Wireless charging safety monitoring management method

Abstract

The invention discloses a wireless charging safety monitoring management method, which comprises the following steps of: monitoring temperature, humidity, current and voltage indexes of wireless charging equipment in real time by deploying a thermistor, a digital humidity sensor and a Hall effect sensor, and preprocessing collected data, including data cleaning, format standardization and abnormal value detection and processing; a data fusion technology is utilized to integrate multi-source information, extract features and analyze correlation among data, an early warning model based on historical data training is established, multi-level early warning is triggered according to different conditions, early warning signals are timely transmitted to related personnel through multiple communication means, and once early warning is triggered, the early warning can be timely carried out. According to a preset emergency response scheme, measures including starting a cooling system, reducing output power and even cutting off a power supply and guaranteeing equipment safety are automatically taken, in addition, safety auditing carried out in each quarter is beneficial to continuously improving an early warning mechanism, and data safety of a monitoring system is guaranteed through an encryption technology and an access control and data backup mode.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless charging, and in particular to a wireless charging safety monitoring and management method. Background Art

[0002] Wireless charging technology is widely welcomed for its convenience and non-contact characteristics. However, in actual applications, due to the lack of effective safety monitoring methods, it may cause equipment damage, reduce user experience and even cause safety accidents. Existing wireless charging devices usually only have basic protection mechanisms, such as overcurrent protection, short-circuit protection, etc. Although these mechanisms can prevent equipment damage to a certain extent, they often react slowly and cannot respond in the first time, and lack continuous monitoring of environmental factors such as temperature and humidity. In addition, existing safety protection mechanisms mostly rely on the protection circuits of the hardware itself and lack intelligent analysis and early warning capabilities at the software level.

[0003] In view of these shortcomings, the present invention proposes a comprehensive safety monitoring and management system, which deploys a variety of temperature, humidity, current, and voltage sensors to monitor the working status of wireless charging equipment and changes in the surrounding environment in real time, and improves the quality and reliability of data by preprocessing and fusing the collected data. By establishing an early warning model based on historical data, possible problems are predicted in advance and corresponding preventive measures are taken to avoid accidents. The preset emergency response plan can respond quickly after a problem is discovered to ensure the safety of equipment and users. Summary of the invention

[0004] The purpose of the present invention is to provide a wireless charging safety monitoring and management method.

[0005] The problem to be solved by the present invention is: to solve the potential safety hazards encountered by wireless charging equipment during use, by deploying various sensors to monitor temperature, humidity, current and voltage parameters in real time, and using data fusion and analysis technology to promptly discover abnormal situations, combined with a cloud computing platform to achieve remote monitoring and early warning, and automatically execute emergency response measures such as cutting off power and starting the cooling system, thereby effectively preventing potential dangers and ensuring the safety of equipment and users.

[0006] A wireless charging safety monitoring and management method, the technical solution adopted is as follows: S1: Deploy temperature sensors, humidity sensors, current and voltage sensors to wireless charging devices, design a data acquisition system, and obtain data from each sensor in real time. The collected data includes temperature, humidity, current, voltage, and power. S2: Preprocess the collected data, including data cleaning, format conversion, outlier detection, and use data fusion technology to integrate data from different sensor sources; S3: Extract features from preprocessed data, including average temperature, maximum current, and minimum voltage, analyze the correlation between data, identify key factors affecting safety, set thresholds as upper temperature limits, current and voltage variation ranges, and humidity thresholds, design early warning logic, and trigger early warning signals when early warning trigger conditions are met. Use historical data to train early warning models and optimize early warning logic. S4: Monitor the status of wireless charging devices in real time through the cloud computing platform, use the Internet of Things technology to connect the devices to the cloud, and realize remote monitoring. When the monitored data meets the warning conditions, the warning signal is immediately triggered. The warning signal is sent through multiple channels, including SMS, email, and mobile application push; S5: Preset emergency response plan. When receiving the warning signal, automatically execute safety measures, including cutting off the power supply, starting the cooling system, recording all emergency response operations, and notifying the operation and maintenance personnel and users simultaneously; S6: Conduct a comprehensive safety audit every quarter to evaluate the safety status of wireless charging equipment, review logs, fault records and warning history, find potential safety hazards, and adjust warning thresholds and logic based on the audit results; S7: Use encryption technology to ensure the security of data transmission between the wireless charging device and the monitoring platform, regularly update encryption keys, use access control lists to limit access rights to the system for different user roles, and regularly back up all data in the monitoring system, including configuration files and log records.

[0007] Furthermore, in S1, a temperature sensor, a humidity sensor, a current sensor, and a voltage sensor are deployed to the wireless charging device to obtain data from each sensor in real time, including: The temperature sensor is an NTC thermistor, which is installed inside the charging board near the heating element and on the surface of the charging device. The humidity sensor is a digital humidity sensor, which is installed in the charging area, away from the power interface that is directly exposed to the outside. The current and voltage sensors are Hall effect sensors, which are installed at the power input and output ends. The power is calculated by measuring the current and voltage during wireless charging and calculating the product of the current and voltage.

[0008] Furthermore, the collected data is preprocessed in S2, and data fusion technology is used to integrate data from different sensor sources, including: S21: Use a low-pass filter to smooth the signal, use a median filter to remove spike noise, select the middle value in the filter window as the filtered value, use linear interpolation to fill missing values, draw a box plot, identify data points outside the upper and lower quartiles as outliers, and identify and process data points that do not conform to the normal working mode based on the current fluctuation working characteristics of the wireless charging device during charging; S22: Use a standard time format to record sensor data, including a unified timestamp format, convert data from different sensors to the same numerical units, use unified units for current and voltage electrical parameters, convert raw data to CSV format, and add metadata fields for each record, including sensor ID and sampling time; S23: Calculate the first quartile Q1 and the third quartile Q3. Any value lower than Q1-1.5*IQR and higher than Q3+1.5*IQR is considered an outlier. IQR is the interquartile range of the data.

[0009] Furthermore, when the warning triggering condition is reached in S3, a warning signal is triggered, and the warning model is trained using historical data to optimize the warning logic, including: S31: Extract key features from the preprocessed data, including average temperature, maximum current, and minimum voltage, calculate feature statistics such as mean, variance, maximum value, and minimum value, and use correlation coefficient matrix and scatter plot to analyze the correlation between features; S32: Set thresholds based on equipment specifications and safety standards, consider the impact of environmental factors and usage conditions, dynamically adjust thresholds, set warning logic for multiple condition combinations, and set different warning levels based on the severity of the warning, including level 1 warning and level 2 warning. Different levels of warnings correspond to different response measures. S33: The model containing multiple warning conditions is ,in Indicates temperature, represents the current, Expressed as voltage, Expressed as humidity, It is a function that determines whether to trigger an alert based on the input variables; Level 1 warning: ; Level 2 warning: as well as ; Level 3 warning: ; Level 4 warning: ; in Indicates the maximum threshold of temperature, Indicates the nominal value of the current, which is the expected value during normal operation. Indicates the nominal value of voltage, Indicates the maximum allowable deviation of the current, that is, the maximum allowable difference between the current and the nominal value. Indicates the maximum allowable voltage deviation, Indicates the critical value of humidity, Indicates the temperature warning threshold. Indicates the warning threshold of the current; S34: When the temperature Exceeded , triggering a first-level warning, if the current or voltage Deviation from its nominal value or Greater than the allowable deviation or , then trigger the second level warning, if the humidity Exceeded , then the third level warning is triggered if the temperature and current exceed their respective warning thresholds at the same time and , a level 4 warning is triggered.

[0010] Furthermore, the emergency response plan is preset in S5, and when the warning signal is received, the safety measures are automatically executed, including: When a level 1 warning is triggered, the built-in and external cooling systems are activated to speed up heat dissipation, and users and operation and maintenance personnel are notified that the temperature is too high. When the second-level warning is triggered, the output power of the wireless charging device is automatically reduced, the fluctuation of current and voltage is reduced, and the time and operation of reducing the charging power are recorded; When the third-level warning is triggered, users are advised to check the humidity of the current charging environment, and are reminded to avoid charging in a high-humidity environment. The operation and maintenance personnel are notified to check the humidity of the environment where the device is located. When the fourth-level warning is triggered, the power supply of the wireless charging device will be immediately cut off, the charging process will be stopped, the cooling system will be started to reduce the temperature of the device, the time and operation of cutting off the power supply and starting the cooling system will be recorded, and the technician will be notified to conduct an immediate inspection.

[0011] The beneficial effects of the present invention are as follows: by deploying various sensors including temperature, humidity, current, and voltage sensors, the working status information of the wireless charging device can be obtained in real time, and potential safety hazards can be discovered in time; The quality of the data is ensured by preprocessing the collected data, such as cleaning, formatting and outlier detection. At the same time, the information from different sources is integrated through data fusion technology, making the data more complete and reliable. By setting reasonable warning thresholds and training warning models based on historical data, it is possible to intelligently determine when to issue warning signals, thus improving the accuracy and timeliness of warnings. Corresponding emergency response plans are preset for different warning levels. Once an abnormality occurs, corresponding safety measures can be automatically executed, reducing the time of human intervention and improving safety; Encryption technology and access control mechanisms are used to protect the security of data transmission, prevent data leakage, and regularly back up important data to ensure the data integrity and reliability of the system; Relevant personnel are notified of warning information through various means including SMS, email, and mobile application push, so that users and operation and maintenance personnel can understand the equipment status at the first time and take corresponding actions. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 The present invention is a flow chart of a wireless charging safety monitoring and management method. DETAILED DESCRIPTION

[0013] The present invention is further clearly and completely described below, but the protection scope of the present invention is not limited thereto.

[0014] A wireless charging safety monitoring and management method, the technical solution adopted is as follows: S1: Deploy temperature sensors, humidity sensors, current and voltage sensors to wireless charging devices, design a data acquisition system, and obtain data from each sensor in real time. The collected data includes temperature, humidity, current, voltage, and power. S2: Preprocess the collected data, including data cleaning, format conversion, outlier detection, and use data fusion technology to integrate data from different sensor sources; S3: Extract features from preprocessed data, including average temperature, maximum current, and minimum voltage, analyze the correlation between data, identify key factors affecting safety, set thresholds as upper temperature limits, current and voltage variation ranges, and humidity thresholds, design early warning logic, and trigger early warning signals when early warning trigger conditions are met. Use historical data to train early warning models and optimize early warning logic. S4: Monitor the status of wireless charging devices in real time through the cloud computing platform, use the Internet of Things technology to connect the devices to the cloud, and realize remote monitoring. When the monitored data meets the warning conditions, the warning signal is immediately triggered. The warning signal is sent through multiple channels, including SMS, email, and mobile application push; S5: Preset emergency response plan. When receiving the warning signal, automatically execute safety measures, including cutting off the power supply, starting the cooling system, recording all emergency response operations, and notifying the operation and maintenance personnel and users simultaneously; S6: Conduct a comprehensive safety audit every quarter to evaluate the safety status of wireless charging equipment, review logs, fault records and warning history, find potential safety hazards, and adjust warning thresholds and logic based on the audit results; S7: Use encryption technology to ensure the security of data transmission between the wireless charging device and the monitoring platform, regularly update encryption keys, use access control lists to limit access rights to the system for different user roles, and regularly back up all data in the monitoring system, including configuration files and log records.

[0015] Furthermore, in S1, a temperature sensor, a humidity sensor, a current sensor, and a voltage sensor are deployed to the wireless charging device to obtain data from each sensor in real time, including: The temperature sensor is an NTC thermistor, which is installed inside the charging board near the heating element and on the surface of the charging device. The humidity sensor is a digital humidity sensor, which is installed in the charging area, away from the power interface that is directly exposed to the outside. The current and voltage sensors are Hall effect sensors, which are installed at the power input and output ends. The power is calculated by measuring the current and voltage during wireless charging and calculating the product of the current and voltage.

[0016] Furthermore, the collected data is preprocessed in S2, and data fusion technology is used to integrate data from different sensor sources, including: S21: Use a low-pass filter to smooth the signal, use a median filter to remove spike noise, select the middle value in the filter window as the filtered value, use linear interpolation to fill missing values, draw a box plot, identify data points outside the upper and lower quartiles as outliers, and identify and process data points that do not conform to the normal working mode based on the current fluctuation working characteristics of the wireless charging device during charging; S22: Use a standard time format to record sensor data, including a unified timestamp format, convert data from different sensors to the same numerical units, use unified units for current and voltage electrical parameters, convert raw data to CSV format, and add metadata fields for each record, including sensor ID and sampling time; S23: Calculate the first quartile Q1 and the third quartile Q3. Any value lower than Q1-1.5*IQR and higher than Q3+1.5*IQR is considered an outlier. IQR is the interquartile range of the data.

[0017] Furthermore, when the warning triggering condition is reached in S3, a warning signal is triggered, and the warning model is trained using historical data to optimize the warning logic, including: S31: Extract key features from the preprocessed data, including average temperature, maximum current, and minimum voltage, calculate feature statistics such as mean, variance, maximum value, and minimum value, and use correlation coefficient matrix and scatter plot to analyze the correlation between features; S32: Set thresholds based on equipment specifications and safety standards, consider the impact of environmental factors and usage conditions, dynamically adjust thresholds, set warning logic for multiple condition combinations, and set different warning levels based on the severity of the warning, including level 1 warning and level 2 warning. Different levels of warnings correspond to different response measures. S33: The model containing multiple warning conditions is ,in Indicates temperature, represents the current, Expressed as voltage, Expressed as humidity, It is a function that determines whether to trigger an alert based on the input variables; Level 1 warning: ; Level 2 warning: as well as ; Level 3 warning: ; Level 4 warning: ; in Indicates the maximum threshold of temperature, Indicates the nominal value of the current, which is the expected value during normal operation. Indicates the nominal value of voltage, Indicates the maximum allowable deviation of the current, that is, the maximum allowable difference between the current and the nominal value. Indicates the maximum allowable voltage deviation, Indicates the critical value of humidity, Indicates the temperature warning threshold. Indicates the warning threshold of the current; S34: When the temperature Exceeded , triggering a first-level warning, if the current or voltage Deviation from its nominal value or Greater than the allowable deviation or , then trigger the second level warning, if the humidity Exceeded , then the third level warning is triggered if the temperature and current exceed their respective warning thresholds at the same time and , a level 4 warning is triggered.

[0018] Furthermore, the emergency response plan is preset in S5, and when the warning signal is received, the safety measures are automatically executed, including: When a level 1 warning is triggered, the built-in and external cooling systems are activated to speed up heat dissipation, and users and operation and maintenance personnel are notified that the temperature is too high. When the second-level warning is triggered, the output power of the wireless charging device is automatically reduced, the fluctuation of current and voltage is reduced, and the time and operation of reducing the charging power are recorded; When the third-level warning is triggered, users are advised to check the humidity of the current charging environment, and are reminded to avoid charging in a high-humidity environment. The operation and maintenance personnel are notified to check the humidity of the environment where the device is located. When the fourth-level warning is triggered, the power supply of the wireless charging device will be immediately cut off, the charging process will be stopped, the cooling system will be started to reduce the temperature of the device, the time and operation of cutting off the power supply and starting the cooling system will be recorded, and the technician will be notified to conduct an immediate inspection.

[0019] The present invention discloses a wireless charging safety monitoring and management method. By deploying NTC thermistors, digital humidity sensors and Hall effect sensors, the temperature, humidity, current and voltage indicators of wireless charging equipment are monitored in real time. The collected data are pre-processed, including data cleaning, format standardization, abnormal value detection and processing, and multi-source information is integrated by using data fusion technology. Features are extracted and the correlation between data is analyzed. An early warning model based on historical data training is established, and multi-level early warnings are triggered according to different conditions. The early warning signals are promptly conveyed to relevant personnel through various communication means. Once the early warning is triggered, measures will be automatically taken according to the preset emergency response plan, including starting the cooling system, reducing the output power and even cutting off the power supply to ensure the safety of the equipment. In addition, the safety audits conducted every quarter are conducive to the continuous improvement of the early warning mechanism, and the data security of the monitoring system is ensured through encryption technology, access control and data backup.

Claims

1. A wireless charging safety monitoring and management method, characterized in that: include: S1: Deploy temperature sensors, humidity sensors, current and voltage sensors to wireless charging devices, design a data acquisition system, and obtain data from each sensor in real time. The collected data includes temperature, humidity, current, voltage, and power. S2: Preprocess the collected data, including data cleaning, format conversion, outlier detection, and use data fusion technology to integrate data from different sensor sources; S3: Extract features from preprocessed data, including average temperature, maximum current, and minimum voltage, analyze the correlation between data, identify key factors affecting safety, set thresholds as upper temperature limits, current and voltage variation ranges, and humidity thresholds, design early warning logic, and trigger early warning signals when early warning trigger conditions are met. Use historical data to train early warning models and optimize early warning logic. S4: Monitor the status of wireless charging devices in real time through the cloud computing platform, use the Internet of Things technology to connect the devices to the cloud, and realize remote monitoring. When the monitored data meets the warning conditions, the warning signal is immediately triggered. The warning signal is sent through multiple channels, including SMS, email, and mobile application push; S5: Preset emergency response plan. When receiving the warning signal, automatically execute safety measures, including cutting off the power supply, starting the cooling system, recording all emergency response operations, and notifying the operation and maintenance personnel and users simultaneously; S6: Conduct a comprehensive safety audit every quarter to evaluate the safety status of wireless charging equipment, review logs, fault records and warning history, find potential safety hazards, and adjust warning thresholds and logic based on the audit results; S7: Use encryption technology to ensure the security of data transmission between the wireless charging device and the monitoring platform, regularly update encryption keys, use access control lists to limit access rights to the system for different user roles, and regularly back up all data in the monitoring system, including configuration files and log records.

2. A wireless charging safety monitoring and management method as claimed in claim 1, characterized in that: In S1, temperature sensors, humidity sensors, current and voltage sensors are deployed to the wireless charging device to obtain data from each sensor in real time, including: The temperature sensor is an NTC thermistor, which is installed inside the charging board near the heating element and on the surface of the charging device. The humidity sensor is a digital humidity sensor, which is installed in the charging area, away from the power interface that is directly exposed to the outside. The current and voltage sensors are Hall effect sensors, which are installed at the power input and output ends. The power is calculated by measuring the current and voltage during wireless charging and calculating the product of the current and voltage.

3. A wireless charging safety monitoring and management method as claimed in claim 1, characterized in that: In S2, the collected data is preprocessed and data fusion technology is used to integrate data from different sensor sources, including: S21: Use a low-pass filter to smooth the signal, use a median filter to remove spike noise, select the middle value in the filter window as the filtered value, use linear interpolation to fill missing values, draw a box plot, identify data points outside the upper and lower quartiles as outliers, and identify and process data points that do not conform to the normal working mode based on the current fluctuation working characteristics of the wireless charging device during charging; S22: Use a standard time format to record sensor data, including a unified timestamp format, convert data from different sensors to the same numerical units, use unified units for current and voltage electrical parameters, convert raw data to CSV format, and add metadata fields for each record, including sensor ID and sampling time; S23: Calculate the first quartile Q1 and the third quartile Q3. Any value lower than Q1-1.5*IQR and higher than Q3+1.5*IQR is considered an outlier. IQR is the interquartile range of the data.

4. A wireless charging safety monitoring and management method as claimed in claim 1, characterized in that: When the warning triggering condition is reached in S3, a warning signal is triggered, and the warning model is trained using historical data to optimize the warning logic, including: S31: Extract key features from the preprocessed data, including average temperature, maximum current, and minimum voltage, calculate feature statistics such as mean, variance, maximum value, and minimum value, and use correlation coefficient matrix and scatter plot to analyze the correlation between features; S32: Set thresholds based on equipment specifications and safety standards, consider the impact of environmental factors and usage conditions, dynamically adjust thresholds, set warning logic for multiple condition combinations, and set different warning levels based on the severity of the warning, including level 1 warning and level 2 warning. Different levels of warnings correspond to different response measures. S33: The model containing multiple warning conditions is ,in Indicates temperature, represents the current, Expressed as voltage, Expressed as humidity, It is a function that determines whether to trigger an alert based on the input variables; Level 1 warning: ; Level 2 warning: as well as ; Level 3 warning: ; Level 4 warning: ; in Indicates the maximum threshold of temperature, Indicates the nominal value of the current, which is the expected value during normal operation. Indicates the nominal value of voltage, Indicates the maximum allowable deviation of the current, that is, the maximum allowable difference between the current and the nominal value. Indicates the maximum allowable voltage deviation, Indicates the critical value of humidity, Indicates the temperature warning threshold. Indicates the warning threshold of the current; S34: When the temperature Exceeded , triggering a first-level warning, if the current or voltage Deviation from its nominal value or Greater than the allowable deviation or , then trigger the second level warning, if the humidity Exceeded , then the third level warning is triggered if the temperature and current exceed their respective warning thresholds at the same time and , a level 4 warning is triggered.

5. A wireless charging safety monitoring and management method as claimed in claim 1, characterized in that: The emergency response plan is preset in S5, and when an early warning signal is received, safety measures are automatically executed, including: When a level 1 warning is triggered, the built-in and external cooling systems are activated to speed up heat dissipation, and users and operation and maintenance personnel are notified that the temperature is too high. When the second-level warning is triggered, the output power of the wireless charging device is automatically reduced, the fluctuation of current and voltage is reduced, and the time and operation of reducing the charging power are recorded; When the third-level warning is triggered, users are advised to check the humidity of the current charging environment, and are reminded to avoid charging in a high-humidity environment. The operation and maintenance personnel are notified to check the humidity of the environment where the device is located. When the fourth-level warning is triggered, the power supply of the wireless charging device will be immediately cut off, the charging process will be stopped, the cooling system will be started to reduce the temperature of the device, the time and operation of cutting off the power supply and starting the cooling system will be recorded, and the technician will be notified to conduct an immediate inspection.

Images