Intelligent electric meter collecting system based on high-speed carrier and multimode communication

Abstract

The invention provides an intelligent electric meter collection system based on high-speed carrier and multimode communication, and the system carries out the stability evaluation of a power line carrier uploading process of power utilization data through a channel state matrix of a high-speed carrier backbone channel and a signal quality vector of a wireless backup channel. Obtaining a stable equilibrium value of the link score in the intelligent electric meter collection; determining a delay interval of communication link switching and a confidence threshold of wireless backup channel activation according to the load fluctuation characteristics of the power utilization data and the historical gradient characteristics of the link scores; and when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds a delay interval, triggering wireless backup channel switching, and recording a switching event to a master station platform. Based on the scheme, self-adaptive adjustment of the switching condition of the wireless backup channel can be realized, so that the error link switching rate of the multimode communication network can be reduced.

Description

Technical Field

[0001] The present application relates to the technical field of electricity meter centralized procurement, and more specifically, to an intelligent electricity meter centralized procurement system based on high-speed carrier and multi-mode communication. Background Art

[0002] The electricity meter centralized procurement system realizes efficient collection of electricity meter data by combining high-speed power line carrier communication with spread spectrum wireless communication. It includes a master station, power line communication concentrator, wireless communication concentrator and smart meters with multiple communication modes. Smart meters can be connected to the concentrator via power lines or wireless relays to achieve flexible networking. The use of high-speed power line carrier communication technology can quickly collect electricity consumption data and enhance the two-way interactive electricity service capabilities. At the same time, it 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 a fixed threshold mechanism to determine link quality, which suffers from serious environmental adaptability deficiencies. The thresholds for parameters such as signal-to-noise ratio and bit error rate are preset to static values, completely ignoring the dynamic characteristics of the power grid's operating state. Power line channel quality fluctuates minute-to-minute or even second-to-second with load fluctuations and weather changes. Meanwhile, the signal strength of wireless backup channels periodically decays due to environmental factors such as building obstruction and seasonal vegetation growth. Fixed thresholds fail to fully utilize channel margins during stable grid operation and are difficult to respond to sudden interference, 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 error switching rate in multimode communication networks has become a challenge facing the industry. Summary of the Invention

[0004] The present 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: The acquisition module is used to use the smart meter terminal to collect electricity consumption data in real time and upload it through the power line carrier in the multi-mode communication network, and use the concentrator to collect multi-link communication data in the multi-mode communication network; A channel assessment module is configured to extract a channel state matrix of a high-speed carrier backbone channel in a multimode communication network and a signal quality vector of a wireless backup channel from the multi-link communication data, and then perform a stability assessment on a power line carrier upload process of the electricity consumption data using the channel state matrix and the signal quality vector to obtain a stable equilibrium value of a link score in the centralized procurement of smart meters; a threshold decision module, configured to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score based on all stable equilibrium values ​​in the historical smart meter centralized procurement, and further determine the hysteresis interval for communication link switching and the confidence threshold for activation of the wireless backup channel based on the historical gradient characteristics and the load fluctuation characteristics; The execution module is used to trigger the wireless backup channel switching when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, and record the switching event to the master station platform.

[0006] The acquisition module is used to use the smart meter terminal to collect electricity consumption data in real time and upload it through the power line carrier in the multi-mode communication network, and use the concentrator to collect multi-link communication data in the multi-mode communication network; A channel assessment module is configured to extract a channel state matrix of a high-speed carrier backbone channel in a multimode communication network and a signal quality vector of a wireless backup channel from the multi-link communication data, and then perform a stability assessment on a power line carrier upload process of the electricity consumption data using the channel state matrix and the signal quality vector to obtain a stable equilibrium value of a link score in the centralized procurement of smart meters; a threshold decision module, configured to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score based on all stable equilibrium values ​​in the historical smart meter centralized procurement, and further determine the hysteresis interval for communication link switching and the confidence threshold for activation of the wireless backup channel based on the historical gradient characteristics and the load fluctuation characteristics; The execution module is used to trigger the wireless backup channel switching when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, and record the switching event to the master station platform.

[0007] In some embodiments, the multimode communication network includes a high-speed power line carrier communication backbone network and a wireless heterogeneous backup network.

[0008] 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 in the multimode communication network from the multi-link communication data specifically includes: Acquire signal power and noise power of a high-speed carrier backbone channel from the multi-link communication data; Determine the channel state matrix of the high-speed carrier backbone channel through all signal powers and noise powers; Obtaining a received signal strength indicator and a signal-to-noise ratio of a 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-noise ratios.

[0009] In some embodiments, performing stability evaluation on 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 the smart meter centralized procurement specifically includes: Normalizing 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 assessment model; The normalized signal quality vector is used as the wireless link compensation factor in the stability evaluation model; A stable evaluation model is used to perform a balanced evaluation on the stability of electricity consumption data in power line carrier upload, and a stable balanced value of the link score in smart meter centralized procurement is obtained.

[0010] In some embodiments, extracting the load fluctuation characteristics of the power consumption data specifically includes: Obtaining the load power of each sampling point from the power consumption data; The load fluctuation characteristics are determined from all load powers.

[0011] In some embodiments, determining the historical gradient feature of the link score based on all stable equilibrium values ​​in historical smart meter centralized procurement specifically includes: Obtain all stable equilibrium values ​​in historical smart meter centralized procurement; The historical gradient characteristics of the link score are determined by all stable equilibrium values.

[0012] In some embodiments, the smart meter terminal is a multi-parameter fusion metering unit based on the DL / T645-2007 protocol.

[0013] 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: Obtaining a time reference value and a scoring reference value for communication link switching; Determining a time adjustment coefficient for communication link switching and a score adjustment coefficient for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics; performing confidence adjustment on the time reference value according to the time adjustment coefficient to obtain a hysteresis interval for communication link switching; The confidence adjustment is performed on the score reference value according to the score adjustment coefficient to obtain a confidence threshold for activation of the wireless backup channel.

[0014] In some embodiments, the concentrator is a multi-protocol gateway device based on an ARM Cortex-M7 core.

[0015] In some embodiments, the master station platform is a cloud-native electricity consumption data aggregation and analysis system.

[0016] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The present application provides a smart meter centralized procurement system based on high-speed carrier and multi-mode communication, which uses smart meter terminals to collect electricity consumption data in real time and upload it through the power line carrier in the multi-mode communication network, and uses a concentrator to collect multi-link communication data in the multi-mode communication network; extracts the channel state matrix of the high-speed carrier backbone channel in the multi-mode communication network and the signal quality vector of the wireless backup channel from the multi-link communication data, and then uses the channel state matrix and the signal quality vector to perform stability evaluation on the power line carrier upload process of the electricity consumption data to obtain a stable equilibrium value of the link score in the smart meter centralized procurement; extracts the load fluctuation characteristics of the electricity consumption data, determines the historical gradient characteristics of the link score through all stable equilibrium values ​​in the historical smart meter centralized procurement, and then determines the hysteresis interval of the communication link switching and the confidence threshold of the wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics; when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, triggers the wireless backup channel switching, and records the switching event to the master station platform.

[0017] It can be seen that in this application, when the stable balance value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, the wireless backup channel switching is triggered and the switching event is recorded to the main station platform; first, the stable balance value is determined to obtain the real-time quality evaluation result of the collaborative work of the power line carrier and the wireless backup channel, thereby providing an accurate basis for link switching decisions, and can effectively identify complex scenarios such as high signal strength but high bit error, avoiding misjudgment caused by one-sided parameters. In the industrial variable frequency interference environment, the stable balance value can improve the accuracy of the characterization of the real link status, thereby accurately distinguishing temporary interference from continuous degradation, and laying a data foundation for subsequent adaptive adjustment; then, the confidence threshold and hysteresis interval are determined. An intelligent switching threshold that dynamically adapts to the operating status of the power grid is obtained, thereby realizing real-time optimization of the switching conditions. When a sharp load fluctuation is detected, the hysteresis interval is automatically increased to prevent false triggering due to instantaneous interference. When the historical gradient shows a continuous degradation trend, the confidence threshold is appropriately lowered to avoid communication interruption in advance. The dual-parameter collaborative adjustment mechanism can reduce the number of false switching in complex working conditions such as typhoon weather, while improving the transmission success rate of key services. By recording the decision basis for each adjustment, the system can also continuously optimize the fuzzy rule base, forming a virtuous cycle of increasing accuracy with use. In summary, based on the above scheme, adaptive adjustment of the wireless backup channel switching conditions can be achieved, thereby reducing the link false switching rate of the multi-mode communication network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0019] 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 the present application; Figure 2 This is a flowchart of determining a hysteresis interval and a confidence threshold according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0021] In order to better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown, this figure is a module structure diagram of a smart meter centralized procurement system based on high-speed carrier and multi-mode communication according to this embodiment of the present application. The diagnosis and monitoring system includes: an acquisition module 101, a channel assessment module 102, a threshold decision module 103 and an execution module 104, which are described as follows: The collection module 101 is used to use the smart meter terminal to collect electricity consumption data in real time and upload it through the power line carrier in the multi-mode communication network, and use the concentrator to collect multi-link communication data in the multi-mode communication network.

[0022] It should be noted that, in this application, the smart meter procurement system with high-speed carrier and multi-mode communication includes smart meter terminals, concentrators, multi-mode communication networks and master station platforms, wherein 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 the ARM Cortex-M7 core; the master station platform is a cloud-native electricity data aggregation and analysis system; electricity consumption data refers to a collection of power parameters such as voltage, current, active power, reactive power, harmonic content, etc. measured by the meter, and the electricity consumption 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.

[0023] In the specific implementation, first, the smart meter has a built-in high-precision metering chip, which collects the original electrical signal at the set sampling rate (the default is 1 time per second), and after analog-to-digital conversion and digital filtering, it is used as valid electricity consumption data. The electricity consumption data is cached locally and waits for upload. In the existing technology, some meters only support timed collection, while the present invention adopts real-time collection to ensure data timeliness; then, the smart meter terminal has a built-in high-speed power line carrier communication module, which uses orthogonal frequency division multiplexing technology to split the data into multiple subcarriers to improve anti-interference ability; after the electricity consumption data is encoded and encrypted, it is transmitted to the concentrator through the power line. In the existing technology, Low-speed narrowband carrier communication (for example: PLC-IoT) has a low transmission rate, while the present invention uses a broadband carrier (2MHz~30MHz) to significantly improve transmission efficiency; finally, the concentrator simultaneously monitors the power line carrier channel and the wireless backup channel (for example: LoRa / NB-IoT), receives data packets from different electricity meters, and records the communication quality of each link, including signal strength, bit error rate, and delay. In the prior art, the concentrator usually relies on only a single communication method, while the present invention ensures reliable data transmission through multi-link parallel collection, thereby using the collection of communication quality of each link as multi-link communication data.

[0024] 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-mode communication network from the multi-link communication data, and then use the channel state matrix and the signal quality vector to perform stability evaluation on the power line carrier upload process of the electricity consumption data to obtain a stable equilibrium value of the link score in the smart meter centralized procurement.

[0025] 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 in the multimode communication network from the multi-link communication data can be achieved by using the following steps: Acquire signal power and noise power of a high-speed carrier backbone channel from the multi-link communication data; Determine the channel state matrix of the high-speed carrier backbone channel through all signal powers and noise powers; Obtaining a received signal strength indicator and a signal-to-noise ratio of a 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-noise ratios.

[0026] It should be noted that, in this application, the channel state matrix is ​​a multidimensional matrix used to quantify the comprehensive quality of the high-speed carrier channel, and the channel state matrix includes the signal-to-noise ratio, carrier-to-interference ratio, and noise floor; the signal quality vector is a multidimensional vector used to quantify the comprehensive quality of the wireless backup channel, and the signal quality vector includes signal strength, signal-to-noise ratio, and delay; signal power refers to the signal energy intensity for effectively transmitting data in power line carrier communication, reflecting the strength of the communication signal; noise power refers to the energy intensity of the interference signal in power line carrier communication, and the noise power includes background noise, pulse noise, etc., reflecting the degree of channel interference; the received signal strength indication indicates the absolute strength of the wireless signal, and the received signal strength indication can be used to judge the communication distance and coverage quality; the signal-to-noise ratio indicates the strength of the wireless signal relative to interference and noise, and the signal-to-noise ratio can be used to evaluate the anti-interference capability of the communication link.

[0027] In the specific implementation, first, the sum of the energy of the effective subcarriers in the carrier frequency band of the multi-link communication data is used as the signal power of the high-speed carrier backbone channel, and the energy detection value of the idle frequency band or silent period in the multi-link communication data is used as the noise power; secondly, the ratio of signal power to noise power is used as the signal-to-noise ratio, and a matrix is ​​constructed by combining the carrier-to-interference ratio (the power ratio of the effective signal to the narrowband interference) and the noise floor (the minimum detectable noise level). Each dimension of the matrix is ​​normalized to eliminate the dimensional difference; then, the received signal strength indication and signal-to-noise ratio of the wireless backup channel are obtained from the multi-link communication data. To eliminate instantaneous fluctuations during the calculation of the signal-to-noise ratio (SNR), an exponentially weighted moving average algorithm can be used to smooth the received signal in multi-link communication data. The channel quality indicator (CQI) sent by the base station is also analyzed as a supplement. Signal purity is assessed through preamble detection to obtain the received signal strength indicator (RSI) and SNR of the wireless backup channel. Finally, the link delay is calculated using the round-trip time of the heartbeat packet, and the packet loss rate is calculated using sequence number detection. The SSI and SNR are mapped to standardized scores ranging from 0 to 100, respectively. A four-dimensional vector is then constructed using the link delay and packet loss rate to serve as the signal quality vector for the wireless backup channel.

[0028] In some embodiments, the stability evaluation of the power line carrier upload process of the electricity consumption data is performed using the channel state matrix and the signal quality vector to obtain a stable equilibrium value of the link score in the smart meter centralized procurement. The following steps can be used: Normalizing 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 assessment model; The normalized signal quality vector is used as the wireless link compensation factor in the stability evaluation model; A stable evaluation model is used to perform a balanced evaluation on the stability of electricity consumption data in power line carrier upload, and a stable balanced value of the link score in smart meter centralized procurement is obtained.

[0029] It should be noted that in this application, the stable balance value refers to the comprehensive link quality score calculated by the stable evaluation model. The stable balance value can reflect the stability level of the collaborative work of the power line carrier and the wireless backup channel. The numerical range of the stable balance value is usually 0~1. The closer to 1, the more reliable the communication link. The stable evaluation model is a mathematical evaluation framework for quantifying the stability of power line carrier communication. The stable evaluation model realizes a comprehensive evaluation of the quality of the communication link by normalizing and weighting the multi-dimensional parameters of the channel state matrix and the signal quality vector. The stable evaluation model converts parameters such as the signal-to-noise ratio and interference level of the carrier channel into a carrier channel stability factor, and converts parameters such as the signal strength and signal-to-noise ratio of the wireless link into a wireless link compensation factor. A linear weighted calculation is performed through preset weight coefficients (the default carrier factor accounts for 70% and the wireless factor accounts for 30%), and finally outputs a stable balance value in the range of 0-1. The stability assessment model adopts a dynamic weight adjustment mechanism to automatically increase the weight of the wireless compensation factor when the grid load fluctuates, ensuring that the assessment results always reflect the actual communication environment and providing an accurate basis for link switching decisions. Among them, 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 ability and signal integrity of the power line channel during data transmission. The wireless link compensation factor is an alternative indicator used to evaluate the quality of the backup wireless communication link. The value of the wireless link compensation factor reflects the compensation ability of the wireless channel when the main link fails.

[0030] 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 ​​in the historical smart meter procurement, and then determine the hysteresis interval of the communication link switching and the confidence threshold for activating the wireless backup channel based on the historical gradient characteristics and the load fluctuation characteristics.

[0031] In some embodiments, extracting the load fluctuation characteristics of the power consumption data may be achieved by using the following steps: Obtaining the load power of each sampling point from the power consumption data; The load fluctuation characteristics are determined from all load powers.

[0032] It should be noted that, in this application, the load fluctuation characteristic is a statistical indicator used to quantify the changing pattern of users' electricity consumption behavior; in specific implementation, first, the sampling frequency is set to once per second, and each sampling is used as a sampling point to obtain multiple sampling points, and then the instantaneous power of each sampling point is obtained from the electricity consumption data as the load power, and the load power of each sampling point can be obtained. The load power refers to the instantaneous value of the active power consumed by the electricity user at a specific moment, and the 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.

[0033] In some embodiments, determining the historical gradient characteristics of the link score based on all stable equilibrium values ​​in historical smart meter centralized procurement can be achieved by the following steps: Obtain all stable equilibrium values ​​in historical smart meter centralized procurement; The historical gradient characteristics of the link score are determined by all stable equilibrium values.

[0034] It should be noted that in this application, the historical gradient feature is a feature used to quantify the trend and fluctuation law of the stable equilibrium value over time; in specific implementation, first, the stable equilibrium value of the smart meter procurement within the historical specified time period (the default is the most recent 1 week) is obtained from the central console of the meter procurement system, and all the stable equilibrium values ​​in the historical smart meter procurement can be obtained; then, the difference between the stable equilibrium values ​​between adjacent smart meter procurements is calculated as the scoring gradient value, and multiple scoring gradient values ​​within the historical specified time period can be obtained, so that the average of all scoring gradient values ​​is used as the historical gradient feature of the link score.

[0035] In some embodiments, the hysteresis interval of the communication link switching and the confidence threshold of the wireless backup channel activation are determined according to the historical gradient characteristics and the load fluctuation characteristics. Figure 2 As described above, this figure is a schematic diagram of the process of determining the hysteresis interval and the confidence threshold in some embodiments of the present application. In this embodiment, the hysteresis interval and the confidence threshold can be determined by the following steps: In step 1031, a time reference value and a scoring reference value for communication link switching are obtained; In step 1032, a time adjustment coefficient for communication link switching and a score adjustment coefficient for wireless backup channel activation are determined based on the historical gradient characteristics and the load fluctuation characteristics. In step 1033, the time reference value is confidence adjusted according to the time adjustment coefficient to obtain a hysteresis interval for communication link switching; In step 1034, the score reference value is confidence adjusted according to the score adjustment coefficient to obtain a confidence threshold for activation of the wireless backup channel.

[0036] 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 switching decisions; the time reference value refers to the pre-set link switching decision delay time standard value, which can be used to prevent false switching caused by instantaneous interference; the scoring reference value is a pre-set wireless backup channel activation scoring standard value, which can be used as a link quality qualification line; 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 switching delay; 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.

[0037] In a specific implementation, first, 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 the 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. Second, a window sliding-based fuzzy logic model is initialized, and the historical gradient characteristics are used as the trend degradation factor and the load fluctuation characteristics are used as the load disturbance factor in the fuzzy logic model. The fuzzy logic model is used to fuzzy quantify the impact of the communication link switching time and the activation of the wireless backup channel, respectively. The quantified results of the fuzzy logic model on the impact of the communication link switching time are used as the time adjustment coefficient, and the quantified results of the fuzzy logic model on the impact of the wireless backup channel activation are 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 to obtain the hysteresis interval for the communication link switching. Finally, the product of the scoring adjustment coefficient and the scoring reference value is used as the confidence threshold for the activation of the wireless backup channel.

[0038] It should be noted that in this application, the fuzzy logic model is a dynamic adaptive decision-making system based on a sliding window. This fuzzy logic model converts precise grid operating parameters into fuzzy linguistic variables, simulating human expert experience for intelligent judgment. The fuzzy logic model uses a time window (default is 15 minutes) as the unit, and receives two input dimensions in real time: historical gradient characteristics (mapped to trend degradation factors) and load fluctuation characteristics (mapped to load disturbance factors). Each dimension is divided into three fuzzy sets: low, medium, and high. Based on 27 preset expert rules, including high trend degradation and high load disturbance → significantly shortened switching time, the Mamdani inference method is used to calculate the activation strength of each rule. Finally, the centroid method is used to defuzzify and output precise time adjustment coefficients and score adjustment coefficients. This fuzzy logic model transforms grid communication stability assessment into a fuzzy rule reasoning problem, retaining the interpretability of expert experience while enabling dynamic parameter adjustment through a sliding window mechanism. This effectively addresses the lack of adaptability of traditional fixed threshold algorithms in complex grid environments.

[0039] The execution module 104 is configured to trigger wireless backup channel switching when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds a hysteresis interval, and record the switching event to the master station platform.

[0040] In specific implementation, the electricity meter centralized procurement system continuously monitors the stable equilibrium value of the link score. When this stable equilibrium value falls below the dynamically calculated confidence threshold and lasts longer than the adaptive hysteresis interval, the primary link is determined to be unreliable and the wireless backup channel switch is automatically triggered. The hysteresis interval can prevent false triggering due to instantaneous fluctuations. The hysteresis interval is dynamically adjusted based on load characteristics and historical communication quality, extending the waiting time to avoid oscillatory switching when the power grid is highly disturbed. The switching event contains complete contextual information such as timestamps, score traces, and channel status, which is reported to the master station platform via the multi-mode communication network for use in building a link reliability knowledge base. This mechanism achieves rapid self-healing of communication link failures and full process traceability through a closed-loop control of evaluation-decision-execution-recording.

[0041] The above describes in detail the example of the smart meter centralized procurement system based on high-speed carrier and multi-mode communication provided by the embodiment of the present application. It can be understood that in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0042] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0043] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A smart meter centralized procurement system based on high-speed carrier and multi-mode communication, comprising a smart meter terminal, a concentrator, a multi-mode communication network and a master station platform, characterized in that: The steps include: The acquisition module is used to use the smart meter terminal to collect electricity consumption data in real time and upload it through the power line carrier in the multi-mode communication network, and use the concentrator to collect multi-link communication data in the multi-mode communication network; A channel assessment module is configured to extract a channel state matrix of a high-speed carrier backbone channel in a multimode communication network and a signal quality vector of a wireless backup channel from the multi-link communication data, and then perform a stability assessment on a power line carrier upload process of the electricity consumption data using the channel state matrix and the signal quality vector to obtain a stable equilibrium value of a link score in the centralized procurement of smart meters; a threshold decision module, configured to extract the load fluctuation characteristics of the electricity consumption data, determine the historical gradient characteristics of the link score based on all stable equilibrium values ​​in the historical smart meter centralized procurement, and further determine the hysteresis interval for communication link switching and the confidence threshold for activation of the wireless backup channel based on the historical gradient characteristics and the load fluctuation characteristics; The execution module is used to trigger the wireless backup channel switching when the stable equilibrium value of the link score is lower than the confidence threshold and the duration exceeds the hysteresis interval, and record the switching event to the master station platform.

2. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication according to claim 1, characterized in that: The multi-mode 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 according to claim 1, characterized in that: Extracting the channel state matrix of the high-speed carrier backbone channel and the signal quality vector of the wireless backup channel in the multimode communication network from the multi-link communication data specifically includes: Acquire signal power and noise power of a high-speed carrier backbone channel from the multi-link communication data; Determine the channel state matrix of the high-speed carrier backbone channel through all signal powers and noise powers; Obtaining a received signal strength indicator and a signal-to-noise ratio of a 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-noise ratios.

4. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication according to claim 1, characterized in that: The stability evaluation of the power line carrier upload process of the electricity consumption data is performed using 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, specifically including: Normalizing 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 assessment model; The normalized signal quality vector is used as the wireless link compensation factor in the stability evaluation model; A stable evaluation model is used to perform a balanced evaluation on the stability of electricity consumption data in power line carrier upload, and a stable balanced value of the link score in smart meter centralized procurement is obtained.

5. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication according to claim 1, characterized in that: Extracting the load fluctuation characteristics of the power consumption data specifically includes: Obtaining the load power of each sampling point from the power consumption data; The load fluctuation characteristics are determined from all load powers.

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

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

8. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication according to claim 1, characterized in that: 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: Obtaining a time reference value and a scoring reference value for communication link switching; Determining a time adjustment coefficient for communication link switching and a score adjustment coefficient for wireless backup channel activation based on the historical gradient characteristics and the load fluctuation characteristics; performing confidence adjustment on the time reference value according to the time adjustment coefficient to obtain a hysteresis interval for communication link switching; The confidence adjustment is performed on the score reference value according to the score adjustment coefficient to obtain a confidence threshold for activation of the wireless backup channel.

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

10. The smart meter centralized procurement system based on high-speed carrier and multi-mode communication according to claim 1, characterized in that: The master station platform is a cloud-native electricity consumption data aggregation and analysis system.

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