297 results about "Single node" patented technology

The invention provides a model training method, device and system based on federated learning and safety protection, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the current training of a target model based on local training data, and obtaining a current model parameter; the local training data is obtained by performing quantum encryption on the original training data; performing homomorphic encryption on the current model parameter to obtain a current encrypted model parameter; uploading the current encryption model parameter to an aggregation node; and receiving global model parameters distributed by the aggregation nodes, and carrying out next training on the target model based on the global model parameters. According to the method, the device and the system provided by the invention, quantum encryption is adopted in an acquisition stage, and homomorphic encryption is adopted in an interaction stage, so that the privacy of an aggregation process is ensured, and a potential attacker cannot obtain model updating details of any single node; in the collaborative training process, it is ensured that data privacy is strictly protected, data are not attacked or tampered, and the efficiency of multi-party collaborative training is improved.

The invention relates to the technical field of data processing and storage, in particular to an asynchronous verification and batch storage system and method for high-concurrency voucher data. Certificate verification is executed based on the memory snapshot, and verification time consumption is improved from the millisecond level of database IO to the microsecond level of memory calculation; a batch persistence mechanism enables the throughput of the system to be improved by dozens of times, and a concurrent storage scene of ten thousand-level TPS can be easily supported; the voucher storage response time is reduced from a hundred millisecond level to a millisecond level; the response is immediately returned to the user after the verification is passed, the user almost does not need to wait, and the operation experience is smooth; the introduction of the message queue enables the system to have peak clipping and valley filling capabilities, and can deal with burst traffic leisurely; the multi-node cluster deployment supports horizontal capacity expansion, the fault of a single node does not influence the overall service availability, and the system reliability is improved.

The invention relates to a configuration implementation method and system for a large-scale FPGA (Field Programmable Gate Array) interconnection system-on-chip. The system comprises 32 data interconnection FPGA core particles and a control FPGA core particle, the control FPGA is connected with the data FPGA through 32 groups of independent Slave Serial interfaces to realize parallel configuration, and state monitoring is carried out through 32 groups of UART (Universal Asynchronous Receiver / Transmitter) interfaces; configuration data are received externally by adopting an RGMII interface, and an upper computer is connected through a system UART. The method comprises the following steps that: after the FPGA is controlled to be self-started from the Flash, an upper computer issues a configuration data packet through the RGMII; the FPGA is controlled to synchronously drive 32 groups of Slave Serial time sequences to complete parallel configuration; the state is fed back in real time through a dual-mode monitoring mechanism, and timeout fault-tolerant logic is implanted. According to the configuration implementation method and system, the configuration efficiency is improved, interface signals are optimized and reduced, single-node fault isolation is supported, and the system availability is improved.

The invention relates to the technical field of power systems and energy management, and discloses an energy-carbon integrated virtual power plant multi-agent collaborative management and control method and system, and the method comprises the steps: S1, constructing a three-layer control architecture comprising a resource sensing layer, a region coordination layer and a global optimization layer; s2, establishing an energy-carbon coupling state space model, and outputting a target optimization result by taking photovoltaic output, energy storage charging and discharging power, a load demand elastic coefficient and a carbon emission right transaction volume as input variables; s3, generating a scheduling instruction by adopting a rolling optimization strategy; and S4, realizing multi-agent collaboration through a distributed consistency algorithm, exchanging power deviation information by each node based on a local constraint condition, and carrying out iterative calculation until the power deviation of all nodes is less than 1%. A three-layer control architecture is constructed, scheduling lag is shortened through decision making and stage processing of the global optimization layer after preprocessing of the regional coordination layer, the limitation of a single node is broken through through cross-regional coordination, and the overall operation efficiency of the virtual power plant is enhanced.

The application relates to a service scheduling and deployment method for Internet of Things edge computing node resources, which is realized based on an Internet of Things edge computing system. The system comprises a cloud data center, a convergence layer core network and a plurality of access layer switch networks connected in sequence. Each access layer switch network is in communication connection with a plurality of edge computing nodes, and each edge computing node is in communication connection with a plurality of data acquisition terminals. The application comprehensively considers the current running state of the edge computing system, network delay problems and running results after deployment, and performs service scheduling and deployment of Internet of Things edge computing node resources on each real-time data stream computing application. The application is suitable for the decision environment of the real-time computing task load and bandwidth dynamic change of the Internet of Things edge computing node, realizes global optimization from the system level, takes into account the single node load capacity, and realizes the optimal scheme and application planning and deployment decision suggestion of the long-period stable operation and real-time task flexible scheduling strategy.

The invention relates to the technical field of network data security, in particular to a power fgOTN power low-delay secure transmission method. Obtaining a service bearing network topology and link transmission parameters, and constructing a network connection matrix; simulating a flow redistribution process after a fault of a single node, and calculating a topology betweenness centrality index of the substation node; the method comprises the following steps: analyzing payload contents of power business data frames at a fgOTN mapping preceding stage, extracting continuous monitoring features and discrete control features, and calculating a real-time business criticality index of each data frame in combination with a frequency deviation and a voltage fluctuation state of a current network; establishing a two-dimensional decision matrix, and generating a defense strategy according to mapping positions of the topological betweenness centrality index and the criticality index value in the matrix; processing the service data according to the generated defense strategy, and mapping the service data to a fine granularity time slot of the fgOTN; and embedding a security state signaling in the frame overhead, and sending the security state signaling to opposite-end equipment through an optical transmission unit.

The invention discloses a data security storage system and method suitable for a resource-constrained network node, and relates to the technical field of data security storage, and the method comprises the steps: carrying out the homomorphic encryption of to-be-transmitted data through employing an elliptic curve public key, packaging a homomorphic ciphertext pair through employing a session key, obtaining a transmission message, and transmitting the transmission message to a network node; transmitting the transmission message to a relay node; determining a target storage node by calculating the distance between the storage node and the aggregated ciphertext pair, performing secondary packaging on the aggregated ciphertext pair by using a storage key, performing disk falling on the target storage node, generating a Verkle proof, and establishing a data storage index; and the secondary ciphertext load is unpacked by using the storage private key, and decryption is performed by using the elliptic curve private key to obtain plaintext data. According to the method, distributed mapping is carried out, secondary packaging is carried out through a storage key derived by homology, lightweight verifiable disk falling and multi-copy redundancy are completed in combination with Verkle certification, and single-node indexing and certification expenses are reduced.

The invention discloses a distributed sensor abnormal event identification method for intelligent traffic, and particularly relates to the technical field of traffic information perception and identification, and the method comprises the following steps: generating an abnormal information initial confidence value through deploying a sensor node with a confidence value dynamic adjustment function; during fusion processing, independent identification priorities of single node anomalies are reserved; after sequence reconstruction of a unified time reference, judging whether a response condition is met or not in combination with trajectory evolution; if yes, an abnormal response process is triggered, a space-time compensation set is constructed based on historical data of the sensing blind area for verification, and finally an abnormal intervention instruction is output and a traffic scheduling system is linked; according to the method, the sensing sensitivity, the fusion accuracy and the response timeliness of the abnormal information in a complex traffic environment are improved, the problem of an identification blind area that single-point abnormity is covered is avoided, state reconstruction and supplementary verification of the sensing blind area are realized, and the rapid intervention capability of an intelligent traffic system on emergencies is enhanced.

The embodiment of the invention provides a distributed data deduplication method and product. The method comprises the steps of obtaining a data key of data to be subjected to deduplication; distributing the data to be subjected to duplicate removal to the ith sub-bucket according to the mixed hash value of the data key; mapping the mixed hash value according to a hash function attribute value configured for the ith sub-bucket to obtain k position numbers; reading numerical values of positions corresponding to the k position numbers from a bit array which is stored at the current moment and corresponds to the ith sub-bucket to obtain k numbers; if it is confirmed that the k numbers are all first numerical values, it is confirmed that the data to be subjected to duplicate removal belong to duplicate data; and if at least one of the k digits is confirmed to be a second numerical value, confirming that the data to be subjected to duplicate removal belongs to new data. According to the embodiment of the invention, the speed and accuracy of big data deduplication processing are remarkably improved, and the problem of single-node calculation overload (different buckets can be distributed to different calculation nodes) is effectively solved.

The invention discloses a PLC-IoT street lamp electric leakage detection method and system based on edge calculation, and relates to the technical field of smart power grids and Internet of Things. According to the invention, the self-adaptive threshold adjustment algorithm is adopted to dynamically calibrate the electric leakage detection threshold, so that the problem that the traditional fixed threshold method is easy to generate false alarm or missing alarm when the environment changes is effectively solved, and the accuracy and reliability of detection are remarkably improved; through a multi-node collaborative decision algorithm and a data fusion technology, the regional electric leakage risk can be accurately judged, misjudgment caused by local interference in single-node detection is avoided, the robustness of the system is enhanced, through a parameter optimization mechanism and a machine learning regression algorithm, an environment correction coefficient and detection algorithm parameters can be dynamically adjusted, and the reliability of the system is improved. The electric leakage risk trend is predicted, dynamic and accurate evaluation of the electric leakage risk is realized, and the intelligent level and the management efficiency of the system are improved.

The embodiment of the invention provides an inference acceleration method and system based on a large language model. The method comprises the following steps of: firstly, reading a query vector, a key vector and a value vector which are calculated by a matrix processing kernel and are subjected to offset addition and quantization operation from a shared cache by virtue of a multi-head attention kernel in a system, respectively calculating an attention score and target key value data, and outputting data to be decoded; the single-node calculation efficiency is guaranteed through customized hardware pipeline design; then, the query vector is recalculated in the decoding stage, the target key value data stored in the high-bandwidth storage key value cache region in the pre-filling stage are directly reused, and resource waste caused by repeated weight loading is avoided; finally, a model parallel strategy is adopted, calculation tasks of the large language model are distributed to all accelerator nodes, distributed execution of large-scale matrix operation among multiple nodes is achieved, the node number can be flexibly expanded according to the model scale in the scheme, and the expansibility of the reasoning acceleration scheme is remarkably improved while reasoning efficiency is guaranteed.

The invention discloses a multi-source property right data verification method, and relates to the technical field of data processing. The method comprises the following steps: acquiring property right data from different data sources, and converting the property right data into a unified field according to semantics of the property right data and a preset mapping rule; after the knowledge graph is constructed based on the uniform field, the business rule vectors are coded into node attributes of the knowledge graph; and traversing nodes and edges in the knowledge graph through a preset graph traversal algorithm, and verifying the property right data according to the business rule vector to obtain a verification result. According to the method, the association relationship between the knowledge graph integration entities is constructed, and the service rule is encoded into the knowledge graph for verification, so that verification is not limited to single node data any more, cross-node verification can be performed in combination with the association information, and the verification accuracy is improved.

The invention provides a disaster recovery database data synchronization method and device, equipment and a storage medium. Relates to the technical field of data processing. The method comprises the steps that logs of all nodes are written into log queues corresponding to all the nodes; determining that the transaction corresponding to the queue head log in each log queue is a single-node transaction or a distributed transaction; writing the submission transaction identifier and the transaction information of the queue head log into a global playback queue according to the sequence of writing the submission transaction identifier of the single-node transaction and the corresponding transaction information and then writing the submission transaction identifier of the multi-node transaction with the minimum submission transaction identifier and the corresponding transaction information; analyzing the transaction information in the global playback queue to obtain statement blocks; writing the statement blocks into at least one playback queue according to the submission transaction identifiers corresponding to the statement blocks; and sending the statement blocks in the playback queues to the disaster recovery database, and enabling the disaster recovery database to execute the statement blocks so as to synchronize data of the source end database in real time. The problem that disaster recovery switching time is long is solved.

The invention discloses an equipment liquid leakage joint prevention control method and electronic equipment, and relates to the technical field of liquid leakage detection, the method depends on a collaborative architecture of a management controller, an interface module and a computing module, and the liquid leakage joint prevention control is realized through a first logic device of the interface module by the management controller. Reading liquid leakage detection data which is transmitted by a second logic device of each node of the calculation module and is subjected to transmission verification; the key information is extracted from the liquid leakage detection data to confirm whether an effective liquid leakage event occurs or not and the corresponding liquid leakage node, so that early warning and accurate positioning of liquid leakage hidden dangers are realized. After an effective liquid leakage event is confirmed, risk grading is carried out on the nodes by combining a pre-configured grading rule based on a three-dimensional space topological relation between the liquid leakage nodes and other nodes; different processing strategies are executed on nodes of different risk levels, an active joint defense and joint control mechanism is formed, the risk of serial hardware damage caused by single-node liquid leakage can be effectively reduced, and the overall reliability and maintainability of equipment are improved.

The invention discloses an abnormal transaction identification method and device based on edge node collaboration and electronic equipment, and relates to the technical field of distribution.The method comprises the steps that an encrypted transaction feature vector transmitted by an infrastructure end is received, a transaction request is generated based on the encrypted transaction feature vector, the transaction request is sent to a plurality of adjacent edge nodes, and the edge nodes send the transaction request to the infrastructure end; and receiving collaborative verification results of a plurality of adjacent edge nodes on the transaction request based on a preset consensus algorithm, performing abnormal transaction identification based on the collaborative verification results of all adjacent edge nodes by using a gradient boosting decision tree model, and executing the transaction request under the condition that the transaction identification result indicates that the transaction is a normal transaction. And a transaction execution result is fed back to the basic equipment end. According to the invention, the technical problem that the protection capability of a localized single-node detection system is easy to reduce for large-scale network attacks or fault conditions of individual nodes in the prior art is solved.

The invention discloses an automatic advertisement screenshot and container automatic expansion method and system in a virtual desktop environment. A virtual display capable of exporting a DMA-BUF is created by deploying a Virtual Display Driver in a kernel of a host machine, and after Chrome is driven by Puppeteer to complete hardware acceleration rendering in a container, zero-copy desktop-level screenshot is carried out on a shared memory frame buffer by utilizing desktop-script-native; and meanwhile, a Kubernetes CRD unified database task and a declarative API are adopted, a Redis Stream second-level index and a predictive HPA algorithm are combined to realize advanced delta t capacity expansion of Pod, and the P99 queuing delay is ensured to be less than 2s. According to the method, single-node concurrency is improved by 3.2 times without extra hardware cost, CPU occupation is reduced by 45%, WebGL / Flash advertisement fidelity is 100%, and the method is suitable for cloud advertisement verification, brand security and programmed creative monitoring scenes.

The invention discloses a vehicle data query method and a related device, a cloud server is divided into a branch node and a coordination node, the coordination node does not store vehicle data and is responsible for query request verification, authority token generation and query result forwarding, and the branch node stores data and is responsible for query request verification. In the whole query process, verification permissions are not concentrated on a single node, multiple verification of identities and permissions is achieved, the whole vehicle query process is encrypted, the query result is further verified at a user side, and a set of high-security data query method is achieved.

The invention relates to a client work order management method and system based on intelligent template dynamic configuration, and the method comprises the following steps: S1, obtaining a business demand document, historical work order data and a field rule library, extracting all business scene universal fields, and storing the universal fields in the field rule library; s2, constructing template metadata according to the field rule base; s3, dynamically generating a work order instance; s4, after the work order is generated, the whole work order processing cycle is driven according to the flow rule, and each work order node and operation action are dynamically executed according to the template flow chart configuration; and S5, regularly and automatically counting the field utilization rate, common process bottlenecks and high-frequency branches through data analysis, generating template optimization suggestions in combination with user feedback and user satisfaction survey data, automatically classifying new problem descriptions by the system according to a pre-trained machine learning model, recommending the most suitable template, and finally, carrying out data analysis on the new problem descriptions. And a work order dispatching strategy is optimized in combination with historical efficiency data. According to the invention, high flexibility and intelligence of work order management are realized.

The invention discloses a wireless fast ad hoc network node security access and key management method based on quantum random numbers, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a dynamic architecture of the Mesh ad hoc network; the constructed Mesh ad hoc network dynamic architecture is updated, and an optimal communication path is searched; the unpredictability of quantum physics is combined with the real-time state of the network to construct a dynamic master key; fragmenting the master key to generate key fragments; the key fragment is stored in a Mesh ad hoc network; and based on the state of the Mesh ad hoc network, judging whether the key fragment is stored again or not. According to the method, the Mesh ad hoc network architecture is quickly established, and a Mesh network distributed key fragmentation and storage mechanism is combined, so that key loss caused by single node leakage and node failure is avoided, node quick ad hoc access and dynamic key updating are supported, and secure communication under frequent topology change is ensured; and a safety management system covering node access, topology change and fault recovery full life cycle is constructed.

This application discloses a method, apparatus, electronic device, and storage medium for splitting a storage cluster, relating to the field of computer technology, particularly to artificial intelligence fields such as cloud computing, distributed storage, and big data processing. The specific implementation scheme is as follows: The data path of the target resource in the original storage cluster is adjusted so that requests to access the target resource are sent to the target node in the original storage cluster; the target node is network isolated; the target node is restarted, and in response to the target node's target startup parameter being set to the target value, the target node is split from the original storage cluster as a new storage cluster; wherein, the target startup parameter is a parameter used to force a node to start as a new single-node cluster; the target node is removed from the original storage cluster, and the network isolation of the target node is lifted.

The application discloses an intelligent decision generation and optimization system and method based on causal inference, relates to the technical field of intelligent decision generation, constructs a time sequence causal chain for a multi-decision node time sequence scene, acquires causal dependence and a conduction path between each decision node, takes single-node local utility maximization as a target, generates an initial local optimal decision, detects a causal conduction overflow degree of the initial local optimal decision in the time sequence causal chain, determines an overflow position and an overflow strength, establishes a global decision optimization rule by taking inhibition of the causal conduction overflow and guarantee of global collaboration as a constraint, corrects the local optimal decision based on the global decision optimization rule, obtains a global collaborative decision sequence, corrects the decision node based on an iteration cycle, and updates and iterates the global collaborative decision sequence, so that the application generates a decision based on local utility and taking global collaboration as a target, improves decision landing efficiency, and realizes continuous optimization of whole life cycle control cost.

This disclosure discloses a method for evaluating the performance of a distributed training system in a single-node environment, wherein the method can be implemented in a computing device, which may be located in a combined processing unit, which may further include a general interconnect interface and other processing units. The compilation unit interacts with the other processing units to jointly perform user-specified computational operations. The combined processing unit may further include a storage device connected to both the computing device and the other processing units for storing data from the computing device and the other processing units.

The application provides a space-time big data distributed privacy computing system and equipment based on homomorphic encryption, and relates to the technical field of data processing. It comprises a space privacy computing operator, which is used for the computing characteristics of vector data and raster data, and realizes homomorphic encryption computing of vector data and raster data by using TFHE homomorphic encryption algorithm and CKKS homomorphic encryption algorithm respectively; a distributed space privacy computing framework is used, each node of the distributed space privacy computing framework is respectively provided with a space privacy computing operator, and the distributed space privacy computing framework realizes multi-node task parallelization and safe cooperation based on a key management mechanism under a distributed environment and a remote Boolean decryption protocol based on inadvertent transmission; each node in the distributed space privacy computing framework is provided with a GPU acceleration architecture, the GPU acceleration architecture is respectively provided with a corresponding CUDA heterogeneous computing optimization scheme for the Boolean logic operation of vector calculation and the matrix operation characteristics of raster calculation, and the processing efficiency of a single node is improved.

Forechannel connections are established from client-side deduplication libraries at clients to a data protection appliance having a server to be upgraded. The client-side libraries receive requests from backup applications at the clients for file data. The forechannel connections are responsible for carrying the file data between the client-side libraries and the appliance. Server-initiated connections are established from the server to the client-side libraries. The server-initiated connections are responsible for carrying instructions from the server to the client-side libraries. While a transfer of the file data is in progress, the client-side libraries are instructed over the server-initiated connections to pause the transfer for the server upgrade. Requests from the backup applications are allowed to continue uninterrupted. After the server has been upgraded, the forechannel and server-initiated connections are re-established.

The application discloses a kind of power grid knowledge graph construction methods based on multi-source data, belong to technical power grid knowledge graph construction field.The power grid knowledge graph construction method based on multi-source data includes, collecting structured, semi-structured and unstructured data related to power grid, obtain multi-source data;Multi-source data is cleaned, standardized and semantically normalized;Construct knowledge graph ontology framework and define power grid core entity and its attribute and relationship, clear the association and logical relationship between equipment;Using semantic matching and instance fusion technology, the same or similar entity information in different data sources is unified as a single node in knowledge graph;The data after fusion processing is converted into graph form, and visual power grid knowledge graph is obtained.The power grid knowledge graph construction method based on multi-source data converts multi-source power grid data into node and edge form, constructs preliminary graph structure, and introduces the calculation method of relationship strength and weight.

The application discloses a kind of node cooperation storage methods and systems for alliance chain ledger data, method includes: for the node in alliance chain group, the performance of node is calculated and is sorted by strength, and the node of early rotation in order is used as main node;Main node will group inside transaction generation collection and package and submit to consensus network;Main node synchronously obtains new block, according to the hash data of block number, the block is determined corresponding by first mapping PG;The straw value of corresponding node is obtained by using pseudo-random hash algorithm to PG number, node ID and the weight of each node copy number;The main account book and the second mapping of sub-account book are stored to the different node of maximum straw value obtained each time. By the technical scheme of the application, the storage pressure of a single node is reduced, the storage performance of alliance chain is improved, the transaction query efficiency of block chain is improved, and the management efficiency of node and block is improved.

The embodiment of the invention provides a data detection method and a related device, and the method comprises the steps: mapping collected domain name system data to a virtual table, and generating a temporary analysis view; detecting the domain name system data in the temporary analysis view based on a time constraint condition and a node aggregation query rule, and determining abnormal nodes in the domain name system data; and for each piece of domain name system data, determining a data detection result based on the type of the abnormal node and the proportion of each type of abnormal node in the total node. According to the method, the dynamic threshold algorithm based on the proportion is introduced, and the data detection result is determined based on the types of the abnormal nodes and the proportion of each type of abnormal nodes in the total node, so that false alarm and missing alarm caused by abnormity or short-term fluctuation of a single node can be effectively reduced, and the detection accuracy is improved.

The invention relates to the field of intelligent card punching and scoring, and discloses a high-altitude exploration card punching and scoring system based on NFC radio frequency and somatosensory interaction, and the system comprises a path and node configuration module which is used for setting NFC card punching nodes in a high-altitude exploration path and setting path parameters; the user identification and clock-in trigger module identifies the bracelet through NFC, records clock-in time and triggers a clock-in signal; the somatosensory action recognition module collects somatosensory data at the clock-in node and calculates an action score according to the path parameters; the clock-in event generation and authentication module generates a unique clock-in token; the integral calculation and path tracking module calculates single node scores and summarizes total scores; and the communication and data uploading module uploads the total score and the clock-in token to a server. According to the invention, the card punching nodes and the NFC radio frequency inductor are integrally arranged, and a body feeling triggering time sequence and standard action template comparison mechanism is combined, so that the technical effects that the card punching behavior is credible and verifiable and the action performance is automatically scored are achieved.

This invention relates to a multi-agent orchestration and execution method supporting complex cyclic graph structures, comprising: S1: receiving workflow configuration and constructing a directed graph data structure; S2: performing a depth-first search traversal on the directed graph data structure, detecting and outputting strongly connected components in the directed graph data structure; S3: abstracting strongly connected components containing multiple nodes into super nodes, and keeping single-node strongly connected components as ordinary nodes, thereby constructing a directed acyclic graph containing super nodes; S4: performing topological sorting on the directed acyclic graph to generate a hierarchical execution plan containing multiple execution layers; S5: scheduling execution items according to the hierarchical execution plan, and when an execution item is the super node, starting a loop executor for iterative execution. This method, by constructing a multi-layered technical system, integrates graph algorithm innovation, unified context management, abstraction layer design, metadata-driven techniques, etc., to achieve automated orchestration and execution of multi-agent workflows supporting cyclic graph structures.

The invention discloses a distributed particle filtering algorithm based on Gaussian mixture and Gossip fusion, which relates to the technical field of distributed state estimation and comprises the following steps: acquiring local posteriori distribution by a sensor node through local particle filtering, exchanging sufficient statistics of GMM between the node and a randomly selected neighbor node based on a Gossip protocol, and obtaining a distributed state estimation result. Iterative fusion of the GMM is realized through weighted mixed importance sampling; and finally, optimizing a global state estimation result through a posterior correction step. According to the method, only full statistics such as a mixed coefficient, a mean vector and a covariance matrix of a Gaussian mixture model (GMM) are exchanged among nodes, original particle transmission is replaced, the communication overhead is remarkably reduced, and the communication complexity is linearly increased along with the network scale; meanwhile, an adaptive GMM component adjustment, a regularization weighting EM algorithm and an iteration updating strategy of random weight matrix control are adopted, the calculation process is simplified on the premise that the estimation precision is guaranteed, the single-node calculation burden is reduced, and large-scale sensor network deployment with limited adaptive resources is achieved.