State monitoring method of electric energy meter connector and electric energy meter connector

Abstract

The invention relates to the technical field of electric energy meter connectors, and particularly provides a state monitoring method of an electric energy meter connector and the electric energy meter connector. According to the method, early warning grading is carried out based on temperature data at a connector plug, a dynamic safety threshold value at the current moment, a temperature change rate and a temperature trend; the state monitoring result of the connector is determined based on the early warning level, the fault type and the residual life, the dynamic safety threshold is determined according to the real-time current data flowing through the connector and the real-time environment temperature, and the temperature trend and the temperature change rate are determined based on the sliding window strategy and the 3 sigma principle; the fault type is determined according to the temperature data, the current data and the historical data, and the residual life is predicted according to the temperature data and the time data corresponding to the temperature data. The problem that the operation state of the electric energy meter connector cannot be monitored in real time in the prior art is solved.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy meters, in particular to a state monitoring method for an electric energy meter connector and an electric energy meter connector. Background Art

[0002] With the rapid development of smart grids, a large number of electricity meters need to be installed for terminal collection and metering, and the performance requirements for electricity meter connectors are constantly increasing.

[0003] Based on operational data and statistical results, the vast majority of meter connector failures are related to overheating, making temperature rise monitoring of meter connectors essential. However, since these connectors only offer structural optimizations to meter installation, performance testing and management of operational meter connectors still require regular inspections by maintenance personnel, lacking real-time monitoring of their operational status.

[0004] Currently, no effective solution has been proposed to the problem that related technologies cannot monitor the operating status of the electric energy meter connector in real time. Summary of the Invention

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

[0006] An embodiment of the present invention provides a method for monitoring the status of an electric energy meter connector, comprising: determining a warning level of a first warning level when temperature data at a connector plug exceeds a 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 warning level of a second warning level when the temperature data exceeds a preset multiple of the dynamic safety threshold, is less than or equal to the dynamic safety threshold, and the temperature trend is abnormal; determining a temperature trend abnormality when temperature data within three consecutive sliding windows exceeds a preset multiple of the dynamic safety threshold based on a sliding window strategy and the 3σ principle; determining a warning level of 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; determining a temperature change rate abnormality based on the 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 a connector status monitoring result based on the warning level, fault type, and remaining life, wherein the fault type is determined based on temperature data, current data, and historical data, and the remaining life is predicted based on the temperature data and the time data corresponding to the temperature data.

[0007] Preferably, before determining the warning level, the method further includes: The dynamic safety threshold is calculated based on the following formula : ; Where, represents the fitting parameters, represents multiplication, Indicates real-time current data, Indicates the resistance of the conductive part of the connector plug. represents the sampling time interval, Indicates the specific heat capacity of the conductive part material, Indicates the mass of the conductive part, Indicates the temperature compensation value, Indicates the ambient temperature correction coefficient, Indicates the real-time ambient temperature.

[0008] Preferably, before determining the condition monitoring result of the connector based on the warning level, fault type and remaining life, the above method also 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 for characterizing different fault types, wherein the classification algorithm model is incrementally trained based on manually labeled fault data on a regular basis, and the classification algorithm model performs feature screening based on Shapley additive interpretation value.

[0009] Preferably, input characteristics are determined based on temperature data, current data and historical data, including: determining the temperature fluctuation amplitude and temperature mean based on temperature data; determining the current fluctuation amplitude and current effective value based on current data; determining the equipment operating life based on historical data; wherein the input characteristics include temperature fluctuation amplitude, temperature mean, current fluctuation amplitude, current effective value and equipment operating life; temperature fluctuation amplitude and current fluctuation amplitude are used to characterize poor contact faults, temperature mean and equipment operating life are used to characterize oxidation faults, and current effective value is used to characterize overload faults.

[0010] Preferably, after inputting the input features into the classification algorithm model and obtaining the classification results, the above method also includes: storing the classification results; calling the classification results, and calculating the fault classification accuracy in combination with the measured fault type corresponding to the classification results; when the decline in the fault classification accuracy exceeds a preset threshold, the classification algorithm model is fully retrained.

[0011] Preferably, before determining the condition monitoring result of the connector based on the warning level, fault type and remaining life, the above method also includes: determining a temperature attenuation curve based on temperature data and time data corresponding to the temperature data; calculating a temperature prediction value based on the temperature attenuation curve and the fault frequency in combination 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 an expected failure time based on the time corresponding to the first time the temperature prediction value exceeds the dynamic safety threshold; and determining the remaining life of the connector based on the expected failure time and the current time.

[0012] Preferably, the status monitoring results of the connector are determined based on the warning level, fault type and remaining life, including: determining the corresponding warning instructions based on the level of the warning level; determining the maintenance opportunity based on the remaining life; determining the status monitoring results based on the warning instructions, fault type and maintenance opportunity, and generating operation and maintenance suggestions.

[0013] Preferably, before determining the warning level, the method further includes: filtering the temperature data and the current data based on the quartile method to obtain data to be processed; and standardizing the data to be processed to obtain target data for determining the warning level.

[0014] An embodiment of the present invention provides an electric energy meter connector, comprising a body, a cover, and a circuit board installed between the body and the cover; a temperature sensor is provided at the plug of the body, and a current sensor is also provided on the body; 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 are provided on the circuit board; the control chip has a built-in clock unit, and when the temperature data at the plug of the control chip is greater than the dynamic safety threshold at the current moment, the control chip determines that the warning level is the first warning level; when 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 the temperature trend is abnormal, the warning level is determined to be 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 warning level is determined to be the third warning level; based on the warning level, fault type and remaining life, the status monitoring result of the connector is determined; among them, the dynamic safety threshold is determined according to 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, when the temperature data in three consecutive sliding windows is greater than the dynamic safety threshold of the preset multiple, the temperature trend is determined to be abnormal; based on the sliding window strategy, when the ratio of the temperature change in a sliding window to the time size of the sliding window is greater than the preset threshold, the temperature change rate is determined to be abnormal; the fault type is determined according to the temperature data, current data and historical data, and the remaining life is predicted based on the temperature data and the time data corresponding to the temperature data.

[0015] Preferably, the body has a groove at the bottom of the plug, and the temperature sensor is arranged in the groove 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.

[0016] The present invention provides a method and connector for monitoring the status of an energy meter connector. The method determines a dynamic safety threshold based on real-time current data and ambient temperature, and uses the dynamic safety threshold and sliding window strategy to classify temperature warning levels. Through triple analysis of warning level, fault type, and remaining life, comprehensive and accurate status monitoring results are obtained. While monitoring the operating status of the energy meter connector in real time, this method reduces safety risks and eases the burden of operation and maintenance. This addresses the problem of related technologies being unable to monitor the operating status of the energy meter connector in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without inventive effort.

[0018] Figure 1 The present invention is a flowchart of a method for monitoring the state of an electric energy meter connector in an embodiment of the present invention.

[0019] Figure 2 It is a structural diagram of an electric energy meter connector in an embodiment of the present invention.

[0020] Figure 3 The temperature sensor is set at Figure 2 Schematic diagram of the position on the connector body.

[0021] Figure 4 yes Figure 2 Schematic diagram of the circuit board structure.

[0022] Figure 5 The display is set to Figure 2 Schematic diagram of the position on the connector body.

[0023] Figure 6 It is a structural diagram of an electronic device in an embodiment of the present invention.

[0024] The above drawings include the following reference numerals: 1—main body; 2—cover; 3—circuit board; 11—temperature sensor; 12—current sensor; 13—plug; 14—OLED display; 31—temperature acquisition interface; 32—current acquisition interface; 33—control chip; 34—HPLC carrier communication module. DETAILED DESCRIPTION

[0025] The following describes embodiments of the present invention in more detail with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying 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 described 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.

[0026] Based on operational data and statistical results, over 70% of meter connector failures are related to overheating. Therefore, measuring the temperature rise of meter connectors is a key test item in full-performance testing. However, since these meter connectors only optimize the installation method of the meter structurally, performance testing and management of meter connectors in operation still require regular inspections by maintenance personnel, lacking real-time monitoring of the meter connector's operating status.

[0027] To do this, please refer to Figure 1 As shown, an embodiment of the present invention provides a method for monitoring the status of an electric energy meter connector, including steps S101 to S104.

[0028] Step S101: When 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, wherein the dynamic safety threshold is determined based on the real-time current data flowing through the connector and the real-time ambient temperature.

[0029] Step S102: When the temperature data is greater than a preset multiple of the dynamic safety threshold and is 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. Based on the sliding window strategy and the 3σ principle, when 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.

[0030] Step S103: When 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. Based on the sliding window strategy, 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, the temperature change rate is determined to be abnormal.

[0031] Step S104, based on the warning level, fault type and remaining life, determine the status monitoring result of the connector, wherein the fault type is determined based on the temperature data, current data and historical data, and the remaining life is predicted based on the temperature data and the time data corresponding to the temperature data.

[0032] Temperature data is collected in real time. It should be noted that real-time collection in the embodiments of the present invention refers to data collected at a certain sampling frequency, and real-time data refers to data collected at a certain sampling frequency. For example, a temperature sensor with an accuracy of ±0.5°C is used to collect temperature data at a sampling frequency of 1Hz.

[0033] The current data flowing through the energy meter connector can be obtained, but is not limited to, via a current sensor. For example, the current sensor can be a Hall effect current sensor. The current data includes, but is not limited to, the effective current value, the peak current value, and the current fluctuation frequency.

[0034] When the energy meter connector is installed in an indoor distribution box, the ambient temperature refers to the air temperature inside the distribution box. When the energy meter connector is installed in an outdoor meter box, the ambient temperature refers to the temperature of the ventilation area within the meter box. The device for collecting ambient temperature is installed inside the indoor distribution box or outdoor meter box. The device for collecting ambient temperature and the device performing the above-mentioned status monitoring method transmit signals using a wired or wireless method.

[0035] By setting the adaptive dynamic safety threshold described above, higher operating temperatures can be allowed when the current flowing through the connector is high current, avoiding the risk of false alarms in high current scenarios caused by the use of fixed thresholds in related technologies. The specific definition of high current can be determined by those skilled in the art based on actual application scenarios. For example, for an energy meter connector in a residential electricity usage scenario, a current greater than 60A is considered high current.

[0036] In application scenarios that pursue higher monitoring accuracy, the dynamic safety threshold is also related to the operation and maintenance cycle deviation, historical fault correlation, material tolerance coefficient, temperature and humidity coupling coefficient, and cabinet heat dissipation efficiency.

[0037] For example, if the maintenance cycle deviates by more than six months, the probability of loosening the meter connector increases, necessitating increased warning sensitivity. The dynamic safety threshold can be lowered by 5%-10%. If the meter connector has been repaired due to elevated temperatures within three months, the dynamic safety threshold can be lowered by 8%-10%. If the ratio of the current temperature data to the maximum temperature tolerance of the connector material is greater than or equal to 0.8, the dynamic safety threshold can be lowered by 8%-15%. In high temperature and high humidity environments, the dynamic safety threshold can be lowered by 5%-10%. For example, if the current ambient temperature is 60°C 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 meter box is greater than or equal to 10°C, indicating poor heat dissipation within the box, the dynamic safety threshold can be lowered by 5%-10%.

[0038] If there are multiple situations that require lowering the dynamic safety threshold at the same time, the situation with the largest reduction ratio of the dynamic safety threshold shall prevail, and the dynamic safety threshold shall be lowered according to the corresponding largest reduction ratio.

[0039] In step S102, the specific value of the preset multiple can be determined by those skilled in the art based on a limited number of experiments. For example, the range of the preset multiple is 0.75-0.85. This embodiment is described below using the preset multiple of 0.8 as an example.

[0040] The sliding window time size can be set to 5-10 minutes or 5-15 seconds. 5-10 minutes is suitable for reducing computing 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 time size and the sampling frequency of the temperature sensor. This embodiment will be described using a sliding window time size of 10 seconds and a step size of 1 second as an example.

[0041] In order to obtain accurate temperature trends, exponential smoothing or autoregressive integrated moving average algorithm can be used to predict temperature trends based on the short-term time series correlation between temperature and current.

[0042] The preset threshold used to determine whether the temperature change rate is abnormal can be determined based on the recommended value of the electricity meter manufacturer, industry standards or specification appendix, or based on the specific application environment of the electricity meter connector. For example, the manufacturer clearly recommends setting the fixed warning threshold for 5(60)A connectors to 2.5℃ / min, that is, the above preset threshold is 2.5℃ / min.

[0043] Specifically, the warning algorithm used to determine the warning level can be a LightGBM model, or a combination of a LightGBM model and a Long Short-Term Memory (LSTM) model. The LightGBM model extracts multiple features from temperature, current, and time data to determine dynamic safety thresholds, temperature trends, and temperature change rates for categorizing warning levels. Incorporating a LightGBM model improves the accuracy of temperature trend calculations, thereby enhancing the accuracy of warning level categorization. The LightGBM model can also be used to predict remaining life, as detailed below.

[0044] The historical data may include, but is not limited to, the installation and commissioning time of the electric energy meter connector, fault records, and operation and maintenance data.

[0045] The equipment's operating age can be determined based on the installation and commissioning time and the current monitoring time. This embodiment will be further explained using the example of oxidation failure analysis based on equipment operating age. Fault records may include, but are not limited to, historical fault types, fault duration, temperature data at the time of the fault, and current conditions. Operation and maintenance data may include, but are not limited to, component replacement types and replacement times.

[0046] By analyzing the warning level, fault type, and remaining life, comprehensive and accurate status monitoring results can be obtained. This allows for real-time monitoring of the operating status of the meter connector, while reducing safety risks and alleviating the burden of operation and maintenance. This embodiment subsequently provides a preferred method for determining the fault type and remaining life.

[0047] Preferably, before determining the warning level based on steps S101 to S103, the method further includes: filtering the temperature data and current data based on the quartile method to obtain data to be processed, and normalizing the data to be processed to obtain target data for determining the warning level.

[0048] In the actual collection process of temperature and current data related to electricity meter connectors, there will inevitably be abnormal outliers, such as temperature jumps caused by transient sensor failures, current spikes caused by electromagnetic interference from the power grid, and instantaneous current fluctuations caused by loose wiring.

[0049] Based on the quartile method, the first quartile Q1, third quartile Q3, and interquartile range IQR (Q3-Q1) corresponding to the above data are calculated. Values ​​outside the range of [Q1-1.5×IQR] to [Q3+1.5×IQR] are identified as abnormal outliers and removed. 1.5×IQR is a common threshold for industrial data filtering. This value can be fine-tuned based on the rated operating conditions of the meter connector, such as the rated current range and normal temperature fluctuation range. For example, 2×IQR is used for extreme operating conditions, which provides strong adaptability.

[0050] Data filtering based on the quartile method can directly adapt to the actual characteristics of temperature and current data monitored on-site, without relying on data distribution assumptions. Furthermore, the computational time is shorter than the data collection cycle, which does not affect the subsequent real-time determination of warning levels.

[0051] The Min-Max normalization algorithm is used to scale the corresponding features of the above-mentioned data to be processed to the interval [0,1] to improve the convergence speed of the subsequent algorithm model.

[0052] In addition, during the training of the algorithm model, it is also necessary to eliminate outliers based on the above-mentioned quartile method and perform feature normalization on the training data based on the above-mentioned Min-Max normalization algorithm. After eliminating outliers and before performing feature normalization, it is preferred to generate simulated samples corresponding to scarce fault types by adding Gaussian noise to enhance the training effect of the algorithm model.

[0053] Preferably, before determining the warning level based on steps S101 to S103, the method further includes: The dynamic safety threshold is calculated based on the following formula : ; Where, represents the fitting parameters, represents multiplication, Indicates real-time current data, Indicates the resistance of the conductive part of the connector plug. represents the sampling time interval, Indicates the specific heat capacity of the conductive part material, Indicates the mass of the conductive part, Indicates the temperature compensation value, Indicates the ambient temperature correction coefficient, Indicates the real-time ambient temperature.

[0054] Fitting parameters The initial value of is 0.5 and is dimensionless. Temperature compensation value The initial value is 30℃. Ambient temperature correction factor The initial value of is 0.2 and dimensionless. Those skilled in the art can adjust the fitting parameters based on a limited number of experiments. , temperature compensation value and ambient temperature correction factor to adjust the specific value.

[0055] Resistance of conductive parts It is the sum of the bulk resistance and contact resistance of the conductive part. The bulk resistance is the inherent resistance of the plug material. The contact resistance is the resistance at the point of contact between the plug and the socket. Those skilled in the art can predetermine or measure the resistance of the conductive part based on the specific model of the energy meter connector and plug used. and quality .

[0056] Sampling time interval is the sampling period of the temperature sensor. The sampling frequency of 1Hz corresponds to a sampling time interval of 1s.

[0057] Exemplarily, when the material of the conductive portion is copper, the material specific heat capacity c of the conductive portion is 460 joules per kilogram degrees Celsius.

[0058] Preferably, before determining the condition monitoring results of the connector 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 input into a classification algorithm model to obtain classification results for characterizing different fault types. The classification algorithm model is incrementally trained regularly based on manually annotated fault data, and the classification algorithm model performs feature screening based on Shapley additive explanatory value.

[0059] Specifically, based on the existing random forest classifier, combined with the Shapley additive explanatory value (SHAP) for feature screening, the above classification algorithm model can be obtained. That is, the above classification algorithm model is an improved random forest classifier, which supports multiple classifications while having strong anti-noise ability and can handle nonlinear features.

[0060] Periodicity can be but is not limited to monthly.

[0061] Incremental training means only adjusting the decision tree weights of the classification algorithm model, which can avoid the high cost of retraining.

[0062] Furthermore, input features are determined based on temperature data, current data, and historical data, including: determining the temperature fluctuation amplitude and temperature mean based on the temperature data; determining the current fluctuation amplitude and current effective value based on the current data; and determining the equipment operating age based on the historical data. The input features include the temperature fluctuation amplitude, temperature mean, current fluctuation amplitude, current effective value, and equipment operating age; the temperature fluctuation amplitude and current fluctuation amplitude are used to indicate poor contact faults, the temperature mean and equipment operating age are used to indicate oxidation faults, and the current effective value is used to indicate overload faults.

[0063] For example, in the temperature fluctuation amplitude T std >3℃ and current fluctuation amplitude I stdWhen the temperature fluctuation range T is less than 5%, the connector has a poor contact fault. The temperature change rate ΔT / Δt can be further added as a judgment condition of instability, that is, when the temperature fluctuation range T is less than 5%, the connector has a poor contact fault. std >3℃, current fluctuation amplitude I std If the temperature is less than 5% and the temperature change rate ΔT / Δt is abnormal, there is a poor contact fault in the connector.

[0064] At the mean temperature T avg As the equipment operates for a certain period of time age If the annual temperature increase is greater than 2°C and the effective current value is normal, there is an oxidation fault in the connector.

[0065] When the effective current value is greater than 1.2 times the rated current and the temperature data rises synchronously with the current data, the connector has an overload fault.

[0066] By combining temperature data, current data and time data for fault diagnosis, we can break through the limitations of single temperature data and improve the accuracy of condition monitoring.

[0067] In addition, compared to the related random forest model, the above 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 loss. For example, the number of trees can be reduced from 100 to 30.

[0068] Furthermore, after inputting the input features into the classification algorithm model and obtaining a classification result, the method further includes storing the classification result. Retrieving the classification result and calculating the fault classification accuracy based on the measured fault type corresponding to the classification result. If the decrease in the fault classification accuracy exceeds a preset threshold, the classification algorithm model is fully retrained.

[0069] For example, the above-mentioned preset threshold for measuring the decline in fault classification accuracy is 5%.

[0070] The classification algorithm model is incrementally trained regularly based on manually labeled fault data, and the classification algorithm model is fully retrained when the decline in fault classification accuracy exceeds a preset threshold, so that the above classification algorithm model has an online learning mechanism.

[0071] Preferably, before determining the condition monitoring result of the connector based on the warning level, fault type, and remaining life in step S104, the method further includes: determining a temperature decay curve based on the temperature data and the time data corresponding to the temperature data. Based on the temperature decay curve and the fault frequency, a temperature prediction value is calculated in conjunction with a time series prediction model, where the fault frequency is the number of times the warning level is determined within a preset time period. An estimated failure time is determined based on the time corresponding to the first time the temperature prediction value exceeds the dynamic safety threshold. The remaining life of the connector is determined based on the estimated failure time and the current time.

[0072] The temperature decay curve represents the mean temperature T avg Trends over time.

[0073] The fault frequency may be, but is not limited to, the number of times the warning level is determined within the past three months.

[0074] The time series prediction model can be a long short-term memory (LSTM) network model. The training data used to train the time series prediction model includes historical temperature series and actual replacement time labels (RUL=0).

[0075] The temperature prediction value is the temperature value within a certain period of time in the future. The specific length of this period of time can be determined by those skilled in the art according to actual needs. For example, the temperature prediction value is the temperature value within the next 7 days.

[0076] Furthermore, the aforementioned early warning and classification algorithm models can be deployed at the edge, while the aforementioned time series prediction model can be deployed in the cloud. Through the closed loop of temperature warning, fault diagnosis, and lifespan prediction, intelligent management of meter connector failures throughout their entire lifecycle is achieved, providing data-driven decision support for power operations and maintenance.

[0077] This example provides the performance metrics required for training each algorithm model: For early warning algorithm training, accuracy must be ≥ 98% and average response time must be ≤ 500ms. For classification algorithm training, a multi-classification F1 score > 90% must be achieved. For time series prediction model training, a residual life (RUL) error must be ≤ 3 days, with a 90% confidence interval covering the actual lifespan.

[0078] To train the algorithm models, a pilot deployment was conducted on 100 electricity meter connectors of varying ages, collecting 100,000 data samples. Fault injection scenarios, such as poor contact, oxidation, and overload, were artificially created to verify the model's response speed and diagnostic accuracy. Model performance in extreme scenarios, such as high temperatures and high current surges, was also recorded to supplement edge case data.

[0079] Preferably, a maintenance model is deployed in the cloud to combine the relevant data of connectors across the entire network and identify common failure modes through clustering algorithms, such as the easy oxidation of a batch of connectors, to optimize the global maintenance strategy.

[0080] Preferably, step S104, determining the condition monitoring results of the connector based on the warning level, fault type, and remaining life, includes: determining a corresponding warning instruction based on the warning level; determining a maintenance opportunity based on the remaining life; and determining the condition monitoring results based on the warning instruction, fault type, and maintenance opportunity for generating operation and maintenance recommendations.

[0081] Specifically, when the warning level is the first warning level, the warning instruction is an immediate alarm, including but not limited to the issuance of sound and light signals. When the warning level is the second warning level, the warning instruction is that the temperature of the connector may exceed the dynamic safety threshold within 10 minutes and requires special attention. When the warning level is the third warning level, the warning instruction is a warning to pay attention. The second and third warning levels correspond to different levels of attention, which can be distinguished by the font color and prompt frequency on the display.

[0082] For example, if the remaining life is less than 30 days and the failure frequency is greater than 5, a recommendation to replace the device within 7 days is generated. If the remaining life is between 30 and 90 days, a recommendation to conduct regular inspections is generated, for example, once every 10 days.

[0083] The operation and maintenance personnel conduct corresponding troubleshooting and repairs based on the fault type in the operation and maintenance recommendations.

[0084] For example, the application process of the above-mentioned state monitoring method is as follows: the temperature sensor collects the current temperature data of 55°C, the current sensor collects the corresponding current data of 10A, and the ambient temperature acquisition device collects the corresponding ambient temperature of 25°C. The early warning algorithm model at the edge calculates the dynamic safety threshold as 40°C. Since the current temperature data is greater than the corresponding dynamic safety threshold, the first early warning level is determined. The classification algorithm model at the edge synchronously analyzes the characteristics: temperature fluctuation amplitude T std is 3.5℃, the current fluctuation amplitude I std The fault was diagnosed as poor contact. The cloud-based time series prediction model calculated the remaining life (RUL) as 15 days and issued a maintenance work order for contact cleaning or replacement within 10 days.

[0085] In summary, the above-mentioned state monitoring method provided by the embodiment of the present invention improves the safety of electricity use and the efficiency of fault handling, can reduce operating costs, and ensure the stability of electricity use. By real-time monitoring of the temperature of the connector, timely warnings can be issued when the temperature rises abnormally, and hidden dangers of overheating caused by poor contact, oxidation, and overload can be discovered in advance, avoiding safety accidents caused by high temperature. The expansion of the circuit fault range caused by connector failure is reduced, and the risk of damage to electrical equipment is reduced. With the help of the fault analysis function, the cause of the temperature abnormality, such as excessive contact resistance and current overload, can be quickly located, shortening the troubleshooting time and reducing the blindness of manual troubleshooting. It is convenient for operation and maintenance personnel to carry out targeted repairs or replacements, improving the accuracy and efficiency of fault handling, and is particularly suitable for large-scale and complex power networks.

[0086] Please refer to Figure 2 As shown, an embodiment of the present invention further provides an electric energy meter connector, comprising a body 1 , a cover plate 2 , and a circuit board 3 installed between the body 1 and the cover plate 2 .

[0087] A temperature sensor 11 is provided at the plug of the body 1 , and a current sensor 12 is also provided on the body 1 .

[0088] 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 .

[0089] The control chip 33 has a built-in clock unit. When the temperature data at the plug is greater than the dynamic safety threshold at the current moment, the control chip 33 determines the warning level to be the first warning level; when the temperature data is greater than the dynamic safety threshold by a preset multiple, is 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; when 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; based on the warning level, fault type and remaining life, the status monitoring result of the connector is determined.

[0090] 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, when the temperature data in three consecutive sliding windows is greater than the dynamic safety threshold by a preset multiple, the temperature trend is determined to be abnormal; based on the sliding window strategy, when the ratio of the temperature change in a sliding window to the time size of the sliding window is greater than the 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 life is predicted based on temperature data and the time data corresponding to the temperature data.

[0091] The clock unit built into the control chip 33 can provide time data in real time.

[0092] The connector terminals of the body 1 are made of highly conductive material to reduce contact resistance.

[0093] The location of the temperature sensor 11 on the connector body 1 is shown in FIG. Figure 3 shown.

[0094] The temperature sensor 11 is connected to the temperature acquisition interface 31 via a serial peripheral interface SPI bus. By installing multiple temperature sensors 11, multi-point temperature monitoring can be performed to fully obtain temperature information.

[0095] The control chip 33 is a microcontroller unit MCU that processes the temperature signal collected by the temperature sensor 11, analyzes state parameters such as the temperature change value per unit time and the terminal temperature peak difference, determines the temperature change trend and stability, avoids misjudgment due to interference factors such as voltage fluctuations, and improves monitoring accuracy. Figure 4 shown.

[0096] The above-mentioned electric energy meter connector can automatically monitor its own operating status in real time, solving the problem that related technologies cannot monitor the operating status of the electric energy meter connector in real time.

[0097] Preferably, the body 1 is provided with a groove at the bottom of the plug 13 , and the temperature sensor 11 is arranged in the groove close to the plug 13 .

[0098] The side of the body 1 away from the circuit board 3 is embedded with an OLED display screen 14, which is connected to the control chip 33 via the integrated circuit IIC bus. Please refer to the position of the OLED display screen 14 on the connector body 1. Figure 5 shown.

[0099] Specifically, the groove is opened according to the shape of the temperature sensor 11. The temperature sensor 11 is tightly attached to the plug 13 and is potted and sealed with plastic, which improves the insulation performance while preventing vibration from causing damage to the device and generating safety hazards.

[0100] Preferably, the circuit board 3 is further provided with an HPLC carrier communication module 34 for uploading temperature warning levels and fault diagnosis information to a cloud server or a local monitoring terminal.

[0101] The present invention also provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to perform the method of the present invention.

[0102] The present invention also provides a computer program product including a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the present invention.

[0103] The present 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, wherein the computer program, when executed by the at least one processor, causes the electronic device to perform the method of the present invention.

[0104] refer to Figure 6 , is a structural block diagram of an electronic device of a server or client of an embodiment of the present invention, which 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 processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0105] like Figure 6 As shown, the electronic device includes a computing unit 601, which can perform various appropriate actions and processes according to 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. RAM 603 can also store various programs and data required for the operation of the electronic device. The computing unit 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0106] Multiple components within the electronic device are connected to the I / O interface 605, including an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 can be any type of device capable of inputting information into the electronic device. The 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. The output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 608 may include, but is not limited to, a magnetic disk or an optical disk. The communication unit 609 allows the electronic device to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks and may include, but is not limited to, a modem, a network card, an infrared communication device, and / or a wireless communication transceiver, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0107] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a CPU, a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing units, various computing units that run machine learning model algorithms, a digital signal processor (DSP), 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 may be implemented as a computer program tangibly embodied in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device via the ROM 602 and / or the communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the above-described methods by any other suitable means (e.g., via firmware).

[0108] The computer programs for implementing the methods of the 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, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0109] In the context of the present invention, a machine-readable medium may 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. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, or infrared system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0110] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "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 other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative and not restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more". The descriptions of the terms "first", "second", etc. are for descriptive purposes only and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features.

[0111] 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 the present invention are all information and data authorized by the user or fully authorized by all parties.

[0112] The various steps described in the method implementation methods provided by the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0113] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments are referenced to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0114] The above-described embodiments merely represent several implementation methods of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that a person of ordinary skill in the art would be able to make various modifications and improvements without departing from the scope of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for monitoring the status of an electric energy meter connector, characterized in that: include: If the temperature data at the connector plug is greater than the current dynamic safety threshold, determining the warning level to be the first warning level, wherein the dynamic safety threshold is determined based on the real-time current data flowing through the connector and the real-time ambient temperature; If the temperature data is greater than the dynamic safety threshold by a preset multiple, and is 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. Based on the sliding window strategy and the 3σ principle, if the temperature data in three consecutive sliding windows is greater than the dynamic safety threshold by a preset multiple, the temperature trend is determined to be abnormal. 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, wherein, based on the sliding window strategy, if the ratio of the temperature change amount 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; Based on the warning level, fault type and remaining life, the status monitoring result of the connector is determined, wherein the fault type is determined based on the temperature data, the current data and historical data, and the remaining life is predicted based on the temperature data and the time data corresponding to the temperature data.

2. The method according to claim 1, characterized in that Before determining the warning level, the method further includes: The dynamic safety threshold is calculated based on the following formula : ； Where, represents the fitting parameters, represents multiplication, represents the real-time current data, Indicates the resistance of the conductive part of the connector plug. represents the sampling time interval, represents the specific heat capacity of the material of the conductive part, represents the mass of the conductive part, Indicates the temperature compensation value, Indicates the ambient temperature correction coefficient, Indicates the real-time ambient temperature.

3. The method according to claim 1, characterized in that Before determining the condition monitoring result of the connector based on the warning level, fault type, and remaining life, the method further includes: determining an input characteristic based on the temperature data, the current data, and the historical data; The input features are input into a classification algorithm model to obtain classification results for characterizing different fault types, wherein the classification algorithm model is incrementally trained regularly based on manually labeled fault data, and the classification algorithm model performs feature screening based on Shapley additive explanatory value.

4. The method according to claim 3, characterized in that Determining an input feature based on the temperature data, the current data, and the historical data includes: determining a temperature fluctuation amplitude and a temperature mean based on the temperature data; determining a current fluctuation amplitude and a current effective value based on the current data; determining the operating life of the equipment based on the historical data; Among them, the input characteristics include the temperature fluctuation amplitude, the temperature average, the current fluctuation amplitude, the current effective value and the equipment operating years; the temperature fluctuation amplitude and the current fluctuation amplitude are used to characterize poor contact faults, the temperature average and the equipment operating years are used to characterize oxidation faults, and the current effective value is used to characterize overload faults.

5. The method according to claim 3, characterized in that After inputting the input features into a classification algorithm model and obtaining a classification result, the method further includes: storing the classification result; Calling the classification result, and calculating the fault classification accuracy rate based on the measured fault type corresponding to the classification result; When the decrease in the fault classification accuracy exceeds a preset threshold, the classification algorithm model is fully retrained.

6. The method according to claim 1, wherein Before determining the condition monitoring result of the connector based on the warning level, fault type, and remaining life, the method further includes: determining a temperature decay curve based on the temperature data and time data corresponding to the temperature data; Based on the temperature attenuation curve and the fault frequency, combined with a time series prediction model, a temperature prediction value is calculated, wherein the fault frequency is the number of times the warning level is determined within a preset time; Determining an estimated failure time based on a time corresponding to when the temperature prediction value first exceeds the dynamic safety threshold; The remaining life of the connector is determined based on the expected failure time and the current time.

7. The method according to claim 1, characterized in that Determining a condition monitoring result of the connector based on the warning level, fault type, and remaining life, including: Determining a corresponding warning instruction based on the level of the warning level; determining a maintenance opportunity based on the remaining life; Based on the early warning instruction, the fault type and the maintenance opportunity, the status monitoring result is determined to generate an operation and maintenance suggestion.

8. The method according to claim 1, characterized in that Before determining the warning level, the method further includes: Filtering the temperature data and the current data based on the quartile method to obtain data to be processed; The data to be processed is standardized to obtain target data for determining the warning level.

9. An electric energy meter connector, characterized in that: It includes a body, a cover plate, and a circuit board installed between the body and the cover plate; A temperature sensor is provided at the plug of the body, and a current sensor is also provided 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 both the temperature acquisition interface and the current acquisition interface; The control chip has a built-in clock unit, and when the temperature data at the plug is greater than the dynamic safety threshold at the current moment, the control chip determines that the warning level is the first warning level; When the temperature data is greater than the dynamic safety threshold value by a preset multiple, is less than or equal to the dynamic safety threshold value, and the temperature trend is abnormal, determining the warning level to be 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, determining the warning level to be the third warning level; Determining a condition monitoring result of the connector based on the warning level, fault type, and remaining life; In which, 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, when the temperature data in three consecutive sliding windows is greater than the dynamic safety threshold of the preset multiple, the temperature trend is determined to be abnormal; based on the sliding window strategy, when the ratio of the temperature change in a sliding window to the time size of the sliding window is greater than the preset threshold, the temperature change rate is determined to be abnormal; the fault type is determined based on the temperature data, the current data and historical data, and the remaining life is predicted based on the temperature data and the time data corresponding to the temperature data.

10. The electric energy meter connector according to claim 9, characterized in that: The body is provided with a groove at the bottom of the plug, and the temperature sensor is arranged in the groove close to the plug; An OLED display screen is embedded on a side of the body away from the circuit board, and the OLED display screen is connected to the control chip via an integrated circuit bus.

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