CN-122027669-A - Electric energy meter data acquisition method and system based on Internet of things

CN122027669ACN 122027669 ACN122027669 ACN 122027669ACN-122027669-A

Abstract

The invention relates to the technical field of intelligent power grid information, and discloses an electric energy meter data acquisition method and system based on the Internet of things. The method comprises the steps of adaptively switching between a steady state mode and a transient state mode by utilizing an event driving mechanism, generating a linear observation vector by a compressed sensing algorithm in the steady state, triggering a high-fidelity sampling capture waveform sequence in the transient state, sending the waveform sequence to an edge intelligent gateway by utilizing an optimization protocol, and restoring the waveform by utilizing a reconstruction model and carrying out semantic encapsulation by the gateway. The system comprises a front end perception module, an edge intelligent gateway integrating a reconstruction module and a semantic analysis engine, and a cloud management platform for performing model training and resource scheduling. The invention relieves the communication pressure and ensures the key data precision through the deep fusion of dynamic perception and edge reconstruction, realizes millisecond response and edge autonomy of the power grid state, and improves the utilization rate of system resources and the safety of data transmission.

Inventors

LIU WEIDONG
WANG LI
WU ZHICHEN
CHEN GENG
ZHANG YANHUA

Assignees

天津瑞芯源智能科技有限责任公司

Dates

Publication Date: 20260512
Application Date: 20260414

Claims (10)

1. The electric energy meter data acquisition method based on the Internet of things is characterized by comprising the following steps of: step S1, monitoring a power grid operation signal in real time through a front end sensing unit deployed at the electric energy meter end, and performing self-adaptive switching between a steady-state operation mode and a transient disturbance mode by utilizing an event driving mechanism; Step S2, under the steady-state operation mode, carrying out sparsification processing on electric energy original data by utilizing a compressed sensing algorithm, mapping the electric energy original data in a time domain to a sparse conversion domain by constructing a sparse basis matrix, carrying out linear projection on coefficients in the sparse conversion domain by adopting a preset measurement matrix to generate a linear observation vector, and dynamically adjusting the projection dimension of the measurement matrix according to received channel available bandwidth feedback to realize closed-loop control of a sampling rate; step S3, under the transient disturbance mode, a high-fidelity sampling instruction is triggered immediately, a high-frequency waveform sequence before and after disturbance is captured by increasing the sampling frequency and the sampling bit depth of an analog-to-digital converter, and a pre-stored waveform before the triggering time is spliced with the high-frequency waveform sequence to add an event identifier; S4, the linear observation vector or the high-frequency waveform sequence is sent to an edge intelligent gateway through an Internet of things transmission protocol; And S5, the edge intelligent gateway performs waveform recovery, space-time alignment and semantic packaging on the received data by using a preset reconstruction model, invokes an integrated generation countermeasure network reconstructor to map the linear observation vector back to a high-dimensional time domain waveform space so as to restore an original steady-state waveform, and uploads the processed structured data packet to a cloud management platform.
2. The method for collecting data of the electric energy meter based on the internet of things according to claim 1, wherein the adaptive switching process determines the mode attribute of the power grid operation signal by extracting a basic electric quantity parameter and calculating a change rate of the basic electric quantity parameter in a preset time step, and comparing the change rate with a preset steady state discrimination threshold.
3. The method for collecting data of an electric energy meter based on the internet of things according to claim 1, wherein the internet of things transmission protocol is extended with a mode identification field and a reconstruction parameter load area at a message header of a protocol data unit, the mode identification field is used for indicating a sampling mode attribute of current load data, and the reconstruction parameter load area is used for carrying an index number of the measurement matrix and data compression ratio information.
4. The method for acquiring the data of the electric energy meter based on the Internet of things according to claim 2 is characterized in that the calculation process of the variability is specifically that a sampling mean value of a current time step is obtained through a gradient monitor and is compared with the statistical characteristics of a previous adjacent time step, and a variability index reflecting the intensity of numerical variation is determined so as to focus on the essential deflection of a power grid running track; in the comparison process, when the fluctuation rate is continuously lower than the preset steady state judgment threshold value, judging that the steady state operation mode is adopted; and when the fluctuation rate breaks through the preset steady state judgment threshold value within the preset observation time or shows continuous oscillation characteristics, judging the transient disturbance mode.
5. The method for acquiring the data of the electric energy meter based on the internet of things according to claim 1, wherein the processing procedure of the measuring matrix in the step S2 is specifically that the measuring matrix adopts a pseudo-random matrix with a cyclic structure, the pseudo-random matrix is synchronously generated at the electric energy meter end and the edge intelligent gateway end through a preset seed key, the front-end sensing unit only stores a single-row vector of the measuring matrix, and full matrix equivalent operation is realized through cyclic shift logic; When linear projection is performed, the sparse coefficient set is combined and accumulated with a group of weight factors with random distribution characteristics, and the dimension of the linear observation vector is reduced along with the reduction of the available bandwidth of the channel by reducing the length of the observation vector or is increased along with the increase of the available bandwidth of the channel by increasing the dimension of projection.
6. The method for acquiring data of the electric energy meter based on the internet of things according to claim 1, wherein the processing procedure of the data in the step S5 is specifically that the generating countermeasure network reconstructor uses a generator to search a basis vector combination matched with the linear observation vector in a high-dimensional manifold space according to the pre-learned power grid waveform characteristic distribution, and restore an original steady-state waveform which keeps the phase precision and the amplitude accuracy; after the restoration is completed, the edge intelligent gateway calculates a secondary mapping value of the reconstructed high-dimensional waveform under the same measurement matrix, and performs difference comparison between the secondary mapping value and the linear observation vector received originally to obtain a consistency residual error index; and if the consistency residual index is out of the preset confidence interval, the edge intelligent gateway sends an instruction for readjusting the sampling strategy to the front end sensing unit.
7. The internet of things-based electric energy meter data acquisition method of claim 6, wherein the front-end sensing unit is configured with a dual-path cache queue, the dual-path cache queue comprising a first path and a second path; the first path is used as a steady-state characteristic path and is used for temporarily storing the linear observation vector subjected to the sparsification treatment and keeping the latest running state in a cyclic coverage mode; the second path is used as a transient original path, and an asynchronous static memory is adopted to circularly store original waveform data obtained by high-frequency sampling; When the event driving mechanism generates a trigger signal, the front end sensing unit sends a locking instruction to enable the second path to stop circularly covering and locking data in a current storage window, and the storage window completely covers a preset number of power frequency periods before and after disturbance occurs so as to realize the tracing of historical events.
8. The method for collecting data of an electric energy meter based on the Internet of things according to claim 7, wherein the self-adaptive switching process further comprises real-time tracking logic for power grid frequency fluctuation, the front-end sensing unit acquires fundamental frequency of a voltage signal through a zero-crossing detection circuit, when frequency deviation exceeding a preset frequency tolerance threshold is detected, the sampling controller executes a forced intervention instruction, stops the current compressed sensing processing logic, and increases sampling bit depth of the analog-to-digital converter from low-precision bit depth to high-precision bit depth, synchronously increases sampling frequency, and captures harmonic components through redundant sampling to ensure acquisition integrity of key electric energy indexes under a frequency unstable working condition.
9. The method for collecting data of the electric energy meter based on the internet of things according to claim 8, wherein the generating countermeasure network reconstructor performs offline training on the cloud management platform, and a training set of the generating countermeasure network reconstructor comprises steady-state waveform samples, distortion waveform samples and corresponding low-dimensional observed values under various typical power loads, and the training process is constrained by adopting a perception loss function and a countermeasure loss function together; And the cloud management platform periodically carries out iterative updating on the global reconstruction model according to the distribution condition of the consistency residual indexes fed back by the edge intelligent gateways, and synchronizes the updated weight parameters to the edge intelligent gateways in an incremental issuing mode so as to adapt the model reasoning process executed by the edge intelligent gateways to the local power grid environment characteristics.
10. An electric energy meter data acquisition system based on the Internet of things, which is characterized by comprising a front-end sensing module, a high-fidelity sampling capturing high-frequency waveform sequence and a data acquisition module, wherein the front-end sensing module is deployed in an intelligent electric energy meter and is used for monitoring an electric network operation signal in real time and executing self-adaptive switching between a steady-state operation mode and a transient disturbance mode through an event driving mechanism, and the front-end sensing module executes a compressed sensing algorithm in a steady state to generate a linear observation vector; The edge intelligent gateway is connected with the front-end sensing module through an internet of things communication link, and is internally integrated with a waveform reconstruction module and a semantic analysis engine, and is used for receiving the linear observation vector or the high-frequency waveform sequence, restoring an original steady-state waveform by utilizing a generating countermeasure network reconstructor, and executing space-time alignment and semantic packaging processing; The cloud management platform is connected with the edge intelligent gateway through a wide area network and is used for executing offline training, global resource scheduling, historical data storage and incremental issuing of model weights of the generated countermeasure network reconstructor.

Description

Electric energy meter data acquisition method and system based on Internet of things Technical Field The invention belongs to the technical field of intelligent power grid information, and particularly relates to an electric energy meter data acquisition method and system based on the Internet of things. Background Along with the acceleration of the construction of the global energy internet and the smart grid, the internet of things technology is increasingly widely applied to the field of power monitoring. As a core component of the power sensing network, the data acquisition and monitoring system of the electric energy meter bears key functions such as power utilization state sensing, load distribution adjustment, electric charge settlement and the like, and is an important foundation stone for realizing energy digital transformation and stable operation of the power system. The electric energy meter data acquisition method and system based on the Internet of things mainly report key indexes such as current, voltage, power and electric energy quality to a remote management platform by using various low-power-consumption wide area network communication protocols through a large-scale intelligent terminal deployed at a user side. The technology aims to provide detailed basic data support for power grid operation analysis, fault early warning and load side management through high-frequency real-time monitoring. The prior art is faced with multiple problems in practical application, the traditional timing quantitative full reporting mode causes congestion of a communication channel and data redundancy, particularly in a complex scene of accessing a massive terminal, a system is easy to lose transient power quality events due to the fact that acquisition frequency is forced to be reduced in order to avoid channel collapse, edge computing power and cloud load have serious mismatch, complex anomaly detection logic generates response delay when the cloud processing is carried out due to the fact that terminal equipment mainly bears data transmission function, real-time feedback requirements of a modern power grid on the power anomaly events are difficult to meet, the system is limited by technical prejudice of uniform data acquisition, steady-state operation data and transient waveform signals cannot be effectively identified and distinguished, a large amount of low-value repeated information and extremely valuable disturbance data are treated equally, and great waste of communication bandwidth and storage resources is caused. Together, these problems result in a serious imbalance between power data collection efficiency and data authenticity. Disclosure of Invention The invention aims to provide an electric energy meter data acquisition method and system based on the Internet of things, which can solve the problems in the background technology. The method solves the technical problems of low acquisition efficiency, mass redundant data congestion channels, slow response of the edge side to transient abnormal events and the like caused by structural problems between communication resource constraint and data precision requirements in the existing electric energy data acquisition system. In order to achieve the purpose, the technical scheme adopted by the invention is that the electric energy meter data acquisition method based on the Internet of things comprises the following steps: s1, monitoring a power grid operation signal in real time through a front end sensing unit arranged at an electric energy meter end, and performing self-adaptive switching between a steady-state operation mode and a transient disturbance mode by utilizing an event driving mechanism; s2, under a steady-state operation mode, performing sparsification processing on electric energy original data by using a compressed sensing algorithm to generate a linear observation vector far lower than an original sampling frequency; s3, under a transient disturbance mode, a high-fidelity sampling instruction is triggered immediately, a high-frequency waveform sequence before and after disturbance occurs is captured, and an event identifier is added; S4, transmitting the linear observation vector or the high-frequency waveform sequence to an edge intelligent gateway through the optimized internet of things transmission protocol; S5, the edge intelligent gateway performs waveform recovery, space-time alignment and semantic packaging on the received data by using a preset reconstruction model, and uploads the processed structured data packet to the cloud management platform. Preferably, the step S1 specifically includes the following steps: s11, extracting basic electric quantity parameters such as voltage, current, power factor and the like acquired by an electric energy meter in real time; S12, calculating the change rate of the basic electric quantity parameter in a preset time step through a gradient monitor; s13, comparing the fluctuation ratio with