Self-adaptive communication method and system for intelligent metering equipment of ubiquitous power internet of things

Abstract

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technology, and more specifically to an adaptive communication method and system for ubiquitous power Internet of Things intelligent metering equipment. Background Art

[0002] With the acceleration of the transformation of the global energy structure, the widespread use of renewable energy has become an irreversible trend. Against this background, the power system is undergoing a transformation from traditional centralized power supply to distributed and intelligent power supply. As an important part of the smart grid, the Ubiquitous Power Internet of Things (UPIoT) aims to achieve comprehensive perception, intelligent analysis and precise control of the power system through the Internet of Things technology. As a key node connecting users and the power grid, smart metering equipment undertakes the important tasks of data collection, transmission and analysis.

[0003] Existing communication methods for metering equipment in the ubiquitous power Internet of Things (UPIoT) are mainly based on traditional power telecontrol communication protocols, such as IEC 104. These protocols have been widely used in power systems and have shown good performance and stability in many aspects. The IEC 104 protocol is a TCP / IP-based communication protocol, mainly used in supervisory control and data acquisition (SCADA) systems in power systems. It achieves interoperability between different devices by defining standardized data formats and communication processes, ensuring the reliability and consistency of data transmission.

[0004] However, in practical applications, traditional power telecontrol communication protocols such as IEC 104 use blocking communication, that is, the sender must wait for the receiver to confirm before continuing to send the next frame of data. Although this method has shown high stability and reliability in early power systems, its efficiency is low and it is easy to cause data transmission delays. In addition, when the transmission bit rate is limited, multi-agent systems usually use quantitative communication to transmit information. Quantitative communication improves communication efficiency by reducing data redundancy, but it also introduces information inaccuracy, which poses a challenge to data accuracy and consistency. Secondly, existing UPIoT metering equipment communication methods usually rely on a fixed network topology, and devices exchange data through preset communication paths. This static network structure is not flexible enough when dealing with large-scale distributed new energy access and dynamic changes in user-side resources.

[0005] Therefore, the present invention discloses an adaptive communication method and system for ubiquitous power Internet of Things smart metering equipment, aiming to solve the problems of quantitative communication inaccuracy, insufficient communication efficiency and poor flexibility existing in the communication of existing smart metering equipment. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention discloses an adaptive communication method and system for ubiquitous power Internet of Things intelligent metering equipment, which improves the communication performance and flexibility of the intelligent metering equipment.

[0007] The present invention adopts the following technical solutions:

[0008] A ubiquitous power Internet of Things intelligent metering equipment adaptive communication method, comprising the following steps:

[0009] S1. When the smart metering equipment is started, it automatically discovers the surrounding neighbor nodes through a dynamic network self-organizing algorithm and builds an adaptive network topology; the dynamic network self-organizing algorithm allows nodes to forward request packets and response packets through intermediate nodes through a multi-hop communication mechanism to expand the network coverage;

[0010] S2. Smart metering equipment regularly evaluates the current network environment through network status monitoring, including network congestion, signal strength and interference;

[0011] S3. Smart metering equipment obtains current task requirements through built-in sensors and user input, including the size, type and priority of business data, and determines the lower limit of business data transmission rate, maximum allowable delay and data sensitivity level through expert system;

[0012] S4. The smart metering equipment sends test data packets to the target node through the established multiple communication links, evaluates the performance of each link, and calculates the channel capacity of each link through the Shannon formula to evaluate the maximum transmission capacity of each link;

[0013] S5. The smart metering equipment selects the optimal link combination according to the data transmission requirements output by S3 and the performance indicators of each link output by S4. S5 includes the following steps:

[0014] S51: Preset multiple candidate link solutions, each solution includes a different link combination;

[0015] S52: Determine the enabled link capacity, the number of enabled links, the number of enabled communication nodes and the average link performance of each solution based on the link state variable, the number of communication nodes, the link performance and the link channel capacity;

[0016] S53: defining a comprehensive performance function f(x) of the scheme, taking into account the enabled link capacity, the number of enabled links, the number of enabled communication nodes, the average link performance and their corresponding weight coefficients;

[0017] S54: Obtaining a link state variable when the data transmission requirement is met and f(x) is maximized through a genetic algorithm;

[0018] S55: selecting a link to be put on standby and a link to be kept enabled according to the link state variable; and dynamically optimizing the link selection strategy through a reinforcement learning algorithm;

[0019] S6, based on the optimal link combination selected in S5, sending data to the target node through a multi-path transmission method;

[0020] S7. During data transmission, the smart metering equipment dynamically adjusts communication parameters, including transmission rate, frequency and power, through an adaptive modulation and coding model based on dynamic spectrum management according to network conditions and link performance;

[0021] S8. The intelligent metering equipment performs data verification at the receiving end through forward error correction coding and backward error correction methods. If a data error is detected, it is corrected through an error compensation algorithm;

[0022] S9. During the entire communication process, the smart metering equipment adopts a multi-layer security mechanism to ensure the secure transmission of data; the multi-layer security mechanism includes a data encryption layer, an identity authentication layer, an intrusion detection layer and an active defense layer.

[0023] As a further technical solution of the present invention, the working method of constructing an adaptive network topology of the dynamic network self-organizing algorithm is:

[0024] U1. When the smart metering equipment is started, the dynamic network self-organizing algorithm sends a request packet through the wireless broadcast protocol. The request packet contains its own node ID, location and communication capability.

[0025] U2, after receiving the request packet, the neighbor node parses the information in the request packet and generates a response packet, which contains the node ID, location and communication capability of the neighbor node, and returns it to the smart metering equipment through the wireless communication channel;

[0026] U3, after receiving the response packet, the smart metering equipment parses the information of the neighboring nodes and stores it in the local database, including the node ID, location and communication capability;

[0027] U4. Based on the extracted node information, the dynamic network self-organizing algorithm constructs a network topology graph and stores it in the form of an adjacency matrix or an adjacency list. At the same time, the dynamic network self-organizing algorithm records the status information of each neighbor node, including timestamp, signal strength, transmission rate, and packet loss.

[0028] U5. Dynamic network self-organizing algorithm dynamically updates the network topology by sending and receiving broadcast packets at regular intervals to ensure the real-time and accuracy of the network.

[0029] As a further technical solution of the present invention, the multi-hop communication mechanism dynamically discovers and maintains the optimal path through the routing protocol AODV, and ensures the communication transmission of data packets on the multi-hop path through the packet forwarding mechanism.

[0030] As a further technical solution of the present invention, in S5, the optimization method of the link selection strategy dynamically optimized by the reinforcement learning algorithm is: first, the state space, action space and reward function are defined through the reinforcement learning model to construct a decision framework for the smart metering equipment in the communication process; the state space is used to include the performance parameters of the current link, the status of the communication node and the demand information for data transmission; the action space is used to define the operation of the smart metering equipment, including enabling or standing by the link; the reward function is used to measure the efficiency of data transmission, the stability of the link and the consumption of resources; according to the reward value fed back by the reward function, the reinforcement learning algorithm gradually updates the decision strategy of the smart metering equipment through a policy iteration method; in the iterative update process, the reinforcement learning algorithm also uses a deep neural network as a function approximator of the reinforcement learning model to approximate the policy function or the value function.

[0031] As a further technical solution of the present invention, the working method of S4 is:

[0032] S41, using a multipath transmission method to send the same test data packet through different links, and evaluating the transmission quality of each link, including signal strength, transmission rate, packet loss rate and delay;

[0033] S42, the intelligent metering equipment comprehensively evaluates the communication quality of each link according to the transmission quality of each link through a link evaluation algorithm based on fuzzy logic; the working steps of the link evaluation algorithm based on fuzzy logic include:

[0034] r1. Define fuzzy sets and divide signal strength, transmission rate, packet loss rate and delay indicators into three fuzzy levels, including weak, medium and strong;

[0035] r2. Define a membership function for each fuzzy set to convert quantitative data into fuzzy values; the parameters of the membership function are adjusted based on expert experience and historical data;

[0036] r3. The smart metering equipment uses a multi-path transmission method to send the same test data packet through different links and collect the communication quality data of each link;

[0037] r4. After receiving the test data packet, the neighbor node records the timestamp, signal strength, transmission rate and packet loss of the received data packet, and returns this information to the smart metering equipment; the smart metering equipment obtains the communication quality data of each link by receiving this feedback information;

[0038] r5. The intelligent metering equipment calculates the membership value of the signal strength through the defined membership function according to the received signal strength data;

[0039] r6. The intelligent metering equipment calculates the membership value of the transmission rate through the defined membership function according to the received transmission rate data;

[0040] r7. The intelligent metering equipment calculates the membership value of the packet loss rate through the defined membership function according to the received packet loss rate data;

[0041] r8. The intelligent metering equipment calculates the membership value of the delay through the defined membership function according to the received delay data;

[0042] r9. Build a fuzzy rule base through expert experience and historical data, and comprehensively evaluate the communication quality of the link through fuzzy rules;

[0043] r10. Inputting the membership value of each link into the fuzzy rule base through the fuzzy reasoning method, performing fuzzy reasoning, and obtaining the comprehensive fuzzy evaluation result of each link; the fuzzy reasoning method synthesizes each evaluation index through fuzzy logic operators and maximum and minimum synthesis method to obtain the final evaluation result;

[0044] r11. Calculate the center of gravity of the fuzzy set by the center of gravity method and convert the fuzzy evaluation results into numerical scores;

[0045] S43. The intelligent metering equipment stores the link evaluation results obtained by the link evaluation algorithm based on fuzzy logic in a local database. The evaluation results include the signal strength, transmission rate, packet loss rate, delay and comprehensive score of each link.

[0046] As a further technical solution of the present invention, in S5, the working method of solving the link state variable by genetic algorithm includes the following steps:

[0047] t1. Generate an initial population based on a heuristic method, where each individual represents a possible solution of a link state variable; the link state variables include enabled link capacity, enabled link number, enabled communication node number and average link performance; based on the properties of the link state variables and the optimization objectives, each individual is represented by binary coding or real number coding, where binary coding is used for discrete variables and real number coding is used for continuous variables;

[0048] t2. Comprehensively consider the enabled link capacity, the number of enabled links, the number of enabled communication nodes, the average link performance and their corresponding weight coefficients, and define the fitness function to evaluate the pros and cons of each individual;

[0049] t3. Calculate the fitness value of each individual through the fitness function;

[0050] t4. Select excellent individuals from the current population to enter the next generation population through a selection operation; the selection operation controls the probability of selection through the fitness value of the individual;

[0051] t5. Generate a new individual through a crossover operation; the crossover operation controls the frequency of the crossover operation through a crossover probability; the crossover probability is set to 0.8;

[0052] t6. Through mutation operation, make slight changes to individuals and introduce new genetic information; the mutation operation includes bit flip mutation, uniform mutation and Gaussian mutation; the mutation operation controls the occurrence frequency of the mutation operation through the mutation probability, and the mutation probability is set between 0.01 and 0.1;

[0053] t7. Set the termination condition to determine whether the genetic algorithm has reached the optimal solution; when the termination condition is met, the algorithm stops iterating and outputs the optimal solution;

[0054] t8. Select the individual with the highest fitness value from the final population as the optimal solution, which is used to represent the link state variable when the data transmission requirements are met and the comprehensive performance is maximized.

[0055] As a further technical solution of the present invention, the adaptive modulation and coding model based on dynamic spectrum management includes a data input layer, a modulation and coding selection layer, a parameter optimization layer, a spectrum resource allocation layer, a communication parameter adjustment layer and an execution layer; the working method of the adaptive modulation and coding model based on dynamic spectrum management is:

[0056] c1. receiving the evaluated network status and link performance data through the data input layer, and temporarily storing the data in a cache;

[0057] c2. Selecting a modulation mode and a coding scheme through the modulation and coding selection layer, and dynamically adjusting the modulation and coding scheme according to changes in link quality; the modulation and coding selection layer constructs a multi-objective optimization model through a multi-criteria decision-making method to select the optimal modulation and coding scheme;

[0058] c3. The parameter optimization layer ensures that the spectrum resources are maximized while meeting the communication quality. The parameter optimization layer dynamically adjusts the communication parameters through the reinforcement learning algorithm, continuously optimizes the settings of the transmission rate, frequency and power, and monitors the communication effect after the parameter adjustment through the real-time feedback mechanism;

[0059] c4. Ensure that each node can communicate on the optimal frequency band through the spectrum resource allocation layer. The spectrum resource allocation layer optimizes the allocation of spectrum resources through dynamic spectrum allocation and spectrum sharing methods, and monitors the use of spectrum resources in real time through spectrum utilization evaluation;

[0060] c5. Ensure that the communication parameters match the current network status and link quality through the communication parameter adjustment layer, which changes the transmission rate, frequency and power parameters in real time through a dynamic adjustment mechanism, and monitors the communication effect after parameter adjustment through a real-time feedback mechanism;

[0061] c6. The optimized communication parameters are set in the smart metering equipment through the execution layer, and the actual communication effect is monitored in real time to verify the parameter adjustment effect, and the detailed information of each parameter adjustment is recorded.

[0062] As a further technical solution of the present invention, the working method of S8 is: encoding the original data through convolutional code, Reed-Solomon code or low-density parity-check code to generate encoded data with redundant information; the encoded data is transmitted to the receiving end through a wireless channel; after the receiving end receives the encoded data, the data is checked and error detected through a backward error correction method. If a data error is detected, the receiving end locates and corrects the error through the redundant information provided by the forward error correction coding algorithm. If the error exceeds the error correction capability of the forward error correction coding, the receiving end requests the sending end to resend the erroneous data packet through a retransmission request mechanism; the forward error correction coding algorithm determines the error position through a sliding window or a polynomial root, and corrects the error through a Viterbi algorithm or polynomial interpolation.

[0063] As a further technical solution of the present invention, the data encryption layer encrypts the transmitted data through the Advanced Encryption Standard AES to ensure data confidentiality; the identity authentication layer ensures network security through a two-way authentication mechanism; the intrusion detection layer monitors network traffic in real time by deploying an intrusion detection system, and promptly discovers and responds to potential network security threats; the intrusion detection system identifies and isolates suspicious nodes through behavioral analysis and anomaly detection methods; the active defense layer actively attracts and misleads potential attackers through a deception defense mechanism to protect the security of real network nodes.

[0064] As a further technical solution of the present invention, an adaptive communication system for ubiquitous power Internet of Things smart metering equipment includes:

[0065] Dynamic network discovery module, used to automatically discover neighboring nodes, build network topology, and record the status information and communication capabilities of each node;

[0066] Network evaluation module, used to regularly evaluate the current network environment, including network congestion, signal strength and interference;

[0067] Demand analysis module, used to analyze the transmission requirements of business data, including the lower limit of transmission rate, maximum allowable delay and data sensitivity level, and set priorities;

[0068] A multipath link evaluation module, used to send test data packets through multiple communication links to evaluate the performance of each link;

[0069] Intelligent link selection module, which is used to select the optimal link combination based on business requirements and link performance evaluation results, and dynamically optimize the link selection strategy through reinforcement learning algorithm;

[0070] A data transmission module, used to send service data to a target node through the optimal link combination selected by the intelligent link selection module, and dynamically adjust communication parameters during transmission through an adaptive modulation and coding model based on dynamic spectrum management;

[0071] The data verification module is used to perform data verification at the receiving end. If data errors are detected, they are corrected through error compensation algorithms.

[0072] Security mechanism module, used to ensure secure data transmission through multi-layer security mechanisms;

[0073] The output end of the dynamic network discovery module is connected to the input ends of the network evaluation module, the demand analysis module, and the multi-path link evaluation module; the output end of the network evaluation module is connected to the input ends of the demand analysis module and the intelligent link selection module; the output end of the demand analysis module is connected to the input ends of the multi-path link evaluation module and the intelligent link selection module; the output end of the multi-path link evaluation module is connected to the input end of the intelligent link selection module; the output end of the intelligent link selection module is connected to the input end of the data transmission module; the output end of the data transmission module is connected to the input end of the data verification module; the output end of the security mechanism module is connected to the above modules.

[0074] The positive beneficial effects of the present invention are:

[0075] 1. Through the dynamic network self-organizing algorithm and multi-hop communication mechanism, the intelligent metering equipment can automatically discover the surrounding neighbor nodes and build an adaptive network topology. This self-organizing network can automatically adjust the topology structure according to the changes in the network environment, ensuring that each node can find the optimal communication path, thereby reducing the data transmission error caused by the fixed network structure. In addition, by regularly evaluating the network environment through network status monitoring, including network congestion, signal strength and interference, potential communication problems can be discovered and resolved in a timely manner, further improving the accuracy and reliability of data transmission.

[0076] 2. During data transmission, the present invention dynamically adjusts communication parameters, including transmission rate, frequency and power, based on dynamic spectrum management and adaptive modulation and coding models according to network conditions and link performance. This dynamic adjustment ensures the best transmission effect under different network conditions, thereby significantly improving communication efficiency. For example, when the network environment is good, the transmission rate can be increased to speed up data transmission; when the network environment is poor, the reliability of data transmission can be ensured by reducing the transmission rate and increasing the power.

[0077] 3. Through the dynamic network self-organizing algorithm and multi-hop communication mechanism, the intelligent metering equipment can automatically adjust the network topology according to the changes in the network environment to ensure the flexibility and adaptability of the network. This adaptive network topology not only expands the network coverage, but also can quickly adjust when the network environment changes to ensure the continuity and stability of data transmission. For example, when a node fails, other nodes can automatically reconfigure the network connection to ensure that data transmission is not affected.

[0078] 4. The present invention dynamically optimizes the link selection strategy through genetic algorithms and reinforcement learning algorithms to ensure that the optimal link combination can be selected under different network conditions. This real-time optimization mechanism enables smart metering equipment to flexibly respond to changes in the network environment and improve the flexibility and adaptability of the network. For example, when the performance of a link decreases, the system can automatically switch to other links with better performance to ensure the efficiency and reliability of data transmission.

[0079] 5. Based on dynamic spectrum management and adaptive modulation and coding models, the present invention enables intelligent metering equipment to dynamically adjust communication parameters and make full use of limited spectrum resources. This dynamic management not only improves the utilization rate of spectrum resources, but also enables flexible adjustments when the network environment changes, ensuring the efficiency and reliability of data transmission. For example, when a frequency band is severely interfered, the system can automatically switch to other available frequency bands to ensure the continuity and stability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a step diagram of an adaptive communication method for ubiquitous power Internet of Things smart metering equipment of the present invention;

[0081] Figure 2 It is a working principle step diagram of the dynamic network self-organizing algorithm of the present invention;

[0082] Figure 3 A schematic diagram of the steps for evaluating the performance of each link of the present invention;

[0083] Figure 4 This is a framework diagram of an adaptive communication system for ubiquitous power Internet of Things smart metering equipment in the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In the above embodiment, if Figure 1-Figure 4 As shown: A ubiquitous power Internet of Things smart metering equipment adaptive communication method includes the following steps:

[0086] S1. When the smart metering equipment is started, it automatically discovers the surrounding neighbor nodes through a dynamic network self-organizing algorithm and builds an adaptive network topology; the dynamic network self-organizing algorithm allows nodes to forward request packets and response packets through intermediate nodes through a multi-hop communication mechanism to expand the network coverage;

[0087] S2. Smart metering equipment regularly evaluates the current network environment through network status monitoring, including network congestion, signal strength and interference;

[0088] S3. Smart metering equipment obtains current task requirements through built-in sensors and user input, including the size, type and priority of business data, and determines the lower limit of business data transmission rate, maximum allowable delay and data sensitivity level through expert system;

[0089] S4. The smart metering equipment sends test data packets to the target node through the established multiple communication links, evaluates the performance of each link, and calculates the channel capacity of each link through the Shannon formula to evaluate the maximum transmission capacity of each link;

[0090] S5. The smart metering equipment selects the optimal link combination according to the data transmission requirements output by S3 and the performance indicators of each link output by S4. S5 includes the following steps:

[0091] S51: Preset multiple candidate link solutions, each solution includes a different link combination;

[0092] S52: Determine the enabled link capacity, the number of enabled links, the number of enabled communication nodes and the average link performance of each solution based on the link state variable, the number of communication nodes, the link performance and the link channel capacity;

[0093] S53: defining a comprehensive performance function f(x) of the scheme, taking into account the enabled link capacity, the number of enabled links, the number of enabled communication nodes, the average link performance and their corresponding weight coefficients;

[0094] S54: Obtaining a link state variable when the data transmission requirement is met and f(x) is maximized through a genetic algorithm;

[0095] S55: selecting a link to be put on standby and a link to be kept enabled according to the link state variable; and dynamically optimizing the link selection strategy through a reinforcement learning algorithm;

[0096] S6, based on the optimal link combination selected in S5, sending data to the target node through a multi-path transmission method;

[0097] S7. During data transmission, the smart metering equipment dynamically adjusts communication parameters, including transmission rate, frequency and power, through an adaptive modulation and coding model based on dynamic spectrum management according to network conditions and link performance;

[0098] S8. The intelligent metering equipment performs data verification at the receiving end through forward error correction coding and backward error correction methods. If a data error is detected, it is corrected through an error compensation algorithm;

[0099] S9. During the entire communication process, the smart metering equipment adopts a multi-layer security mechanism to ensure the secure transmission of data; the multi-layer security mechanism includes a data encryption layer, an identity authentication layer, an intrusion detection layer and an active defense layer.

[0100] In a specific embodiment, the specific implementation hardware architecture of the present invention includes:

[0101] Central Processing Unit (CPU): The core processing unit of smart metering equipment, responsible for running various algorithms and control logic, including dynamic network self-organizing algorithms, genetic algorithms, reinforcement learning algorithms, etc.

[0102] Wireless communication module: supports multiple wireless communication standards (such as Wi-Fi, LoRa, NB-IoT, etc.) for communicating with other nodes.

[0103] Spectrum sensing module: used to monitor the spectrum resource status in the network environment in real time, including signal strength, channel quality and interference.

[0104] Sensor module: Built-in sensors are used to obtain current task requirements, including the size, type, and priority of business data.

[0105] Storage module: used to store network status data, link performance evaluation results, task requirement information, etc.

[0106] Security module: includes encryption chip and security processor, which are used to realize functions such as data encryption, identity authentication, intrusion detection and active defense.

[0107] Power management module: responsible for power supply and energy management to ensure that the device can work normally in low power mode.

[0108] The dynamic network self-organizing algorithm runs the self-organizing algorithm through the CPU, uses the wireless communication module to send and receive information about neighboring nodes, and builds an adaptive network topology. The spectrum sensing module provides the necessary spectrum resource information to help the algorithm select the optimal communication path. The dynamic network self-organizing algorithm can automatically adjust the network topology according to changes in the network environment to ensure that each node can find the optimal communication path, thereby improving the flexibility and coverage of the network. In actual work, the dynamic network self-organizing algorithm implements adaptive communication of smart metering equipment through the following steps:

[0109] Startup and node discovery: When the smart metering equipment starts, it sends a broadcast discovery message (Hello message) through the wireless communication module to request information about the surrounding nodes. After receiving the request, the neighboring node replies with a response packet containing its own information. These response packets are transmitted to the CPU of the smart metering equipment through the wireless communication module, and the CPU parses the response packet to extract the information of the neighboring nodes.

[0110] Network topology construction: The CPU uses graph theory algorithms (such as the minimum spanning tree algorithm, Dijkstra algorithm, etc.) to construct network topology based on the collected neighbor node information. The minimum spanning tree algorithm ensures that the connection cost between all nodes is minimized, while the Dijkstra algorithm can find the shortest path between any two nodes. The constructed network topology is stored in the storage module for subsequent communication.

[0111] Multi-hop communication: When direct communication between two nodes is not feasible, the smart metering equipment selects the optimal multi-hop path through routing algorithms (such as AODV, DSDV, etc.). The intermediate nodes act as relays to forward request packets and response packets. The routing algorithm ensures the efficiency and reliability of data transmission and avoids communication interruptions caused by single-hop communication limitations.

[0112] Network maintenance: The dynamic network self-organizing algorithm also includes a network maintenance mechanism to regularly check the connectivity and node status of the network topology. The CPU monitors the network environment in real time through the spectrum sensing module and the network status monitoring module to detect the online status of the nodes. If a node failure or new node is detected, the algorithm will automatically adjust the network topology to ensure the stability and coverage of the network. For example, when a node fails, the algorithm will reselect the path to ensure that data transmission is not affected.

[0113] Compared with the existing communication technology of smart metering equipment, the dynamic network self-organizing algorithm can automatically adjust the network topology according to the changes in the network environment to ensure that each node can find the optimal communication path. This is difficult to achieve in the existing fixed network topology, because the fixed network topology often cannot be adjusted in time when facing changes in the network environment, resulting in low communication efficiency and poor reliability. The dynamic network self-organizing algorithm allows nodes to forward request packets and response packets through intermediate nodes through a multi-hop communication mechanism, which expands the network coverage and improves the flexibility and adaptability of the network. The multi-hop communication mechanism forwards request packets and response packets through intermediate nodes as relays, expanding the coverage of the network. In a complex network environment, especially in scenarios where nodes are sparsely distributed or the geographical environment is complex, the multi-hop communication mechanism can ensure the continuity and integrity of data transmission and avoid communication interruptions caused by single-hop communication limitations. This is difficult to achieve in the existing single-hop communication technology, because the coverage of single-hop communication is limited and is easily affected by the geographical environment and obstacles. The dynamic network self-organizing algorithm also includes a network maintenance mechanism to regularly check the connectivity and node status of the network topology. The CPU monitors the network environment in real time through the spectrum sensing module and the network status monitoring module to detect the online status of the node. If a node failure or a new node is detected, the algorithm will automatically adjust the network topology to ensure the stability and coverage of the network. This is difficult to achieve in the existing static network topology structure, because static network topology often requires manual intervention to restore network connectivity when facing node failure or new node addition. The dynamic network self-organizing algorithm ensures the dynamic adjustment and self-healing capabilities of the network by automatically adjusting the network topology, improving the stability and reliability of the network. In addition, the dynamic network self-organizing algorithm reduces the dependence on manual intervention and reduces the cost of network maintenance by automatically discovering neighbor nodes, building network topology and multi-hop communication mechanisms. In the large-scale ubiquitous power Internet of Things, there are a large number of nodes, a complex network environment, and high manual maintenance costs. The dynamic network self-organizing algorithm ensures the efficient operation of the network through automated management, reduces maintenance costs, and improves the overall economy and practicality of the system.

[0114] The following experiments verify the positive and beneficial effects of the dynamic network self-organization algorithm in the ubiquitous power Internet of Things smart metering equipment. The experimental environment includes: Central Processing Unit (CPU): High-performance embedded processor, such as ARM Cortex-A9, responsible for running the self-organization algorithm and control logic. Wireless communication module: Supports multiple wireless communication standards (such as Wi-Fi, LoRa, NB-IoT, etc.) for communicating with other nodes. Spectrum sensing module: Used to monitor the spectrum resource status in the network environment in real time, including signal strength, channel quality and interference. Sensor module: Built-in sensors are used to obtain current task requirements, including the size, type and priority of business data. Storage module: Used to store network status data, link performance evaluation results, task requirement information, etc. Power management module: Responsible for power supply and energy management to ensure that the device can work normally even in low power mode.

[0115] The experiment was divided into two groups: Group A applied the dynamic network self-organizing algorithm, and Group B applied the traditional static network topology algorithm. Both groups of experiments were conducted under the same experimental conditions, and each group conducted five experiments to ensure the reliability and repeatability of the experimental results. The number of nodes in the experiment included 10 smart metering equipment nodes, evenly distributed in the laboratory. The communication standard is the LoRa wireless communication standard. Simulating the data acquisition task, each node sends a data packet every 10 seconds, and the data packet size is 100 bytes. The network environment setting has certain signal interference and network congestion. The data records are shown in Table 1;

[0116] Table 1 Comparative experimental record table

[0117]

[0118]

[0119] As can be seen from data table 1, the average delay of group A (dynamic network self-organizing algorithm) is 50 milliseconds, while the average delay of group B (traditional algorithm) is 80 milliseconds. The dynamic network self-organizing algorithm significantly reduces the delay of data transmission and improves the real-time performance of communication. The average packet loss rate of group A is 2%, while the average packet loss rate of group B is 11%. The dynamic network self-organizing algorithm significantly reduces the packet loss rate and improves the reliability of data transmission through multi-hop communication mechanism and optimized network topology. The network coverage of group A is always 10 nodes, while the network coverage of group B fluctuates between 7 and 8 nodes. The dynamic network self-organizing algorithm ensures complete coverage of the network and improves the flexibility and adaptability of the network by automatically discovering neighbor nodes and building an adaptive network topology.

[0120] Network status monitoring monitors the spectrum resource status in the network environment in real time, including signal strength, channel quality and interference. These data are transmitted to the CPU through the wireless communication module for processing and analysis. Network status monitoring ensures that data transmission is carried out on the best link, avoids communication inaccuracy caused by network congestion or interference, and improves the stability and accuracy of communication. In specific implementation, spectrum sensing receives and sends wireless signals through the RF front end, including antennas, low noise amplifiers, mixers and filters. The RF front end converts the received wireless signal into an intermediate frequency or baseband signal for further processing. The intermediate frequency or baseband signal is preprocessed by the signal processing unit, including operations such as amplification, filtering and sampling. The signal processing unit converts the analog signal into a digital signal for subsequent digital signal processing. The digital signal is analyzed by the spectrum analysis unit to extract the spectrum characteristics of the signal. Commonly used spectrum analysis methods include Fourier transform (FFT), wavelet transform and cyclic spectrum analysis. The spectrum analysis unit can identify the frequency components, signal strength and interference of the signal. Channel quality assessment includes the following steps:

[0121] Signal strength measurement: Measures the strength of the signal to assess the signal quality of the link. Links with high signal strength generally have better communication performance.

[0122] Signal-to-Noise Ratio (SNR) calculation: Calculate the signal-to-noise ratio to assess the noise level of the channel. Links with high signal-to-noise ratios typically have lower bit error rates.

[0123] Bit Error Rate (BER) measurement: By sending test packets and receiving feedback packets, the bit error rate is calculated. Links with lower bit error rates are generally more reliable.

[0124] Link Quality Index (LQI) calculation: The Link Quality Index (LQI) is calculated by comprehensively considering indicators such as signal strength, signal-to-noise ratio, and bit error rate. Links with high LQI values ​​generally have better communication performance.

[0125] Interference detection avoids communication inaccuracies caused by interference by detecting and identifying interference sources. Interference detection usually includes the following steps:

[0126] Spectrum scanning: The spectrum sensing module is used to scan the entire frequency band to detect the signal strength and interference of each frequency band.

[0127] Interference source location: Identify the location and strength of interference sources through spectrum analysis. Common interference sources include other wireless devices, electromagnetic interference, etc.

[0128] Interference avoidance: Based on the interference detection results, the frequency band with the least interference is selected for communication. Interference avoidance can be achieved through dynamic spectrum management and adaptive modulation coding.

[0129] The mission requirement assessment obtains the current mission requirements through the sensor module, including the size, type and priority of the business data. These data are transmitted to the CPU through the internal bus and evaluated by the expert system to determine the lower limit of the business data transmission rate, the maximum allowable delay and the sensitivity level of the data. In the specific implementation, the mission requirement assessment ensures that the parameter settings of data transmission meet the actual needs, avoids communication inaccuracies caused by improper parameter settings, and improves the reliability and efficiency of data transmission.

[0130] In the link performance evaluation, the smart metering equipment sends a test data packet to the target node through the wireless communication module, receives the feedback data packet and calculates the link performance. These data are transmitted to the CPU through the internal bus, and the channel capacity of each link is calculated using the Shannon formula to evaluate the maximum transmission capacity of each link. The link performance evaluation evaluates the comprehensive link performance through the link evaluation algorithm based on fuzzy logic to ensure that the selected link has the highest transmission capacity, thereby improving the communication efficiency. In the specific implementation, first, the smart metering equipment uses a multi-path transmission method through the wireless communication module to send the same test data packet through different links. The selection of each link is based on the network topology constructed by the dynamic network self-organizing algorithm to ensure that the test data packet can cover different communication paths. The multi-path transmission method collects the communication quality data of each link through the collaborative work of multiple nodes, including signal strength, transmission rate, packet loss rate and delay. These data are transmitted to the CPU of the smart metering equipment through the wireless communication module for processing and analysis, ensuring the comprehensiveness and accuracy of the evaluation data. Secondly, the link evaluation algorithm based on fuzzy logic converts quantitative data into fuzzy values ​​through fuzzy set definition and membership function. The signal strength, transmission rate, packet loss rate and delay indicators are divided into three fuzzy levels, including weak, medium and strong. The fuzzy level of each indicator is defined by the membership function, which converts quantitative data into fuzzy values. The parameters of the membership function are adjusted through expert experience and historical data to ensure that the membership function can accurately reflect the actual status of each indicator. This fuzzification processing method can better handle uncertainty and ambiguity, and improve the scientificity and rationality of the evaluation. Then, the intelligent metering equipment constructs a fuzzy rule base through expert experience and historical data, and defines various fuzzy rules for comprehensive evaluation of the communication quality of the link. The rules in the fuzzy rule base are combined with various evaluation indicators through fuzzy logic operators and maximum and minimum synthesis methods to obtain the final evaluation results. The construction of the fuzzy rule base is based on a large amount of actual data and expert experience, which ensures the comprehensiveness and reliability of the evaluation results. Through the fuzzy rule base, the intelligent metering equipment can comprehensively consider the influence of multiple indicators to ensure the comprehensiveness and reliability of the evaluation results. Then, the intelligent metering equipment inputs the membership value of each link into the fuzzy rule base for fuzzy reasoning. The fuzzy reasoning method combines various evaluation indicators through fuzzy logic operators and maximum and minimum synthesis methods to obtain the comprehensive fuzzy evaluation results of each link. The fuzzy reasoning method can handle complex nonlinear relationships and ensure the scientificity and rationality of the evaluation results. Through fuzzy reasoning, intelligent metering equipment can comprehensively consider the influence of multiple indicators and obtain comprehensive evaluation results of each link. Finally, the center of gravity of the fuzzy set is calculated by the center of gravity method, and the fuzzy evaluation results are converted into numerical scores. The numerical score reflects the comprehensive communication quality of each link. The higher the score, the stronger the transmission capacity of the link.The centroid method is a commonly used fuzzy set defuzzification method. By calculating the centroid of the fuzzy set, the fuzzy evaluation results are converted into specific numerical scores, ensuring the intuitiveness and operability of the evaluation results. The intelligent metering equipment stores the link evaluation results obtained by the link evaluation algorithm based on fuzzy logic in the local database. The evaluation results include the signal strength, transmission rate, packet loss rate, delay and comprehensive score of each link. These evaluation results provide a basis for the subsequent optimal link selection and communication parameter adjustment.

[0131] Compared with the existing link evaluation technology, the link evaluation algorithm based on fuzzy logic comprehensively evaluates multiple indicators such as signal strength, transmission rate, packet loss rate and delay, ensuring the comprehensiveness and accuracy of the evaluation results. Traditional link evaluation technology usually only focuses on a single indicator, which is difficult to fully reflect the communication quality of the link. In addition, the link evaluation algorithm based on fuzzy logic converts quantitative data into fuzzy values ​​through membership functions, and comprehensively evaluates the communication quality of the link through fuzzy rule bases and fuzzy reasoning methods. This method can better handle uncertainty and fuzziness, ensuring the scientificity and rationality of the evaluation results. Traditional link evaluation technology is usually based on fixed thresholds and simple mathematical models, which is difficult to handle complex and changing network environments. Secondly, the link evaluation algorithm based on fuzzy logic ensures the real-time and accuracy of the evaluation data through multi-path transmission methods and real-time data collection. The fuzzy rule base and fuzzy reasoning methods can comprehensively consider the influence of multiple indicators to ensure the high accuracy of the evaluation results. Traditional link evaluation technology is usually based on static data and simple models, which is difficult to adapt to dynamically changing network environments. At the same time, the link evaluation algorithm based on fuzzy logic constructs a fuzzy rule base through expert experience and historical data, which can dynamically adjust the evaluation parameters according to the actual network environment to ensure the adaptability and flexibility of the evaluation results. Traditional link evaluation technologies are usually based on fixed parameters and models, which are difficult to adapt to changes in different network environments. In addition, the link evaluation algorithm based on fuzzy logic improves communication efficiency by comprehensively evaluating the communication quality of each link to ensure that the selected link has the highest transmission capacity. Traditional link evaluation technologies usually select links based on a single indicator, which makes it difficult to ensure the efficiency and reliability of communication. Finally, the link evaluation algorithm based on fuzzy logic reduces dependence on manual intervention and reduces maintenance costs through automated evaluation and dynamic adjustment. Traditional link evaluation technologies usually require manual intervention and regular maintenance, which increases maintenance costs and workload.

[0132] The design of comparative experiments verifies the effect of the link evaluation algorithm based on fuzzy logic. The hardware working environment of the link evaluation algorithm based on fuzzy logic includes: Intelligent metering equipment: equipped with wireless communication module, internal bus, CPU and local database. Wireless communication network: used for data transmission between intelligent metering equipment and target nodes. Test equipment: used to send test data packets and receive feedback data packets. Data recording and analysis system: used to store and analyze experimental data.

[0133] A group of identical hardware was selected to conduct comparative experiments using the fuzzy logic-based link evaluation algorithm (Group A) and the traditional algorithm (Group B). Both groups of experiments were conducted under the same experimental conditions, including the same network environment, packet size, and sending frequency. The experimental results of Groups A and B were compared. Each group conducted five experiments and recorded the link performance evaluation results of each experiment. The experimental data records are shown in Table 2:

[0134] Table 2 Experimental record form

[0135]

[0136]

[0137] Through comparative experiments, it can be seen that the link evaluation algorithm based on fuzzy logic (Group A) is superior to the traditional algorithm (Group B) in terms of signal strength, transmission rate, packet loss rate, delay and comprehensive score. The average comprehensive score of Group A is 88.94, while the average comprehensive score of Group B is 78.15. This proves that the link evaluation algorithm based on fuzzy logic can significantly improve the accuracy of link performance evaluation and communication efficiency in the adaptive communication method of ubiquitous power Internet of Things smart metering equipment.

[0138] In the optimal link selection, the CPU selects the optimal link combination through genetic algorithms and reinforcement learning algorithms based on task requirements and link performance evaluation results. These algorithms run on the CPU, interact with the storage module and the wireless communication module through the internal bus, and dynamically optimize the link selection strategy. The optimal link selection ensures that the optimal link combination is selected while meeting the data transmission requirements, further improving communication efficiency and reliability. In the specific implementation, the genetic algorithm is used to obtain the link state variables when the data transmission requirements are met and the maximum value is reached; the genetic algorithm is an optimization algorithm that simulates natural selection and genetic mechanisms, which can find the optimal solution in a complex search space. In the adaptive communication method of ubiquitous power Internet of Things smart metering equipment, the genetic algorithm is used to optimize the link state variables to meet the data transmission requirements and reach the maximum value.

[0139] The implementation principle of the genetic algorithm includes the steps of encoding, initial population generation, fitness evaluation, selection, crossover, mutation and termination conditions. In the encoding stage, the link state variables are encoded as chromosomes in the genetic algorithm. In the initial population generation stage, a set of chromosomes is randomly generated as the initial solution. In the fitness evaluation stage, the fitness value of each chromosome is evaluated according to the preset objective function (i.e., the comprehensive performance function). In the selection stage, excellent chromosomes are selected as parents according to the fitness value. In the crossover stage, offspring are generated by exchanging some genes of the parent chromosomes. In the mutation stage, the offspring chromosomes are randomly mutated to increase the diversity of solutions. In the termination condition stage, when the preset number of iterations is reached or the fitness value is no longer significantly improved, the algorithm terminates and outputs the optimal solution.

[0140] Based on S54, S55 selects the link to be in standby and to remain enabled according to the optimal link state variable obtained by the genetic algorithm. The link selection strategy is dynamically optimized by the reinforcement learning algorithm. The reinforcement learning algorithm is a machine learning algorithm that learns the optimal strategy by trial and error. In the adaptive communication method of the ubiquitous power Internet of Things smart metering equipment, the reinforcement learning algorithm is used to dynamically optimize the link selection strategy. The implementation principle of the reinforcement learning algorithm includes the steps of state representation, action space, reward function, strategy learning and strategy update. In the state representation stage, the current communication environment and link state are represented as the state of the reinforcement learning algorithm. In the action space stage, the link combination that the smart metering equipment can select as an action is defined. In the reward function stage, the reward value is defined according to the efficiency and quality of data transmission. In the strategy learning stage, the smart metering equipment learns the optimal link selection strategy by continuously trying different actions and observing the reward value. In the strategy update stage, the link selection behavior of the smart metering equipment is updated according to the learned strategy.

[0141] The genetic algorithm works in S54 by iterative search. First, an initial population containing multiple link state variable combinations is generated. Then, the fitness value of each link state variable is evaluated according to the comprehensive performance function. Next, the link state variable with a higher fitness value is selected as the parent generation, and the offspring is generated through crossover and mutation operations. This process is repeated until the preset number of iterations is reached or the fitness value is no longer significantly improved. Finally, the optimal link state variable combination is output.

[0142] In S55, the smart metering equipment first selects the links to be in standby and remain enabled according to the optimal link state variables obtained by the genetic algorithm. Then, the smart metering equipment begins to observe the changes in the communication environment and link state, and learns the optimal link selection strategy according to the reinforcement learning algorithm. During the learning process, the smart metering equipment continuously tries different link combinations and obtains reward values ​​based on the efficiency and quality of data transmission. Based on the reward value, the smart metering equipment updates its link selection strategy to gradually approach the optimal solution.

[0143] In the working state of S54, the genetic algorithm continuously iterates to search for the optimal combination of link state variables. This process can make full use of the global search ability and parallel computing ability of the genetic algorithm to quickly find the link state variables that meet the data transmission requirements and are at the maximum value. Through the optimization of S54, the efficiency and quality of data transmission can be significantly improved, and the communication delay and packet loss rate can be reduced. At the same time, the robustness and adaptability of the genetic algorithm enable this method to adapt to different communication environments and link state changes.

[0144] In the working state of S55, the intelligent metering equipment continuously learns and optimizes the link selection strategy through the reinforcement learning algorithm. This process can make full use of the trial and error learning and strategy optimization capabilities of reinforcement learning, and gradually approach the optimal link selection strategy. Through the optimization of S55, the intelligent metering equipment can dynamically adjust the link selection strategy according to the real-time communication environment and link status changes to adapt to different data transmission requirements. This can not only improve the stability and reliability of data transmission, but also reduce communication costs and energy consumption. At the same time, the self-learning ability and adaptability of reinforcement learning enable this method to continuously adapt to new communication environments and link status changes, and maintain long-term optimization effects.

[0145] Compared with the existing technology, the genetic algorithm has the global search capability and can find the optimal solution in a complex search space. This enables S54 to fully consider all possible combinations of link state variables and find the optimal solution that meets the data transmission requirements and is at the maximum value. Both the genetic algorithm and the reinforcement learning algorithm are adaptive and robust, and can adapt to different communication environments and link state changes. This enables S54 and S55 to maintain stable optimization effects in different scenarios. The reinforcement learning algorithm has dynamic optimization capabilities and can adjust the link selection strategy according to the real-time communication environment and link state changes. This enables S55 to dynamically adjust the link selection strategy according to the real-time data transmission requirements and improve the stability and reliability of data transmission. By optimizing the link selection strategy, S54 and S55 can reduce unnecessary communication overhead and energy consumption and improve the utilization of communication resources. This is of great significance to the ubiquitous power Internet of Things, which helps to reduce operating costs and environmental impact.

[0146] Multipath transmission is based on the selected optimal link combination, and the smart metering equipment sends data to the target node through the wireless communication module. The multipath transmission method improves the reliability and efficiency of data transmission by using multiple links to transmit data simultaneously. In the specific implementation, multipath transmission not only improves the reliability of data transmission, but also significantly improves the communication efficiency, especially in the case of network congestion or partial link failure, multipath transmission can ensure the continuity and integrity of data. In the specific implementation, when the smart metering equipment sends data to the target node through the wireless communication module, it will dynamically evaluate and select the optimal link combination according to parameters such as real-time network conditions, link quality, and transmission delay. These link combinations can be based on different frequency bands, different communication technologies, or different physical paths to ensure the diversity and flexibility of data transmission. By dynamically adjusting the link combination, the multipath transmission method can optimize the utilization of network resources in real time, alleviate congestion problems on a single path, improve bandwidth utilization, and thus improve communication efficiency. At the same time, when a link fails or packet loss occurs, the system can quickly switch to other paths to ensure the continuity and integrity of data. In addition, the multipath transmission method also provides a fault recovery and fault tolerance mechanism, which can automatically correct errors or lost data packets during data transmission, further improving the reliability of data transmission. This intelligent selection and dynamic adjustment mechanism can be implemented based on machine learning algorithms or preset decision rules. Through continuous learning and optimization, smart metering equipment can gradually improve the accuracy and efficiency of link selection.

[0147] In dynamic spectrum management and adaptive modulation and coding, the smart metering equipment monitors the network status and link performance in real time through the spectrum sensing module. Based on these data, the CPU dynamically adjusts the communication parameters, including transmission rate, frequency and power, through the adaptive modulation and coding model based on dynamic spectrum management. Adaptive modulation and coding selects the most appropriate modulation method and coding scheme through a multi-criteria decision method. Dynamic spectrum management and adaptive modulation and coding ensure the best transmission effect under different network conditions, thereby improving the flexibility and adaptability of communication. In specific implementation, the adaptive modulation and coding model based on dynamic spectrum management realizes the adaptive communication of smart metering equipment through the following steps:

[0148] Spectrum sensing: The RF front end is responsible for receiving and sending wireless signals, including antennas, low noise amplifiers, mixers, and filters. The RF front end converts the received wireless signals into intermediate frequency or baseband signals for further processing. The signal processing unit preprocesses the intermediate frequency or baseband signals, including operations such as amplification, filtering, and sampling. The signal processing unit converts analog signals into digital signals for subsequent digital signal processing. The spectrum analysis unit performs spectrum analysis on digital signals to extract the spectrum characteristics of the signals. Common spectrum analysis methods include Fourier transform (FFT), wavelet transform, and cyclic spectrum analysis. The spectrum analysis unit can identify the frequency components, signal strength, and interference of the signal.

[0149] Data processing: The spectrum sensing module collects network status and link performance data in real time, including signal strength, channel quality, and interference. These data are transmitted to the CPU through the wireless communication module for processing and analysis. Based on the collected data, the CPU dynamically adjusts the communication parameters, including transmission rate, frequency, and power, through an adaptive modulation and coding model based on dynamic spectrum management. The CPU selects the most appropriate modulation mode and coding scheme based on link performance and network conditions.

[0150] Multi-criteria decision-making method: Select the most appropriate modulation method (such as BPSK, QPSK, 16-QAM, 64-QAM, etc.) based on link performance and network conditions. The selection of the modulation method takes into account signal strength, channel quality and interference to ensure the best transmission effect under different network conditions. Select the most appropriate coding scheme (such as convolutional coding, Turbo coding, LDPC coding, etc.) based on link performance and network conditions. The selection of the coding scheme takes into account transmission rate, frequency, power, signal strength, channel quality and interference to ensure the best transmission effect under different network conditions. The multi-criteria decision-making method comprehensively considers multiple factors, including transmission rate, frequency, power, signal strength, channel quality and interference, to select the most appropriate modulation method and coding scheme. The multi-criteria decision-making method uses weighted summation, hierarchical analysis method (AHP) and other methods to ensure that the selected modulation method and coding scheme can comprehensively consider the influence of multiple factors.

[0151] Compared with existing communication technologies, the adaptive modulation and coding model based on dynamic spectrum management monitors network conditions and link performance in real time to ensure the real-time and accuracy of data. Traditional communication technologies are usually based on static data and simple models, which are difficult to adapt to dynamically changing network environments. Through dynamic spectrum management, smart metering equipment can select the best frequency band for communication according to network conditions and link performance to avoid interference and congestion. Traditional communication technologies are usually based on fixed frequency bands and parameters, which are difficult to adapt to changes in different network environments. Through adaptive modulation and coding, smart metering equipment can dynamically adjust the modulation mode and coding scheme according to link performance and network conditions to ensure the best transmission effect under different network conditions. Traditional communication technologies are usually based on fixed modulation modes and coding schemes, which are difficult to adapt to changes in different network environments. The multi-criteria decision method comprehensively considers multiple factors, including transmission rate, frequency, power, signal strength, channel quality and interference, to select the most appropriate modulation mode and coding scheme. The multi-criteria decision method ensures that the selected modulation mode and coding scheme can comprehensively consider the influence of multiple factors, thereby improving the efficiency and reliability of communication. In addition, the adaptive modulation and coding model based on dynamic spectrum management ensures the flexibility and adaptability of communication through real-time monitoring and dynamic adjustment, and improves the efficiency and reliability of communication. Traditional communication technologies usually require manual intervention and regular maintenance, which increases maintenance costs and workload.

[0152] Specifically, the hardware working environment of the adaptive modulation and coding model based on dynamic spectrum management mainly includes: Intelligent metering equipment: The equipment has built-in spectrum sensing module, CPU, communication module, etc., which are used to monitor network status and link performance in real time, and dynamically adjust communication parameters according to the data. Spectrum sensing module: used to collect spectrum information in the network in real time, including available spectrum resources, interference conditions, etc. Communication module: supports multiple communication protocols and technologies for data transmission and reception. Data processing unit (CPU): responsible for processing and analyzing the data collected by the spectrum sensing module, and calculating the optimal communication parameter combination based on the principles of dynamic spectrum management. Power supply module: provides a stable power supply for the entire system.

[0153] Based on the above hardware, a comparative experiment was designed to verify the positive and beneficial effects of the adaptive modulation and coding model based on dynamic spectrum management. A set of hardware was applied to the model (Group A) and the traditional communication model of adaptive modulation and coding without dynamic spectrum management (Group B) for comparative experiments. Both groups of experiments were conducted under the same experimental conditions, including the same network environment, interference conditions, data transmission volume, etc. Each group of experiments was conducted five times to ensure the stability and reliability of the results. During the experiment, we recorded the key indicators such as transmission rate, bit error rate, energy consumption, etc. of each experiment, and conducted comparative analysis. The experimental records are shown in Table 3:

[0154] Table 3 Model comparison experiment record table

[0155]

[0156]

[0157] It can be seen from the data table 1 that the adaptive modulation and coding model based on dynamic spectrum management (group A) is superior to the traditional model (group B) in terms of transmission rate, bit error rate and energy consumption. The average transmission rate of group A is about 25% higher than that of group B, the bit error rate is reduced by about 67%, and the energy consumption is reduced by about 17%. These results show that the adaptive modulation and coding model based on dynamic spectrum management can significantly improve communication efficiency, reduce bit error rate and energy consumption, and has positive and beneficial effects.

[0158] In forward error correction coding and backward error correction, smart metering equipment implements forward error correction coding and backward error correction through built-in encoders and decoders. The encoder adds redundant information at the sending end, and the decoder performs data verification at the receiving end. If data errors are detected, they are corrected through error compensation algorithms. Forward error correction coding and backward error correction ensure the accuracy and reliability of data transmission, and maintain the stability and accuracy of communication even when the network environment changes. In specific implementation, forward error correction coding and backward error correction are implemented through the following steps:

[0159] Forward error correction coding: The smart metering equipment uses a built-in encoder at the transmitter to encode the original data block to generate a coded data block with redundant information. The encoder encodes the original data block according to the selected coding method (such as convolutional code, Reed-Solomon code, LDPC code, etc.) to generate redundant information. Among them, the convolutional code generates redundant information by sliding window, and the most common method is to use recursive systematic convolutional code (RSC). The Reed-Solomon code generates redundant information by the polynomial root method, and the most common method is to use the polynomial interpolation method. Low-density parity-check codes generate redundant information by sparse matrix, and the most common method is to use the belief propagation algorithm (BP).

[0160] The encoded data block is sent to the receiving end through the wireless communication module. The way of adding redundant information depends on the selected encoding method. For example, the convolutional code generates redundant information by sliding window, the Reed-Solomon code generates redundant information by polynomial root method, and the LDPC code generates redundant information by sparse matrix method. The receiving end uses the built-in decoder to decode the received encoded data block to detect and correct the errors generated during the transmission process. The decoding process usually includes two methods: soft decision and hard decision. Among them, the convolutional code is decoded by the Viterbi Algorithm, and soft decision and hard decision are commonly used. The Reed-Solomon code is decoded by the polynomial interpolation method, and the Berlekamp-Massey algorithm is commonly used. The low-density parity check code is decoded by the belief propagation algorithm (BP), and the layer-by-layer iterative method is commonly used.

[0161] Backward Error Correction: After the decoder detects data errors at the receiving end, it sends a retransmission request to the sending end through the feedback channel. Error detection can be performed through methods such as checksum and CRC. After receiving the retransmission request, the sending end retransmits the erroneous data block. The retransmission request can be implemented through the ARQ protocol, including stop-and-wait ARQ, continuous ARQ, and selective repeat ARQ. The receiving end receives and decodes again until the data is correct. The retransmission process is performed through the feedback channel to ensure the accuracy and reliability of the data.

[0162] Compared with existing communication technologies, forward error correction coding improves the reliability of data transmission by adding redundant information. Even when the network environment changes, errors generated during transmission can be detected and corrected through redundant information. For example, convolutional codes generate redundant information through sliding windows, Reed-Solomon codes generate redundant information through polynomial roots, and LDPC codes generate redundant information through sparse matrices. This ensures the reliability and stability of data transmission. Backward error correction further improves the accuracy of data transmission through a retransmission mechanism. After the receiving end detects a data error, it sends a retransmission request to the sending end through a feedback channel, and the sending end resends the erroneous data block. This ensures the accuracy and reliability of data transmission.

[0163] In the multi-layer security mechanism, the smart metering equipment implements a multi-layer security mechanism through a security module, including a data encryption layer, an identity authentication layer, an intrusion detection layer, and an active defense layer. The encryption chip is responsible for data encryption and decryption, and the security processor is responsible for identity authentication and intrusion detection. The multi-layer security mechanism ensures the security and privacy protection of data transmission, prevents data leakage and malicious attacks, and improves the overall security of the system. The data encryption layer encrypts the transmitted data through the Advanced Encryption Standard AES to ensure data confidentiality; the identity authentication layer ensures only network security through a two-way authentication mechanism; the intrusion detection layer monitors network traffic in real time by deploying an intrusion detection system, and promptly discovers and responds to potential network security threats; the intrusion detection system identifies and isolates suspicious nodes through behavioral analysis and anomaly detection methods; the active defense layer actively attracts and misleads potential attackers through a deception defense mechanism to protect the security of real network nodes.

[0164] The specific implementation steps of this system are: Startup and network self-organization: When the smart metering equipment is started, the CPU runs the dynamic network self-organization algorithm, sends and receives information about neighboring nodes through the wireless communication module, and builds an adaptive network topology. The spectrum sensing module monitors the spectrum resource status in the network environment in real time and provides necessary spectrum resource information.

[0165] Network status monitoring: The spectrum sensing module monitors the spectrum resource status in the network environment in real time, including signal strength, channel quality and interference. These data are transmitted to the CPU through the wireless communication module for processing and analysis, and the current network environment is regularly evaluated.

[0166] Mission requirement assessment: The sensor module obtains the current mission requirements, including the size, type and priority of the business data. These data are transmitted to the CPU through the internal bus and evaluated by the expert system to determine the lower limit of the business data transmission rate, the maximum allowable delay and the sensitivity level of the data.

[0167] Link performance evaluation: The smart metering equipment sends test data packets to the target node through the wireless communication module, receives feedback data packets and calculates the link performance. These data are transmitted to the CPU through the internal bus, and the Shannon formula is used to calculate the channel capacity of each link to evaluate the maximum transmission capacity of each link.

[0168] Optimal link selection: The CPU selects the optimal link combination through genetic algorithms and reinforcement learning algorithms based on task requirements and link performance evaluation results. These algorithms run on the CPU and interact with the storage module and wireless communication module through the internal bus to dynamically optimize the link selection strategy.

[0169] Multipath transmission: Based on the selected optimal link combination, the smart metering equipment sends data to the target node through the wireless communication module. The multipath transmission method improves the reliability and efficiency of data transmission by using multiple links to transmit data simultaneously.

[0170] Dynamic spectrum management and adaptive modulation and coding: Smart metering equipment monitors network status and link performance in real time through spectrum sensing modules. The CPU dynamically adjusts communication parameters, including transmission rate, frequency, and power, based on these data. Adaptive modulation and coding selects the most appropriate modulation mode and coding scheme through a multi-criteria decision-making method.

[0171] Forward error correction coding and backward error correction: Smart metering equipment implements forward error correction coding and backward error correction through built-in encoders and decoders. The encoder adds redundant information at the sending end, and the decoder performs data verification at the receiving end. If data errors are detected, they are corrected through error compensation algorithms.

[0172] Multi-layer security mechanism: Smart metering equipment implements a multi-layer security mechanism through security modules, including data encryption layer, identity authentication layer, intrusion detection layer and active defense layer. The encryption chip is responsible for data encryption and decryption, and the security processor is responsible for identity authentication and intrusion detection.

[0173] In the specific implementation, the dynamic network self-organizing algorithm and multi-hop communication mechanism ensure that each node can find the optimal communication path, avoiding data transmission errors caused by fixed network structure. Network status monitoring and link performance evaluation ensure that data transmission is carried out on the best link, avoiding communication inaccuracy caused by network congestion or interference. The multi-path transmission method not only improves the reliability of data transmission but also significantly improves communication efficiency by using multiple links to transmit data simultaneously. Dynamic spectrum management and adaptive modulation and coding ensure the best transmission effect under different network conditions by optimizing the utilization of spectrum resources and selecting the most appropriate modulation mode and coding scheme. The dynamic network self-organizing algorithm can automatically adjust the network topology according to changes in the network environment to ensure the flexibility and coverage of the network. The optimal link selection dynamically optimizes the link selection strategy through genetic algorithms and reinforcement learning algorithms to ensure that the optimal link combination can be selected under different network conditions, further improving the flexibility and adaptability of the network. Forward error correction coding and backward error correction methods ensure the accuracy and reliability of data transmission, and maintain the stability and accuracy of communication even when the network environment changes. The multi-path transmission method improves the reliability of data transmission and ensures the continuity and integrity of data by using multiple links to transmit data simultaneously. The multi-layer security mechanism ensures the security and privacy protection of data transmission through the data encryption layer, identity authentication layer, intrusion detection layer and active defense layer. The encryption chip and security processor in the security module provide strong security protection to prevent data leakage and malicious attacks, and improve the overall security of the system.

[0174] Although the specific embodiments of the present invention are described above, it should be understood by those skilled in the art that these specific embodiments are only illustrative, and those skilled in the art may omit, replace, and change the details of the above methods and systems in various ways without departing from the principles and essence of the present invention. For example, merging the above method steps so as to perform substantially the same functions in substantially the same manner to achieve substantially the same results is within the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. An adaptive communication method for ubiquitous power Internet of Things smart metering equipment, characterized in that: The following steps are involved: S1. When the smart metering equipment is started, it automatically discovers the surrounding neighbor nodes through a dynamic network self-organizing algorithm and builds an adaptive network topology; the dynamic network self-organizing algorithm allows nodes to forward request packets and response packets through intermediate nodes through a multi-hop communication mechanism to expand the network coverage; S2. Smart metering equipment regularly evaluates the current network environment through network status monitoring, including network congestion, signal strength and interference; S3. Smart metering equipment obtains current task requirements through built-in sensors and user input, including the size, type and priority of business data, and determines the lower limit of business data transmission rate, maximum allowable delay and data sensitivity level through expert system; S4. The smart metering equipment sends test data packets to the target node through the established multiple communication links, evaluates the performance of each link, and calculates the channel capacity of each link through the Shannon formula to evaluate the maximum transmission capacity of each link; S5. The smart metering equipment selects the optimal link combination according to the data transmission requirements output by S3 and the performance indicators of each link output by S4. S5 includes the following steps: S51: Preset multiple candidate link solutions, each solution includes a different link combination; S52: Determine the enabled link capacity, the number of enabled links, the number of enabled communication nodes and the average link performance of each solution based on the link state variable, the number of communication nodes, the link performance and the link channel capacity; S53: defining a comprehensive performance function f(x) of the scheme, taking into account the enabled link capacity, the number of enabled links, the number of enabled communication nodes, the average link performance and their corresponding weight coefficients; S54: Obtaining a link state variable when the data transmission requirement is met and f(x) is maximized through a genetic algorithm; S55: selecting a link to be put on standby and a link to be kept enabled according to the link state variable; and dynamically optimizing the link selection strategy through a reinforcement learning algorithm; S6, based on the optimal link combination selected in S5, sending data to the target node through a multi-path transmission method; S7. During data transmission, the smart metering equipment dynamically adjusts communication parameters, including transmission rate, frequency and power, through an adaptive modulation and coding model based on dynamic spectrum management according to network conditions and link performance; S8. The intelligent metering equipment performs data verification at the receiving end through forward error correction coding and backward error correction methods. If a data error is detected, it is corrected through an error compensation algorithm; S9. During the entire communication process, the smart metering equipment adopts a multi-layer security mechanism to ensure the secure transmission of data; the multi-layer security mechanism includes a data encryption layer, an identity authentication layer, an intrusion detection layer and an active defense layer.

2. According to claim 1, a ubiquitous power Internet of Things intelligent metering equipment adaptive communication method is characterized by: The working method of constructing the adaptive network topology of the dynamic network self-organizing algorithm is: U1. When the smart metering equipment is started, the dynamic network self-organizing algorithm sends a request packet through the wireless broadcast protocol. The request packet contains its own node ID, location and communication capability. U2, after receiving the request packet, the neighbor node parses the information in the request packet and generates a response packet, which contains the node ID, location and communication capability of the neighbor node, and returns it to the smart metering equipment through the wireless communication channel; U3, after receiving the response packet, the smart metering equipment parses the information of the neighboring nodes and stores it in the local database, including the node ID, location and communication capability; U4. Based on the extracted node information, the dynamic network self-organizing algorithm constructs a network topology graph and stores it in the form of an adjacency matrix or an adjacency list. At the same time, the dynamic network self-organizing algorithm records the status information of each neighbor node, including timestamp, signal strength, transmission rate, and packet loss. U5. Dynamic network self-organizing algorithm dynamically updates the network topology by sending and receiving broadcast packets at regular intervals to ensure the real-time and accuracy of the network.

3. According to claim 1, a ubiquitous power Internet of Things intelligent metering equipment adaptive communication method is characterized by: The multi-hop communication mechanism dynamically discovers and maintains the optimal path through the routing protocol AODV, and ensures the communication transmission of data packets on the multi-hop path through the packet forwarding mechanism.

4. The adaptive communication method for ubiquitous power Internet of Things intelligent metering equipment according to claim 1 is characterized in that: In S5, the optimization method of the link selection strategy dynamically optimized by the reinforcement learning algorithm is: first, the state space, action space and reward function are defined through the reinforcement learning model to construct a decision framework for the smart metering equipment in the communication process; the state space is used to include the performance parameters of the current link, the status of the communication node and the demand information for data transmission; the action space is used to define the operation of the smart metering equipment, including enabling or standing by the link; the reward function is used to measure the efficiency of data transmission, the stability of the link and the consumption of resources; according to the reward value fed back by the reward function, the reinforcement learning algorithm gradually updates the decision strategy of the smart metering equipment through the policy iteration method; in the iterative update process, the reinforcement learning algorithm also uses a deep neural network as a function approximator of the reinforcement learning model to approximate the policy function or the value function.

5. The adaptive communication method for ubiquitous power Internet of Things intelligent metering equipment according to claim 1 is characterized by: The working method of S4 is: S41, using a multipath transmission method to send the same test data packet through different links, and evaluating the transmission quality of each link, including signal strength, transmission rate, packet loss rate and delay; S42, the intelligent metering equipment comprehensively evaluates the communication quality of each link according to the transmission quality of each link through a link evaluation algorithm based on fuzzy logic; The working steps of the link evaluation algorithm based on fuzzy logic include: r1. Define fuzzy sets and divide signal strength, transmission rate, packet loss rate and delay indicators into three fuzzy levels, including weak, medium and strong; r2. Define a membership function for each fuzzy set to convert quantitative data into fuzzy values; the parameters of the membership function are adjusted based on expert experience and historical data; r3. The smart metering equipment uses a multi-path transmission method to send the same test data packet through different links and collect the communication quality data of each link; r4. After receiving the test data packet, the neighbor node records the timestamp, signal strength, transmission rate and packet loss of the received data packet, and returns this information to the smart metering equipment; the smart metering equipment obtains the communication quality data of each link by receiving this feedback information; r5. The intelligent metering equipment calculates the membership value of the signal strength through the defined membership function according to the received signal strength data; r6. The intelligent metering equipment calculates the membership value of the transmission rate through the defined membership function according to the received transmission rate data; r7. The intelligent metering equipment calculates the membership value of the packet loss rate through the defined membership function according to the received packet loss rate data; r8. The intelligent metering equipment calculates the membership value of the delay through the defined membership function according to the received delay data; r9. Build a fuzzy rule base through expert experience and historical data, and comprehensively evaluate the communication quality of the link through fuzzy rules; r10. Inputting the membership value of each link into the fuzzy rule base through the fuzzy reasoning method, performing fuzzy reasoning, and obtaining the comprehensive fuzzy evaluation result of each link; the fuzzy reasoning method synthesizes each evaluation index through fuzzy logic operators and maximum and minimum synthesis method to obtain the final evaluation result; r11. Calculate the center of gravity of the fuzzy set by the center of gravity method and convert the fuzzy evaluation results into numerical scores; S43. The intelligent metering equipment stores the link evaluation results obtained by the link evaluation algorithm based on fuzzy logic in a local database. The evaluation results include the signal strength, transmission rate, packet loss rate, delay and comprehensive score of each link.

6. The adaptive communication method for ubiquitous power Internet of Things intelligent metering equipment according to claim 1 is characterized by: In S5, the working method of solving the link state variable by genetic algorithm includes the following steps: t1. Generate an initial population based on a heuristic method, where each individual represents a possible solution of a link state variable; the link state variables include enabled link capacity, enabled link number, enabled communication node number and average link performance; based on the properties of the link state variables and the optimization objectives, each individual is represented by binary coding or real number coding, where binary coding is used for discrete variables and real number coding is used for continuous variables; t2. Comprehensively consider the enabled link capacity, the number of enabled links, the number of enabled communication nodes, the average link performance and their corresponding weight coefficients, and define the fitness function to evaluate the pros and cons of each individual; t3. Calculate the fitness value of each individual through the fitness function; t4. Select excellent individuals from the current population to enter the next generation population through a selection operation; the selection operation controls the probability of selection through the fitness value of the individual; t5. Generate a new individual through a crossover operation; the crossover operation controls the frequency of the crossover operation through a crossover probability; the crossover probability is set to 0.8; t6. Through mutation operation, make slight changes to individuals and introduce new genetic information; the mutation operation includes bit flip mutation, uniform mutation and Gaussian mutation; the mutation operation controls the occurrence frequency of the mutation operation through the mutation probability, and the mutation probability is set between 0.01 and 0.1; t7. Set the termination condition to determine whether the genetic algorithm has reached the optimal solution; when the termination condition is met, the algorithm stops iterating and outputs the optimal solution; t8. Select the individual with the highest fitness value from the final population as the optimal solution, which is used to represent the link state variable when the data transmission requirements are met and the comprehensive performance is maximized.

7. The adaptive communication method for ubiquitous power Internet of Things intelligent metering equipment according to claim 1 is characterized by: The adaptive modulation and coding model based on dynamic spectrum management includes a data input layer, a modulation and coding selection layer, a parameter optimization layer, a spectrum resource allocation layer, a communication parameter adjustment layer and an execution layer; the working method of the adaptive modulation and coding model based on dynamic spectrum management is: c1. receiving the evaluated network status and link performance data through the data input layer, and temporarily storing the data in a cache; c2. Selecting a modulation mode and a coding scheme through the modulation and coding selection layer, and dynamically adjusting the modulation and coding scheme according to changes in link quality; The modulation and coding selection layer constructs a multi-objective optimization model through a multi-criteria decision-making method to select the optimal modulation and coding scheme; c3. The parameter optimization layer ensures that the spectrum resources are maximized while meeting the communication quality. The parameter optimization layer dynamically adjusts the communication parameters through the reinforcement learning algorithm, continuously optimizes the settings of the transmission rate, frequency and power, and monitors the communication effect after the parameter adjustment through the real-time feedback mechanism; c4. Ensure that each node can communicate on the optimal frequency band through the spectrum resource allocation layer. The spectrum resource allocation layer optimizes the allocation of spectrum resources through dynamic spectrum allocation and spectrum sharing methods, and monitors the use of spectrum resources in real time through spectrum utilization evaluation; c5. Ensure that the communication parameters match the current network status and link quality through the communication parameter adjustment layer, which changes the transmission rate, frequency and power parameters in real time through a dynamic adjustment mechanism, and monitors the communication effect after parameter adjustment through a real-time feedback mechanism; c6. The optimized communication parameters are set in the smart metering equipment through the execution layer, and the actual communication effect is monitored in real time to verify the parameter adjustment effect, and the detailed information of each parameter adjustment is recorded.

8. The adaptive communication method for ubiquitous power Internet of Things intelligent metering equipment according to claim 1 is characterized by: The working method of S8 is: encoding the original data through convolutional code, Reed-Solomon code or low-density parity-check code to generate encoded data with redundant information; the encoded data is transmitted to the receiving end through a wireless channel; after the receiving end receives the encoded data, the data is checked and error detected through a backward error correction method. If a data error is detected, the receiving end locates and corrects the error through the redundant information provided by the forward error correction coding algorithm. If the error exceeds the error correction capability of the forward error correction coding, the receiving end requests the sending end to resend the erroneous data packet through a retransmission request mechanism; the forward error correction coding algorithm determines the error position through a sliding window or a polynomial root, and corrects the error through a Viterbi algorithm or polynomial interpolation.

9. The adaptive communication method for ubiquitous power Internet of Things intelligent metering equipment according to claim 1 is characterized by: The data encryption layer encrypts the transmitted data through the Advanced Encryption Standard AES to ensure data confidentiality; the identity authentication layer ensures network security through a two-way authentication mechanism; the intrusion detection layer monitors network traffic in real time by deploying an intrusion detection system, and promptly discovers and responds to potential network security threats; the intrusion detection system identifies and isolates suspicious nodes through behavioral analysis and anomaly detection methods; the active defense layer actively attracts and misleads potential attackers through a deception defense mechanism to protect the security of real network nodes.

10. A ubiquitous power Internet of Things intelligent metering equipment adaptive communication system, characterized by: A method for adaptive communication of ubiquitous power Internet of Things smart metering equipment as described in any one of claims 1 to 9, comprising: Dynamic network discovery module, used to automatically discover neighboring nodes, build network topology, and record the status information and communication capabilities of each node; Network evaluation module, used to regularly evaluate the current network environment, including network congestion, signal strength and interference; Demand analysis module, used to analyze the transmission requirements of business data, including the lower limit of transmission rate, maximum allowable delay and data sensitivity level, and set priorities; A multipath link evaluation module, used to send test data packets through multiple communication links to evaluate the performance of each link; Intelligent link selection module, which is used to select the optimal link combination based on business requirements and link performance evaluation results, and dynamically optimize the link selection strategy through reinforcement learning algorithm; A data transmission module, used to send service data to a target node through the optimal link combination selected by the intelligent link selection module, and dynamically adjust communication parameters during transmission through an adaptive modulation and coding model based on dynamic spectrum management; The data verification module is used to perform data verification at the receiving end. If data errors are detected, they are corrected through error compensation algorithms. Security mechanism module, used to ensure secure data transmission through multi-layer security mechanisms; The output end of the dynamic network discovery module is connected to the input ends of the network evaluation module, the demand analysis module, and the multi-path link evaluation module; the output end of the network evaluation module is connected to the input ends of the demand analysis module and the intelligent link selection module; the output end of the demand analysis module is connected to the input ends of the multi-path link evaluation module and the intelligent link selection module; the output end of the multi-path link evaluation module is connected to the input end of the intelligent link selection module; the output end of the intelligent link selection module is connected to the input end of the data transmission module; the output end of the data transmission module is connected to the input end of the data verification module; the output end of the security mechanism module is connected to the above modules.

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