380 results about "Heterogeneous network" patented technology

The invention discloses a vehicle-mounted cross-protocol data time sequence cooperative processing method, which comprises the following steps: establishing a unified time reference, and providing an accurate space-time coordinate system for heterogeneous network nodes by electing a global master clock and constructing a clock relation model and a periodic correction mechanism; systematic cognition and management of communication resources are realized, and a verified protocol capability table is generated by automatically identifying protocol types and variants; constructing an automatic mapping system from demand to execution, performing formalized packaging on a target scene by introducing a unified intermediate model, generating a cross-protocol mapping rule based on the model and a protocol capability table, and then forming a global scheduling table in combination with a unified time reference; the cooperative communication of all nodes is finally realized by using the global scheduling table, and the end-to-end delay is constrained within the preset range, so that the problem that the end-to-end time sequence in the vehicle-mounted heterogeneous network is uncontrollable is solved, and the controllability and reliability of the time sequence of the vehicle-mounted network are improved.

The invention relates to the technical field of network communication, and discloses a vehicle-mounted edge computing data recording system and method based on protocol adaptive analysis, and the system comprises a multi-protocol network access controller which is used for capturing an original data frame and extracting a physical feature triple; the identification analysis engine is used for executing hash operation on the triple to generate a physical feature code and retrieving a physical logic address mapping table; the direct memory access controller is used for responding to a target memory address pointer hit by retrieval and directly writing a data load into an input buffer area of the functional operation module, and by constructing a direct addressing mechanism based on Hash mapping, thorough decoupling of vehicle-mounted heterogeneous network physical topology and edge computing logic is achieved.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention discloses a wide-area time traceability credible authentication method in a power heterogeneous network environment, and belongs to the technical field of power system informatization and automation. Comprising the following steps: acquiring various high-precision time source information in a power heterogeneous network environment, and acquiring an initial time reference of the power system time synchronization device; generating uniform reference time, and embedding a digital watermark containing time source information and credibility thereof for each timestamp by using the digital watermark embedding module; controlling the power system time synchronization device to sequentially generate time transmission fingerprints at all levels of network nodes; constructing a unique time fingerprint chain covering the whole link; packaging the time fingerprint of each time transmission node and the traceability information thereof, and performing uplink storage; precise prediction and optimal control of time transmission errors are realized; if the overall cumulative time error exceeds a range of + / -10 [mu] s, the predictive correction is maintained until the error is controlled within + / -10 [mu] s.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention discloses a drug relocation model construction method for simultaneously predicting drug-target interaction and drug-disease incidence relation, and belongs to the field of drug research and development, and the method comprises the following steps: integrating heterogeneous networks and attribute characteristics of drugs, targets and diseases, learning multi-relation node embedding by using RGCN, and constructing a drug relocation model for simultaneously predicting drug-target interaction and drug-disease incidence relation; a Gelato algorithm is combined to enhance a network structure, an auto-covariance is introduced to calculate a potential association score, and drug-target interaction and drug-disease association are synchronously predicted; the weighted cross entropy and N-pair loss joint optimization is adopted, unbiased training is realized, the problems of class imbalance and network sparseness are solved, and the model generalization ability and prediction precision are improved.

The invention discloses a heterogeneous network embedding method based on dynamic heterogeneous network decomposition and a hierarchical space-time attention mechanism. The method comprises the following steps: dynamic network decomposition: decomposing a heterogeneous network sequence into independent subgraph sets according to edge types; multi-order node attention aggregation: for each sub-graph, fusing information of nodes and different edge types; semantic level attention is embedded, wherein global semantic embedding is generated in combination with global relation type weight and meta-path long-short-term memory network coding; time dynamic modeling: respectively calculating a time attention weight and a Monte Carlo sampling approximate Horkes intensity, and then aggregating and splicing results of the time attention weight and the Monte Carlo sampling approximate Horkes intensity to obtain a final embedding result. According to the method, through cross-granularity semantic modeling and efficient time sequence dependence learning, an innovative solution is provided for node representation of the dynamic heterogeneous network.

The invention provides an end-network cooperative transmission system and method for a multi-mode network. The system comprises a terminal service demand layer which is used for identifying the service type of a terminal in a multi-mode network, generating structured service demand information and transmitting the service demand information to a network collaborative decision-making layer; the network cooperation decision-making layer is used for fusing the service demand information and the link state information, generating a transmission optimization strategy including path selection, priority mapping, bandwidth allocation and rate control, and transmitting the transmission optimization strategy to the transmission optimization strategy execution layer; and the transmission optimization strategy execution layer is used for executing a transmission optimization strategy, collecting link state information in the multi-mode network in real time through INT, and transmitting the link state information to the network cooperation decision layer. According to the method, efficient scheduling of heterogeneous network resources and high-reliability transmission guarantee of differentiated services are achieved, and the method is suitable for key service scenes sensitive to time delay and reliability, such as Internet of Vehicles and ultra-high-definition video transmission.

The invention discloses a resource allocation and configuration method and device and electronic equipment. The method comprises the following steps: converting network resources in a private network into programmable objects, and constructing a resource label model according to attribute information of the programmable objects and the network resources; receiving service demand information, extracting a service intention in the service demand information, and converting the service intention into a standardized intermediate instruction; generating a resource allocation scheme according to the standardized intermediate instruction and the network resource use state information, and evaluating the resource allocation scheme in a digital twin environment of the private network; and under the condition that the evaluation result of the resource allocation scheme in the digital twin environment meets a preset requirement, converting the resource allocation scheme into a control instruction, and sending the control instruction to the network resource. The technical problems of low network resource allocation efficiency and slow service response speed caused by non-uniform resource description and high service demand analysis difficulty in a multi-manufacturer heterogeneous network environment are solved.

The invention discloses a multi-source sensing airport scene moving target cooperative monitoring and conflict early warning system, and relates to the technical field of airport scene traffic management, a millimeter wave radar, a laser radar and the like form a heterogeneous network, and the whole area of an airport is synchronously covered through a TSN network; cascading filtering denoising is adopted, GPS time service and Kalman filtering are combined to realize space-time calibration, and a data complementation mechanism is established; based on a multi-modal model classification target, the tracking continuity is kept through an improved algorithm; fusing multiple factors and predicting a path by using an ST-GNN model to adapt to different scenes; constructing a four-dimensional judgment space to evaluate risks in a grading manner, and performing grading response and multi-channel information pushing according to the risks; and the system performance is continuously optimized through federated learning and digital twinborn verification. According to the invention, the method achieves the precise monitoring of the whole area of the airport scene, improves the target recognition and tracking stability, has the self-optimization capability, balances the operation safety and efficiency, and supports the high-quality development of aviation logistics and general aviation services.

The invention discloses a model identification method, device and equipment for multi-unmanned aerial vehicle radio frequency signals and a storage medium, and relates to the technical field of mode identification and classification, and the method comprises the steps: carrying out the preprocessing of the unmanned aerial vehicle radio frequency signals, and obtaining a radio frequency time-frequency graph; performing feature extraction on the radio frequency time-frequency graph through a multi-branch heterogeneous network model to obtain a first branch feature, a second branch feature and a third branch feature; performing global feature coding, branch attention generation and feature weighting on the features of the three branches through a cross-branch attention interaction module to obtain attention enhancement features; the attention enhancement features are refined through a local and global feature refining module, and fusion features are obtained; and inputting the fusion features into a full connection layer and an activation function for classification to obtain a multi-unmanned aerial vehicle model identification result. According to the invention, the accuracy of identifying the radio frequency signal type of the unmanned aerial vehicle and the capability of resisting complex electromagnetic environment interference can be improved in a multi-vehicle coexistence scene.

The invention discloses an optical fiber wireless heterogeneous network intelligent monitoring system and method based on energy-information co-transmission, and relates to the technical field of optical fiber wireless heterogeneous network intelligent monitoring, the system comprises a central base station unit, an energy-information co-transmission optical fiber transmission unit and a micro-nano integration unit; the central base station unit is integrated with a high-power laser with a first preset wavelength, a light receiving unit with a second preset wavelength and a data processing subunit; the high-power laser is used for generating an optical signal for transmitting energy; the light receiving unit is used for receiving monitoring data; the data processing subunit is used for realizing clock recovery, code element synchronization, serial decoding and data display functions; and the energy-information co-transmission optical fiber transmission unit takes a multi-core optical cable as a physical carrier and adopts a wavelength division multiplexing technology to realize isolated transmission of energy and information, one end of the energy-information co-transmission optical fiber transmission unit is in communication connection with the central base station unit, and the other end of the energy-information co-transmission optical fiber transmission unit is in communication connection with each micro-nano integrated monitoring unit.

The invention discloses an intelligent metasurface auxiliary directional charger deployment method based on a heterogeneous graph neural network and deep reinforcement learning, and belongs to the field of wireless energy supplementation of the Internet of Things. The method aims at solving the problems that at present, related work is mostly one-time static geometric deployment, the coverage range is limited, flexibility is insufficient, the optimization granularity is coarse, high-quality charging full coverage is difficult to achieve under the cost constraint, and the overall charging effectiveness of a network is difficult to maximize. According to the method, a heterogeneous network graph fusing the relation of sensor nodes, directional chargers and intelligent super surfaces (RIS) is constructed, a heterogeneous graph neural network is deployed in a base station in a centralized mode to extract the network structure and energy state characteristics, and a near-end strategy optimization algorithm is further combined. And joint optimization of a directional charger deployment position and a charging direction as well as an RIS position and reflection configuration is realized. According to the method, the charging coverage rate, the charging utility and the deployment cost are taken as a comprehensive target, the optimal deployment strategy can be intelligently generated, the charging coverage is expanded, the charging utility is improved and the node failure rate is reduced on the premise of controllable cost, so that the purpose of prolonging the service life of the network is achieved.

The invention relates to an automobile heterogeneous network intrusion detection method based on QLoRA and knowledge distillation, and belongs to the technical field of intelligent networked automobile on-board network intrusion detection safety. The technical problems that an existing method is difficult to give consideration to light weight and high precision and cannot adapt to resource constraints of a vehicle-mounted embedded environment are solved. According to the technical scheme, the method comprises the steps that self-adaptive mapping and feature extraction are conducted on original data from a CAN bus and the Ethernet, and a unified annotation data set is constructed; a knowledge distillation framework based on BERT is designed, google-bert is used as a teacher model, tiansz-bert is used as a student model, model parameters are compressed by adopting a QLoRA quantification technology, and partial layers are frozen; knowledge of the teacher model is transmitted to the student model through knowledge distillation, and training is carried out in combination with soft and hard label loss. The technical effects are that lightweight intrusion detection is realized, resource consumption is reduced, high detection precision is maintained, and various network attack threats can be identified.

The invention discloses a tower crane bolt axial force intelligent ultrasonic detection method and system based on multi-modal data fusion. Firstly, a multi-stage influence factor system is constructed based on a fuzzy hierarchical comprehensive evaluation theory, and a high-risk region identification model is established; and calculating a regional risk value based on the model, and determining a monitoring point distribution scheme. A heterogeneous network composed of a fiber grating strain sensor and an ultrasonic sensor is arranged in a selected area, and strain and ultrasonic data are synchronously collected. And inputting the multi-modal data into a deep belief network for feature fusion, and realizing health state dynamic evaluation through a fuzzy recognition model. And based on an evaluation result, performing fatigue life prediction by adopting an improved rain flow counting method and an accumulative damage theory. And finally, data fusion display and intelligent early warning are realized through the remote monitoring platform. According to the invention, full-chain intelligent health management from risk identification, real-time monitoring to life prediction is realized.

The invention relates to the technical field of protocol conversion, in particular to an E1 / IP video conference protocol conversion device and system, and the system comprises a frame structure mapping module, a signaling adaptation module, a media stream packaging module, a buffer area control module and a synchronous coordination module. According to the invention, the synchronization mark and the load field of the physical layer framing structure are extracted, the maximum transmission unit parameter is combined, the frame packet boundary is accurately identified, the cross-network data positioning accuracy is improved, the call control mapping relation is established based on key field identification, and the session continuity between heterogeneous signaling is enhanced; the audio and video sampling rate and timestamp fundamental frequency are matched and verified, the consistency of media stream transmission time delay is guaranteed, interface throughput and time slot occupation changes are dynamically monitored, a buffer regulation and control instruction is generated, and the data transmission stability under burst flow is improved; the synchronization accuracy of the protocol conversion system in a heterogeneous network environment is realized, and the reliability of real-time services such as video conferences and the like is enhanced.

The invention provides a time-space structured sparse optical coding deepwater heterogeneous network data synchronization method and system, and the method comprises the steps: carrying out the sparse decomposition of the time sequence clock deviation data of each current underwater node at a transmitting end device of a deepwater heterogeneous network based on a trained over-complete dictionary, and obtaining an original sparse vector; converting the dynamic compression observation data corresponding to the original sparse vector into an optical signal, and transmitting the optical signal to a receiving end device of the deepwater heterogeneous network by a transmitting end device through an umbilical cable; demodulating the optical signal into an observation vector through receiving end equipment, inputting the observation vector into the trained deep expansion network model, and outputting a reconstructed sparse vector by the deep expansion network model; and recovering the reconstructed sparse vector into a clock skew signal through the over-complete dictionary, and generating a clock synchronization calibration instruction to realize data synchronization of the corresponding underwater node. According to the invention, the problems of high bandwidth occupation, low signal ratio and poor reconstruction time efficiency existing in the underwater network time synchronization technology can be solved.

The invention provides a network information page presentation method, a network information page presentation system and electronic equipment. The method comprises the steps of obtaining a user intention; analyzing the user intention based on a large language model to determine a task type and a data source corresponding to the task type; according to the data sources and the task types, obtaining original data from the plurality of heterogeneous network data sources in parallel; carrying out analysis and structured processing on the original data to obtain structured data; fusing the structured data to generate an information set; on the basis of the information set, natural language content conforming to a predefined layout specification is generated through a large language model; and according to the layout specification, rendering the natural language content into a personalized user interface and presenting the personalized user interface. According to the method provided by the invention, on one hand, the user information acquisition cost and complexity are effectively reduced; and on the other hand, the information acquisition efficiency and quality are improved, and personalized user experience is provided for customers.

The invention relates to the technical field of mobile communication and Internet of Things, and discloses a heterogeneous network space multi-level mobile Internet of Things cloud system based on artificial intelligence, which comprises a data acquisition module for acquiring real-time state data of a mobile Internet of Things terminal and generating a standardized network-service joint situation vector; the endogenous interference sensing module is used for generating a network interference association diagram based on the vector; the service-network dynamic decoupling module is used for reversely reconstructing terminal service characteristics according to the association diagram and decoupling a key network state sequence; the business intention abstraction module constructs a heterogeneous network cooperation state target according to the sequence; the collaborative scheduling strategy generation module is used for generating a multi-dimensional collaborative scheduling instruction by using a collaborative decision model; the network resource self-growing module is used for performing state sequence evolution after the rehearsal instruction and constructing a compensation network plane; and the instruction distribution and verification module is used for executing the instruction of the fusion compensation logic and updating the association diagram. According to the method, the problems of heterogeneous network interference and scheduling lag of the mobile Internet of Things are effectively solved.

The invention relates to the technical field of artificial intelligence and brain-computer interfaces, and discloses a brain-computer interface method and system based on dynamic hierarchical splitting federated learning and a medium, and the method comprises the steps that a client reports a local state; the reinforcement learning agent dynamically decides a joint action for each client based on the global state; the client calculates and uploads an intermediate activation value to the central server; and the server completes residual calculation and returns the gradient, and the client and the server update the subnet parameters respectively. According to the method, joint optimization is carried out on split layer selection and wireless resource and computing resource allocation through reinforcement learning, dynamic self-adaptive decision is realized by taking comprehensive reward of model identification precision, training time delay and client energy consumption as a target, communication overhead and training time delay are remarkably reduced on the premise of strictly protecting user data privacy, and user experience is improved. The terminal energy consumption is reduced, and the training efficiency and the final recognition performance of the model in the dynamic heterogeneous network environment are improved.

The invention discloses a distributed wireless network user switching control method based on deep reinforcement learning, and relates to the technical field of edge computing. According to the invention, a heterogeneous network scene formed by a plurality of access points (AP) and a plurality of user equipment (STA) is constructed. Each piece of user equipment operates independently, candidate access points are screened based on local observation information, the cost for switching to each candidate point is comprehensively calculated, and the cost comprises RSSI difference value cost, switching delay cost and switching cost for suppressing frequent switching. And furthermore, with the goal of minimizing the total switching cost of all user equipment as a target, a switching decision problem is converted into a Markov decision process, and a decision strategy is optimized by using a deep reinforcement learning model, so that an optimal switching instruction is output. The method effectively reduces the switching time delay and the failure rate, improves the network stability and the user experience, and is especially suitable for high-speed mobile environments such as the Internet of Vehicles.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

The invention discloses a drug-disease association prediction method, system, device and medium, and relates to the technical field of drug relocalization, and the method comprises the steps: constructing a drug similarity network and a disease similarity network; obtaining a binary adjacency matrix through k-neighbor graphs of different nodes in the two similarity networks, carrying out weighted fusion on the binary adjacency matrix through an attention coefficient to obtain a soft adjacency matrix, and carrying out fine-grained graph convolution updating to obtain drug features and disease features; extracting heterogeneous node representations of drugs and diseases in the biochemical heterogeneous network, and carrying out dynamic weight distribution on different representations to obtain final embedding of drug nodes and disease nodes; and splicing the final embedding of the drug nodes and the final embedding of the disease nodes, and performing prediction according to the spliced features to obtain the drug-disease association probability. According to the method, the potential complementary relationship between the two is fully mined, and the heterogeneous feature fusion effect is improved, so that the performance and robustness of drug-disease association prediction are integrally enhanced.

The invention discloses an unmanned aerial vehicle control system and method for Internet of Things communication, and relates to the technical field of unmanned aerial vehicle control, the unmanned aerial vehicle control system comprises a data acquisition module, a decision processing module and the like, a switching instruction is generated through multi-dimensional data fusion, cross-layer collaboration, dynamic spectrum scheduling and three-level link guarantee are realized, and the unmanned aerial vehicle control system and method are applied to unmanned aerial vehicle control. The system has the advantages that through a multi-dimensional data fusion algorithm and a cross-layer cooperation mechanism, link quality, flight state and task type data are acquired in real time by using the data acquisition module, and a precise network switching instruction is generated through the decision processing module; in cooperation with a pre-registered address caching mechanism of the cross-layer cooperation module and a dynamic spectrum allocation strategy of the spectrum scheduling module, the defects of heterogeneous network switching decision lag, cross-layer protocol conflict and dynamic spectrum adaptation deficiency in the prior art are effectively solved, and the scheme realizes intelligent seamless switching of the unmanned aerial vehicle between heterogeneous networks such as a cellular network and a satellite network.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for generating a heterogeneous network of blockchains. The method includes generating a platform blockchain having a single replicated state machine. The method includes generating at least one subnet based in part on the platform blockchain, the subnet including at least one blockchain, the generating causing bonding or burning of a base asset of the platform blockchain. The method includes specifying validators for the at least one subnet. The platform blockchain includes a source of truth for transactions across the subnets.

An apparatus of a user equipment (UE) configured to perform an uplink (UL) transmission in an asymmetric multiple transmit receive point (TRP) deployment scenario. The user equipment may determine and maintain timing advances (TA) tor respective transmit receive points in a heterogeneous network The heterogeneous network may include macro node supporting downlink (DL) and UL communications and micro and pico nodes with DL communications disabled.

The application discloses a data transmission method and system of a heterogeneous network, the method comprising: acquiring network priorities of each available network within a preset range; acquiring data transmission information of a data packet to be transmitted, so as to divide a plurality of available networks into a plurality of primary transmission networks and a plurality of backup transmission networks; determining target transmission data streams of each primary transmission network; when the target transmission data streams are allocated to the primary transmission networks for data transmission, real-time monitoring of real-time primary network performance indexes of each primary transmission network and real-time backup network performance indexes of each backup transmission network is performed to determine a to-be-switched network in the plurality of primary transmission networks and a to-be-transmitted network in the plurality of backup transmission networks; and untransmitted data streams of the to-be-switched network are allocated to the to-be-transmitted network for data transmission, so that the efficiency of data transmission is improved.

The application discloses an RGBT target tracking training system, a training method and an inference method. The system comprises a multi-layer teacher network, a lightweight student network, a first distillation branch used for extracting feature statistics and calculating alignment loss of a task response graph, a second distillation branch used for extracting a word element relationship matrix and calculating relationship loss, a relationship control module used for generating a dynamic mask to control word element relationship visibility according to a training stage, and an auxiliary supervision module comprising a fusion tracking head and a modal-specific tracking head for multi-head supervision of student features. Through a dual-branch distillation strategy of architecture decoupling, feature-level forced alignment of a heterogeneous network is avoided. Through a progressive relationship exposure mechanism, relationship complexity is dynamically controlled to ensure optimization stability. Through a modal preservation learning strategy, single-modal discriminative features are preserved. The application can significantly improve inference speed while maintaining high tracking accuracy, and effectively balances model accuracy and running efficiency.

The invention discloses a dynamic optimization method, device and equipment based on heterogeneous network topology and a storage medium, which are applied to the technical field of network optimization, and the method comprises the following steps: obtaining equipment state data of a target network; processing the equipment state data to obtain equipment fingerprint data; processing the equipment state data and the equipment fingerprint data by using an incremental maximum likelihood topological reasoning technology to obtain a real-time topological graph; inputting the real-time topological graph into a fault diagnosis model for processing to obtain a root cause analysis result; performing state modeling processing on the traffic time sequence data, the equipment fingerprint data and the root cause analysis result by using a generative technology to obtain a deduction result; and performing strategy optimization processing on the deduction result by using a multi-agent reinforcement learning technology to obtain a network dynamic optimization scheme. The method provided by the invention can solve the technical problems in the prior art that the network management response is lagged, the flexibility is poor, and the global state is difficult to perceive in real time and the fault is difficult to locate accurately.

The invention relates to the technical field of wireless communication, in particular to an ad hoc network transmission method suitable for a field environment, which comprises the following steps: S1, heterogeneous network architecture design: network nodes are deployed in a non-signal area and are responsible for data acquisition and Mesh relay transmission; the gateway nodes are deployed at the boundary of a signal area and are responsible for 4G / LoRa protocol conversion and cloud forwarding, and each node has the capability of automatically identifying the signal intensity of the current environment; and S3, an intelligent transmission mechanism is adopted, in the starting stage, after the nodes are powered on, cellular network signals are automatically detected, and according to the signal strength, the roles of the network nodes and the competitive gateway nodes are autonomously determined. An autonomous relay transmission mechanism based on the Mesh network is constructed, the network reliability is improved, autonomous roaming and multi-path transmission of the nodes are achieved, the communication success rate in a severe environment is improved, energy consumption distribution is optimized, and the situation that specific nodes deplete energy too early is avoided through dynamic role distribution.