamic multi-path data transmission and reception across heterogeneous radio access networks. The method includes selecting between sequential, concurrent, or redundant multipath transmission strategies based on real-time measurements of link quality parameters, including signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), delay, and spectrum availability. The system further enables adaptive transmission power control, modulation scheme selection, and carrier configuration based on physical layer feedback, minimizing interference and optimizing spectral efficiency. Peer-to-peer communication between terminals is supported without base station mediation, reducing RAN load. Multi-base station connectivity is enabled, allowing terminals to simultaneously transmit through multiple nodes and aggregate at the core network. The architecture supports real-time topology control, dynamic link adaptation, and transmission parameter adjustment based on physical-layer constraints.

The invention relates to the technical field of fault diagnosis, in particular to an intelligent monitoring system for a sewage treatment pipeline, which comprises a topological structure analysis module for collecting pipeline network nodes and communication information, calculating the connection number and geometric attributes of multiple nodes, identifying the pipeline length and the connection direction between adjacent nodes, and analyzing the pipeline network nodes; and calculating the length of a loop formed among multiple nodes and a topological structure, analyzing structural stability features, and obtaining topological geometric feature data. According to the invention, by analyzing the geometric and topological data of the sewage treatment pipeline system, the understanding of the stability of the pipeline structure is optimized, so that the accuracy of fault prediction is improved, the flow and pressure changes are monitored in real time, and the response to dynamic adjustment and abnormal phenomena in the system is more sensitive and timely; the flow gradient and the pressure difference are analyzed, so that the recognition capability of an abnormal mode is enhanced, a problem area can be quickly recognized and processed, the operation efficiency is improved, and the environmental risk and the maintenance cost are reduced.

The invention relates to the technical field of intelligent customer service control, and discloses a federated learning driven customer service robot cooperative control method and system. The method comprises the following steps: deploying a local intention recognition model at a plurality of nodes, collecting a user dialogue stream, extracting a semantic behavior track fragment, and generating a behavior feature vector set containing a time sequence and context association; the federal cooperative controller performs periodic aggregation, constructs a cross-node feature alignment mapping table based on trajectory similarity, and generates a global behavior feature distribution map; calculating node feature offset, screening high-contribution-degree nodes in combination with a sparse activation threshold, and allocating aggregation tasks; a knowledge distillation compression model is used at the high-contribution-degree nodes, weight updating parameters are extracted, compensation coefficients are added, and an encrypted updating package is generated; and the federal cooperative controller carries out heterogeneous fusion on the encrypted packet, reconstructs a global intention decision tree and carries out segmentation and distribution, so that efficient cooperation and optimization are realized, and privacy protection and service adaptability are considered.

The invention discloses an industrial equipment fault detection method fusing a complex relation and space-time dependence, and belongs to the technical field of industrial anomaly detection, and the method comprises the steps: constructing a plurality of adjacent matrixes, carrying out the weighted fusion to form an enhanced adjacent matrix, and comprehensively and accurately describing the complex multi-dimensional relation between industrial equipment; designing a spatial-temporal feature extraction module, extracting spatial features in parallel by using a graph convolutional neural network and a random graph attention network, extracting time features through time convolution and a multi-head attention mechanism, and dynamically fusing the spatial-temporal features by means of a gating mechanism to generate graph-level features; a state judgment layer composed of a plurality of node-level binary classifiers and a voting mechanism are adopted to comprehensively judge classification results of all nodes, so that the stability and reliability of judgment of the overall state of the industrial control system are enhanced, and the risk of misjudgment is reduced; the problems of equipment relation modeling and multi-dimensional information fusion are effectively solved, features are extracted and fused more accurately, and the accuracy and adaptability of anomaly detection are improved.

The invention relates to the field of safety protection and emergency management, and discloses an abnormal behavior identification method based on hotel monitoring and dynamic inspection, which comprises the following steps: modeling a hotel area, dividing a monitoring range into a plurality of nodes, and calculating the risk weight of each node based on historical behavior data and real-time collected data; based on the risk weight, generating an optimal inspection path of the inspection robot through a path planning optimization algorithm; and in a multi-robot cooperation scene, the inspection path is dynamically adjusted based on the game optimization model, and inspection resource conflicts are avoided. Through cooperation of the inspection robot, the fixed camera, the path planning and the game optimization module, efficient inspection path planning and abnormal behavior recognition are realized, path selection is optimized, blind areas are avoided, the accuracy of abnormal behavior recognition is improved by adopting a spatial-temporal feature extraction and deep learning model, the game optimization model coordinates cooperation of multiple robots, and the accuracy of abnormal behavior recognition is improved. And path conflicts are reduced, and the system adaptability is enhanced through intelligent path planning and dynamic adjustment.

The invention relates to the technical field of data monitoring, and discloses an intelligent data monitoring method and a monitoring platform, and the intelligent data monitoring method comprises the steps: constructing a dynamic heterogeneous hypergraph model, and representing the complex relation and time sequence evolution characteristics among multi-source data nodes; based on a dynamic heterogeneous hypergraph model, realizing high-order relationship representation and calculation, and obtaining embedded representation of nodes and relationships; performing heterogeneous graph information transmission and aggregation by utilizing embedded representation of nodes and relationships, and capturing complex interaction characteristics among multiple nodes; according to the complex interaction characteristics, an abnormal propagation rate model is established, and accurate prediction of an abnormal diffusion path is realized; based on the abnormal propagation rate model and the abnormal diffusion path, multi-level collaborative anomaly detection is implemented, and collaborative faults across subsystems are identified; according to the invention, the abnormal propagation path can be predicted in advance, the abnormal prediction accuracy is improved, and the system fault response time is advanced.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention relates to the technical field of the Internet of Things, in particular to a short circuit fault monitoring system and method based on the Internet of Things. According to the method, fault mode identification is carried out based on current characteristic waveforms, prediction is carried out based on historical data, current waveform data are captured in real time by using a high-speed sampling current sensor, and abnormal changes in the current waveforms are identified and marked; an RNN deep learning model is constructed, features are extracted from waveforms, the features are compared with known fault modes, potential short circuit faults are recognized, the constructed deep learning model conducts reasoning based on real-time data, abnormal waveform features are quickly recognized, and therefore quick response is achieved, the fault detection time is greatly shortened, and the fault detection efficiency is improved. According to the method, short-circuit faults in a high-speed circuit are effectively handled, power flow data of multiple nodes are processed in real time by adopting a distributed data aggregation and analysis technology, a Bayesian network model is constructed, potential short-circuit faults are identified when power flow is abnormal, and specific nodes and paths where the faults occur are positioned.

The invention relates to the technical field of cluster video stream processing, and discloses a video stream real-time coding and decoding transmission method under a cluster. The method comprises the following steps: firstly, acquiring video stream coding parameter text data, link state time sequence data and equipment performance index data of multiple nodes of a target cluster to form a transmission link data set; semantic analysis is carried out on the coding parameter text data to obtain a coding semantic feature vector, dynamic fluctuation features are extracted from the link state time sequence data to obtain a link fluctuation feature vector, and cross-modal fusion is carried out to generate a fusion transmission feature set; generating an abnormal association degree score set by using a pre-trained multi-layer sensing network model, and obtaining an abnormal source node and an equipment defect type by combining root cause tracing according to the abnormal association degree score set; and finally, generating a dynamic optimization strategy and feeding back to the transmission control system to trigger parameter calibration. According to the method, the abnormal root cause can be accurately traced, the transmission parameters are optimized, and the cluster video stream transmission quality is improved.

The invention discloses a micro-grid dynamic scheduling method based on deep learning, and the method comprises the steps: fusing industrial Internet of Things collection and GIS positioning, and constructing a multivariable original spatio-temporal data set covering multiple nodes; extracting multi-scale features through multi-resolution wavelets and Fourier transform, combining the multi-scale features with a dynamic adjacency matrix, and realizing feature adaptive distribution and nonlinear dynamic modeling by using multi-scale attention gating, graph convolution and a time sequence neural network model; the micro-grid load and state prediction accuracy, the system generalization ability and the abnormal response level can be effectively improved, and powerful support is provided for intelligent scheduling and abnormal analysis.

The invention discloses a digital content tracing system based on a block chain technology, and particularly relates to the technical field of block chains. The tampering risk of the digital content in the distributed storage process is evaluated by analyzing the multi-node data synchronization behavior; the transaction overstock risk judgment module evaluates the transaction overstock and local congestion hidden danger according to the transaction verification rate in the propagation process; and the reliability evaluation module judges the reliability of the digital content tracing chain in combination with the tampering risk and the local congestion hidden danger. The abnormal risk assessment module analyzes operation behavior abnormity through the intelligent contract log, and the conflict possibility assessment module detects consistency deviation of circulation records; and the potential risk assessment module synthesizes the multi-dimensional risk, generates a potential risk assessment result of the tracing chain, and determines whether to trigger an on-chain responsibility node tracing verification mechanism, so that potential hidden dangers in the tracing chain can be comprehensively identified, and an efficient and reliable solution is provided for block chain digital content tracing.

The invention relates to the technical field of edge computing, in particular to a safety production monitoring method based on edge collaboration, which comprises the following steps: collecting multi-node clock pulses, calculating adjacent time differences to form a pulse difference parameter set, carrying out statistical distribution, setting a dynamic tolerance boundary, and constructing an effective deviation parameter set; the method comprises the following steps of: generating a weighted smooth deviation result by combining clock stability and link delay weighted normalization, sorting monitoring data according to a timestamp to identify duplication abrupt change exception, constructing a cross-node exception time sequence by associating nodes and time information, and judging a safety state transition result of equipment. A time difference parameter is constructed through multi-node reference pulse arrival time, a dynamic deviation constraint is formed by combining distribution characteristics, clock stability and link measurement weighted smoothing are introduced, cross-node time reference consistency is kept, operation data unified time sequence rearrangement is carried out, a sudden change event reliably reappears in a time window, and an abnormal identifier is associated with a node identity. And forming a cross-node event evolution link.

The invention provides a local cache resource optimization method and system combined with a node state, and relates to the technical field of cloud storage optimizing.The method comprises the steps that firstly, a state index set, including indexes such as resource use, security levels, survivability and transmission cost, of multiple nodes in a distributed network is obtained; calculating a target cache value of the current node to the target information resource according to the target cache value, generating a local cache decision parameter after correction of a dynamic adjustment factor, if the local cache decision parameter exceeds a node cache trigger threshold value, storing the target information resource into a local cache of the current node and generating a cache resource distribution update instruction, and updating the cache resource distribution update instruction. And adjusting the adjacent node cache synchronization strategy according to the cache resource distribution updating instruction, thereby realizing the cooperative operation of the cache resources, and improving the resource utilization rate and access efficiency of the distributed network.

The invention relates to the technical field of intelligent power distribution system state monitoring, in particular to a cable connection state online monitoring method for a low-voltage cable branch box, which comprises the following steps of: applying a disturbance excitation signal to a connection node of the cable branch box, and acquiring transient voltage, current and phase response parameters of the node in different excitation periods; establishing a mapping relation between the node electrical characteristics and the connection state; performing dynamic reference calibration on the acquired signals based on a multi-node space-time coupling matrix to form an adaptive judgment reference of electrical coupling between nodes; a connection degradation sensitive index is extracted through feature sequence differential calculation, a connection state quantitative evaluation result is generated, and real-time updating and anomaly recognition are carried out according to the state change trend; according to the method, the problems of misjudgment and delay caused by a monitoring mode depending on indirect parameters such as temperature and current are solved, high environmental adaptability and degradation identification precision are achieved, and the operation safety and monitoring reliability of the low-voltage power distribution system are greatly improved.

The invention relates to the technical field of water conservancy monitoring, in particular to a remote monitoring and early warning system for a water conservancy monitoring terminal, which comprises a data acquisition module, an edge calculation module, an information transmission module, a central analysis module and an execution module. According to the method, the accuracy and continuity of water conservancy monitoring data are ensured through the technologies of timestamp detection, interpolation calculation, data cleaning, median filtering and the like; according to the method, abnormal changes in key water conservancy data such as water level, flow and pressure can be identified through real-time data analysis, and abnormal fluctuations of a trend part or a periodic part can be found in time in combination with seasonal decomposition and abnormal value analysis; according to the invention, the problems of bandwidth limitation and uneven network load in the traditional remote monitoring system are solved by dynamically adjusting the communication bandwidth, frequency and priority management. Particularly, under the conditions of multiple nodes and large data volume, data transmission delay and loss can be effectively avoided, and the real-time performance and stability are ensured.

The invention provides an electric power inspection tool body intelligent path optimization method and system based on reinforcement learning, and relates to the field of power supply systems, and the method comprises the steps: building a to-be-inspected region environment model which comprises a plurality of to-be-inspected nodes; according to the historical operation information of the plurality of nodes to be inspected, a risk association relationship among the plurality of nodes to be inspected is determined, an inspection path optimization model based on a deep Q network is constructed, and a reward function of the inspection path optimization model is related to the risk association relationship among the plurality of nodes to be inspected, a real-time risk value of each node to be inspected and inspection energy consumption; determining a real-time risk value of each node to be inspected according to the real-time operation information of the plurality of nodes to be inspected and the risk association relationship; and generating a real-time electric power inspection optimization path according to the real-time risk value of each node to be inspected through the inspection path optimization model, and controlling the electric power inspection robot to perform electric power inspection, so that the electric power inspection efficiency and the real-time performance of responding to the electric power fault risk are improved, and the energy consumption is reduced.

The invention relates to the technical field of enterprise data management, in particular to a distributed collaborative office data processing method and system, and the method comprises the following steps: based on employee task completion records, evaluating various capability processing capabilities of employees through extracting and analyzing the project completion data, feedback frequency and interaction data of the employees, and calculating the task completion data of the employees; the task process of the current project is analyzed, the requirements of the target project for various skills are identified, and a capability and requirement data set is formed; according to the invention, through real-time monitoring and analysis of interaction data and feedback frequency of employees, capability of the employees is accurately assessed and project requirements are matched, reasonable human resource configuration is realized and flexibility of project management is improved, and through dynamic adjustment of data access authority, information security is ensured and a data leakage risk is reduced. Real-time data synchronization and consistency verification guarantee that data is accurate among multiple nodes, the team is helped to identify the reason of low efficiency in combination with analysis of cooperation efficiency, and the cooperation efficiency among team members is enhanced.

The invention provides a fuse slow fusing fault detection method based on phase voltage correlation characteristics. The method comprises the following steps: acquiring three-phase phase voltages of a plurality of nodes at the upstream and the middle and downstream of a line, performing normalization and sliding window processing on the acquired phase voltages, and inputting the phase voltages into a neural network model for training; the fuse anomaly detection method based on the neural network is constructed according to the characteristic that phase voltage may slightly drop when the fuse is slowly fused and by combining priori knowledge of voltage regular change of each node of a line, and a phase voltage reconstruction value is updated in real time by mining nonlinear characteristics among voltage data of each node through the neural network. And finally, according to whether the root-mean-square error between the actual value and the reconstruction value of the phase voltage exceeds an adaptive threshold value, identifying that the fuse is abnormal. Example results show that the method can effectively identify the slow fusing process of the fuse, and a foundation is laid for reliable operation of a protection system and improvement of operation and maintenance efficiency.

The invention discloses a prestressed beam simulation method, system and device based on a vector type finite element, and relates to the technical field of finite element simulation, and the method comprises the steps: building a prestressed beam numerical model through a vector type finite element method; dispersing the prestressed beam numerical model into a plurality of nodes and a plurality of prestressed beam units; calculating a stiffness matrix of each prestressed beam unit; determining a control equation of each node based on the stiffness matrix; and solving the control equation of each node by adopting a central difference method, and updating the state of each node according to the solving result to finish the simulation of the prestressed beam. The method can achieve the simulation of the complex behaviors of the prestressed beam, and is high in calculation efficiency and good in calculation stability.

The invention discloses an intelligent network data security protection method, system and device and a medium thereof, and the method comprises the following steps: obtaining network flow data, monitoring the network flow of the network flow data through a flow collection device and a network monitoring tool, extracting the feature information of a data packet, carrying out the preprocessing based on the feature information of the data packet, and obtaining the network flow data; according to the convolutional neural network and the LSTM, time sequence features are captured, modeling is carried out according to the convolutional neural network, and a relation between devices in the network and a topological structure of a data stream are determined; on the basis of privacy protection and calculation sharing of homomorphic encryption, encryption calculation is carried out through a Paillier encryption algorithm, summation and multiplication operations are carried out on encrypted data, the encrypted data are distributed on multiple nodes, local training is carried out on the basis of a federal learning technology, and the data are obtained. According to the method, deep adaptive learning is combined with the graph neural network, so that the detection precision of an attack mode is improved, particularly modeling of complex attack behaviors is realized, data privacy protection is ensured, and meanwhile, the calculation efficiency is improved.

The invention provides a charging station access node selection method and system considering the vulnerability of a power distribution network, and belongs to the field of charging station site selection. The method comprises the steps that a power distribution network topological structure of a power distribution network is acquired, and the power distribution network topological structure comprises a plurality of power distribution nodes; calculating a plurality of node vulnerability parameter sets of the plurality of power distribution network nodes according to the power distribution node vulnerability index set; based on a Jaya-AHP method and an entropy weight method, performing index weight fusion distribution on the power distribution node vulnerability index set to obtain a vulnerability index weight set; and according to the vulnerability index weight set and the multiple node vulnerability parameter sets, calculating and screening to obtain an optimal access node, and performing charging station access. By accurately identifying the vulnerability difference of each node of the power distribution network, the centralized layout of the charging station in a node area with relatively high vulnerability is effectively avoided, and the access node with low vulnerability and proper geographic position is preferably selected, so that the impact of the charging load on the power grid is reduced.

The invention discloses a management system of a server and the server, relates to the technical field of computers, and realizes efficient interconnection and centralized management between a plurality of computing nodes and a management module by introducing an interface module between the computing module and the management module. Specifically, each computing node comprises a computing unit and a programmable logic device, a plurality of computing nodes are connected through the programmable logic device in the interface module, and the first management module obtains the operation data of each computing node through the interface module, so that the design that one management module manages multiple nodes is realized, the management units can be reduced, and the management efficiency is improved. And the cost is reduced, so that the expandability and the management efficiency of the system are improved.

The invention relates to the technical field of low-altitude Internet of Things, and particularly provides a target situation awareness method and system based on the low-altitude Internet of Things, and the method comprises the steps: firstly obtaining a target awareness data set which is cooperatively collected by multiple nodes in the low-altitude Internet of Things and comprises three-dimensional position data and environment disturbance data, and then carrying out the feature extraction of the target awareness data set, the method comprises the following steps: generating spatial distribution characteristics and continuous change characteristics of a target object, inputting the spatial distribution characteristics and the continuous change characteristics into a pre-trained situation awareness model to generate situation association characteristics, and obtaining current situation attributes and situation development tendency information of the target object through a situation classification layer; and finally, according to the current situation attribute and the situation development tendency information, generating a collaborative awareness instruction containing a target positioning identifier, and sending the collaborative awareness instruction to a low-altitude Internet of Things management terminal, thereby comprehensively and accurately sensing the low-altitude target situation, providing an effective collaborative awareness instruction for the low-altitude Internet of Things, and improving the safety and management efficiency of the low-altitude field.

The invention relates to a knowledge graph construction method and device, equipment and a storage medium. The knowledge graph construction method comprises the steps of obtaining a to-be-processed target text; extracting first text information from the target text according to preset first prompt information; wherein the first text information represents fact information of a plurality of entities in the target text; semantic recognition is conducted on the first text information, and the content type of the target text is determined; defining a plurality of nodes in a knowledge graph and an association relationship between at least two nodes according to the content type; and mapping the first text information into the knowledge graph to obtain a target knowledge graph of the target text. According to the method provided by the invention, the integrity and accuracy of information extraction are remarkably improved, meanwhile, the high-quality knowledge graph conforming to the text content type is constructed, and the precision and practicability of text analysis are improved.

The invention discloses a cross-currency financial transaction real-time settlement and reconciliation system and method. The system is composed of a transaction matching module, a settlement engine module, an intelligent contract module, a reconciliation checking module and a distributed account book module. The transaction matching module completes multi-currency order matching according to a price-time priority rule; the clearing engine module is based on an intelligent contract adjustment algorithm and a plurality of parallel channels; the reconciliation checking module generates a bill abstract for each liquidation result, and constructs a tamper-proof abstract chain; the distributed account book module synchronizes a multi-node balance state through an adjacent weighted aggregation algorithm and verifies abstract consistency; meanwhile, a load balancing and feedback control mechanism is built in, and a clearing path and channel task distribution are dynamically adjusted. According to the invention, second-level matching, high-frequency reconciliation and auditing and tamper-proof accounting management in a multi-institution and multi-currency environment are realized, and an efficient, safe and extensible technical solution is provided for cross-border payment and settlement.

In various examples, data storage is managed using a distributed storage management system that is resilient. Data blocks of a logical block device may be distributed across multiple nodes in a cluster. The logical block device may correspond to a file system volume associated with a file system instance deployed on a selected node within a distributed block layer of a distributed file system. Each data block may have a location in the cluster identified by a block identifier associated with each data block. Each data block may be replicated on at least one other node in the cluster. A metadata object corresponding to a logical block device that maps to the file system volume may be replicated on at least another node in the cluster. Each data block and the metadata object may be hosted on virtualized storage that is protected using redundant array independent disks (RAID).

Embodiments of the present disclosure relate to a method, a device, and a computer program product for determining a node of a decision tree. The method includes determining multiple features of multiple modals corresponding to input information. The method further includes generating a multi-modal feature representation by combining the multiple features of the multiple modals. The method further includes determining a target path in a decision tree that is associated with the multi-modal feature representation, the decision tree comprising multiple nodes. The method further includes determining, in the target path based on the multi-modal feature representation, a target node associated with the input information and used to indicate a question or an answer. This method enables the fusion of feature representations corresponding to input information of different modals to determine a multi-modal feature representation. In this way, it is possible to determine richer and more accurate user intentions.

Methods and apparatus are described for enhanced node communication within a wireless node network having nodes and a server. The method begins with a first node associating with a second node, and the first node capturing relevant node information. The first node transmits the relevant node information to the server via the second node operating as an intermediary for indirect communication with the server. A third node captures relevant node information and transmits the relevant node information to the server without using the second node as an intermediary or bridge to the server. Each of the first node, the second node, and the third node may be wireless transceiver-based nodes and respectively implemented as integrated circuits.