Intelligent electric energy meter system based on RF mesh ad hoc network and communication method thereof

Abstract

The invention relates to the technical field of intelligent electric energy meters, in particular to an intelligent electric energy meter system based on an RF net-shaped ad hoc network and a communication method of the intelligent electric energy meter system. The system adopts an intelligent electric energy meter with an integrated gateway function as a gateway node in an RF network and uploads power utilization data and state information to a head end system; independent gateway equipment does not need to be additionally deployed, so that the hardware procurement cost, the installation and debugging cost and the long-term operation and maintenance cost are remarkably reduced; meanwhile, according to the intelligent electric energy meter system communication method based on the RF net-shaped ad hoc network, the optimal main path and the optimal standby path are selected for data transmission by dynamically calculating the comprehensive routing metric value, the reliability of the network is greatly improved through the multi-path redundancy design, and the system has the higher fault self-healing capacity; in addition, the intelligent electric energy meter integrated with the gateway function can monitor and optimize the network topology structure, the data flow distribution and the node resource utilization rate in real time through a built-in network management system, and the data transmission efficiency and the network stability are further improved.

Description

Technical Field

[0001] The present application relates to the technical field of smart electric energy meters, and in particular to a smart electric energy meter system based on an RF mesh ad hoc network and a communication method thereof. Background Art

[0002] Smart meter systems are at the core of modern power management, enabling real-time collection and transmission of power data through advanced metering infrastructure (AMI). Traditional AMI systems rely on radio frequency (RF) mesh networks to transmit meter data to the utility's headend system via independent gateways or data concentrator units (DCUs). For example, patent application publication number CN113423082A provides a method for efficiently collecting AMI system terminal data, allowing meters to directly report multi-dimensional data, including monthly and daily data, load curves, and some key events, to a concentrator or gateway. The concentrator or gateway then forwards this data to the headend system, enabling centralized management and analysis of the data.

[0003] In actual deployments, these gateway devices are often placed on infrastructure such as substations or utility poles, leveraging cellular LTE networks to achieve stable communication with headend systems. However, the complex design of independent gateways requires high-precision electronic components and multiple radio communication devices, which not only increases initial investment costs but also makes subsequent maintenance complex and expensive. Summary of the Invention

[0004] Based on this, it is necessary to provide a smart energy meter system based on RF mesh ad hoc network and its communication method to address the high cost, complex installation and difficult maintenance problems caused by the current smart meter system relying on independent gateways for data transmission.

[0005] In a first aspect, the present application provides a communication method for a smart energy meter system based on an RF mesh ad hoc network, wherein the smart energy meter includes a common smart energy meter and a smart energy meter with an integrated gateway function, wherein the common smart energy meter and the smart energy meter with an integrated gateway function communicate via an RF network, and the smart energy meter with an integrated gateway function communicates with a head-end system via a cellular network or satellite communication, the method comprising:

[0006] Step S1, each node in the RF network periodically evaluates the link quality with adjacent nodes and calculates a comprehensive routing metric based on the link quality;

[0007] Step S2, each node in the RF network selects a primary path and a backup path according to the comprehensive routing metric value;

[0008] Step S3: The smart energy meter with integrated gateway function sends power consumption data and status information to the head-end system, and optimizes data flow and network performance through the built-in network management system;

[0009] Step S4: the head-end system receives, stores and analyzes the power usage data and the status information.

[0010] Furthermore, the link quality evaluation indicators include signal strength indication, link quality indication, bit error rate and link availability history record.

[0011] Furthermore, the calculation formula of the comprehensive routing metric is: comprehensive routing metric = α×(link quality index)+β×(remaining power percentage)+γ×(inverse of hop count)+δ×(congestion index), where α, β, γ, and δ are all weight coefficients.

[0012] Furthermore, the method further comprises:

[0013] In step S5, each node in the RF network switches paths when detecting a high-traffic path or a communication link interruption.

[0014] Furthermore, the judgment indicators of the high-traffic path include data packet transmission rate, queue backlog length, bandwidth utilization and data volume transmitted within a time window.

[0015] Furthermore, step S2 includes:

[0016] Step S21, obtaining a reference threshold;

[0017] Step S22: The path whose comprehensive routing metric value is higher than the reference threshold is selected as the primary path, and the path with the second highest comprehensive routing metric value is selected as the backup path.

[0018] Furthermore, the built-in network management system optimizes data flow and network performance by:

[0019] Step S31: Periodically send detection packets to draw a real-time network topology map, assign relay and terminal roles based on location and power status, reduce the amount of active node data in high-density areas, and enhance connectivity in low-density areas;

[0020] Step S32: intelligently merge similar data from adjacent nodes, assign transmission priorities based on data types, and allocate different communication time slots to different nodes;

[0021] Step S33, adjusting the node sleep period according to network traffic and battery status, dynamically adjusting the node transmit power according to the link quality, and automatically switching to a low-interference channel when interference is detected;

[0022] Step S34, dynamically adjust the synchronization period according to the network delay status, adjust the retransmission timeout and maximum retransmission number based on the network congestion level, and set the broadcast packet TTL and propagation range;

[0023] Step S35 , identifying abnormal nodes, continuously evaluating the quality of all active links, predicting potential failures, and triggering local network reorganization when a key node failure or network partition is detected.

[0024] Furthermore, the abnormal node identification step includes:

[0025] Step S351, calculating the node's communication behavior abnormality index, energy behavior abnormality index, data consistency abnormality index, and network security abnormality index;

[0026] Step S352, obtaining a total anomaly score based on the weight of each anomaly indicator and the calculation result of step S351;

[0027] Step S353: Obtain an abnormality level of the node according to the total abnormality score; the abnormality level includes no abnormality, slight abnormality, moderate abnormality and severe abnormality.

[0028] Furthermore, the steps of local network reorganization include:

[0029] Step S354: When a single node failure is detected, the faulty node is isolated and path reconstruction and topology adjustment are performed;

[0030] Step S355: When a gateway node failure is detected, a backup gateway is enabled and load balancing and topology adjustment are performed;

[0031] Step S356: When a large-scale network partition is detected, the RF network is divided into multiple subnets and the availability of each subnet is evaluated. A bridge connection is established between the available subnets through the central nodes of the available subnets. The gateway node with the best performance is selected as the regional coordinator to rebuild the topology.

[0032] In the second aspect, the present application also provides a smart electricity meter system based on an RF mesh ad hoc network, the system including an ordinary smart electricity meter, a smart electricity meter with integrated gateway function and a head-end system, the ordinary smart electricity meter and the smart electricity meter with integrated gateway function communicate through an RF network, and the smart electricity meter with integrated gateway function communicates with the head-end system through a cellular network or a satellite communication network, and the ordinary smart electricity meter, the smart electricity meter with integrated gateway function and the head-end system communicate according to the communication method of the smart electricity meter system based on the RF mesh ad hoc network described in the first aspect.

[0033] The aforementioned smart energy meter system based on RF mesh ad hoc networks uses smart energy meters with integrated gateway functions as gateway nodes in the RF network, directly uploading electricity consumption data and status information to the headend system without the need for additional deployment of independent gateway devices, thereby significantly reducing hardware procurement costs, installation and commissioning costs, and long-term operation and maintenance costs. At the same time, the aforementioned smart energy meter system communication method based on RF mesh ad hoc networks dynamically calculates comprehensive routing metrics to select the optimal primary and backup paths for data transmission. This multi-path redundancy design significantly improves network reliability and gives the system stronger fault self-healing capabilities. Furthermore, the smart energy meter with integrated gateway function has a built-in network management system that can monitor and optimize network topology, data traffic distribution, and node resource utilization in real time, further improving data transmission efficiency and network stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Schematic diagram of the structure of a smart electric energy meter system based on an RF mesh ad hoc network in one embodiment;

[0035] Figure 2 1. A flow chart of a communication method for a smart electric energy meter system based on an RF mesh ad hoc network in one embodiment;

[0036] Figure 3 A topology diagram of a smart electric energy meter system based on an RF mesh ad hoc network in one embodiment;

[0037] Figure 4 A network topology diagram of an adjusted smart energy meter system in a single-node failure scenario in one embodiment;

[0038] Figure 5 A network topology diagram of an adjusted smart energy meter system in a gateway node failure scenario in one embodiment;

[0039] Figure 6 This is a network topology diagram of the smart electric energy meter system after adjustment in a large-scale network partitioning scenario in one embodiment. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0041] Example 1

[0042] like Figure 1As shown, the present application provides a smart electricity meter system based on an RF mesh ad hoc network, including an ordinary smart electricity meter and a smart electricity meter with integrated gateway function. The in-meter gateway is a revolutionary design that integrates the gateway function directly into the smart electricity meter instead of existing as an independent external device. In addition, the smart electricity meter with integrated gateway function also has the metering function of an ordinary smart electricity meter, while supporting RF mesh network communication and cellular LTE communication. A single gateway meter can serve 300 ordinary electricity meters connected by RF mesh. Furthermore, in some embodiments, the smart electricity meter with integrated gateway function can also integrate satellite communication function to be suitable for data transmission in remote areas.

[0043] Ordinary smart energy meters use Mesh Sub-GHz technology to form an RF mesh network. In this network, each ordinary smart energy meter can communicate with multiple adjacent meters to form multi-path redundancy. During data transmission, the network can automatically select the optimal path for transmission. Ordinary smart energy meters and smart energy meters with integrated gateway functions communicate via the RF network. Smart energy meters with integrated gateway functions exchange data with the head-end system via cellular networks (such as LTE / 5G) or satellite communications. The system adopts a three-layer communication architecture design. The first layer is ordinary smart energy meters that achieve point-to-point or multi-point communication through the RF mesh network, forming a self-organizing, self-healing local network, enhancing the flexibility and reliability of network coverage. The second layer is the smart energy meter with integrated gateway function that acts as a bridge between ordinary smart energy meters and the wide area network, achieving seamless connection between the local network and the wide area network. The third layer transmits data to the power company's head-end system via cellular networks such as LTE / 5G or satellite communication networks.

[0044] Specifically, such as Figure 2 As shown, this embodiment also provides a communication method for a smart electric energy meter system based on an RF mesh ad hoc network, specifically comprising:

[0045] In step S1, each node in the RF network periodically evaluates the link quality with adjacent nodes and calculates a comprehensive routing metric based on the link quality.

[0046] In an RF mesh ad hoc network, each smart energy meter (i.e., node) has the ability to communicate with neighboring nodes. These nodes regularly evaluate the link quality between their directly connected neighbors at pre-set intervals. Link quality evaluation may involve multiple metrics, such as signal strength, bit error rate, and packet loss rate. In a preferred embodiment, link quality evaluation metrics include signal strength indicator (RSSI), link quality indicator (LQI), bit error rate (BER), and link availability history. A link quality index can be calculated based on the evaluation metrics of each link quality. For example, the link quality index can be a weighted average of the evaluation metrics. A comprehensive routing metric is then calculated by combining the remaining power of the standard smart energy meter, the inverse number of hops from the standard smart energy meter to the smart energy meter with integrated gateway functionality, and the congestion index. This metric comprehensively considers multiple aspects of link quality and can fully and accurately reflect the performance and reliability of the link in terms of data transmission, providing a scientific and reasonable basis for subsequent data transmission path selection. In a preferred embodiment, the calculation formula of the comprehensive routing metric is: Comprehensive routing metric = α × (link quality index) + β × (remaining power percentage) + γ × (inverse of hop count) + δ × (congestion index), where α, β, γ, and δ are weight coefficients that can be dynamically adjusted according to network characteristics. The initial value of the weight coefficient can be set by those skilled in the art. The congestion index is the ratio of the average queue length of the current path to the congestion threshold. For example, Figure 3 The network topology diagram of a common smart energy meter (Tablei, i = 1, 2..., 6) and a smart energy meter with integrated gateway function (Gateway1) is shown. Taking the path from Table4 to Gateway1 as an example, when the initial values of the weight coefficients are α = 0.4, β = 0.2, γ = 0.2, δ = 0.2, and the RSSI is -65dBm (the full score is -30dBm), its normalized value is ((-30) - (-65)) / (( -30)-(-100))=0.58, the battery power of Table 4 is 85%, its normalized value is 0.85, the inverse of the hop count is 2 hops, its normalized value is 1 / 2=0.5, the average queue length of the current path is 20 data packets, the congestion threshold is 50 data packets, its normalized value is 20 / 50=0.4; the calculated comprehensive routing metric value = 0.4×0.58+0.2×0.85+0.2×0.5+0.2×0.4=0.582.

[0047] In addition, this embodiment also provides a dynamic adjustment strategy for the weight coefficient. Specifically, in a high-load (congestion) scenario, the weight of the congestion index can be increased and the weight of the link quality index can be reduced to give priority to paths with lighter loads. In a low-battery scenario, the weight of the remaining battery percentage can be increased and the weight of the hop count can be reduced to avoid using nodes that are about to run out of power. In a signal-unstable scenario, the weight of the link quality index can be increased and the weight of the congestion index can be reduced to give priority to paths with the best signal quality.

[0048] In step S2, each node in the RF network selects a primary path and a backup path according to the comprehensive routing metric.

[0049] During the RF network initialization phase, a flooded route request packet (RREQ) is sent through a gateway node—a smart energy meter with integrated gateway functionality. All nodes then record multiple possible paths. During the data transmission phase, each node selects a primary and backup path based on the comprehensive routing metric calculated in step S1. Path decision-making is prioritized as follows: nodes with sufficient battery life are considered first, followed by paths with fewer hops, then links with a high historical stability, and finally, areas with light network load.

[0050] Specifically, step S2 includes:

[0051] Step S21: Obtain a reference threshold.

[0052] Step S22: The path with the integrated routing metric value higher than the reference threshold is selected as the primary path, and the path with the second highest integrated routing metric value is selected as the backup path.

[0053] The primary path is the preferred path for data transmission. Data is typically transmitted along this path to ensure it reaches its destination quickly and reliably. To improve data transmission reliability and fault tolerance, in addition to the primary path, each node also selects one or more suboptimal paths as backup paths based on the overall routing metric. Backup paths are used when the primary path experiences a failure (such as a link interruption or signal interference). If the primary path fails to transmit data properly, the node automatically switches to the backup path and continues the data transmission task, thus ensuring the continuity and stability of data transmission.

[0054] Furthermore, each node in the RF network can switch paths when a high-traffic path is detected or a communication link is interrupted. The judgment indicators of a high-traffic path include packet transmission rate, queue backlog length, bandwidth utilization, and the amount of data transmitted within the time window. For example, when the packet transmission rate exceeds 100 packets / second, the queue backlog length exceeds 30% of the average value, the bandwidth utilization exceeds 80%, or the data transmitted within 5 minutes exceeds 10MB, the current path can be judged as a high-traffic path. In addition, each node recalculates the routing metric every 60 seconds, and immediately triggers path reselection when a significant drop in path performance (such as more than 20%) is detected. Historical routing performance data can also be saved for long-term network performance optimization.

[0055] In step S3, the smart energy meter with integrated gateway function sends the electricity consumption data and status information to the head-end system, and optimizes the data flow and network performance through the built-in network management system.

[0056] The status information may include the power status, communication status, and hardware status of each smart meter. Smart meters with integrated gateway functionality send their own collected electricity usage data and status information, as well as data and status information obtained from other common smart meters, to the headend system.

[0057] Specifically, the steps for the built-in network management system to optimize data flow and network performance include:

[0058] Step S31 , periodically sending detection packets to draw a real-time network topology map, assigning relay and terminal roles based on location and power status, reducing the amount of active node data in high-density areas, and enhancing connectivity in low-density areas.

[0059] Specifically, smart energy meters with integrated gateway functions periodically send probe packets across the network. These probe packets propagate between nodes. Upon receiving a probe packet, each node records the packet's source and path. By collecting this information from all nodes, a real-time network topology can be constructed, revealing the connectivity and distribution of each node within the network. Node roles are appropriately assigned based on each node's geographic location and battery charge level. For example, a node with sufficient battery power and a critical location might be designated as a relay node, responsible for forwarding data from other nodes; while a node with low battery power or a relatively marginal location might be designated as a terminal node, primarily responsible for data collection and transmission. In areas with high node density, the number of active nodes and data transmission volume can be appropriately reduced to avoid network congestion and resource waste. In areas with low node density, measures can be taken to enhance connectivity between nodes, such as adding relay nodes or adjusting node transmission parameters, to ensure reliable data transmission.

[0060] Step S32: intelligently merge similar data of adjacent nodes, assign transmission priorities according to data types, and allocate different communication time slots to different nodes.

[0061] Adjacent nodes may collect similar data, such as similar voltage and current data. By merging these similar data using intelligent algorithms, the amount of data transmitted within the network can be reduced, improving network bandwidth utilization. Different types of data have different importance and real-time requirements. For example, emergency alarm data requires priority transmission, while routine monitoring data can be transmitted later. Data is assigned different transmission priorities based on its type to ensure that important data is transmitted in a timely manner. Furthermore, technologies such as time division multiple access (TDMA) are used to allocate different communication time slots to different nodes in the network, avoiding data transmission conflicts between nodes and improving network transmission efficiency and reliability.

[0062] Step S33: adjust the node sleep period according to network traffic and battery status, dynamically adjust the node transmission power according to link quality, and automatically switch to a low-interference channel when interference is detected.

[0063] Specifically, when network traffic is low, some nodes can be put into a dormant state to conserve power. When network traffic is high, these nodes can be awakened to increase network transmission capacity. Furthermore, the node's sleep cycle is appropriately adjusted based on its battery status to extend its lifespan. Link quality between nodes is affected by various factors, such as distance and interference. By monitoring link quality in real time, the node's transmit power is dynamically adjusted to minimize transmit power while ensuring reliable data transmission, reducing energy consumption and interference. If interference is detected on the current channel, the node can automatically switch to a less noisy channel to ensure stable and reliable data transmission.

[0064] Step S34: dynamically adjust the synchronization period according to the network delay status, adjust the retransmission timeout and the maximum number of retransmissions based on the network congestion level, and set the broadcast packet TTL and propagation range.

[0065] Network latency can affect the accuracy of synchronization between nodes. Dynamically adjust the node synchronization period based on the current network latency to ensure accurate time synchronization between nodes and avoid data transmission errors caused by time asynchrony. When network congestion occurs, appropriately increase the retransmission timeout and maximum number of retransmissions to reduce data loss caused by network congestion. When the network is unobstructed, these parameters can be appropriately reduced to improve network transmission efficiency. If broadcast packets are not restricted, they can cause network congestion. By setting the broadcast packet's time to live (TTL) and range, you can control the reach of broadcast packets and avoid broadcast storms.

[0066] Step S35 , identifying abnormal nodes, continuously evaluating the quality of all active links, predicting potential failures, and triggering local network reorganization when a key node failure or network partition is detected.

[0067] Specifically, by real-time monitoring of various node parameters, such as signal strength, data transmission quality, and battery charge, nodes that may be experiencing anomalies are identified. For example, if a node's signal strength is consistently low or its data transmission error rate is high, this node may be considered anomaly. The quality of all active links in the network is continuously evaluated, including indicators such as link stability and bandwidth utilization. Based on the evaluation results, link problems are promptly identified and appropriate measures are taken to optimize them. Predictive algorithms are used to predict potential node failures based on the node's historical data and current status. For example, if a node's battery charge is rapidly declining, it may be predicted that the node is about to run out of power. When a critical node failure is detected or a network partition occurs (i.e., the network is divided into multiple disconnected parts), a local network reorganization mechanism is automatically triggered. By reallocating node roles and adjusting the network topology, normal network operation is restored, ensuring reliable data transmission.

[0068] Local network reconfiguration is based on the following core principles: 1. Minimum connection cost: Prioritize reconnection solutions with the shortest paths to minimize network reconfiguration communication overhead; 2. High node reliability: Node reliability is assessed based on historical communication quality, battery life, and node hardware status, prioritizing nodes with high reliability; 3. Dynamic routing adaptation: Real-time calculation of routing metrics allows for rapid response to network topology changes to ensure communication continuity. Key aspects of the reconfiguration technology include: 1. Rapid fault detection: Second-level response to node anomalies and millisecond-level path switching; 2. Intelligent routing algorithm: Machine learning-based route prediction and automatic learning of optimal network connection patterns; 3. Multipath redundancy: Maintaining backup paths and enabling rapid switching to ensure communication continuity; 4. Secure reconfiguration: Encrypted authentication during the reconfiguration process prevents attacks during network reconfiguration. Furthermore, the reconfigured local network is evaluated according to reconfiguration performance indicators, and further adjustments are made if they do not meet the evaluation criteria. Among them, the reassembly performance indicators may include reassembly response time, communication recovery rate, data loss rate and network availability, and when the reassembly response time is less than 1 second, the communication recovery rate is greater than 99.5%, the data loss rate is less than 0.1% and the network availability is greater than 99.99%, the reassembly network is judged to meet the evaluation criteria.

[0069] In a preferred embodiment, the step of identifying abnormal nodes includes:

[0070] Step S351 , calculating the node's communication behavior anomaly index, energy behavior anomaly index, data consistency anomaly index, and network security anomaly index.

[0071] Among them, the communication behavior abnormality indicators include abnormal data packet transmission rate, communication frequency and routing dynamic change rate. The calculation formula of abnormal data packet transmission rate is: abnormal data packet transmission rate = (number of abnormal data packets / total number of data packets) × 100%. When the abnormal data packet transmission rate is less than 1%, the data transmission is judged to be normal. When the abnormal data packet transmission rate is greater than or equal to 1% and less than or equal to 5%, the data transmission is judged to be slightly abnormal. When the abnormal data packet transmission rate is greater than 5%, the data transmission is judged to be seriously abnormal. The communication frequency is the fluctuation of the communication frequency per unit time. The allowable fluctuation range is ±20%. If it exceeds this fluctuation range, the communication frequency is lower than 30% of the normal communication frequency for a long time, or exceeds 200% of the normal frequency, it is considered to be abnormal communication frequency. The routing dynamic change rate is the number of routing path changes per unit time. Its normal range is less than or equal to 3 times / hour. When the routing dynamic change rate is greater than 5 times / hour, or more than 2 times within 2 minutes, the routing behavior is judged to be abnormal.

[0072] Abnormal energy behavior indicators include the battery energy consumption rate and the standby current value. The battery energy consumption rate is the ratio of the actual battery decay rate to the expected decay rate. For example, when the battery energy consumption rate is less than 20% / year, the battery is judged to be decaying normally; when the battery energy consumption rate is greater than or equal to 20% / year and less than or equal to 40% / year, the battery is judged to be slightly abnormal; when the battery energy consumption rate is greater than 40% / year, the battery is judged to be seriously abnormal. When the standby current value is less than 10mA, the standby current is judged to be normal; when the standby current value is greater than 50mA and the high current lasts for more than 30 minutes, the standby current is judged to be abnormal.

[0073] Data consistency anomaly indicators include the packet verification failure rate and the data duplication or omission rate. When the packet verification failure rate is less than 0.1%, data transmission consistency is considered normal. When the packet verification failure rate is greater than or equal to 0.1% and less than or equal to 1%, data transmission consistency is considered slightly abnormal. When the packet verification failure rate is greater than 1%, data transmission consistency is considered severely abnormal. When the data duplication rate is less than 0.05% or the data omission rate is less than 0.1%, data transmission consistency is considered normal. When the data duplication rate is greater than 0.5% or the data omission rate is greater than 1%, data transmission consistency is considered abnormal.

[0074] Network security anomaly indicators include the number of encryption failures, security certificate verification failures, and illegal access attempts. For example, when the number of encryption failures is less than 3 times per day, encrypted communication is considered normal; when the number of encryption failures is greater than 10 times per day, encrypted communication is considered abnormal. When the number of security certificate verification failures reaches 3, security certificate verification is considered a failure. When the number of illegal access attempts is 0 times per day, unauthorized access attempts are considered normal; when the number of illegal access attempts is 1-3 times per day, unauthorized access attempts are considered slightly abnormal; and when the number of illegal access attempts is greater than 5 times per day, unauthorized access attempts are considered severely abnormal.

[0075] In step S352, a total anomaly score is calculated based on the weight of each anomaly indicator and the calculation result of step S351. The weight distribution can be dynamically adjusted according to the actual scenario. For example, the weight of the communication behavior indicator can be higher than the energy behavior indicator to reflect the core stability requirements of the network.

[0076] Step S353: Obtain the abnormality level of the node according to the total abnormality score; the abnormality level includes no abnormality, slight abnormality, moderate abnormality and severe abnormality.

[0077] Total anomaly score = ∑(index value × weight). When the total anomaly score is less than 10, the node is considered normal. When the total anomaly score is greater than or equal to 10 and less than or equal to 30, the node is considered slightly abnormal. When the total anomaly score is greater than or equal to 31 and less than or equal to 60, the node is considered moderately abnormal. When the total anomaly score is greater than 60, the node is considered severely abnormal.

[0078] Furthermore, differentiated processing strategies are implemented for nodes with different levels of abnormality: Nodes with minor abnormalities are primarily monitored, including logging, triggering alarm notifications, and maintaining network connectivity; nodes with moderate abnormalities are required to limit communication priority, initiate data transmission restrictions, or trigger self-diagnosis procedures; and nodes with severe abnormalities are immediately isolated, removed from the network, and undergo manual intervention. Furthermore, gateway nodes deploy real-time monitoring and adaptive mechanisms, including continuous collection of node behavior data, dynamic adjustment of abnormality thresholds based on machine learning, establishment of node behavior baseline profiles, and real-time early warning capabilities. For example, models trained with historical data can predict node abnormality trends, or the communication frequency fluctuation range can be dynamically adjusted based on network load to improve system robustness and response efficiency.

[0079] In a preferred embodiment, the step of local network reorganization includes:

[0080] Step S354: When a single node failure is detected, the failed node is isolated and path reconstruction and topology adjustment are performed.

[0081] Specifically, Figure 3Taking the network topology shown in the figure as an example, when the communication of Table2 node is completely interrupted, the battery is exhausted, or the encrypted communication fails continuously, the Table2 node is removed from the network topology, all communication permissions of Table2 are frozen, and the original path (Table5→Table2→Gateway1) is adjusted to the new path (Table5→Table3→Gateway1). The adjusted topology is shown in the figure below. Figure 4 shown.

[0082] Step S355: When a gateway node failure is detected, a backup gateway is enabled and load balancing and topology adjustment are performed.

[0083] Specifically, when a failure is detected in a gateway node, the system will automatically enable a pre-configured backup gateway. The backup gateway usually has similar functions and configurations to the primary gateway, and can quickly take over network communication tasks when the primary gateway fails, ensuring that the connection between networks is not interrupted. After the backup gateway is enabled, the data traffic in the network will be redistributed to the backup gateway. In order to prevent the backup gateway from being overloaded due to excessive traffic and affecting network performance, the system will load balance the network traffic and evenly distribute the data traffic to the various interfaces or processing units of the backup gateway, so that the backup gateway can efficiently handle network communication tasks. Similarly, Figure 3 Taking the network topology shown in the figure as an example, when the communication quality of Gateway1 continues to decline, the data forwarding success rate is less than 60%, or multiple security verification failures occur, the backup gateway Gateway2 is activated, some node routes are switched to Gateway2, and load balancing is performed, Table1, Table4, and Table5 are assigned to Gateway2, and Table2, Table3, and Table6 are retained on Gateway1. The adjusted topology is shown in the figure below. Figure 5 shown.

[0084] Step S356: When a large-scale network partition is detected, the RF network is divided into multiple subnets and the availability of each subnet is evaluated. A bridge connection is established between the available subnets through the central nodes of the available subnets. The gateway node with the best performance is selected as the regional coordinator to rebuild the topology.

[0085] Subnets can be divided based on factors such as the network's physical layout, node distribution, and service requirements, ensuring that each subnet maintains a certain degree of independence and integrity. After the subnets are divided, the availability of each subnet needs to be evaluated. Evaluation metrics can include the number of nodes within the subnet, node communication capabilities, and subnet bandwidth resources. This evaluation provides an understanding of the operational status and performance level of each subnet, providing a basis for subsequent bridge connection and regional coordinator selection. After identifying available subnets, bridge connections need to be established between them through the central node of the available subnets. The central node is typically a node with strong communication and processing capabilities within the subnet, capable of facilitating data transfer between subnets. Establishing bridge connections enables communication between available subnets, restoring overall network connectivity.

[0086] In order to effectively manage and coordinate the network after a large-scale network partition, it is necessary to select the gateway node with the best performance as the regional coordinator. The regional coordinator is responsible for coordinating the communication resources of each subnet, coordinating the data transmission and business interaction between subnets, and ensuring that the network can operate efficiently and stably after the partition is restored. After completing the above steps, it is necessary to rebuild the network topology based on the new network structure and node connection relationship. Figure 3 Taking the network topology shown in the figure as an example, when it is detected that the network where Gateway1 is located is physically damaged, communication between large-scale nodes is interrupted, or more than 30% of the nodes are disconnected, a large-scale network partitioning and reorganization is performed. The reconstructed network topology is as follows: Figure 6 As shown in the figure, Table 1 and Table 2 form subnet 1, Table 4 and Table 5 form subnet 2, Table 3 and Table 6 form subnet 3, and Gateway 1 is the regional coordinator for subnets 1, 2, and 3.

[0087] In step S4, the head-end system receives, stores and analyzes the power consumption data and status information.

[0088] Specifically, the headend system features two-way communication capabilities, enabling it to not only receive data from meters but also send control commands to them, enabling remote control and significantly improving the flexibility and efficiency of power management. The headend system stores and analyzes acquired power usage data and status information, generating reports, bills, and analysis results to inform power companies' decision-making. Furthermore, the headend system supports remote configuration and management of meter networks, allowing operators to set parameters and manage equipment without having to be physically present on-site. This further simplifies power management processes and reduces operational costs.

[0089] The RF mesh ad hoc network-based smart energy meter system of this embodiment uses a smart energy meter with an integrated gateway function as a gateway node in the RF network, directly uploading electricity consumption data and status information to the head-end system without the need to deploy additional independent gateway devices, thereby significantly reducing hardware procurement costs, installation and commissioning costs, and long-term operation and maintenance costs. At the same time, the communication method of the smart energy meter system based on the RF mesh ad hoc network of this embodiment dynamically calculates the comprehensive routing metric value to select the optimal primary path and backup path for data transmission. This multi-path redundancy design greatly improves the reliability of the network and gives the system stronger fault self-healing capabilities. In addition, the smart energy meter with an integrated gateway function has a built-in network management system that can monitor and optimize the network topology, data traffic distribution, and node resource utilization in real time, further improving data transmission efficiency and network stability.

[0090] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A communication method for a smart electric energy meter system based on RF mesh ad hoc network, characterized in that: The smart energy meter includes an ordinary smart energy meter and an intelligent energy meter with integrated gateway function, wherein the ordinary smart energy meter and the intelligent energy meter with integrated gateway function communicate via an RF network, and the intelligent energy meter with integrated gateway function communicates with the head-end system via a cellular network or satellite communication. The method includes: Step S1, each node in the RF network periodically evaluates the link quality with adjacent nodes and calculates a comprehensive routing metric based on the link quality; Step S2, each node in the RF network selects a primary path and a backup path according to the comprehensive routing metric value; Step S3: The smart energy meter with integrated gateway function sends power consumption data and status information to the head-end system, and optimizes data flow and network performance through the built-in network management system; Step S4: the head-end system receives, stores and analyzes the power usage data and the status information.

2. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 1, characterized in that: The evaluation indicators of the link quality include signal strength indicator, link quality indicator, bit error rate and link availability history record.

3. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 1, characterized in that: The calculation formula of the comprehensive routing metric is: comprehensive routing metric = α×(link quality index)+β×(remaining power percentage)+γ×(inverse of hop count)+δ×(congestion index), where α, β, γ, and δ are all weight coefficients.

4. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 1, characterized in that: The method further comprises: In step S5, each node in the RF network switches paths when detecting a high-traffic path or a communication link interruption.

5. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 4, characterized in that: The judgment indicators of the high-traffic path include data packet transmission rate, queue backlog length, bandwidth utilization and data volume transmitted within a time window.

6. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 1, characterized in that: Step S2 includes: Step S21, obtaining a reference threshold; Step S22: The path whose comprehensive routing metric value is higher than the reference threshold is selected as the primary path, and the path with the second highest comprehensive routing metric value is selected as the backup path.

7. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 1, characterized in that: The built-in network management system optimizes data flow and network performance by: Step S31: Periodically send detection packets to draw a real-time network topology map, assign relay and terminal roles based on location and power status, reduce the amount of active node data in high-density areas, and enhance connectivity in low-density areas; Step S32: intelligently merge similar data from adjacent nodes, assign transmission priorities based on data types, and allocate different communication time slots to different nodes; Step S33, adjusting the node sleep period according to network traffic and battery status, dynamically adjusting the node transmit power according to the link quality, and automatically switching to a low-interference channel when interference is detected; Step S34, dynamically adjust the synchronization period according to the network delay status, adjust the retransmission timeout and maximum retransmission number based on the network congestion level, and set the broadcast packet TTL and propagation range; Step S35 , identifying abnormal nodes, continuously evaluating the quality of all active links, predicting potential failures, and triggering local network reorganization when a key node failure or network partition is detected.

8. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 7, characterized in that: The abnormal node identification step includes: Step S351, calculating the node's communication behavior abnormality index, energy behavior abnormality index, data consistency abnormality index, and network security abnormality index; Step S352, obtaining a total anomaly score based on the weight of each anomaly indicator and the calculation result of step S351; Step S353: Obtain an abnormality level of the node according to the total abnormality score; the abnormality level includes no abnormality, slight abnormality, moderate abnormality and severe abnormality.

9. The communication method of the smart electric energy meter system based on RF mesh ad hoc network according to claim 7, characterized in that: The steps for local network reorganization include: Step S354: When a single node failure is detected, the faulty node is isolated and path reconstruction and topology adjustment are performed; Step S355: When a gateway node failure is detected, a backup gateway is enabled and load balancing and topology adjustment are performed; Step S356: When a large-scale network partition is detected, the RF network is divided into multiple subnets and the availability of each subnet is evaluated. A bridge connection is established between the available subnets through the central nodes of the available subnets. The gateway node with the best performance is selected as the regional coordinator to rebuild the topology.

10. A smart electric energy meter system based on RF mesh ad hoc network, characterized in that: The system includes an ordinary smart energy meter, a smart energy meter with integrated gateway function and a head-end system. The ordinary smart energy meter and the smart energy meter with integrated gateway function communicate through an RF network, and the smart energy meter with integrated gateway function communicates with the head-end system through a cellular network or a satellite communication network. The ordinary smart energy meter, the smart energy meter with integrated gateway function and the head-end system communicate according to the smart energy meter system communication method based on RF mesh ad hoc network described in any one of claims 1-9.

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