A data acquisition interaction method of an electric energy meter data terminal

Abstract

The application discloses a kind of data acquisition interaction methods of electric energy meter data terminal, including instruction wake-up and parameter initialization, dynamic sampling of environmental perception, abnormal detection driven by space-time characteristics, multi-path quantum-classical hybrid encryption transmission and feedback retransmission and buffer synchronization steps.It dynamically adjusts sampling frequency by fusing power consumption load and environmental fluctuation data, extracts space-time characteristics using Mixer-Transformer architecture and updates abnormal threshold value combining EWMA, selects quantum or national encryption based on path quality score, and transmits in parallel by fragmentation, and synchronizes the buffer according to the data timeliness classification.This method improves the mutation response speed, reduces the false alarm rate and transmission packet loss rate, reduces the data synchronization delay after network recovery, and is suitable for precise collection and safe transmission of electric energy data in complex environment.

Description

Technical Field

[0001] This invention relates to the field of electricity metering technology, specifically to a data acquisition and interaction method for an electricity meter data terminal, which is particularly suitable for accurate acquisition, secure transmission, and anomaly detection of electricity data in complex environments. Background Technology

[0002] With the rapid development of smart grids, electricity meter data terminals, as key nodes between the power grid and users, directly affect the grid's dispatch efficiency and electricity management level through the timeliness, accuracy, and security of data collection and transmission.

[0003] Existing technologies for data acquisition and transmission in electricity meter terminals have several shortcomings: In terms of data acquisition, most rely solely on adjusting sampling strategies based on the absolute value of electricity load, neglecting the impact of environmental factors (such as temperature, humidity, and vibration) on acquisition accuracy, resulting in significant data deviations when environmental fluctuations are large; anomaly detection often uses fixed threshold ratios, making it difficult to adapt to changes in data characteristics across different time and space scenarios, leading to a high false alarm rate; data transmission often employs single-path switching and uses limited encryption methods, making data loss or leakage prone to occur when network quality is poor or under attack; and the cache synchronization mechanism is simple, only storing data without considering its timeliness, resulting in high data synchronization delays after network recovery.

[0004] Therefore, there is an urgent need for a method that can combine environmental perception to achieve dynamic sampling, adopt efficient anomaly detection methods, and have high-security transmission and intelligent cache synchronization to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a data acquisition and interaction method for an electricity meter data terminal, so as to improve the adaptability of data acquisition, reduce the false alarm rate of abnormal data, enhance the security and reliability of data transmission, and improve the efficiency of cache synchronization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A data acquisition and interaction method for an electricity meter data terminal includes the following steps:

[0008] S1: Command wake-up and parameter initialization: The energy meter data terminal wakes up from the low-power sleep state in response to the acquisition command from the remote control center, parses the acquisition time period and acquisition type in the command, and initializes the acquisition parameters;

[0009] S2: Dynamic sampling for environmental perception: Electricity load data and environmental fluctuation data are collected through current sensors, voltage sensors, and environmental sensors. The sampling frequency is dynamically adjusted based on the load change rate prediction model and the environmental coupling model, specifically satisfying the following requirements:

[0010]

[0011] in

[0012] is the load sensitivity coefficient (dimensionless), which characterizes the weight of the influence of electrical load on the sampling frequency;

[0013] The environmental fluctuation sensitivity coefficient (dimensionless) characterizes the weight of the impact of environmental fluctuations on the sampling frequency.

[0014] Real-time electrical load power value (unit: kW);

[0015] The standard deviation of environmental data (calculated based on temperature, humidity, and vibration sensor data);

[0016] S3: Spatiotemporal feature-driven anomaly detection: The Mixer-Transformer architecture is used to extract spatiotemporal correlation features from the collected data. The anomaly judgment threshold is dynamically updated through exponential weighted moving average (EWMA) to remove abnormal data that deviates from the spatiotemporal dimension.

[0017] S4: Multi-path quantum-classical hybrid encryption transmission: The encryption algorithm is selected based on the transmission path quality score. If the transmission is not in fiber optic and the score is >0.8, quantum key distribution (QKD) encryption is enabled; otherwise, the national standard SM9 encryption is enabled. The data is then fragmented and transmitted in parallel through the two paths with the highest scores.

[0018] S5: Feedback retransmission and cache synchronization: Receive confirmation signals from the remote control center. If the transmission fails and the number of retries has not exceeded the limit, switch the path and increase the sampling frequency to retransmit. If the retries exceed the limit, store the data in the local cache and mark it with a time stamp. Upload it synchronously after the network recovers.

[0019] Furthermore, the load change rate prediction model in S2 employs the ARIMA time series algorithm, specifically including:

[0020] Standard deviation of load data within the time window Compared with historical standard deviation Generate adaptive threshold Where α takes values ​​of 0.6-0.8 and β takes values ​​of 0.05-0.3, when the real-time load change rate exceeds At that time, the sampling frequency will be increased to 1.5 times the baseline value.

[0021] Furthermore, the Mixer-Transformer architecture in S3 includes:

[0022] The Temporal Convolution module captures periodic load fluctuations, while the Feature Hybridization module (MLP) analyzes the correlation between voltage, current, and environmental parameters, outputting a spatiotemporal feature vector. The anomaly detection formula is:

[0023]

[0024] in It is dynamically updated from historical data via EWMA.

[0025] Furthermore, the multipath parallel transmission in S4 satisfies:

[0026] After data is fragmented, it is transmitted synchronously through power line carrier path and wireless communication path (LoRa / NB-IoT). The receiving end reassembles the complete data packet based on the data window marked by P0 / P1 code.

[0027] Furthermore, the path quality scoring formula in S4 is as follows:

[0028]

[0029] in:

[0030]

[0031] Furthermore, the cache synchronization mechanism in S5 includes:

[0032] Local cached data is sorted by timestamp, and time-sensitive data is uploaded first after the network is restored (time-sensitive > environmentally sensitive data > regular data).

[0033] Furthermore, the environmental sensor includes a temperature and humidity sensor and a vibration sensor, and the environmental fluctuation standard deviation is... The calculation cycle is synchronized with the data collection period.

[0034] Furthermore, the specific method for dynamically updating the abnormal threshold in S3 is as follows:

[0035]

[0036] in The historical deviation attenuation factor is taken as 0.85 to 0.95.

[0037] Furthermore, during the wireless communication path switching, if the packet loss rate is greater than 10%, the power line carrier path will be forcibly used to transmit the fragmented main data packet.

[0038] The present invention also provides an electricity meter data terminal, comprising:

[0039] An environmental sensing module (including temperature, humidity, and vibration sensors), a quantum encryption chip (supporting QKD and SM9), and a Mixer-Transformer coprocessor are used to execute any of the methods described above.

[0040] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0041] This invention improves the speed of response to sudden changes by dynamically sampling based on environmental perception and adjusting the sampling frequency by combining power load data and environmental fluctuation data, compared to existing methods that only rely on the absolute value of the load.

[0042] Anomaly detection driven by spatiotemporal features is adopted. Features are extracted using the Mixer-Transformer architecture and thresholds are dynamically updated using EWMA, thereby improving the false alarm rate.

[0043] Multi-path quantum-classical hybrid encryption transmission combines path quality scoring to select encryption algorithms and performs data fragmentation and parallel transmission, thereby reducing packet loss rate.

[0044] The feedback retransmission and cache synchronization mechanism prioritizes data uploads based on timeliness, reducing data synchronization latency after network recovery. Attached Figure Description

[0045] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0046] Figure 2 This is the overall flowchart of the present invention. Detailed Implementation

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0048] like Figure 1 and Figure 2 As shown in the figure, this embodiment provides a data acquisition and interaction method for an electricity meter data terminal, including the following steps:

[0049] S1: Command Wake-up and Parameter Initialization: The electricity meter data terminal is normally in a low-power sleep state. When the remote control center sends a data acquisition command, the terminal is woken up. The command includes the data acquisition period (e.g., 8:00-20:00 daily) and the data acquisition type (e.g., active power, reactive power). After parsing, the terminal initializes the data acquisition parameters, including the reference sampling frequency (e.g., 100Hz) and data transmission protocol.

[0050] S2: Dynamic sampling of environmental perception: Obtaining electrical load data by collecting current data through current sensors and voltage data through voltage sensors. Temperature and humidity data are collected by temperature and humidity sensors, and vibration data is collected by vibration sensors as environmental fluctuation data. The standard deviation of the environmental data is then calculated. Load sensitivity coefficient The environmental fluctuation sensitivity coefficient is set to 0.03. Take 0.02, according to the formula Adjust the sampling frequency.

[0051] Meanwhile, the ARIMA time series algorithm is used as the load change rate prediction model, based on the standard deviation of load data over the past hour. Standard deviation from historical average Generate adaptive threshold When the real-time load change rate exceeds At that time, the sampling frequency will be increased to 1.5 times the baseline value.

[0052] S3: Spatiotemporal Feature-Driven Anomaly Detection: The TemporalConv1d module in the Mixer-Transformer architecture employs three convolutional layers to capture periodic load fluctuations within one hour; the Feature Hybridization Module (MLP) contains two fully connected layers to analyze the correlation between parameters such as voltage, current, temperature, humidity, and vibration, outputting a spatiotemporal feature vector. .

[0053] The anomaly detection threshold is dynamically updated using EWMA, and the formula is as follows: ,in .when When this happens, the data is identified as abnormal and removed.

[0054] S4: Multi-path quantum-classical hybrid encrypted transmission: Real-time detection of bandwidth (BW), delay, and packet loss rate (Loss) for power line carrier path, LoRa path, and NB-IoT path. MaxBW is set to 100Mbps, and MaxDelay is set to 500ms, according to the formula... Calculate the score for each path.

[0055] If a path score is greater than 0.8, quantum key distribution (QKD) encryption is enabled; otherwise, SM9 encryption is enabled. Data is divided into two parts and transmitted in parallel through the two paths with the highest scores (e.g., power line carrier path and LoRa path). The receiving end reassembles the data packets based on data windows marked with P0 / P1 codes. When the path is optical fiber, QKD encryption is enforced.

[0056] S5: Feedback Retransmission and Cache Synchronization: After receiving data, the remote control center returns an acknowledgment signal containing a digital signature. After the terminal verifies the signature, if the transmission is successful, it records parameters such as bandwidth and latency of the path and updates the path score weight. If the transmission fails and the number of retries (e.g., 2) does not exceed the limit, it switches to the path with the second-highest score and increases the sampling frequency to 1.2 times the current frequency for retransmission. If the retries exceed the limit, the data is stored in a local 1GB cache and marked with a time stamp. After the network recovers, it prioritizes uploading time-sensitive data (such as real-time electricity consumption data), followed by environmentally sensitive data and regular data.

[0057] When the packet loss rate of the wireless communication path (such as the LoRa path) is greater than 10%, the power line carrier path will be forcibly enabled to transmit fragmented master data packets.

[0058] This embodiment also provides an energy meter data terminal, including an environmental sensing module (containing a temperature and humidity sensor and a vibration sensor), a quantum encryption chip (supporting QKD and SM9), and a Mixer-Transformer coprocessor, which can execute the above methods.

[0059] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A data acquisition and interaction method for an electricity meter data terminal, characterized in that, Includes the following steps: S1: Command wake-up and parameter initialization: The energy meter data terminal wakes up from the low-power sleep state in response to the acquisition command from the remote control center, parses the acquisition time period and acquisition type in the command, and initializes the acquisition parameters; S2: Dynamic sampling for environmental perception: Electricity load data and environmental fluctuation data are collected through current sensors, voltage sensors, and environmental sensors. The sampling frequency is dynamically adjusted based on the load change rate prediction model and the environmental coupling model, specifically satisfying the following requirements: ,in: The load sensitivity coefficient characterizes the weight of the influence of electrical load on the sampling frequency; This is the environmental fluctuation sensitivity coefficient, which characterizes the weight of the impact of environmental fluctuations on the sampling frequency; Real-time electrical load power value, unit: kW; The standard deviation of environmental data is calculated based on temperature, humidity, and vibration sensor data. S3: Spatiotemporal feature-driven anomaly detection: The Mixer-Transformer architecture is used to extract spatiotemporal correlation features from the collected data. The anomaly judgment threshold is dynamically updated through exponential weighted moving average to remove abnormal data that deviates from the spatiotemporal dimension. S4: Multi-path quantum-classical hybrid encrypted transmission: The encryption algorithm is selected based on the quality score of the transmission path. If the transmission is not fiber optic and the score is >0.8, quantum key distribution encryption is enabled; otherwise, the national standard SM9 encryption is enabled. The data is then fragmented and transmitted in parallel through the two paths with the highest scores. S5: Feedback retransmission and cache synchronization: Receive confirmation signals from the remote control center. If the transmission fails and the number of retries has not exceeded the limit, switch the path and increase the sampling frequency to retransmit. If the retries exceed the limit, store the data in the local cache and mark it with a time stamp. Upload it synchronously after the network is restored. The Mixer-Transformer architecture in S3 includes: The temporal convolution module captures periodic load fluctuations, while the feature fusion module analyzes the correlation between voltage, current, and environmental parameters, outputting a spatiotemporal feature vector. The anomaly detection formula is: ,in: The spatiotemporal feature vectors are extracted using the Mixer-Transformer architecture; The predicted value of the feature vector; The dynamic anomaly threshold is updated using an exponentially weighted moving average. The specific method for dynamically updating the anomaly threshold in S3 is as follows: ,in: This is the historical deviation attenuation factor, with a value range of 0.85 to 0.95; This is the current time step.

2. The method according to claim 1, characterized in that, The load change rate prediction model in S2 uses the ARIMA time series algorithm, specifically including: Standard deviation of load data within the time window Compared with historical standard deviation Generate adaptive thresholds. When the real-time load change rate exceeds At that time, the sampling frequency will be increased to 1.5 times the reference value; in: The historical load standard deviation is calculated based on load data within a time window. The historical volatility weighting factor has a value range of 0.6 to 0.

8. The reference offset is defined as 0.05 to 0.

3.

3. The method according to claim 1, characterized in that, The multipath parallel transmission in S4 satisfies: After data is fragmented, it is transmitted synchronously through power line carrier path and wireless communication path respectively. The receiving end reassembles the complete data packet based on the data window marked by P0 / P1 code.

4. The method according to claim 1, characterized in that, The path quality scoring formula in S4 is: ,in: Furthermore, QKD encryption is forcibly enabled when the path is fiber optic; The measured bandwidth of the current transmission path; unit: Mbps; The maximum nominal bandwidth supported by the system; unit: Mbps; Network latency for the current transmission path, in milliseconds (ms). The maximum allowable delay threshold, with a value range of 100ms to 500ms; The packet loss rate of the current transmission path, in units of 0.0 to 1.0 (percentage). The bandwidth weighting coefficient is dimensionless, with a numerical range of 0.2 to 0.

6. The delay weighting coefficient is dimensionless, with a numerical range of 0.1 to 0.

5. The packet loss rate weighting coefficient is dimensionless, with a value range of 0.2 to 0.

4.

5. The method according to claim 1, characterized in that, The cache synchronization mechanism in S5 includes: Local cached data is sorted by timestamp. Once the network is restored, time-sensitive data will be uploaded first, followed by time-sensitive data > data sensitive to environmental fluctuations > regular data.

6. The method according to claim 1, characterized in that, The environmental sensors include temperature and humidity sensors and vibration sensors, and the environmental fluctuation standard deviation... The calculation cycle is synchronized with the data collection period.

7. The method according to claim 3, characterized in that, When the wireless communication path is switched, if the packet loss rate is greater than 10%, the power line carrier path will be forcibly used to transmit the fragmented main data packet.

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