Intelligent electric meter data detection processing system

Abstract

The invention provides an intelligent electric meter data detection and processing system, which comprises the following data detection and processing modules: a data acquisition module for signal perception: utilizing a flexible thin film sensor, directly fitting the surface of a wire, supporting high-frequency current detection, accurately capturing a fast transient current, and conditioning the signal: using an analog low-pass filter to suppress radio frequency interference; according to the intelligent electric meter data detection and processing system, leap-type improvement is achieved in the four dimensions of precision, safety, intelligence and energy efficiency, the inherent defects of a traditional electric meter are overcome, and the intelligent electric meter data detection and processing system becomes a core fulcrum of digital transformation of a novel electric power system; software definition and ecological expansion enable the electricity meter to be evolved into an energy internet core node from a metering tool.

Description

Technical Field

[0001] The present invention relates to the technical field of smart meters, and in particular to a smart meter data detection and processing system. Background Art

[0002] The meter data detection and processing system is a key system used to manage meter data in the power sector. It communicates with the meter to efficiently collect data such as power consumption, voltage, and current. It can perform multi-dimensional detection on this data to ensure its accuracy, completeness, and consistency, and promptly identify abnormal values ​​and missing values. With the help of data analysis algorithms, the system can deeply analyze electricity consumption behavior patterns and predict power load. At the same time, it will clean and convert the data to generate intuitive reports and charts. Once the data is abnormal, the system will quickly alarm and notify the relevant personnel. The system is widely used. Power companies use it to monitor power grids, optimize scheduling, and set electricity prices; industrial enterprises use it to achieve energy management and reduce electricity costs; smart buildings rely on it to monitor electricity consumption and improve energy efficiency. In short, the meter data detection and processing system plays a vital role in improving the operating efficiency of the power system, optimizing energy management, and promoting energy conservation and consumption reduction.

[0003] However, the existing system has a low sampling rate (usually ≤1 kHz), which is unable to capture high-frequency harmonics and microsecond transient events (such as voltage sags). This leads to the risk of harmonic resonance being missed when new energy is connected to the grid. The current transformer has poor linearity, with errors exceeding 2% under low load or overload conditions. Small current theft is difficult to identify, and data processing relies on the cloud. In addition, physical protection relies on mechanical seals that are easy to forge. There is no magnetic field detection function, which makes it difficult to resist theft by strong magnets. At the same time, there are problems such as a single communication protocol (such as only supporting PLC) and a high failure rate in meter reading in complex network environments. Summary of the Invention

[0004] Technical problems solved

[0005] In view of the shortcomings of the existing technology, the present invention provides a smart meter data detection and processing system, which solves the problems of low sampling rate, weak physical protection function and single communication protocol of the existing system.

[0006] Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a smart meter data detection and processing system, including the following data detection and processing modules:

[0008] Data acquisition module:

[0009] Signal sensing: Utilizes flexible thin film sensors that adhere directly to the surface of the conductor, supporting high-frequency current detection and accurately capturing fast transient currents;

[0010] Signal conditioning: Use an analog low-pass filter to suppress radio frequency interference, and use an operational amplifier to amplify the weak signal to a range suitable for ADC input;

[0011] Analog-to-digital conversion: 16-bit or 24-bit ADC chips are used, supporting thousands of samples per second to capture instantaneous waveform details, synchronously sampling voltage and current to ensure phase consistency and avoid calculation errors;

[0012] Data processing module:

[0013] Data preprocessing: High-frequency noise is filtered out through a hardware low-pass filter, and the FIR algorithm is run on a digital signal processor to eliminate clutter other than power frequency harmonics.

[0014] Data calibration and compensation: The built-in temperature sensor monitors the ambient temperature in real time and corrects the temperature drift of the sensor. At the same time, the algorithm compensates for the inherent phase difference between the voltage and current signals to ensure the accuracy of power calculation.

[0015] Data transmission: Use LZ0 or ZigZag encoding to compress historical data to reduce transmission bandwidth, give priority to real-time data transmission, upload historical data in batches, and communicate with the master station regularly to confirm online status;

[0016] Communication transmission module:

[0017] Communication protocols: LoRaWAN and PLC protocols are used. LoRaWAN: long distance, low rate, suitable for dense urban deployment. PLC: transmits data through power lines without additional wiring.

[0018] Data encapsulation and encryption: Use TLS / SSL encrypted channels to prevent data tampering or eavesdropping;

[0019] Data storage module:

[0020] Data generation and classification: Update instantaneous values ​​of voltage, current, and power every second, and record statistical value event logs at fixed intervals;

[0021] Data cache: temporarily storing unprocessed sample data in RAM;

[0022] Data compression and local writing: Use differential encoding to store the difference between consecutive timestamp data and append historical data in chronological order to reduce Flash wear;

[0023] Anti-theft module:

[0024] (1) Big data analysis: Identify abnormal users through power consumption pattern analysis, and install current imbalance detection components, magnetic field interference detection components, physical tampering detection components, and temperature detection components inside the electricity meter.

[0025] Preferably, the data processing module collects current waveform and voltage event data in real meter deployment, builds a million-level sample library, uses neural architecture search to automatically generate a TinyML model suitable for the MCU, and then injects the model into the meter through OTA update to support online learning.

[0026] Preferably, the data processing module runs a lightweight neural network at the meter end, and identifies the start and stop of a single device through the total current waveform without the need for an additional sub-meter. At the same time, it uses LSTM to analyze time series data to provide early warning of equipment failure or electricity theft, with an accuracy rate increased by more than 30% compared to the traditional threshold method.

[0027] Preferably, FRAM storage medium is selected in the data storage module for high-frequency data writing, which has both high speed and high durability. At the same time, Huffman coding is used to perform entropy coding on high-frequency data, which increases the compression rate by 20%-40%.

[0028] Preferably, in the data storage module, when the network is interrupted, new data is temporarily stored in a temporary partition of the Flash, and a super capacitor is used to ensure that the current storage operation is completed in the event of a sudden power outage.

[0029] Preferably, the current imbalance detection component in the anti-theft module includes a high-precision CT sensor to measure the current difference between the phase line and the neutral line. 相 =I 零 ,If the difference exceeds the threshold, it triggers suspicion of electricity theft, and the magnetic field interference detection component includes a Hall sensor: ,detects the magnetic field strength around the meter and identifies the illegal use of strong magnets to interfere with the meter measurement.

[0030] Preferably, the physical tamper detection component in the anti-theft module includes a light sensor to monitor whether the meter housing is illegally opened, and the temperature detection component includes an infrared thermistor to detect abnormal temperature increase of the current transformer.

[0031] Preferably, the anti-theft module also includes an integrated Infineon SL197 security chip to store keys and perform encryption operations to prevent electricity thieves from tampering with the meter firmware or data. A self-destruct circuit is used internally to automatically erase sensitive data when illegal cover opening is detected.

[0032] Beneficial effects

[0033] The present invention provides a smart meter data detection and processing system, which has the following beneficial effects:

[0034] 1. The smart meter data detection and processing system of the present invention has achieved a leapfrog improvement in the five dimensions of accuracy, security, intelligence, energy efficiency, and economy. It not only solves the inherent defects of traditional meters, but also becomes the core fulcrum of the digital transformation of new power systems. Software definition and ecological expansion enable meters to evolve from metering tools to core nodes of the energy Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is an overall flow chart of a smart meter data detection and processing system proposed by the present invention;

[0036] Figure 2 This is a flow chart of the data processing module of the smart meter data detection and processing system proposed by the present invention;

[0037] Figure 3 This is a flow chart of the data storage module of the smart meter data detection and processing system proposed by the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Example:

[0040] like Figure 1-3 As shown, an embodiment of the present invention provides a smart meter data detection and processing system, including the following data detection and processing modules:

[0041] Data acquisition module:

[0042] Signal sensing: Flexible thin-film sensors adhere directly to the surface of wires, supporting high-frequency current detection and accurately capturing fast transient currents. Based on the Faraday magneto-optical effect, the sensor measures the magnetic field changes generated by the current through optical fibers, enabling contactless, electromagnetic interference-resistant current detection suitable for high-voltage scenarios. Temperature and humidity sensors are integrated into the meter to predict equipment failures based on electricity usage data.

[0043] Signal conditioning: Use an analog low-pass filter to suppress radio frequency interference, and use an operational amplifier to amplify the weak signal to a range suitable for ADC input;

[0044] Analog-to-digital conversion: 16-bit or 24-bit ADC chips are used, supporting thousands of samples per second to capture instantaneous waveform details, synchronously sampling voltage and current to ensure phase consistency and avoid calculation errors;

[0045] Data processing module:

[0046] Data preprocessing: High-frequency noise is filtered out through a hardware low-pass filter. The FIR algorithm is run on a digital signal processor to eliminate clutter other than power frequency harmonics. The root mean square (RMS) algorithm is used to obtain the effective values ​​of voltage and current. The power calculation formula is: No power: ; Apparent power: In the power, V represents the effective value of voltage, I represents the effective value of current, and cosϕ represents the power factor. In the reactive power, V and I have the same meanings as in the power, and sinϕ determines the direction and magnitude of reactive power based on the sine value of the phase difference. Finally, in the apparent power, V and I represent the effective values ​​of voltage and current, and their definitions are the same as above.

[0047] Data calibration and compensation: The built-in temperature sensor monitors the ambient temperature in real time and corrects the temperature drift of the sensor. At the same time, the algorithm compensates for the inherent phase difference between the voltage and current signals to ensure the accuracy of power calculation.

[0048] Data transmission: Use LZ0 or ZigZag encoding to compress historical data to reduce transmission bandwidth, give priority to real-time data transmission, upload historical data in batches, and communicate with the master station regularly to confirm online status;

[0049] Communication transmission module:

[0050] Communication protocols: LoRaWAN and PLC protocols are used. LoRaWAN: long distance, low rate, suitable for dense urban deployment. PLC: transmits data through power lines without additional wiring.

[0051] Data encapsulation and encryption: Use TLS / SSL encrypted channels to prevent data tampering or eavesdropping;

[0052] Data storage module:

[0053] Data generation and classification: Update instantaneous values ​​of voltage, current, and power every second, and record statistical value event logs at fixed intervals;

[0054] Data cache: temporarily storing unprocessed sample data in RAM;

[0055] Data compression and local writing: Differential encoding is used to store the differences of continuous timestamp data, and historical data is appended in chronological order to reduce Flash wear. A layered encryption strategy is adopted. Sensitive data (such as user electricity usage behavior) is encrypted using the AES-256 or SM4 algorithm. The key is stored in the hardware security module (HSM). Ordinary data (such as voltage and current values) is only CRC-checked and not encrypted to save computing power. At the same time, when the storage space reaches 95%, the earliest data is deleted according to the "first in, first out" (FIFO) principle, and critical data (such as data on the first day of each month) is not overwritten. For Flash wear leveling, a dynamic address mapping algorithm (such as FTL) can be used to disperse write operations and extend the Flash life to more than 10 years. For extreme temperature data protection, industrial-grade storage chips (-40℃~85℃) are selected, and temperature-adaptive write speed control is added at the software layer.

[0056] Anti-theft module:

[0057] (1) Big data analysis: Abnormal users are identified through electricity usage pattern analysis. At the same time, current imbalance detection components, magnetic field interference detection components, physical tampering detection components, and temperature detection components are installed inside the electricity meter. In addition to the above-mentioned anti-theft methods, the TinyML model can be run on the electricity meter for real-time inspection, and complex model secondary verification can be performed in the cloud. According to changes in regional electricity theft patterns, the new version of the detection algorithm can be remotely pushed (such as OTA upgrade), thereby achieving the effect of intelligent anti-theft.

[0058] The data processing module collects current waveform and voltage event data from real electricity meter deployments, builds a million-level sample library, and uses neural architecture search to automatically generate a TinyML model suitable for the MCU. The model is then injected into the electricity meter through OTA updates to support online learning.

[0059] The data processing module runs a lightweight neural network on the meter side, identifying the start and stop of individual devices through the total current waveform without the need for additional sub-meters. At the same time, it uses LSTM to analyze time series data to provide early warning of equipment failures or electricity theft, with an accuracy rate increased by more than 30% compared to traditional threshold methods.

[0060] FRAM storage medium is selected in the data storage module for high-frequency data writing, which combines high speed and high durability. At the same time, Huffman coding is used to entropy encode high-frequency data, which increases the compression rate by 20%-40%.

[0061] When the network is interrupted in the data storage module, new data is temporarily stored in the temporary partition of the Flash. At the same time, super capacitors are used to ensure that the current storage operation is completed in the event of a sudden power outage.

[0062] The current imbalance detection component in the anti-theft module includes a high-precision CT sensor to measure the current difference between the phase line and the neutral line. 相=I 零 ,If the difference exceeds the threshold, it triggers suspicion of electricity theft, and the magnetic field interference detection component includes a Hall sensor: ,detects the magnetic field strength around the meter and identifies the illegal use of strong magnets to interfere with the meter measurement.

[0063] The physical tamper detection component in the anti-theft module includes a light sensor to monitor whether the meter housing has been illegally opened, and the temperature detection component includes an infrared thermistor to detect abnormal temperature increases in the current transformer.

[0064] The anti-theft module also includes an integrated Infineon SL197 security chip, which stores keys and performs encryption operations to prevent electricity thieves from tampering with the meter firmware or data. It uses an internal self-destruct circuit to automatically erase sensitive data when an illegal cover opening is detected.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart meter data detection and processing system, characterized in that: Includes the following data detection and processing modules: Data acquisition module: Signal sensing: Utilizes flexible thin film sensors that adhere directly to the surface of the conductor, supporting high-frequency current detection and accurately capturing fast transient currents; Signal conditioning: Use an analog low-pass filter to suppress radio frequency interference, and use an operational amplifier to amplify the weak signal to a range suitable for ADC input; Analog-to-digital conversion: 16-bit or 24-bit ADC chips are used, supporting thousands of samples per second to capture instantaneous waveform details, synchronously sampling voltage and current to ensure phase consistency and avoid calculation errors; Data processing module: Data preprocessing: High-frequency noise is filtered out through a hardware low-pass filter, and the FIR algorithm is run on a digital signal processor to eliminate clutter other than power frequency harmonics. Data calibration and compensation: The built-in temperature sensor monitors the ambient temperature in real time and corrects the temperature drift of the sensor. At the same time, the algorithm compensates for the inherent phase difference between the voltage and current signals to ensure the accuracy of power calculation. Data transmission: Use LZ0 or ZigZag encoding to compress historical data to reduce transmission bandwidth, give priority to real-time data transmission, upload historical data in batches, and communicate with the master station regularly to confirm online status; Communication transmission module: Communication protocols: LoRaWAN and PLC protocols are used. LoRaWAN: long distance, low rate, suitable for dense urban deployment. PLC: transmits data through power lines without additional wiring. Data encapsulation and encryption: Use TLS / SSL encrypted channels to prevent data tampering or eavesdropping; Data storage module: Data generation and classification: Update instantaneous values ​​of voltage, current, and power every second, and record statistical value event logs at fixed intervals; Data cache: temporarily storing unprocessed sample data in RAM; Data compression and local writing: Use differential encoding to store the difference between consecutive timestamp data and append historical data in chronological order to reduce Flash wear; Anti-theft module: (1) Big data analysis: Identify abnormal users through power consumption pattern analysis, and install current imbalance detection components, magnetic field interference detection components, physical tampering detection components, and temperature detection components inside the electricity meter.

2. The smart meter data detection and processing system according to claim 1, characterized in that: The data processing module collects current waveform and voltage event data in real electricity meter deployments, builds a million-level sample library, uses neural architecture search to automatically generate a TinyML model suitable for the MCU, and then injects the model into the electricity meter through OTA updates to support online learning.

3. The smart meter data detection and processing system according to claim 1, characterized in that: The data processing module runs a lightweight neural network on the meter side, identifying the start and stop of individual devices through the total current waveform without the need for additional sub-meters. At the same time, it uses LSTM to analyze time series data to provide early warning of equipment failures or electricity theft, with an accuracy rate increased by more than 30% compared to traditional threshold methods.

4. The smart meter data detection and processing system according to claim 1, characterized in that: The data storage module uses FRAM storage medium for high-frequency data writing, which has both high speed and high durability. At the same time, Huffman coding is used to entropy encode high-frequency data, which increases the compression rate by 20%-40%.

5. The smart meter data detection and processing system according to claim 1, characterized in that: In the data storage module, when the network is interrupted, new data is temporarily stored in the temporary partition of the Flash, and a super capacitor is used to ensure that the current storage operation is completed in the event of a sudden power outage.

6. The smart meter data detection and processing system according to claim 1, characterized in that: The current imbalance detection component in the anti-theft module includes a high-precision CT sensor to measure the current difference between the phase line and the neutral line. 相 =I 零 ,If the difference exceeds the threshold, it triggers suspicion of electricity theft, and the magnetic field interference detection component includes a Hall sensor: ,detects the magnetic field strength around the meter and identifies the illegal use of strong magnets to interfere with the meter measurement.

7. The smart meter data detection and processing system according to claim 1, characterized in that: The physical tamper detection component in the anti-theft module includes a light sensor to monitor whether the meter housing is illegally opened, and the temperature detection component includes an infrared thermistor to detect abnormal temperature increase of the current transformer.

8. The smart meter data detection and processing system according to claim 1, characterized in that: The anti-theft module also includes an integrated Infineon SL197 security chip that stores keys and performs encryption operations to prevent electricity thieves from tampering with the meter firmware or data. It also uses an internal self-destruct circuit to automatically erase sensitive data when an illegal cover opening is detected.

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