Remote monitoring method for operation data of smart electric meter box

Abstract

The invention discloses an intelligent electric meter box operation data remote monitoring method, and relates to the technical field of intelligent power grids, and the method comprises the following steps: an intelligent electric meter sends a test data packet through an initially set transmission window, and verifies the communication quality; the method comprises the following steps: acquiring communication quality data in a test data packet sending process in real time, and preprocessing the acquired original data to generate a data set containing various communication quality indexes; by monitoring the communication quality in real time and intelligently evaluating the interference risk, the intelligent electric meter can adaptively adjust the transmission window and mode, the data transmission efficiency is improved, and loss and delay are reduced. And when the spectrum interference is relatively strong, a transmission window is reduced and segmented transmission is adopted to ensure the data stability, so that communication interruption or data distortion is avoided. According to the method, the power monitoring system can obtain accurate data in time, and the monitoring capability, the fault early warning precision and the energy management efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and particularly to a method for remotely monitoring the operation data of a smart meter box. Background Art

[0002] Remote monitoring of the operation data of a smart meter box refers to integrating sensors, communication modules, and processing units in the meter box to collect real-time power metering data (such as voltage, current, power, power consumption, etc.) and operating status (such as temperature, opening of the cover, tilting, abnormal power consumption, etc.) in the meter box, and remotely transmitting the data to the background management platform through wireless communication (such as NB-IoT, 4G, LoRa, etc.) to achieve remote monitoring, data analysis, and abnormal warning of the operation of the meter box. This method improves the intelligent management ability of the power supply system, helps the operation and maintenance department to achieve remote inspection, fault diagnosis, and energy consumption management, reduces labor costs, and improves power supply safety and efficiency.

[0003] The prior art has the following deficiencies: The wireless communication module of a smart meter usually relies on a specific wireless frequency band (such as GPRS, NB-IoT, LoRa, etc.) for data transmission to achieve remote monitoring and real-time feedback of meter data. However, in the prior art, the wireless communication module may be subject to spectrum interference from other devices, such as radio transmitters, communication towers, radar systems, etc. The electromagnetic wave signals emitted by these devices may overlap or be close to the working frequency band of the smart meter, resulting in signal interference.

[0004] This spectrum interference will cause instability in the data transmission process of the wireless communication module of the smart meter, specifically manifested as problems such as data transmission failure, data loss, or transmission delay. Due to the existence of interference signals, the data communication link between the smart meter and the remote monitoring platform may not be established, or there may be frequent error transmissions during the communication process, resulting in the meter data not being uploaded to the monitoring system in a timely and accurate manner. This will not only affect the accuracy of real-time monitoring and data update, but also may cause the power management platform to be unable to obtain the operation status information of the power system, thus affecting key decisions such as power dispatching, fault warning, and energy consumption management.

[0005] In some severe cases, the continuous existence of spectrum interference may cause the wireless communication of the smart meter to completely fail, resulting in the paralysis of the power monitoring system. At this time, the monitoring platform cannot obtain any data from the meter, cannot detect equipment failures or abnormal situations in a timely manner, greatly increasing the risks and potential hazards of power system operation and maintenance, and may lead to power supply interruption, increased operating costs, and safety problems.

[0006] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure. Therefore, it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0007] The object of the present invention is to provide a method for remotely monitoring the operation data of an intelligent meter box. By real-time monitoring of communication quality and intelligent assessment of interference risks, the intelligent meter can adaptively adjust the transmission window and mode, improve data transmission efficiency, and reduce loss and delay. When the spectrum interference is strong, the transmission window is reduced and segmented transmission is adopted to ensure data stability and avoid communication interruption or data distortion. This method ensures that the power monitoring system can obtain accurate data in a timely manner, improves the monitoring ability, fault warning accuracy, and energy management efficiency, so as to solve the problems in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions: A method for remotely monitoring the operation data of an intelligent meter box, including the following steps: The intelligent meter sends test data packets through an initially set transmission window to verify the communication quality; Real-time obtain the communication quality data during the sending process of the test data packets, and preprocess the obtained raw data to generate a data set containing various communication quality indicators; Extract the key indicators reflecting the spectrum interference during the data transmission process from the data set, comprehensively analyze the extracted key indicators, and quantify the interference degree during the current data transmission process; Input the analyzed key indicators into a pre-trained machine learning model, intelligently evaluate the data transmission process through the model, and divide the transmission process according to the evaluation results; According to the evaluation results of the machine learning model, divide the data transmission process into two categories: "there is interference risk" and "no interference risk"; For no interference risk, increase the data transmission window to improve the transmission efficiency; for the existence of interference risk, reduce the data transmission window, reduce the amount of data transmitted, and split the data into several equal data blocks through a segmented transmission strategy, and send them in batches to ensure the stable transmission of data.

[0009] Preferably, the initially set transmission window is set according to the communication network environment, data transmission requirements, and the hardware performance of the meter. The specific steps are as follows: First, based on the communication technology supported by the meter, determine the data transmission rate range according to the bandwidth and delay characteristics of different technologies; Secondly, evaluate the hardware performance of the meter, including processing power, memory, and storage capacity, to ensure that the set window size does not exceed the device processing capacity; Next, select the data packet size and transmission interval according to the stability and reliability of the network environment; Finally, perform test data transmission, evaluate the performance of the set transmission window in actual use, and adjust the window size according to the feedback to optimize the efficiency and stability of data transmission.

[0010] Preferably, extract key indicators reflecting spectrum interference during data transmission from the data set. The extracted indicators include the efficiency change in the modulation and demodulation process and the wireless channel utilization rate. Perform a comprehensive analysis of the efficiency change in the modulation and demodulation process and the wireless channel utilization rate under the detection window to generate a modulation and demodulation efficiency reference value and a channel utilization rate reference value respectively, and quantify the interference degree in the current data transmission process through the modulation and demodulation efficiency reference value and the channel utilization rate reference value.

[0011] Preferably, the specific steps for comprehensively analyzing the efficiency change in the modulation and demodulation process under the detection window to generate a modulation and demodulation efficiency reference value are as follows: During the modulation and demodulation process, individually evaluate each transmission unit and calculate the efficiency ratio. The efficiency ratio is used to measure the relationship between the number of successfully demodulated signal blocks in reality and the theoretical transmission load and the influence of interference. The calculation expression is Ryan: , where is the modulation and demodulation efficiency ratio of the th transmission unit, is the number of successfully demodulated signal blocks in the th transmission unit, is the total number of data transmission blocks in the th transmission unit, is the number of detected wireless interference events during the th transmission unit; After obtaining the efficiency ratio of each transmission unit, comprehensively process the obtained efficiency ratios to generate a modulation and demodulation efficiency reference value to reflect the modulation and demodulation efficiency status of the entire monitoring window. The calculation expression is as follows: , where is the modulation and demodulation efficiency reference value, is the weight factor of the th transmission unit, is the total number of transmission units.

[0012] Preferably, the specific steps for comprehensively analyzing the wireless channel utilization rate under the detection window to generate a channel utilization rate reference value are as follows: Statistically record the set of time slot numbers occupied by all data packet transmissions in the detection window as , , is the time slot number occupied by the th, is the total number of time slot numbers. Since time slots may be occupied discontinuously due to interference, in order to measure the tightness and continuity of occupancy, a data packet distribution characteristic factor is introduced, and the calculation expression is as follows: , where is the data packet distribution characteristic factor, is the occupied time slot number; Further, to measure the difference between the channel utilization rate and the ideal state quantitatively, an effective data occupancy deviation index is introduced, and the calculation expression of the deviation index is as follows: , where is the maximum number of data blocks that the current channel can theoretically accommodate, is the number of data blocks that are actually successfully transmitted and effectively received, is the effective data occupancy deviation index; The data packet distribution characteristic factor and the effective data occupancy deviation index are fused to generate a channel utilization rate reference value, and the generation formula is as follows: , where is the channel utilization rate reference value.

[0013] Preferably, the analyzed modulation and demodulation efficiency reference value and the channel utilization rate reference value are input into a pre-trained machine learning model. The machine learning model generates a spectrum interference tolerance coefficient, and the data transmission process is intelligently evaluated through the spectrum interference tolerance coefficient, and the transmission process is divided according to the evaluation result.

[0014] Preferably, when the spectrum interference tolerance coefficient generated during the intelligent evaluation of the data transmission process by the pre-trained machine learning model is compared and analyzed with the preset spectrum interference tolerance coefficient reference threshold, the data transmission process is divided, and the division steps are as follows: If the spectrum interference tolerance coefficient is less than the preset spectrum interference tolerance coefficient reference threshold, the data transmission process is divided into a state with interference risk; if the spectrum interference tolerance coefficient is greater than or equal to the preset spectrum interference tolerance coefficient reference threshold, the data transmission process is divided into a state without interference risk.

[0015] Preferably, for the state without interference risk, the specific steps to increase the data transmission window to improve the transmission efficiency are as follows: When it is determined that the current data transmission process is in a state without interference risk, it indicates that the communication link has good stability and anti-interference ability. To improve the data transmission efficiency, the original data transmission strategy is optimized based on the communication state, the data transmission window is dynamically expanded, and more data blocks are transmitted in each round of communication, so as to reduce the communication frequency and improve the bandwidth utilization rate. The transmission window adjustment formula is as follows: , where, is the optimized data transmission window size, is the default base window size, is the spectrum interference tolerance coefficient, is the reference threshold of the spectrum interference tolerance coefficient, is the natural logarithm function, is the base of the natural logarithm.

[0016] Preferably, for the presence of interference risk, the data transmission window is reduced, the amount of data transmitted is decreased, and the data is split into several equal data blocks through a segmented transmission strategy and sent in batches to ensure the stable transmission of data. The specific steps are as follows: When the machine learning model evaluates that there is a current spectrum interference risk, first adjust the current data transmission window size to reduce the probability of failure of large data volume transmission under interference. The data transmission window adjustment formula is as follows: , where, is the adjusted data transmission window under the interference environment, is the maximum data transmission window in the interference-free environment, is the maximum compression ratio, is the compression non-linear exponential factor; After the window is reduced, further enhance the communication stability and reduce the retransmission cost, split the data transmitted once, reduce the risk of transmission failure caused by interference, and thus improve the reliability of data transmission. The splitting formula is as follows: , where, is the number of data blocks, is the minimum data block size, is the minimum guarantee segment capacity coefficient, is the interference perception enhancement factor.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention: By real-time monitoring of the communication quality, intelligent evaluation of the interference risk and dynamic adjustment of the data transmission strategy, the present invention enables the smart meter to adaptively adjust the transmission window size and transmission mode in different network environments, thereby maximizing the data transmission efficiency and reducing data loss or delay. Especially in the case of strong spectrum interference, the reduced transmission window and segmented transmission strategy can ensure the stability of data and avoid communication interruption or data distortion caused by interference. Finally, this method can ensure that the power monitoring system obtains accurate and timely meter data, improving the monitoring ability, fault warning accuracy and energy management efficiency of the power system. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments described in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0019] Figure 1 This is the method flow chart of the remote monitoring method for the operation data of the intelligent meter box of the present invention. Detailed implementation manners

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.

[0021] The present invention provides a remote monitoring method for the operation data of an intelligent meter box as Figure 1 shown, including the following steps: The smart meter sends test data packets through an initially set transmission window to verify the communication quality; The role of the test data packets is to initially verify the stability and signal quality of the wireless communication link. By sending a small number of data packets, the meter can test whether the communication is smooth under the wireless network conditions and collect real-time communication quality data (such as signal strength, latency, bit error rate, etc.). This preliminary test helps to evaluate the interference degree of the current channel and ensure that the meter can make appropriate adaptive adjustments during actual data transmission.

[0022] Through the test data packets, the smart meter can provide a basis for subsequent collection of communication quality data, ensuring a preliminary understanding of the communication environment before starting large-scale data transmission.

[0023] The initially set transmission window is set according to the communication network environment, data transmission requirements, and the hardware performance of the electricity meter. The specific steps are as follows: First, based on the communication technologies supported by the electricity meter (such as GPRS, NB-IoT, LoRa, etc.), determine the data transmission rate range according to the bandwidth and latency characteristics of different technologies. Second, evaluate the hardware performance of the electricity meter, including processing power, memory, and storage capacity, to ensure that the set window size does not exceed the device's processing capacity. Then, according to the stability and reliability of the network environment, select the packet size and transmission interval. For example, if the network environment is relatively stable, a larger transmission window can be set to improve efficiency; if the network fluctuates greatly, a smaller window is set to reduce the impact of interference. Finally, conduct test data transmission, evaluate the performance of the set transmission window in actual use, and adjust the window size according to the feedback to optimize the efficiency and stability of data transmission.

[0024] Obtain the communication quality data during the test packet sending process in real time, and preprocess the obtained raw data to generate a data set containing various communication quality indicators. The obtained communication quality data includes key performance indicators such as signal strength, signal-to-noise ratio, data transmission rate, latency, and bit error rate. These communication quality data reflect the actual condition of the current wireless channel and help the system evaluate the stability of data transmission. Data collection is not only carried out during the test phase but also real-time monitored throughout the data transmission process. By obtaining these data in real time, the electricity meter can sense any signs of interference or signal attenuation, providing data support for subsequent interference risk assessment.

[0025] Obtain and monitor the communication quality data in real time, providing a basis for subsequent interference identification and adjustment of data transmission strategies to ensure that the smart electricity meter can identify network anomalies in a timely manner.

[0026] The obtained communication quality data may contain noise, outliers, or incomplete data, so it needs to be preprocessed. The tasks of preprocessing include data cleaning, denoising, normalization, and data filling, etc., to ensure that the data input into the subsequent analysis model is accurate and consistent. This process usually uses techniques such as filtering, smoothing, and interpolation to standardize data from different sources, thereby eliminating unnecessary interference and improving the quality of the data.

[0027] Data preprocessing ensures the consistency, accuracy, and availability of the data, which is crucial for subsequent analysis, avoiding misjudgment or model training failure caused by data noise or outliers.

[0028] The role of generating a data set containing various communication quality metrics is to provide comprehensive support for subsequent interference assessment and data transmission optimization. By collecting and aggregating multiple key communication quality metrics such as signal strength, signal-to-noise ratio, transmission rate, latency, and bit error rate, the stability and performance of the current communication environment can be comprehensively understood. The generation of the data set enables the smart meter to systematically monitor the changes in network quality at different times and conditions, and provides an accurate data basis for subsequent analysis. These metrics help identify potential problems such as signal degradation, network congestion, or spectrum interference, thus providing data support for the meter to adjust its data transmission strategy (such as increasing or decreasing the transmission window, adjusting the transmission rate, etc.), ensuring that the meter maintains efficient and stable transmission performance in a dynamically changing communication environment.

[0029] Extract the key metrics reflecting spectrum interference during the data transmission process from the data set, and conduct a comprehensive analysis of the extracted key metrics to quantify the degree of interference in the current data transmission process; Extract the key metrics reflecting spectrum interference during the data transmission process from the data set. The extracted metrics include the change in the efficiency of the modulation and demodulation process and the utilization rate of the wireless channel. Conduct a comprehensive analysis of the change in the efficiency of the modulation and demodulation process and the utilization rate of the wireless channel under the detection window to generate a reference value for the modulation and demodulation efficiency and a reference value for the channel utilization rate respectively. Quantify the degree of interference in the current data transmission process through the reference value for the modulation and demodulation efficiency and the reference value for the channel utilization rate.

[0030] A decrease in the efficiency of the modulation and demodulation process usually indicates spectrum interference during the data transmission process. The modulation and demodulation process is the process of converting digital data into analog signals suitable for transmission over a wireless channel (modulation), and converting the analog signals back into digital data at the receiving end (demodulation). The efficiency of this process is usually closely related to the quality and stability of the signal. In the case of spectrum interference, the signals on the wireless channel are affected by noise, interference, and signal attenuation, resulting in a decrease in signal quality, thus reducing the efficiency of the modulation and demodulation process. Spectrum interference usually introduces additional noise, resulting in a decrease in the signal-to-noise ratio (SNR) of the signal. This requires the modem to use more energy and time to correctly decode the data when receiving the signal. In addition, interference may also cause frequent adjustments to the modulation method, thus affecting the demodulation efficiency. For example, the modem of the meter may need to switch to a lower-efficiency modulation method (such as from QPSK modulation to BPSK modulation) to cope with interference. Although this method can ensure a certain transmission reliability in a noisy environment, it will reduce the transmission rate and thus affect the efficiency of the entire data transmission process. Therefore, a decrease in the efficiency of the modulation and demodulation process is an important indicator of spectrum interference, reflecting the deterioration of signal quality and the interference pressure faced by the communication system.

[0031] The specific steps for comprehensively analyzing the efficiency change in the modulation and demodulation process under the detection window to generate a modulation and demodulation efficiency reference value are as follows: During the modulation and demodulation process, each transmission unit is evaluated separately, and the efficiency ratio is calculated. The efficiency ratio is used to measure the relationship between the number of successfully demodulated signal blocks in reality and the theoretical transmission load and the influence of interference. The calculation expression is as follows: In the formula, is the modulation and demodulation efficiency ratio of the th transmission unit, which is an important indicator for measuring the degree of influence of spectrum interference on this unit during the modulation and demodulation process. is the number of successfully demodulated signal blocks in the th transmission unit, reflecting the actual data recovery ability of this unit. is the total number of data transmission blocks of the th transmission unit, that is, the theoretically transmitted load. is the number of detected wireless interference events during the th transmission unit, such as abnormal signal activities like signal dips, bandwidth narrowing, burst noise, frequency drift, receive error flags, etc. By calculating the efficiency ratio of each transmission unit, the deviation between the actual demodulation success rate and the theoretical transmission load under spectrum interference conditions can be accurately measured. This step can reveal the specific impact of interference on the data demodulation performance at the unit level at the micro level, providing accurate and fine-grained basic data for subsequent overall efficiency evaluation.

[0032] After obtaining the efficiency ratio of each transmission unit, the obtained efficiency ratios are integrated to generate a modulation and demodulation efficiency reference value to reflect the modulation and demodulation efficiency status of the entire monitoring window. The calculation expression is as follows: In the formula, is the modulation and demodulation efficiency reference value. is the weight factor of the th transmission unit. is the total number of transmission units, a factor used to weight the importance or representativeness of each transmission unit, used to control the contribution degree of each unit to the final .

[0033] The modulation and demodulation efficiency ratios of multiple transmission units are weighted and integrated to form a global and quantifiable modulation and demodulation efficiency reference value, thereby comprehensively reflecting the communication quality status within the entire detection window. By introducing the weight factor, the influence degree of the severely interfered area can be highlighted, making the evaluation result more targeted and sensitive, providing solid data support for the accurate identification of spectrum interference and the adjustment of dynamic transmission strategies.

[0034] The smaller the modulation and demodulation efficiency reference value generated after comprehensively analyzing the efficiency change of the modulation and demodulation process under the detection window, the more spectrum interference there is during the data transmission process. The modulation and demodulation efficiency reference value reflects the efficiency of the modulation and demodulation process under specific conditions. Especially in the face of interference, the signal quality will be affected, resulting in a decrease in the efficiency of the modulation and demodulation process. When spectrum interference exists, the signal quality of the wireless channel deteriorates, the signal-to-noise ratio decreases, making the demodulation process more difficult and requiring more processing time and resources to correctly decode the signal. Therefore, in an interference environment, the modulation and demodulation efficiency reference value will decrease, indicating that the system needs to use more complex modulation and demodulation methods to maintain the stability of data transmission. This usually leads to a decrease in the transmission rate, an increase in the transmission time, and power consumption. On the contrary, when the data transmission process is not affected by spectrum interference, the modulation and demodulation process can work at a higher efficiency, and the performance value of the modulation and demodulation efficiency reference value is usually larger, reflecting better signal quality and a smoother transmission process.

[0035] A decrease in the utilization rate of the wireless channel usually indicates that spectrum interference exists during the data transmission process. The utilization rate of the wireless channel refers to the ratio of the effective data transmitted by the wireless channel within a specific time, which reflects the usage efficiency of the channel. When spectrum interference exists, the signal quality will be affected, resulting in problems such as a decrease in signal strength, an increase in noise, and an increase in the error rate. These interference factors cause wireless devices to repeatedly send data packets or reduce the transmission rate to cope with signal attenuation and errors, thereby wasting bandwidth and resources, and reducing the effective utilization rate of the wireless channel. In a high-interference environment, a large amount of the available bandwidth of the wireless channel is consumed to correct errors and retransmit lost data packets, resulting in a significant reduction in the time for effective data transmission. Especially in the case of spectrum congestion, multiple devices compete for the same frequency band, and the decrease in channel utilization is particularly obvious. Therefore, by monitoring the utilization rate of the wireless channel, the existence of spectrum interference can be indirectly identified, providing a basis for optimizing data transmission strategies (such as adjusting the transmission window or selecting a better frequency band) to help improve the transmission efficiency and stability of the system.

[0036] The specific steps for generating the channel utilization rate reference value by comprehensively analyzing the utilization rate of the wireless channel under the detection window are as follows: Statistically record the set of time slot numbers occupied by all data packet transmissions in the detection window, denoted as , , is the time slot number occupied by the data packet, is the total number of time slot numbers. Since time slots may be occupied discontinuously due to interference, to measure the tightness and continuity of the occupation, a data packet distribution characteristic factor is introduced, and the calculation expression is as follows: , where, is the data packet distribution characteristic factor, that is, within the detection window, the "density" or "continuity" of the data packet distribution in the channel is the occupied time slot number; The core idea of this formula is to capture the density of the data packet distribution in the channel. If the data packets are continuously and evenly distributed in the time slots (that is, high utilization rate and low interference), then the interval between adjacent time slots will be very small, resulting in the overall value being relatively large; on the contrary, if the data is sparsely distributed and there are channel holes (which often occur in case of high interference), then this value becomes smaller. The factor reflects the "continuity compactness" of channel utilization, which does not involve the average density, but is a weighted reverse measure of the spatial distribution characteristics.

[0037] To further quantitatively measure the difference between the channel utilization rate and the ideal state, an effective data occupancy ratio deviation index is introduced. By using this deviation index, the relative deviation degree between the number of actually transmitted data blocks and the theoretical maximum capacity within the current detection window is measured. The calculation expression of the deviation index is as follows: , where is the maximum number of data blocks that the current channel can theoretically accommodate (obtained based on frequency resources, time slot configuration, etc.), is the number of actually successfully transmitted and effectively received data blocks (excluding retransmitted or incorrect packets), is the effective data occupancy ratio deviation index, which is used to quantify the deviation degree between the actually transmitted data volume and the theoretical maximum transmission capacity within a specific detection window; By calculating the effective data occupancy ratio deviation index, the deviation between the actual data transmission volume and the theoretical maximum transmission volume is quantified, reflecting the impact of spectrum interference or other network problems on the channel utilization rate. The deviation index provides a basis for subsequent channel utilization rate evaluation, which helps smart meters adjust data transmission strategies according to the interference degree and optimize network performance.

[0038] Fusing the data packet distribution characteristic factor and the effective data occupancy ratio deviation index , a channel utilization rate reference value is generated. The generation formula is as follows: , where is the channel utilization rate reference value.

[0039] This definition combines two dimensions: the density of channel space utilization and the deviation degree of the transmission quantity: When the channel is densely used and the data transmission is close to the theoretical optimum, is large, is small, takes a relatively large value, indicating low interference and high utilization rate; When the channel is sparse, there are many retransmissions, and the amount of effective data is small, small, large, it drops significantly, reflecting the possibility of spectrum interference.

[0040] The smaller the reference value of channel utilization rate generated by comprehensively analyzing the wireless channel utilization rate under the detection window, the more spectrum interference there is during data transmission. The reference value of channel utilization rate reflects the ratio of the effective data transmitted by the wireless channel within a given monitoring window. When spectrum interference exists, the communication quality will be affected, resulting in a higher error rate, latency, and retransmission phenomenon during data transmission. These interferences reduce the signal transmission efficiency, thus occupying more bandwidth to retransmit lost data packets and perform error correction. As a result, the effective utilization rate of the channel drops, manifested as a smaller reference value of channel utilization rate. Conversely, if the quality of the wireless channel is good, with less or no interference, the channel can transmit data efficiently, reducing the need for retransmission and error correction, and the reference value of channel utilization rate will be larger, indicating that the data transmission process is not significantly affected by spectrum interference.

[0041] Input the analyzed key metrics into a pre-trained machine learning model, and use the model to intelligently evaluate the data transmission process and divide the transmission process according to the evaluation results; Input the analyzed reference value of modulation and demodulation efficiency and the reference value of channel utilization rate into a pre-trained machine learning model, generate a spectrum interference tolerance coefficient through the machine learning model, use the spectrum interference tolerance coefficient to intelligently evaluate the data transmission process, and divide the transmission process according to the evaluation results.

[0042] The pre-trained machine learning model refers to the process of using a large amount of historical data and known interference scenarios during data collection and processing, and using machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) to train the model so that it can accurately predict the impact of spectrum interference on the data transmission process. During the training phase, the model analyzes the relationship between different input features (such as the reference value of modulation and demodulation efficiency, the reference value of channel utilization rate, etc.) and spectrum interference, and learns how to obtain the interference tolerance coefficient based on the input features. The training process involves supervised learning, where the input data (features) is associated with the target output (such as the spectrum interference tolerance coefficient). By using the labeled training dataset, the model gradually adjusts its internal weights and parameters to improve the prediction accuracy for new data.

[0043] The core objective of a trained machine learning model is to be able to intelligently evaluate the spectrum interference tolerance based on real-time data in practical applications. After the input reference values (such as modulation and demodulation efficiency and channel utilization rate) are processed by the model, a spectrum interference tolerance coefficient reflecting the current interference situation will be generated. This coefficient can quantify the adaptability and tolerance of smart meters to interference in a specific environment. Based on this evaluation result, the system can divide the current data transmission process into two situations: "no interference risk" and "interference risk", and thus dynamically adjust the data transmission strategy, such as increasing or decreasing the data transmission window or adopting a segmented transmission strategy, to ensure the stable transmission of data. The accuracy and generalization ability of the model largely depend on the quality of the training data, the rationality of feature selection, and the complexity of the model itself. Therefore, the pre-trained machine learning model is the key to realizing the adaptive adjustment of smart meters and improving the efficiency and stability of data transmission.

[0044] The machine learning model is not limited here, and any machine learning model that can achieve comprehensive analysis of the modulation and demodulation efficiency reference value and the channel utilization rate reference value to generate a spectrum interference tolerance coefficient is acceptable. To implement the technical solution of the present invention, the present invention provides a specific implementation method; The spectrum interference tolerance coefficient is generated according to the following formula: , where and are respectively the preset proportionality coefficients of the modulation and demodulation efficiency reference value and the channel utilization rate reference value , and and are both greater than 0.

[0045] The preset proportionality coefficient refers to the coefficients and used in the formula. These two coefficients are used to represent the contribution degrees of the modulation and demodulation efficiency reference value and the channel utilization rate reference value to the generation of the spectrum interference tolerance . Specifically, and respectively represent the weights of the modulation and demodulation efficiency and the channel utilization rate on . These two coefficients are preset, that is, determined through historical data, experiments or expert experience, and are used to reflect the importance of each parameter in the interference tolerance. and are usually greater than 0, indicating that these two parameters, the modulation and demodulation efficiency reference value and the channel utilization rate reference value ), on All have positive contributions. These preset proportionality coefficients help the machine learning model better reflect the influence of different communication quality parameters (modulation and demodulation efficiency reference value and channel utilization reference value ) on the spectrum interference tolerance, and provide a quantitative basis for interference evaluation during the data transmission process.

[0046] It can be seen from the spectrum interference tolerance coefficient that the smaller the modulation and demodulation efficiency reference value generated after comprehensively analyzing the efficiency change of the modulation and demodulation process under the detection window, and the smaller the channel utilization reference value generated after comprehensively analyzing the wireless channel utilization under the detection window, the smaller the spectrum interference tolerance coefficient generated when the data transmission process is intelligently evaluated by the pre-trained machine learning model, indicating that the probability of the data transmission process being affected by spectrum interference is greater. On the contrary, it indicates that the probability of the data transmission process being affected by spectrum interference is smaller.

[0047] According to the evaluation results of the machine learning model, the data transmission process is divided into two categories: "interference risk" and "no interference risk"; The spectrum interference tolerance coefficient generated when the data transmission process is intelligently evaluated by the pre-trained machine learning model is compared and analyzed with the preset spectrum interference tolerance coefficient reference threshold to divide the data transmission process. The division steps are as follows: If the spectrum interference tolerance coefficient is less than the preset spectrum interference tolerance coefficient reference threshold, the data transmission process is divided into having interference risk; if the spectrum interference tolerance coefficient is greater than or equal to the preset spectrum interference tolerance coefficient reference threshold, the data transmission process is divided into having no interference risk.

[0048] For no interference risk, increase the data transmission window to improve the transmission efficiency; for interference risk, reduce the data transmission window, reduce the amount of data transmitted, and split the data into several equal data blocks through the strategy of segmented transmission and send them in batches to ensure the stable transmission of data; By intelligently adjusting the data transmission window size and adopting a segmented transmission strategy, the transmission efficiency and stability of smart meters in different communication environments are ensured, thus maximizing the reliable transmission of data. In the case of no interference risk, increasing the data transmission window can significantly improve the transmission efficiency. In an environment with good signal quality and less interference, the smart meter can transmit more data blocks at one time, reduce the overhead in the transmission process, improve the utilization rate of the bandwidth, reduce the time delay caused by multiple transmissions, thereby accelerating the data transmission process and enhancing the operating efficiency and response speed of the overall system. For an environment with interference risk, narrowing the data transmission window can effectively reduce data loss or transmission errors during the transmission process. In the case of poor signal quality, unstable network or strong interference, transmitting a large amount of data may lead to signal distortion, data loss or transmission errors. Therefore, it is necessary to reduce the amount of data transmitted each time to increase the probability of successful transmission each time. In addition, adopting a segmented transmission strategy, splitting large data into multiple equal data blocks and sending them in batches, can avoid the retransmission or failure of the entire data packet due to signal loss in the case of strong interference, ensuring that even if some data blocks are lost, they can be supplemented through subsequent retransmission and batch transmission, thus ensuring the stability and integrity of data transmission. This strategy comprehensively considers the changes in the communication environment. By flexibly adjusting the transmission strategy, it can not only improve the efficiency without interference, but also ensure the stability with interference, thereby greatly enhancing the adaptability and reliability of the meter system in a dynamically changing environment.

[0049] For the case of no interference risk, the specific steps to increase the data transmission window to improve the transmission efficiency are as follows: When it is judged that the current data transmission process is in a state of no interference risk, it indicates that the communication link has good stability and anti-interference ability. To improve the data transmission efficiency, the original data transmission strategy is optimized based on the communication state, and the data transmission window is dynamically expanded to transmit more data blocks in each round of communication, thereby reducing the communication frequency and improving the bandwidth utilization rate. The transmission window adjustment formula is as follows: , where is the optimized data transmission window size, indicating the transmission window size that the smart meter should adopt in the current communication environment (unit: number of data blocks), is the default basic window size, representing the conservative transmission amount used before evaluation, is the spectrum interference tolerance coefficient, is the reference threshold of the spectrum interference tolerance coefficient, is the natural logarithm function, used to compress the growth rate so that relatively large growth will not cause excessive expansion, is the base of the natural logarithm.

[0050] When When the electricity meter has strong resistance to interference, the transmission window will be enlarged accordingly. However, the amplification ratio will tend to be flat as the margin increases, thus avoiding the communication risks brought by too fast expansion and ensuring the system stability and robustness while improving efficiency.

[0051] This mechanism embodies the design principle of "maximizing transmission efficiency under the premise of security" and is an adaptive, highly elastic, and intelligent data transmission optimization strategy.

[0052] For the case of interference risk, the data transmission window is reduced, the amount of data transmitted is decreased, and the data is split into several equal data blocks through the strategy of segmented transmission and sent in batches to ensure the stable transmission of data. The specific steps are as follows: When the machine learning model evaluates that there is a current spectrum interference risk , first, the size of the current data transmission window is adjusted to reduce the probability of failure of large - data - volume transmission under interference. The data transmission window adjustment formula is as follows: , where is the adjusted data transmission window under the interference environment, is the maximum data transmission window in the interference - free environment, is the maximum compression ratio, , which defines the maximum proportion that the window can be compressed in the presence of spectrum interference, is the compression non - linear exponential factor, which controls the change speed and curve slope of window compression and is used to amplify or buffer the window adjustment effect brought; After identifying the spectrum interference risk, by non - linearly and dynamically reducing the data transmission window, the amount of data transmitted in a single transmission is actively reduced, thereby reducing the loss probability of data packets and the risk of transmission failure in the interference environment. This mechanism enables the smart electricity meter to flexibly adjust the transmission strategy according to the current communication environment and improve the stability of data transmission and the anti - interference ability of the system.

[0053] After completing the window reduction, to further enhance the communication stability and reduce the re - transmission cost, the data transmitted in a single transmission is split to reduce the risk of transmission failure caused by interference, thereby improving the reliability of data transmission. According to the adjusted window and the interference evaluation result, calculate how many data blocks need to be split to effectively cope with the interference in the network. The splitting formula is as follows: , where is the number of data blocks, which is calculated to determine how many data blocks are needed to effectively transmit data, especially when the interference is high, is the minimum data block size, representing the minimum capacity of each data block, ensuring that the basic size of each data block will not be too small in the case of high interference, is the minimum guaranteed segment capacity factor, which is used to set the minimum capacity of the data block to ensure that even in the environment with the strongest interference, the data block will not be reduced to an unusable level. is the interference perception enhancement factor, which controls the response sensitivity of the system to interference. When the interference is stronger, it will increase the number of data blocks to cope with a more complex transmission environment.

[0054] According to the current spectrum interference risk, dynamically calculate the number of data blocks that need to be split, so as to ensure the stability and reliability of data transmission. In a high-interference environment, after the data transmission window is reduced, it may be necessary to further split the data into multiple small blocks for transmission. This is because in a severely interfered situation, larger data blocks are prone to loss or errors. By adjusting the number of data blocks, it can ensure that the data is more stable during transmission, reduce the packet loss rate, and improve the data recovery ability.

[0055] By real-time monitoring the communication quality, intelligently evaluating the interference risk, and dynamically adjusting the data transmission strategy, the present invention enables the smart meter to adaptively adjust the transmission window size and transmission mode in different network environments, thereby maximizing the data transmission efficiency and reducing data loss or delay. Especially in the case of strong spectrum interference, the transmission window reduction and segmented transmission strategy can ensure the stability of the data and avoid communication interruption or data distortion caused by interference. Ultimately, this method can ensure that the power monitoring system obtains accurate and timely meter data, improving the monitoring ability, fault warning accuracy, and energy management efficiency of the power system.

[0056] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0057] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

[0058] It should be noted that in this text, if there are relative terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0059] It should be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution is prior or subsequent, and the order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0060] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0061] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0062] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, in each embodiment of the present application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0064] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

[0065] Only some exemplary embodiments of the present invention have been described above by way of illustration. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A remote monitoring method for operation data of a smart electric meter box, characterized in that: The following steps are involved: The smart meter sends a test data packet through the initially set transmission window to verify the communication quality; Acquire the communication quality data in the process of sending the test data packet in real time, and pre-process the acquired raw data to generate a data set containing multiple communication quality indicators; Extract key indicators reflecting spectrum interference during data transmission from the data set, conduct comprehensive analysis on the extracted key indicators, and quantify the degree of interference during the current data transmission process; The analyzed key indicators are input into the pre-trained machine learning model, and the data transmission process is intelligently evaluated through the model, and the transmission process is divided according to the evaluation results; According to the evaluation results of the machine learning model, the data transmission process is divided into two categories: "there is interference risk" and "there is no interference risk"; If there is no interference risk, increase the data transmission window to improve transmission efficiency. If there is interference risk, reduce the data transmission window, reduce the amount of data transmitted, and split the data into several equivalent data blocks through the segmented transmission strategy and send them in batches to ensure stable data transmission.

2. The remote monitoring method for operation data of a smart electric meter box according to claim 1 is characterized in that: The initial transmission window is set according to the communication network environment, data transmission requirements and the hardware performance of the meter. The specific steps are as follows: First, based on the communication technology supported by the meter, the data transmission rate range is determined according to the bandwidth and latency characteristics of different technologies; Second, evaluate the hardware performance of the meter, including processing power, memory, and storage capacity, to ensure that the set window size does not exceed the device's processing capabilities; Next, select the packet size and transmission interval based on the stability and reliability of the network environment; Finally, test data transmission is performed to evaluate the performance of the set transmission window in actual use, and the window size is adjusted based on the feedback to optimize the efficiency and stability of data transmission.

3. The remote monitoring method for operation data of a smart electric meter box according to claim 1 is characterized in that: Key indicators reflecting spectrum interference in the data transmission process are extracted from the data set. The extracted indicators include changes in modulation and demodulation process efficiency and wireless channel utilization. The modulation and demodulation process efficiency changes and wireless channel utilization are comprehensively analyzed under the detection window to generate modulation and demodulation efficiency reference values ​​and channel utilization reference values, respectively. The modulation and demodulation efficiency reference values ​​and channel utilization reference values ​​are used to quantify the degree of interference in the current data transmission process.

4. The remote monitoring method for operation data of a smart electric meter box according to claim 3 is characterized in that: The specific steps of comprehensively analyzing the changes in the modulation and demodulation process efficiency under the detection window to generate the modulation and demodulation efficiency reference value are as follows: During the modulation and demodulation process, each transmission unit is evaluated individually and the efficiency ratio is calculated. The efficiency ratio is used to measure the relationship between the number of signal blocks that are actually demodulated successfully and the theoretical transmission load and interference impact. The calculation expression is: , where It is The modulation and demodulation efficiency ratio of each transmission unit, It is The number of successfully demodulated signal blocks in a transmission unit, It is The total number of data transfer blocks of the transmission unit, It is The number of wireless interference events detected during a transmission unit; After obtaining the efficiency ratio of each transmission unit, the obtained efficiency ratios are integrated to generate a modulation and demodulation efficiency reference value to reflect the modulation and demodulation efficiency status of the entire monitoring window. The calculation expression is as follows: , where is the reference value of modulation and demodulation efficiency, It is The weight factor of the transmission unit, is the total number of transmission units.

5. The remote monitoring method for operation data of a smart electric meter box according to claim 3 is characterized in that: The specific steps for comprehensively analyzing the wireless channel utilization in the detection window to generate a channel utilization reference value are as follows: The set of time slot numbers occupied by all data packet transmissions in the statistical detection window is recorded as , , It is The occupied time slot number, is the total number of time slot numbers. Time slots may be occupied discontinuously due to interference. In order to measure the density and continuity of occupancy, a data packet distribution characteristic factor is introduced. The calculation expression is as follows: , where is the packet distribution characteristic factor, It is The occupied time slot number; To further quantitatively measure the difference between the channel utilization and the ideal state, the effective data ratio deviation index is introduced. The deviation index calculation expression is as follows: , where The maximum number of data blocks that the current channel can theoretically accommodate, is the number of data blocks that were actually successfully transmitted and effectively received, is the effective data ratio deviation index; Packet distribution characteristic factor and effective data ratio deviation index Fusion, generate channel utilization reference value, the generation formula is as follows: , where It is the reference value of channel utilization.

6. The remote monitoring method for operation data of a smart electric meter box according to claim 3 is characterized in that: The analyzed modulation and demodulation efficiency reference value and channel utilization reference value are input into the pre-trained machine learning model, and the spectrum interference tolerance coefficient is generated by the machine learning model. The data transmission process is intelligently evaluated by the spectrum interference tolerance coefficient, and the transmission process is divided according to the evaluation results.

7. The remote monitoring method for operation data of a smart electric meter box according to claim 6 is characterized in that: The spectrum interference tolerance coefficient generated by the pre-trained machine learning model when intelligently evaluating the data transmission process is compared with the pre-set spectrum interference tolerance coefficient reference threshold, and the data transmission process is divided. The division steps are as follows: If the spectrum interference tolerance coefficient is less than a preset spectrum interference tolerance coefficient reference threshold, the data transmission process is classified as having interference risk; if the spectrum interference tolerance coefficient is greater than or equal to a preset spectrum interference tolerance coefficient reference threshold, the data transmission process is classified as having no interference risk.

8. The remote monitoring method for operation data of a smart electric meter box according to claim 7 is characterized in that: For a situation without interference risk, the specific steps to increase the data transmission window and improve transmission efficiency are as follows: When it is judged that the current data transmission process is in a state without interference risk, it means that the communication link has good stability and anti-interference ability. In order to improve the data transmission efficiency, the original data transmission strategy is optimized based on the communication status, and the data transmission window is dynamically expanded to transmit more data blocks in each round of communication, thereby reducing the communication frequency and improving the bandwidth utilization. The transmission window adjustment formula is as follows: , where is the optimized data transmission window size, is the default base window size, is the spectrum interference tolerance coefficient, is the reference threshold of the spectrum interference tolerance coefficient, is the natural logarithm function, is the base of natural logarithms.

9. The remote monitoring method for operation data of a smart electric meter box according to claim 7 is characterized in that: In case of interference risk, the data transmission window is shortened, the amount of data transmitted is reduced, and the data is split into several equal data blocks through the segmented transmission strategy and sent in batches to ensure stable data transmission. The specific steps are as follows: When the machine learning model evaluates that there is a spectrum interference risk, the current data transmission window size is first adjusted to reduce the probability of failure of large data transmission under interference. The data transmission window adjustment formula is as follows: , where is the data transmission window adjusted under interference environment, It is the maximum data transmission window in an interference-free environment. is the maximum compression ratio, is the compression nonlinear exponential factor; After the window is narrowed, the communication stability is further enhanced and the retransmission cost is reduced. The single-transmission data is split to reduce the risk of transmission failure caused by interference, thereby improving the reliability of data transmission. The splitting formula is as follows: , where is the number of data blocks, is the minimum data block size, is the minimum guaranteed section capacity factor, is the interference perception enhancement factor.

Images