An intelligent electric meter collection system based on high-speed carrier and multi-mode communication

Abstract

This application provides a smart meter centralized data acquisition system based on high-speed carrier and multi-mode communication. It assesses the stability of the power line carrier uploading process of electricity consumption data using the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel, obtaining a stable equilibrium value of the link score in smart meter centralized data acquisition. Then, based on the load fluctuation characteristics of the electricity consumption data and the historical gradient characteristics of the link score, it determines the hysteresis interval for communication link switching and the confidence threshold for activating the wireless backup channel. When the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, wireless backup channel switching is triggered, and the switching event is recorded to the main station platform. Based on the above scheme, adaptive adjustment of the wireless backup channel switching conditions can be achieved, thereby reducing the link mis-switching rate of the multi-mode communication network.

Description

Technical Field

[0001] This application relates to the field of electricity meter centralized procurement technology, and more specifically, to a smart electricity meter centralized procurement system based on high-speed carrier and multi-mode communication. Background Technology

[0002] The electricity meter centralized collection system achieves efficient collection of electricity meter data through a combination of high-speed power line carrier communication and spread spectrum wireless communication. It includes a master station, power line communication concentrators, wireless communication concentrators, and smart meters with various communication modes. Smart meters can be connected to the concentrators via power lines or wireless relays to achieve flexible networking. Utilizing high-speed power line carrier communication technology, it can quickly collect electricity consumption data, improve two-way interactive electricity service capabilities, and also has functions such as power outage event reporting, communication performance monitoring, and network optimization, which can significantly improve operation and maintenance efficiency.

[0003] Traditional smart meter communication systems generally use fixed threshold mechanisms to judge link quality, which suffers from serious environmental adaptability defects. By presetting thresholds for parameters such as signal-to-noise ratio (SNR) and bit error rate (BER) to static values, they completely ignore the dynamic characteristics of the power grid's operating state. Power line channel quality fluctuates by minutes or even seconds depending on load fluctuations and weather changes. Simultaneously, the signal strength of the wireless backup channel is periodically attenuated by environmental factors such as building obstruction and seasonal vegetation growth. Fixed thresholds cannot fully utilize channel margins during stable power grid operation and are difficult to respond to sudden interference in a timely manner, resulting in low communication efficiency and delayed fault recovery. Therefore, how to achieve adaptive adjustment of wireless backup channel switching conditions to reduce the link handover error rate of multi-mode communication networks has become a major challenge for the industry. Summary of the Invention

[0004] This application provides a smart meter centralized procurement system based on high-speed carrier and multi-mode communication, which can realize adaptive adjustment of wireless backup channel switching conditions, thereby reducing the link error switching rate of the multi-mode communication network.

[0005] This application provides a smart meter centralized procurement system based on high-speed carrier and multi-mode communication, including:

[0006] The data acquisition module is used to collect electricity consumption data in real time using smart meter terminals and upload it via power line carrier in a multimode communication network. It also uses a concentrator to collect multi-link communication data in the multimode communication network.

[0007] The channel evaluation module is used to extract the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data. Then, the stability of the power line carrier uploading process of the electricity data is evaluated by the channel state matrix and the signal quality vector to obtain the stable equilibrium value of the link score in the smart meter centralized procurement.

[0008] The threshold decision module is used to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score through all stable equilibrium values ​​in the historical smart meter collection, and then determine the hysteresis interval of communication link switching and the confidence threshold for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics.

[0009] The execution module is used to trigger a wireless backup channel switch and record the switch event to the main station platform when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval.

[0010] The data acquisition module is used to collect electricity consumption data in real time using smart meter terminals and upload it via power line carrier in a multimode communication network. It also uses a concentrator to collect multi-link communication data in the multimode communication network.

[0011] The channel evaluation module is used to extract the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data. Then, the stability of the power line carrier uploading process of the electricity data is evaluated by the channel state matrix and the signal quality vector to obtain the stable equilibrium value of the link score in the smart meter centralized procurement.

[0012] The threshold decision module is used to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score through all stable equilibrium values ​​in the historical smart meter collection, and then determine the hysteresis interval of communication link switching and the confidence threshold for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics.

[0013] The execution module is used to trigger a wireless backup channel switch and record the switch event to the main station platform when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval.

[0014] In some embodiments, the multimode communication network includes a high-speed power line carrier communication backbone network and a wireless heterogeneous backup network.

[0015] In some embodiments, extracting the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data specifically includes:

[0016] The signal power and noise power of the high-speed carrier backbone channel are obtained from the multi-link communication data;

[0017] The channel state matrix of the high-speed carrier backbone channel is determined by all signal power and noise power.

[0018] Obtain the received signal strength indication and signal-to-dryness ratio of the wireless backup channel from the multi-link communication data;

[0019] The signal quality vector of the wireless backup channel is determined by all received signal strength indicators and signal-to-dryness ratio.

[0020] In some embodiments, the stability assessment of the power line carrier upload process of electricity consumption data using the channel state matrix and the signal quality vector, to obtain a stable equilibrium value of the link score in smart meter centralized procurement, specifically includes:

[0021] Normalize the channel state matrix and the signal quality vector;

[0022] Initialize a stable evaluation model based on weighted scoring;

[0023] The normalized channel state matrix is ​​used as the carrier-channel stability factor in the stability evaluation model.

[0024] The normalized signal quality vector is used as the wireless link compensation factor in the stability evaluation model.

[0025] A stability assessment model is used to conduct a balanced assessment of the stability of electricity consumption data during power line carrier transmission, resulting in a stable equilibrium value for the link score in smart meter centralized procurement.

[0026] In some embodiments, extracting the load fluctuation characteristics of the electricity consumption data specifically includes:

[0027] The load power of each sampling point is obtained from the electricity consumption data;

[0028] The load fluctuation characteristics are determined by all load power.

[0029] In some embodiments, determining the historical gradient characteristics of the link score through all stable equilibrium values ​​in historical smart meter data collection specifically includes:

[0030] Obtain all stable equilibrium values ​​from historical smart meter procurement;

[0031] The historical gradient characteristics of the link score are determined by all stable equilibrium values.

[0032] In some embodiments, the smart meter terminal is a multi-parameter fusion metering unit based on the DL / T645-2007 protocol.

[0033] In some embodiments, determining the hysteresis interval for communication link switching and the confidence threshold for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics specifically includes:

[0034] Obtain the time reference value and scoring reference value for communication link switching;

[0035] The timing adjustment coefficient for communication link switching and the scoring adjustment coefficient for wireless backup channel activation are determined using the historical gradient characteristics and the load fluctuation characteristics.

[0036] The time base value is confidence-adjusted based on the time adjustment coefficient to obtain the hysteresis interval for communication link switching;

[0037] The confidence threshold for activating the wireless backup channel is obtained by adjusting the confidence of the scoring benchmark value based on the scoring adjustment coefficient.

[0038] In some embodiments, the concentrator is a multiprotocol gateway device based on an ARM Cortex-M7 core.

[0039] In some embodiments, the main platform is a cloud-native electricity data aggregation and analysis system.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] This application provides a smart meter centralized procurement system based on high-speed carrier and multi-mode communication. The system uses smart meter terminals to collect electricity consumption data in real time and uploads it via power line carrier in a multi-mode communication network. A concentrator collects multi-link communication data from the multi-link communication network. The system extracts the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data. Then, it uses the channel state matrix and the signal quality vector to evaluate the stability of the power line carrier upload process of the electricity consumption data, obtaining a stable equilibrium value for the link score in the smart meter centralized procurement. The system extracts the load fluctuation characteristics of the electricity consumption data and determines the historical gradient characteristics of the link score using all stable equilibrium values ​​from historical smart meter centralized procurement. Then, based on the historical gradient characteristics and the load fluctuation characteristics, it determines the hysteresis interval for communication link switching and the confidence threshold for wireless backup channel activation. When the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, wireless backup channel switching is triggered, and the switching event is recorded to the main station platform.

[0042] Therefore, in this application, when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, the wireless backup channel is switched, and the switching event is recorded to the main station platform. First, determining the stable equilibrium value yields the real-time quality assessment result of the power line carrier and wireless backup channel working together, thus providing an accurate basis for link switching decisions. This effectively identifies complex scenarios such as high signal strength but high bit error rate, avoiding misjudgments due to parameter bias. In industrial frequency converter interference environments, the stable equilibrium value can improve the accuracy of representing the actual link state, thereby accurately distinguishing between temporary interference and continuous degradation, laying a data foundation for subsequent adaptive adjustment. Then, determining the confidence threshold and hysteresis interval... By obtaining an intelligent switching threshold that dynamically adapts to the power grid's operating state, real-time optimization of switching conditions is achieved. When drastic load fluctuations are detected, the hysteresis interval is automatically increased to prevent accidental triggering due to transient interference. When historical gradients show a continuous deterioration trend, the confidence threshold is appropriately lowered to avoid communication interruptions in advance. The dual-parameter collaborative adjustment mechanism enables the system to reduce the number of erroneous switching events under complex operating conditions such as typhoons, while improving the transmission success rate of critical services. By recording the decision basis for each adjustment, the system can also continuously optimize the fuzzy rule base, forming a virtuous cycle of becoming more accurate with use. In summary, based on the above scheme, adaptive adjustment of wireless backup channel switching conditions can be achieved, thereby reducing the link erroneous switching rate of multi-mode communication networks. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a module structure diagram of a smart meter centralized procurement system based on high-speed carrier and multi-mode communication, according to some embodiments of this application;

[0045] Figure 2 This is a flowchart illustrating the process of determining the hysteresis interval and confidence threshold according to some embodiments of this application. Detailed Implementation

[0046] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0047] To better understand the above technical solutions, a detailed description of the technical solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. (Refer to...) Figure 1As shown in the figure, this is a modular structure diagram of a smart meter data acquisition system based on high-speed carrier and multi-mode communication according to this embodiment of the present application. The diagnostic and monitoring system includes: a data acquisition module 101, a channel evaluation module 102, a threshold decision module 103, and an execution module 104, which are described below:

[0048] The data acquisition module 101 is used to collect electricity consumption data in real time using a smart meter terminal and upload it via power line carrier in a multi-mode communication network, and to collect multi-link communication data in the multi-mode communication network using a concentrator.

[0049] It should be noted that, in this application, the smart meter centralized procurement system with high-speed carrier and multi-mode communication includes a smart meter terminal, a concentrator, a multi-mode communication network, and a master station platform. The multi-mode communication network includes a high-speed power line carrier communication backbone network and a wireless heterogeneous backup network. The smart meter terminal is a multi-parameter fusion metering unit based on the DL / T645-2007 protocol. The concentrator is a multi-protocol gateway device based on an ARM Cortex-M7 core. The master station platform is a cloud-native electricity data aggregation and analysis system. Electricity data refers to the collection of power parameters measured by the meter, such as voltage, current, active power, reactive power, and harmonic content. This electricity data is used in this application to measure and analyze user electricity consumption behavior. Multi-link communication data refers to the data packets received by the concentrator from different communication links and their corresponding channel status information.

[0050] In specific implementation, firstly, the smart meter has a built-in high-precision metering chip that collects raw electrical signals at a set sampling rate (default is once per second). After analog-to-digital conversion and digital filtering, the collected signals become valid electricity consumption data. This data is cached locally and awaits uploading. In existing technologies, some meters only support timed data collection, while this invention uses real-time collection to ensure data timeliness. Secondly, the smart meter terminal has a built-in high-speed power line carrier communication module that uses orthogonal frequency division multiplexing (OFDM) technology to divide the data onto multiple subcarriers to improve anti-interference capabilities. After encoding and encryption, the electricity consumption data is transmitted to the concentrator via the power line. In existing technologies... Low-speed narrowband carrier communication (e.g., PLC-IoT) has a low transmission rate, while the present invention uses a wideband carrier (2MHz~30MHz) to significantly improve transmission efficiency. Finally, the concentrator simultaneously monitors the power line carrier channel and the wireless backup channel (e.g., LoRa / NB-IoT), receives data packets from different meters, and records the communication quality of each link, including signal strength, bit error rate, and latency. In the prior art, the concentrator usually relies on only a single communication method, while the present invention ensures reliable data transmission through parallel acquisition of multiple links, thereby using the collection of communication quality of each link as multi-link communication data.

[0051] The channel evaluation module 102 is used to extract the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel in the multi-link communication data, and then use the channel state matrix and the signal quality vector to evaluate the stability of the power line carrier uploading process of the electricity consumption data, so as to obtain a stable equilibrium value of the link score in the smart meter centralized procurement.

[0052] In some embodiments, extracting the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data can be achieved by the following steps:

[0053] The signal power and noise power of the high-speed carrier backbone channel are obtained from the multi-link communication data;

[0054] The channel state matrix of the high-speed carrier backbone channel is determined by all signal power and noise power.

[0055] Obtain the received signal strength indication and signal-to-dryness ratio of the wireless backup channel from the multi-link communication data;

[0056] The signal quality vector of the wireless backup channel is determined by all received signal strength indicators and signal-to-dryness ratio.

[0057] It should be noted that, in this application, the channel state matrix is ​​a multi-dimensional matrix used to quantify the overall quality of a high-speed carrier channel, which includes the signal-to-dryness ratio (SDR), carrier-to-interference ratio (CRI), and noise floor; the signal quality vector is a multi-dimensional vector used to quantify the overall quality of a wireless backup channel, which includes signal strength, SDR, and delay; signal power refers to the signal energy intensity of effectively transmitted data in power line carrier communication, reflecting the strength of the communication signal; noise power refers to the energy intensity of interference signals in power line carrier communication, including background noise, impulse noise, etc., reflecting the degree of channel interference; the received signal strength indicator represents the absolute strength of the wireless signal, which can be used to determine communication distance and coverage quality; the SDR represents the strength of the wireless signal relative to interference and noise, which can be used to evaluate the anti-interference capability of the communication link.

[0058] In specific implementation, firstly, the sum of the energy of effective subcarriers within the carrier frequency band of the multi-link communication data is taken as the signal power of the high-speed carrier backbone channel, and the energy detection value of the idle frequency band or quiet period in the multi-link communication data is taken as the noise power; secondly, the ratio of signal power to noise power is taken as the signal-to-interference-ratio (SIR), and a matrix is ​​constructed by combining the carrier-to-interference ratio (the power ratio of effective signal to narrowband interference) and the noise floor (the lowest detectable noise level). Each dimension of the matrix is ​​normalized to eliminate dimensional differences; then, the received signal strength indication and SIR of the wireless backup channel are obtained from the multi-link communication data. In the calculation of the signal-to-dryness ratio (SDR), to eliminate instantaneous fluctuations, an exponentially weighted moving average algorithm can be used to smooth the received signal in multi-link communication data. Additionally, the channel quality indicator (CMI) sent by the base station is analyzed as a supplement. Simultaneously, the signal purity is evaluated through preamble detection. This yields the received signal strength indicator (RSI) and SMI of the wireless backup channel. Finally, the link delay is calculated using the heartbeat packet round-trip time, and the packet loss rate is calculated using sequence number detection. The RSI and SMI are mapped to standardized scores of 0-100, and then combined with the link delay and packet loss rate to construct a four-dimensional vector as the signal quality vector for the wireless backup channel.

[0059] In some embodiments, the stability assessment of the power line carrier upload process of electricity consumption data using the channel state matrix and the signal quality vector, to obtain a stable equilibrium value of the link score in smart meter centralized procurement, can be achieved through the following steps:

[0060] Normalize the channel state matrix and the signal quality vector;

[0061] Initialize a stable evaluation model based on weighted scoring;

[0062] The normalized channel state matrix is ​​used as the carrier-channel stability factor in the stability evaluation model.

[0063] The normalized signal quality vector is used as the wireless link compensation factor in the stability evaluation model.

[0064] A stability assessment model is used to conduct a balanced assessment of the stability of electricity consumption data during power line carrier transmission, resulting in a stable equilibrium value for the link score in smart meter centralized procurement.

[0065] It should be noted that in this application, the stable equilibrium value refers to the comprehensive link quality score calculated by the stability assessment model. This stable equilibrium value can reflect the stability level of the power line carrier and the wireless backup channel working together. The value range of the stable equilibrium value is usually 0 to 1, and the closer it is to 1, the more reliable the communication link is. The stability assessment model is a mathematical assessment framework for quantifying the stability of power line carrier communication. This stability assessment model achieves a comprehensive evaluation of the communication link quality by normalizing and weighting the multi-dimensional parameters of the channel state matrix and the signal quality vector. The stability assessment model transforms parameters such as the signal-to-dryness ratio and interference level of the carrier channel into carrier channel stability factors, and at the same time transforms parameters such as the signal strength and signal-to-dryness ratio of the wireless link into wireless link compensation factors. The model performs linear weighting calculation through preset weight coefficients (the default is 70% for the carrier factor and 30% for the wireless factor), and finally outputs a stable equilibrium value in the range of 0-1. The stability assessment model employs a dynamic weight adjustment mechanism, automatically increasing the weight of the wireless compensation factor when the power grid load fluctuates. This ensures that the assessment results always reflect the real communication environment and provide an accurate basis for link switching decisions. Among these, the carrier channel stability factor is a comprehensive indicator used to quantify the reliability of high-speed power line carrier communication. The value of the carrier channel stability factor reflects the anti-interference capability and signal integrity of the power line channel during data transmission. The wireless link compensation factor is an indicator used to assess the substitutability of backup wireless communication links. The value of the wireless link compensation factor reflects the compensation capability of the wireless channel when the primary link fails.

[0066] The threshold decision module 103 is used to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score through all stable equilibrium values ​​collected in the historical smart meter collection, and then determine the hysteresis interval of communication link switching and the confidence threshold for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics.

[0067] In some embodiments, extracting the load fluctuation characteristics of the electricity consumption data can be achieved using the following steps:

[0068] The load power of each sampling point is obtained from the electricity consumption data;

[0069] The load fluctuation characteristics are determined by all load power.

[0070] It should be noted that, in this application, the load fluctuation characteristic is a statistical indicator used to quantify the changing patterns of user electricity consumption behavior. In specific implementation, firstly, the sampling frequency is set to once per second, and each sampling is taken as a sampling point, thus obtaining multiple sampling points. Then, the instantaneous power of each sampling point is obtained from the electricity consumption data as the load power, thus obtaining the load power of each sampling point. This load power refers to the instantaneous value of active power consumed by the electricity user at a specific moment, and this load power can reflect the real-time energy consumption of the electrical equipment. Then, the standard deviation of all load powers can be used as the load fluctuation characteristic.

[0071] In some embodiments, determining the historical gradient characteristics of the link score using all stable equilibrium values ​​from historical smart meter data collection can be achieved through the following steps:

[0072] Obtain all stable equilibrium values ​​from historical smart meter procurement;

[0073] The historical gradient characteristics of the link score are determined by all stable equilibrium values.

[0074] It should be noted that, in this application, the historical gradient feature is a feature used to quantify the trend and fluctuation pattern of the stable equilibrium value over time. In specific implementation, firstly, the stable equilibrium value of smart meter procurement within a specified historical time period (default is the most recent week) is obtained from the central control console of the electricity meter procurement system, thus obtaining all stable equilibrium values ​​in the historical smart meter procurement. Then, the difference between the stable equilibrium values ​​of adjacent smart meter procurement is calculated as the scoring gradient value, thus obtaining multiple scoring gradient values ​​within the specified historical time period. The average of all scoring gradient values ​​is then used as the historical gradient feature of the link score.

[0075] In some embodiments, the hysteresis interval for communication link switching and the confidence threshold for wireless backup channel activation are determined based on the historical gradient characteristics and the load fluctuation characteristics, with reference to... Figure 2 The diagram is a flowchart illustrating the determination of the hysteresis interval and confidence threshold in some embodiments of this application. In this embodiment, the determination of the hysteresis interval and confidence threshold can be achieved using the following steps:

[0076] In step 1031, the time reference value and scoring reference value for communication link switching are obtained;

[0077] In step 1032, the time adjustment coefficient for communication link switching and the score adjustment coefficient for wireless backup channel activation are determined by the historical gradient characteristics and the load fluctuation characteristics.

[0078] In step 1033, the time reference value is confidence-adjusted according to the time adjustment coefficient to obtain the hysteresis interval of the communication link switching;

[0079] In step 1034, the confidence adjustment of the scoring benchmark value is performed according to the scoring adjustment coefficient to obtain the confidence threshold for the activation of the wireless backup channel.

[0080] It should be noted that, in this application, the confidence threshold is the minimum threshold reflecting the current network quality requirements; the hysteresis interval is the dynamic delay time used for handover decisions; the time reference value refers to a pre-set standard value for the link handover decision delay time, which can be used to prevent false handovers caused by transient interference; the scoring reference value is a pre-set scoring standard value for the activation of the wireless backup channel, which can serve as the pass / fail line for link quality; the time adjustment coefficient is a dynamic parameter used to correct the time reference value, which can reflect the impact of changes in the network environment on the handover delay; and the scoring adjustment coefficient is a dynamic parameter used to correct the scoring reference value, which can reflect the impact of load changes on link quality requirements.

[0081] In specific implementation, firstly, preset parameters are read from the system configuration library of the electricity meter centralized procurement system to obtain the time reference value and scoring reference value for communication link switching. The time reference value is set according to the requirements of the communication protocol (e.g., DL / T645), and the scoring reference value can be determined through laboratory testing. Secondly, a fuzzy logic model based on window sliding is initialized. Historical gradient features are used as the trend degradation factor in the fuzzy logic model, and load fluctuation features are used as the load disturbance factor. The fuzzy logic model is used to fuzzify the impact of communication link switching time and wireless backup channel activation, respectively. The quantification result of the fuzzy logic model on the impact of communication link switching time is used as the time adjustment coefficient, and the quantification result of the fuzzy logic model on the impact of wireless backup channel activation is used as the scoring adjustment coefficient. Then, the product of the time adjustment coefficient and the time reference value is used as the adjustment value. The difference between the time reference value and the adjustment value is used as the lower limit of the hysteresis interval, and the sum of the time reference value and the adjustment value is used as the upper limit of the hysteresis interval, thus obtaining the hysteresis interval of communication link switching. Finally, the product of the scoring adjustment coefficient and the scoring reference value is used as the confidence threshold for wireless backup channel activation.

[0082] It should be noted that the fuzzy logic model in this application is a dynamic adaptive decision-making system based on a sliding window. This fuzzy logic model transforms precise power grid operating parameters into fuzzy linguistic variables and simulates human expert experience for intelligent judgment. The model receives two input dimensions in real time: historical gradient features (mapped to trend deterioration factors) and load fluctuation features (mapped to load disturbance factors), using a time window (default 15 minutes). Each dimension is divided into three fuzzy sets: low, medium, and high. Through 27 preset expert rules, including those indicating high trend deterioration and high load disturbance—significantly shortening the switching time—the activation intensity of each rule is calculated using the Mamdani inference method. Finally, the precise time adjustment coefficient and score adjustment coefficient are output through defuzzification using the centroid method. This fuzzy logic model transforms power grid communication stability assessment into a fuzzy rule inference problem, preserving the interpretability of expert experience while achieving dynamic parameter adjustment through the sliding window mechanism. This effectively solves the problem of insufficient adaptability of traditional fixed threshold algorithms in complex power grid environments.

[0083] The execution module 104 is used to trigger a wireless backup channel switch when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, and to record the switch event to the main station platform.

[0084] In practice, the electricity meter centralized procurement system continuously monitors the stable equilibrium value of the link score. When this stable equilibrium value falls below a dynamically calculated confidence threshold and the duration exceeds the adaptive hysteresis interval, the main link is deemed unreliable, and a wireless backup channel switch is automatically triggered. This hysteresis interval prevents false triggering due to instantaneous fluctuations. The hysteresis interval is dynamically adjusted based on load characteristics and historical communication quality, extending the waiting time during periods of significant grid disturbances to avoid oscillating switching. The switching event includes complete contextual information such as a timestamp, scoring trajectory, and channel status, which is reported to the main station platform via a multi-mode communication network to build a link reliability knowledge base. This mechanism, through closed-loop control of evaluation-decision-execution-recording, enables rapid self-healing of communication link failures and full traceability throughout the entire process.

[0085] The foregoing detailed examples of a smart meter centralized data collection system based on high-speed carrier and multi-mode communication provided in this application. It is understood that the corresponding device, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0086] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0087] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A smart meter centralized procurement system based on high-speed carrier and multi-mode communication, the system comprising smart meter terminals, concentrators, a multi-mode communication network, and a master station platform, characterized in that, Includes the following steps: The data acquisition module is used to collect electricity consumption data in real time using smart meter terminals and upload it via power line carrier in a multimode communication network. It also uses a concentrator to collect multi-link communication data in the multimode communication network. The channel evaluation module is used to extract the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data. Then, the stability of the power line carrier uploading process of the electricity data is evaluated by the channel state matrix and the signal quality vector to obtain the stable equilibrium value of the link score in the smart meter centralized procurement. The threshold decision module is used to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score through all stable equilibrium values ​​in the historical smart meter collection, and then determine the hysteresis interval of communication link switching and the confidence threshold for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics. The execution module is used to trigger a wireless backup channel switch and record the switch event to the main station platform when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval. Specifically, extracting the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel from the multi-link communication data includes: The signal power and noise power of the high-speed carrier backbone channel are obtained from the multi-link communication data; The channel state matrix of the high-speed carrier backbone channel is determined by all signal power and noise power. Obtain the received signal strength indication and signal-to-dryness ratio of the wireless backup channel from the multi-link communication data; The signal quality vector of the wireless backup channel is determined by all received signal strength indicators and signal-to-dryness ratio. Specifically, the stability assessment of the power line carrier upload process of electricity consumption data using the channel state matrix and the signal quality vector, to obtain a stable equilibrium value for the link score in smart meter centralized procurement, includes: Normalize the channel state matrix and the signal quality vector; Initialize a stable evaluation model based on weighted scoring; The normalized channel state matrix is ​​used as the carrier-channel stability factor in the stability evaluation model. The normalized signal quality vector is used as the wireless link compensation factor in the stability evaluation model. A stability assessment model is used to conduct a balanced assessment of the stability of electricity consumption data during power line carrier transmission, resulting in a stable equilibrium value for the link score in smart meter centralized procurement.

2. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, The multimode communication network includes a high-speed power line carrier communication backbone network and a wireless heterogeneous backup network.

3. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, Extracting the load fluctuation characteristics of the electricity consumption data specifically includes: The load power of each sampling point is obtained from the electricity consumption data; The load fluctuation characteristics are determined by all load power.

4. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, The historical gradient characteristics of the link score are determined by using all stable equilibrium values ​​from historical smart meter procurement, specifically including: Obtain all stable equilibrium values ​​from historical smart meter procurement; The historical gradient characteristics of the link score are determined by all stable equilibrium values.

5. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, The smart meter terminal is a multi-parameter fusion metering unit based on the DL / T645-2007 protocol.

6. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, The determination of the hysteresis interval for communication link switching and the confidence threshold for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics specifically includes: Obtain the time reference value and scoring reference value for communication link switching; The timing adjustment coefficient for communication link switching and the scoring adjustment coefficient for wireless backup channel activation are determined using the historical gradient characteristics and the load fluctuation characteristics. The time base value is confidence-adjusted based on the time adjustment coefficient to obtain the hysteresis interval for communication link switching; The confidence threshold for activating the wireless backup channel is obtained by adjusting the confidence of the scoring benchmark value based on the scoring adjustment coefficient.

7. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, The concentrator is a multi-protocol gateway device based on the ARM Cortex-M7 core.

8. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication as described in claim 1, characterized in that, The main platform is a cloud-native electricity data aggregation and analysis system.

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