Transformer iron core health state monitoring method, equipment and medium
By deploying high-temperature stable intelligent sensors in the transformer core and combining them with self-calibration functions and dynamic early warning threshold assessment, the problem of unstable operation of transformer monitoring equipment under high temperature and high load environments has been solved, enabling accurate assessment and timely early warning of the health status of the transformer core.
Patent Information
- Application Number
- CN202511641487.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2026-02-06
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing transformer monitoring equipment is unstable in the high-temperature and high-load environment of electric vehicle charging stations, which reduces the reliability of the monitoring system, makes it impossible to obtain continuous and accurate transformer core operating condition data, distorts the health status assessment results, and makes it impossible to identify potential faults in a timely manner.
It employs intelligent sensors with high-temperature stability and self-calibration capabilities to collect temperature, pressure, and vibration data in real time. The data is then dynamically assessed using a health status prediction model, and the warning threshold is adjusted based on real-time load and ambient temperature to achieve self-calibration and early warning notification.
It improves the stability and reliability of monitoring equipment under high temperature and high load environments, ensures the accuracy and timeliness of health status assessment, enables accurate early warning of potential faults, and avoids distortion, missed reports, and false alarms in the monitoring system.
Smart Images
Figure CN121476770A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power equipment condition monitoring, and specifically to a method, device and medium for monitoring the health status of a transformer core. Background Art
[0002] With the popularization of electric vehicles, charging stations, as infrastructure, the number of charging stations and service demands are both growing rapidly. The concentration and increase of charging demands make the power transformers in charging stations need to operate under high load for a long time, and the operation time is significantly extended. In this working mode, the transformer will generate a large amount of heat. Especially in high-temperature summers or peak charging periods, the internal temperature will rise sharply, posing a severe test to the health status of the core components of the transformer, especially the core, which is directly related to the safety and stability of the operation of the charging station.
[0003] Currently, most of the health monitoring systems for transformers are designed for traditional power systems and do not fully consider this special application scenario of electric vehicle charging stations at the initial design stage. The charging station environment has significant characteristics of high temperature and high load, and the monitoring devices supporting the existing monitoring systems usually are not easy to maintain working stability and measurement accuracy for a long time in such high-temperature environments.
[0004] Therefore, when the existing technology is applied to the monitoring of transformers in charging stations, the following technical problems mainly exist:
[0005] When the current transformer monitoring technology is applied to the special working scenario of high temperature and high load in electric vehicle charging stations, the monitoring device itself is not easy to withstand the influence of high temperature and cannot maintain working stability for a long time, thereby greatly reducing the reliability of the monitoring system.
[0006] Due to the unstable operation of the monitoring device, the existing monitoring methods cannot obtain continuous and accurate data reflecting the working conditions of the transformer core, resulting in serious distortion of the health status assessment results. Therefore, the operation personnel cannot identify and investigate potential fault hazards in time, which poses a threat to the safe and stable operation of the charging station.
[0007] For this reason, the present invention proposes a method, device and medium for monitoring the health status of a transformer core to solve the above-mentioned problems. Summary of the Invention
[0008] Aiming at the deficiencies of the existing technology, the present invention provides a method, device and medium for monitoring the health status of a transformer core to solve the problems raised in the above background art.
[0009] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for monitoring the health status of a transformer core, including:
[0010] Step 1: Real-time acquisition of operational data collected by intelligent sensors installed on the transformer core. The intelligent sensors have the ability to operate stably at high temperatures and have a self-calibration function. The operational data includes temperature data, pressure data, and vibration data.
[0011] Step 2: The operational data is fused to generate a comprehensive health feature value, and the comprehensive health feature value is input into the health status prediction model. The health status prediction model dynamically adjusts the early warning threshold of the health assessment based on the real-time operating load and ambient temperature of the charging station, and outputs the health status assessment result.
[0012] Step 3: Compare the health status assessment result with the dynamically adjusted early warning threshold in real time. When the health status assessment result reaches the early warning threshold, the early warning mechanism is automatically triggered and an early warning notification is sent.
[0013] Step four: Control the smart sensor to automatically perform the self-calibration function according to a preset cycle in order to correct the measurement deviation.
[0014] Preferably, the real-time acquisition of operating data collected by the intelligent sensor installed on the transformer core includes: receiving the temperature data, pressure data, and vibration data sent by the intelligent sensor through a wireless communication module.
[0015] Preferably, the step of fusing the operational data to generate a comprehensive health feature value includes: assigning dynamic weighting coefficients to the temperature data, pressure data, and vibration data based on the real-time operational load, and obtaining the comprehensive health feature value by performing a weighted summation operation on the operational data;
[0016] The health status prediction model is a machine learning model trained based on historical operational data.
[0017] Preferably, the early warning threshold for dynamically adjusting the health assessment includes: calculating a load adjustment amount based on the real-time operating load, calculating a temperature adjustment amount based on the ambient temperature, and summing a baseline threshold with the load adjustment amount and the temperature adjustment amount to generate the early warning threshold;
[0018] Sending the warning notification includes: generating structured information containing potential fault types and maintenance recommendations, and sending the structured information to the terminal equipment of the charging station operator.
[0019] Preferably, controlling the smart sensor to automatically perform the self-calibration function according to a preset period includes: triggering the smart sensor to immediately perform the self-calibration function when the drift of the measurement data of any smart sensor exceeds a preset range.
[0020] Preferably, step one further includes:
[0021] Sub-step At each sampling time Acquire the raw temperature data collected by the smart sensor. Raw pressure data Compared with the original vibration data This constitutes the original running dataset;
[0022] Sub-step The validity of data mutations is verified for each data item in the original running dataset by calculating the data change rate. And with the preset mutation threshold The validity of the data is determined by comparison, specifically the rate of change of the data. The calculation method is as follows:
[0023] ,
[0024] in, Raw temperature data Raw pressure data or raw vibration data At the current sampling time The value, For the previous sampling time The value, The sampling time interval;
[0025] If the calculated rate of change of data Less than or equal to the corresponding mutation threshold The original running dataset is determined to be a valid dataset;
[0026] Sub-step The data in the effective dataset is subjected to sliding window filtering to generate the running data, which includes the temperature data. Stress data Vibration data The calculation method for the running data is as follows:
[0027] ,
[0028] in, To be at the current sampling time The final running data is generated after being processed by sliding window filtering; To sample time within the sliding window Historical data points that were deemed valid; The current moment; The amount of time to go back from the current moment; The window size for the sliding window; The counting index for the time step; This represents the sampling time interval.
[0029] Preferably, step two further includes:
[0030] Sub-step Obtain the operating data generated in step one and the real-time operating load retrieved by the charging station operation management system. And based on the real-time operating load Dynamic weighting coefficients are calculated using a preset weighting function, and these dynamic weighting coefficients include those related to the temperature data. Corresponding dynamic temperature weighting coefficient With the pressure data Corresponding dynamic pressure weighting coefficient and the vibration data Corresponding dynamic vibration weighting coefficient And the sum of all dynamic weight coefficients is ;
[0031] Sub-step The normalized operational data is weighted and fused using the dynamic weighting coefficients to generate the comprehensive health feature value. The comprehensive health characteristic value The calculation method is as follows:
[0032] ,
[0033] in, and The preset temperature data normalization boundary values, and The preset pressure data normalization boundary values, and These are the preset boundary values for normalizing the vibration data;
[0034] Sub-step The comprehensive health characteristic value The data is input into the health status prediction model, which is a machine learning model trained based on historical operational data. The model then analyzes the comprehensive health feature values. Perform analysis and calculations to output a quantitative assessment result of the health status. .
[0035] Preferably, step three further includes:
[0036] Sub-step Obtain real-time operating load from the charging station operation management system. With ambient temperature The dynamically adjusted early warning threshold is calculated according to the following method. :
[0037] ,
[0038] in, The preset health status benchmark threshold, This is the load impact factor. As the baseline operating load, The environmental temperature influence coefficient. The reference ambient temperature;
[0039] Sub-step The health status assessment results output in step two With the dynamically adjusted warning threshold Real-time numerical comparison is performed, and alarm judgment conditions are met. ≥ When this occurs, an alarm activation signal is generated;
[0040] Sub-step Upon receiving the alarm activation signal, the early warning mechanism is immediately triggered, and a health status assessment result is generated. The dynamically adjusted early warning threshold The system also provides structured early warning notifications with preset potential fault types and maintenance recommendations, and sends these structured early warning notifications to the terminal devices of charging station operators.
[0041] Preferably, step four further includes:
[0042] Sub-step At each sampling time The real-time measurement values collected by the intelligent sensor under preset stable operating conditions are compared with the corresponding stored reference values to calculate the measurement data drift. The measured data drift amount The calculation method is as follows:
[0043] ,
[0044] in, These are real-time measurement values collected by the intelligent sensor under stable operating conditions. A pre-calibrated reference value for the smart sensor;
[0045] Sub-step Start the self-calibration cycle timer and simultaneously monitor the drift of the measured data. With the preset drift threshold The determination is made when the preset calibration trigger condition is met, namely, the self-calibration cycle timer reaches the preset cycle, or the measurement data drift is determined. Greater than the drift threshold At that time, a calibration execution command is generated;
[0046] Sub-step The system sends the calibration execution command to the smart sensor. Upon receiving the calibration execution command, the smart sensor automatically executes its built-in self-calibration function to generate a new calibration compensation value. The system receives the new calibration compensation value. Then, the calibration parameters used to correct the subsequent running data are updated, and the self-calibration cycle timer is reset.
[0047] A terminal device includes a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the aforementioned method for monitoring the health status of a transformer core.
[0048] Preferably, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for monitoring the health status of a transformer core.
[0049] This invention provides a method, device, and medium for monitoring the health status of transformer cores. It has the following beneficial effects:
[0050] 1. This invention employs a technical solution that deploys intelligent sensors with high-temperature stability and self-calibration capabilities in the transformer core and performs periodic self-calibration control. This achieves the technical effect of improving the working stability and long-term reliability of monitoring equipment under high-temperature and high-load environments. Compared with the existing technology that uses traditional monitoring equipment for data acquisition, this invention solves the problem that the monitoring equipment itself is difficult to withstand the effects of high temperatures and maintain working stability for a long time, which leads to a decrease in the overall reliability of the monitoring system.
[0051] 2. This invention employs a technical solution that integrates and processes multi-dimensional operational data, utilizing a health status prediction model that dynamically adjusts warning thresholds based on real-time load and ambient temperature for assessment and early warning. This ensures the accuracy and authenticity of health status assessment results, achieving timely and accurate early warning of potential faults. Compared to existing technologies that rely on distorted data collected by unstable monitoring equipment for health status assessment, this invention addresses the shortcomings of failing to obtain continuous and accurate operating data, which leads to distorted health status assessment results and hinders timely identification and troubleshooting of potential faults. Attached Figure Description
[0052] Figure 1 This is a flowchart of the present invention. Detailed Implementation
[0053] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.
[0054] The present invention will now be described in detail with reference to the accompanying drawings:
[0055] Example:
[0056] Please see the appendix Figure 1 This invention provides a method for monitoring the health status of a transformer core, comprising:
[0057] Step 1: Real-time acquisition of operational data collected by intelligent sensors installed on the transformer core. The intelligent sensors have the ability to operate stably at high temperatures and have self-calibration functions. The operational data includes temperature data, pressure data, and vibration data.
[0058] Step 2: The operational data is fused and processed to generate a comprehensive health feature value, which is then input into the health status prediction model. The health status prediction model dynamically adjusts the early warning threshold of the health assessment based on the real-time operating load and ambient temperature of the charging station, and outputs the health status assessment result.
[0059] Step 3: Compare the health status assessment results with the dynamically adjusted warning thresholds in real time. When the health status assessment results reach the warning thresholds, the warning mechanism is automatically triggered and a warning notification is sent.
[0060] Step four: Control the smart sensor to automatically perform self-calibration according to a preset cycle to correct measurement deviations;
[0061] Step one further includes:
[0062] Sub-step At each sampling time Acquire raw temperature data collected by smart sensors Raw pressure data Compared with the original vibration data This constitutes the original running dataset;
[0063] Sub-step Perform data mutation validity checks on each data item in the original running dataset by calculating the data change rate. And with the preset mutation threshold Comparison is used to determine data validity, and the rate of data change. The calculation method is as follows:
[0064] ,
[0065] in, Raw temperature data Raw pressure data or raw vibration data At the current sampling time The value, For the previous sampling time The value, The sampling time interval;
[0066] If the calculated rate of change of data Less than or equal to the corresponding mutation threshold The original running dataset was determined to be a valid dataset.
[0067] Sub-step The data in the valid dataset is processed by a sliding window filter to generate runtime data, which includes temperature data. Stress data Vibration data The calculation method for the running data is as follows:
[0068] ,
[0069] in, To be at the current sampling time The final running data is generated after being processed by sliding window filtering; To sample time within the sliding window Historical data points that were deemed valid; The current moment; The amount of time to go back from the current moment; The window size for the sliding window; The counting index for the time step; This represents the sampling time interval.
[0070] Step two further includes:
[0071] Sub-step Obtain the operational data generated in step one and the real-time operational load retrieved from the charging station operation management system. And based on real-time operating load Dynamic weighting coefficients are calculated using a preset weighting function. These dynamic weighting coefficients include those related to temperature data. Corresponding dynamic temperature weighting coefficient , and stress data Corresponding dynamic pressure weighting coefficient and vibration data Corresponding dynamic vibration weighting coefficient And the sum of all dynamic weight coefficients is ;
[0072] Sub-step The normalized operational data is weighted and fused using dynamic weighting coefficients to generate comprehensive health feature values. Comprehensive health characteristic value The calculation method is as follows:
[0073] ,
[0074] in, and The preset temperature data normalization boundary values, and The preset pressure data normalization boundary values, and These are the preset boundary values for normalizing the vibration data;
[0075] Sub-step Comprehensive health characteristic values The data is input into a health status prediction model, which is a machine learning model trained based on historical operational data. The model then analyzes the comprehensive health feature values. Perform analysis and calculations to output quantitative health status assessment results. .
[0076] Step three further includes:
[0077] Sub-step Obtain real-time operating load from the charging station operation management system. With ambient temperature The dynamically adjusted early warning threshold is calculated using the following method. :
[0078] ,
[0079] in, The preset health status benchmark threshold, This is the load impact factor. As the baseline operating load, The environmental temperature influence coefficient. The reference ambient temperature;
[0080] Sub-step The health status assessment results output in step two With dynamically adjusted early warning thresholds Real-time numerical comparison is performed, and alarm judgment conditions are met. ≥ When this occurs, an alarm activation signal is generated;
[0081] Sub-step Upon receiving an alarm activation signal, the early warning mechanism is immediately triggered, and a health status assessment result is generated. Dynamically adjusted early warning thresholds It also provides pre-set structured early warning notifications with potential fault types and maintenance suggestions, and sends these notifications to the terminal devices of charging station operators.
[0082] Step four further includes:
[0083] Sub-step At each sampling time The system compares the real-time measurement values collected by the smart sensor under preset stable operating conditions with the corresponding stored reference values to calculate the measurement data drift. Measurement data drift The calculation method is as follows:
[0084] ,
[0085] in, These are real-time measurement values collected by the intelligent sensor under stable operating conditions. A pre-calibrated reference value for the smart sensor;
[0086] Sub-step Start the self-calibration cycle timer and simultaneously monitor the drift of the measurement data. With the preset drift threshold The determination is made when the preset calibration trigger conditions are met, namely, the self-calibration cycle timer reaches the preset cycle, or the measurement data drift is determined. Greater than the drift threshold At that time, a calibration execution command is generated;
[0087] Sub-step The system sends a calibration execution command to the smart sensor. Upon receiving the command, the smart sensor automatically executes its built-in self-calibration function to generate a new calibration compensation value. The system receives a new calibration compensation value. Then, update the calibration parameters used to correct subsequent running data and reset the self-calibration cycle timer.
[0088] By deploying intelligent sensors with high-temperature stability capabilities in the transformer core and introducing data mutation validity verification and sliding window filtering at the initial stage of data acquisition, the authenticity and reliability of monitoring information are ensured from the data source. This hardware-level approach overcomes the impact of the harsh environment of charging stations (high temperature and high load) on the sensors. Furthermore, algorithmic preprocessing eliminates abnormal mutations and noise interference, providing a high-quality, highly stable data foundation for subsequent accurate analysis. This completely solves the fundamental problem of monitoring system failure caused by unreliable front-end acquisition equipment in existing technologies.
[0089] By dynamically weighting and fusing multi-dimensional operational data based on real-time operating load and utilizing machine learning models for in-depth analysis, a comprehensive and intelligent assessment of the transformer core's health status is achieved. This approach abandons the simplistic approach of traditional monitoring methods that rely on isolated threshold judgments of single data points. Instead, it organically combines multiple physical quantities through algorithms and leverages the model's learning capabilities to uncover complex relationships between data points. This allows for accurate and comprehensive quantification of the core's true health level, overcoming the technical shortcomings of existing technologies that suffer from distorted assessment results due to simplistic evaluation methods.
[0090] By dynamically adjusting the warning threshold based on real-time operating load and ambient temperature, and performing real-time comparison to automatically trigger structured warning notifications, an adaptive and proactive fault warning system is constructed. Its advantage lies in the fact that the warning criteria are not static but intelligently adapt to the normal state fluctuations of the transformer under different operating conditions, thereby significantly improving the accuracy of warnings and effectively avoiding missed or false alarms caused by fixed thresholds. This truly realizes the transformation from passive response to proactive prediction, solving the pain point of existing technologies' inability to identify potential fault hazards in a timely and accurate manner.
[0091] By controlling intelligent sensors to perform a combination of periodic and event-triggered self-calibration functions, a self-sustaining mechanism is established to ensure the long-term stable operation of the system. The value of this step lies in its ability to automatically compensate for measurement deviations caused by long-term sensor use, component aging, or environmental changes, ensuring that the data input into the analysis model maintains high accuracy throughout its lifecycle. This, in turn, guarantees the long-term effectiveness and reliability of the monitoring and early warning process, addressing the shortcomings of existing monitoring systems where accuracy declines over time due to lack of maintenance.
[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A method for monitoring the health status of a transformer core, characterized in that, include: Step 1: Real-time acquisition of operational data collected by intelligent sensors installed on the transformer core. The intelligent sensors have the ability to operate stably at high temperatures and have self-calibration functions. The operational data includes temperature data, pressure data and vibration data. Step 2: The operational data is fused and processed to generate a comprehensive health feature value, which is then input into the health status prediction model. The health status prediction model dynamically adjusts the early warning threshold of the health assessment based on the real-time operating load and ambient temperature of the charging station, and outputs the health status assessment result. Step 3: Compare the health status assessment results with the dynamically adjusted warning thresholds in real time. When the health status assessment results reach the warning thresholds, the warning mechanism is automatically triggered and a warning notification is sent. Step four: Control the smart sensor to automatically perform self-calibration according to a preset cycle to correct measurement deviations.
2. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, The real-time acquisition of operational data collected by the intelligent sensors installed on the transformer core includes: receiving the temperature data, pressure data, and vibration data sent by the intelligent sensors through a wireless communication module.
3. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, The step of fusing the operational data to generate a comprehensive health feature value includes: assigning dynamic weighting coefficients to the temperature data, pressure data, and vibration data based on the real-time operational load, and obtaining the comprehensive health feature value by performing a weighted summation operation on the operational data; The health status prediction model is a machine learning model trained based on historical operational data.
4. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, The dynamic adjustment of the health assessment warning threshold includes: calculating the load adjustment amount based on the real-time operating load, calculating the temperature adjustment amount based on the ambient temperature, and summing the benchmark threshold with the load adjustment amount and the temperature adjustment amount to generate the warning threshold. Sending the early warning notification includes: generating structured information containing potential fault types and maintenance suggestions, and sending the structured information to the terminal device of the charging station operator; The method of controlling the smart sensor to automatically perform the self-calibration function according to a preset period includes: triggering the smart sensor to immediately perform the self-calibration function when the drift of the measurement data of any smart sensor exceeds a preset range.
5. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, Step one further includes: Sub-step At each sampling time Acquire the raw temperature data collected by the smart sensor. Raw pressure data Compared with the original vibration data This constitutes the original running dataset; Sub-step The validity of data mutations is verified for each data item in the original running dataset by calculating the data change rate. And with the preset mutation threshold The validity of the data is determined by comparison, specifically the rate of change of the data. The calculation method is as follows: ， in, Raw temperature data Raw pressure data or raw vibration data At the current sampling time The value, For the previous sampling time The value, The sampling time interval; If the calculated rate of change of data Less than or equal to the corresponding mutation threshold The original running dataset is determined to be a valid dataset; Sub-step The data in the effective dataset is subjected to sliding window filtering to generate the running data, which includes the temperature data. Stress data Vibration data The calculation method for the running data is as follows: ， in, To be at the current sampling time The final running data is generated after being processed by sliding window filtering; To sample time within the sliding window Historical data points that were deemed valid; The current moment; The amount of time to go back from the current moment; The window size for the sliding window; The counting index for the time step; This represents the sampling time interval.
6. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, Step two further includes: Sub-step Obtain the operating data generated in step one and the real-time operating load retrieved by the charging station operation management system. And based on the real-time operating load Dynamic weighting coefficients are calculated using a preset weighting function, and these dynamic weighting coefficients include those related to the temperature data. Corresponding dynamic temperature weighting coefficient With the pressure data Corresponding dynamic pressure weighting coefficient and the vibration data Corresponding dynamic vibration weighting coefficient And the sum of all dynamic weight coefficients is ; Sub-step The normalized operational data is weighted and fused using the dynamic weighting coefficients to generate the comprehensive health feature value. The comprehensive health characteristic value The calculation method is as follows: ， in, and The preset temperature data normalization boundary values, and The preset pressure data normalization boundary values, and These are the preset boundary values for normalizing the vibration data; Sub-step The comprehensive health characteristic value The data is input into the health status prediction model, which is a machine learning model trained based on historical operational data. The model then analyzes the comprehensive health feature values. Perform analysis and calculations to output a quantitative assessment result of the health status. .
7. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, Step three further includes: Sub-step Obtain real-time operating load from the charging station operation management system. With ambient temperature The dynamically adjusted early warning threshold is calculated according to the following method. : ， in, The preset health status benchmark threshold, This is the load impact factor. As the baseline operating load, The environmental temperature influence coefficient. The reference ambient temperature; Sub-step The health status assessment results output in step two With the dynamically adjusted warning threshold Real-time numerical comparison is performed, and alarm judgment conditions are met. ≥ When this occurs, an alarm activation signal is generated; Sub-step Upon receiving the alarm activation signal, the early warning mechanism is immediately triggered, and a health status assessment result is generated. The dynamically adjusted early warning threshold The system also provides structured early warning notifications with preset potential fault types and maintenance recommendations, and sends these structured early warning notifications to the terminal devices of charging station operators.
8. The method for monitoring the health status of a transformer core according to claim 1, characterized in that, Step four further includes: Sub-step At each sampling time The real-time measurement values collected by the intelligent sensor under preset stable operating conditions are compared with the corresponding stored reference values to calculate the measurement data drift. The measured data drift amount The calculation method is as follows: ， in, These are real-time measurement values collected by the intelligent sensor under stable operating conditions. A pre-calibrated reference value for the smart sensor; Sub-step Start the self-calibration cycle timer and simultaneously monitor the drift of the measured data. With the preset drift threshold The determination is made when the preset calibration trigger condition is met, namely, the self-calibration cycle timer reaches the preset cycle, or the measurement data drift is determined. Greater than the drift threshold At that time, a calibration execution command is generated; Sub-step The system sends the calibration execution command to the smart sensor. Upon receiving the calibration execution command, the smart sensor automatically executes its built-in self-calibration function to generate a new calibration compensation value. The system receives the new calibration compensation value. Then, the calibration parameters used to correct the subsequent running data are updated, and the self-calibration cycle timer is reset.
9. A terminal device, characterized in that, The device includes a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, it implements a method for monitoring the health status of a transformer core as described in any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements a method for monitoring the health status of a transformer core as described in any one of claims 1 to 8.