Iot-based electric energy metering box operation monitoring method and system

Abstract

The application discloses a kind of based on Internet of Things electric energy metering box operation monitoring method and system, it is related to electric power metering equipment monitoring technical field.For the problem that metering box current freezes time and temperature collection time are not synchronized, temperature lag leads to instantaneous matching distortion during load sudden change, false alarm and miss frequently, the method is with preset thermal time constant to the load current square is exponentially weighted average, constructs and each temperature collection time alignment equivalent heat current;With terminal temperature rise divided by equivalent heat current square obtains thermal resistance characteristic value, establishes quantile baseline according to heat level grading;According to the contact deterioration of multi-grade synchronous drift determination, single-grade offset or accompanied by environmental temperature lifting is determined as environmental disturbance;Determination result is reported by Internet of Things link grading, generates maintenance work order after review by monitoring platform.The application can realize early identification of wiring terminal contact deterioration without increasing temperature measuring element or improving collection frequency, and is suitable for remote operation of transformer area metering box.

Description

Technical Field

[0001] This invention relates to the field of power metering equipment monitoring technology, and more specifically, to an Internet of Things-based method for monitoring the operation of power metering boxes, as well as a system, electronic equipment, and computer-readable storage medium for implementing the method. Background Technology

[0002] Electricity metering boxes are installed on the low-voltage side of the distribution area. Inside the box, electricity meters, incoming and outgoing line switches, and wiring terminals are centrally located. It is the intersection of electricity information collection and power supply safety. In a medium-sized county-level power supply area, there are often tens of thousands of metering boxes that are exposed to the outdoors year-round. Due to reasons such as loose crimping and oxidation of copper and aluminum contact surfaces, the contact resistance of the wiring terminals inside the box deteriorates. The Joule heat generated when the load current flows through the box causes the terminals to overheat continuously. This can lead to inaccurate meter readings by burning the meter tail wires, or even cause a fire in the box.

[0003] Currently, the industry implements periodic operation management of metering devices in accordance with DL / T 448 "Technical Management Regulations for Electricity Metering Devices". Temperature measuring elements and data acquisition terminals are generally installed in the metering boxes to report data such as terminal temperature and load current to a monitoring platform via an IoT link, where the platform performs overheat detection. The detection logic generally follows two routes. One is the threshold route, which sets fixed limits or dynamic limits that adjust with the environment for terminal temperature, triggering an alarm when these limits are exceeded. The other is the correlation route, which uses the correlation between temperature and current to establish a baseline model, identifying anomalies based on the degree to which the measured temperature deviates from the baseline. Some solutions also filter data according to steady-state conditions, first eliminating periods with large current fluctuations and rapid temperature changes, and then using the remaining steady-state data to invert the contact resistance.

[0004] These two lines operate reasonably well in stable load environments such as substation busbars and cable joints managed according to the "Condition-Based Maintenance and Testing Procedures for Transmission and Transformation Equipment." However, after being moved to the metering boxes in the distribution areas, false alarms and missed alarms occur frequently. Maintenance personnel often simply raise the limits after multiple ineffective work attempts, and the monitoring loses its ability to detect problems early. The reason is that the load and data conditions of the metering boxes are very different from those in the aforementioned environments. On the one hand, residential loads show obvious peaks, with induction cookers and air conditioners being used intensively during evening cooking hours. The outgoing current can jump several times within one or two collection cycles. The steady-state window with smooth current fluctuations is almost non-existent during peak electricity consumption periods. Screening based on steady-state conditions would precisely filter out the most noteworthy time of day. On the other hand, the current in the metering boxes is taken from the frozen data of the electricity meter, and the terminal temperature is taken from independent temperature measuring elements. Both are reported by the acquisition terminal at their respective cycles, typically on the order of 15 minutes, and the recording times are not aligned.

[0005] After on-site comparison at the distribution center, the inventors discovered that the root of the problem was not the threshold value, but rather the assumption in existing methods that temperature and current are comparable at the same moment. Terminals are metallic bodies with heat capacity; temperature is a cumulative response to their historical heating patterns over a period of time, not an instantaneous function of the current. After a load surge, the terminal temperature needs to climb for over ten minutes to reach a new equilibrium. Directly pairing the current temperature with the current during this climbing phase will systematically distort the temperature-current relationship. Furthermore, the practice of using minute-level dense sampling to fully record the climbing curve and identify thermal parameters is simply not feasible for a 15-minute data acquisition system. Previous methods have attempted to identify the thermal time constant and temperature rise coefficient using transient processes on connector-type equipment, but these also rely on equally spaced dense sampling, which cannot be transferred to the sparse and asynchronous data conditions of the metering box.

[0006] Therefore, for power metering boxes, there is a need for an operation monitoring method that can reliably identify terminal contact degradation and distinguish terminal degradation from environmental disturbances without adding temperature sensing elements or increasing the acquisition frequency, using existing sparse asynchronous acquisition data. Summary of the Invention

[0007] The problem this invention aims to solve is that, under conditions of sparse data collected by the metering box, asynchronous current recording and temperature acquisition, and frequent and sudden load changes, existing monitoring methods frequently produce false alarms and missed alarms due to their reliance on simultaneous pairing of temperature and current or on parameter identification based on dense sampling, and cannot achieve early identification of terminal contact deterioration.

[0008] To address the aforementioned technical problems, according to one aspect of the present invention, an IoT-based method for monitoring the operation of an electricity metering box is provided. The method includes: Step S1, where a data acquisition terminal inside the electricity metering box periodically acquires the frozen load current value of each outgoing circuit, the terminal temperature of the corresponding terminal, and the ambient temperature inside the box. The recording time of the frozen load current value and the acquisition time of the terminal temperature are asynchronous. Step S2, for each acquisition time of the terminal temperature, using a preset thermal time constant as an attenuation parameter, the squares of the frozen load current values ​​prior to that acquisition time are exponentially weighted and averaged over the time interval between the recording time and the acquisition time. The square root of the weighted average is then taken to obtain the equivalent thermal current aligned with that acquisition time. Step S3, the terminal temperature rise is obtained by subtracting the ambient temperature inside the box from the terminal temperature, and the thermal resistance characteristic is obtained by dividing the terminal temperature rise by the square of the equivalent thermal current. The thermal resistance characteristic value is assigned to the corresponding grade according to the preset current grade in which the equivalent thermal current falls, and the grade baseline is formed by the quantile of the assigned thermal resistance characteristic value in each grade; Step S4, the offset ratio of the thermal resistance characteristic value of each grade relative to the grade baseline in the most recent evaluation period is calculated. When the number of grades with an offset ratio exceeding the preset drift threshold is not less than the preset number of grades, and this situation continues for not less than the preset evaluation period, it is determined that the corresponding terminal has contact deterioration. When only a single grade has offset, or when the offset occurs simultaneously with the rise in the ambient temperature inside the box, it is determined to be an environmental disturbance; Step S5, when contact deterioration is determined, the acquisition terminal reports the offset ratio of each grade of the deteriorated terminal and the original sampling sequence of the corresponding evaluation period to the monitoring platform through the Internet of Things communication link. After the monitoring platform reviews and confirms, a maintenance work order is generated. If no determination is made, only summary data is reported according to the preset reporting period.

[0009] In one alternative implementation, the preset thermal time constant is not identified, but selected from a pre-configured candidate set: the equivalent thermal current and thermal resistance characteristic value are calculated for each candidate thermal time constant, the sample standard deviation of the thermal resistance characteristic value within the same range is compared, the candidate value whose standard deviation does not exceed that of the other candidates is selected, and the value is reselected according to the calibration cycle to adapt to the differences in terminal installation method and heat dissipation conditions.

[0010] In other optional implementations, current grading is based on the ratio of equivalent thermal current to the rated current of the outgoing circuit. Sampling times with a ratio that is too low are not included in the calculation due to insufficient temperature rise signal-to-noise ratio. The typical lower limit is 0.2 times the rated current. The grading baseline is the median of the thermal resistance characteristic value within the baseline window period and is updated on a rolling basis. Once a terminal is determined to have contact degradation, the baseline stops updating until it is repaired and repositioned to prevent the degradation trend from being gradually absorbed by the baseline and passivating the determination. When the proportion of terminals with offset in the same metering box reaches a preset proportion, it is treated as a box-level common cause disturbance and the degradation determination for that period is revoked. The monitoring platform can also review the reported original sampling sequence in conjunction with the meteorological temperature data of the substation area and determine the work order priority according to the offset amplitude. The summary data includes the median of the thermal resistance characteristic value of each grading, the number of samples, and the grading number, so that the normal uplink flow of a single box is kept at about 50 bytes per time.

[0011] According to another aspect of the present invention, an Internet of Things (IoT)-based power metering box operation monitoring system is provided. The system includes a data acquisition terminal installed within the power metering box and a monitoring platform connected to the acquisition terminal via an IoT communication link. The acquisition terminal includes a data acquisition module, a time-scale alignment module, a tiered baseline module, a degradation judgment module, and a tiered reporting module, respectively responsible for data acquisition, equivalent thermal current construction, tiering and baseline maintenance, degradation and disturbance discrimination, and two-level data reporting. The monitoring platform includes a verification module and a work order module.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device including a processor and a memory, the memory storing a computer program, the computer program being executed by the processor to implement the Internet of Things-based power metering box operation monitoring method described in any of the above implementations.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program for executing the Internet of Things-based power metering box operation monitoring method described in any of the above implementations.

[0014] Based on the method and system provided in the above embodiments of the present invention, temperature sampling is no longer rigidly paired with instantaneous current, but rather with equivalent thermocurrent weighted and accumulated according to thermal time constant. Sampling during periods of sudden load changes thus transforms from a source of interference into effective samples, allowing full utilization of existing sparse and asynchronous data acquisition without the need to add temperature measuring elements or increase the acquisition frequency for monitoring purposes. Contact degradation manifests as a synchronous drift of the thermal resistance characteristic values ​​of each current band. Environmental disturbances only cause partial band shifts or are accompanied by a synchronous rise in ambient temperature, allowing for differentiation between the two and reducing ineffective work. Judgment and verification are separated at the acquisition terminal and monitoring platform levels, with only summary data reported under normal circumstances, controlling communication burden and platform pressure. Therefore, the embodiments of the present invention achieve early identification of terminal contact degradation and identification of common environmental causes without modifying existing acquisition hardware.

[0015] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0016] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 A flowchart of an IoT-based power metering box operation monitoring method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the operation monitoring system for an Internet of Things-based electricity metering box according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the terminal temperature hysteresis response and instantaneous pairing deviation under sudden load change conditions. Figure 4 This is a schematic diagram illustrating the process of aligning the equivalent thermal current with the terminal temperature acquisition time. Figure 5 This is a schematic diagram illustrating the determination of consistency drift between the grading baseline and cross-grading. Figure 6 A schematic diagram of the process for selecting candidate thermal time constants; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the drift of thermal resistance characteristic values ​​relative to the graded baseline during the degradation evolution of the wiring terminals in the embodiment. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. The values ​​of thermal time constants, grading boundaries, drift thresholds, period parameters, and current and temperature values ​​used in the following implementations are all illustrative examples and do not constitute a limitation on the scope of protection of this invention. Those skilled in the art can adjust the corresponding parameters according to the actual conditions of the transformer substation; the adjusted implementation methods still fall within the scope of protection of this invention.

[0018] Those skilled in the art will understand that the terms "first," "second," etc., in the embodiments of this invention are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they indicate a necessary logical order between them. It should also be understood that in the embodiments of this invention, "multiple" can refer to two or more, and "at least one" can refer to one, two, or more. Similar reference numerals in the various figures indicate similar items; once an item is defined in one figure, it will not be discussed again in subsequent figures.

[0019] Exemplary methods Figure 1 This is a flowchart of an IoT-based method for monitoring the operation of an energy metering box according to an embodiment of the present invention. Figure 1 As shown, the method begins with step S1.

[0020] In step S1, the data acquisition terminal inside the energy metering box periodically acquires the load current freeze value of each outgoing circuit, the terminal temperature of the corresponding wiring terminal, and the ambient temperature inside the box. The load current freeze value refers to the current reading stored by the energy meter according to a preset freeze cycle. The data acquisition terminal reads the current from the meter in batches using the DL / T 645 protocol or an object-oriented data exchange protocol. Each freeze value carries the freeze time recorded by the energy meter. The terminal temperature is provided by a temperature sensing element attached to the crimping part of the wiring terminal. The temperature sensing element can be an NTC thermistor, platinum resistance thermometer, or a digital temperature sensor such as DS18B20; this invention is not limited to these. The ambient temperature inside the box is provided by another temperature sensing element located away from the heat source and suspended in the middle of the box. The data acquisition terminal records the acquisition time of each terminal temperature and ambient temperature using its own clock. It is worth noting that the freeze time of the energy meter is determined by the meter side, while the acquisition time of the temperature sensing element is determined by the terminal side. The two sets of times do not need to be aligned in this method, nor does any hardware need to be modified for alignment. This is precisely the problem to be solved in the subsequent step S2. The freezing period and temperature acquisition period are typically 15 minutes, but can also be 5 minutes, 30 minutes, or other values, and they are not required to be equal.

[0021] Optionally, the load current can be obtained from the frozen data of the electricity meter instead of being taken from the data acquisition terminal via a current transformer, and the root mean square value can be recorded at a fixed period. In this case, the recording time is still given by the terminal-side clock, and the subsequent processing of this method remains unchanged. An electricity metering box usually has multiple outgoing circuits. The acquisition and processing of each circuit are independent and executed in parallel. The failure of meter communication in one circuit only affects the current data of that circuit and does not affect the other circuits. Considering that the HPLC link may experience short-term congestion during peak hours in the distribution area, the data acquisition terminal retryes the frozen values ​​that fail to be read according to the protocol. The data retrieved by the retry still carries the original frozen time. Late data can be inserted into the accumulation queue according to its frozen time and participate in the calculation without being misaligned due to transmission delay - this is the convenience brought about by anchoring the time stamp on the data generation side rather than the receiving side.

[0022] In step S2, for each acquisition moment of the terminal temperature, the acquisition terminal constructs an equivalent thermocurrent aligned with that acquisition moment. The equivalent thermocurrent refers to the current obtained by taking the square root of the weighted average of the squares of the frozen load current values ​​before the acquisition moment, using a preset thermal time constant as the attenuation parameter, and then applying an exponential weighted average over time intervals. Specifically, let the acquisition moment of the terminal temperature be t, and the frozen load current values ​​before it be I1, I2, ..., I... n The time intervals from the freezing point to t are Δt1, Δt2, ..., Δt n If the preset thermal time constant is τ, then the weight of each frozen value is e. -Δtᵢ / τ The square of the equivalent thermocurrent is equal to the sum of the products of each weight and the square of the corresponding current, divided by the sum of the weights.

[0023] This construction is based on a well-known thermal fact: the temperature rise of a metal terminal with heat capacity does not change instantaneously with the current, but rather approaches an equilibrium value commensurate with the heating power in an exponentially relaxed manner. The closer the heating is to the current moment, the greater its contribution to the current temperature, while the contribution of earlier heating decreases exponentially. For example... Figure 3 As shown, after a load jump, the terminal temperature needs to undergo a ramp-up period of approximately three times the preheating time constant before reaching a new equilibrium. During this ramp-up, the temperature-current relationship obtained by pairing instantaneous current with temperature will fluctuate significantly depending on the sampling point. The exponentially weighted average precisely compresses a heating history into an equivalent quantity using the same weighting structure, making "several past freezing values" and "this current temperature" physically comparable. In other words, step S2 is not correcting the data, but rather identifying and pairing the true cause of the temperature sampling with it.

[0024] For example, assuming a preset thermal time constant τ of 10 minutes, the temperature of a certain terminal is collected at 20:07. The three frozen values ​​of the load current before this are 37.6A at 19:30, 40.3A at 19:47, and 77.9A at 20:04, with time intervals of 37 minutes, 20 minutes, and 3 minutes, respectively. The corresponding weights are 0.0247, 0.1353, and 0.7408, respectively. The squares of the three currents are 1413.8, 1624.1, and 6068.4, respectively. After weighted averaging, the square of the equivalent thermal current is approximately 5273. Taking the square root, the equivalent thermal current is approximately 72.6A. This alignment process is as follows: Figure 4 As shown, the lighter load from more than 30 minutes ago has only a weak effect, while the load that jumped just 3 minutes ago is dominant. The equivalent thermal current falls between the two, which is consistent with the actual thermal state of the terminal as it is currently climbing.

[0025] Optionally, the weighted average accumulation window is truncated at three times the preset thermal time constant before the current acquisition time. Earlier frozen values ​​have a weight of less than 5%, and discarding them does not affect accuracy and reduces storage. When the acquisition terminal is powered on and there are fewer than two frozen values ​​in the window, no equivalent thermal current is output at this acquisition time. Optionally, if the acquisition terminal detects a jump correction in its own clock, the pairings that cross the jump time are invalidated, and accumulation starts again after the jump.

[0026] It is necessary to anticipate a potential failure scenario: if the preset thermal time constant deviates from the actual relaxation characteristics of the terminals, the equivalent thermal current will systematically be faster or slower than the actual thermal state, resulting in a systematic deviation in the calculated thermal resistance characteristic value. However, this deviation is essentially constant within the same tier—it depends on the statistical pattern of load fluctuations within that tier, and this statistical pattern is stable within the baseline window—and is therefore absorbed by the tier baseline as a whole. Step S4 compares the offset relative to the baseline and is not sensitive to this constant deviation. In other words, this method naturally has a relaxed requirement for the accuracy of the thermal time constant, which is the inherent reason why coarse selection is sufficient and precise identification is unnecessary. What truly needs to be avoided is only mismatch at the order of magnitude level; a candidate set covering 5 to 40 minutes is sufficient to encompass the vast majority of terminal specifications.

[0027] Regarding the value of the preset thermal time constant, this method does not require precise identification, but rather adopts a method such as... Figure 6The preliminary selection process involves pre-configuring a candidate set consisting of at least three candidate thermal time constants, such as four ranges: 5 minutes, 10 minutes, 20 minutes, and 40 minutes, covering the magnitude range of thermal time constants for common terminal specifications and installation methods. For each candidate value, an equivalent thermal current is constructed according to this step, and the thermal resistance characteristic value is calculated according to step S3. Then, the sample standard deviation of the thermal resistance characteristic value within the same range is statistically analyzed. The closer the selected thermal time constant is to the actual relaxation characteristics of the terminal, the more the calculated thermal resistance characteristic value at the same heating level clusters in one place, and the smaller the standard deviation. Based on this, candidate values ​​whose sample standard deviation does not exceed that of the other candidates are selected as the preset thermal time constant. For example, the sample standard deviations of a certain terminal in the same range under the four candidate values ​​are 0.42, 0.19, 0.31, and 0.55 (all in units of 10). -3 ℃ / A 2 If the selected action is not specified, then a 10-minute timeframe is chosen. The selected action is repeated according to the preset calibration cycle, which can be 30 to 90 days, typically 60 days. For enclosures where heat dissipation conditions vary significantly with the seasons, a shorter cycle can be chosen. The advantage of this approach is that the dense sampling required for parameter identification does not exist in the metrology enclosure scenario. The coarse selection only compares the statistical clustering of several candidates, without additional requirements on data density. Furthermore, the deviation between candidate timeframes is absorbed by the timeframe baseline and does not affect the drift determination.

[0028] In step S3, the data acquisition terminal subtracts the ambient temperature inside the chamber from the terminal temperature to obtain the terminal temperature rise. The terminal temperature rise is then divided by the square of the equivalent thermal current to obtain the thermal resistance characteristic value at the time of data acquisition. The terminal temperature rise refers to the difference between the terminal temperature and the ambient temperature inside the chamber; this difference eliminates the influence of diurnal and seasonal temperature variations. The thermal resistance characteristic value refers to the steady-state temperature rise corresponding to the square of a unit current, with units of °C / A. 2 Its physical meaning is the ratio of the product of terminal contact resistance and thermal resistance: when the contact resistance increases due to oxidation of the contact surface or loosening of the crimp, the same current generates more Joule heat, and the characteristic value of thermal resistance rises accordingly. Therefore, it is a direct observation reflecting the degree of contact deterioration. Using the previous example, with a terminal temperature of 52.4℃ and an ambient temperature of 35.1℃, the terminal temperature rise is 17.3℃. Dividing this by the square of the equivalent thermal current, 5273, the characteristic value of thermal resistance is approximately 3.28 × 10⁻⁶. -3 ℃ / A 2 As a comparison, if the value is directly paired with the current frozen value of 77.9A at the same time using existing methods, the resulting value is approximately 2.85 × 10⁻⁶. -3 ℃ / A 2 The value is about 13% lower than expected; if paired with 37.6A due to asynchronous misalignment, the value is approximately 12.24 × 10⁻⁶. -3 ℃ / A 2The result was approximately 2.7 times higher than expected. Through repeated comparisons and verifications, the inventors discovered that the same physical state yielded results several times different under instantaneous pairing, while remaining stable under equivalent thermocurrent pairing. This is precisely the reason why this method does not produce false alarms during periods of sudden load changes.

[0029] After obtaining the thermal resistance characteristic value, it is assigned to the corresponding current range according to the preset current range into which the equivalent thermal current falls. Within each range, the quantile of the assigned thermal resistance characteristic value forms the range baseline. The preset current range refers to several intervals pre-divided according to the ratio of the equivalent thermal current to the rated current of the outgoing circuit, for example, divided by 0.2, 0.4, 0.6, 0.8, and 1.0. When the ratio is lower than the lower limit of 0.2, the temperature rise at the sampling time is less than 1°C and the signal-to-noise ratio is poor, so it is not included in the calculation of the thermal resistance characteristic value and subsequent judgment. The lower limit can be adjusted between 0.15 and 0.3. For areas with long-term light loads, the lower limit can be appropriately lowered and the lower range can be densified. The reason for dividing by ratio instead of absolute current value is that the rated current of the outgoing line of different metering boxes in the same area is not the same. Dividing by ratio allows all boxes to share the same range configuration, and the platform does not need to maintain parameters for each box individually. The grading boundaries do not need to be uniform; they can be adjusted according to the histogram of the load distribution in the area to ensure that the sample size of each grade is roughly equal. Grades with too few samples will have large fluctuations in quantiles and unstable baselines. A typical approach is to take the distribution of equivalent thermal current over the most recent 30 days and place the boundaries at a position where the sample proportion of each grade is not less than 15%. The grading baseline refers to the median of all thermal resistance characteristic values ​​within the baseline window period of that grade. The baseline window period can be 14 to 60 days, typically 30 days, and should be updated on a rolling basis according to the update cycle, which can be 1 to 7 days, typically 1 day. The reason for establishing baselines for each grade instead of sharing a single baseline is that the actual heat dissipation of the terminals is not strictly linear. Natural convection and radiation heat dissipation increase nonlinearly with temperature rise, and the thermal resistance characteristic values ​​under different heating levels naturally have systematic differences. Mixing them together will misinterpret this difference as drift. After grading, the internal conditions of each grade are similar, and the baseline is meaningful for comparison. The median is not sensitive to individual bad points, so it is preferred over the mean; other quantiles such as the 60th percentile can also be used, but this invention is not limited to them.

[0030] The initial establishment of the tiered baseline involves an accumulation period. In the initial few days after the metering box is put into operation or after maintenance and relocation, the sample size for each tier is small, and quantiles fluctuate wildly. During this stage, only data is archived and accumulated; it is not used for drift determination. The typical accumulation period is 7 days, but it can be shortened to 3 days for areas with gentler load fluctuations. The criterion for ending the accumulation period can also be based on sample size rather than the number of days—any tier with 30 accumulated samples is considered ready for baseline assessment. Each tier is independently ready and independently assessed, with the first one to reach full capacity used first. Maintenance and relocation are initiated by maintenance personnel through the monitoring platform during work order closure. The reset command clears and rebuilds the baselines and accumulation queues for all tiers of that terminal, preventing contamination of the new baseline by degraded data from before maintenance.

[0031] In step S4, the acquisition terminal calculates the offset ratio of each grade according to the evaluation period and makes a judgment. The evaluation period refers to the most recent statistical window, typically 24 hours, and is rolled over according to the acquisition cycle. The offset ratio refers to the difference between the median thermal resistance characteristic value of the grade and the grade baseline within the evaluation period, divided by the grade baseline. If a grade has no samples within the evaluation period—which is often the case for high-heat grades under light load at night—the grade is recorded as an invalid grade in this evaluation period. It is neither counted in the number of grades that have reached the offset nor considered as not offset. It will be reinstated for judgment after accumulating at least 3 samples. When the number of grades with an offset ratio exceeding the preset drift threshold is not less than the preset number of grades, and this situation continues for at least the preset number of evaluation cycles, it is determined that the corresponding wiring terminal has contact deterioration. The preset drift threshold can be set between 10% and 30%, with 15% being typical. It is determined by replaying historical data collected in the area and statistically analyzing the number of false alarms and missed alarms under different thresholds, taking a middle ground. For high-risk areas such as densely populated locations, the threshold can be tightened to 10%. The preset number of risk levels is 2 to 4, with 2 being typical. The preset number of assessment cycles is 2 to 6, with 3 being typical, corresponding to a confirmation period of approximately 1 to 2 days. Optionally, a fast track can be maintained in addition to the continuous assessment: when the offset ratio of any level exceeds twice the preset drift threshold within a single assessment period, the waiting period for the continuous assessment cycle is skipped, and the process directly proceeds to step S5 for reporting. This balances sudden loosening—gradual deterioration follows the conservative continuous confirmation process, while sudden deterioration uses the fast track, with the two paths not interfering with each other.

[0032] Consistency across different timeframes is the core criterion for this step. Figure 5 As shown. Increased contact resistance is a change in the physical properties of the terminals themselves. Regardless of the load's heating level, the thermal resistance characteristic value will increase proportionally, manifesting as simultaneous drift of multiple terminals. Environmental disturbances such as direct sunlight, open cabinet doors, and proximity to heat sources alter heat dissipation conditions or the temperature field, often manifesting only at specific heating levels or occurring synchronously with a rise in the ambient temperature inside the cabinet. Therefore, when only a single terminal shows a shift, or when the shift occurs synchronously with a rise in the ambient temperature inside the cabinet, it is considered an environmental disturbance and no contact degradation treatment is applied. Considering that common factors such as overall cabinet heating may cause multiple terminals to drift simultaneously, optionally, when the proportion of terminals with a shift ratio exceeding a preset drift threshold within the same energy metering box reaches a preset percentage, it is treated as a cabinet-level common-cause disturbance. The aforementioned common-cause disturbance refers to a situation where changes in heat dissipation conditions affecting the entire enclosure, such as exposure to sunlight or obstructed ventilation, cause a simultaneous false increase in the thermal resistance characteristic values ​​of multiple terminals within the same enclosure. In this case, the contact degradation judgment of each terminal during the assessment period is revoked, and the enclosure-level common-cause disturbance event is reported to the monitoring platform for record-keeping. The preset percentage can be between 50% and 80%, with 60% being typical.

[0033] In step S5, the acquisition terminal reports the results in a tiered manner according to the judgment results. When contact degradation is determined, the offset ratio of each grade of the degraded terminal and the original sampling sequence of the corresponding assessment period are reported to the monitoring platform via the IoT communication link. The original sampling sequence includes the terminal temperature, ambient temperature inside the box, load current freezing value, and corresponding equivalent thermal current at each sampling time during the assessment period. After verification and confirmation by the monitoring platform, a maintenance work order is generated. Optionally, during the verification, the meteorological temperature data of the area where the metering box is located during the corresponding assessment period is retrieved, and the terminal temperature rise in the original sampling sequence is checked one by one. After eliminating the interference of high ambient temperature periods, the work order priority is determined according to the amplitude of the offset ratio of each grade. When contact degradation is not determined, the acquisition terminal only reports summary data according to the preset reporting period. The summary data includes the median thermal resistance characteristic value, sample quantity, and grade number of each grade within the reporting period. The reporting period can be from 1 hour to 24 hours, with 4 hours being typical. The monitoring platform archives and displays the trends of the summary data, allowing maintenance personnel to browse the health overview of each box and terminal by area. If the verification fails to confirm degradation, the platform sends a false alarm mark to the acquisition terminal. The acquisition terminal retains the judgment record for that assessment period along with the mark. The maintenance side can then use the accumulated false alarm records to adjust the drift threshold of the box accordingly, gradually aligning the judgment parameters with the characteristics of the area. The IoT communication link can be any of the following forms: HPLC, NB-IoT, Cat.1, or RS-485 cascade. The networking and transmission of the link itself are mature technologies and will not be elaborated here. Since the original sampling sequence is only reported when degradation is determined, the normal uplink of a single box is only about 50 bytes of summary data each time, thus controlling the access pressure and communication costs of the monitoring platform.

[0034] Exemplary System Figure 2 This is a schematic diagram of the operation monitoring system for an Internet of Things-based electricity metering box according to an embodiment of the present invention. Figure 2 As shown, the system includes a data acquisition terminal 100 installed in an energy metering box and a monitoring platform 200 connected to the data acquisition terminal 100 via an Internet of Things (IoT) communication link. It should be noted that the division of the modules within the data acquisition terminal 100 is illustrative and represents only a logical functional division; in actual implementation, they can be integrated into the same processing chip within the data acquisition terminal.

[0035] The data acquisition terminal 100 includes a data acquisition module 110, a time-stamp alignment module 120, a graded baseline module 130, a degradation judgment module 140, and a graded reporting module 150. The data acquisition module 110 supports the data acquisition terminal in performing... Figure 1Step S1 in the illustrated embodiment is executed by the time-scale alignment module 120, which executes step S2. Step S3 is executed by the graded baseline module 130, which executes step S4. Step S5 is executed by the graded reporting module 150. Specifically, the data acquisition module 110 reads the load current freeze value from the electricity meter and drives the temperature sensing element to complete temperature acquisition. The time-scale alignment module 120 maintains the current square weighted cumulative amount of each loop and outputs the equivalent thermal current at each temperature acquisition moment. The graded baseline module 130 completes the calculation, archiving, and baseline rolling update of the thermal resistance characteristic value. The degradation judgment module 140 performs cross-grade consistency judgment and distinguishes between environmental disturbances and box-level common-cause disturbances. The graded reporting module 150 organizes the reporting of summary data or original sampling sequences according to the judgment results. The monitoring platform 200 includes a verification module 210 and a work order module 220. The verification module 210 is used to verify and confirm the data reported by the acquisition terminal, and the work order module 220 is used to generate and dispatch maintenance work orders after verification and confirmation.

[0036] In one possible implementation, the data acquisition terminal 100 is based on a microcontroller, with five modules implemented as function calls in firmware. The current square weighted cumulative value is stored in on-chip memory as a fixed number of registers, and each circuit and each terminal occupies only about 60 bytes of runtime memory. The monitoring platform 200 is deployed on the main station side above the distribution area concentrator. The verification module 210 and the work order module 220 can interface with the alarm and work order subsystem of the existing power consumption information acquisition main station, without the need to build a separate platform. The system provided in this embodiment is used to execute the method described in any of the aforementioned implementations and can achieve the same technical effect. The mechanism of the processing performed by each module will not be repeated here.

[0037] Exemplary electronic devices Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Figure 7 As shown, the electronic device 700 includes a processor 710 and a memory 720. The processor 710 can be a microcontroller in a data acquisition terminal, such as an STM32L4 series low-power chip, or a server processing unit with data processing capabilities on the monitoring platform side. The memory 720 can include one or more combinations of volatile memory and non-volatile memory, storing a computer program that, when executed by the processor 710, implements the IoT-based power metering box operation monitoring method described in any of the above implementations of this invention. Optionally, the electronic device 700 may also include an input device 730 and an output device 740, such as a protocol communication interface, a temperature sensing element interface, and an IoT communication module, these components being interconnected via a bus system. For simplicity, Figure 7 Only some components relevant to this invention are shown in the diagram.

[0038] Exemplary computer program products and computer-readable storage media In addition to the methods, systems, and electronic devices described above, embodiments of the present invention can also be computer program products, comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the IoT-based power metering box operation monitoring method described in the foregoing sections of this specification. The computer program product can be written in any combination of one or more programming languages ​​to execute program code for performing the operations of the embodiments of the present invention. This program code can be executed entirely on the data acquisition terminal, or partially on the data acquisition terminal and partially on the monitoring platform.

[0039] Embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the IoT-based power metering box operation monitoring method described in this specification. The computer-readable storage medium may be any combination of one or more readable media, such as random access memory, read-only memory, flash memory, portable compact disk read-only memory, or any suitable combination thereof.

[0040] Example

[0041] The following combination Figure 8 Taking a meter box in a mixed commercial and residential area in southern China as an example, this paper reviews the entire process of implementing this method in a real-world degradation evolution scenario. The rated current of the main outgoing circuit of the meter box is 100A. A 10kΩ NTC temperature sensing element is mounted on the B-phase terminal of the circuit. The temperature sensing lead is connected to the analog acquisition port of the acquisition terminal inside the box. The acquisition terminal is based on an STM32L4 microcontroller and is connected to the concentrator of the area via an HPLC link. The concentrator then connects to the monitoring platform. The freezing period and temperature acquisition period are both 15 minutes. The preset thermal time constant is selected as 10 minutes after preliminary selection. The grading boundaries are divided according to the rated current ratios of 0.2, 0.4, 0.6, 0.8, and 1.0. The drift threshold is 15%, the number of grading levels is 2, the number of evaluation periods is 3, the evaluation period is 24 hours, and the baseline window period is 30 days.

[0042] During a certain evening rush hour, the load current of this circuit froze at 41.2A at 18:45, 43.8A at 19:00, and jumped to 82.6A at 19:15, corresponding to the concentrated activation of residential air conditioning. At 19:22, the data acquisition terminal recorded a terminal temperature of 48.9℃ and an ambient temperature of 34.6℃. The time-scale alignment module used a 10-minute thermal time constant to perform an exponentially weighted average of the squares of the three froze values, yielding an equivalent thermal current of approximately 75.9A, with a ratio to the rated current of 0.759, falling within the 0.6 to 0.8 range; the terminal temperature rise was 14.3℃, and the thermal resistance characteristic value was 2.48 × 10⁻⁶.-3 ℃ / A 2 The baseline for this classification at that time was 2.41 × 10⁻⁶. -3 ℃ / A 2 The offset ratio is approximately 2.9%, which is far below the 15% drift threshold. This sample is archived as a normal sample and does not trigger any action.

[0043] In the following weeks, the terminal slowly deteriorated due to loose crimping bolts and oxidation of the contact surface. For example... Figure 8 As shown, taking the first week after maintenance as the baseline, the median thermal resistance characteristic values ​​for the 0.6 to 0.8 range in weeks 0, 2, 4, and 6 are 2.43, 2.59, 2.91, and 3.27 respectively (all units are 0.001℃ / A). 2 The baseline for each grade is 2.41, and the offset percentages relative to the baseline are approximately 0.8%, 7.5%, 20.7%, and 35.7%, respectively. From week 4 onwards, the offset percentages for the three grades (0.4 to 0.6, 0.6 to 0.8, and 0.8 to 1.0) are approximately 18.9%, 20.7%, and 17.6%, respectively, exceeding the 15% threshold and remaining above it for three consecutive evaluation cycles. The 0.2 to 0.4 grade, due to its low heat generation level and offset of approximately 8.4%, did not reach the threshold, and the degradation judgment module determined that this terminal had contact degradation. During this period, no synchronous increase in the ambient temperature inside the enclosure was observed, and there was no offset in any of the other terminal grades within the same enclosure; environmental disturbances and enclosure-level common causes were ruled out. The graded reporting module reported the offset percentages of the three grades along with the original sampling sequence for that evaluation period to the monitoring platform. The verification module retrieved meteorological data from the distribution area, confirming that the local temperature was stable and there were no records of extreme high temperatures during the assessment period. It then checked the original sampling sequences one by one, confirming that the offset was widespread across different time periods and heat levels, rather than concentrated at specific points in time. Further checks of the summary data of other terminals in the same box confirmed that the offset was limited to this terminal. All three checks pointed to terminal degradation, confirming the verification. Based on the maximum offset of 20.7%, falling within the 15% to 30% range, a medium-priority maintenance work order was generated—if it exceeded 30%, it was processed with high priority and a shortened response time. Maintenance personnel arrived on-site and inspected the terminal, confirming that approximately one-quarter of the crimp bolts were loose, and oxidation and discoloration were visible on the contact surface, consistent with the initial assessment. After the maintenance team polished the contact surface and retightened it to the specified torque, the monitoring platform issued a repair order. The baselines for each grade of the terminal were re-established within a week, and normal monitoring was subsequently restored.

[0044] The other terminals in the same enclosure formed a natural control during the same monitoring period. Taking terminal 1 (phase A, with a load configuration similar to phase B) as an example, the thermal resistance characteristic values ​​of each grade consistently deviated from the baseline within ±5% throughout the entire monitoring period, never triggering a judgment. During a week of continuous high temperatures in late July, the ambient temperature inside the enclosure repeatedly rose above 41℃. The thermal resistance characteristic values ​​of the 0.8 to 1.0 grades of this terminal increased by approximately 9%, but the ambient temperature inside the enclosure rose synchronously during the same period, and no deviation was observed in the other grades. According to the criterion in step S4, this was classified as an environmental disturbance, and no alarm was generated. This week happened to be a high-incidence period for false alarms in the threshold route—adjacent batches of enclosures using a fixed limit of 65℃ in the same area triggered four temperature over-limit alarms during that week, and no terminal abnormalities were found during on-site inspections. The comparison shows that the cross-grade consistency criterion neither overlooks real degradation nor is it swayed by high temperatures.

[0045] As the load in the back-end area lightened in October, the equivalent thermal current of this circuit remained below 0.2 times the rated current for an extended period. Therefore, it was not included in the thermal resistance characteristic value calculation during most sampling times, and the grading range of 0.6 to 0.8 and above was marked as invalid for most evaluation periods. During the light-load period, this method remained silent, without any false judgments due to sparse samples. The grading baseline continued to be used after the light-load period ended without drift—this is precisely what the removal of low-heating grades and the handling of invalid grades jointly ensured: it is better to refrain from drawing conclusions when information is insufficient than to force a judgment amidst noise.

[0046] When the problem occurs, the absolute temperature of the terminal is not particularly noticeable: in week 4, the terminal temperature is approximately 54.5°C under an 82.6A load, and in week 6, at an ambient temperature of 36.2°C, it is only approximately 58.5°C, neither of which reaches the conventional fixed limit of 65°C. Following the threshold-based approach, it would take several more weeks or even longer to trigger an alarm, by which time the contact resistance would have increased further. This method advances the detection point to approximately two weeks before the temperature exceeds the limit.

[0047] As Comparative Example 1, data collected from the same area within the same week were replayed using the existing instantaneous pairing method. Due to the temperature still being in the climbing phase during the evening peak load change period, the temperature-current relationship obtained from instantaneous pairing repeatedly fluctuated between 16% and 2.6 times lower, resulting in 11 false offset alarms within a week when the same 15% drift threshold was applied. In contrast, this method resulted in 0 false alarms for the same set of data. As Comparative Example 2, equivalent heat-current pairing was retained, but current grading was canceled, allowing all samples to share a single baseline. The cross-seasonal data was then replayed. Due to the nonlinear increase in natural convection heat dissipation with temperature rise, the thermal resistance characteristic value during the summer high-load period is naturally lower than that during the winter light-load period. Mixing the data together caused the shared baseline to fluctuate slowly with the seasons, resulting in a lower baseline accumulated in winter after the start of summer, leading to 3 false drift alarms in June. After grading, the heat dissipation conditions within each gradation are similar, this seasonal fluctuation disappeared, and there were 0 false alarms during the same period. The two comparative examples respectively demonstrate the necessity of time-scale alignment and grading baselines, neither of which can be omitted.

[0048] The above playback data is an illustrative example based on the measured load curve. The specific values ​​for different substations may vary, but the comparison relationship between instantaneous pairing and the systemic distortion during the sudden change period and the equivalent heat current pairing remains stable.

[0049] In terms of project deployment, this method offers one modification path for both existing metering boxes and newly installed metering boxes. For existing boxes, firmware upgrades are used to embed time-scale alignment, baseline grading, and degradation assessment procedures into the existing data acquisition terminals. Terminal temperature sensors are installed according to circuits and connected to the nearest backup data acquisition port of the terminal. The entire box modification can be completed during routine maintenance during power outages. For newly installed boxes, the wiring of the temperature sensors and data acquisition terminals is pre-fabricated at the factory. After commissioning and a baseline accumulation period, the boxes automatically enter monitoring mode. Neither path requires replacing the electricity meter, altering the meter wiring, or relying on additional communication channels, minimizing disruption to the transformer substation upgrade process.

[0050] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions described in the various embodiments of the present invention.

Claims

1. A method for monitoring the operation of an electricity metering box based on the Internet of Things, characterized in that, include: Step S1: The data acquisition terminal in the power metering box periodically acquires the load current freezing value of each outgoing circuit, the terminal temperature of the corresponding wiring terminal, and the ambient temperature inside the box. The recording time of the load current freezing value and the acquisition time of the terminal temperature are not synchronized. Step S2: For each acquisition moment of terminal temperature, using a preset thermal time constant as the attenuation parameter, the squares of the frozen values ​​of each load current before the acquisition moment are exponentially weighted and averaged according to the time interval between the recording moment and the acquisition moment. The square root of the weighted average result is taken to obtain the equivalent thermal current aligned with the acquisition moment. Step S3: Subtract the ambient temperature inside the box from the terminal temperature to obtain the terminal temperature rise. Divide the terminal temperature rise by the square of the equivalent thermal current to obtain the thermal resistance characteristic value. According to the preset current range into which the equivalent thermal current falls, classify the thermal resistance characteristic value into the corresponding range. In each range, form the range baseline by the quantile of the thermal resistance characteristic value that has been classified. Step S4: Calculate the offset ratio of the thermal resistance characteristic value of each grade relative to the grade baseline during the most recent evaluation period. When the number of grades with an offset ratio exceeding the preset drift threshold is not less than the preset number of grades, and this situation continues for not less than the preset number of evaluation cycles, it is determined that the corresponding terminal has contact deterioration. When only a single grade shifts, or when the shift occurs simultaneously with the rise in ambient temperature inside the chamber, it is determined to be an environmental disturbance and no contact degradation treatment is performed. Step S5: When it is determined that there is contact deterioration, the acquisition terminal reports the offset ratio of each grade of the deteriorated terminal and the original sampling sequence of the corresponding evaluation period to the monitoring platform via the Internet of Things communication link. After the monitoring platform reviews and confirms the report, it generates a maintenance work order. If no contact degradation is determined, the acquisition terminal reports summary data of thermal resistance characteristic values ​​for each grade to the monitoring platform according to the preset reporting cycle.

2. The method according to claim 1, characterized in that, A candidate set consisting of at least three candidate thermal time constants is pre-configured. For each candidate thermal time constant, the corresponding equivalent thermocurrent is obtained according to step S2, and the corresponding thermal resistance characteristic value is obtained according to step S3. The sample standard deviation of the thermal resistance characteristic value corresponding to each candidate thermal time constant within the same grade is calculated. The candidate thermal time constant whose sample standard deviation does not exceed that of the other candidates is taken as the value of the preset thermal time constant, and is reselected according to the preset calibration cycle.

3. The method according to claim 1, characterized in that, The preset current grading method is as follows: it is divided into at least three grades according to the ratio of equivalent thermal current to the rated current of the outgoing circuit. Sampling times with a ratio lower than the preset lower limit are not included in the calculation of thermal resistance characteristic value and the determination of contact degradation.

4. The method according to claim 1, characterized in that, The value of the grading baseline is determined by the median of all thermal resistance characteristic values ​​during the most recent baseline window period within the corresponding grading, and is updated on a rolling basis according to a preset update cycle; Once a terminal block is determined to have contact deterioration, the baseline of each corresponding segment of the terminal block will stop updating until it is repaired.

5. The method according to claim 1, characterized in that, When the proportion of terminals in the same energy metering box whose offset ratio exceeds the preset drift threshold reaches the preset proportion, it is determined to be a box-level common cause disturbance. The contact degradation judgment of each terminal in the evaluation period is cancelled, and the box-level common cause disturbance event is reported to the monitoring platform via the Internet of Things communication link.

6. The method according to claim 1, characterized in that, After receiving the reported data of the deteriorated terminals, the monitoring platform retrieves the meteorological temperature data of the power metering box area during the corresponding assessment period, verifies the terminal temperature rise in the original sampling sequence one by one, eliminates the interference of the high temperature period, and determines the priority of the maintenance work order according to the amplitude of the offset ratio of each grade.

7. The method according to claim 3, characterized in that, The preset lower limit is 0.2 times the rated current.

8. An Internet of Things-based power metering box operation monitoring system, characterized in that, This includes a data acquisition terminal installed in the electricity metering box and a monitoring platform connected to the data acquisition terminal via an Internet of Things (IoT) communication link; The data acquisition terminal includes: a data acquisition module, used to acquire the load current freeze value of the outgoing circuit, the terminal temperature of the wiring terminal and the ambient temperature inside the box according to their respective cycles; The time-scale alignment module is used to perform an exponential weighted average of the square of the load current freezing value with a preset thermal time constant and then take the square root to obtain the equivalent thermocurrent aligned with the acquisition time of the terminal temperature. The grading baseline module is used to divide the difference between the terminal temperature and the ambient temperature inside the box by the square of the equivalent thermal current to obtain the thermal resistance characteristic value, and to categorize and archive the samples according to the preset current and form the grading baseline based on the quantile of the samples already included in the grading. The degradation judgment module is used to determine whether there is contact degradation of the terminal block and to distinguish environmental disturbances based on the offset ratio of the thermal resistance characteristic value of each grade relative to the grade baseline, the number of grades that have reached the offset and the duration of the offset. The hierarchical reporting module is used to report the offset ratio of each grade and the original sampling sequence to the monitoring platform when contact degradation is detected, and to report summary data according to the preset reporting cycle in other cases. The monitoring platform includes a verification module and a work order module. The verification module is used to verify and confirm the data reported by the acquisition terminal, and the work order module is used to generate maintenance work orders after verification and confirmation.

9. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 7.

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