Intelligent monitoring and self-healing control method and system for ring main unit based on edge computing

Abstract

The application relates to the technical field of ring network cabinets, and discloses a ring network cabinet intelligent monitoring and self-recovery control method and system based on edge computing, which comprises the following steps: collecting voltage data, current data and environmental temperature data on the edge side; when the voltage data or the current data changes in value, a time delay compensation observation window is established; the voltage data, the current data and the environmental temperature data are matched and verified to determine whether a synchronization constraint condition is met; when it is determined that the synchronization constraint condition is not met, the environmental temperature data is time-series shifted based on a time difference value; when it is determined that the synchronization constraint condition is met, the environmental temperature data is time-series shifted based on a time delay coefficient; synchronized sensing data is obtained, and a self-recovery control instruction is generated; and the time difference value is integrated into a historical time delay sequence, and the time delay coefficient is updated. The application reflects the real lag characteristics of environmental quantities on electrical changes, and improves the reliability of operation state judgment.

Description

Technical Field

[0001] This invention relates to the field of ring main unit technology, and more specifically, to a method and system for intelligent monitoring and self-healing control of ring main units based on edge computing. Background Technology

[0002] Ring main units (RNBs), as key switching devices in power distribution systems, are widely used in urban power distribution networks, industrial power supply systems, and new energy access scenarios. Their operating status directly affects the safety, stability, and continuity of the power supply system. With the continuous improvement of the intelligence level of power distribution equipment, online monitoring technology for RNBs is gradually evolving from traditional manual inspections and periodic maintenance to real-time sensing, status assessment, and anomaly early warning based on multi-source sensor data.

[0003] Existing patent CN120103041A discloses an intelligent sensing online monitoring system and method for ring main units. This system forms energy parameters through integration over a 50-millisecond sampling period, captures short-term energy change characteristics, extracts abnormal segments based on a mutation rate threshold, extracts the direction and amplitude of energy changes to pinpoint the anomaly origin, continuously collects instantaneous leakage current values, extracts the maximum and average amplitudes to calculate the offset, quantifies insulation fluctuations, calls time window data to calculate the amplitude fluctuation rate, and combines the offset to perform multi-threshold discrimination to assess the degree of degradation. It encapsulates energy parameters, amplitude offsets, and insulation level information, and performs synchronization consistency correction of node deviations to ensure reliable data synchronization. This system can achieve continuous sensing of the ring main unit's operating status to a certain extent. However, in practical applications, the sensing of the ring main unit's operating status faces a long-standing unresolved problem: a natural difference in response time lag between rapidly changing electrical quantities and slowly changing environmental quantities. Electrical quantities such as partial discharge, current mutations, and voltage fluctuations typically occur quickly, while temperature, insulation status, or other environmentally related quantities are often affected by thermal inertia, structural conduction, and cumulative effects, resulting in relatively delayed and slow-responding changes. Directly aligning and fusing different types of data at the same point in time during monitoring can easily lead to situations where electrical changes have occurred but environmental quantities have not yet responded, or environmental quantities have changed but electrical quantities have returned to normal. This reduces the accuracy of multi-source data synchronization and alignment, and affects the reliability of operational status judgment.

[0004] Therefore, it is necessary to design an intelligent monitoring and self-healing control method and system for ring main units based on edge computing to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes an intelligent monitoring and self-healing control method and system for ring main units based on edge computing, which aims to solve the problem that the reliability of the operation status judgment is affected by situations where electrical changes have occurred but environmental quantities have not yet responded, or environmental quantities have changed but electrical quantities have returned to normal.

[0006] In one aspect, this invention proposes a method for intelligent monitoring and self-healing control of ring main units based on edge computing, comprising:

[0007] Voltage, current, and ambient temperature data of the ring main unit are collected at the edge side, and the collection timestamp is marked.

[0008] When the voltage or current data changes, a time-delay compensation observation window is established. Within the time-delay compensation observation window, the characteristic change time of the voltage or current data and the characteristic response time of the ambient temperature data are identified, and the time difference between the characteristic change time and the characteristic response time is determined. Based on electrical-environmental constraints, the voltage data, current data, and ambient temperature data are matched and verified to determine whether the synchronization constraints are met.

[0009] When it is determined that the synchronization constraint condition is not met, the ambient temperature data is time-shifted based on the time difference to obtain synchronized sensing data; when it is determined that the synchronization constraint condition is met, the ambient temperature data is time-shifted based on the time delay coefficient to obtain synchronized sensing data.

[0010] The self-healing control command is generated based on the obtained synchronous sensing data; and the time difference is incorporated into the historical time delay sequence to update the time delay coefficient.

[0011] Furthermore, when the voltage or current data changes numerically, the establishment of a time-delay compensation observation window includes:

[0012] The rate of change of the voltage data and the rate of change of the current data are obtained; when the rate of change of the voltage data is greater than the voltage change threshold or the rate of change of the current data is greater than the current change threshold, the current change time is marked as the window start time; the duration of the time delay compensation observation window is determined according to the time delay coefficient, and the window end time is determined; the time period between the window start time and the window end time is used as the time delay compensation observation window.

[0013] Furthermore, determining the time difference between the feature change time and the feature response time includes:

[0014] The start time of the window is taken as the feature change time; the ambient temperature data within the time delay compensation observation window is traversed, and the rate of change of the ambient temperature data is detected. The moment when the rate of change changes from zero to positive is taken as the feature response time; the first acquisition timestamp of the feature change time and the second acquisition timestamp of the feature response time are obtained; the difference between the first acquisition timestamp and the second acquisition timestamp is determined as the time difference.

[0015] Furthermore, based on electrical-environmental constraints, when performing matching verification on the voltage data, current data, and ambient temperature data to determine whether the synchronization constraints are met, the process includes:

[0016] Based on the voltage and current data, the theoretical heating power value is determined; based on the thermal resistance parameter and the theoretical heating power value, the theoretical temperature rise value is determined; the theoretical temperature rise value is added to the ambient reference temperature to obtain the theoretical temperature value.

[0017] Furthermore, based on electrical-environmental constraints, when performing matching verification on the voltage data, current data, and ambient temperature data to determine whether the synchronization constraints are met, the process also includes:

[0018] The ambient temperature data is shifted along the time axis based on the time delay coefficient to obtain pre-aligned ambient temperature data. Within the time delay compensation observation window, the theoretical temperature value is compared point by point with the pre-aligned ambient temperature data to obtain a temperature deviation sequence. When the maximum deviation value in the temperature deviation sequence is less than or equal to the temperature deviation threshold, it is determined that the synchronization constraint condition is met. When the maximum deviation value in the temperature deviation sequence is greater than the temperature deviation threshold, it is determined that the synchronization constraint condition is not met.

[0019] Furthermore, when performing a time-series offset on the ambient temperature data based on the time difference, the process includes:

[0020] The offset and offset direction are determined based on the time difference; the timestamps of each data point in the ambient temperature data are adjusted so that the characteristic response time of the ambient temperature data coincides with the characteristic change time of the voltage data or current data on the time axis, thereby obtaining the synchronous sensing data.

[0021] Furthermore, when performing time-series offset on the ambient temperature data based on the time delay coefficient, the following steps are included:

[0022] The offset and offset direction are determined based on the time delay coefficient; the ambient temperature data sequence within the time delay compensation observation window is shifted along the time axis by the offset to obtain the synchronous sensing data.

[0023] Furthermore, the self-healing control commands include normal maintenance commands, abnormal adjustment commands, and fault trip commands.

[0024] Furthermore, when updating the time delay coefficient by incorporating the time difference into the historical time delay sequence, the process includes:

[0025] The time difference is stored at the end of the historical time delay sequence, and the historical data at the head of the historical time delay sequence is removed; a weighted average is performed on all the data in the adjusted historical time delay sequence to obtain an updated value; the updated value is assigned to the time delay coefficient.

[0026] Compared with existing technologies, the advantages of this invention are as follows: By establishing a time-delay compensation observation window triggered by rapidly changing electrical quantities such as voltage and current, and extracting the time difference between the moment of change of electrical quantity characteristics and the moment of response of ambient temperature characteristics within the window, an explicit characterization of the dynamic response relationship between "fast variables and slow variables" is achieved. This reflects the true lag characteristics of environmental quantities to electrical changes, improving the timing alignment accuracy of multi-source data from the source. Based on the matching and verification mechanism of electrical-environmental constraints, the rationality of the synchronization results is verified by constructing a correspondence between theoretical temperature and actual temperature. This ensures that data alignment no longer depends solely on the time dimension, but combines physical mechanisms for dual constraint judgment, thereby avoiding misalignment problems caused by noise disturbances or abnormal sampling and improving the reliability of synchronized sensing data. Differentiated timing compensation strategies are adopted for different synchronization states: when synchronization constraints are not met, dynamic correction is performed using the time difference calculated in real time; when synchronization constraints are met, stable compensation is performed based on the time delay coefficient obtained from historical evolution. This balances real-time performance and stability, enabling rapid response to sudden operating conditions while maintaining low computational overhead during stable operation, thus improving overall operating efficiency. By continuously integrating the real-time acquired time difference into the historical time delay sequence and updating it, the self-learning and adaptive adjustment of the time delay parameters are realized, overcoming the problem of error accumulation caused by parameter drift during long-term operation.

[0027] On the other hand, this application also provides an edge computing-based intelligent monitoring and self-healing control system for ring main units, used to apply the above-mentioned edge computing-based intelligent monitoring and self-healing control method for ring main units, including:

[0028] The data acquisition unit is configured to acquire voltage, current, and ambient temperature data of the ring main unit at the edge side and mark the acquisition timestamp.

[0029] The verification unit is configured to establish a time-delay compensation observation window when the voltage or current data changes; within the time-delay compensation observation window, identify the characteristic change time of the voltage or current data and the characteristic response time of the ambient temperature data, and determine the time difference between the characteristic change time and the characteristic response time; based on electrical-environmental constraints, perform matching verification on the voltage data, current data, and ambient temperature data to determine whether the synchronization constraints are met.

[0030] The adjustment unit is configured to, when it is determined that the synchronization constraint condition is not met, perform a time-series offset on the ambient temperature data based on the time difference to obtain synchronized sensing data; and when it is determined that the synchronization constraint condition is met, perform a time-series offset on the ambient temperature data based on the time delay coefficient to obtain synchronized sensing data.

[0031] The self-healing unit is configured to generate self-healing control instructions based on the obtained synchronous sensing data; and to incorporate the time difference into the historical time delay sequence to update the time delay coefficient.

[0032] It is understandable that the above-mentioned edge computing-based intelligent monitoring and self-healing control methods and systems for ring main units have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0033] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0034] Figure 1 A flowchart illustrating the edge computing-based intelligent monitoring and self-healing control method for ring main units provided in this embodiment of the invention;

[0035] Figure 2 This is a functional block diagram of an edge computing-based intelligent monitoring and self-healing control system for ring main units provided in an embodiment of the present invention. Detailed Implementation

[0036] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] In some embodiments of this application, see Figure 1 As shown, a method for intelligent monitoring and self-healing control of ring main units based on edge computing is proposed, including:

[0038] S100: Collect voltage, current and ambient temperature data of the ring main unit on the edge side and mark the collection timestamp.

[0039] Specifically, in step S100, edge acquisition and processing nodes are deployed within or near the ring main unit. These edge nodes establish communication connections with voltage sensors, current sensors, and ambient temperature sensors to synchronously acquire and preprocess multi-source data during the ring main unit's operation. The voltage sensors preferably employ capacitive voltage divider or electromagnetic measurement structures, connected to the busbar or feeder side, to continuously acquire instantaneous or effective values ​​of the three-phase voltage. The current sensors preferably use Rogowski coils or Hall effect sensors, arranged around a conductor, to acquire load current and short-circuit current changes. The ambient temperature sensors are located in key heat-generating areas or ventilation channels inside the unit to acquire temperature data reflecting changes in the internal thermal environment. The analog or digital signals output by each sensor are first input to the edge nodes, where they are filtered, amplified, and converted from analog to digital to form a unified data stream. Edge nodes time-stamp the collected data based on a built-in unified clock source or through a network time synchronization mechanism (such as a local high-precision crystal oscillator or a time protocol synchronized with the upper-level system), binding each sampled data point with the corresponding collection time to generate an information structure containing data values ​​and timestamps. The timestamps can be accurate to milliseconds or higher, and are used to identify the positional relationship of the data in a unified timeline.

[0040] S200: When voltage or current data changes, a time-delay compensation observation window is established; within the time-delay compensation observation window, the characteristic change time of voltage or current data and the characteristic response time of ambient temperature data are identified, and the time difference between the characteristic change time and the characteristic response time is determined; based on electrical-environmental constraints, the voltage data, current data and ambient temperature data are matched and verified to determine whether the synchronization constraints are met.

[0041] In some embodiments of this application, when establishing a time-delay compensation observation window because the voltage or current data changes numerically, the following steps are included:

[0042] Acquire the rate of change of voltage data and the rate of change of current data; when the rate of change of voltage data is greater than the voltage change threshold or the rate of change of current data is greater than the current change threshold, mark the current change moment as the start moment of the window; determine the duration of the time delay compensation observation window based on the time delay coefficient, and determine the end moment of the window; use the time period between the start moment and the end moment of the window as the time delay compensation observation window.

[0043] In some embodiments of this application, determining the time difference between the feature change time and the feature response time includes:

[0044] The window start time is taken as the feature change time; the ambient temperature data within the time delay compensation observation window is traversed, and the rate of change of the ambient temperature data is detected. The moment when the rate of change changes from zero to positive is taken as the feature response time; the first acquisition timestamp of the feature change time and the second acquisition timestamp of the feature response time are obtained; the difference between the first acquisition timestamp and the second acquisition timestamp is determined as the time difference.

[0045] In some embodiments of this application, when matching and verifying voltage data, current data, and ambient temperature data based on electrical-environmental constraints to determine whether synchronization constraints are met, the process includes:

[0046] Based on voltage and current data, determine the theoretical heating power value; based on thermal resistance parameters and theoretical heating power value, determine the theoretical temperature rise value; add the theoretical temperature rise value to the ambient reference temperature to obtain the theoretical temperature value.

[0047] In some embodiments of this application, when performing matching verification on voltage data, current data, and ambient temperature data based on electrical-environmental constraints to determine whether synchronization constraints are met, the method further includes:

[0048] Based on the time delay coefficient, the ambient temperature data is shifted along the time axis to obtain pre-aligned ambient temperature data. Within the time delay compensation observation window, the theoretical temperature value is compared point by point with the pre-aligned ambient temperature data to obtain a temperature deviation sequence. When the maximum deviation value in the temperature deviation sequence is less than or equal to the temperature deviation threshold, it is determined that the synchronization constraint condition is met; when the maximum deviation value in the temperature deviation sequence is greater than the temperature deviation threshold, it is determined that the synchronization constraint condition is not met.

[0049] Specifically, in step S200, continuous monitoring is first performed on adjacent sampling points. The voltage and current data are judged to have changed beyond the normal fluctuation range by differential operation, rate of change calculation or local trend fitting. The normal fluctuation range can be set based on the rated operating status of the ring main unit, historical stable operating data or preset statistical thresholds. When the rate of change of the voltage data is greater than the corresponding voltage change threshold or the rate of change of the current data is greater than the corresponding current change threshold, it indicates that the current electrical state has undergone a significant observable change. At this time, the acquisition timestamp corresponding to the current change is marked as the start time of the window, and a time delay compensation observation window is established based on the start time of the window. For example, assuming a feeder is operating stably, its current sampling values ​​are 100A, 102A, 101A, and 103A over several consecutive sampling periods. The difference between adjacent sampling points is within ±3A, corresponding to a change rate of no more than approximately 3%, which can be considered normal fluctuation. However, when the current sampling value suddenly increases from 103A to 125A at a certain moment, the difference between adjacent values ​​reaches 22A, corresponding to a change rate of approximately 21%, significantly exceeding the preset change threshold (e.g., 10%). This can be identified as an abnormal change event, and this moment is marked as the trigger moment. Similarly, for voltage data, for example, a small fluctuation in bus voltage around the rated 10kV (e.g., from 9.9kV to 10.1kV) is within the normal range. However, if it drops from 10.0kV to 9.2kV in a short period of time, the significantly excessive change can be obtained through differential calculation or sliding window change rate calculation, thereby triggering the establishment of the time-delay compensation observation window. The duration of the time-delay compensation observation window is not fixed but dynamically set in conjunction with the time-delay coefficient. Since historical time-delay sequences have not yet been accumulated during the initial operation phase, the time-delay coefficient can be obtained by combining preset parameters with online initial estimation. An empirical initial time-delay coefficient range can be pre-set based on the ring mains cabinet's structural characteristics, material thermal conductivity, and typical operating load conditions. For example, based on historical test data or factory calibration data of similar equipment, the typical delay time from electrical quantity change to ambient temperature response can be set to between several seconds and tens of seconds, and the median value within this range can be selected as the initial time-delay coefficient. During the initial operation phase after startup, several voltage or current change events can be observed in real time. After each time delay compensation observation window is triggered, the corresponding time difference is calculated according to the extraction method of characteristic change time and characteristic response time, and these initial time differences are gradually used to form an initial time delay sample set. After the number of samples reaches the preset minimum statistical number (e.g., several valid events are collected continuously), the initial sample set is statistically processed, such as taking the mean or weighted mean, to obtain a time delay estimate that is more consistent with the actual operating conditions of the current equipment, and the estimated value is updated and assigned to the time delay coefficient.The time delay coefficient reflects the typical response time required for a ring main unit to transmit electrical changes to ambient temperature changes under current operating conditions. Therefore, the end time of the observation window can be obtained by superimposing the start time and duration of the window, ensuring that the observation window fully covers the temperature response process and avoiding feature omissions due to an excessively short window or irrelevant data interference due to an excessively long window. For example, if the time delay coefficient of a certain ring main unit under typical load conditions is found to be 8 seconds during historical operation, then when a sudden current change is detected at a certain moment and this moment is marked as the start time t1 of the window, the duration of the observation window can be set to a time scale corresponding to the time delay coefficient. For example, it can be directly taken as 8 seconds or a safety margin factor (such as 1.2 times) can be introduced on this basis, resulting in a duration of approximately 9.6 seconds. Thus, the end time of the window is determined as t1 + 9.6 seconds. Within this time range, the ambient temperature can usually complete the entire process from the initial state to a significant response change, which is beneficial for accurately capturing the characteristic response time of the temperature. When the operating conditions of the equipment change, the time delay coefficient can also be dynamically adjusted. Under high load or high ambient temperature conditions, the equipment generates more heat and changes in thermal inertia. The time lag coefficient after historical updates may increase to 12 seconds, and the corresponding observation window duration should also be extended (e.g., 12 seconds or about 14 seconds after multiplying by the margin) to avoid ending the observation window before the temperature response is fully manifested, which would lead to feature omission. Conversely, under low load or good heat dissipation conditions, the time lag coefficient may decrease to 5 seconds. At this time, the window duration is shortened accordingly, thereby reducing the introduction of irrelevant data and improving computational efficiency.

[0050] Specifically, in step S200, within the time-delay compensation observation window, feature extraction processing is performed on the voltage or current data. The moment of change triggering, the moment when the rate of change reaches a local peak, or the moment when the slope of the change curve abruptly changes is determined as the characteristic change moment. Simultaneously, trend analysis is performed on the ambient temperature data, preferably using first-order difference, sliding rate of change detection, or continuous increment discrimination to identify the changing trend of the temperature sequence. When the rate of change changes from zero or near zero to a positive value and remains positive, it indicates that the temperature has changed from a relatively stable state to a continuously rising state. Therefore, the moment when the sign of the rate of change changes is determined as the characteristic response moment of the ambient temperature. From a physical perspective, this moment is the turning point in the trend of temperature response relative to electrical changes. Subsequently, the first and second acquisition timestamps corresponding to the characteristic change moment and the characteristic response moment are extracted respectively, and the time difference is calculated by the difference between the two. The time difference is used to characterize the degree of lag in the response of ambient temperature relative to changes in voltage or current data, thereby providing a basis for subsequent time series offset and time delay updates. For example, within a certain operating cycle, the current data rapidly increases from 98A to 120A at time t2=10:00:05, and reaches 130A and 128A at the next two sampling points, respectively. Calculations of the rate of change show that the rate of change reaches a local peak near 10:00:06, or curve fitting identifies this moment as the most significant abrupt change in slope. Therefore, 10:00:05 or 10:00:06 can be identified as the characteristic change moment of the current data. Simultaneously, within the corresponding time-delay compensation observation window, the ambient temperature data undergoes first-order difference processing. Assuming the temperature remains relatively stable at around 35.0℃ between 10:00:05 and 10:00:09, and continuously rises to 35.3℃, 35.8℃, and 36.2℃ from 10:00:10, the sliding rate of change detection identifies the temperature sequence as transitioning from stability to a continuous increase at 10:00:10; this moment is the characteristic response moment. Further extract the acquisition timestamps corresponding to the two times mentioned above. For example, if the first acquisition timestamp is 10:00:05 and the second acquisition timestamp is 10:00:10, the difference between the two is 5 seconds. This time difference represents the degree of lag in the response of the ambient temperature to the change in current under the current operating conditions.

[0051] Specifically, in step S200, after extracting the time difference, electrical-environmental constraints are further introduced. These constraints refer to a consistency judgment rule established based on the physical mechanism of the ring main unit's operation, defining the coupling relationship that voltage, current, and ambient temperature data should satisfy. Electrical quantities (such as voltage and current) determine the equipment's heating level. The heating process is converted into heat through conductor resistance loss, contact resistance, and load changes, and is gradually transferred to the outside of the equipment via material thermal resistance and heat dissipation paths, thus causing changes in ambient temperature. Therefore, under a given operating condition, electrical characterization quantities reflecting the degree of heating (such as equivalent heating power or load intensity) can be calculated based on voltage and current data. Combined with equipment structural parameters (such as thermal resistance and heat dissipation coefficient), the theoretical temperature rise trend can be derived, resulting in the corresponding theoretical temperature change sequence. Based on this, the theoretical temperature change sequence is compared with the actual collected ambient temperature data after time alignment. When both are within the preset error range in terms of change direction, change magnitude, and time evolution trend, it indicates that the response of ambient temperature to electrical changes conforms to actual physical laws, and the electrical-environmental constraint condition is satisfied. Conversely, if there is a significant deviation between the two, such as a significant increase in electrical load but no reasonable temperature rise, or a sudden temperature rise but no corresponding change in electrical quantity, it indicates that there is a time sequence misalignment, sensing abnormality, or interference between the data, and it should be determined that the synchronization constraint condition is not satisfied. Preferably, voltage data, current data, and ambient temperature data are matched and verified to determine whether the current multi-source data meets the synchronization constraint condition. The theoretical heat generation power value is determined based on the voltage and current data. The theoretical heat generation power value is used to characterize the expected heat generation level of the ring main unit under the current electrical load condition. Subsequently, combined with the thermal resistance parameters (or heat dissipation path parameters, or preset heat conduction parameters) of the ring main unit structure, the theoretical temperature rise value is calculated, and the theoretical temperature rise value is added to the ambient reference temperature to obtain the theoretical temperature value corresponding to the current electrical operating condition. The thermal resistance parameter can be obtained in various ways. For example, it can be preset based on the thermal simulation analysis results or factory test calibration data during the equipment design phase. Alternatively, it can be back-calibrated in the early stage of equipment operation by loading typical operating conditions, collecting electrical quantity and temperature response data, and combining them with the thermal balance relationship. It can also be corrected online through historical operating data during long-term operation to improve the accuracy of the parameter. The ambient reference temperature is preferably a reference temperature that reflects the external environment or initial thermal state of the equipment. It can be acquired in real time by temperature sensors installed outside the ring main unit or at the air inlet, or by selecting the stable average value of the ambient temperature as the reference value when the equipment is in a low-load or steady-state operation phase. This ensures that the theoretical temperature value can truly reflect the temperature change trend under the coupling effect of the current electrical operating conditions and environmental conditions.To eliminate the impact of historical time lag, the ambient temperature data is first shifted along the time axis based on the time lag coefficient to obtain pre-aligned ambient temperature data. Then, within the time lag compensation observation window, the theoretical temperature value and the pre-aligned ambient temperature data are compared point by point to form a temperature deviation sequence. This point-by-point comparison can be performed sequentially at each time point after alignment using a unified sampling timestamp. The temperature deviation sequence reflects the degree of agreement between the theoretical and actual temperatures. When the maximum deviation value in the temperature deviation sequence is less than or equal to the temperature deviation threshold, it indicates that under the current time lag compensation conditions, the ambient temperature data and electrical data conform to the expected physical response law, and the synchronization constraint condition is met. Conversely, when the maximum deviation value is greater than the temperature deviation threshold, it indicates that the data alignment result under the existing compensation parameters still has a large deviation, and the synchronization constraint condition is not met. The temperature deviation threshold measures the allowable deviation range between the theoretical temperature value and the actual ambient temperature data. Its setting is determined comprehensively based on multiple factors such as equipment operating characteristics, historical data statistics, and measurement error range. A value of 2℃ is preferred.

[0052] S300: When it is determined that the synchronization constraint conditions are not met, the ambient temperature data is time-shifted based on the time difference to obtain synchronized sensing data; when it is determined that the synchronization constraint conditions are met, the ambient temperature data is time-shifted based on the time delay coefficient to obtain synchronized sensing data.

[0053] In some embodiments of this application, when performing time-series offset on ambient temperature data based on time difference, the following steps are included:

[0054] The offset and offset direction are determined based on the time difference; the timestamps of each data point in the ambient temperature data are adjusted so that the characteristic response time of the ambient temperature data coincides with the characteristic change time of the voltage or current data on the time axis, thus obtaining synchronous sensing data.

[0055] In some embodiments of this application, when performing time-series offset on ambient temperature data based on a time delay coefficient, the following steps are included:

[0056] The offset and offset direction are determined based on the time delay coefficient; the ambient temperature data sequence within the time delay compensation observation window is shifted along the time axis by the offset to obtain synchronous sensing data.

[0057] Specifically, in step S300, when the determination result is that the synchronization constraint condition is not met, it indicates that the current ambient temperature data has a significant response lag or time misalignment relative to the voltage or current data. In this case, a dynamic time-series offset method based on the time difference is preferred for compensation. First, the offset amount and offset direction of the ambient temperature data are determined according to the obtained time difference value. The offset direction is determined by the chronological relationship between the characteristic response time and the characteristic change time. When the characteristic response time of the ambient temperature lags behind the electrical characteristic change time, the ambient temperature data is shifted forward along the time axis; when there is an early response or abnormal fluctuation, the data is shifted backward according to the corresponding relationship. Subsequently, the timestamps corresponding to each data point in the ambient temperature data sequence are uniformly adjusted. That is, while keeping the temperature value of each data point unchanged, the time labels are corrected as a whole so that the characteristic response time of the adjusted ambient temperature data is aligned with the characteristic change time of the voltage or current data on the time axis. During the adjustment process, the continuity of the data within the window can be checked to avoid data breakage or overlap caused by timestamp offset. If necessary, the data can be smoothed by interpolation or resampling to ensure the integrity of the data sequence. After the above processing, synchronous sensing data based on real-time time difference correction is obtained, which can accurately reflect the correspondence between electrical changes and temperature response. When the synchronization constraint condition is met, it indicates that the time delay relationship between electrical quantities and ambient temperature is stable under the current operating condition. At this time, a fast compensation strategy based on the time delay coefficient can be adopted to reduce computational complexity. Preferably, a unified time offset and its direction are determined based on the time delay coefficient updated from the historical time delay sequence, and this offset is applied to the ambient temperature data sequence within the time delay compensation observation window. Data alignment is achieved by translating the entire sequence along the time axis. The time delay coefficient represents the average response lag characteristic statistically obtained over a period of time. Therefore, when the synchronization constraint condition is met, a stable and consistent data correction effect can be achieved with a small computational overhead. During implementation, the translated data sequence can also be checked for time alignment and subjected to necessary resampling to ensure a one-to-one correspondence with voltage and current data under a unified time reference.

[0058] S400: Generates self-healing control commands based on the obtained synchronous sensing data; and incorporates the time difference into the historical time delay sequence to update the time delay coefficient.

[0059] In some embodiments of this application, the self-healing control commands include normal maintenance commands, abnormal adjustment commands, and fault trip commands.

[0060] In some embodiments of this application, when incorporating the time difference into the historical time delay sequence and updating the time delay coefficient, the following steps are included:

[0061] Store the time difference at the end of the historical time delay sequence and remove the historical data at the head of the historical time delay sequence; then perform a weighted average on all the data in the adjusted historical time delay sequence to obtain the updated value; and assign the updated value to the time delay coefficient.

[0062] Specifically, in step S400, after acquiring the synchronized sensing data processed by time offset, the edge side first performs state discrimination processing on the synchronized sensing data. The synchronized sensing data includes at least time-aligned voltage data, current data, and ambient temperature data. By comprehensively analyzing the numerical relationship, trend of change, and coupling characteristics of the three data on the same time axis, the current operating state of the ring main unit can be determined as a stable operating state, an abnormal operating state, or a fault operating state. Specifically, when the matching relationship between voltage, current, and temperature meets the preset safe operating range, and the trend of each parameter change is stable, it is judged as a normal operating state; when there is abnormal temperature rise, high current, or voltage fluctuation exceeding the normal range but not yet reaching the fault threshold, it is judged as an abnormal operating state; when there is a sudden change in current, a sharp rise in temperature, or severe mismatch of multiple parameters, it is judged as a fault operating state. Based on the above state discrimination results, corresponding self-healing control commands are further generated. In some embodiments, the self-healing control commands include normal maintenance commands, abnormal adjustment commands, and fault trip commands. Among them, the normal maintenance command is used to maintain the current operating strategy when the operation is normal, without triggering additional control actions; the abnormal adjustment command is used to adjust the operating parameters when a slight abnormality or trend deviation is detected, such as adjusting load distribution, reducing operating current, or activating auxiliary cooling devices to suppress further development of the abnormality; the fault trip command is used to output a trip control signal to control the circuit breaker when a fault operating state is determined, so as to realize the rapid isolation of the faulty circuit, thereby ensuring the safety of equipment and system. All of the above control commands can be directly sent to the actuator through the edge-side control interface to achieve low-latency local closed-loop control. For example, the edge side acquires synchronous sensing data after time alignment, in which the voltage is stable at about 10kV, the current fluctuates slightly around 120A, and the ambient temperature slowly rises from 30℃ to 31℃. The trends of these three values ​​are consistent and within the preset safety range. At this time, it is determined to be a normal operating state, and a normal maintenance command is generated accordingly to maintain the current operating strategy without additional intervention. As the load gradually increases, the current rises from 120A to 160A, and the theoretical temperature rise prediction should also increase accordingly. However, the actual ambient temperature rises to 35℃ in a short period of time, and the rate of temperature rise is slightly higher than the historical normal level, but has not yet reached the danger threshold. This is judged as an abnormal operating state. In response to this situation, the edge side generates abnormal adjustment commands, such as automatically adjusting part of the load to an adjacent circuit, reducing the load current of that circuit, or starting the cabinet fan for forced cooling, thereby suppressing the temperature rise from continuing to expand. When a sudden fault scenario occurs, such as the current suddenly jumping from 160A to over 500A at a certain moment, while the ambient temperature rapidly rises to over 45℃ in a short period of time, and the voltage fluctuates significantly or even drops, multiple parameters are severely mismatched, and this is judged as a fault operating state.In response to this situation, a fault trip command is immediately generated, and the circuit breaker tripping signal is sent directly from the edge side to disconnect the faulty circuit within milliseconds, preventing equipment damage or the escalation of the accident.

[0063] Specifically, in step S400, while generating control commands, to achieve continuous adaptive optimization of the environmental response time delay characteristics, this application further performs dynamic updating processing on the time difference values. Preferably, the currently calculated time difference value is used as the latest sample data and stored at the end of the historical time delay sequence, while the earliest data item at the head of the historical time delay sequence is removed simultaneously to maintain a constant length of the historical time delay sequence, thereby forming a sliding update data queue. Based on this, each time difference value in the updated historical time delay sequence is weighted and averaged according to a preset weight. Newer time difference values ​​are assigned higher weights to enhance the responsiveness to changes in the current operating state, while older data are assigned lower weights to maintain overall stability. For example, assuming the length of the historical time delay sequence is set to 5, the time differences in the current sequence are: 4s, 5s, 6s, 7s, 8s (arranged in chronological order, with 8s being the latest acquired data). To enhance responsiveness to changes in current operating conditions, incremental weights can be assigned to the aforementioned data, for example, weights of 0.1, 0.15, 0.2, 0.25, and 0.3, with the most recent data receiving the highest weight. Based on this, a weighted average is calculated for the historical time-delay sequence, yielding a result of approximately 6.5 seconds. This 6.5 seconds is then used as the updated time-delay coefficient. Compared to a simple average (6 seconds), this weighted result is more biased towards recent data (7 seconds, 8 seconds), reflecting the trend of slower temperature response under current operating conditions. In the next update, if the newly acquired time difference is 9 seconds, it is added to the end of the sequence, while the earliest 4 seconds are removed, resulting in a new sequence: 5 seconds, 6 seconds, 7 seconds, 8 seconds, and 9 seconds. This is then weighted again according to the same weighting rules, continuously achieving dynamic tracking and updating of the time-delay coefficient. The new time-delay estimate obtained through the above weighted calculation is assigned to the time-delay coefficient for subsequent steps in constructing the time-delay compensation observation window and processing the temporal offset of environmental temperature data. Preferably, after calculating the weighted average of the historical time delay sequence, a new time delay estimate is obtained. The edge side first writes this value into the parameter register or cache variable corresponding to the time delay coefficient, which is then used as the current valid time delay parameter for subsequent calculations. In practice, this assignment process can adopt a double buffering or shadow variable mechanism. That is, the updated value is first written into a temporary time delay coefficient variable. After completing the data processing task of the current cycle, the temporary variable is switched to the formal time delay coefficient at the beginning of the next processing cycle, thereby avoiding parameter mutations during the calculation process from affecting the results.

[0064] Based on another preferred embodiment described above, see [link to preferred embodiment]. Figure 2As shown, this embodiment provides an edge computing-based intelligent monitoring and self-healing control system for ring main units, used to apply the above-mentioned edge computing-based intelligent monitoring and self-healing control method for ring main units, including:

[0065] The data acquisition unit is configured to acquire voltage, current, and ambient temperature data of the ring main unit at the edge side and mark the acquisition timestamp.

[0066] The verification unit is configured to establish a time-delay compensation observation window when the voltage or current data changes; within the time-delay compensation observation window, it identifies the characteristic change time of the voltage or current data and the characteristic response time of the ambient temperature data, and determines the time difference between the characteristic change time and the characteristic response time; based on the electrical-environmental constraints, it performs matching verification on the voltage data, current data, and ambient temperature data to determine whether the synchronization constraints are met.

[0067] The adjustment unit is configured to, when it is determined that the synchronization constraint conditions are not met, perform a time-series offset on the ambient temperature data based on the time difference to obtain synchronized sensing data; and when it is determined that the synchronization constraint conditions are met, perform a time-series offset on the ambient temperature data based on the time delay coefficient to obtain synchronized sensing data.

[0068] The self-healing unit is configured to generate self-healing control commands based on the obtained synchronous sensing data; and to incorporate the time difference into the historical time delay sequence to update the time delay coefficient.

[0069] In summary, by establishing a time-delay compensation observation window triggered by rapidly changing electrical quantities such as voltage and current, and extracting the time difference between the moment of change in electrical quantity characteristics and the moment of response of ambient temperature characteristics within the window, an explicit characterization of the dynamic response relationship between "fast variables and slow variables" is achieved. This reflects the true lag characteristics of environmental quantities to electrical changes, improving the time-series alignment accuracy of multi-source data from the source. Based on the matching and verification mechanism of electrical-environmental constraints, the rationality of the synchronization results is verified by constructing a correspondence between theoretical and actual temperatures. This ensures that data alignment no longer relies solely on the time dimension but incorporates dual constraint judgment based on physical mechanisms, thereby avoiding misalignment problems caused by noise disturbances or abnormal sampling and improving the reliability of synchronized sensing data. Differentiated time-series compensation strategies are adopted for different synchronization states: when synchronization constraints are not met, dynamic correction is performed using the time difference calculated in real time; when synchronization constraints are met, stable compensation is performed based on the time delay coefficient obtained from historical evolution. This approach balances real-time performance and stability, enabling rapid response to sudden operating conditions while maintaining low computational overhead during stable operation, thus improving overall operating efficiency. By continuously integrating the real-time acquired time difference into the historical time delay sequence and updating it, the self-learning and adaptive adjustment of the time delay parameters are realized, overcoming the problem of error accumulation caused by parameter drift during long-term operation.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for intelligent monitoring and self-healing control of ring main units based on edge computing, characterized in that, include: Voltage, current, and ambient temperature data of the ring main unit are collected at the edge side, and the collection timestamp is marked. When the voltage or current data changes, a time-delay compensation observation window is established. Within the time-delay compensation observation window, the characteristic change time of the voltage or current data and the characteristic response time of the ambient temperature data are identified, and the time difference between the characteristic change time and the characteristic response time is determined. Based on electrical-environmental constraints, the voltage data, current data, and ambient temperature data are matched and verified to determine whether the synchronization constraints are met. When it is determined that the synchronization constraint condition is not met, the ambient temperature data is time-shifted based on the time difference to obtain synchronized sensing data; when it is determined that the synchronization constraint condition is met, the ambient temperature data is time-shifted based on the time delay coefficient to obtain synchronized sensing data. Generate self-healing control instructions based on the obtained synchronous sensing data; The time difference is then incorporated into the historical time delay sequence to update the time delay coefficient.

2. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 1, characterized in that, When the voltage or current data changes numerically, the establishment of a time-delay compensation observation window includes: The rate of change of the voltage data and the rate of change of the current data are obtained; when the rate of change of the voltage data is greater than the voltage change threshold or the rate of change of the current data is greater than the current change threshold, the current change time is marked as the window start time; the duration of the time delay compensation observation window is determined according to the time delay coefficient, and the window end time is determined; the time period between the window start time and the window end time is used as the time delay compensation observation window.

3. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 2, characterized in that, Determining the time difference between the feature change time and the feature response time includes: The start time of the window is taken as the feature change time; the ambient temperature data within the time delay compensation observation window is traversed, and the rate of change of the ambient temperature data is detected. The moment when the rate of change changes from zero to positive is taken as the feature response time; the first acquisition timestamp of the feature change time and the second acquisition timestamp of the feature response time are obtained; the difference between the first acquisition timestamp and the second acquisition timestamp is determined as the time difference.

4. The edge computing-based intelligent monitoring and self-healing control method for ring main units according to claim 3, characterized in that, When performing a matching verification on the voltage data, current data, and ambient temperature data based on electrical-environmental constraints to determine whether the synchronization constraints are met, the process includes: Based on the voltage and current data, the theoretical heating power value is determined; based on the thermal resistance parameter and the theoretical heating power value, the theoretical temperature rise value is determined; the theoretical temperature rise value is added to the ambient reference temperature to obtain the theoretical temperature value.

5. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 4, characterized in that, Based on electrical-environmental constraints, when performing a matching verification on the voltage data, current data, and ambient temperature data to determine whether the synchronization constraints are met, the process further includes: The ambient temperature data is shifted along the time axis based on the time delay coefficient to obtain pre-aligned ambient temperature data. Within the time delay compensation observation window, the theoretical temperature value is compared point by point with the pre-aligned ambient temperature data to obtain a temperature deviation sequence. When the maximum deviation value in the temperature deviation sequence is less than or equal to the temperature deviation threshold, it is determined that the synchronization constraint condition is met. When the maximum deviation value in the temperature deviation sequence is greater than the temperature deviation threshold, it is determined that the synchronization constraint condition is not met.

6. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 1, characterized in that, When performing time-series offset on the ambient temperature data based on the time difference, the following is included: The offset and offset direction are determined based on the time difference; the timestamps of each data point in the ambient temperature data are adjusted so that the characteristic response time of the ambient temperature data coincides with the characteristic change time of the voltage data or current data on the time axis, thereby obtaining the synchronous sensing data.

7. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 1, characterized in that, When performing time-series offset on the ambient temperature data based on the time lag coefficient, the following is included: The offset and offset direction are determined based on the time delay coefficient; the ambient temperature data sequence within the time delay compensation observation window is shifted along the time axis by the offset to obtain the synchronous sensing data.

8. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 1, characterized in that, The self-healing control commands include normal maintenance commands, abnormal adjustment commands, and fault trip commands.

9. The intelligent monitoring and self-healing control method for ring main units based on edge computing according to claim 1, characterized in that, When incorporating the time difference into the historical time delay sequence and updating the time delay coefficient, the following steps are included: The time difference is stored at the end of the historical time delay sequence, and the historical data at the head of the historical time delay sequence is removed; a weighted average is performed on all the data in the adjusted historical time delay sequence to obtain an updated value; the updated value is assigned to the time delay coefficient.

10. A ring main unit intelligent monitoring and self-healing control system based on edge computing, used to apply the edge computing-based ring main unit intelligent monitoring and self-healing control method as described in any one of claims 1-9, characterized in that, include: The data acquisition unit is configured to acquire voltage, current, and ambient temperature data of the ring main unit at the edge side and mark the acquisition timestamp. The verification unit is configured to establish a time-delay compensation observation window when the voltage or current data changes; within the time-delay compensation observation window, identify the characteristic change time of the voltage or current data and the characteristic response time of the ambient temperature data, and determine the time difference between the characteristic change time and the characteristic response time; based on electrical-environmental constraints, perform matching verification on the voltage data, current data, and ambient temperature data to determine whether the synchronization constraints are met. The adjustment unit is configured to, when it is determined that the synchronization constraint condition is not met, perform a time-series offset on the ambient temperature data based on the time difference to obtain synchronized sensing data; and when it is determined that the synchronization constraint condition is met, perform a time-series offset on the ambient temperature data based on the time delay coefficient to obtain synchronized sensing data. The self-healing unit is configured to generate self-healing control commands based on the obtained synchronous sensing data. The time difference is then incorporated into the historical time delay sequence to update the time delay coefficient.

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