Method for monitoring state of electric energy meter connector and electric energy meter connector

Abstract

The present application relates to the technical field of electric energy meter connector, and particularly provides a state monitoring method and electric energy meter connector, the method is based on the temperature data of connector plug, the dynamic safety threshold of current time, temperature change rate and temperature trend to carry out early warning level classification, based on early warning level, fault type and residual life to determine the state monitoring result of connector, wherein, the dynamic safety threshold is determined according to the real-time current data and real-time environmental temperature of flowing through the connector, the temperature trend and temperature change rate are determined based on sliding window strategy and 3 sigma principle, the fault type is determined according to temperature data, current data and historical data, and the residual life is predicted according to temperature data and time data corresponding to temperature data. To solve the problem that related technology cannot monitor the running state of electric energy meter connector in real time.

Description

Technical Field

[0001] This invention relates to the field of electricity meter technology, and in particular to a method for monitoring the status of an electricity meter connector and an electricity meter connector. Background Technology

[0002] With the rapid development of smart grids, a large number of electricity meters need to be installed for end-point data collection and metering, which continuously increases the performance requirements for electricity meter connectors.

[0003] Based on operational data and statistical results, the vast majority of electricity meter connector failures are related to excessive temperature. Therefore, monitoring the temperature rise of electricity meter connectors is necessary. However, since the relevant electricity meter connectors only optimize the installation method of the electricity meter structurally, performance testing and management of the connectors in use still require regular inspections by maintenance personnel, lacking real-time monitoring of the operating status of electricity meter connectors.

[0004] There is currently no effective solution to the problem that related technologies cannot monitor the operating status of electricity meter connectors in real time. Summary of the Invention

[0005] The present invention provides a method for monitoring the status of an electricity meter connector and an electricity meter connector, which at least solves the problem that related technologies cannot monitor the operating status of electricity meter connectors in real time.

[0006] This invention provides a method for monitoring the status of an electricity meter connector, comprising: determining a first warning level when the temperature data at the connector plug is greater than the current dynamic safety threshold, wherein the dynamic safety threshold is determined based on real-time current data flowing through the connector and real-time ambient temperature; determining a second warning level when the temperature data is greater than a preset multiple of the dynamic safety threshold, less than or equal to the dynamic safety threshold, and exhibits an abnormal temperature trend, wherein an abnormal temperature trend is determined based on a sliding window strategy and the 3σ principle when the temperature data within three consecutive sliding windows is greater than a preset multiple of the dynamic safety threshold; determining a third warning level when the temperature data is less than or equal to the dynamic safety threshold and exhibits an abnormal temperature change rate, wherein an abnormal temperature change rate is determined based on a sliding window strategy when the ratio of the temperature change within a sliding window to the time size of the sliding window is greater than a preset threshold; and determining the status monitoring result of the connector based on the warning level, fault type, and remaining lifespan, wherein the fault type is determined based on temperature data, current data, and historical data, and the remaining lifespan is predicted based on temperature data and the corresponding time data.

[0007] Preferably, before determining the warning level, the above method further includes:

[0008] Calculate the dynamic safety threshold based on the following formula. :

[0009] ;

[0010] In the formula, Represents the fitted parameters, Indicates multiplication. This represents real-time current data. This indicates the resistance of the conductive part at the connector plug. Indicates the sampling time interval. This indicates the specific heat capacity of the material in the conductive part. Indicates the mass of the conductive part. This represents the temperature compensation value. This represents the ambient temperature correction factor. This indicates the real-time ambient temperature.

[0011] Preferably, before determining the condition monitoring results of the connector based on the warning level, fault type, and remaining life, the above method further includes: determining input features based on temperature data, current data, and historical data; inputting the input features into a classification algorithm model to obtain classification results, which are used to characterize different fault types. In this process, the classification algorithm model is incrementally trained periodically based on manually labeled fault data, and the classification algorithm model is used for feature selection based on Shapley additive interpretation values.

[0012] Preferably, the input features are determined based on temperature data, current data, and historical data, including: determining the temperature fluctuation amplitude and average temperature based on temperature data; determining the current fluctuation amplitude and effective current value based on current data; and determining the equipment's service life based on historical data. The input features include temperature fluctuation amplitude, average temperature, current fluctuation amplitude, effective current value, and equipment service life. Temperature fluctuation amplitude and current fluctuation amplitude are used to characterize poor contact faults, average temperature and equipment service life are used to characterize oxidation faults, and the effective current value is used to characterize overload faults.

[0013] Preferably, after inputting the input features into the classification algorithm model and obtaining the classification result, the above method further includes: storing the classification result; calling the classification result and calculating the fault classification accuracy by combining it with the actual fault type corresponding to the classification result; and performing full retraining on the classification algorithm model if the decrease in fault classification accuracy exceeds a preset threshold.

[0014] Preferably, before determining the status monitoring results of the connector based on the warning level, fault type, and remaining lifespan, the above method further includes: determining a temperature decay curve based on temperature data and the corresponding time data; calculating a predicted temperature value based on the temperature decay curve and fault frequency, combined with a time series prediction model, wherein the fault frequency is the number of times the warning level is determined within a preset time; determining the expected fault time based on the time corresponding to when the predicted temperature value first exceeds the dynamic safety threshold; and determining the remaining lifespan of the connector based on the expected fault time and the current time.

[0015] Preferably, the status monitoring results of the connector are determined based on the warning level, fault type, and remaining lifespan, including: determining the corresponding warning instruction based on the warning level; determining the maintenance timing based on the remaining lifespan; and determining the status monitoring results based on the warning instruction, fault type, and maintenance timing to generate operation and maintenance recommendations.

[0016] Preferably, before determining the warning level, the above method further includes: filtering temperature data and current data based on the quartile method to obtain data to be processed; and standardizing the data to be processed to obtain target data, which is used to determine the warning level.

[0017] This invention provides an energy meter connector, including a body, a cover plate, and a circuit board installed between the body and the cover plate. A temperature sensor is installed at the plug of the body, and a current sensor is also installed on the body. The circuit board has a temperature acquisition interface corresponding to the temperature sensor, a current acquisition interface corresponding to the current sensor, and a control chip connected to both the temperature acquisition interface and the current acquisition interface. The control chip has a built-in clock unit. When the temperature data at the plug is greater than the current dynamic safety threshold, the control chip determines the warning level as a first warning level; when the temperature data is greater than a preset multiple of the dynamic safety threshold, less than or equal to the dynamic safety threshold, and the temperature trend is abnormal, the control chip determines the warning level as a second warning level; when the temperature data is less than or equal to the dynamic safety threshold... In cases where the temperature change rate is abnormal, the warning level is determined to be the third warning level. Based on the warning level, fault type, and remaining lifespan, the status monitoring results of the connector are determined. Among them, the dynamic safety threshold is determined based on the real-time current data flowing through the body and the real-time ambient temperature. Based on the sliding window strategy and the 3σ principle, if the temperature data in three consecutive sliding windows is greater than a preset multiple of the dynamic safety threshold, the temperature trend is determined to be abnormal. Based on the sliding window strategy, if the ratio of the temperature change amount in a sliding window to the time size of the sliding window is greater than a preset threshold, the temperature change rate is determined to be abnormal. The fault type is determined based on temperature data, current data, and historical data, and the remaining lifespan is predicted based on temperature data and the corresponding time data.

[0018] Preferably, the body has a groove at the bottom of the plug, and the temperature sensor is placed in the groove and close to the plug; an OLED display is embedded on the side of the body away from the circuit board, and the OLED display is connected to the control chip through an integrated circuit bus.

[0019] This invention provides a method for monitoring the status of an electricity meter connector and an electricity meter connector itself. It determines a dynamic safety threshold based on real-time current data and real-time ambient temperature, and classifies temperature warning levels based on the dynamic safety threshold and a sliding window strategy. Through triple analysis of warning level, fault type, and remaining lifespan, comprehensive and accurate status monitoring results can be obtained. This method reduces safety risks and alleviates the maintenance burden while monitoring the operating status of the electricity meter connector in real time. It addresses the problem that related technologies cannot monitor the operating status of electricity meter connectors in real time. Attached Figure Description

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other embodiments based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the steps of a status monitoring method for an electricity meter connector in an embodiment of the present invention.

[0022] Figure 2 This is a schematic diagram of the structure of an electricity meter connector in an embodiment of the present invention.

[0023] Figure 3 The temperature sensor is set in Figure 2 A schematic diagram showing the location of the connector on the main body.

[0024] Figure 4 yes Figure 2 A schematic diagram of the circuit board structure.

[0025] Figure 5 Is the display screen set? Figure 2 A schematic diagram showing the location of the connector on the main body.

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0027] The above figures include the following reference numerals:

[0028] 1—Body; 2—Cover plate; 3—Circuit board;

[0029] 11—Temperature sensor; 12—Current sensor; 13—Plug; 14—OLED display;

[0030] 31—Temperature acquisition interface; 32—Current acquisition interface; 33—Control chip; 34—HPLC carrier communication module. Detailed Implementation

[0031] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0032] Based on operational data and statistical results, over 70% of electricity meter connector failures are related to excessive temperature. Therefore, temperature rise measurement of electricity meter connectors is a crucial testing item in full-performance testing. However, since the relevant electricity meter connectors only optimize the installation method structurally, performance testing and management of the connectors in use still require regular inspections by maintenance personnel, lacking real-time monitoring of the operating status of electricity meter connectors.

[0033] Therefore, please refer to Figure 1 As shown, the present invention provides a method for monitoring the status of an electricity meter connector, including steps S101 to S104.

[0034] Step S101: If the temperature data at the connector plug is greater than the dynamic safety threshold at the current moment, the warning level is determined to be the first warning level. The dynamic safety threshold is determined based on the real-time current data flowing through the connector and the real-time ambient temperature.

[0035] Step S102: If the temperature data is greater than a preset multiple of the dynamic safety threshold, less than or equal to the dynamic safety threshold, and the temperature trend is abnormal, the warning level is determined to be the second warning level. Specifically, based on the sliding window strategy and the 3σ principle, if the temperature data is greater than a preset multiple of the dynamic safety threshold within three consecutive sliding windows, the abnormal temperature trend is determined.

[0036] Step S103: If the temperature data is less than or equal to the dynamic safety threshold and the temperature change rate is abnormal, the warning level is determined to be the third warning level. Specifically, based on the sliding window strategy, if the ratio of the temperature change within a sliding window to the time size of the sliding window is greater than a preset threshold, the temperature change rate is determined to be abnormal.

[0037] Step S104: Based on the warning level, fault type and remaining lifespan, determine the status monitoring results of the connector. The fault type is determined based on temperature data, current data and historical data, and the remaining lifespan is predicted based on temperature data and the time data corresponding to the temperature data.

[0038] Temperature data is acquired in real time. It should be noted that in this embodiment of the invention, real-time acquisition refers to acquisition at a certain sampling frequency, and real-time data refers to data acquired at a certain sampling frequency. For example, a temperature sensor with an accuracy of ±0.5℃ is used to acquire temperature data at a sampling frequency of 1Hz.

[0039] The current data flowing through the electricity meter connector can be, but is not limited to, acquired through a current sensor. For example, the aforementioned current sensor is a Hall effect current sensor. The aforementioned current data includes, but is not limited to, the RMS current value, the peak current value, and the current fluctuation frequency.

[0040] When the electricity meter connector is installed inside an indoor distribution box, the ambient temperature refers to the air temperature inside the distribution box. When the electricity meter connector is installed inside an outdoor metering box, the ambient temperature refers to the temperature of the ventilated area inside the metering box. The device for collecting ambient temperature is located inside either the indoor distribution box or the outdoor metering box. The device for collecting ambient temperature and the device performing the above-described condition monitoring method transmit signals via wired or wireless means.

[0041] By setting an adaptive dynamic safety threshold, a higher operating temperature can be allowed even when the current flowing through the connector is high, avoiding the risk of false alarms in high-current scenarios caused by using fixed thresholds in related technologies. The specific definition of "high current" can be determined by those skilled in the art based on the actual application scenario. For example, for electricity meter connectors used in residential daily electricity consumption scenarios, a current flowing through the connector greater than 60A is considered a high current.

[0042] In applications that pursue higher monitoring accuracy, dynamic safety thresholds are also related to maintenance cycle deviations, historical fault correlations, material tolerance coefficients, temperature and humidity coupling coefficients, and enclosure heat dissipation efficiency.

[0043] For example, if the maintenance cycle deviation exceeds 6 months, the probability of loosening of the electricity meter connectors increases, requiring improved early warning sensitivity; in this case, the dynamic safety threshold can be reduced by 5%-10%. If the electricity meter connector has been repaired within 3 months due to temperature rise, the dynamic safety threshold can be reduced by 8%-10%. If the ratio of the current temperature data to the connector material's maximum withstand temperature is greater than or equal to 0.8, the dynamic safety threshold can be reduced by 8%-15%. In high-temperature and high-humidity environments, the dynamic safety threshold can be reduced by 5%-10%. For example, if the current ambient temperature is 60℃ and the humidity is 80%, the dynamic safety threshold can be lowered by 7%. If the temperature difference between the inside and outside of the electricity meter box is greater than or equal to 10℃, the box's heat dissipation is poor, requiring a 5%-10% reduction in the dynamic safety threshold.

[0044] If multiple situations requiring a reduction in the dynamic security threshold exist simultaneously, the situation with the largest reduction in the dynamic security threshold shall prevail, and the dynamic security threshold shall be reduced accordingly based on the maximum reduction ratio.

[0045] In step S102, the specific value of the preset multiple can be determined by those skilled in the art through a limited number of experiments. For example, the range of the preset multiple is 0.75-0.85. This embodiment will be described below using a preset multiple of 0.8 as an example.

[0046] The sliding window duration can be set to 5-10 minutes or 5-15 seconds. 5-10 minutes is suitable for reducing computational resource consumption, while 5-15 seconds is suitable for improving monitoring accuracy. The step size of the sliding window can be determined based on the sliding window duration and the sampling frequency of the temperature sensor. This embodiment will subsequently use a sliding window duration of 10 seconds and a step size of 1 second as an example.

[0047] To obtain accurate temperature trends, exponential smoothing or autoregressive integral moving average algorithms can be used to predict temperature trends based on the short-term time-series correlation between temperature and current.

[0048] The preset threshold used to determine whether the rate of temperature change is abnormal can be determined based on the electricity meter manufacturer's recommended value, industry standards or specification appendices, or it can be determined based on the specific application environment of the electricity meter connector. For example, the manufacturer has specified that the fixed warning threshold for the 5(60)A connector should be set at 2.5℃ / min, which is the preset threshold mentioned above.

[0049] Specifically, the early warning algorithm model used to determine the warning level can be a gradient boosting tree (LightGBM) model, or a combination of a gradient boosting tree model and a long short-term memory (LSTM) network model. Based on the gradient boosting tree model, multiple features are extracted from temperature, current, and time data to obtain dynamic safety thresholds, temperature trends, and temperature change rates, thus enabling warning level classification. Combining the LSTM network model can improve the accuracy of temperature trend calculation, thereby improving the accuracy of warning level classification. Simultaneously, the LSTM network model can also be used to predict remaining lifespan, as will be explained later.

[0050] Historical data may include, but is not limited to, the installation and commissioning time of electricity meter connectors, fault records, and operation and maintenance data.

[0051] The service life of equipment can be determined based on the installation and commissioning time and the current monitoring time. This embodiment will further illustrate this by using oxidation fault analysis based on the service life of the equipment as an example. Fault records can include, but are not limited to, historical fault types, fault duration, temperature data at the time of the fault, and current conditions. Maintenance data can include, but is not limited to, component replacement types and replacement times.

[0052] By analyzing warning levels, fault types, and remaining lifespan, comprehensive and accurate condition monitoring results can be obtained. This allows for real-time monitoring of the operating status of electricity meter connectors while reducing safety risks and alleviating maintenance burdens. This embodiment subsequently provides preferred methods for determining fault types and remaining lifespan.

[0053] Preferably, before determining the warning level based on steps S101 to S103, the method further includes: filtering the temperature and current data based on the quartile method to obtain data to be processed; and standardizing the data to be processed to obtain target data, which is used to determine the warning level.

[0054] In the actual data collection process, the temperature and current data related to the electricity meter connectors will inevitably contain abnormal outliers, such as temperature jumps caused by momentary sensor failures, current spikes caused by electromagnetic interference from the power grid, and momentary current fluctuations caused by loose wiring.

[0055] Based on the quartile method, the first quartile Q1, the third quartile Q3, and the interquartile range IQR = Q3 - Q1 corresponding to the above data are calculated. Values ​​exceeding the range of [Q1 - 1.5 × IQR] to [Q3 + 1.5 × IQR] are identified as outliers and removed. Here, 1.5 × IQR is a common threshold for industrial data filtering, which can be finely adjusted according to the rated operating conditions of the electricity meter connector, such as the rated current range and normal temperature fluctuation range. For example, 2 × IQR can be used under extreme conditions, demonstrating strong adaptability.

[0056] Data filtering based on the quartile method has two advantages: firstly, it can directly adapt to the actual characteristics of temperature and current data monitored on-site, without relying on assumptions about data distribution; secondly, the calculation time is shorter than the data acquisition cycle, so it will not affect the real-time determination of the subsequent warning level.

[0057] The Min-Max normalization algorithm is used to scale the corresponding features of the above data to the [0,1] interval to improve the convergence speed of the subsequent algorithm model.

[0058] In addition, during the training of the algorithm model, it is also necessary to remove outliers from the training data based on the above quartile method and perform feature normalization processing based on the above Min-Max standardization algorithm. After outlier removal and before feature normalization processing, it is preferable to generate simulated samples corresponding to rare fault types by adding Gaussian noise to enhance the training effect of the algorithm model.

[0059] Preferably, before determining the warning level based on steps S101 to S103, the above method further includes:

[0060] Calculate the dynamic safety threshold based on the following formula. :

[0061] ;

[0062] In the formula, Represents the fitted parameters, Indicates multiplication. This represents real-time current data. This indicates the resistance of the conductive part at the connector plug. Indicates the sampling time interval. This indicates the specific heat capacity of the material in the conductive part. Indicates the mass of the conductive part. This represents the temperature compensation value. This represents the ambient temperature correction factor. This indicates the real-time ambient temperature.

[0063] Fitting parameters The initial value is 0.5, dimensionless. Temperature compensation value. The initial value is 30℃. Ambient temperature correction factor. The initial value is 0.2, which is dimensionless. Those skilled in the art can adjust the fitting parameters based on a finite number of experiments. Temperature compensation value and ambient temperature correction factor The specific values ​​will be adjusted.

[0064] Resistance of conductive parts It is the sum of the intrinsic resistance of the conductive part and the contact resistance. The intrinsic resistance is the inherent resistance of the plug's own material. The contact resistance is the resistance at the contact point between the plug and the socket. Those skilled in the art can determine or measure the resistance of the conductive part in advance based on the specific model of the electricity meter connector and plug used. and quality .

[0065] Sampling time interval This is the sampling period of the temperature sensor; a sampling frequency of 1Hz corresponds to a sampling time interval of 1s.

[0066] For example, when the material of the conductive part is copper, the specific heat capacity c of the conductive part is 460 joules per kilogram of degree Celsius.

[0067] Preferably, before determining the connector's condition monitoring results based on the warning level, fault type, and remaining life in step S104, the method further includes: determining input features based on temperature data, current data, and historical data. The input features are then input into a classification algorithm model to obtain classification results, which are used to characterize different fault types. The classification algorithm model is periodically incrementally trained based on manually labeled fault data, and the model uses Shapley additive interpretation values ​​for feature selection.

[0068] Specifically, by combining the Shapley Additive Interpretation Value (SHAP) with the existing random forest classifier for feature selection, the above classification algorithm model can be obtained. That is, the above classification algorithm model is an improved random forest classifier, which supports multi-class classification, has strong noise resistance, and can handle nonlinear features.

[0069] Regularly can be, but is not limited to, monthly.

[0070] Incremental training refers to adjusting only the decision tree weights of the classification algorithm model, which avoids the high cost of retraining.

[0071] Furthermore, input features are determined based on temperature data, current data, and historical data, including: determining temperature fluctuation amplitude and average temperature based on temperature data; determining current fluctuation amplitude and RMS current based on current data; and determining the equipment's service life based on historical data. The input features include temperature fluctuation amplitude, average temperature, current fluctuation amplitude, RMS current, and equipment service life; temperature fluctuation amplitude and current fluctuation amplitude are used to characterize poor contact faults, average temperature and equipment service life are used to characterize oxidation faults, and the RMS current is used to characterize overload faults.

[0072] For example, in the temperature fluctuation range T std >3℃ and current fluctuation amplitude I stdIn cases where the percentage is less than 5%, the connector exhibits poor contact. A further criterion can be added: an unstable temperature change rate ΔT / Δt. Specifically, this means that within a temperature fluctuation range T... std >3℃, current fluctuation amplitude I std If the temperature change rate is less than 5% and the temperature change rate ΔT / Δt is abnormal, the connector has a poor contact fault.

[0073] At the average temperature T avg With the service life t of the equipment age If the annual temperature increase is greater than 2°C and the effective current value is normal, the connector has an oxidation fault.

[0074] If the effective current value is greater than 1.2 times the rated current, and the temperature data increases synchronously with the current data, the connector has an overload fault.

[0075] By combining temperature data, current data, and time data for fault diagnosis, the limitations of single temperature data can be overcome, and the accuracy of condition monitoring can be improved.

[0076] Furthermore, compared to the relevant random forest model, the classification algorithm model can be simplified by reducing the number of trees for local preliminary judgment, which helps to improve classification speed and reduce computational cost. For example, the number of trees can be reduced from 100 to 30.

[0077] Furthermore, after inputting the input features into the classification algorithm model and obtaining the classification result, the above method also includes: storing the classification result; calling the classification result and calculating the fault classification accuracy by combining it with the actual measured fault type corresponding to the classification result; and performing a full retraining of the classification algorithm model if the decrease in fault classification accuracy exceeds a preset threshold.

[0078] For example, the preset threshold used to measure the decline in fault classification accuracy is 5%.

[0079] The classification algorithm model is incrementally trained on manually labeled fault data on a regular basis, and a full retraining is performed on the classification algorithm model when the decrease in fault classification accuracy exceeds a preset threshold, so that the above classification algorithm model has an online learning mechanism.

[0080] Preferably, before determining the connector's status monitoring results based on the warning level, fault type, and remaining lifespan in step S104, the method further includes: determining a temperature decay curve based on temperature data and the corresponding time data; calculating a predicted temperature value based on the temperature decay curve and fault frequency, combined with a time series prediction model, wherein the fault frequency is the number of times the warning level is determined within a preset time period; determining the expected fault time based on the time corresponding to when the predicted temperature value first exceeds the dynamic safety threshold; and determining the connector's remaining lifespan based on the expected fault time and the current time.

[0081] Temperature decay curve characterizes the average temperature T avg Trends over time.

[0082] The frequency of failures can be, but is not limited to, the number of times a warning level has been determined within the past 3 months.

[0083] The aforementioned time series prediction model can be a Long Short-Term Memory (LSTM) network model. The training data used to train the aforementioned time series prediction model includes historical temperature sequences and actual time labels that have changed (RUL=0).

[0084] Temperature forecasts are temperature values ​​for a future period of time, the specific range of which can be determined by those skilled in the art based on actual needs. For example, a temperature forecast might be the temperature value for the next 7 days.

[0085] Furthermore, the aforementioned early warning and classification algorithms can be deployed at the edge, while the time series prediction model can be deployed in the cloud. Through a closed loop of temperature early warning, fault diagnosis, and lifespan prediction, intelligent management of the entire lifecycle of electricity meter connector faults is achieved, providing data-driven decision support for power operation and maintenance.

[0086] This embodiment provides the performance metrics required for training each algorithm model: The training of the early warning algorithm model requires an accuracy ≥ 98% and an average response time ≤ 500ms. The training of the classification algorithm model requires a multi-class F1 score > 90%. The training of the time series prediction model requires a remaining lifetime error ≤ 3 days and a 90% confidence interval covering the actual lifetime.

[0087] To train the various algorithm models, a pilot deployment was conducted using 100 electricity meter connectors with different service lives, collecting 100,000 data samples. This included artificially creating scenarios such as poor contact, oxidation, and overload to simulate fault injection and verify the model's response speed and diagnostic accuracy. The model's performance under extreme scenarios, such as high-temperature environments and high-current surges, was recorded to supplement edge case data.

[0088] Preferably, a maintenance model is deployed in the cloud to combine relevant data on connectors across the entire network and use clustering algorithms to identify common fault patterns, such as a batch of connectors being prone to oxidation, in order to optimize the global maintenance strategy.

[0089] Preferably, step S104, based on the warning level, fault type, and remaining lifespan, determines the status monitoring results of the connector, including: determining the corresponding warning instruction based on the warning level; determining the maintenance timing based on the remaining lifespan; and determining the status monitoring results based on the warning instruction, fault type, and maintenance timing to generate operation and maintenance recommendations.

[0090] Specifically, at the first warning level, the warning instruction is an immediate alarm, including but not limited to issuing audible and visual signals. At the second warning level, the warning instruction indicates that the connector temperature may exceed the dynamic safety threshold within 10 minutes, requiring close monitoring. At the third warning level, the warning instruction is a simple reminder to pay attention. The level of attention required for the second and third warning levels differs, which can be distinguished by the font color and frequency of the prompts on the display screen.

[0091] For example, if the remaining lifespan is less than 30 days and the failure frequency is greater than 5 times, a replacement recommendation within 7 days is generated. If the remaining lifespan is between 30 and 90 days, a recommendation for regular inspection is generated, such as once every 10 days.

[0092] The maintenance personnel will conduct corresponding troubleshooting and repair based on the fault types in the maintenance recommendations.

[0093] For example, the application process of the above-mentioned state monitoring method is as follows: the temperature sensor collects the current temperature data as 55℃, the current sensor collects the corresponding current data as 10A, and the ambient temperature acquisition device collects the corresponding ambient temperature as 25℃. The edge-end early warning algorithm model calculates the dynamic safety threshold as 40℃. Since the current temperature data is greater than the corresponding dynamic safety threshold, the first early warning level is determined. The edge-end classification algorithm model synchronously analyzes the characteristics: temperature fluctuation amplitude T. std The temperature is 3.5℃, and the current fluctuation amplitude I std The failure rate was 1%, diagnosed as a poor contact fault. The cloud-based time series prediction model calculated the remaining usable life (RUL) to be 15 days, and a maintenance work order was sent to schedule contact cleaning or replacement within 10 days.

[0094] In summary, the status monitoring method provided by the embodiments of this invention improves power safety and fault handling efficiency, reduces operating costs, and ensures power stability. By monitoring connector temperature in real time, it provides timely warnings when temperatures rise abnormally, enabling early detection of overheating hazards caused by poor contact, oxidation, or overload, thus preventing safety accidents due to high temperatures. It reduces the scope of circuit faults caused by connector failures, lowering the risk of equipment damage. With fault analysis capabilities, it can quickly pinpoint the causes of abnormal temperatures, such as excessive contact resistance or current overload, shortening troubleshooting time and reducing the blind spots of manual troubleshooting. It facilitates targeted repairs or replacements by maintenance personnel, improving the accuracy and efficiency of fault handling, and is particularly suitable for large-scale, complex power networks.

[0095] Please refer to Figure 2 As shown, an embodiment of the present invention also provides an electricity meter connector, including a body 1, a cover plate 2, and a circuit board 3 installed between the body 1 and the cover plate 2.

[0096] A temperature sensor 11 is installed at the plug of the main body 1, and a current sensor 12 is also installed on the main body 1.

[0097] The circuit board 3 is provided with a temperature acquisition interface 31 corresponding to the temperature sensor 11, a current acquisition interface 32 corresponding to the current sensor 12, and a control chip 33 connected to both the temperature acquisition interface 31 and the current acquisition interface 32.

[0098] The control chip 33 has a built-in clock unit. When the temperature data at the plug is greater than the current dynamic safety threshold, the control chip 33 determines the warning level as the first warning level; when the temperature data is greater than a preset multiple of the dynamic safety threshold, less than or equal to the dynamic safety threshold, and the temperature trend is abnormal, the control chip 33 determines the warning level as the second warning level; when the temperature data is less than or equal to the dynamic safety threshold, and the temperature change rate is abnormal, the control chip 33 determines the warning level as the third warning level; based on the warning level, fault type, and remaining lifespan, the control chip 33 determines the status monitoring result of the connector.

[0099] The dynamic safety threshold is determined based on real-time current data flowing through the body and real-time ambient temperature. Based on the sliding window strategy and the 3σ principle, an abnormal temperature trend is determined when the temperature data within three consecutive sliding windows exceeds a preset multiple of the dynamic safety threshold. Based on the sliding window strategy, an abnormal temperature change rate is determined when the ratio of the temperature change within a sliding window to the time size of the sliding window exceeds a preset threshold. The fault type is determined based on temperature data, current data, and historical data, and the remaining lifetime is predicted based on temperature data and the corresponding time data.

[0100] The clock unit built into the control chip 33 can provide time data in real time.

[0101] The connectors of the body 1 are made of highly conductive material to reduce contact resistance.

[0102] Please refer to the location of the temperature sensor 11 on the connector body 1. Figure 3 As shown.

[0103] Temperature sensor 11 is connected to temperature acquisition interface 31 via serial peripheral interface SPI bus. By installing multiple temperature sensors 11, multi-point temperature monitoring can be performed to obtain comprehensive temperature information.

[0104] The control chip 33 is a microcontroller unit (MCU) that processes the temperature signal collected by the temperature sensor 11, analyzes status parameters such as temperature change per unit time and terminal temperature peak difference, determines the temperature change trend and stability, avoids misjudgments caused by interference factors such as voltage fluctuations, and improves monitoring accuracy. Please refer to the diagram for the structure of circuit board 3. Figure 4 As shown.

[0105] The aforementioned electricity meter connector can automatically monitor its own operating status in real time, solving the problem that related technologies cannot monitor the operating status of electricity meter connectors in real time.

[0106] Preferably, the body 1 has a groove at the bottom of the plug 13, and the temperature sensor 11 is disposed in the groove and closely attached to the plug 13.

[0107] An OLED display screen 14 is embedded on the side of the main body 1 away from the circuit board 3. The OLED display screen is connected to the control chip 33 via an integrated circuit IIC bus. Please refer to [reference needed] for the location of the OLED display screen 14 on the connector main body 1. Figure 5 As shown.

[0108] Specifically, the groove is formed according to the shape of the temperature sensor 11. The temperature sensor 11 is tightly attached to the plug 13 and encapsulated in plastic to improve insulation performance and prevent vibration from causing damage to the device and creating safety hazards.

[0109] Preferably, the circuit board 3 is also provided with an HPLC carrier communication module 34, which is used to upload temperature warning level and fault diagnosis information to a cloud server or a local monitoring terminal.

[0110] The present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0111] The present invention also provides a computer program product, including a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform the method of the embodiments of the present invention.

[0112] This invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the method of this invention.

[0113] refer to Figure 6 This is a structural block diagram of an electronic device, either a server or a client, according to an embodiment of the present invention. It is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0114] like Figure 6 As shown, the electronic device includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0115] Multiple components in the electronic device are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information into the electronic device. Input unit 606 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 608 may include, but is not limited to, disks and optical discs. Communication unit 609 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0116] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 602 and / or communication unit 609. In some embodiments, the computing unit 601 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0117] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more". The descriptions of terms such as "first", "second", etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features.

[0120] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties.

[0121] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0122] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0123] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A method for monitoring the state of an electrical energy meter connector, characterized by, The method comprises the following steps: determining a first warning level when the temperature data at the connector plug is greater than a dynamic safety threshold at the current time, wherein the dynamic safety threshold is determined according to real-time current data flowing through the connector and real-time environmental temperature; determining a second warning level when the temperature data is greater than a preset multiple of the dynamic safety threshold, less than or equal to the dynamic safety threshold, and the temperature trend is abnormal, wherein the temperature trend is determined to be abnormal when the temperature data in three consecutive sliding windows is greater than the dynamic safety threshold multiplied by the preset multiple based on a sliding window strategy and a 3σ principle; determining a third warning level when the temperature data is less than or equal to the dynamic safety threshold, and the temperature change rate is abnormal, wherein the temperature change rate is determined to be abnormal when the ratio of the temperature change in a sliding window to the time size of the sliding window is greater than a preset threshold based on the sliding window strategy; determining a state monitoring result of the connector based on the warning level, the fault type, and the remaining life, wherein the fault type is determined according to the temperature data, the current data, and historical data, and the remaining life is predicted based on the temperature data and time data corresponding to the temperature data; wherein, before determining the state monitoring result of the connector based on the warning level, the fault type, and the remaining life, the method further comprises: determining a temperature decay curve based on the temperature data and time data corresponding to the temperature data; calculating a temperature prediction value based on the temperature decay curve and the failure frequency in combination with a time series prediction model, wherein the failure frequency is the number of times the warning level is determined within a preset time; determining a predicted failure time based on the time corresponding to when the temperature prediction value first exceeds the dynamic safety threshold; determining the remaining life of the connector based on the predicted failure time and the current time.

2. The method of claim 1, wherein, Before determining the warning level, the method further comprises: The dynamic safety threshold is calculated based on the following formula : ； wherein represents a fitting parameter, represents a multiplication, represents the real-time current data, represents the resistance of the electrically conductive part of the connector plug, represents the sampling time interval, represents the specific heat capacity of the electrically conductive part, represents the mass of the electrically conductive part, represents a temperature compensation value, represents an ambient temperature correction factor, represents the real-time ambient temperature.

3. The method of claim 1, wherein, Before determining the state monitoring result of the connector based on the warning level, the fault type, and the remaining life, the method further comprises: determining input features based on the temperature data, the current data, and the historical data; inputting the input features into a classification algorithm model to obtain a classification result representing different fault types, wherein the classification algorithm model is periodically incrementally trained based on manually annotated fault data, and the classification algorithm model is based on Shapley additive explanation value for feature screening.

4. The method of claim 3, wherein, Determining input features based on the temperature data, the current data, and the historical data comprises: determining a temperature fluctuation amplitude and a temperature mean value based on the temperature data; determining a current fluctuation amplitude and a current effective value based on the current data; determining a device operating age based on the historical data; The input features include the temperature fluctuation amplitude, the temperature mean value, the current fluctuation amplitude, the current effective value, and the equipment operation age; the temperature fluctuation amplitude and the current fluctuation amplitude are used to characterize a poor contact fault, the temperature mean value and the equipment operation age are used to characterize an oxidation fault, and the current effective value is used to characterize an overload fault.

5. The method of claim 3, wherein, After the input features are input into a classification algorithm model to obtain a classification result, the method further comprises: storing the classification result; calling the classification result, combining the corresponding measured fault type of the classification result, and calculating a fault classification accuracy rate; in a case where a decline amplitude of the fault classification accuracy rate exceeds a preset threshold, performing full-amount retraining on the classification algorithm model.

6. The method of claim 1, wherein, Based on the early warning level, the fault type, and the remaining life, a state monitoring result of the connector is determined, including: determining a corresponding early warning instruction based on the level of the early warning level; determining a maintenance time based on the remaining life; determining the state monitoring result based on the early warning instruction, the fault type, and the maintenance time, for generating an operation and maintenance suggestion.

7. The method of claim 1, wherein, Before determining the early warning level, the method further comprises: filtering the temperature data and the current data based on a quartile method to obtain to-be-processed data; performing standardization processing on the to-be-processed data to obtain target data, for determining the early warning level.

8. An electrical energy meter connector, characterized by The body, a cover plate, and a circuit board mounted between the body and the cover plate are included; A temperature sensor is arranged at a plug of the body, and a current sensor is further arranged on the body; The circuit board is provided with a temperature acquisition interface corresponding to the temperature sensor, a current acquisition interface corresponding to the current sensor, and a control chip connected to the temperature acquisition interface and the current acquisition interface; The control chip is internally provided with a clock unit, and in a case where the temperature data at the plug is greater than a dynamic safety threshold at a current time, the control chip determines that the early warning level is a first early warning level; in a case where the temperature data is greater than a preset multiple of the dynamic safety threshold, is less than or equal to the dynamic safety threshold, and a temperature trend is abnormal, the control chip determines that the early warning level is a second early warning level; in a case where the temperature data is less than or equal to the dynamic safety threshold, and a temperature change rate is abnormal, the control chip determines that the early warning level is a third early warning level; Based on the early warning level, the fault type, and the remaining life, a state monitoring result of the connector is determined; The dynamic safety threshold is determined according to real-time current data flowing through the body and real-time environmental temperature, and the temperature trend is determined to be abnormal in the case that temperature data in three continuous sliding windows is greater than the dynamic safety threshold by a preset multiple based on a sliding window strategy and a 3sigma principle; the temperature change rate is determined to be abnormal in the case that a ratio of a temperature change amount in a sliding window to a time size of the sliding window is greater than a preset threshold based on the sliding window strategy; the fault type is determined according to the temperature data, the current data and historical data, the remaining life is predicted according to the temperature data and time data corresponding to the temperature data, and the method comprises the following steps: determining a temperature attenuation curve based on the temperature data and time data corresponding to the temperature data; calculating a temperature prediction value based on the temperature attenuation curve and a fault frequency in combination with a time series prediction model, wherein the fault frequency is a number of times of determining the early warning level in a preset time; determining a predicted failure time based on a time corresponding to a first time when the temperature prediction value is greater than the dynamic safety threshold; determining the remaining life of the connector based on the predicted failure time and a current time.

9. The electrical energy meter connector of claim 8, wherein, The body is provided with a recess at a bottom of the plug, and the temperature sensor is arranged in the recess and closely attached to the plug; an OLED display screen is embedded in a side of the body away from the circuit board, and the OLED display screen is connected to the control chip through an integrated circuit bus.

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