Research on a new management model of distribution Internet of Things

Based on the characteristics of controllable intelligence of the Internet of Things (IoT) and the requirements of the new distribution Network for function and transmission delay, this study proposes a method of combining edge collaborative computing and distribution Network station area, and builds a distribution Network management structure model by combining the Packet Transport Network (PTN) Network structure. The multi-terminal node distribution model of distributed IoT is established. Finally, a distribution IoT management model is constructed based on the edge multi-node cooperative reasoning algorithm and collaborative computing architecture model. The purpose of this paper is to solve the problem of large reasoning delay caused by heavy computing tasks in distribution cloud servers. The final results show that the model reduces the inference delay of cloud computing when a large number of smart device terminals of distribution IoT are connected to the network.

collaboration, and proposed a new implementation scheme for quick power supply restoration of distribution networks 6 .Li et al. adopted edge computing technology to transfer computing and storage tasks from cloud computing centers to edge devices closer to data sources for processing, reducing data transmission delay and improving data processing speed and real-time.A special data processing framework for distribution network load multi-source, strong coupling data acquisition, cleaning and aggregation is constructed, and the energy efficiency management system of the distribution network is further constructed by studying the quality optimization of power grid and distributed energy scheduling 7 .Literature 8 proposes a unified energy management architecture applied to distributed edge computing of renewable energy, which improves the response speed of distributed energy control.Literature 9 constructed the edge computing architecture of smart grid model.Taking intelligent measurement data acquisition as an example, the role of edge computing in data analysis, its efficiency and security were analyzed in detail.Literature 10 designed and realized the service collaboration of distribution IoT based on the pipeline mode computing model around the edge computing requirements, which could reduce the computational load, but the computational speed was not studied.Literature 11 takes intelligent terminals of power distribution as the core, provides collaborative data assistance based on edge computing, and realizes quantitative calculation and intelligent identification of local topology data in low-voltage station areas.In the literature 12 , the whole distribution station area is taken as the edge computer node, the characteristic indicators of the station area are established through monitoring data, and the weight is selected to judge the operation state of the station area, so as to further reduce the computational redundancy.In literature 13 , the collaborative management architecture of the power company's jurisdiction area is designed based on edge computing technology for the IoT in the distribution area.In literature 14 , large-scale distributed co-simulation technology is applied to smart grid system and a new co-simulator is designed.Literature 15 and literature 16 used edge computing technology to design diversified load management, optimized the data processing flow, and proposed solutions to the heterogeneous problem of intelligent terminal devices in the distribution IoT.The above researches show that edge computing can be effectively applied to smart distribution networks, but most of the research focuses on the design of distribution network management structure, and the optimization operation and efficient work of distribution Internet of Things are not studied in depth.
Edge computing extends from cloud computing to intelligent terminals and edge nodes of the Internet of Things, which can improve computing speed and processing efficiency.A large number of sensing and computing terminals are arranged in the station area.By using the data acquisition and processing capabilities of these edge devices and the interactive capabilities with users, the information is preprocessed, which saves computing resources for the cloud computing center.The two can be unified into a whole, and the precise control and realtime response of the entire distribution IoT can be realized 17,18 .According to the dimensions of collaborative computing between cloud computing and edge computing, cloud edge collaboration can be divided into the following four categories: (1) resource collaboration; (2) data collaboration; (3) intelligent collaboration; (4) service collaboration.Based on the above four categories 19 .This paper studies the model of reducing inference delay in the process of distributed IoT cloud computing, designs a multi-terminal distribution model of distribution IoT, and constructs a distribution IoT management model based on edge multi-node collaborative computing, thus completing the inference optimization task of distributed cloud computing.

Edge computing
Through the integration of computing, communication and control technologies, the intelligent terminal of IoT has formed an organic whole of information system and physical systems 20 .There are some shortcomings in the current calculation methods of distribution networks, such as fast calculation, data management, data terminal integration, analysis and decision-making, etc.The calculation rate and processing efficiency of the existing calculation methods are relatively slow, but they can be solved by the technologies of cloud computing and big data analysis 21 .However, at present, the data environment is complex and redundant, and the structure is diverse.The steps of analyzing and discriminating all kinds of information are tedious, and the real-time response characteristics of information are difficult to meet, which has a certain impact on the stability of energy transmission and the reliability of intelligent control in distribution network 22 .Compared with the feature that cloud computing can be processed remotely and uniformly in the master station, edge computing extends from cloud computing to intelligent terminals and edge nodes of the Internet of Things, and its main purpose is to realize the functions of real-time collection and calculation of localized data information, online diagnosis, ms-level rapid response and precise control of controlled nodes.A single distribution terminal adopts edge calculation, so that data can be calculated and solved on the edge side, and the solution results can be uploaded to the cloud center, which is more conducive to real-time monitoring of the running state and quick and intelligent processing of data.Traditional control is to control the electrical signal, while edge calculation is to control the information.It integrates the key capabilities of communication networks, computing preprocessing, data storage and application programs with edge nodes as the core 23 .At present, all industries are striving to promote the integration of operational, information, communication (OICT) proposed by the Edge Computing Alliance 19 , but in fact, with the wide application of big data and other technologies, the transformation from OICT to data technology (DT) will also be realized, and finally digital automation technology (AT) and intelligent control services will be provided.Figure 1 shows the schematic diagram of data positioning for edge calculation.

Connecting edge computing with Internet of Things and distribution network
The traditional distribution network can usually be divided into the main station, sub-stations and terminal layers.With the deepening reform of the power network and the massive access of distributed energy, the traditional distribution network has also changed from the passive distribution network with unidirectional transmission to the active distribution network with bidirectional transmission capacity 24,25 , which also makes the distribution system look forward to the change.At present, with the large-scale power electronic equipment and intelligent controllable devices put into use, there are some shortcomings in the traditional control technology and power communication grid.Only by introducing the IoT technology into the power grid can we meet the demand of new distribution system 26 .At present, all layers of distribution equipment in the new distribution network are combined with embedded systems, which makes the traditional distribution network form information interaction with other equipment in the station area based on information communication network, thus constructing a large-scale distribution Internet of Things with physical network, information equipment and computing units coupled.Finally, it can further promote the integration of edge computing, IoT and distribution network, and make the distribution network realize intelligent control at different levels.

Multi-terminal node distribution model of Internet of Things
In the distribution network, distribution intelligent terminals can be used as edge computing nodes, with computing, storage and communication functions.The terminal of the smart platform can manage the connected communication devices, sensing devices, measurement devices, and execution devices, and can seamlessly connect to the data center and cloud platform.Through various appropriate communication means, the measurement data is collected, and the local data processing and analysis decisions are carried out.The adjacent intelligent station terminals can form distributed computing nodes, and the measurement data and processing results of the edge nodes of each distributed terminal intelligent device can be shared.Through various suitable communication means, measurement data is collected, and local data processing and analysis are carried out.The terminals in the adjacent intelligent station area can form distributed computing nodes, and the measurement data and processing results of the edge nodes of the intelligent devices of each distributed terminal can be shared.The schematic diagram of device data and information sharing is shown in Fig. 2: In this diagram, the central server acts as a centralized management and coordination role.Each smart station terminal is connected to a central server and communicates with other terminals.
Data sharing can be achieved in the following ways: 1. Data upload: The intelligent station terminal can upload the collected measurement data and processing results to the central server.These data can include sensor readings, algorithm outputs, etc.A central server is responsible for storing and managing this data, making it accessible to other terminals.2. Data download: The terminal can download data uploaded by other terminals from the central server.In this way, each terminal can obtain data from the entire distributed network for further processing and analysis.3. Data sharing: Data can also be shared directly between terminals without going through the central server.
This method can improve the data transmission speed and reduce the network load.Terminals can send data to other terminals through point-to-point communication or multicast.4. Collaborative computing: Distributed terminals can use collaborative computing technology to divide tasks into sub-tasks and execute them in parallel on each terminal.Each terminal is responsible for processing part of the data and passing the results to a central server or other terminal for further integration and analysis.
Through these ways, the adjacent intelligent station terminals can realize the sharing of data and information, thus forming a distributed computing node, providing more powerful computing and analysis capabilities.www.nature.com/scientificreports/Power distribution IoT is an extension of the IoT technology in the field of power distribution networks.The characteristics of power distribution IoT cannot be reflected through the traditional 3-layer physical architecture (master station, substation, terminal).In order to reflect the characteristics of distribution Internet of Things, this paper designs a distribution Internet of Things architecture based on the PTN network model according to the PE-P-CE structure proposed by the PTN network, combined with the characteristics of edge computing technology to reduce the computation amount and the information interaction of the IoT.Distribution IoT is mainly composed of three types of devices, namely CE devices, PE devices and P devices.The structure diagram of edge technology is shown in Fig. 3.
The CE devices are terminal edge unit level IoT devices located in the active distribution network in the PTN access layer, which can realize self-sensing, self-computing, interchangeable, extensible and self-determining functions.The CE devices in the distribution IoT mainly include distribution transformers, terminal feedback units, intelligent monitoring units, data transmission units, centralized processing units, switch devices, and configured communication modules.The CE devices carry out real-time monitoring, data collection, and intelligent control of terminal devices.Distribution transformer monitoring terminal units monitor the status of distributed power supplies, energy storage devices, and electricity meters.The sub-station device is the edge access device installed in the PTN convergence layer, which plays the role of convergence and access of all edge data.It is called the electronic distribution station in the IoT at the active distribution network system level.It can organize and configure the control instructions of the distribution network by itself, and make self-decisions and optimizations.Multiple CE devices are pooled and connected to PE devices.Through status collection, data interaction, edge analysis and cloud integration, global management in the distribution area is finally realized.As the core layer of the PTN structure, the master station equipment can realize comprehensive information perception in the distribution IoT, integrate and process the data of distributed terminal intelligent devices, conduct in-depth analysis, and then make scientific decisions and issue commands.The master station equipment monitors the status of the distribution network in real time, and the sub-station equipment uploads the information required to be uploaded by distributed terminal intelligent devices to the master station.Moreover, the master station can read real-time data from each CE device, and use the information obtained above to determine the operation status of the smart distribution network.

Construction of collaborative management and control model for Internet of Things
At present, it is difficult to solve the demand of multi-node distribution Internet of Things scenarios by adopting centralized single architecture scheme.Edge computing mode is introduced, and the number of intelligent devices in distribution network terminals is set as edge nodes.Edge computing is the first way to preprocess data.For the edge node management function undertaken by the cloud server, the cloud edge collaboration mechanism can ensure the low delay of data interaction between the distribution network devices.In practical applications, the communication delay of data transmission between devices is still quite high and may even exceed the calculation delay.The following strategies can be used to improve the communication delay of data transmission between devices.First, we can consider optimizing the network topology to ensure more efficient data transmission paths.By carefully designing the network topology, we can reduce the transmission distance of data packets, thus reducing communication delays 27 .Secondly, increasing network bandwidth is another effective measure.Increasing bandwidth can increase data transfer rates and reduce communication delays.This can be achieved by upgrading network hardware or using higher-performance communication equipment 28 .To further optimize communication performance, caching and prefetch technologies can be introduced.By implementing a caching mechanism on the device side, frequent access to the cloud can be reduced, thereby reducing communication latency 29 .Additionally, adopting faster communication protocols is also a way to solve high communication latency issues.Selecting appropriate communication protocols improves data transmission efficiency and accelerates inter-device communications 30 .www.nature.com/scientificreports/At present, the terminal equipment of the distribution network is complex, including distributed energy, energy storage equipment, flexible load and some controllable equipment.The information interaction of each equipment is mixed, and there is interference in the transmission process.According to the requirements of terminal intelligent devices in the distribution network for request delay and functional integrity, the modeler considers the distribution network and its information system structure, and designs a multi-dimensional collaborative algorithm to meet the requirements.The proposed topology model includes master device node, edge node, user terminal layer and edge device of distribution IoT .The master node C and the edge node set directly managed by the master node C have direct access, and the edge node is provided with its own management center, and the user terminal can access the edge node.A brief topology is shown in Fig. 4.
The distribution network is managed hierarchically, and the multi-level centralized and distributed combination of global, intermediate and local control structures is adopted to construct the distribution network management and control structure, as shown in Fig. 5. Based on this structure, first of all, tasks are generated by terminal devices and sent to nodes in the cloud to perform processing computation.
The interface definitions in the Fig. 5 are shown in Table 1.
According to the physical structure of the distribution network system, the management and control model is used to represent it hierarchically.The various types of equipment and control modes in the model have the following characteristics: 1. Multiple terminal devices can realize information interaction and real-time analysis.2. In the same structure layer, the effects of different control modes are not coupled and separated from each other, so that the control objectives can be achieved independently, and the influence among their control effects can be ignored.

Multi-node collaborative computing method for minimizing effect time target
The traditional load balancing strategy is mainly used in parallel systems.For external data requests, it distributes tasks to multiple processing units, but there are also some shortcomings, such as poor dynamic performance of the algorithm, insufficient consideration of the real-time service capability required by the processing units, and generation of task response and scheduling decisions 31 .Literature 32 adopts the dynamic equilibrium strategy of linear regression, which can predict the real-time data of terminal edge nodes and then feed back, but it can't meet the coordination needs of cloud and edge because it doesn't consider the propagation delay.Therefore, taking into account the computing capacity of nodes and the propagation delay between edge and cloud centers, a collaborative computing model involving multiple nodes is adopted to minimize response time and achieve collaboration between cloud computing and edge computing.By implementing reasonable task division, node collaborative computing, task scheduling and optimization, as well as error processing and fault tolerance mechanisms, this multi-node collaborative computing model can effectively enhance task execution efficiency and system performance [33][34][35] .
According to the collaborative architecture model of master device and edge computing described in Section "Multi-terminal node distribution model of Internet of Things", the algorithm is described with the minimum delay as the goal.The data interaction of terminal intelligent devices reaches the edge node first, and the edge node makes the action decision through collaborative control, which involves the round-trip delay between the edge and the master device, the terminal computing processing delay of the master device, and finally the data processing can be completed only after the edge node calculates the delay.Specific parameters to be considered are shown in Table 2.
For the data exchange task of terminal equipment, the proposed algorithm is represented by a quaternary data set: where st i is the number of subtasks included in the j i task, c i is the clock cycle required to complete the target task, λ i is the weight of j i task in computing resources, and μ i is the weight of j i task to computing resources.
The main equipment and edge nodes are characterized by ternary data sets: where cn k is the corresponding node calculated, cpu k is the vacancy rate of computing resources, mem k is the idle rate of computing memory during node calculation, and t k is the time for a single terminal intelligent device to run independently on the corresponding edge computing node.Based on the above definition, the cloud computing time can be quantitatively analyzed with the minimum delay as the goal.When the edge node k performs the data interaction task i, the calculated time delay is: When the main device executes the data interaction task i, the calculation time delay is: Then, it can be found that the request response time RT edge (i,k) of the edge node is: When the master node executes the data interaction task, the request response time RT cloud (i,0) is: In order to minimize the response time of the request, the method adopted is to choose the local edge and the main device node with short response to perform this task.After the task is submitted, the scheduling decision is made by the edge node first.In order to judge which node is the task executor, T edge is removed from the calculation response time for comparison during the decision-making.The judgment formula is as follows: (1) www.nature.com/scientificreports/There are two data transmission flows from edge node to master device node and from master device node to edge node in data collaboration, all of which are acted by each edge node's own collaboration controller.The specific process of data collaboration is shown in Fig. 6.
Actually, edge nodes in the system only undertake some application functions.When a user terminal accesses the system, the edge node and the master node need to jointly respond.The master device node is responsible for the global management of the distribution IoT in the whole region, and provides storage capacity for edge nodes.Edge nodes realize device management and control, collaborative decision-making and task execution, and jointly provide application layer services for user terminals.For the tasks submitted by the terminal at the edge node, the collaborative controller of the edge node first adopts scheduling decision or task forwarding according to the task type.The specific workflow is shown in Fig. 7. Based on the above scheme, the distribution network is partitioned, the edge nodes are determined after numbering each distributed terminal device, and the deployment scheme is constructed with the minimum delay as the goal.In the process of reasoning task, the distance between information generating unit and edge nodes, communication environment and other factors will affect the final output result.In order to further reduce the delay, deep neural network (DNN) and equipment edge synergy reasoning algorithm can be introduced.Divide DNN first, reasonably use computing resources to reduce computing complexity and redundancy of edge servers.By training DNN models with different capacities and multiple nodes, select DNN models suitable for application requirements, thus reducing computing burden and total delay.During normal operation, with the different running time, the output data of different DNN layers are also different, which shows great heterogeneity.In practice, the layer module running for a long time may not output data efficiently.Therefore, the DNN is divided into two parts, and the redundant and complicated part can be calculated in the server first in a low transmission efficiency mode, thus reducing the end-to-end waiting time.

Simulation experiment
Firstly, the proposed collaborative reasoning algorithm is simulated and verified on the cloud simulation platform.In the simulation experiment, a cloud computing center and three edge nodes are set up, and 30 groups of computing tasks are set up.Each group of computing tasks has three task units of the same type, and they are submitted to the three edge nodes respectively, and each group of computing tasks is separated by 1 ms to simulate the change of transmission delay in the actual working process.In the process of simulation, the time delay of service agent module is changed to simulate, and the effect of task coordination processing submitted to edge nodes is simulated and compared.Figure 8 depicts the execution time of different computing tasks submitted to edge nodes under different scheduling methods.The solid line represents the collaborative reasoning algorithm of minimizing response time proposed in this paper, the short dotted line represents the polling scheduling algorithm, and the long dotted line represents the random scheduling algorithm.The results show that the method based on minimizing response time has stable task completion time and relatively short execution time.
Average the data in Fig. 8, and the average execution time of different methods is shown in Fig. 9.The results show that this method has the shortest average execution time.
As can be seen from Figs. 8 and 9, the system efficiency can be improved by reducing the time for users to submit tasks to edge nodes.This is mainly because the algorithm fully considers the computing capacity gap between cloud computing center and edge nodes and realizes it through optimized scheduling decisions.
In order to build the heterogeneous edge node network environment, four different edge devices are used: JetsonTX1, Jetson TX2, Jetson TK1 and Jetson NaNo.Each time the network topology is generated, it is randomly assigned to the edge nodes.Firstly, the characteristic state indicators of four edge devices are preprocessed by weight matrix.And the smaller the number, the better the running state.The processing results of state indicators are shown in Table 3.
For different computing performance parameters of four types of equipments, the Paleo framework is used to test and run the delay parameters on the edge nodes of each branch, and the running progress is recorded as shown in Table 4.
Compared with the delay time of existing cloud servers, it can be found that the efficiency has been improved in the later stage of optimization.In the method based on edge computing, the average delay of different networks is simulated, and the bandwidth between nodes is set to 1Mbps.The results are shown in Table 5.
The test results show that compared with other experiments, when the instructions uploaded by the information generation unit are cancelled from the same partition in the network, the simulation experiment data analysis takes the lowest time with the same inference accuracy.( 7)

Conclusion
Aiming at the problems of heavy computing tasks, excessive reasoning delay, and high redundancy of complex data acquisition, this paper adopts cloud-edge coordination based on edge computing technology and the combination of IoT and intelligent distribution network, and develops coordination strategies through three aspects: computing, data and service coordination.Based on the PTN network structure, a multi-terminal node distribution model of distribution IoT is proposed, and a distributed management model of distribution network IoT based on edge multi-node collaborative computing is established.Finally, the distribution autonomy and collaborative management and control of the distribution network are realized, effectively avoiding the problem of traditional centralized management.

Table 1 .
Data interface definition. .When the superior control fails, each subordinate control will not respond, and it can continue to work according to the operation rules.

Table 2 .
Minimum delay parameter description.

Table 4 .
The experiment results.

Table 5 .
Comparison of experimental results.