A high-precision measurement system and device for measuring a switch

Abstract

The application discloses a kind of high-precision measurement system and device of measuring switch, it is related to measurement technical field, including control module, measurement module, computing module and storage module, measurement module, for obtaining analog signal and conversion into digital signal, transmission to control module, signal processing is carried out to digital signal, generates first measurement data, transmission to main control processing unit and dispatch computing module, first measurement data is handled in layers, obtains second measurement data, and transmission to communication module, data synchronization is carried out to second measurement data, obtains third measurement data, and transmission to storage module, store data after synchronization third measurement data, through high-precision signal acquisition and multi-component processing offset interference precision, utilize parallel computing and priority scheduling shorten response, optimize data aggregation, by cross-platform synchronization and distributed technology realizes data sharing, using hierarchical storage and block chain storage evidence guarantee efficiency safety, enable intelligent power grid operation and maintenance.

Description

Technical Field

[0001] This invention relates to the field of measurement technology, and in particular to a high-precision measurement system and device for measuring switches. Background Technology

[0002] In recent years, with the intelligent development of smart grids, industrial automation, and new energy power distribution systems, measurement switches, as core equipment for power parameter monitoring, are of paramount importance in terms of measurement accuracy and data processing capabilities. With the large-scale integration of distributed energy, the stringent requirements of industrial control for power quality, and the rapid deployment of new energy vehicle charging networks and energy storage systems, the power system exhibits complex characteristics of multi-source grid connection, dynamic load changes, and interconnected and coordinated equipment. Measurement switches not only need to accurately collect the instantaneous values ​​of power frequency current and voltage, as well as the fundamental and harmonic components of basic electrical parameters, but also need to complete data calibration, anomaly identification, and multi-dimensional analysis in real time to provide reliable basis for grid dispatching, equipment protection, and energy efficiency management.

[0003] Traditional measurement systems are limited by the insufficient environmental adaptability of single sensors, making them prone to measurement deviations under temperature drift and electromagnetic noise interference. Data processing relies on a single-core serial architecture, which makes it difficult to balance high-precision signal processing and real-time anomaly response, resulting in delays in overload and short-circuit fault identification and handling. Cross-device data synchronization lacks a unified protocol and security mechanism, failing to meet the collaborative needs of heterogeneous platforms such as edge computing gateways, data centers, and mobile terminals. The tight coupling between hardware and software functions leads to inefficient allocation of resources for critical tasks, severely restricting the reliability and scalability of the system in complex scenarios. Summary of the Invention

[0004] The technical problems solved by this invention are: traditional measurement systems are limited by the insufficient environmental adaptability of single sensors, and are prone to measurement deviations under temperature drift and electromagnetic noise interference; data processing relies on a single-core serial architecture, which makes it difficult to balance high-precision signal processing and real-time anomaly response, resulting in delays in the identification and handling of overload and short-circuit faults; cross-device data synchronization lacks a unified protocol and security mechanism, which cannot meet the collaborative needs of heterogeneous platforms such as edge computing gateways, data centers and mobile terminals; the tight coupling of hardware and software functions leads to low efficiency in the allocation of resources for critical tasks, which seriously restricts the reliability and scalability of the system in complex scenarios.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a high-precision measurement system for a measuring switch, comprising a control module, a measurement module, a calculation module and a storage module;

[0006] The measurement module is used to acquire analog signals and convert them into digital signals, which are then transmitted to the control module.

[0007] The control module is used to process the digital signal, generate first measurement data, transmit it to the main control processing unit, and schedule the calculation module.

[0008] The calculation module is used to perform layered processing on the first measurement data, obtain the second measurement data, and transmit it to the communication module.

[0009] The communication module is used to synchronize the second measurement data to obtain the third measurement data and transmit it to the storage module.

[0010] The storage module is used to store the third measurement data after data synchronization.

[0011] As a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, the analog signal is converted into a digital signal by an integrated sensor and an ADC;

[0012] The analog signal includes the instantaneous value of the power frequency current and the instantaneous value of the power frequency voltage;

[0013] The digital signal includes current amplitude and voltage amplitude.

[0014] As a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, the signal processing includes signal conditioning and adaptation, fundamental component extraction and harmonic component extraction, and data format conversion.

[0015] The signal conditioning and adaptation performs range matching and level calibration on the digital signal to make the digital signal parameters conform to the processing range of the auxiliary processing unit;

[0016] The fundamental component extraction and harmonic component extraction separate the power frequency fundamental component and each harmonic component from the digital signal to generate component data;

[0017] The component data includes frequency, fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, amplitude of each harmonic voltage, and phase information;

[0018] The data format conversion transforms the processed component data into a unified format compatible with the shared memory of the main control processing unit, generating the first measurement data.

[0019] The first measurement data includes fundamental frequency, harmonic frequency, fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, amplitude of each harmonic voltage, and phase information;

[0020] The first measurement data is transmitted to the main control processing unit.

[0021] As a preferred embodiment of the high-precision measurement system for the measurement switch described in this invention, the first measurement data is subjected to layered processing, including dynamic calibration processing, multi-dimensional data aggregation processing, and real-time anomaly feature identification processing.

[0022] The dynamic calibration process, based on the first measurement data, periodically calibrates the electrical parameters according to the metrological accuracy requirements through a preset temperature compensation logic and noise suppression circuit, generating real-time metrological data.

[0023] The electrical parameters include the fundamental current amplitude, the fundamental voltage amplitude, the amplitude of each harmonic current, and the amplitude of each harmonic voltage;

[0024] The periodic calibration includes:

[0025] The electrical parameters are calibrated to generate real-time metering data;

[0026] The real-time metering data is subjected to error threshold verification;

[0027] The real-time metering data is subject to first regulation;

[0028] The real-time metering data includes the effective value of current, the effective value of voltage, active power, and reactive power;

[0029] The first regulation specifically includes:

[0030] If the error threshold verification is successful, the data is synchronously transmitted to the multi-dimensional data aggregation processing module and the real-time anomaly feature recognition processing module.

[0031] If the error threshold verification fails, the auxiliary processing unit will be automatically triggered to reacquire the digital signal and start the secondary calibration process.

[0032] As a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, the multi-dimensional data aggregation processing is divided according to a preset time period or power distribution area.

[0033] The real-time metering data of the branch lines within the power distribution area are aggregated in both time and space to generate regional analysis results.

[0034] The regional analysis results are then subject to a second adjustment;

[0035] The analysis results include total electricity consumption, load curve, and power factor distribution;

[0036] The second regulation specifically includes:

[0037] The regional analysis results are stored in a priority hierarchy, which includes real-time data, statistical data, and aggregated data for abnormal periods.

[0038] The real-time data is stored in a cache.

[0039] The statistical-level data is stored in a large-capacity Flash memory.

[0040] The aggregated data during the abnormal period is automatically tagged and encrypted before being transmitted to the data center.

[0041] As a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, the real-time anomaly feature identification processing specifically includes:

[0042] Preset electrical parameter thresholds for real-time anomaly feature identification and processing;

[0043] The real-time metering data is scanned cycle by cycle to identify abnormal features that exceed the electrical parameter threshold and generate timestamped event records. The event records are then subject to third regulation.

[0044] The electrical parameter thresholds include overload current thresholds corresponding to the fundamental current amplitude and effective current value, undervoltage voltage thresholds corresponding to the fundamental voltage amplitude and effective voltage value, and harmonic distortion rate thresholds corresponding to the amplitudes of each harmonic current and the amplitudes of each harmonic voltage.

[0045] The event log includes the anomaly type, the time of occurrence, the duration, and the deviation value of the electrical parameters;

[0046] The anomaly types include overload, short circuit, and phase anomaly;

[0047] The third regulation specifically includes:

[0048] Send a trip trigger signal to the protection device;

[0049] Anomaly warning information is pushed to the mobile terminal via the communication module;

[0050] An independent index for abnormal events is created in the storage module, supporting data backtracking queries for n periods before and after an abnormality.

[0051] As a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, the hierarchical processing is implemented in parallel through a heterogeneous computing architecture of the main control processing unit and the auxiliary processing unit of the control module;

[0052] The auxiliary processing unit is used to perform signal processing and real-time temperature data acquisition on the digital signals transmitted by the measurement module.

[0053] The main control processing unit scheduling and computing module realizes parallel processing of dynamic calibration processing, multi-dimensional data aggregation processing and real-time abnormal feature identification processing through hardware task queues;

[0054] The hardware task queue receives scheduling instructions from the main control processing unit and encapsulates the task instructions for dynamic calibration processing, multi-dimensional data aggregation processing, and real-time anomaly feature identification processing into task units with priority tags.

[0055] The task unit includes a calibration task unit, an aggregation task unit, and an identification task unit;

[0056] The abnormal feature identification processing corresponds to a high-priority identification task unit;

[0057] The dynamic calibration processing corresponds to a medium-priority calibration task unit;

[0058] The multi-dimensional data aggregation processing corresponds to a low-priority aggregation task unit;

[0059] The task queue uses a hardware arbitration mechanism to achieve parallel scheduling of calibration task units, aggregation task units, and identification task units based on the priority tags;

[0060] The calibration task unit includes temperature compensation logic parameters, noise suppression circuit control instructions, and error threshold verification, and is executed in real time by the dynamic calibration process;

[0061] The hardware task queue supports task prefetching and pipelined operation, and enables high-speed interaction between calibration task data and shared memory through dual-port RAM;

[0062] After calibration, the data is synchronized to multi-dimensional data aggregation processing and real-time anomaly feature identification processing via a shared memory dual buffer.

[0063] When the abnormal event is triggered, the main control processing unit automatically suspends non-critical aggregation tasks through the task priority label and function correlation judgment mechanism, so that the abnormal handling response time is less than the preset time.

[0064] The automatic pause of non-critical aggregation tasks includes:

[0065] By using the priority arbitration mechanism of the hardware task queue, the scheduling priority of the aggregated task unit is reduced to a low priority, and the occupied shared memory double buffer resources are released.

[0066] The mechanism includes:

[0067] Priority label matching: Identifies tasks in the aggregation task unit that are marked as "aggregated data" and have a low priority.

[0068] Functional relevance determination: Determine whether the aggregated task unit does not directly participate in real-time abnormal feature identification or dynamic calibration processing;

[0069] The second measurement data includes real-time measurement data, regional analysis results, and event records.

[0070] In a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, data synchronization of the second measurement data specifically includes:

[0071] The second measurement data is synchronized in real time or periodically between the control module, calculation module, communication module and storage module through one or more communication protocols.

[0072] The synchronization process employs CRC checksum and timestamp alignment mechanisms.

[0073] Based on HarmonyOS distributed soft bus technology, the second measurement data is synchronized and shared across devices in the HarmonyOS ecosystem, including edge computing gateways, data centers, and mobile terminals, to generate the third measurement data.

[0074] In a preferred embodiment of the high-precision measurement system for the measuring switch described in this invention, the storage module is used to store the third measurement data after data synchronization, specifically including:

[0075] A hierarchical indexing mechanism is constructed based on the control module, measurement module, calculation module and storage module, and a circular buffer is used for high-speed reading and writing of real-time data;

[0076] Statistical data is indexed using hash indexes to accelerate retrieval, and abnormal data is stored in encrypted form and automatically generated with blockchain-based evidence tags.

[0077] The present invention provides a high-precision measuring device for a measuring switch, comprising a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs a high-precision measuring system for the measuring switch as described in any of the above claims.

[0078] The beneficial effects of this invention are as follows: By employing high-precision signal acquisition and multi-component processing technology, it accurately acquires power frequency and harmonic components, offsetting environmental interference to improve measurement accuracy; leveraging a parallel computing architecture and priority scheduling, it enables parallel processing of data calibration and anomaly identification tasks, shortening anomaly response time and optimizing data aggregation efficiency and storage scalability in large-scale scenarios; based on cross-platform synchronization protocols and distributed soft bus technology, it achieves real-time secure data sharing among heterogeneous devices; and by adopting a hierarchical storage architecture and blockchain notarization, it ensures real-time data read / write efficiency and the immutability of abnormal data. Overall, through collaborative innovation in hardware and algorithms, it comprehensively improves the accuracy of power parameter measurement, the timeliness of system response, and the security of data assets, empowering efficient operation and maintenance of smart grids. Attached Figure Description

[0079] Figure 1 This is a basic flowchart of a high-precision measurement system for a measuring switch provided in one embodiment of the present invention. Detailed Implementation

[0080] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0081] Example, refer to Figure 1 As an embodiment of the present invention, a high-precision measurement system for a measuring switch is provided, including a control module, a measurement module, a calculation module and a storage module;

[0082] The measurement module is used to acquire analog signals and convert them into digital signals, which are then transmitted to the control module.

[0083] The control module is used to process digital signals, generate the first measurement data, transmit it to the main control processing unit, and schedule the calculation module.

[0084] The calculation module is used to perform hierarchical processing on the first measurement data, obtain the second measurement data, and transmit it to the communication module;

[0085] The communication module is used to synchronize the second measurement data, obtain the third measurement data, and transmit it to the storage module.

[0086] The storage module is used to store the third measurement data after data synchronization.

[0087] In one embodiment, the measurement module integrates sensors and an ADC converter to acquire analog signals of instantaneous power frequency current and voltage values. These signals are then converted into digital signals including current and voltage amplitudes and transmitted to the control module. The control module sequentially performs signal conditioning and adaptation (range matching, level calibration), and extracts fundamental and harmonic components. This generates component data including frequency, fundamental current amplitude, fundamental voltage amplitude, harmonic current amplitude, harmonic voltage amplitude, and phase information. After format conversion, the first measurement data is generated and transmitted to the main control processing unit. The calculation module performs layered processing on the first measurement data: dynamic calibration processing calibrates electrical parameters through temperature compensation and noise suppression circuits, generating real-time effective values ​​of current, voltage, active power, and reactive power. Metering data, after error verification, is synchronized to the multi-dimensional data aggregation and real-time anomaly identification module. The multi-dimensional data aggregation module aggregates real-time data by time / region, generates analysis results of total electricity consumption and load curves, and stores them hierarchically. The anomaly identification module scans the real-time data, identifies overload and short-circuit anomalies, and generates event records with timestamps. The communication module uses a multi-protocol combination of CRC check and timestamp alignment mechanism to achieve synchronization of the second measurement data among modules. With the help of HarmonyOS distributed soft bus technology, it shares data with external devices to generate the third measurement data. The storage module builds a hierarchical index, uses a circular buffer for real-time data, establishes a hash index for statistical data, encrypts anomaly data, and generates blockchain storage tags, ultimately achieving efficient data storage and secure management.

[0088] Analog signals are converted into digital signals through integrated sensors and ADCs;

[0089] Analog signals include the instantaneous values ​​of power frequency current and power frequency voltage;

[0090] Digital signals include current amplitude and voltage amplitude.

[0091] Signal processing includes signal conditioning and adaptation, fundamental component extraction and harmonic component extraction, and data format conversion;

[0092] Signal conditioning and adaptation involves range matching and level calibration of digital signals to ensure that the digital signal parameters conform to the processing range of the auxiliary processing unit.

[0093] Fundamental component extraction and harmonic component extraction separate the fundamental frequency component and each harmonic component from the digital signal to generate component data;

[0094] The component data includes frequency, fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, amplitude of each harmonic voltage, and phase information;

[0095] Data format conversion converts the processed component data into a unified format compatible with the shared memory of the main control processing unit, generating the first measurement data;

[0096] The first measurement data includes the fundamental frequency, harmonic frequencies, fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, amplitude of each harmonic voltage, and phase information;

[0097] The first measurement data is transmitted to the main control processing unit.

[0098] In one embodiment, the measurement module acquires analog signals of instantaneous power frequency current and voltage values ​​via integrated current and voltage sensors (ranges of 0-5A and 0-220V respectively, adaptable to common parameters of low-voltage power distribution systems). These signals are then converted into digital signals (including current and voltage amplitudes) by a 16-bit resolution, 10kHz sampling rate ADC converter (satisfying the Nyquist sampling theorem and accurately capturing harmonic components up to the 20th order). The control module performs signal conditioning and adaptation on the digital signals: range matching is achieved through a programmable amplifier, conditioning the ±5V signal output from the sensors to the ADC input range of 0-3.3V, and level calibration is performed based on a temperature compensation meter (temperature drift error less than or equal to ±0.1%). The signal parameters are made to meet the processing requirements of the auxiliary processing unit. Then, a Fast Fourier Transform (FFT) algorithm with a window length of 20ms is used to separate the fundamental frequency and harmonic components within the 20th order, generating component data with frequency (accuracy up to 0.01Hz, amplitude accuracy 0.2%, phase accuracy 0.1°), including the fundamental frequency (50Hz±0.5%), harmonic frequencies (integer multiples of the fundamental frequency), corresponding amplitude and phase information. Finally, through the data format conversion module, the component data is encapsulated into the first measurement data in 32-bit floating-point format and transmitted to the main control processing unit via the shared memory bus for subsequent hierarchical processing.

[0099] The first measurement data is processed in layers, including dynamic calibration processing, multi-dimensional data aggregation processing, and real-time anomaly feature identification processing.

[0100] Dynamic calibration processing, based on the first measurement data, periodically calibrates the electrical parameters according to the metrological accuracy requirements through preset temperature compensation logic and noise suppression circuit, and generates real-time metrological data.

[0101] The electrical parameters include the fundamental current amplitude, the fundamental voltage amplitude, the amplitude of each harmonic current, and the amplitude of each harmonic voltage;

[0102] Periodic calibration includes:

[0103] Electrical parameters are calibrated to generate real-time metering data;

[0104] Error threshold verification is performed on real-time metering data;

[0105] First adjustment is made to the real-time metering data;

[0106] Real-time metering data includes RMS current, RMS voltage, active power, and reactive power;

[0107] The first round of regulation specifically includes:

[0108] If the error threshold verification is successful, the data is synchronously transmitted to the multi-dimensional data aggregation processing module and the real-time anomaly feature recognition processing module.

[0109] If the error threshold verification fails, the auxiliary processing unit will be automatically triggered to reacquire the digital signal and start the secondary calibration process.

[0110] In one embodiment, during dynamic calibration, the calculation module, based on the first measurement data (including electrical parameters: fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, and amplitude of each harmonic voltage), and according to the 0.2S-level metering accuracy requirement (compliant with GB / T 17883 Electrical Energy Metering Standard), uses preset temperature compensation logic (compensation range -40℃ to +85℃, temperature drift error less than or equal to ±0.1%). The system uses a FS (Firmware Function) and an 8th-order Butterworth noise suppression circuit (cutoff frequency 20kHz, suppressing high-frequency electromagnetic interference) to periodically calibrate electrical parameters (1s cycle, covering 50 cycles of the power frequency signal). After calibration, real-time metering data is generated, including RMS current (accuracy ±0.2%), RMS voltage (accuracy ±0.2%), active power (accuracy ±0.5%), and reactive power (accuracy ±0.5%). The calibrated data enters the error threshold verification stage: the error thresholds for RMS current and RMS voltage are set at 0.2%, and the power error threshold is set at 0.5% (matching the metering accuracy level). If the verification is successful, the real-time metering data is synchronously transmitted to the multi-dimensional data aggregation module and the anomaly identification module through the dual-port RAM. If it fails, the auxiliary processing unit is automatically triggered to re-acquire the digital signal for 10 power frequency cycles (200ms) at a sampling rate of 10kHz (consistent with the ADC), and the secondary calibration process (including dynamic correction of the temperature compensation coefficient) is started to ensure that the data error after calibration is stable within the threshold range.

[0111] Multi-dimensional data aggregation and processing, divided according to preset time periods or power distribution areas;

[0112] Real-time metering data of branch lines within the power distribution area are aggregated in both time and space to generate regional analysis results;

[0113] Secondary adjustments are made to the regional analysis results;

[0114] The analysis results include total electricity consumption, load curves, and power factor distribution;

[0115] The second round of regulation specifically includes:

[0116] Regional analysis results are stored in a hierarchical manner according to priority, with priorities including real-time data, statistical data, and aggregated data from abnormal periods;

[0117] Real-time data is stored in a cache;

[0118] Statistical data is stored in large-capacity Flash memory;

[0119] Aggregated data from abnormal periods is automatically tagged and encrypted before being transmitted to the data center.

[0120] In one embodiment, during multi-dimensional data aggregation, a two-dimensional division is performed based on a preset time period (1 second for real-time, 15 minutes for statistical, and trigger-based acquisition during abnormal periods) and the power distribution area (such as feeder transformer areas and building power distribution units). Spatiotemporal aggregation is then performed on the real-time metering data (including RMS current, RMS voltage, and power) of each branch line: In the time dimension, real-time data is aggregated in a 1-second cycle to generate a load curve, while statistical data is aggregated in a 15-minute cycle to form a trend analysis of total electricity consumption and power factor distribution; in the spatial dimension, all branch data within the distribution area are aggregated according to the distribution area hierarchy (such as transformer area → feeder → substation) to generate a panoramic view of regional electricity consumption. The aggregated data is then subject to a second control measure: real-time data (updated at a frequency of less than or equal to 1 second) is stored in a high-speed SRAM cache to meet millisecond-level real-time query requirements; statistical data (generating 24×4 15-minute granular records daily) is written to a large-capacity NAND flash memory. Flash memory (storage capacity greater than or equal to 32GB, supporting 10 years of historical data storage) is used to create a hash index to accelerate retrieval; data from abnormal periods (aggregated data within 5 minutes before and after triggering overload and harmonic exceedance events) is automatically timestamped and transmitted to the data center via 4G / 5G channels using the AES-128 encryption algorithm (compliant with power industry data security standards). At the same time, a blockchain evidence tag (using the SHA-256 hash algorithm) is generated in the local storage module to ensure that the data is tamper-proof.

[0121] Real-time anomaly feature recognition and processing specifically includes:

[0122] Preset electrical parameter thresholds for real-time anomaly feature identification and processing;

[0123] The real-time metering data is scanned cycle by cycle to identify abnormal features that exceed the electrical parameter threshold and generate timestamped event records. The event records are then subject to third-party regulation.

[0124] The electrical parameter thresholds include the overload current threshold corresponding to the fundamental current amplitude and the effective value of the current, the undervoltage voltage threshold corresponding to the fundamental voltage amplitude and the effective value of the voltage, and the harmonic distortion rate threshold corresponding to the amplitude of each harmonic current and the amplitude of each harmonic voltage.

[0125] The event log includes the anomaly type, the time of occurrence, the duration, and the deviation of electrical parameters;

[0126] The types of anomalies include overload, short circuit, and phase anomalies;

[0127] The third regulation specifically includes:

[0128] Send a trip trigger signal to the protection device;

[0129] Anomaly warning information is pushed to the mobile terminal via the communication module;

[0130] An independent index for abnormal events is created in the storage module, supporting data backtracking queries for n periods before and after an abnormality.

[0131] In one embodiment, during real-time anomaly feature identification processing, preset electrical parameter thresholds (compliant with GB / T 14549 power quality standard) are used: the overload current threshold is set to 1.2 times the rated current (to avoid false triggering due to instantaneous fluctuations), the undervoltage voltage threshold is set to 0.9 times the rated voltage (to adapt to the safety threshold of low-voltage power distribution systems), and the total harmonic distortion (THD) threshold is set to 5% for voltage THD and 8% for current THD (to meet IEEE standards). The 519-2014 Low Voltage System Harmonic Limits standard uses a timer to trigger a cycle-by-cycle scan (cycle 20ms, consistent with the power frequency signal cycle) to compare real-time metering data against thresholds. When a threshold is exceeded, an event record with a millisecond-level timestamp is immediately generated, including the anomaly type (overload, short circuit, and phase anomaly), occurrence time, duration, and deviation of electrical parameters (e.g., current exceeding the threshold by 15%). The third control mechanism executes simultaneously: a hard-wired trip trigger signal is sent to the protection device (response time less than 50ms, meeting the requirements for rapid action of intelligent circuit breakers), and a warning message including anomaly codes (e.g., "E001-Overload Event-Line A") is pushed to the mobile terminal via the MQTT protocol. At the same time, an independent index is established in the storage module (based on event timestamp + device ID), automatically associating the original data and aggregated data of 10 (n=10) power frequency cycles (200ms) before and after the anomaly, supporting waveform backtracking and in-depth analysis before and after the fault.

[0132] Layered processing achieves parallel processing through a heterogeneous computing architecture of the main control processing unit and auxiliary processing units in the control module;

[0133] The auxiliary processing unit is used for signal processing and real-time acquisition of temperature data from the digital signals transmitted by the measurement module.

[0134] The main control processing unit schedules the computing module, which uses a hardware task queue to achieve parallel processing of dynamic calibration, multi-dimensional data aggregation, and real-time anomaly feature identification.

[0135] The hardware task queue receives scheduling instructions from the main control processing unit and encapsulates the task instructions for dynamic calibration processing, multi-dimensional data aggregation processing, and real-time anomaly feature identification processing into task units with priority tags.

[0136] The task unit includes a calibration task unit, an aggregation task unit, and an identification task unit;

[0137] The anomaly feature identification and processing task unit is assigned a high priority.

[0138] Dynamic calibration processing corresponds to a medium-priority calibration task unit;

[0139] Multi-dimensional data aggregation processing corresponds to low-priority aggregation task units;

[0140] The task queue uses a hardware arbitration mechanism to achieve parallel scheduling of calibration task units, aggregation task units, and identification task units based on priority tags;

[0141] The calibration task unit includes temperature compensation logic parameters, noise suppression circuit control instructions, and error threshold verification, which are executed in real time by dynamic calibration processing;

[0142] The hardware task queue supports task prefetching and pipelined operations, and enables high-speed interaction between calibration task data and shared memory through dual-port RAM;

[0143] After calibration, the data is synchronized to multi-dimensional data aggregation processing and real-time anomaly feature identification processing via a shared memory dual buffer.

[0144] When an abnormal event is triggered, the main control processing unit automatically suspends non-critical aggregation tasks through the task priority label and function correlation judgment mechanism, so that the abnormal handling response time is less than the preset time.

[0145] Automatically pausing non-critical aggregation tasks includes:

[0146] By using the priority arbitration mechanism of the hardware task queue, the scheduling priority of the aggregated task unit is reduced to a low priority, and the occupied shared memory double buffer resources are released.

[0147] The mechanism includes:

[0148] Priority label matching: Identifies tasks in the aggregation task unit that are marked as "aggregated data" and have a low priority.

[0149] Functional relevance determination: Determine whether the aggregated task unit does not directly participate in real-time abnormal feature identification or dynamic calibration processing;

[0150] The second set of measurement data includes real-time measurement data, regional analysis results, and event logs.

[0151] In one embodiment, during layered processing, a heterogeneous computing architecture of "ARM main control processing unit + FPGA auxiliary processing unit" is adopted (adapting to the low-power, high-performance requirements of embedded systems). The auxiliary processing unit acquires digital signals (10kHz sampling rate) output from the measurement module and temperature sensor data (accuracy ±0.5℃) in real time, simultaneously completing signal conditioning and temperature compensation processing. The main control processing unit schedules the computing module through a hardware task queue, encapsulating dynamic calibration, data aggregation, and anomaly identification tasks into priority-tagged task units: the identification task unit (anomaly feature identification) is set to high priority (interrupt response level IRQ1), the calibration task unit (dynamic calibration) is set to medium priority (IRQ2), and the aggregation task unit (multi-dimensional data aggregation) is set to low priority (IRQ3). The hardware task queue is based on a 3-level priority arbitration mechanism (compliant with the AMBAAHB bus protocol) and supports... The system employs task prefetching and an 8-stage pipeline operation, enabling high-speed interaction (160MB / s bandwidth) between calibration task data and shared memory via dual-port RAM (256KB storage capacity). After calibration, the data is synchronized to subsequent processing modules via a dual-buffered shared memory (128KB capacity per buffer) to ensure data consistency. When an abnormal event is triggered, the main control processing unit automatically lowers the priority of the aggregation task unit to the lowest level (IRQ4) by matching priority tags (identifying tasks with the "aggregated data" tag and a priority of IRQ3) and determining functional relevance (non-abnormal handling related tasks), thus releasing the dual-buffered resources and strictly controlling the abnormal handling response time to within 50ms (meeting the rapid action requirements of the protection device). The second measurement data (real-time measurement data, regional analysis results, and event records) are processed in parallel through a task queue scheduling mechanism, ensuring the real-time performance and stability of the system under complex operating conditions.

[0152] Data synchronization of the second measurement data specifically includes:

[0153] Real-time or periodic synchronization of the second measurement data is achieved between the control module, calculation module, communication module, and storage module through one or more communication protocols.

[0154] The synchronization process employs CRC checksum and timestamp alignment mechanisms.

[0155] Based on HarmonyOS distributed soft bus technology, the second measurement data is synchronized and shared across devices in the HarmonyOS ecosystem, including edge computing gateways, data centers, and mobile terminals, to generate the third measurement data.

[0156] The storage module is used to store the third measurement data after data synchronization, specifically including:

[0157] A hierarchical indexing mechanism is constructed based on the control module, measurement module, calculation module and storage module, and a circular buffer is used for high-speed reading and writing of real-time data;

[0158] Statistical data is indexed using hash indexes to accelerate retrieval, and abnormal data is stored in encrypted form and automatically generated with blockchain-based evidence tags.

[0159] In one embodiment, during data synchronization and storage, the second measurement data synchronization between the control module, computing module, and communication module is achieved through a multi-protocol stack (SPI / UART / Ethernet): real-time data uses 1ms periodic SPI communication (rate 10Mbps), and periodic data is transmitted via UART at a baud rate of 9600. Both data are embedded with 16-bit CRC checksums (compliant with ISO 3309 standard) and millisecond-level timestamps (accuracy ±1ms) to ensure data integrity and time consistency. Based on HarmonyOS distributed soft bus technology (supporting L2 / L3 layer cross-device communication), an encrypted channel (AES-256 algorithm) is established with HarmonyOS ecosystem devices in edge computing gateways and data centers to achieve cross-device synchronization of the second measurement data and generate third measurement data including device ID and timestamp. The storage module employs a three-tiered indexing mechanism: real-time data (update frequency less than or equal to 1ms) is stored in a 256KB circular buffer (SRAM material), supporting high-speed read and write operations of 100,000 times per second; statistical data (aggregated at 15-minute granularity) is indexed using the MurmurHash3 algorithm and stored in a 256MB high-capacity Flash (NAND material), with retrieval time less than or equal to 10ms; abnormal data is encrypted using AES-128 and written to an independent storage partition, and a blockchain evidence tag (including a 512-bit hash value) is generated using the SHA-256 algorithm to ensure data immutability. This tiered storage strategy improves the overall read and write performance of the system by 40%, meeting the real-time access and security auditing requirements of the smart grid for massive amounts of power data.

[0160] In one embodiment, a high-precision measuring device for a measuring switch is provided, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, the processor performs a high-precision measuring system for the measuring switch.

[0161] This invention utilizes high-precision signal acquisition and multi-component processing technology to accurately acquire power frequency and harmonic components, offsetting environmental interference to improve measurement accuracy. Leveraging a parallel computing architecture and priority scheduling, it enables parallel processing of data calibration and anomaly identification tasks, shortening anomaly response time and optimizing data aggregation efficiency and storage scalability in large-scale scenarios. Based on cross-platform synchronization protocols and distributed soft bus technology, it achieves real-time secure data sharing among heterogeneous devices. Employing a hierarchical storage architecture and blockchain notarization, it ensures real-time data read / write efficiency and the immutability of anomaly data. Through collaborative innovation in hardware and algorithms, this invention comprehensively improves the accuracy of power parameter measurement, the timeliness of system response, and the security of data assets, empowering efficient operation and maintenance of smart grids.

[0162] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A high-precision measurement system for a measuring switch, characterized in that, It includes a control module, a measurement module, a calculation module, and a storage module; The measurement module is used to acquire analog signals and convert them into digital signals, which are then transmitted to the control module. The control module is used to process the digital signal, generate first measurement data, transmit it to the main control processing unit, and schedule the calculation module. The calculation module is used to perform layered processing on the first measurement data, obtain the second measurement data, and transmit it to the communication module. The communication module is used to synchronize the second measurement data to obtain the third measurement data and transmit it to the storage module. The storage module is used to store the third measurement data after data synchronization; The first measurement data is processed in layers, including dynamic calibration processing, multi-dimensional data aggregation processing, and real-time anomaly feature identification processing. The hierarchical processing achieves parallel processing through a heterogeneous computing architecture of the main control processing unit and the auxiliary processing unit of the control module; The auxiliary processing unit is used to perform signal processing and real-time temperature data acquisition on the digital signals transmitted by the measurement module. The main control processing unit scheduling and computing module realizes parallel processing of dynamic calibration processing, multi-dimensional data aggregation processing and real-time abnormal feature identification processing through hardware task queues; The hardware task queue receives scheduling instructions from the main control processing unit and encapsulates the task instructions for dynamic calibration processing, multi-dimensional data aggregation processing, and real-time anomaly feature identification processing into task units with priority tags. The task unit includes a calibration task unit, an aggregation task unit, and an identification task unit; The abnormal feature identification processing corresponds to a high-priority identification task unit; The dynamic calibration processing corresponds to a medium-priority calibration task unit; The multi-dimensional data aggregation processing corresponds to a low-priority aggregation task unit; The task queue uses a hardware arbitration mechanism to achieve parallel scheduling of calibration task units, aggregation task units, and identification task units based on the priority tags; The calibration task unit includes temperature compensation logic parameters, noise suppression circuit control instructions, and error threshold verification, and is executed in real time by the dynamic calibration process; The hardware task queue supports task prefetching and pipelined operation, and enables high-speed interaction between calibration task data and shared memory through dual-port RAM; After calibration, the data is synchronized to multi-dimensional data aggregation processing and real-time anomaly feature identification processing via a shared memory dual buffer. When an abnormal event is triggered, the main control processing unit automatically suspends non-critical aggregation tasks through the task priority label and function correlation judgment mechanism, so that the abnormal handling response time is less than the preset time. The automatic pause of non-critical aggregation tasks includes: By using the priority arbitration mechanism of the hardware task queue, the scheduling priority of the aggregated task unit is reduced to a low priority, and the occupied shared memory double buffer resources are released. The mechanism includes: Priority label matching: Identifies tasks in the aggregation task unit that are marked as "aggregated data" and have a low priority. Functional relevance determination: Determine whether the aggregated task unit does not directly participate in real-time abnormal feature identification or dynamic calibration processing; The second measurement data includes real-time measurement data, regional analysis results, and event records.

2. The high-precision measurement system for the measuring switch as described in claim 1, characterized in that: The analog signal is converted into a digital signal through an integrated sensor and ADC; The analog signal includes the instantaneous value of the power frequency current and the instantaneous value of the power frequency voltage; The digital signal includes current amplitude and voltage amplitude.

3. The high-precision measurement system for the measuring switch as described in claim 2, characterized in that: The signal processing includes signal conditioning and adaptation, fundamental component extraction and harmonic component extraction, and data format conversion. The signal conditioning and adaptation performs range matching and level calibration on the digital signal to make the digital signal parameters conform to the processing range of the auxiliary processing unit; The fundamental component extraction and harmonic component extraction separate the power frequency fundamental component and each harmonic component from the digital signal to generate component data; The component data includes frequency, fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, amplitude of each harmonic voltage, and phase information; The data format conversion transforms the processed component data into a unified format compatible with the shared memory of the main control processing unit, generating the first measurement data. The first measurement data includes fundamental frequency, harmonic frequency, fundamental current amplitude, fundamental voltage amplitude, amplitude of each harmonic current, amplitude of each harmonic voltage, and phase information; The first measurement data is transmitted to the main control processing unit.

4. The high-precision measurement system for the measuring switch as described in claim 3, characterized in that: The dynamic calibration process, based on the first measurement data, periodically calibrates the electrical parameters according to the metrological accuracy requirements through a preset temperature compensation logic and noise suppression circuit, generating real-time metrological data. The electrical parameters include the fundamental current amplitude, the fundamental voltage amplitude, the amplitude of each harmonic current, and the amplitude of each harmonic voltage; The periodic calibration includes: The electrical parameters are calibrated to generate real-time metering data; The real-time metering data is subjected to error threshold verification; The real-time metering data is subject to first regulation; The real-time metering data includes the effective value of current, the effective value of voltage, active power, and reactive power; The first regulation specifically includes: If the error threshold verification is successful, the data is synchronously transmitted to the multi-dimensional data aggregation processing module and the real-time anomaly feature recognition processing module. If the error threshold verification fails, the auxiliary processing unit will be automatically triggered to reacquire the digital signal and start the secondary calibration process.

5. The high-precision measurement system for the measuring switch as described in claim 4, characterized in that: The multi-dimensional data aggregation processing is divided according to a preset time period or power distribution area; The real-time metering data of the branch lines within the power distribution area are aggregated in both time and space to generate regional analysis results. The regional analysis results are then subject to a second adjustment. The analysis results include total electricity consumption, load curve, and power factor distribution; The second regulation specifically includes: The regional analysis results are stored in a priority hierarchy, which includes real-time data, statistical data, and aggregated data for abnormal periods. The real-time data is stored in a cache. The statistical-level data is stored in a large-capacity Flash memory. The aggregated data during the abnormal period is automatically tagged and encrypted before being transmitted to the data center.

6. The high-precision measurement system for the measuring switch as described in claim 5, characterized in that: The real-time anomaly feature identification process specifically includes: Preset electrical parameter thresholds for real-time anomaly feature identification and processing; The real-time metering data is scanned cycle by cycle to identify abnormal features that exceed the electrical parameter threshold and generate timestamped event records. The event records are then subject to third regulation. The electrical parameter thresholds include overload current thresholds corresponding to the fundamental current amplitude and effective current value, undervoltage voltage thresholds corresponding to the fundamental voltage amplitude and effective voltage value, and harmonic distortion rate thresholds corresponding to the amplitudes of each harmonic current and the amplitudes of each harmonic voltage. The event log includes the anomaly type, the time of occurrence, the duration, and the deviation value of the electrical parameters; The anomaly types include overload, short circuit, and phase anomaly; The third regulation specifically includes: Send a trip trigger signal to the protection device; Anomaly warning information is pushed to the mobile terminal via the communication module; An independent index for abnormal events is created in the storage module, supporting data backtracking queries for n periods before and after an abnormality.

7. The high-precision measurement system for the measuring switch as described in claim 6, characterized in that: Synchronizing the second measurement data specifically includes: The second measurement data is synchronized in real time or periodically between the control module, calculation module, communication module and storage module through one or more communication protocols. The synchronization process employs CRC checksum and timestamp alignment mechanisms. Based on HarmonyOS distributed soft bus technology, the second measurement data is synchronized and shared across devices in the HarmonyOS ecosystem, including edge computing gateways, data centers, and mobile terminals, to generate the third measurement data.

8. The high-precision measurement system for the measuring switch as described in claim 7, characterized in that: The storage module is used to store the third measurement data after data synchronization, specifically including: A hierarchical indexing mechanism is constructed based on the control module, measurement module, calculation module and storage module, and a circular buffer is used for high-speed reading and writing of real-time data; Statistical data is indexed using hash indexes to accelerate retrieval, and abnormal data is stored in encrypted form and automatically generated with blockchain-based evidence tags.

9. A high-precision measuring device for a measuring switch, characterized in that: The system includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform a method using a high-precision measurement system employing a measuring switch as described in any one of claims 1-8.

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