Power Edge Cloud Collaborative Management Method Based on HarmonyOS

Abstract

The present invention relates to a power edge cloud collaborative management method based on HarmonyOS, which includes the following steps: S1: Deploy intelligent edge nodes, set up embedded AI acceleration units, and run HarmonyOS on all intelligent edge nodes to achieve seamless collaboration of devices using its distributed characteristics; S2: Adopt Mesh network technology to achieve self-organizing connection of edge devices, design direct communication paths between edge devices, and achieve rapid processing of local problems. S3: In response to the real-time requirements of the power system, optimize the MQTT / CoAP protocol stack to improve the efficiency and reliability of message delivery, and utilize the network slicing characteristics of 5G to allocate dedicated network resources for different types of power applications. S4: Build a security architecture based on the zero-trust principle to enhance device access control and data transmission security, use blockchain technology for identity authentication and data integrity protection to prevent data tampering and forgery. The present invention effectively improves the local distributed decision-making ability and global collaborative ability.

Description

Technical Field

[0001] The present invention relates to the technical field of edge computing, and in particular, to a power edge cloud collaborative management method based on HarmonyOS. Background Art

[0002] With the rapid development of the power system towards the intelligent direction, the traditional centralized cloud computing mode has been difficult to meet the power grid's requirements for low-latency, high-reliability, and high-security communication. The wide application of smart grids, such as distributed energy access, dynamic energy management, real-time fault protection, etc., requires edge devices to have stronger independent processing capabilities to cope with emergencies and massive data processing. On the other hand, the complexity and diversity of the power system also pose requirements for differentiated resource allocation and precise security control of the communication network. At the same time, with the rise of emerging technologies such as edge computing, blockchain, and 5G network slicing, in order to further improve the collaborative management ability of the power system, the collaborative management mode based on edge cloud has become a better choice. Summary of the Invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide a power edge cloud collaborative management method based on HarmonyOS, which can effectively improve the local distributed decision-making ability and global collaborative ability.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] A power edge cloud collaborative management method based on HarmonyOS, comprising the following steps

[0006] S1: Deploy intelligent edge nodes, set up an embedded AI acceleration unit, and run HarmonyOS on all intelligent edge nodes to achieve seamless collaboration of devices using its distributed characteristics;

[0007] S2: Adopt Mesh network technology to achieve self-organizing connection of edge devices, improve the robustness and self-healing ability of the system; reduce dependence on the cloud, design a direct communication path between edge devices, and achieve rapid processing of local problems.

[0008] S3: In response to the real-time requirements of the power system, optimize the MQTT / CoAP protocol stack to improve the efficiency and reliability of message transmission, and utilize the network slicing characteristics of 5G to allocate dedicated network resources for different types of power applications to improve communication quality.

[0009] S4: Build a security architecture based on the zero-trust principle to enhance device access control and data transmission security, and use blockchain technology for identity authentication and data integrity protection to prevent data tampering and forgery.

[0010] Further, S1 is specifically as follows:

[0011] S11: Each intelligent edge node runs HarmonyOS and acts as an intelligent terminal, with edge storage, computing capabilities, distributed service capabilities, and supports AI inference; through the broadcast discovery mechanism of the soft bus in the HarmonyOS distributed architecture, dynamic networking between edge nodes is completed to establish a collaborative working environment and achieve resource sharing of storage, computing, and task management for distributed devices; through the HarmonyOS distributed communication framework, direct communication between edge nodes is preferentially supported, and then combined with the cloud for task linkage management:

[0012] S12: Based on the distributed soft bus of HarmonyOS, node devices automatically discover and complete spatial perception and connection; resource sharing among edge nodes, including storage and computing capabilities, and multiple nodes collaborate to execute large tasks;

[0013] S13: Through the HarmonyOS distributed communication framework, peer-to-peer data exchange is preferentially realized between edge devices, reducing dependence on the cloud.

[0014] Furthermore, based on the distributed soft bus of HarmonyOS, node devices automatically discover and complete spatial perception and connection, specifically as follows:

[0015] In the HarmonyOS environment, the distributed soft bus supports the use of the broadcast discovery protocol to complete the spatial perception of multiple nodes through broadcast access or peripheral scanning;

[0016] Based on the node identifier, each device automatically authenticates and securely accesses the group;

[0017] Perceive the relative distance and topological relationship between nodes, and generate a collaborative communication networking topology according to the network quality and hardware capabilities of the devices;

[0018] Each node contributes its local storage space to the collaborative group:

[0019] Use the distributed file system of HarmonyOS to realize distributed file storage and shared access; multiple nodes collaborate to write the front-end data acquisition records and dynamically synchronize them to other edge nodes;

[0020] Cold data and hot data are respectively extracted from different nodes through hierarchical management to improve access speed.

[0021] Within the collaborative group, based on the size of the computing task and the node load situation, tasks are dynamically divided: CPU / NPU-intensive tasks are preferentially distributed to high-performance nodes; storage-intensive tasks are distributed and managed on nodes with larger storage space; and through the distributed soft bus, requests are uniformly scheduled to call the resources of other idle nodes to execute large-scale analysis tasks.

[0022] Furthermore, S2 is specifically as follows:

[0023] Deploy the Mesh network technology. After the intelligent edge device is powered on, it automatically joins the Mesh network using the broadcast discovery mechanism;

[0024] Establish a network topology, generate an adjacency matrix. The edge nodes periodically send node information based on the distributed soft bus of HarmonyOS, including: node ID and cost information, where the cost information includes link delay, RSSI, load, and remaining bandwidth; Store the information of directly connected nodes through an adjacency list:

[0025] A dj (i) = {j ∈ Nodes ∣ j is directly connected to i}

[0026] Among them, the adjacency matrix is expressed as:

[0027]

[0028] w ij represents the link weight;

[0029] Based on the link quality assessment, dynamically plan the optimal communication path between nodes;

[0030] Transmit data between devices through multi-hop routing. At the same time, detect the node status in real time and automatically bypass the failed nodes. After each routing adjustment is completed, synchronously update the network topology to the entire network;

[0031] According to the optimal communication path between nodes, establish a point-to-point direct communication link without cloud intervention; Regularly update the routing table and dynamically adjust the communication path;

[0032] Through the cooperation of the edge layer, quickly detect problems. If a node failure or temperature anomaly is found, the corresponding tasks are directly taken over by neighboring nodes, and the local problems are quickly processed through data cooperation and control logic transmission;

[0033] Through task dynamic distribution and the linkage between edge nodes, achieve the rapid scheduling of distributed logic;

[0034] In coordination with the distributed architecture, HarmonyOS decomposes the computing, data sharing, and control logic parts of the device to the edge layer.

[0035] Furthermore, based on the link quality assessment (RSSI, bandwidth), dynamically plan the optimal communication path between nodes, specifically as follows:

[0036] Periodically monitor the quality metrics of link l ij including RSSI, bandwidth, and load;

[0037] Update the link weight w ij and store it in the adjacency matrix;

[0038] ;

[0039] Among them, α, β, and γ are weight coefficients; RSSI ij is the signal strength of link l ij ; BW ij is the remaining available bandwidth of link l ij ; C j is the current load of the receiving node;

[0040] It is defined that in the network graph G = (N, A), find the optimal path P from the source node s to the target node t s→t , so that the total path weight is minimized; N is the set of intelligent edge nodes of the network; A is the set of communication links in the network;

[0041] Use the Dijkstra algorithm to generate the optimal path to the target node in real time according to the dynamic adjacency matrix,

[0042] Initialize the shortest distance d[i] of the current node i to ∞, for i ∈ N, except d[s] = 0;

[0043] Update the distances of neighbor nodes:

[0044] ;

[0045] Continuously iterate through the unvisited nodes until the target node t is visited;

[0046] Total path weight:

[0047]

[0048] When the RSSI or bandwidth of link l ij changes, adjust the weight w ij in real time, and recalculate the path P; if the RSSI ij is too low or BW ij = 0, mark the weight as infinity (∞), and trigger replanning.

[0049] Furthermore, according to the optimal communication path between nodes, establish a point-to-point direct communication link without cloud intervention; regularly update the routing table and dynamically adjust the communication path, specifically as follows:

[0050] Each node independently maintains a dynamic routing table to record all target nodes and their communication paths. The structure of the routing table of node i :

[0051] ;

[0052] Among them, NextHop is the next-hop node to the target node;

[0053] Each edge node looks up its routing table according to the target node t to obtain the next-hop node NextHop:

[0054] NextHop = R i [t].NextHop;

[0055] If NextHop is the target node t, direct communication is carried out; R i [t] is the routing table obtained according to the target node t;

[0056] Otherwise, the data is forwarded to the next-hop node until the target node is reached;

[0057] Data transmission is forwarded hop by hop through the nodes in the target path, following the process below:

[0058] The source node generates the path P according to the routing table s→t ;

[0059] The data packet is forwarded hop by hop according to each node in the path;

[0060] The target node t receives and processes the data;

[0061] In the HarmonyOS environment, its distributed soft bus is utilized to support direct communication between different edge devices, eliminating intermediate nodes and cloud intervention.

[0062] Furthermore, in response to the real-time requirements of the power system, the MQTT / CoAP protocol stack is optimized to improve the efficiency and reliability of message transmission, as follows:

[0063] MQTT uses the lightweight version of MQTT-SN to adapt to the resource-constrained situation of edge devices: uses UDP-based transmission to avoid complex connection management and improve real-time performance;

[0064] CoAP uses the Non-confirmable mode to process low-priority messages, reducing handshake latency; optimizes ACK messages and combines with the fast timeout retransmission mechanism of UDP to ensure reliable transmission of critical data;

[0065] CBOR encoding is enabled for MQTT / CoAP message content using efficient compression to replace traditional JSON, reducing data volume; data merging compression is enabled in scenarios with high link load, binding multiple data for transmission to reduce message-level overhead;

[0066] Improve the QoS strategy:

[0067] MQTT QoS mechanism:

[0068] QoS 0: Low-priority monitoring signal, used for non-critical monitoring data, and UDP is used for transmission to reduce the overhead of establishing connections;

[0069] QoS 1: Medium-priority power acquisition signal, with an optimized retransmission strategy. Set the dynamic retransmission timeout Tretry(i’), and adjust the retransmission interval of messages according to network latency and packet loss rate:

[0070]

[0071] where, is the th retry count, is the base retransmission time, is the maximum retransmission time;

[0072] QoS 2: Critical protection control signal: By means of 5G URLLC slices, avoid the delay caused by retransmission,

[0073] In CoAP, improve the timeout hypothesis model:

[0074] According to the particularity of the acquisition environment, dynamically update the timeout time RTT estimate value:

[0075] ;

[0076] where a is the weight; R(s') refers to the actual network round-trip time measured at the current moment s';

[0077] Use EWMA to smooth the measurement of RTT, reduce unnecessary retries, especially in an unstable network;

[0078] Integrate a time synchronization module based on PTP / NTP in the protocol stack to ensure strict timestamp accuracy for power protection signals and event messages;

[0079] Add a priority identifier to MQTT / CoAP messages and process them through a dual-queue scheduling mechanism.

[0080] Furthermore, utilize the network slicing characteristics of 5G to allocate dedicated network resources for different types of power applications to improve communication quality, as follows:

[0081] Divide three types of slices according to different service requirements of the power system:

[0082] URLLC slice: Used for protection and control communication, such as line protection, fault detection, and automation control signals;

[0083] Configure high-priority resource scheduling to ensure bandwidth and reliability (e.g., prevent data packet loss through HARQ (Hybrid ARQ));

[0084] eMBB (Enhanced Mobile Broadband) slice: Used to transmit data that requires high bandwidth, such as power status monitoring and video monitoring;

[0085] Configure the throughput to be greater than the threshold and adapt to the intelligent monitoring system to transmit large data waveforms, event records, or analysis results;

[0086] mMTC (Massive Machine Type Communication) slice, for data collection of wide-area power terminal devices (such as electricity meters and distribution network devices), optimizing terminal energy consumption and signaling overhead.

[0087] The network slice resource allocation is based on the actual load demand of the power system and is dynamically adjusted:

[0088] Slice resource allocation function:

[0089] The slice resource Rs is determined by the allocation ratio and is dynamically adjusted according to the task priority and latency requirements:

[0090] ;

[0091] where, P s is the slice priority; Q s is the slice real-time load; R total is the total network bandwidth; is the weighted sum of the priorities and real-time load factors of all n' slices, and the subscript i'' is used to traverse all slices;

[0092] And according to the real-time monitoring of the power system, use the network slice orchestrator to dynamically adjust the weights of each slice,

[0093] Use the 5G QoS flow mechanism, combined with the priorities of MQTT / CoAP messages, to provide the most suitable slices for power communications with different priorities.

[0094] Furthermore, S4 is specifically as follows:

[0095] Design the identity authentication and permission management of the power system according to the zero-trust principle, configure a unique identifier for each edge node, and establish secure communication between devices using the PKI public key system; implement real-time identity verification when the node connects, accesses, or communicates, and continuously verify the reliability of the device.

[0096] Introduce blockchain in data exchange and exception handling: Use blockchain to record device behavior logs (such as running status or instructions) to ensure the immutability of data; Provide the ability to deposit evidence on the chain to achieve data auditing and tracking.

[0097] Use lightweight AES encryption algorithm to encrypt all communication data of edge nodes; Protect CoAP transmission based on UDP's TLS.

[0098] A power edge cloud collaborative management system based on HarmonyOS, including a processor, a memory, and a computer program stored on the memory. When the processor executes the computer program, it specifically executes the steps in a power edge cloud collaborative management method based on HarmonyOS as described above.

[0099] The present invention has the following beneficial effects:

[0100] 1. Based on the distributed characteristics of HarmonyOS and combined with an embedded AI acceleration unit, the present invention can achieve the intelligence and efficient collaboration of power edge devices, reduce the dependence on cloud computing, and significantly improve the real-time performance and stability of edge communication by introducing Mesh network technology and optimizing the MQTT / CoAP protocol stack design;

[0101] 2. Utilizing the network slicing characteristics of 5G, the present invention can provide customized communication resources for different types of power service scenarios (such as protection control, status monitoring, and massive data collection) to meet diverse needs, and through the integration of zero-trust architecture and blockchain technology, provide reliable identity authentication and data integrity guarantee for the power system, comprehensively enhancing the security and defense capabilities of the power grid;

[0102] 3. By utilizing HarmonyOS and a distributed architecture, the present invention gives full play to the edge computing ability, reduces the dependence on the cloud, and improves the autonomy of the system and the ability to handle local problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0104] The following further elaborates the present invention in detail with reference to the drawings and specific embodiments:

[0105] Refer to Figure 1 , in the present invention, a power edge cloud collaborative management method based on HarmonyOS is provided, including the following steps

[0106] S1: Deploy intelligent edge nodes, set up an embedded AI acceleration unit, and run HarmonyOS on all intelligent edge nodes to achieve seamless collaboration of devices using its distributed characteristics;

[0107] S2: Adopt Mesh network technology to achieve self-organizing connection of edge devices, improve the robustness and self-healing ability of the system; reduce dependence on the cloud, design direct communication paths between edge devices, and achieve rapid processing of local problems;

[0108] S3: For the real-time requirements of the power system, optimize the MQTT / CoAP protocol stack to improve the efficiency and reliability of message transmission. Utilize the network slicing characteristics of 5G to allocate dedicated network resources for different types of power applications and improve communication quality;

[0109] S4: Build a security architecture based on the zero-trust principle to enhance device access control and data transmission security. Use blockchain technology for identity authentication and data integrity protection to prevent data tampering and forgery.

[0110] In this embodiment, S1 is as follows:

[0111] S11: Each intelligent edge node runs HarmonyOS and acts as an intelligent terminal, with edge storage, computing capabilities, distributed service capabilities, and supports AI inference; through the broadcast discovery mechanism of the soft bus in the distributed architecture of HarmonyOS, complete dynamic networking between edge nodes, establish a collaborative working environment, and achieve resource sharing of storage, computing, and task management for distributed devices; through the distributed communication framework of HarmonyOS, give priority to supporting direct connection communication between edge nodes, and then combine with the cloud for task linkage management:

[0112] S12: Based on the distributed soft bus of HarmonyOS, node devices automatically discover and complete space perception and connection; edge node resource sharing, including storage and computing capabilities, and multiple nodes cooperate to execute large tasks;

[0113] S13: Through the distributed communication framework of HarmonyOS, give priority to achieving point-to-point data exchange between edge devices and reduce dependence on the cloud.

[0114] In this embodiment, based on the distributed soft bus of HarmonyOS, node devices automatically discover and complete space perception and connection, as follows:

[0115] In the HarmonyOS environment, the distributed soft bus supports the use of the broadcast discovery protocol to complete the space perception of multiple nodes through broadcast access or peripheral scanning;

[0116] Based on node identifiers (such as MAC addresses, device IDs), each device automatically authenticates and securely accesses the group;

[0117] Perceive the relative distance and topological relationship between sensing nodes (such as distance sensing technology: ultrasonic sensors or signal strength evaluation), and generate a collaborative communication networking topology according to the network quality and hardware capabilities of the device (such as NPU computing power, memory size).

[0118] Each node contributes its local storage space to the collaborative group:

[0119] Use the distributed file system of HarmonyOS (Harmony Distributed File System, HDFS) to achieve file storage distribution and shared access; multiple nodes collaborate to write the front-end data acquisition records (such as power monitoring data such as voltage and current), and dynamically synchronize them to other edge nodes;

[0120] Cold data and hot data are respectively extracted from different nodes through hierarchical management to improve access speed.

[0121] Within the collaborative group, based on the size of the computing task and the node load situation, tasks are dynamically divided: CPU / NPU-intensive tasks (such as real-time evaluation of power equipment failures) are preferentially distributed to high-performance nodes; storage-intensive tasks (such as aggregation of historical data for load prediction) are distributed and managed on nodes with larger storage space; and large-scale analysis tasks are executed by uniformly scheduling requests through the distributed soft bus to call the resources of other idle nodes.

[0122] In this embodiment, S2 is as follows:

[0123] Deploy the Mesh network technology, and after the intelligent edge device is powered on, it automatically joins the Mesh network using the broadcast discovery mechanism;

[0124] Establish a network topology, generate an adjacency matrix, and the edge nodes periodically send node information based on the distributed soft bus of HarmonyOS, including: node ID and cost information, where the cost information includes link delay, RSSI (signal strength), load, and remaining bandwidth; store the information of directly connected nodes through an adjacency list:

[0125] A dj (i)={j∈Nodes ∣ j is directly connected to i}

[0126] Among them, the adjacency matrix is expressed as:

[0127]

[0128] w ij Represents the link weight;

[0129] Based on the link quality assessment, dynamically plan the optimal communication path between nodes;

[0130] Transmit data between devices through multi-hop routing, and at the same time, real-time detect the node status, automatically bypass the failed nodes, and after each routing adjustment is completed, synchronously update the network topology to the whole network;

[0131] Establish a point-to-point direct communication link according to the optimal communication path between nodes without cloud intervention; regularly update the routing table and dynamically adjust the communication path;

[0132] Through the cooperation of the edge layer, quickly detect problems. If node failures or temperature anomalies are found, the corresponding tasks are directly taken over by neighboring nodes, and the local problems are quickly processed through data cooperation and control logic transfer;

[0133] Through dynamic task distribution and the linkage between edge nodes, achieve fast scheduling of distributed logic;

[0134] In cooperation with the distributed architecture, HarmonyOS decomposes the computing, data sharing, and control logic parts of the device to the edge layer.

[0135] In this embodiment, based on link quality assessment (RSSI, bandwidth), dynamically plan the optimal communication path between nodes, specifically as follows:

[0136] Periodically monitor the quality metrics of link l ij including RSSI, bandwidth, and load;

[0137] Update the link weight w ij and store it in the adjacency matrix;

[0138] ;

[0139] where α, β, γ are weight coefficients; RSSI ij is the signal strength of link l ij ; BW ij is the remaining available bandwidth of link l ij ; C j is the current load of the receiving node;

[0140] Define in the network graph G=(N,A), find the optimal path P s→t from the source node s to the target node t, so that the total weight of the path is minimized; N is the set of intelligent edge nodes in the network; A is the set of communication links in the network;

[0141] Use the Dijkstra algorithm to generate the optimal path to the target node in real time according to the dynamic adjacency matrix,

[0142] Initialize the shortest distance d[i]=∞ of the current node i, for i∈N, except d[s]=0;

[0143] Update the distances of neighboring nodes:

[0144] ;

[0145] Continuously iterate through unvisited nodes until the target node t is visited;

[0146] Total path weight:

[0147]

[0148] When the RSSI or bandwidth of link l ij changes, adjust the weight w ij in real time, and recalculate the path P; if the RSSI ij is too low or the BW ij = 0, mark the weight as infinity (∞), and trigger replanning.

[0149] In this embodiment, according to the optimal communication path between nodes, a point-to-point direct communication link is established without cloud intervention; the routing table is updated regularly, and the communication path is adjusted dynamically, as follows:

[0150] Each node independently maintains a dynamic routing table that records all target nodes and their communication paths. The routing table structure of node i :

[0151] ;

[0152] where NextHop is the next-hop node to the target node;

[0153] Each edge node looks up its routing table according to the target node t to obtain the next-hop node NextHop:

[0154] NextHop = R i [t].NextHop;

[0155] If NextHop is the target node t, communicate directly; R i [t] is the routing table obtained according to the target node t;

[0156] Otherwise, forward the data to the next-hop node until the target node is reached;

[0157] Data transmission is forwarded hop by hop through the nodes in the target path, according to the following process:

[0158] The source node generates the path P according to the routing table s→t ;

[0159] The data packet is forwarded hop by hop through each node in the path;

[0160] The target node t receives and processes the data;

[0161] In the HarmonyOS environment, its distributed soft bus is utilized to support direct communication between different edge devices, eliminating intermediate nodes and cloud intervention.

[0162] In this embodiment, to meet the real-time requirements of the power system, the MQTT / CoAP protocol stack is optimized to improve the efficiency and reliability of message transmission, as follows:

[0163] MQTT (Message Queuing Telemetry Transport) uses the lightweight version of MQTT-SN to adapt to the resource-constrained situation of edge devices: it uses UDP-based transmission (instead of TCP) to avoid complex connection management and improve real-time performance;

[0164] CoAP (Constrained Application Protocol) uses the Non-confirmable mode to handle low-priority messages, reducing handshake latency; it optimizes ACK messages and combines with the fast timeout retransmission mechanism of UDP to ensure reliable transmission of critical data;

[0165] CBOR (Concise Binary Object Representation) encoding is enabled for MQTT / CoAP message content using efficient compression to replace traditional JSON, reducing data volume; data merging compression is enabled in scenarios with high link load, binding multiple pieces of data for transmission to reduce message-level overhead;

[0166] The power system requires communication with different reliability levels, such as low-latency protection signals and highly reliable data acquisition. The QoS strategy is improved as follows:

[0167] MQTT QoS mechanism (for real-time power scenarios):

[0168] QoS 0: Low-priority monitoring signals, used for non-critical monitoring data, and UDP transmission is used to reduce the overhead of establishing connections;

[0169] QoS 1: Medium-priority power acquisition signals, with the retransmission strategy optimized. The dynamic retransmission timeout Tretry(i’) is set, and the retransmission interval of messages is adjusted according to network latency and packet loss rate:

[0170]

[0171] where is the number of retry attempts, is the base retransmission time, is the maximum retransmission time;

[0172] QoS 2: Critical protection control signals: By leveraging 5G URLLC slices, the delay caused by retransmission is avoided.

[0173] In CoAP, improve the timeout assumption model:

[0174] According to the particularity of the acquisition environment, dynamically update the estimated value of the timeout time RTT (round-trip time):

[0175] ;

[0176] Among them, a is the weight; R(s') refers to the actual network round-trip time measured at the current moment s';

[0177] Use EWMA (Exponentially Weighted Moving Average) to smooth the measurement of RTT, reduce unnecessary retries, especially in an unstable network;

[0178] Integrate a time synchronization module based on PTP (Precision Time Protocol) / NTP (Network Time Protocol) in the protocol stack to ensure strict timestamp accuracy of power protection signals and event messages;

[0179] Add a priority identifier Tenqueue to the MQTT / CoAP message (such as adding a priority field to the message header) and process it through a dual-queue scheduling mechanism (high / normal priority queues).

[0180] High priority: Abnormal information of grid equipment (such as overload, short-circuit signals);

[0181] Normal priority: Monitoring data, diagnostic status;

[0182] Tenqueue = P(x) + Tarrival(x)

[0183] Among them, P(x) represents the priority of message x, and Tarrival(x) represents the message arrival time. High-priority messages are guaranteed to be consumed first.

[0184] In this embodiment, utilize the network slicing characteristics of 5G to allocate dedicated network resources for different types of power applications to improve communication quality, as follows:

[0185] Divide three types of slices according to different service requirements of the power system:

[0186] URLLC (Ultra-Reliable Low-Latency Communication) slice: Used for protection and control communication, such as line protection, fault detection, and automation control signals, with a latency requirement of less than 1 ms;

[0187] Configure high-priority resource scheduling to ensure bandwidth and reliability (such as preventing data packet loss through HARQ (Hybrid ARQ));

[0188] eMBB (Enhanced Mobile Broadband) slice: used to transmit data that requires high bandwidth, such as power status monitoring and video monitoring;

[0189] Configure the throughput to be greater than the threshold and adapt to the intelligent monitoring system to transmit large data waveforms, event records or analysis results;

[0190] mMTC (Massive Machine Type Communication) slice, for data collection of wide-area power terminal devices (such as electricity meters, distribution network devices), optimizing terminal energy consumption and signaling overhead.

[0191] The network slice resource allocation is based on the actual load demand of the power system and dynamically adjusted:

[0192] Slice resource allocation function:

[0193] The slice resource Rs is determined by the allocation ratio and is dynamically adjusted according to the task priority and latency requirements:

[0194] ;

[0195] where P s is the slice priority; Q s is the slice real-time load; R total is the total network bandwidth; is the weighted sum of the priorities and real-time load factors of all n' slices, and the subscript i'' is used to traverse all slices;

[0196] And according to the real-time monitoring of the power system, use the network slice orchestrator to dynamically adjust the weights of each slice, for example:

[0197] When an accident occurs, the weight of the URLLC slice for protecting communication will be significantly increased;

[0198] In normal monitoring scenarios, more resources are allocated to the eMBB and mMTC slices.

[0199] Utilize the 5G QoS flow mechanism, combined with the priorities of MQTT / CoAP messages, to provide the most suitable slice for power communications with different priorities.

[0200] Configure 5QI (5G QoS Identifier) to define latency and reliability targets for each flow:

[0201] The high-priority field matches URLLC;

[0202] The medium and low-priority fields match eMBB or mMTC.

[0203] In this embodiment, S4 is specifically:

[0204] Design the identity authentication and permission management of the power system according to the zero-trust principle, configure a unique identifier for each edge node, and establish secure communication between devices using the PKI public key system; implement real-time identity verification when the node connects, accesses, or communicates, and continuously verify the reliability of the device.

[0205] Introduce blockchain in data exchange and exception handling: use blockchain to record device behavior logs (such as running status or instructions) to ensure data immutability; provide the ability to deposit evidence on the chain to achieve data auditing and tracking.

[0206] Use the lightweight AES encryption algorithm to encrypt all communication data of edge nodes; protect CoAP transmission based on UDP-TLS.

[0207] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0208] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0209] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable apparatus provide steps for realizing the functions specified in one process or a plurality of processes and / or blocks. Figure 1 One process or a plurality of processes and / or blocks Figure 1 Steps for realizing the functions specified in one block or a plurality of blocks.

[0211] As described above, the above are only the preferred embodiments of the present invention, and are not limitations on the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A power edge cloud collaborative management method based on HarmonyOS, characterized in that It includes the following steps S1: Deploy intelligent edge nodes, set up embedded AI acceleration units, and run HarmonyOS on all intelligent edge nodes to achieve seamless collaboration of devices by leveraging its distributed characteristics; S2: Adopt Mesh network technology to achieve self-organizing connection of edge devices, and design direct communication paths between edge devices to achieve rapid processing of local problems; S3: Optimize the MQTT / CoAP protocol stack for the real-time requirements of the power system, and utilize the network slicing characteristics of 5G to allocate dedicated network resources for different types of power applications; S4: Build a security architecture based on the zero-trust principle to enhance device access control and data transmission security, use blockchain technology for identity authentication and data integrity protection to prevent data tampering and forgery; The specific content of S2 is as follows: Deploy Mesh network technology. After the intelligent edge device is powered on, it automatically joins the Mesh network using the broadcast discovery mechanism; Establish a network topology and generate an adjacency matrix. The edge nodes periodically send node information based on the distributed soft bus of HarmonyOS, including: node ID and cost information, where the cost information includes link delay, RSSI, load, and remaining bandwidth; store the information of directly connected nodes through an adjacency list: A dj (i) = {j ∈ Nodes | j is directly connected to i} Among them, the adjacency matrix is expressed as: ； w ij represents a link weight; Based on link quality assessment, dynamically plan the optimal communication path between nodes; Transmit data between devices through multi-hop routing, and at the same time, detect the node status in real time, automatically bypass failed nodes, and synchronously update the network topology to the entire network after each routing adjustment is completed; Establish a point-to-point direct communication link according to the optimal communication path between nodes without cloud intervention; regularly update the routing table and dynamically adjust the communication path; Quickly detect problems through edge layer collaboration. If a node failure or temperature anomaly is found, the adjacent node directly takes over the corresponding task, and the local problem is quickly processed through data collaboration and control logic transfer; Through task dynamic distribution and linkage between edge nodes, achieve rapid scheduling of distributed logic; In cooperation with the distributed architecture, HarmonyOS decomposes the computing, data sharing, and control logic parts of the device to the edge layer.

2. The method for power edge cloud collaborative management based on HarmonyOS according to claim 1, wherein The specific content of S1 is as follows: S11: Each intelligent edge node runs HarmonyOS and acts as an intelligent terminal, with edge storage, computing capabilities, distributed service capabilities, and supports AI inference; through the broadcast discovery mechanism of the soft bus in the HarmonyOS distributed architecture, complete dynamic networking between edge nodes, establish a collaborative working environment, and achieve resource sharing of storage, computing, and task management of distributed devices; through the HarmonyOS distributed communication framework, preferentially support direct communication between edge nodes, and then combine with the cloud for task linkage management: S12: Based on the distributed soft bus of HarmonyOS, node devices automatically discover and complete spatial perception and connection; share edge node resources, including storage and computing capabilities, and multiple nodes collaborate to execute large tasks; S13: Through the HarmonyOS distributed communication framework, preferentially achieve point-to-point data exchange between edge devices to reduce dependence on the cloud.

3. The method for power edge cloud collaborative management based on HarmonyOS according to claim 2, wherein, The distributed soft bus based on HarmonyOS enables node devices to automatically discover and complete space perception and connection, as follows: In the HarmonyOS environment, the distributed soft bus supports the use of the broadcast discovery protocol to complete the space perception of multiple nodes through broadcast access or peripheral scanning; Based on the node identifier, each device automatically authenticates and securely accesses the group; Perceive the relative distance and topological relationship between nodes, and generate a collaborative communication networking topology according to the network quality and hardware capabilities of the devices; Each node contributes its local storage space to the collaborative group: Use the distributed file system of HarmonyOS to achieve distributed file storage and shared access; multiple nodes collaborate to write the data acquisition records at the front end and dynamically synchronize them to other edge nodes; Cold data and hot data are extracted from different nodes through hierarchical management respectively to improve the access speed; Within the collaborative group, tasks are dynamically divided based on the size of the computing task and the node load: CPU / NPU-intensive tasks are preferentially distributed to high-performance nodes; storage-intensive tasks are distributed and managed on nodes with larger storage space; and large-scale analysis tasks are executed by uniformly scheduling requests through the distributed soft bus to call the resources of other idle nodes.

4. The method for power edge cloud collaborative management based on HarmonyOS according to claim 1, characterized in that, The dynamic planning of the optimal communication path between nodes based on link quality assessment is as follows: Periodically monitor the quality metrics of link l ij , including RSSI, bandwidth, and load; Update the link weight w ij and store it in the adjacency matrix; ； where α, β, γ are weight coefficients; RSSI ij is the signal strength of link l ij ; BW ij is the remaining available bandwidth of link l ij ; C j is the current load of the receiving node Define in the network graph G = (N, A), find the optimal path P from the source node s to the target node t s→t , to minimize the total weight of the path; N is the set of intelligent edge nodes of the network; A is the set of communication links in the network; Use the Dijkstra algorithm to generate the optimal path to the target node in real time according to the dynamic adjacency matrix, Initialize the shortest distance d[i] of the current node i to ∞, for i ∈ N, except d[s]=0; Update the distance of the neighbor nodes: ； Continuously iterate through the unvisited nodes until the target node t is visited; Total path weight: ； When the link l ij experiences a change in RSSI or bandwidth, adjust the weight w ij in real time and recalculate the path P; if the RSSI ij is too low or the BW ij = 0, mark the weight as infinity (∞) and trigger replanning.

5. The power edge cloud collaborative management method based on HarmonyOS according to claim 1, characterized in that, The establishment of a point-to-point direct communication link according to the optimal communication path between nodes without cloud intervention; regularly update the routing table and dynamically adjust the communication path, as follows: Each node independently maintains a dynamic routing table to record all target nodes and their communication paths. The structure of the routing table of node i : ； Among them, NextHop is the next-hop node to the target node; R i [t] is the routing table obtained according to the target node t; Each edge node looks up its routing table according to the target node t to obtain the next-hop node NextHop: NextHop=R i [t].NextHop; If NextHop is the target node t, then communicate directly; Otherwise, forward the data to the next-hop node until the target node is reached; Data transmission is forwarded hop by hop through the nodes in the target path, according to the following process: The source node generates path P according to the routing table s→t ; The data packet is forwarded hop by hop through each node in the path; The target node t receives and processes the data; In the HarmonyOS environment, use its distributed soft bus to support direct communication between different edge devices, eliminating intermediate nodes and cloud intervention.

6. The method for collaborative management of power edge cloud based on HarmonyOS according to claim 1, wherein The optimization of the MQTT / CoAP protocol stack for the real-time requirements of the power system is as follows: MQTT uses the lightweight version of MQTT-SN to adapt to the resource-constrained situation of edge devices: use UDP-based transmission to avoid complex connection management and improve real-time performance; CoAP uses the Non-confirmable mode to process low-priority messages to reduce handshake latency; optimize the ACK message and combine it with the fast timeout retransmission mechanism of UDP to ensure the reliable transmission of key data; Use efficient compression to enable CBOR encoding for MQTT / CoAP message content, replacing traditional JSON to reduce data volume; enable data merging and compression in scenarios with high link load, bundle multiple data for transmission, and reduce message-level overhead; Improved QoS policy: MQTT QoS mechanism: QoS 0: low priority monitoring signal, used for non-critical monitoring data, using UDP transmission to reduce the overhead of establishing connections; QoS 1: Medium priority power collection signal, optimized retransmission strategy, set dynamic retransmission timeout Tretry(i'), adjust the message retransmission interval according to network delay and packet loss rate: ； Among them, is the th retry count, is the base retransmission time, is the maximum retransmission time; QoS 2: Key protection control signal: With 5G URLLC slicing, delays caused by retransmission are avoided. In CoAP, the timeout assumption model is improved: Dynamically update the estimated timeout RTT value based on the particularity of the collection environment: ； Where a is the weight; R(s') refers to the actual network return time measured at the current time s'; Use EWMA to smoothly measure RTT and reduce unnecessary retries, especially in unstable networks; Integrate a PTP / NTP-based time synchronization module into the protocol stack to ensure strict timestamp accuracy for power protection signals and event messages; Add priority identifiers to MQTT / CoAP messages and process them through a dual-queue scheduling mechanism.

7. The method for collaborative management of power edge cloud based on HarmonyOS according to claim 6, characterized in that The network slicing feature of 5G is used to allocate dedicated network resources for different types of power applications, as follows: According to the different business requirements of the power system, three types of slices are divided: URLLC slice: used for protection and control communications, including line protection, fault detection, and automation control signals; Configure high-priority resource scheduling and prevent data packet loss through HARQ; eMBB slice: used to transmit data that requires high bandwidth, including power status monitoring and video surveillance data; The configuration throughput is greater than the threshold and is adapted to the intelligent monitoring system to transmit big data waveforms, event records or analysis results; mMTC slicing collects data from wide-area power terminal devices, optimizing terminal energy consumption and signaling overhead; Network slice resource allocation is based on the actual load demand of the power system and is adjusted dynamically: Slice resource allocation function: The sliced resource Rs is determined by the allocation ratio and is dynamically adjusted according to the task priority and latency requirements: ； Among them, P s is the slice priority; Q s is the slice real-time load; R total is the total network bandwidth; is the weighted sum of the priorities and real-time load factors of all n' slices; Based on the real-time monitoring of the power system, the network slice orchestrator is used to dynamically adjust the weight of each slice. The QoS flow mechanism of 5G is utilized, combined with the priority of MQTT / CoAP messages, to provide the most suitable slices for power communications of different priorities.

8. The method for collaborative management of power edge cloud based on HarmonyOS according to claim 1, wherein, The S4 is specifically: Design the identity authentication and permission management of the power system according to the zero-trust principle, configure a unique identifier for each edge node, and use the PKI public key system to establish secure communication between devices; implement real-time identity authentication when nodes are connected, accessed or communicated, and continuously verify the reliability of the equipment; Introducing blockchain in data exchange and exception handling: Using blockchain to record device behavior logs to ensure that data cannot be tampered with; providing on-chain evidence storage capabilities to achieve data auditing and tracking; Use the lightweight AES encryption algorithm to encrypt all communication data of edge nodes; use TLS over UDP to protect CoAP transmission.

9. A power edge cloud collaborative management system based on HarmonyOS, characterized in that, It includes a processor, a memory, and a computer program stored on the memory. When the processor executes the computer program, it specifically executes the steps in a method for power edge cloud collaborative management based on HarmonyOS as described in any one of claims 1-8.

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