181 results about "Network integration" patented technology

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention provides a computing power resource scheduling method and system based on cloud network integration. Selecting a computing power task to be scheduled as a current scheduling task, and generating a node resource adaptation matrix based on the real-time load data, the cloud network topological relation between the nodes and the dynamic bandwidth data; then calculating the lowest scheduling cost of each node according to the matrix, and determining a dynamic adjustment factor when a resource allocation conflict occurs in the current scheduling task; based on the lowest scheduling cost, the computing power demand scale and the data transmission estimated overhead, calculating the final scheduling overhead for scheduling the current scheduling task to each node; and finally, allocating tasks to a target node according to the final scheduling overhead, updating a running task queue, if a conflict occurs, calling a dynamic adjustment factor to execute resource reallocation, and updating the queue after the reallocation succeeds. And circulating the process until all tasks are scheduled. According to the scheme, optimal scheduling of computing power resources in a cloud network convergence environment can be realized.

The invention discloses a battery health management system and method, and belongs to the technical field of battery management, the system comprises an implantable sensor network, an edge and cloud collaborative computing platform, a multi-modal prediction model and a dynamic optimization execution unit; the implantable sensor network integrates various sensors to collect multi-physical field parameters in a battery, and the energy collection unit supplies power through cross-shielding communication transmission. The edge and cloud collaboration platform processes data, trains a model and carries out block chain evidence storage; the multi-modal prediction model is fused with space-time double-flow Transform and causal reasoning, SOH and RUL are predicted, and thermal runaway is early warned; the dynamic optimization execution unit realizes efficient control through layered equalization and self-adaptive thermal management; the method comprises an initialization stage, an operation stage and a maintenance stage to form a closed loop. The method breaks through the traditional limitation, improves the safety, reliability and economy of the battery, and is suitable for electric vehicles, energy storage power stations and other scenes.

The invention provides a vehicle network integration cooperative regulation and control method and system. The method comprises the following steps that 1, the system collects access parameters of multiple types of charging facilities; 2, constructing a power distribution network safety evaluation model based on the data collected in the step 1; 3, determining a charging label of the accessed electric vehicle according to the product of the real-time SOC and the SOH; 4, comparing the three-phase voltage deviation, the current unbalance degree and the load growth rate of the power distribution network with the history in the same period to obtain an overload risk rate, and further calculating a power grid safety margin coefficient; 5, calculating a final safety evaluation result to truly reflect the vulnerability level of the current power grid; and step 6, constructing a digital twinborn model of the target area, performing simulation verification on the initial instruction, and collecting equipment state, vehicle response and power grid operation data after regulation and control. By applying the technical scheme, an efficient and collaborative vehicle network interaction system can be realized, and deep fusion and collaborative development of the energy and traffic fields can be promoted.

The invention discloses a cloth surface flaw detection method and device based on texture perception and anomaly detection. The method comprises the following steps: acquiring a surface image of detected cloth; inputting the surface image of the detected cloth into a deep learning network model; a multi-scale feature map is extracted through the backbone network; processing the feature map through the texture perception feature extraction module so as to fuse cross-channel and cross-space texture information; integrating anomaly detection branches through the check network, and outputting feature maps of different scales; performing frequency domain enhancement and spatial domain feature extraction operation and fusion on the features through the adaptive frequency domain convolution module; and outputting a detection result through the YoloHead detection head so as to judge whether the cloth has flaws or not. According to the method, the texture feature information in the cloth image can be effectively utilized, and the detection accuracy and robustness of the cloth surface flaws and the recognition capability of unknown flaws are improved.

The invention provides a lightweight medical image segmentation network (ABUNet) based on asymmetry and an implementation method thereof, and the method comprises the following steps: S1, in a coding stage, proposing a feature subtraction convolution block (FSCB), and implementing O (C2 / N)-level parameter compression (N is a group number) by using channel feature difference operation; in a lightweight scene, the FSCB can effectively reduce feature redundancy, directly highlights key features of a lesion area, and is superior to traditional feature operation based on addition and multiplication; s2, in a decoding stage, a feature addition convolution block (FACB) is designed, a multi-branch feature fusion mechanism is adopted, and the alignment precision of different feature representations is improved under the condition that the calculation cost is not increased; and S3, in a bridging stage, a multi-scale deep convolutional block (MSDB) is constructed, and the multi-scale context modeling capability of the model is remarkably enhanced by utilizing heterogeneous kernel parallel computing, so that more accurate lesion feature extraction is realized. And S4, in a network integration stage, an FSCB module is integrated into an encoder part of a U-shaped architecture, an FACB module is integrated into a decoder part, and an MSDB module is used for processing grouping characteristics in a bridging module to construct an asymmetric model ABUNet. The asymmetric architecture overcomes the limitation of symmetry of a traditional encoder-decoder, and effectively balances high segmentation precision and calculation efficiency.

The invention discloses a milling process control method based on reinforcement learning, which comprises the following steps: collecting and processing multi-source process data in real time, normalizing and extracting features, and forming state feature vectors; the state feature vectors are input into a DSAC-T reinforcement learning controller, the opening degree of a feeding valve and the rotation speed adjustment of a main shaft are used as action spaces, a reinforcement learning strategy network is constructed, and a process drift online detection sub-module is integrated; training a controller to maximize the rice yield, minimize the broken rice rate and energy consumption, and combining the process safety index to obtain an optimal strategy; deploying the trained controller in an actual scene, and outputting an adjustment instruction to implement closed-loop control; when the actual rice grain size or broken rice rate exceeds the model prediction deviation, online incremental learning is automatically triggered, and control parameters are optimized; the intelligent, self-adaptive and safe control of the rice milling process is realized. According to the method, adaptive optimization control of the rice milling process is realized, and the method has efficient response capability to dynamic disturbance and abnormal states.

The invention relates to a tunneling action generation method and system based on time-space depth fusion multi-task prediction in the technical field of shield engineering data processing, and the method and system integrate local and global time sequence information through a dynamic depth fusion network, achieve the fusion of multiple time-space scales, improve the perception capability of complex working conditions, and improve the efficiency of shield engineering data processing. Meanwhile, future state prediction and control candidates are output, action fusion is carried out on a strategy layer, performance loss caused by prediction-control splitting is reduced, prediction-control integration is achieved, an uncertainty head is introduced, explicit constraint is carried out on a loss function and strategy fusion layer, the risk under stratum sudden change or sensing noise is effectively restrained, and the prediction-control performance is improved. Uncertainty constraint security is realized; on the basis of experience playback, weight self-adaption and noise removal, online self-adaption updating requirements of different stratums and tunneling stages are met, online self-adaption updating can be achieved, and the action generation capacity of tunneling stability control under the complex stratums and noise conditions is remarkably improved.

The invention discloses an intelligent insulation parameter dynamic monitoring and regulation system based on a GD32 microcontroller, and belongs to the field of power electronic insulation monitoring. According to the system, a distributed monitoring network is constructed by taking GD32 as a core processor, a multi-channel time-sharing sampling and dynamic impedance measurement algorithm is integrated, GA / GB dual-channel independent sampling is realized by virtue of a state machine, and the sampling efficiency is improved by 80% compared with that of a traditional scheme. A programmable resistance network and a low-noise operational amplifier are adopted, 1 pF to 100 nF impedance matching and 5-200-time dynamic gain adjustment are achieved, an optical coupler switching dynamic impedance measurement method is originally created, four sets of voltage are adopted for time-sharing conduction of a solid-state relay, and 1.5% high precision is achieved through an improved voltage division model. LC-pi type filtering is adopted at a signal input end, and the high-frequency noise suppression ratio is larger than 20 dB; and a CAN module is newly added, so that a CAN2.0B extension frame and an adjustable Baud rate of 10k-1Mbps are supported. Actual measurement shows that the system can measure 10-65M omega insulation resistance in new energy and rail transit scenes, the dynamic response is smaller than or equal to 200ms, the system is suitable for the fields of medical equipment, industrial sensors and the like, and the overall performance is improved by three times compared with that of a traditional scheme.

The invention discloses a low-altitude defense scene low-slow small target identification method, a terminal, a medium and a product. According to the method, firstly, a video frame is processed through a multi-scale feature adaptive target detection network, the network integrates a global self-attention mechanism to capture a remote dependency relationship, shallow details and deep semantic information are fused by adopting a dynamic weighting multi-scale feature fusion structure, and a target bounding box and a category are output in combination with an optimized loss function. And then, a time sequence level multi-target identity keeping and trajectory generating module is used, stable association of cross-frame target identities is realized through fusion of Kalman filtering motion prediction and appearance feature matching, and a target life cycle is managed in cooperation with a trajectory maintenance mechanism. According to the method, the problems of low-speed small target feature weakening, complex background interference, unstable multi-target tracking and the like are effectively solved, the recognition precision, the anti-interference capability and the tracking continuity are remarkably improved, and meanwhile, the low-altitude defense real-time requirement is met.

The invention discloses a self-adaptive spatial transformation network for cross-medium optical distortion, and solves the problem that the existing spatial transformation network is difficult to consider both global transformation and local nonlinear optical distortion. The network is integrated in a target detection model, and the method comprises the steps that firstly, a parameter prediction network synchronously outputs affine transformation parameters and a water surface fluctuation physical parameter diagram; then, the local distortion grid generation network generates a distortion sampling grid based on the parameter diagram and a predefined water surface fluctuation physical model; meanwhile, the grid generator generates an affine sampling grid according to the affine transformation parameters; then, a grid fusion device fuses the two grids to generate a final sampling grid; and finally, the sampler samples the input image or the feature map according to the final sampling grid to obtain an output image or a feature map. Through a space transformation mechanism guided by physical prior, optical distortion caused by water surface fluctuation can be effectively relieved, and the accuracy and robustness of a target detection model in a cross-medium underwater scene are remarkably improved.

A method for training an ensemble (1) of neural networks (10) for the real-time control of a dynamic system (100) is presented, wherein the ensemble (1) of neural networks (10) is trained with training data from an input space (2). Each of the neural networks (10) of the ensemble (1) is trained exclusively with training data from a corresponding subset (20) of the input space (2). The subsets (20) belonging to the different neural networks (10) of the ensemble (1) are disjoint, and the training of the ensemble (1) of neural networks (10) is subject to at least one constraint, namely that the output of the ensemble (1) of neural networks (10) is continuous at at least one transition (21) between two adjacent subsets (20) of the input space (2). Furthermore, a dynamic system is presented.

The invention is suitable for the technical field of communication, and provides a 5G lightweight core network-based computing network integration intelligent scheduling method, which comprises the following steps of: analyzing a computing task and a resource demand vector thereof by acquiring a task demand, computing power and a network resource state, further matching the demand with the resource state, and applying an improved particle swarm optimization or convex optimization algorithm and the like to obtain a computing network integration intelligent scheduling result. Generating an optimal task scheduling strategy, determining a mapping relation between the task and a computing power node and a network path, distributing the task to an edge node for execution according to the strategy, guaranteeing the data transmission efficiency through a UPF local shunting technology, and finally updating a global scheduling model through federal learning iteration based on a task execution result. And a closed-loop scheduling system with continuous optimization capability is formed. Unified management and integrated operation of computing power and network resources are realized, and through a lightweight core network and an intelligent scheduling algorithm, the task time delay is remarkably reduced, and the resource utilization rate is improved.

The invention relates to the technical field of deep learning, in particular to a forage grass yield prediction model construction method based on deep learning, which comprises the steps of constructing a multi-source data fusion module, establishing a cold start mechanism, designing a multi-modal deep learning prediction network, integrating a physical constraint mechanism and constructing a management decision support system. A seasonal attribution analysis function is realized; in the prior art, a simple data superposition or static weighted fusion scheme is generally adopted, and inherent defects of deficiency, different scales and heterogeneity of multi-source data are difficult to process, so that the fusion feature quality is poor; according to the method, firstly, a data blank is accurately filled through an intelligent algorithm based on space-time continuity, then heterogeneous data is unified to a standard grid by using a multi-scale pyramid engine, and finally, deep fusion is performed through an attention mechanism for dynamically calculating importance of each data source; the integrity, the consistency and the information density of the input data are remarkably improved, and a solid and reliable data foundation is laid for subsequent accurate prediction.

The embodiment of the invention discloses an automatic rock extraction method and system based on deep learning and Mars rover camera images, and aims to solve the problems that an existing Mars rock segmentation algorithm is insufficient in generalization ability under a complex earth surface background, high in model calculation load and difficult to deploy on satellite-borne edge equipment. The core of the method is that a lightweight encoder-decoder network is constructed, and the network integrates three key modules: a frequency-assisted enhancement Mama module, which accurately captures rock texture and contour by fusing the global sequence modeling capability of Mama and the frequency domain enhancement of wavelet transform; the multi-scale feature intensifier is used for adaptively fusing multi-level features by using a parallel double attention mechanism; and the boundary perception auxiliary branch improves the integrity of the segmented contour through an explicit edge supervision and feature decoupling mechanism. According to the method, the rock extraction precision is remarkably improved, meanwhile, the model complexity and the calculation overhead are greatly reduced, the method is suitable for outer space exploration scenes with limited communication bandwidth and calculation resources such as Mars rovers, and effective balance of high precision and light weight is achieved.

The invention relates to the technical field of kitchen appliance control, and discloses a kitchen appliance integrated control system based on dynamic power distribution, which comprises a power monitoring module, a load analysis module, a dynamic distribution module, a safety protection module, a user interaction module and a network integration module. The current, voltage and instantaneous power consumption value of each device are collected in real time through a power monitoring module, and whether a safe power interval is exceeded is judged; analyzing a total load state and a dynamic demand trend based on a load analysis module, and predicting a potential overload risk; the dynamic allocation module dynamically adjusts the power allocation proportion of key and non-key equipment in combination with a user priority rule; the safety protection module monitors in real time and automatically executes graded protection measures; and the user interaction module provides a graphical interface to support priority configuration and power upper limit setting so as to realize strategy closed-loop optimization.

The invention discloses an offshore high-power wind power plant rapid prediction system and method based on wind resource refined evaluation and field network integration. The system comprises a wind resource fine evaluation module used for comprehensively considering wind speed, wind direction, turbulence intensity, atmospheric stability and marine environment parameters to obtain spatial and temporal distribution of wind energy resources; the high-power wind turbine comprehensive modeling module is used for establishing a wind power plant unit power output model in combination with the power curve and a Jensen wake flow model; the neural network prediction module is used for realizing rapid prediction of future power; and the field network integrated rapid solution and grid connection module combines the predicted power with the power grid constraint condition, and outputs the adjusted power curve and power grid operation characteristics by adopting a rapid solution and optimization method. According to the method, the impact of wind power fluctuation on the power grid can be effectively reduced, the wind power grid-connected friend performance and the power grid stability are improved, rapid solving and dispatching optimization of the generating capacity of the high-power wind power plant in the deep and far sea are achieved, and the method has high engineering application value.

The invention discloses an MJS reinforcement parameter optimization method based on finite element-neural network integration. The method comprises the following steps: firstly, constructing a finite element automatic modeling script system based on an ABAQUS platform, taking geological parameters and reinforcement preliminary design parameters of a construction area as input initial codes of automatic modeling, and then modifying different corresponding reinforcement parameters in the codes to realize parameterized modeling and batch model generation; reading and formatting strength and deformation results in a. Odb file output by a calculation result of the ABAQUS platform by utilizing Python, and fusing the strength and deformation results with on-site deformation monitoring data to form a unified database; then, constructing a forward modeling / inversion model based on PINNs by utilizing a Python language; performing preliminary prediction on site deformation by using the forward modeling model; continuously optimizing and updating the reinforcement design target parameters through the inversion model; and finally, comprehensively deciding and determining a foundation reinforcement parameter combination according to the calculation result of the forward / inversion model.

This disclosure relates to decentralized integration solutions for securely conducting legal transactions and / or other types of transactions. In certain embodiments, a transaction platform includes a decentralized network integration system that interfaces with decentralized network systems, including blockchain and / or distributed storage systems, to securely conduct legal transactions and / or other types of transactions. The transaction platform can protect sensitive data associated with the transactions using techniques that combine zero-knowledge proofs (ZKP) with blockchain-based smart contract technologies. The transaction platform also can protect the integrity of transaction documentation using techniques that combine distributed storage and smart contract technologies. This disclosure also describes techniques for implementing AI-powered dispute resolution processes and tokenized ecosystems on the transaction platform.

The invention discloses a thermal error prediction method and system based on WFGN, and the method comprises the steps: S1, obtaining temperature sequence data and part error data, and converting the data into graph structure data; s2, constructing a data set by using the graph structure data, and dividing the data set into a training set, a verification set and a test set; s3, constructing a multi-domain fusion graph neural network which comprises a Fourier transform-based convolutional network used for capturing global features, a wavelet transform-based convolutional network used for capturing local features and a full-connection network, and performing connection through a feature fusion layer; s4, training the multi-domain fusion graph neural network; s5, predicting a corresponding error result according to the temperature sequence data detected during part machining; a deep learning technology is utilized, a Fourier transform convolutional network, a wavelet transform convolutional network and a full-connection network are fused, thermal errors generated at different machine tool machining temperatures are learned and automatically predicted, global frequency features are extracted through Fourier transform, local multi-scale change features are captured through wavelet transform, and the local multi-scale change features are obtained. And a high-precision prediction result is output through full-connection network integration, so that the precision and efficiency of thermal error prediction are effectively improved.

The invention relates to the technical field of distributed photovoltaic power generation, in particular to a charging station planning model considering distributed photovoltaic effective consumption. The three-network integration unit considers the vehicle demand and the cooperative relationship of the road network and the power network based on a multi-objective optimization module, and realizes the optimal planning layout of the charging station and the distributed photovoltaic system through a multi-objective function; the distribution processing unit distributes and processes the initial configuration of the charging station and the distributed photovoltaic system through a heuristic optimization algorithm according to the cooperative relationship of the three-network integration unit, and provides an initial solution for optimization calculation; and the optimization iteration unit is used for gradually optimizing the configuration of the charging station and the distributed photovoltaic through an iteration optimization algorithm based on the initial solution of the distribution processing unit. An initial solution is improved step by step through an iterative optimization algorithm, the optimality or approximate optimality of a final scheme is ensured, meanwhile, the method has high robustness and adaptability, and a feasible solution can be found in a complex environment.

The present disclosure relates to the technical field of communications. Provided are a computing and network service processing method and a related device. The method comprises: on the basis of a computing and network service request, determining a computing and network service data volume, a computing and network service priority, a terminal-side computing delay range and a local round-trip delay; interacting with another network element to determine a computing power QoS, so as to determine a PCC rule; and finally deciding a computing and network QoS policy and a PFCP rule. By means of interaction, a computing and network QoS indicator required by a computing and network service is pre-allocated, resources required by a network and computing power are allocated, and collaborative scheduling is performed on computing power resources and network resources, so as to achieve the objective of computing and network integration.

The invention discloses a hydraulic engineering progress prediction system based on big data. According to the system, standard engineering multi-source data is used; extracting feature data in the standard engineering multi-source data to obtain feature engineering multi-source data; an improved LSTM long-short term memory network is adopted, a space-time attention mechanism is integrated to establish an LSTM prediction model, and the LSTM prediction model is utilized to learn time attention at different historical moments and differentiated influence weights of space attention of different engineering parts on a prediction target; and inputting the differentiated influence weight and the feature engineering multi-source data into an XGBoost integrated learning model to quantify the comprehensive influence of the complex interaction among multiple factors on the progress, and outputting node completion prediction time and risk level signals. And the accuracy of progress prediction is greatly improved.

The application discloses a reverse analysis method based on disassembly function recovery, which is used for solving the embedded reverse analysis process including satellite firmware embedded operating system and the like. Through a multi-step machine learning process, the method combines disassembler output and actual binary data to achieve efficient analysis of embedded operating system firmware. The specific steps include evaluating the effectiveness of the disassembler, creating training data, using neural networks to integrate multiple disassembler outputs to improve analysis accuracy, and further optimizing prediction performance through a context-aware integration model. The application can improve the accuracy and efficiency of reverse analysis, and has advantages in bypassing obfuscation mechanisms, improving string matching accuracy, and handling encrypted or special file systems.

The invention relates to a server-less packet processing service with isolated virtual network integration. A program to be executed to perform packet processing operations on packets associated with a resource group and security settings for the resource group are received. The program is transmitted to a set of fast path nodes that are assigned to the resource set based on metadata of the set. Relative to a particular packet, a secure operation based on the settings is performed and the program is executed at a fast path node. A packet routing operation corresponding to the received packet is performed based at least in part on a result of the program.

A loopback test system and method of using the system to test optical connectivity of an equipment rack including an optical shuffle device and at least one server, with each server having server ports. The loopback test system includes a plurality of loopback optical interfaces that are each coupled to a respective network-side optical interface or a respective spare optical interface of the optical shuffle device. Shuffle optical waveguides of the optical shuffle device and loopback optical waveguides of the loopback test system cross-connect each server port in a first subset of the server ports to a respective server port in a second subset of the server ports, with at least one of the cross-connections passing through a spare optical interface and two of the network-side optical interfaces.

The application provides a Mars crater detection method and system, relates to the technical field of planetary remote sensing image processing and target detection, and comprises the following steps: constructing a CAFE-Net network based on a YOLO single-stage detection model, integrating a multi-scale context perception module, a feature enhancement unit and an efficient feature fusion network, training the model after pre-processing MDCD and DACD data sets, and outputting visualized and structured detection results through model inference and non-maximum suppression post-processing, so that the problems of high false detection rate of craters and dome structures and high missing detection rate of small craters in the prior art are effectively solved, the precision and recall rate are improved, the method is suitable for deep space exploration scenes such as Mars probe landing site selection and Mars geological age determination, and can be automatically processed in an end-to-end mode and is convenient to deploy.

The application discloses a cross-individual EEG emotion recognition method based on course learning and multi-source domain adaptation, which constructs a multi-modal feature extraction network, integrates time sequence, frequency domain and brain network connectivity feature extraction modules, so as to fully mine and fuse multi-dimensional emotion-related information. In view of the challenges of large individual difference and inconsistent data distribution in cross-subject emotion recognition, the application introduces a multi-source domain adaptation mechanism, effectively aligns the feature distribution of the source domain and the target domain, and improves the discrimination ability and migration performance of the model in the target domain. In addition, in order to alleviate the convergence instability and local optimum problem caused by difficult samples in the early stage of model training, the application introduces a course learning strategy, guides the target domain data to participate in training according to the sample difficulty from shallow to deep, thereby significantly enhancing the convergence, generalization ability and robustness of the model, and finally effectively improving the overall performance of cross-subject EEG emotion recognition.

The invention discloses a double-side impedance trajectory optimization high-frequency wireless electric energy transmission system and method based on multi-mode matching network integration and a near-zero impedance angle rectifier, and relates to the field of high-frequency wireless electric energy transmission. The problems that the impedance conversion function of an existing high-frequency resonant rectifier structure shows nonlinear capacitive change, a large number of reactive components are generated, the impedance angle fluctuates greatly along with the load, the voltage stress is high, the efficiency is sharply reduced in the wide load range and the like are solved. One end of a multi-mode matching network is connected with a Class-E power amplifier and the other end of the multi-mode matching network is connected with a coupling coil through impedance track optimization at a primary side, so that output power change compression under wide-range load change is realized; the secondary side is connected with the coupling coil through a near-zero impedance angle rectifier, the near-zero impedance angle rectifier comprises a three-unit novel resonant rectifier, the three-unit novel resonant rectifier comprises three-mode smooth input impedance, the three-mode smooth impedance is connected in parallel, and impedance angle compression and high-efficiency operation under a wide load are achieved.

The application provides a PLC system communication method and system based on a DCS redundant network, relates to the technical field of industrial automation control system communication network integration, and comprises the following steps: network architecture reconstruction, direct access of a programmable logic controller (PLC) to a redundant industrial Ethernet of a distributed control system (DCS), and formation of a unified local area network; IP address planning, allocation of addresses of the same preset IP network segment to controllers of the DCS, PLCs, industrial personal computers and optional communication modules, and ensuring direct communication between devices; the application can save special communication modules and supporting equipment, reduce direct hardware cost, reduce cabinet space occupation and wiring cost by fully utilizing the existing redundant network infrastructure of the DCS; for a system containing multiple PLCs, the cost advantage is more obvious, no additional investment in redundant communication hardware is needed, and the overall investment is further reduced.