Remote monitoring system of single-phase ammeter

Abstract

The invention discloses a remote monitoring system for a single-phase ammeter, and relates to the technical field of power monitoring control, the remote monitoring system comprises an acquisition module, a control module and a monitoring module, the acquisition module is used for acquiring analog electric signals of a single-phase circuit and running state information of the ammeter, and electric signal digital codes and state digital identifiers are respectively generated through a conversion unit; the electric signal digital code and the state digital identifier are processed through the processing unit to obtain electric parameter data and digital state information, the electric parameter data and the digital state information are stored in the storage unit and transmitted to the monitoring module, the electric parameter data and the digital state information are displayed to a user, and a remote instruction is issued to the control module; comprehensive information is obtained through multi-dimensional collection and digital conversion, the abnormal leakage and false alarm rate is detected and reduced, hierarchical linkage and conflict resolution are achieved, linkage query and hierarchical early warning are supported, a complete closed loop is formed, an efficient and reliable scheme is provided for intelligent power grid user side management, and energy conservation and power grid efficiency improvement are facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power monitoring control, and particularly relates to a remote monitoring system of a single-phase electric meter. BACKGROUND

[0002] In recent years, with the deepening of the construction of smart grid and the refinement of user-side power management, the importance of the remote monitoring system of the single-phase electric meter in realizing real-time collection of power consumption data, abnormal state early warning and remote control is increasingly prominent. It is not only a core link of building a new power consumption ecology, but also a key support for improving the efficiency of power grid operation. In recent years, with the access of distributed energy, the popularization of peak-valley electricity price policy and the increasing attention of users to power safety, the market has put forward higher requirements for the remote monitoring system of the single-phase electric meter in terms of data collection comprehensiveness, abnormality recognition accuracy, control strategy flexibility and human-computer interaction convenience.

[0003] The existing remote monitoring system of the single-phase electric meter has some deficiencies. The data collection dimension is single, only basic voltage and current signals can be obtained, the digital processing capability of the electric meter running state information is insufficient, and it is difficult to meet the equipment full life cycle management demand. The abnormality detection strategy is simple, and it mainly depends on single threshold judgment, lacks correlation analysis of the trend of electric parameter change and cross-signal logic verification, resulting in high false negative rate and false positive rate of abnormality recognition. The response mechanism of the control module is fixed, and the hierarchical control strategy and cross-device linkage mechanism are not built. When facing complex power consumption scenarios, the regulation and control efficiency is low, and the control strategy conflict resolution capability is lacking, which easily causes confusion in command execution. The data display and interaction function is simple, and it is impossible to realize the linkage query of electric parameters and device state information and the visual hierarchical early warning of abnormal information, so the user operation experience and fault tracing efficiency are limited. SUMMARY

[0004] The present application solves the technical problems that the existing remote monitoring system of the single-phase electric meter has some deficiencies. The data collection dimension is single, only basic voltage and current signals can be obtained, the digital processing capability of the electric meter running state information is insufficient, and it is difficult to meet the equipment full life cycle management demand. The abnormality detection strategy is simple, and it mainly depends on single threshold judgment, lacks correlation analysis of the trend of electric parameter change and cross-signal logic verification, resulting in high false negative rate and false positive rate of abnormality recognition. The response mechanism of the control module is fixed, and the hierarchical control strategy and cross-device linkage mechanism are not built. When facing complex power consumption scenarios, the regulation and control efficiency is low, and the control strategy conflict resolution capability is lacking, which easily causes confusion in command execution. The data display and interaction function is simple, and it is impossible to realize the linkage query of electric parameters and device state information and the visual hierarchical early warning of abnormal information, so the user operation experience and fault tracing efficiency are limited.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a remote monitoring system for a single-phase electricity meter includes a data acquisition module, a control module, and a monitoring module; The acquisition module is used to acquire analog electrical signals of a single-phase circuit and operating status information of the meter, and generates digital codes for electrical signals and digital identifiers for status through a conversion unit, and transmits them to the control module. The control module is used to process the digital encoding of the electrical signal and the digital identifier of the status through the processing unit to obtain electrical parameter data and digital status information, store them in the storage unit and transmit them to the monitoring module; The monitoring module is used to display the electrical parameter data and digital status information to the user, and to send remote commands to the control module.

[0006] In a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the analog electrical signal includes a voltage signal and a current signal. The operating status information includes event logs, switch status, working mode, communication status, number of times the cover was opened, number of times undervoltage occurred, and power failure information; The conversion unit includes analog-to-digital conversion and digital-to-digital conversion; The analog electrical signal is converted into a digital code by analog-to-digital conversion. A status digital identifier is generated by digitizing the aforementioned operational status information; The digital encoding of the electrical signal includes voltage digital encoding and current digital encoding; The status digital identifiers include event record identifiers, switch status identifiers, operating mode identifiers, communication status identifiers, cover opening count identifiers, undervoltage count identifiers, and power failure information identifiers.

[0007] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the process of digitally encoding the electrical signal and digitally identifying the status to obtain electrical parameter data and digital status information by a processing unit includes: The voltage digital code is converted into a voltage value using a decoding algorithm, and the current digital code is converted into a current value. Electrical parameter data are calculated based on the voltage and current values ​​and the phase difference between the voltage and current values. The electrical parameter data includes active power, reactive power, power factor, and energy consumption. The status digital identifier is parsed and converted into corresponding textual description information and quantitative data through a preset mapping table of correspondence between identifiers and operating status information to form digital status information. The digital status information includes event type description, switch status description, working mode description and communication status description. The quantitative data includes cumulative number of times the cover was opened, cumulative number of times the voltage was low, cumulative number of times the power was lost, and the corresponding timestamp information of the cumulative number of times the cover was opened, the cumulative number of times the voltage was low, and the cumulative number of times the power was lost. The electrical parameter data and digital status information are stored in the storage unit.

[0008] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the process of calculating the electrical parameter data based on a preset strategy to generate a data monitoring signal includes: The preset strategy includes a trend analysis strategy and a correlation analysis strategy, which are executed in parallel. The trend analysis strategy includes: Based on preset time intervals, voltage values, current values, active power, and power factor are continuously collected and trend analysis is performed within a specific time window. The trend analysis identifies the changing trends of the voltage, current, active power, and power factor by calculating the rate of change, fluctuation amplitude, or cumulative change of the voltage, current, active power, and power factor within a time window. When the changing trend conforms to the preset abnormal trend characteristics, a trend abnormality monitoring signal is generated; The correlation analysis strategy includes: Establish logical relationships between voltage values, current values, active power, and power factor; When the correlation between the voltage value, current value, active power, and power factor deviates from the conventional correlation logic, an abnormal correlation monitoring signal is generated. The conventional correlation logic is constructed based on the physical characteristics and normal operating rules of voltage, current, active power, and power factor. Abnormal deviation scenarios of the conventional correlation logic include: The voltage value is normal, but the current value is abnormal; The deviation between the active power and the theoretically calculated values ​​of voltage and current exceeds the normal range; The trend and magnitude of the power factor change do not match the trend and magnitude of the voltage and current values. The data monitoring signals include trend anomaly monitoring signals and correlation anomaly monitoring signals.

[0009] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, a control signal is generated by matching the data monitoring signal or the remote command of the monitoring module through a preset strategy library. The preset strategy library includes an anomaly level-control strategy matching mechanism, a remote command multi-dimensional parsing mechanism, a cross-device linkage control interaction mechanism, and a control strategy conflict resolution mechanism. The control signals include a first control signal, a second control signal, a third control signal, and a fourth control signal; The first control signal is generated through the anomaly level-control strategy matching mechanism. The second control signal is generated through the remote command multivariate parsing mechanism; A third control signal is generated through the cross-device linkage control interaction mechanism. A fourth control signal is generated through the aforementioned control strategy conflict resolution mechanism; The anomaly level-control strategy matching mechanism includes: The system presets different urgency levels for different types of anomalies, including Level 1, Level 2, and Level 3 anomalies. When a Level 1 anomaly is triggered, the hard real-time control channel is activated, a power failure protection signal is generated, and an alarm is sent to the user via a short message unit. When a level 2 anomaly is triggered, the soft regulation control channel is activated to generate load adjustment signals for single-phase electricity meter equipment according to a preset timing sequence, and non-critical electrical equipment is cut off in stages. When a level 3 anomaly is triggered, an early warning signal is generated, and the local audible and visual alarm of the single-phase electricity meter is triggered and marked with a red dot on the monitoring module interface. The first control signal includes a power failure protection signal, a load adjustment signal, and a warning signal.

[0010] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the remote command multivariate parsing mechanism includes: Perform semantic-action matching on remote commands issued by the monitoring module; If it is a closing command or a opening command, the user's authority and the power grid status are verified by the command verification algorithm. Once the verification is successful, a switch control signal is generated. If it is a parameter calibration command, a calibration trigger signal is generated and sent to the acquisition module, and an instruction to send back electrical parameter data and digital status information is generated and sent to the control module. If it is a data query command, generate a data package signal of electrical parameter data and digital status information, encapsulate the electrical parameter data and digital status information according to a preset compression format and upload it; The second control signal includes a switch control signal, a calibration trigger signal, and a data packetization signal.

[0011] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the cross-device linkage control interaction mechanism includes: It integrates a standardized protocol conversion unit, supporting the control module to interact with external devices for control signals; When a correlation signal is detected that the voltage value is normal but the current value is abnormal, a high-energy-consuming device identification signal is generated for the smart socket to locate the abnormal power-consuming terminal. When a peak-valley arbitrage instruction is received from a user through the monitoring module, a charging and discharging control signal for the energy storage device is generated according to a preset time period, which is used to link the energy storage device to charge during off-peak hours and discharge during peak hours. The third control signal includes a high-energy-consuming device identification signal and an energy storage device charging and discharging control signal.

[0012] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the control strategy conflict resolution mechanism includes: When the control module receives both the control and remote commands triggered by the data monitoring signal, the first conflict is triggered and handled through priority arbitration logic. When the power failure protection signal corresponding to the first-level anomaly is received by the control module, the power failure protection signal has the highest priority, triggering the second conflict and interrupting the execution of all other control commands; When multiple control signals with the same functional attributes are received by the control module, a third conflict is triggered. The control signal received last is taken as the valid signal and executed according to the order of reception time, and the control signal received earlier is automatically invalidated. When the first, second, or third conflict occurs, a policy conflict log signal is generated to record the conflict type and processing result. The fourth control signal includes the policy conflict log signal.

[0013] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the storage and analysis of the electrical parameter data, digital status information, monitoring signals, and control signals, and the marking of abnormal information, specifically includes: The electrical parameter data, digital status information, monitoring signals, and control signals are used to construct an electrical parameter database, a digital status information log library, a monitoring signal log library, and a control signal log library according to a time series. The electrical parameter database stores voltage values, current values, active power, reactive power, power factor, and energy consumption according to a preset time granularity. The digital status information log library is associated with digital status information and quantitative data; The monitoring signal log library is associated with the generation time, triggering conditions, and corresponding electrical parameter data snapshots of the monitoring signals; The control signal log library is associated with the generation time, trigger source, and execution result of the control signal; The electrical parameter data, digital status information, monitoring signals, and control signals are analyzed from multiple dimensions using an anomaly pattern recognition algorithm, specifically including: The deviation rate is obtained by comparing the real-time electrical parameter data with the historical benchmark values ​​for the same period. When the deviation rate exceeds a preset first threshold, it is marked as an abnormal parameter fluctuation. The frequency of triggering of trend anomaly monitoring signals or related anomaly monitoring signals within a preset time period is statistically analyzed, and when the number of triggers reaches a preset second threshold, it is marked as an anomaly frequent warning. Verify the timing correlation between monitoring signals, control signals, electrical parameter data, and digital status information; When the electrical parameter data at the time the monitoring signal or control signal is generated is within the normal threshold range or the digitized status information is normal, it is marked as a suspected false alarm signal. Perform time-series-based fluctuation trend analysis on the quantitative data in the digital status information and mark it as an early warning of abnormal operation of single-phase electricity meter equipment; The fluctuation trend analysis includes calculating the cumulative growth rate and the number of sudden changes in the quantitative data within a preset time period; Based on the abnormal parameter fluctuations, frequent abnormality warnings, suspected false alarm signals, and abnormal operation warnings, an abnormality identification code is generated. The anomaly identification code includes the data item name, the anomaly urgency level, and the timestamp corresponding to the anomaly being identified. The data item names correspond to specific data items of electrical parameter data, digital status information, monitoring signals and control signals. The specific data items include voltage value, current value, active power, reactive power, power factor, energy consumption, event type description, switch status description, working mode description, communication status description, quantitative data, trend anomaly monitoring signal, associated anomaly monitoring signal, first control signal, second control signal, third control signal and fourth control signal. The abnormal identification code is then associated with and stored in conjunction with the corresponding electrical parameter data, digital status information, monitoring signals, control signals, and processing suggestions to form a traceable abnormal record file.

[0014] As a preferred embodiment of the remote monitoring system for a single-phase electricity meter according to the present invention, the step of displaying the electrical parameter data and digital status information to the user includes: The system presents electrical parameter data and digital status information in real time through a visual interface, supporting dynamic curves, dashboards, and tables for display, and enabling linked queries of electrical parameter data and digital status information. It provides a user data query function interface, allowing users to filter data by time interval, data item name, or anomaly identifier code, and supports exporting or printing comprehensive reports, which include trend analysis, status statistics, and anomaly tracing. Highlight the marked abnormal information, including displaying the associated digital status information summary in the abnormal code floating window, marking abnormal data fields with red borders, differentiated visual markers for different urgency levels, and flashing reminders of real-time abnormal events; The remote commands include closing commands, opening commands, parameter calibration commands, data query commands, and peak-valley arbitrage commands.

[0015] The beneficial effects of this invention are as follows: By acquiring and digitally converting multi-dimensional signals, it comprehensively obtains electrical parameters and meter operating status information, meeting the needs of equipment lifecycle management; by adopting a dual strategy of trend analysis and correlation analysis, it effectively reduces the false alarm and missed alarm rates, and improves the accuracy of anomaly identification; by leveraging a preset strategy library, it achieves hierarchical response, cross-device linkage, and conflict resolution, flexibly responding to complex power consumption scenarios and improving control efficiency and command execution stability; through multi-database storage, multi-dimensional analysis, and a visual interface, it enables linked querying of electrical parameters and status information, and hierarchical early warning of anomalies, facilitating user operation and fault tracing. It forms a complete closed loop from acquisition, detection, and control to interaction, providing an efficient and reliable technical solution for smart grid user-side power management, and powerfully promoting energy conservation, carbon reduction, and grid operation efficiency improvement. Attached Figure Description

[0016] Figure 1 This is a basic flowchart illustrating a remote monitoring system for a single-phase electricity meter, as provided in one embodiment of the present invention. Detailed Implementation

[0017] 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.

[0018] Example, refer to Figure 1 As an embodiment of the present invention, a remote monitoring system for a single-phase electricity meter is provided, comprising a data acquisition module, a control module, and a monitoring module; The acquisition module is used to acquire analog electrical signals from single-phase circuits and the operating status information of meters. It generates digital codes for electrical signals and digital identifiers for status through a conversion unit and transmits them to the control module. The control module is used to process the digital encoding of electrical signals and the digital identification of status through the processing unit to obtain electrical parameter data and digital status information, which are then stored in the storage unit and transmitted to the monitoring module. The monitoring module is used to display electrical parameter data and digital status information to users, and to send remote commands to the control module.

[0019] In one embodiment, the acquisition module can acquire analog electrical signals and meter operating status information of a single-phase circuit. Through analog-to-digital conversion and digital conversion, it generates digital codes for the electrical signals and digital status identifiers, which are then transmitted to the control module. The control module decodes, processes, and logically analyzes the analog-to-digital signals to obtain electrical parameter data and digital status information. This data is stored in a storage unit and then transmitted to the monitoring module. The monitoring module then displays the electrical parameter data and digital status information to the user in real time through a visual interface. The user can also send remote commands to the control module. Through the coordinated operation of these three modules, a complete closed loop of data acquisition, processing and analysis, and interactive control is constructed. This achieves real-time and accurate monitoring of the single-phase meter's operating status, intelligent processing of abnormal signals, and efficient response to remote commands, effectively improving the management efficiency and electricity safety of the smart grid user side.

[0020] Analog electrical signals include voltage signals and current signals; Operating status information includes event logs, switch status, operating mode, communication status, number of times the lid was opened, number of times undervoltage occurred, and power failure information; The conversion unit includes analog-to-digital conversion and digital-to-analog conversion; Digital codes for electrical signals are generated by performing analog-to-digital conversion on analog electrical signals; A status digital identifier is generated by digitizing the operational status information; Digital encoding of electrical signals includes voltage digital encoding and current digital encoding; The status digital identifiers include event log identifiers, switch status identifiers, operating mode identifiers, communication status identifiers, cover opening count identifiers, undervoltage count identifiers, and power failure information identifiers.

[0021] In one embodiment, the acquisition module achieves multi-dimensional data acquisition and digital processing through sensors and a conversion unit. The sensors acquire analog electrical signals of voltage and current in the single-phase circuit in real time, while simultaneously obtaining operational status information such as meter event records, switch status, operating mode, communication status, number of times the meter was opened, number of times undervoltage occurred, and power failure information. The conversion unit uses a 16-bit ADC chip (such as AD7689) to perform analog-to-digital conversion on the analog electrical signals, generating digital codes for voltage and current. The operational status information is then digitized using a preset encoding table. Specifically, a mapping rule between fixed-length fields and enumerated values ​​is used to generate digital status identifiers, including event record identifiers, switch status identifiers, operating mode identifiers, communication status identifiers, number of times the meter was opened, number of times undervoltage occurred, and power failure information identifiers. The digital codes for the electrical signals and the digital status identifiers are then transmitted to the control module, achieving comprehensive acquisition and standardized digital conversion of single-phase circuit electrical parameters and meter operational status. The encoding rules of the preset encoding table are as follows: Event log identifier: 8-bit binary encoding is used (the high 4 bits are the event category and the low 4 bits are the event subclass). For example, the closing event is encoded as 0001 0001 (the high 4 bits 0001 indicate a switch event and the low 4 bits 0001 indicate a closing event). Switch status indicator: 1-bit binary code (1 indicates closed, 0 indicates open). Operating mode identifier: 2-bit binary code (00 indicates normal mode, 01 indicates calibration mode, 10 indicates maintenance mode). Communication status identifier: 4-bit binary code (high 2 bits for communication type, low 2 bits for connection status), such as RS485 online communication code is 00 11; Number of times the lid has been opened: 16-bit unsigned integer code (directly stores the cumulative number of times, such as 0x0003 for 3 times the lid has been opened); Undervoltage count identifier: 16-bit unsigned integer code (stores the number of undervoltage occurrences, such as 0x0005 for 5 undervoltage occurrences); Power failure information identifier: 64-bit timestamp encoding (using Unix timestamp, accurate to milliseconds, such as August 20, 2025, 10:00:00.500ms encoded as 0x5F4B04B001F4).

[0022] The electrical signal is digitally encoded and its status is digitally identified by the processing unit to obtain electrical parameter data and digitized status information, including: The voltage digital code is converted into a voltage value through a decoding algorithm, and the current digital code is converted into a current value. Electrical parameter data are calculated based on voltage and current values ​​and the phase difference between voltage and current values. Electrical parameter data includes active power, reactive power, power factor, and energy consumption; By using a pre-defined mapping table that corresponds to the identifiers and the operational status information, the status digital identifiers are parsed and converted into corresponding textual descriptions and quantitative data to form digital status information. Digital status information includes event type description, switch status description, working mode description and communication status description. Quantitative data includes cumulative number of times the cover was opened, cumulative number of times the voltage was low, cumulative number of times the power was lost, and the corresponding timestamp information for the cumulative number of times the cover was opened, the cumulative number of times the voltage was low, and the cumulative number of times the power was lost. Electrical parameter data and digital status information are stored in the storage unit.

[0023] In one embodiment, the processing unit performs data processing and status parsing through the following steps: First, it uses a preset decoding algorithm to perform a linear mapping conversion between the voltage digital code and the current digital code (e.g., voltage value = digital code × full scale / 2¹). 6 The system combines a 16-bit ADC with a resolution of 0.0038V to achieve high-precision restoration of voltage and current signals. Secondly, by synchronously acquiring the phase difference between voltage and current signals (based on real-time measurement using a phase-locked loop circuit), electrical parameter data is generated according to the power calculation formula (active power P=UIcosθ, reactive power Q=UIsinθ). The power factor cosθ is directly calculated from the phase difference, and the energy consumption is obtained by integrating the active power over time, ensuring that the electrical parameter calculations conform to electrical engineering principles. For status digital identifiers, the processing unit calls the built-in mapping table (such as the high 4 bits of the event record identifier corresponding to the event category enumeration value), parses the 8-bit binary event code into text descriptions of closing events and communication interruptions, and extracts the cumulative number of times the cover is opened, the cumulative number of times undervoltage is reached, and a 64-bit timestamp in 16-bit unsigned integer form, forming quantified data with a time dimension.

[0024] Based on a preset strategy, the electrical parameter data is processed to generate data monitoring signals, including: The preset strategies include trend analysis strategies and correlation analysis strategies, which are executed in parallel. Trend analysis strategies include: Based on preset time intervals, voltage values, current values, active power, and power factor are continuously collected and trend analysis is performed within a specific time window. Trend analysis identifies trends in voltage, current, active power, and power factor by calculating the rate of change, fluctuation range, or cumulative change of these parameters within a time window. When the trend of change matches the preset abnormal trend characteristics, a trend abnormality monitoring signal is generated; Association analysis strategies include: Establish logical relationships between voltage values, current values, active power, and power factor; When the correlation between voltage and current values ​​and active power and power factor deviates from the normal correlation logic, an abnormal correlation monitoring signal is generated. Conventional correlation logic is built upon the physical characteristics and normal operating rules of voltage, current, active power, and power factor. Abnormal deviations from conventional correlation logic include: The voltage value is normal, but the current value is abnormal. The deviation between the active power and the theoretically calculated voltage and current values ​​exceeds the normal range; The trend and magnitude of the power factor change do not match the trends and magnitudes of the voltage and current values. Data monitoring signals include trend anomaly monitoring signals and correlation anomaly monitoring signals.

[0025] In one embodiment, the processing unit performs trend analysis and correlation analysis on electrical parameter data in parallel based on a preset strategy to generate data monitoring signals: The trend analysis strategy continuously samples voltage, current, active power, and power factor within a preset 1-minute time window. By calculating the rate of change of each parameter within the window (e.g., voltage change rate = (current value - previous value) / rated voltage × 100%), fluctuation amplitude (maximum value - minimum value), and cumulative change, it identifies the parameter change trend. When the voltage fluctuation amplitude exceeds the rated value ±5% and the power factor continuously decreases by more than 0.1 within 10 minutes, which meets the preset abnormal trend characteristics, a trend abnormality monitoring signal is generated. The correlation analysis strategy constructs conventional correlation logic based on the physical formula P=UIcosθ. It sets abnormal deviation scenarios such as the deviation between the measured value and the theoretical calculated value of active power exceeding 3%, the voltage being normal but the current exceeding the rated value by 120%, the power factor change trend, and the power factor change amplitude not matching the changes in voltage and current values. When real-time data triggers the above scenarios, a correlation abnormality monitoring signal is generated. The two types of strategies run in real time through independent threads and the thresholds can be configured remotely.

[0026] Based on data monitoring signals or remote instructions from the monitoring module, control signals are generated through matching with a preset strategy library. The preset strategy library includes an anomaly level-control strategy matching mechanism, a remote command multi-dimensional parsing mechanism, a cross-device linkage control interaction mechanism, and a control strategy conflict resolution mechanism; The control signals include a first control signal, a second control signal, a third control signal, and a fourth control signal; The first control signal is generated through an anomaly level-control strategy matching mechanism; A second control signal is generated through a remote command multivariate parsing mechanism; A third control signal is generated through a cross-device linkage control interaction mechanism; A fourth control signal is generated through a control strategy conflict resolution mechanism; The anomaly level-control strategy matching mechanism includes: Preset different urgency levels for different anomaly types, including Level 1, Level 2, and Level 3 anomalies; When a Level 1 anomaly is triggered, the hard real-time control channel is activated, a power failure protection signal is generated, and an alarm is sent to the user via a short message unit. When a level 2 anomaly is triggered, the soft regulation control channel is activated to generate load adjustment signals for single-phase electricity meter equipment according to a preset timing sequence, and non-critical electrical equipment is cut off in stages. When a level 3 anomaly is triggered, an early warning signal is generated, and the local audible and visual alarm of the single-phase electricity meter is triggered and marked with a red dot on the monitoring module interface. The first control signals include power failure protection signals, load adjustment signals, and early warning signals.

[0027] In one embodiment, the control module relies on a preset strategy library to intelligently generate and execute control signals. It presets thresholds for level one anomalies (e.g., voltage drop below 150V and current exceeding rated value by 200%), level two anomalies (e.g., voltage fluctuation of ±10% for 5 minutes and power factor less than 0.7), and level three anomalies (e.g., communication interruption for 10 minutes and opening the lid more than 3 times per day). When a level one anomaly is triggered, power-off protection is executed through a hard real-time channel (e.g., direct relay control) and an SMS alarm is sent via the GSM module. When a level two anomaly is triggered, non-critical loads such as the air conditioner and water heater are cut off according to priority through a soft adjustment channel (e.g., PLC logic control). When a level three anomaly is triggered, the local buzzer and LED of the electricity meter are activated, and an alarm is displayed on the monitoring interface. Red warning points are marked; simultaneously, semantic matching and permission verification are performed on the instructions issued by the monitoring module (e.g., only administrators can execute closing / opening commands), generating signals for switch control and parameter calibration. It supports packaging historical data in JSON format and encrypting and uploading it, and integrates Modbus and MQTT protocol conversion units. When an abnormal current is detected, it sends an identification signal to the smart socket to locate high-energy-consuming devices. When receiving peak-valley arbitrage instructions, it issues charging and discharging sequences to the energy storage device (e.g., charging during off-peak hours 0:00-6:00 and discharging during peak hours 18:00-22:00), and handles conflicts through priority arbitration logic (e.g., first-level abnormal power failure signals take precedence over remote closing commands), and records conflict logs by timestamp (e.g., on 2025-08-20 10:05, a first-level power failure signal interrupted the remote opening command).

[0028] Remote command multivariate parsing mechanisms include: Perform semantic-action matching on remote commands issued by the monitoring module; If it is a closing command or a opening command, the user's authority and the power grid status are verified by the command verification algorithm. Once the verification is successful, a switch control signal is generated. If it is a parameter calibration command, a calibration trigger signal is generated and sent to the acquisition module, and an instruction to send back electrical parameter data and digital status information is generated and sent to the control module. If it is a data query command, generate a data package signal containing electrical parameter data and digital status information, encapsulate the electrical parameter data and digital status information according to a preset compression format, and upload it. The second control signal includes a switch control signal, a calibration trigger signal, and a data packetization signal.

[0029] In one embodiment, the remote command multivariate parsing mechanism is implemented as follows: After receiving the remote command from the monitoring module, the control module first performs semantic-action matching to identify the command type and trigger the corresponding processing logic. If it is a closing or opening command, the built-in command verification algorithm verifies the user's permissions (e.g., distinguishing between administrator and ordinary user permission levels) and the grid status (e.g., whether the voltage or current value is within the safe range). After successful verification, a switch control signal is generated to drive the relay to perform the operation, avoiding illegal operation or opening / closing with faults. If it is a parameter calibration command, a calibration trigger signal is generated and sent to the acquisition module, synchronously transmitting the command back to the control module for power generation parameter data and digital status information, realizing the calibration of meter metering accuracy and the synchronization of status data. If it is a data query command, a data packaging signal is generated, and the electrical parameter data (e.g., voltage and active power) and digital status information (e.g., switch status and number of times the cover is opened) are packaged in a preset ZIP compression format and uploaded to the monitoring module via the TCP / IP protocol.

[0030] Cross-device linkage control and interaction mechanisms include: It integrates a standardized protocol conversion unit, supporting the control module to interact with external devices for control signals; When a correlation signal is detected that the voltage value is normal but the current value is abnormal, a high-energy-consuming device identification signal is generated for the smart socket to locate the abnormal power-consuming terminal. When a peak-valley arbitrage instruction is received from a user through the monitoring module, a charging and discharging control signal for the energy storage device is generated according to a preset time period, which is used to link the energy storage device to charge during off-peak hours and discharge during peak hours. The third control signal includes high-energy-consuming equipment identification signal and energy storage equipment charging and discharging control signal.

[0031] In one embodiment, the cross-device linkage control interaction mechanism is implemented in the following way: the control module integrates a standardized protocol conversion unit to support the conversion and adaptation of Modbus and MQTT protocols, enabling control signal interaction with external devices such as smart sockets and energy storage devices; when an abnormal signal is detected where the voltage value is within the normal range of 200-240V but the current value exceeds 120% of the rated value, a high-energy-consuming device identification signal is immediately generated for the connected smart socket, and the abnormal power-consuming terminal (such as a faulty appliance) is located by analyzing the current data of each socket; when a peak-valley arbitrage instruction is received from the user through the monitoring module, a charging and discharging control signal for the energy storage device is generated according to a preset time period (such as the off-peak period 0:00-6:00 and the peak period 18:00-22:00), and the energy storage device is linked to charge at a rate of 0.5C during the off-peak period and discharge at a rate of 0.3C during the peak period.

[0032] Control strategy conflict resolution mechanisms include: When the control module receives both the control and remote commands triggered by the data monitoring signal, the first conflict is triggered and handled through priority arbitration logic. When the power failure protection signal corresponding to the first-level anomaly is received by the control module, the power failure protection signal has the highest priority, triggering the second conflict and interrupting the execution of all other control commands; When multiple control signals with the same functional attributes are received by the control module, a third conflict is triggered. The control signal received last is taken as the valid signal and executed according to the order of reception time, and the control signal received earlier is automatically invalidated. When the first, second, or third conflict occurs, a policy conflict log signal is generated to record the conflict type and the handling result. The fourth control signal includes the policy conflict log signal.

[0033] In one embodiment, the control strategy conflict resolution mechanism is implemented as follows: When the control module simultaneously receives automatic control triggered by data monitoring signals (such as secondary abnormal load adjustment) and remote commands (such as user-issued closing commands), a first conflict is triggered. This is handled through a preset priority arbitration logic—by default, primary abnormal signals are greater than remote emergency commands (such as immediate tripping), which are greater than automatic control signals, which are greater than ordinary remote commands. For example, when the conflict involves automatic control and ordinary remote commands that are not primary abnormalities, the command is executed according to the principle of prioritizing manual intervention over remote commands. When a power outage protection signal corresponding to a primary abnormality is received, a second conflict is triggered. Because this signal has the highest priority, all currently executing control commands are immediately interrupted, and the conflict is resolved through hardware... The real-time channel enforces power outage protection and locks other command inputs until the anomaly is resolved. When multiple control signals with the same functional attributes are received (such as two consecutive tripping commands), a third conflict is triggered. The control module sorts the signals according to the signal reception timestamp, retaining only the last signal as a valid command. The first received signal is automatically invalidated (such as the first tripping command being overwritten by the second). All conflict scenarios generate a policy conflict log signal, recording the conflict type (first conflict, second conflict, and third conflict), trigger time, conflict signal details, and processing results (such as 2025-08-20 14:30 First conflict: Remote closing command overwrites secondary abnormal load adjustment signal). The log is stored in EEPROM for subsequent auditing.

[0034] The system stores and analyzes electrical parameter data, digital status information, monitoring signals, and control signals, and marks abnormal information. Specifically, this includes: Electrical parameter data, digital status information, monitoring signals, and control signals are used to construct an electrical parameter database, a digital status information log library, a monitoring signal log library, and a control signal log library based on time series data. The electrical parameter database stores voltage values, current values, active power, reactive power, power factor, and energy consumption according to a preset time granularity. The digital status information log library links digital status information with quantitative data; The monitoring signal log library is associated with the generation time, triggering conditions, and corresponding electrical parameter data snapshots of the monitoring signals; The control signal log library associates the generation time, trigger source, and execution result of control signals; The electrical parameter data, digital status information, monitoring signals, and control signals are analyzed from multiple dimensions using anomaly pattern recognition algorithms, specifically including: The deviation rate is obtained by comparing the real-time electrical parameter data with the historical benchmark values ​​for the same period. When the deviation rate exceeds the preset first threshold, it is marked as abnormal parameter fluctuation. The frequency of triggering of trend anomaly monitoring signals or related anomaly monitoring signals within a preset time period is statistically analyzed, and when the number of triggers reaches a preset second threshold, it is marked as an anomaly frequent warning. Verify the timing correlation between monitoring signals, control signals, electrical parameter data, and digital status information; When the electrical parameter data at the time of generation of monitoring or control signals are within the normal threshold range or the digital status information is normal, it is marked as a suspected false alarm signal. Perform time-series-based fluctuation trend analysis on the quantitative data in the digital status information and mark it as an early warning of abnormal operation of single-phase electricity meter equipment; Volatility trend analysis includes calculating the cumulative growth rate and number of abrupt changes of quantitative data within a preset time period; Based on abnormal parameter fluctuations, frequent abnormality warnings, suspected false alarms, and abnormal operation warnings, generate abnormal identification codes; The anomaly identification code includes the data item name, the anomaly urgency level, and the timestamp corresponding to the anomaly being identified; The data item names correspond to specific data items of electrical parameter data, digital status information, monitoring signals and control signals. Specific data items include voltage value, current value, active power, reactive power, power factor, energy consumption, event type description, switch status description, working mode description, communication status description, quantitative data, trend anomaly monitoring signal, associated anomaly monitoring signal, first control signal, second control signal, third control signal and fourth control signal; The abnormal identification code is associated with and stored with the corresponding electrical parameter data, digital status information, monitoring signals, control signals and handling suggestions to form a traceable abnormal record file.

[0035] In one embodiment, the storage and analysis of electrical parameter data is achieved through the following method: An electrical parameter database, a digital status information log database, a monitoring signal log database, and a control signal log database are constructed according to a time series. The electrical parameter database stores voltage values ​​(normal range 200-240V), current values ​​(less than or equal to 120% of rated value), active power, reactive power, power factor (normal range 0.8-1.0), and energy consumption at a time granularity of 1 minute (conforming to the conventional frequency of power data acquisition). The digital status information log database associates quantitative data of switch status (open and closed) with the number of operations, and communication status (connected and disconnected) with the duration of interruptions. The monitoring signal log database associates signal generation time, triggering conditions (such as voltage fluctuation ±5%), and corresponding snapshots of electrical parameters. The control signal log database associates generation time, triggering sources (such as first-level anomalies and remote commands), and execution results (success and failure). During multi-dimensional analysis, the deviation rate of real-time electrical parameters from historical benchmark values ​​exceeds ±10% (the first threshold is set at ±10%, according to GB / T). The following are flagged as abnormal parameter fluctuations: 12325 power supply voltage deviation standard; if the trend or associated abnormal monitoring signal triggers more than 3 times within 1 hour (the preset time is 1 hour, based on the short-term clustering characteristics of power grid faults, in accordance with the power operation and maintenance short-term high-frequency abnormal priority handling specification), it is flagged as an abnormal frequent occurrence warning; if the voltage is between 200-240V and the current is less than or equal to 120% of the rated value and the status information is normal when the monitoring signal or control signal is generated, it is flagged as a suspected false alarm signal; 24-hour trend analysis is performed on the digital status quantification data (such as the number of times the cover is opened). If the cumulative growth rate exceeds 50% or the number of sudden changes is greater than or equal to 5 times (refer to the experience of electricity consumption behavior analysis), it is flagged as an abnormal operation warning. The generated abnormal identification code includes the data item name (such as current value and trend abnormal monitoring signal), the degree of abnormality (level 1 abnormality, level 2 abnormality and level 3 abnormality, corresponding to the severity of the fault) and the identification timestamp, and is associated with the corresponding data, signal and handling suggestions (such as parameter fluctuation abnormality suggestion to check the line) for storage, forming a traceable abnormal record archive.

[0036] Displaying electrical parameter data and digital status information to users includes: The system presents electrical parameter data and digital status information in real time through a visual interface, supporting dynamic curves, dashboards, and tables for display, and enabling linked queries of electrical parameter data and digital status information. It provides a user data query function interface, allowing users to filter data by time interval, data item name, or anomaly identifier code, and supports exporting or printing comprehensive reports, which include trend analysis, status statistics, and anomaly tracing. Highlight the marked abnormal information, including displaying the associated digital status information summary in the abnormal code floating window, marking abnormal data fields with red borders, differentiated visual markers for different urgency levels, and flashing reminders of real-time abnormal events; Remote commands include closing commands, opening commands, parameter calibration commands, data query commands, and peak-valley arbitrage commands.

[0037] In one embodiment, the display of electrical parameter data and digital status information to the user is achieved by: developing a visualization interface using a web front-end framework, and presenting dynamic curves of electrical parameters (time axis accuracy of 1 minute), a dashboard (displaying power factor and energy consumption), and data tables in real time based on the ECharts chart library. It supports clicking on a certain time point on the curve to query the digital status information of the corresponding time, such as the switching status and communication interruption duration. The data query interface allows users to filter data by time interval (e.g., the last 24 hours and this month), data item name (e.g., active power and switch status), or anomaly identification code (e.g., Y20250820143001). Query results can be exported as a comprehensive report in PDF / Excel format. The report automatically generates trend analysis line charts, status statistics bar charts, and anomaly tracing timelines (e.g., electrical parameter fluctuation records 10 minutes before and after an anomaly signal trigger). Anomaly information is displayed through multi-dimensional visualization: an anomaly code floating window displays a summary of associated status information (e.g., communication interruption duration of 15 minutes), anomaly data fields are highlighted with red borders, first-level anomalies are marked with a red flashing icon, and second-level anomalies are marked with an orange static icon. When the remote command receiving module parses closing and opening commands, it first verifies user permissions (e.g., only administrators can execute opening commands). After the parameter calibration command is triggered, a comparison report of electrical parameters before and after calibration is automatically generated. Data query commands support the separate display of real-time and historical data. Peak-valley arbitrage commands preset charging and discharging periods (e.g., off-peak period 0:00-8:00) through the interface calendar component and generate strategy execution logs.

[0038] This invention comprehensively acquires electrical parameters and meter operating status information through multi-dimensional signal acquisition and digital conversion, meeting the needs of equipment lifecycle management. It employs a dual strategy of trend analysis and correlation analysis to effectively reduce false alarms and missed alarms, improving the accuracy of anomaly identification. Utilizing a pre-set strategy library, it achieves tiered response, cross-device linkage, and conflict resolution, flexibly addressing complex power consumption scenarios and improving control efficiency and command execution stability. Through multi-database storage, multi-dimensional analysis, and a visual interface, it enables linked querying of electrical parameters and status information, and tiered anomaly warnings, facilitating user operation and fault tracing. From acquisition, detection, and control to interaction, it forms a complete closed loop, providing an efficient and reliable technical solution for smart grid user-side power management, powerfully promoting energy conservation, carbon reduction, and improved grid operation efficiency.

[0039] 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.

[0040] 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 remote monitoring system for single phase meters, characterized by, The system comprises a collection module, a control module and a monitoring module. The collection module is configured to collect analog electrical signals of a single-phase circuit and running state information of an electric meter, generate electrical signal digital codes and state digital identifiers through a conversion unit, and transmit the electrical signal digital codes and the state digital identifiers to the control module. The control module is configured to process the electrical signal digital codes and the state digital identifiers through a processing unit, obtain electrical parameter data and digitized state information, store the electrical parameter data and the digitized state information in a storage unit, and transmit the electrical parameter data and the digitized state information to the monitoring module. The monitoring module is configured to display the electrical parameter data and the digitized state information to a user and issue remote instructions to the control module.

2. The system for remote monitoring of single phase energy meters of claim 1, wherein: The analog electrical signals comprise voltage signals and current signals. The running state information comprises event records, switch states, working modes, communication states, cover opening times, under-voltage times and power-off information. The conversion unit comprises an analog-to-digital conversion and a digitization conversion. The analog electrical signals are converted into electrical signal digital codes through the analog-to-digital conversion. The running state information is converted into state digital identifiers through the digitization conversion. The electrical signal digital codes comprise voltage digital codes and current digital codes. The state digital identifiers comprise event record identifiers, switch state identifiers, working mode identifiers, communication state identifiers, cover opening time identifiers, under-voltage time identifiers and power-off information identifiers.

3. The system for remote monitoring of single phase meters of claim 2, wherein: The electrical signal digital codes and the state digital identifiers are processed through the processing unit to obtain electrical parameter data and digitized state information, including: The voltage digital codes are converted into voltage values and the current digital codes are converted into current values through a decoding algorithm; The electrical parameter data are calculated based on the voltage values and the current values and phase differences between the voltage values and the current values; The electrical parameter data comprise active power, reactive power, power factor and power consumption; The state digital identifiers are analyzed through a preset identifier and running state information corresponding relationship mapping table, converted into corresponding textual description information and quantitative data to form digitized state information; The digitized state information comprises event type descriptions, switch state descriptions, working mode descriptions and communication state descriptions, and the quantitative data comprises cover opening cumulative times, under-voltage cumulative times, power-off cumulative times and corresponding time stamp information of the cover opening cumulative times, the under-voltage cumulative times and the power-off cumulative times; The electrical parameter data and the digitized state information are stored in the storage unit.

4. The system for remote monitoring of single phase energy meters of claim 3, wherein: The electrical parameter data are operated based on a preset strategy to generate data monitoring signals, including: The preset strategy comprises a trend analysis strategy and a correlation analysis strategy, and the trend analysis strategy and the correlation analysis strategy are executed in parallel; The trend analysis strategy comprises: The voltage values, the current values, the active power and the power factor in a specific time window are continuously collected and analyzed based on a preset time interval; The trend analysis identifies the change trend of the voltage values, the current values, the active power and the power factor by calculating the change rate, fluctuation amplitude or cumulative change amount of the voltage values, the current values, the active power and the power factor in the time window; When the change trend meets a preset abnormal trend characteristic, a trend abnormality monitoring signal is generated. The correlation analysis strategy comprises: constructing a conventional correlation relationship logic between voltage values, current values, and active power and power factor; generating a correlation anomaly monitoring signal when the correlation relationship between the voltage values, current values, and active power and power factor deviates from the conventional correlation relationship logic; the conventional correlation relationship logic is constructed based on physical characteristics and normal operation rules of voltage values, current values, active power and power factor, and abnormal deviation scenarios of the conventional correlation relationship logic comprise: the voltage value is normal and the current value is abnormal; the active power deviates from the theoretically calculated value of the voltage value and the current value beyond a normal range; a change trend and a change amplitude of the power factor do not match a change trend and a change amplitude of the voltage value and the current value; the data monitoring signal comprises a trend anomaly monitoring signal and a correlation anomaly monitoring signal.

5. The system for remote monitoring of single phase energy meters of claim 4, wherein: According to the data monitoring signal or a remote instruction of the monitoring module, a control signal is generated through a preset strategy library by matching; the preset strategy library comprises an abnormal level-control strategy matching mechanism, a remote instruction multi-element analysis mechanism, a cross-device linkage control interaction mechanism, and a control strategy conflict resolution mechanism; the control signal comprises a first control signal, a second control signal, a third control signal, and a fourth control signal; the first control signal is generated through the abnormal level-control strategy matching mechanism; the second control signal is generated through the remote instruction multi-element analysis mechanism; the third control signal is generated through the cross-device linkage control interaction mechanism; the fourth control signal is generated through the control strategy conflict resolution mechanism; the abnormal level-control strategy matching mechanism comprises: presetting emergency level grades of different abnormal types, the emergency level grades comprising a first-level abnormality, a second-level abnormality, and a third-level abnormality; when the first-level abnormality is triggered, a hard real-time control channel is enabled, a power-off protection signal is generated, and an alarm is sent to a user through a short message unit; when the second-level abnormality is triggered, a soft adjustment control channel is started, a load adjustment signal of a single-phase electric meter device is generated according to a preset time sequence, and non-critical electric equipment is hierarchically removed; when the third-level abnormality is triggered, a warning prompt signal is generated, and a local sound-light alarm of a single electric meter and a red dot marking on an interface of a monitoring module are triggered; the first control signal comprises a power-off protection signal, a load adjustment signal, and a warning prompt signal.

6. The system for remote monitoring of single phase energy meters of claim 5, wherein: The remote instruction multi-element analysis mechanism comprises: performing semantic-action matching on a remote instruction issued by the monitoring module; if it is a closing or opening instruction, a user's authority and a power grid state are verified through an instruction verification algorithm, and a switch control signal is generated when the verification is passed; if it is a parameter calibration instruction, a calibration trigger signal is generated to the acquisition module, and an electric parameter data and digital state information return instruction is generated to the control module; if it is a data query instruction, a data packaging signal of the electric parameter data and the digital state information is generated, the electric parameter data and the digital state information are packaged in a preset compression format, and are uploaded; the second control signal comprises a switch control signal, a calibration trigger signal, and a data packaging signal.

7. The system for remote monitoring of single phase energy meters of claim 6, wherein: the cross-device linkage control interaction mechanism comprises: The integrated standardized protocol conversion unit supports the control module to interact with external devices through control signals; When an associated abnormal signal of a normal voltage value and an abnormal current value is detected, a high-energy-consumption device identification signal is generated to the intelligent socket to locate an abnormal power consumption terminal; When a peak-valley arbitrage instruction issued by a user through the monitoring module is received, a storage device charging and discharging control signal is generated according to a preset time period, and the storage device is linked to charge in a low valley period and discharge in a peak period; The third control signal includes a high-energy-consumption device identification signal and a storage device charging and discharging control signal.

8. The system for remote monitoring of single phase energy meters of claim 7, wherein: The control strategy conflict resolution mechanism includes: When the control triggered by the data monitoring signal is received by the control module at the same time as the remote instruction, a first conflict is triggered, and is processed through priority arbitration logic; When the power-off protection signal corresponding to the first-level abnormality is received by the control module, the power-off protection signal has the highest priority, a second conflict is triggered, and the execution of all other control instructions is interrupted; When multiple control signals with the same function attribute are received by the control module, a third conflict is triggered, the last received control signal is taken as the effective signal and executed in the order of receiving time, and the first received control signal is automatically invalidated; When the first conflict, the second conflict or the third conflict occurs, a strategy conflict log signal is generated to record the conflict type and the processing result; The fourth control signal includes the strategy conflict log signal.

9. The system for remote monitoring of single phase energy meters of claim 8, wherein: The electrical parameter data, the digitized state information, the monitoring signal and the control signal are stored and analyzed and abnormal information is marked, specifically including: The electrical parameter data, the digitized state information, the monitoring signal and the control signal are stored in an electrical parameter database, a digitized state information log library, a monitoring signal log library and a control signal log library in the order of time sequence; The electrical parameter database stores voltage value, current value, active power, reactive power, power factor and power consumption according to a preset time granularity; The digitized state information log library is associated with digitized state information and quantitative data; The monitoring signal log library is associated with the generation time of the monitoring signal, the trigger condition and the corresponding electrical parameter data snapshot; The control signal log library is associated with the generation time of the control signal, the trigger source and the execution result; The electrical parameter data, the digitized state information, the monitoring signal and the control signal are analyzed through an abnormal mode recognition algorithm in multiple dimensions, specifically including: The real-time electrical parameter data is subjected to deviation calculation with historical same-period reference values to obtain a deviation rate; When the deviation rate exceeds a preset first threshold value, it is marked as parameter fluctuation abnormality; The trigger frequency of a trend abnormality monitoring signal or an associated abnormality monitoring signal within a preset time is counted, and when the trigger frequency reaches a preset second threshold value, it is marked as abnormal frequency early warning; The time sequence correlation of the monitoring signal, the control signal, the electrical parameter data and the digitized state information is verified; When the electrical parameter data at the time when the monitoring signal or the control signal is generated is within a normal threshold range or the digitized state information is not abnormal, it is marked as a suspected false alarm signal; The quantitative data in the digitized state information is subjected to fluctuation trend analysis based on time sequence, and is marked as single-phase electric meter device abnormal operation early warning; The fluctuation trend analysis includes calculating the cumulative growth rate and the number of mutations of the quantified data within a preset period of time; According to the parameter fluctuation anomaly, the frequent abnormal warning, the suspected false alarm signal and the abnormal operation warning, an abnormal identification code is generated; The abnormal identification code includes data item name, abnormal emergency level and abnormal identification corresponding time stamp; The data item name corresponds to specific data items of electrical parameter data, digitized state information, monitoring signals and control signals, including voltage value, current value, active power, reactive power, power factor, power consumption, event type description, switch state description, working mode description, communication state description, quantified data, trend anomaly monitoring signal, associated anomaly monitoring signal, first control signal, second control signal, third control signal and fourth control signal; And the abnormal identification code is associated with the corresponding electrical parameter data, digitized state information, monitoring signal, control signal and processing suggestion to form a traceable abnormal record file.

10. The system for remote monitoring of single phase energy meters of claim 9, wherein: The electrical parameter data and digitized state information are displayed to the user, including: Real-time presentation of electrical parameter data and digitized state information through a visual interface, supporting dynamic curve chart, instrument panel and table form for display, and realizing linkage query of electrical parameter data and digitized state information; Providing user data query function interface, allowing users to filter data by time interval, data item name or abnormal identification code, supporting export or printing of comprehensive report, including trend analysis, state statistics and abnormal traceability; The marked abnormal information is highlighted, including abnormal code floating window display associated with digitized state information summary, red border marking abnormal data field, differentiated visual marking for different emergency level, flashing reminder of real-time abnormal event; The remote instructions include closing command, opening command, parameter calibration command, data query command and peak-valley arbitrage command.

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