Electronic device overvoltage protection control method, electronic device, storage medium and computer program product

Abstract

The application discloses a control method for overvoltage protection of an electronic device, an electronic device, a storage medium and a computer program product, relates to the technical field of overvoltage protection of electronic devices, and discloses a control method for overvoltage protection of an electronic device, comprising the following steps: acquiring a filtered voltage; comparing the filtered voltage with overvoltage judgment thresholds at all levels to determine and accumulate a record of a current overvoltage level, and performing graded lock control based on the current overvoltage level; periodically extracting a fault fingerprint vector of an output voltage response curve in a lock control state, and comparing the fault fingerprint vector with a preset rule library in terms of similarity, so that a system recovery process is started when the similarity meets a preset recovery condition; and calculating a health index according to the weight coefficients of the overvoltage levels, so that a maintenance warning signal is sent when the health index is lower than a preset health threshold, thereby solving the problems that there is no active diagnosis and recovery mechanism in the prior art, and the health state of the device cannot be quantified.

Description

Technical Field

[0001] This application relates to overvoltage protection for electronic equipment, and more particularly to control methods, electronic equipment, storage media, and computer program products for overvoltage protection of electronic equipment. Background Technology

[0002] With the increasing integration and complexity of electronic devices, overvoltage protection technology faces challenges such as adaptability to dynamic operating conditions, multi-dimensional fault identification, and environmental compatibility. Existing overvoltage protection technologies for electronic devices mostly adopt a single fixed threshold-triggered shutdown protection strategy, lacking a graded response mechanism for different overvoltage amplitudes. This leads to overprotection affecting business continuity during minor disturbances or insufficient protection measures during serious faults. At the same time, traditional solutions often rely on manual reset or fixed-delay recovery after locking, failing to proactively diagnose whether the fault has been eliminated and thus posing a risk of blind restart. Furthermore, they neglect the cumulative analysis of historical overvoltage frequency and level, making it difficult to achieve early preventative maintenance warnings for faults through quantitative assessment of equipment health. This fails to meet the needs of intelligent and refined operation and maintenance of power systems in high-reliability scenarios. Summary of the Invention

[0003] The purpose of this application is to provide a control method for overvoltage protection of electronic equipment, an overvoltage protection electronic device, a storage medium, and a computer program product, which improves the accuracy and reliability of overvoltage protection and reduces the risk of false triggering and component damage.

[0004] This application provides a control method for overvoltage protection of electronic equipment, the technical solution of which is as follows: The output voltage of the power supply is sampled and preprocessed to obtain the filtered voltage; Multiple overvoltage judgment thresholds are preset, each corresponding to a different overvoltage level. The filtered voltage is compared with each of the overvoltage judgment thresholds to determine and accumulate the current overvoltage level. Based on the current overvoltage level, graded locking control is performed. In the locked control state, the fault fingerprint vector of the response curve under the output voltage is periodically extracted, and the fault fingerprint vector is compared with the similarity of the preset rule base. When the similarity meets the preset recovery conditions, the system recovery process is started. The number of overpressure levels at each level is counted, and a health index is calculated by weighting the overpressure levels at each level according to their weighting coefficients. A maintenance warning signal is issued when the health index is lower than a preset health threshold.

[0005] Furthermore, this application also proposes that the step of sampling and preprocessing the output voltage of the power supply to obtain the filtered voltage includes: The output voltage of the power supply is sampled by a resistor divider network to obtain a sampled voltage, and the sampled voltage is then filtered by a low-pass filter to remove high-frequency noise, thus obtaining the filtered voltage.

[0006] Furthermore, this application also proposes multiple overvoltage determination thresholds, including a first overvoltage threshold, a second overvoltage threshold, and a third overvoltage threshold. The step of comparing the filtered voltage with each level of the overvoltage determination threshold to determine and accumulate the current overvoltage level, and performing graded locking control based on the current overvoltage level, includes: If the filtered voltage is greater than the first overvoltage threshold and less than the second overvoltage threshold, the current overvoltage level is determined to be the first overvoltage level. The power switch drive circuit is set to a low-level flag, the switch drive circuit is controlled to stop working, and the number of times the first overvoltage level is recorded in the historical overvoltage level database is accumulated. If the filtered voltage is greater than the second overvoltage threshold and less than the third overvoltage threshold, the current overvoltage level is determined to be the second overvoltage level, and a hard shutdown command is generated to control the fast discharge circuit to conduct in order to clamp the bus voltage to a safe range, cut off the enable signal of the main power path, and accumulate and record the number of times the second overvoltage level is recorded in the historical overvoltage level database. If the filtered voltage is greater than the third overvoltage threshold, the current overvoltage level is determined to be the third overvoltage level, the overvoltage protection relay is triggered to cut off the power input to the whole machine, and the number of times the third overvoltage level is recorded in the historical overvoltage level database is accumulated. Wherein, the first overvoltage threshold is less than the second overvoltage threshold, the second overvoltage threshold is less than the third overvoltage threshold, and the historical overvoltage level database stores the frequency of occurrence of various overvoltage level events experienced by electronic devices during operation.

[0007] Furthermore, this application also proposes that the step of periodically extracting the fault fingerprint vector of the response curve under the output voltage in the locked control state, and comparing the fault fingerprint vector with a preset rule base for similarity, so as to start the system recovery process when the similarity meets the preset recovery conditions, includes: In the locked control state, a turn-on drive signal is generated according to a preset diagnostic time interval to control the power switch drive circuit to turn on according to a first duty cycle, so as to generate a transient voltage response curve for the output voltage of the power supply. The response curve of the transient voltage is acquired, and a sequence of characteristic parameters including rise time, peak response voltage, decay time, voltage change rate, and residual voltage is extracted to construct the current fault fingerprint vector. Retrieve historical normal fingerprint vectors that match the current ambient temperature and load status from the preset rule base, calculate the feature difference degree between the current faulty fingerprint vector and the historical normal fingerprint vector, and generate the similarity score based on the feature difference degree. The similarity score is compared with a preset similarity threshold to determine whether the system recovery process can be initiated.

[0008] Furthermore, this application also proposes that the step of retrieving historical normal fingerprint vectors that match the current ambient temperature and load status from the preset rule base, calculating the feature difference degree between the current faulty fingerprint vector and the historical normal fingerprint vector, and generating the similarity score based on the feature difference degree includes: Calculate the normalized difference between each feature parameter in the current fault fingerprint vector and the corresponding feature parameter in the historical normal fingerprint vector; The comprehensive feature difference is obtained by multiplying each of the normalized differences by the corresponding preset weight coefficient and then summing them. The similarity score is calculated by subtracting the comprehensive feature difference from the preset full score, or by substituting the comprehensive feature difference into a preset inverse mapping function.

[0009] Furthermore, this application also proposes that the step of comparing the similarity score with a preset similarity threshold to determine whether the recovery conditions are met includes: When the similarity score is greater than or equal to the preset similarity threshold, the single diagnosis is determined to be passed and the consecutive pass count is accumulated; or when the similarity score is less than the preset similarity threshold, the consecutive pass count is cleared to zero. If the consecutive pass count reaches a preset consecutive pass count threshold, the fault troubleshooting condition is determined to be met, and the system recovery process is initiated.

[0010] Furthermore, this application also proposes that the step of counting the number of times each overpressure level is measured and calculating a health index based on the weighting coefficients of each overpressure level, so as to issue a maintenance warning signal when the health index is lower than a preset health threshold, includes: Read the cumulative number of times each overvoltage level is recorded in the historical overvoltage level database; Multiply the cumulative number of occurrences at each level by their respective weighting coefficients and sum them to obtain the cumulative risk value. The current health index is obtained by subtracting the accumulated risk value from the preset health baseline value. When the current health index is lower than the preset health threshold, the maintenance warning signal is issued to prompt the user to maintain the electronic device.

[0011] Furthermore, this application also proposes an overvoltage protection electronic device, the electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for overvoltage protection of the above-mentioned electronic device.

[0012] Furthermore, this application also proposes a storage medium, which is a computer-readable storage medium, storing a computer program on the storage medium, wherein when the computer program is executed by a processor, it implements the steps of the control method for overvoltage protection of the above-mentioned electronic device.

[0013] Furthermore, this application also proposes a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the control method for overvoltage protection of the above-mentioned electronic equipment.

[0014] As can be seen from the above, the control method, overvoltage protection electronic device, storage medium, and computer program product for overvoltage protection of electronic equipment provided in this application solve the problems of false operation or insufficient response caused by single threshold protection, lack of active diagnosis and recovery mechanism, and inability to quantify the health status of equipment in the prior art by sampling and preprocessing the power supply output voltage, setting multi-level overvoltage thresholds to perform hierarchical locking control, extracting fault fingerprint vectors in the locked state for similarity comparison to start the recovery process, and calculating the health index by counting the number of overvoltage levels to issue an early warning. It has the advantages of realizing refined hierarchical response of overvoltage protection, supporting active diagnosis and intelligent recovery of fault status, and providing equipment health assessment to realize preventive maintenance. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating an embodiment of the control method for overvoltage protection of electronic equipment in this application; Figure 2 A schematic diagram of a sub-flow for step S200 of the control method for overvoltage protection of electronic equipment in this application; Figure 3 A schematic diagram of a sub-flow for step S300 of the control method for overvoltage protection of electronic equipment in this application; Figure 4 A schematic diagram of a sub-flow for step S330 of the control method for overvoltage protection of electronic equipment in this application; Figure 5 A schematic diagram of a sub-flow for step S340 of the control method for overvoltage protection of electronic equipment in this application; Figure 6 A schematic diagram of a sub-flow for step S400 of the control method for overvoltage protection of electronic equipment in this application; Figure 7 This is a schematic diagram of the hardware operating environment involved in the control method for overvoltage protection of electronic devices in the embodiments of this application.

[0018] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0020] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0021] Traditional overvoltage protection technologies for electronic equipment often employ a single fixed threshold-triggered shutdown, lacking a graded response for different overvoltage amplitudes, which can easily lead to overprotection or underprotection. Existing solutions often rely on manual reset or fixed-delay recovery after locking, failing to proactively diagnose whether the fault has been eliminated and posing a risk of blind restarting. Furthermore, traditional methods neglect the cumulative analysis of historical overvoltage frequency and severity, making it difficult to quantitatively assess equipment health and achieve early preventative maintenance warnings, thus failing to meet the demands for intelligent and refined operation and maintenance of power systems in high-reliability scenarios.

[0022] like Figures 1 to 7 As shown, this application proposes a control method for overvoltage protection of electronic equipment. The method includes: sampling and preprocessing the output voltage of the power supply to obtain a filtered voltage; pre-setting multiple overvoltage judgment thresholds, each corresponding to a different overvoltage level; comparing the filtered voltage with each overvoltage judgment threshold to determine and cumulatively record the current overvoltage level; performing graded locking control based on the current overvoltage level; periodically extracting fault fingerprint vectors from the response curve under the output voltage during the locking control state; comparing the similarity of the fault fingerprint vectors with a preset rule base; initiating a system recovery process when the similarity meets a preset recovery condition; counting the number of overvoltage levels at each level; and calculating a health index based on the weighting coefficients of each overvoltage level; issuing a maintenance warning signal when the health index is lower than a preset threshold.

[0023] For ease of understanding, the following explains some key terms in this embodiment: Filtered voltage refers to a smooth and stable voltage signal obtained by sampling the power supply output voltage, preprocessing it to remove noise and interference components, and using it for subsequent overvoltage condition judgment.

[0024] Overvoltage judgment threshold refers to the pre-set voltage limit value used to distinguish different overvoltage levels. When the filtered voltage exceeds these thresholds, the system will determine the corresponding overvoltage level according to its range.

[0025] Overvoltage level refers to classifying overvoltage conditions into different severity levels based on the comparison between the filtered voltage and the overvoltage judgment threshold, such as slight overvoltage, moderate overvoltage, or severe overvoltage.

[0026] Lockout control refers to the measures taken by the system to limit or interrupt power output to protect electronic equipment when an overvoltage condition is detected. It can take graded responses according to different overvoltage levels.

[0027] A fault fingerprint vector is a set of parameters extracted from the power supply output and its transient response curve under locked control conditions, which can characterize the current working state or fault characteristics of the equipment.

[0028] The preset rule base refers to a database that stores fault fingerprint vectors and their corresponding features under various known normal or fault states. It is used to compare with the currently extracted fault fingerprint vectors to evaluate the device status.

[0029] Similarity refers to the degree of matching between the current fault fingerprint vector and the reference fingerprint vector in the preset rule base. It is used to determine whether the fault has been eliminated or whether the system has the conditions for recovery.

[0030] Recovery conditions refer to the preset criteria for determining that the fault has been eliminated and the system can safely restart when the system is in a locked control state, after analyzing the fault fingerprint vector and comparing its similarity.

[0031] The health index is a quantitative indicator that assesses the overall health status of electronic devices by statistically analyzing the frequency of different overpressure levels and assigning different weight coefficients to each level according to its severity.

[0032] The weighting coefficient refers to the numerical factor assigned to different overpressure levels when calculating the health index, which reflects the relative importance of each level to the health of the equipment.

[0033] Maintenance warning signals are alerts issued by the system when the health index falls below a preset safety threshold, reminding users or management systems to inspect and maintain electronic devices.

[0034] like Figure 1 As shown, this application proposes a control method for overvoltage protection of electronic equipment, which includes: Step S100: Sample and preprocess the output voltage of the power supply to obtain the filtered voltage; Step S200: Multiple overvoltage judgment thresholds are preset, each corresponding to a different overvoltage level. The filtered voltage is compared with each overvoltage judgment threshold to determine and accumulate the current overvoltage level. Based on the current overvoltage level, graded locking control is performed. Step S300: Under the locked control state, periodically extract the fault fingerprint vector of the response curve under the output voltage, and compare the similarity of the fault fingerprint vector with the preset rule base, so as to start the system recovery process when the similarity meets the preset recovery conditions. Step S400: Count the number of times each overpressure level occurs, and calculate the health index based on the weighting coefficient of each overpressure level, so as to issue a maintenance warning signal when the health index is lower than the preset health threshold.

[0035] For ease of understanding, the following explains some key terms in this embodiment: First, the power supply's output voltage is sampled and preprocessed to obtain a filtered voltage. To obtain accurate voltage information, various methods can be used to sample the power supply's output voltage. For example, the output voltage can be periodically sampled directly using an analog-to-digital converter (ADC) to convert the analog voltage signal into a digital signal. In some implementations, the output voltage can also be directly sensed by a voltage sensor, and the sensed voltage signal can be transmitted to the processing unit. After obtaining the sampled voltage, preprocessing is required to eliminate noise and interference in the signal. Preprocessing may include performing a moving average on the sampled data or using a digital filtering algorithm to process the sampled data, thereby obtaining a smooth filtered voltage, providing a reliable input for subsequent overvoltage detection.

[0036] Furthermore, multiple overvoltage judgment thresholds are pre-set, each corresponding to a different overvoltage level. The filtered voltage is compared with each overvoltage judgment threshold to determine and accumulate the current overvoltage level. Based on the current overvoltage level, graded locking control is executed. To achieve a refined response to different levels of overvoltage, the system can pre-set multiple overvoltage judgment thresholds. These thresholds can be divided according to the voltage withstand capability of the electronic equipment, system stability requirements, and fault severity. For example, two or more thresholds can be set, corresponding to different overvoltage levels such as mild, moderate, and severe. In actual operation, the real-time acquired filtered voltage is compared with these preset overvoltage judgment thresholds. When the filtered voltage exceeds a certain threshold, the system determines that it is currently in the corresponding overvoltage level and accumulates the number of occurrences of that overvoltage level. Based on the determined current overvoltage level, the system will execute graded locking control. For example, for mild overvoltage, soft shutdown measures can be taken, such as reducing output power or temporarily stopping some non-critical functions; for moderate overvoltage, moderate shutdown measures can be taken, such as cutting off some power supply paths; for severe overvoltage, hard shutdown measures can be taken, such as completely cutting off the main power input, in order to protect electronic equipment from damage to the greatest extent.

[0037] Furthermore, under locked control conditions, the system periodically extracts fault fingerprint vectors from the response curves of the output voltage and compares these vectors with a preset rule base. When the similarity meets preset recovery conditions, the system initiates a recovery process. When an electronic device enters locked control mode, to avoid blind restarts, the system can periodically perform fault diagnosis. Specifically, under locked conditions, a specific excitation signal can be applied to the power output to generate a transient voltage response. The response curve of this transient voltage is then acquired, and a series of characteristic parameters characterizing the device's state are extracted from this curve. For example, the shape, time, or frequency characteristics of the response curve can be extracted, thereby constructing a fault fingerprint vector for the current device. This fault fingerprint vector is compared with reference fingerprint vectors in a preset rule base. The preset rule base stores fingerprint vectors under various known normal or fault states. By comparison, the similarity between the current fault fingerprint vector and the reference fingerprint vectors in the rule base can be calculated. When the calculated similarity reaches or exceeds a preset recovery condition, it indicates that the fault may have been eliminated, and the system can initiate a recovery process to attempt to restore normal operation.

[0038] Finally, the system counts the number of overvoltage events at each level and calculates a health index based on the weighting coefficients of each level. A maintenance warning signal is issued when the health index falls below a preset health threshold. To assess the long-term health of electronic equipment and perform preventative maintenance, the system continuously counts the occurrence of each overvoltage level. For example, it can record the frequency of mild, moderate, and severe overvoltage. Since different overvoltage levels cause varying degrees of damage, the system can preset different weighting coefficients for each level; for example, the weighting coefficient for severe overvoltage can be higher than that for mild overvoltage. Subsequently, the counts of each overvoltage level are weighted and calculated using their corresponding weighting coefficients to obtain a comprehensive health index. This health index quantifies the cumulative risk and overall health of the electronic equipment after an overvoltage event. When the calculated health index falls below the preset health threshold, the system issues a maintenance warning signal, prompting the user or management system to inspect, maintain, or replace the electronic equipment to prevent potentially more serious malfunctions.

[0039] This embodiment effectively solves the problems of overprotection or underprotection caused by traditional single-threshold protection strategies by sampling and preprocessing the power supply output voltage and implementing hierarchical locking control based on multiple overvoltage judgment thresholds. Furthermore, in the locked control state, by periodically extracting fault fingerprint vectors and comparing their similarity with a preset rule base, proactive fault diagnosis is achieved, avoiding the risk of blind restarts. In addition, by statistically analyzing the number of overvoltage levels and calculating a weighted health index, a means of quantitatively assessing equipment health is provided, enabling timely maintenance warnings and meeting the needs of intelligent and refined operation and maintenance of power systems in high-reliability scenarios.

[0040] In some of the embodiments described above in this application, the output voltage of the power supply is sampled and preprocessed to obtain a filtered voltage to provide an accurate voltage signal for subsequent overvoltage level judgment and locking control. However, in the implementation process, the sampled voltage may be affected by high-frequency noise interference, such as power switch noise or external electromagnetic interference, resulting in inaccurate filtered voltage, which in turn affects the comparison result of the overvoltage judgment threshold, causing false triggering or missed triggering of protection actions, and reducing the reliability and stability of the overvoltage protection system.

[0041] In this regard, this application further proposes the following steps for sampling and preprocessing the output voltage of the power supply to obtain the filtered voltage: The output voltage of the power supply is sampled by a resistor divider network to obtain a sampled voltage, and the sampled voltage is then filtered by a low-pass filter to remove high-frequency noise, thus obtaining the filtered voltage.

[0042] Specifically, a resistor divider network is a circuit composed of two or more resistors in series. Its main function is to reduce a high input voltage by a preset ratio, thereby obtaining a lower output voltage. In overvoltage protection scenarios for electronic devices, the power supply output voltage is usually high, and direct measurement may pose safety hazards or exceed the measurement range of the measuring equipment. Therefore, a resistor divider network can safely reduce the high voltage proportionally and convert it into a low voltage signal that can be processed by subsequent circuits (such as analog-to-digital converters), providing a basis for subsequent voltage measurement and judgment. For example, two series resistors R1 and R2 can be used, where R1 is connected to the power supply output terminal and R2 is connected to ground, and the sampling voltage is drawn from the connection point of R1 and R2; alternatively, multiple series resistors can be used, with multiple voltage division ratios provided by drawing them out at different nodes to adapt to different measurement needs or improve voltage division accuracy. The voltage signal obtained through the resistor divider network is the sampling voltage, which is a preliminary quantization of the original power supply output voltage and represents the instantaneous value of the original output voltage.

[0043] Subsequently, the sampled voltage is processed by a low-pass filter. A low-pass filter is an electronic filter that allows signals below a certain cutoff frequency to pass through while attenuating signals above that cutoff frequency. In power supply systems, the sampled voltage is often affected by various high-frequency noises, which may originate from power switching operations, electromagnetic interference, or other transient events. A low-pass filter effectively filters out these high-frequency noise components, retaining the DC and low-frequency effective components of the voltage signal, thus making the sampled voltage signal smoother and more stable. For example, a passive RC (resistor-capacitor) low-pass filter can be used, where the cutoff frequency is determined by selecting appropriate resistor and capacitor values; it has a simple structure and low cost. Alternatively, an active low-pass filter can be used, such as a Sallen-Key filter based on an operational amplifier (Op-Amp) or a multi-stage RC filter. These filters can provide higher filtering accuracy and steeper attenuation characteristics to more effectively suppress noise. After processing by the low-pass filter, the sampled voltage signal, with high-frequency noise components removed, is the filtered voltage. It is a pure and stable voltage signal, directly used for subsequent overvoltage threshold comparison.

[0044] The above technical solution achieves several key improvements. First, by using a resistor divider network to safely and accurately scale down the power supply's output voltage, high voltage is converted into a measurable low voltage signal. This effectively avoids the electrical risks associated with directly measuring high voltage and provides a reliable sampling voltage for subsequent processing. Second, a low-pass filter effectively filters out high-frequency noise components such as power switching noise and electromagnetic interference, eliminating transient fluctuations and ensuring the smoothness and stability of the voltage signal. The resulting filtered voltage is pure and accurate, significantly improving the accuracy of comparing it with various overvoltage thresholds. This effectively prevents false or missed triggering of protection actions due to noise interference, greatly enhancing the reliability and stability of the electronic equipment overvoltage protection system and ensuring the precise execution of graded locking control.

[0045] In some of the embodiments described above in this application, a tiered locking control based on the current overvoltage level is proposed to provide a response mechanism for different overvoltage amplitudes. However, in its implementation, the lack of specific threshold definitions and corresponding action details may result in inaccurate protection measures, making it impossible to effectively distinguish between minor, moderate and severe overvoltage situations. Consequently, it may overprotect during minor disturbances, affecting business continuity, or provide insufficient protection during severe faults.

[0046] In this regard, such as Figure 2 As shown, this application further proposes step S200, which involves comparing the filtered voltage with the overvoltage determination thresholds at each level to determine and accumulate the current overvoltage level, and performing graded locking control based on the current overvoltage level. Step S210: If the filtered voltage is greater than the first overvoltage threshold and the filtered voltage is less than the second overvoltage threshold, the current overvoltage level is determined to be the first overvoltage level. The power switch drive circuit is set to a low-level flag, the switch drive circuit is controlled to stop working, and the number of times the first overvoltage level in the historical overvoltage level database is accumulated and recorded. Step S220: If the filtered voltage is greater than the second overvoltage threshold and the filtered voltage is less than the third overvoltage threshold, the current overvoltage level is determined to be the second overvoltage level, and a hard shutdown command is generated to control the fast discharge circuit to conduct in order to clamp the bus voltage to a safe range, cut off the enable signal of the main power path, and accumulate and record the number of the second overvoltage level in the historical overvoltage level database. Step S230: If the filtered voltage is greater than the third overvoltage threshold, the current overvoltage level is determined to be the third overvoltage level, the overvoltage protection relay is triggered to cut off the input power of the whole machine, and the number of times the third overvoltage level is recorded in the historical overvoltage level database is accumulated. Wherein, the first overvoltage threshold is less than the second overvoltage threshold, the second overvoltage threshold is less than the third overvoltage threshold, and the historical overvoltage level database stores the frequency of occurrence of various overvoltage level events experienced by electronic devices during operation.

[0047] Specifically, the multiple overvoltage thresholds, namely the first overvoltage threshold, the second overvoltage threshold, and the third overvoltage threshold, are preset voltage reference points used to divide the power supply's output voltage (filtered voltage) into different overvoltage severity ranges. These thresholds can be stored in the device's non-volatile memory, such as EEPROM or flash memory, and loaded into the processor or dedicated comparator circuit during system startup; alternatively, these thresholds can be configured via a hardware resistor divider network or a programmable digital potentiometer to adapt to different power supply designs and protection requirements. Presetting these thresholds means they are determined and configured before the device operates, representing fixed parameters determined during system design based on power supply characteristics, load requirements, and safety standards. Each threshold corresponds to a specific overvoltage level, such as minor overvoltage, moderate overvoltage, or severe overvoltage. These thresholds can be determined through engineering calculations and experimental verification based on the power supply's rated output voltage, maximum permissible transient voltage, and the system's overvoltage tolerance; alternatively, they can be set according to industry standards (such as IEC, UL, etc.) or the specifications of specific application scenarios.

[0048] Comparing the filtered voltage with the overvoltage thresholds at each level is a crucial step in determining whether the current power supply output voltage exceeds a specific safe range. The comparison can be implemented using a hardware comparator circuit; when the filtered voltage exceeds a certain threshold, the comparator outputs a high-level signal, triggering the corresponding logic circuit. Alternatively, the comparison can be performed by digitizing the filtered voltage using a microcontroller or digital signal processor (DSP) analog-to-digital converter (ADC), and then comparing the value with a stored threshold using a software algorithm. Accumulating and recording the current overvoltage level provides data support for subsequent health assessments and fault analysis. This can be achieved by maintaining a counter array or hash table in memory; each time an overvoltage level is determined, the corresponding counter is incremented. Implementing graded lockout control based on the current overvoltage level means taking different levels of protection measures according to the severity of the overvoltage, rather than a blanket shutdown. Lockout control means that once triggered, the system enters a controlled state until specific conditions are met before it can recover. Hierarchical locking control can be implemented using state machine logic, where each overvoltage level corresponds to a specific protection state, and the conditions for entering and exiting that state are defined; alternatively, it can be implemented using a priority interrupt system, where different overvoltage levels trigger interrupts of different priorities, and the interrupt service routine executes the corresponding locking control actions.

[0049] When the filtered voltage is greater than the first overvoltage threshold but less than the second overvoltage threshold, it is determined to be the first overvoltage level. This is a mild response for minor overvoltage situations. At this time, the power switch drive circuit is set to a low-level flag. By applying a low-level signal to the enable pin of the drive chip, the power switch drive circuit can be stopped, thereby cutting off the drive signal to the main power switch, putting it into a shutdown state, stopping power supply to the load, and preventing further damage to the equipment due to overvoltage. Controlling the switch drive circuit to stop working typically means cutting off the drive signal to the main power switch, putting it into a shutdown state, and thus stopping power supply to the load to prevent further damage to the equipment due to overvoltage. Setting the power switch drive circuit to a low-level flag can be achieved by sending a low-level signal to the enable pin of the drive chip, or by controlling the PWM (Pulse Width Modulation) output to zero via software. Simultaneously, the number of occurrences of the first overvoltage level in the historical overvoltage level database is accumulated and recorded for long-term health assessment.

[0050] When the filtered voltage exceeds the second overvoltage threshold but falls below the third overvoltage threshold, it is classified as the second overvoltage level. This is a response to moderate overvoltage conditions and is more severe than the first overvoltage level. At this point, a hard shutdown command is generated to control the fast discharge loop to clamp the bus voltage to a safe range and cut off the enable signal for the main power path. The hard shutdown command typically means an immediate, forced power cut-off and can be implemented using dedicated hardware logic circuitry (such as a comparator-triggered SR latch), which immediately outputs a shutdown signal upon triggering. The fast discharge loop can consist of one or more high-power resistors and a fast switch (such as a MOSFET or IGBT). When an overvoltage occurs, the switch conducts, dissipating excess energy on the bus through the resistor. The enable signal for cutting off the main power path can be controlled by the EN (Enable) pin of the power management chip or by disconnecting the main power path via a relay. Simultaneously, the number of times the second overvoltage level has occurred in the historical overvoltage level database is accumulated and recorded.

[0051] When the filtered voltage exceeds the third overvoltage threshold, it is determined to be a third overvoltage level, which is the highest level of response for severe overvoltage situations. At this time, the overvoltage protection relay is triggered to cut off the power supply to the entire device, directly disconnecting the power supply to prevent catastrophic damage. Triggering the overvoltage protection relay can be achieved through a drive circuit. When the third overvoltage level is detected, the relay coil is energized, causing the relay contacts to open, thereby cutting off the power supply to the entire device. The relay can be an electromagnetic relay or a solid-state relay, the selection depending on the required response speed, isolation capability, and current carrying capacity. Simultaneously, the number of times the third overvoltage level has occurred in the historical overvoltage level database is accumulated and recorded.

[0052] It is worth noting that the first overvoltage threshold is less than the second overvoltage threshold, and the second overvoltage threshold is less than the third overvoltage threshold. This progressive relationship ensures the rationality of the logical sequence and graded response of overvoltage protection; that is, as the overvoltage amplitude increases, the severity of the protection measures also increases. This relationship is a fundamental design requirement and must be strictly adhered to when setting thresholds. Furthermore, the software or hardware configuration should include a verification mechanism for this threshold sequence to prevent configuration errors from causing confusion in the protection logic.

[0053] Through the above technical solution, this application achieves accurate response to overvoltage events by specifically defining three overvoltage thresholds and corresponding graded locking control steps, thus solving the problem of inaccurate graded response. First, multiple overvoltage judgment thresholds are set and pre-assigned to different overvoltage levels. This allows the system to finely differentiate based on voltage severity, avoiding insufficient or excessive protection caused by a single threshold. The filtered voltage is compared with each threshold level to determine the current overvoltage level, ensuring accurate classification based on the actual voltage value and providing a reliable basis for subsequent responses. When implementing graded locking control based on the current overvoltage level, specific measures are taken for different levels: When the filtered voltage exceeds the first overvoltage threshold but does not reach the second overvoltage threshold, it is determined to be the first overvoltage level. A low-level flag is set to control the switch drive circuit to stop operating. This is a mild response, avoiding over-protection for minor disturbances that could affect business continuity. Simultaneously, the number of recorded instances is accumulated to support historical analysis. When the filtered voltage exceeds the second overvoltage threshold but does not reach the third overvoltage threshold, it is determined to be the second overvoltage level. A hard shutdown command is generated to control the fast discharge circuit to conduct the clamping voltage and cut off the enable signal, providing moderate protection against damage caused by moderate overvoltages. When the filtered voltage exceeds the third overvoltage threshold, it is determined to be the third overvoltage level. A relay is triggered to cut off the power supply, achieving the most stringent protection to cope with severe faults. This graded response mechanism effectively balances protection strength and business continuity, avoiding the drawbacks of traditional single-threshold protection strategies and significantly improving the intelligence and precision of overvoltage protection for electronic equipment.

[0054] In some of the embodiments described above in this application, a method is proposed to periodically extract fault fingerprint vectors and compare them to initiate a system recovery process under locked control state in order to actively diagnose whether the fault has been eliminated. However, in its implementation, there is a lack of specific diagnostic mechanisms to ensure the accuracy and efficiency of the diagnosis. It cannot dynamically adapt to changes in the environment and load status, resulting in blind fault judgment and the risk of false recovery or delayed recovery.

[0055] In this regard, such as Figure 3As shown, this application further proposes step S300, which involves periodically extracting the fault fingerprint vector of the response curve under the output voltage in the locked control state, comparing the fault fingerprint vector with a preset rule base for similarity, and initiating the system recovery process when the similarity meets the preset recovery conditions. This step includes: Step S310: In the locked control state, a turn-on drive signal is generated according to a preset diagnostic time interval to control the power switch drive circuit to turn on according to the first duty cycle, so as to generate a transient voltage response curve for the output voltage of the power supply. Step S320: Acquire the response curve of the transient voltage, and extract the feature parameter sequence including rise time, peak response voltage, decay time, voltage change rate and residual voltage to construct the current fault fingerprint vector; Step S330: Retrieve historical normal fingerprint vectors that match the current ambient temperature and load status from the preset rule base, calculate the feature difference degree between the current fault fingerprint vector and the historical normal fingerprint vector, and generate the similarity score based on the feature difference degree. Step S340: Compare the similarity score with a preset similarity threshold to determine whether the system recovery process can be started.

[0056] Specifically, in the locked control state, a turn-on drive signal is generated according to a preset diagnostic time interval, controlling the power switch drive circuit to conduct according to a first duty cycle, thereby stimulating the generation of a transient voltage response curve for the power supply's output voltage. This step aims to actively and controllably provide a brief excitation to the power supply system in the locked state to observe its transient response behavior under specific conditions. By generating a turn-on drive signal and controlling the power switch drive circuit to conduct with the first duty cycle, the operating state of the power supply under light load or specific load conditions can be simulated, thereby stimulating and capturing the transient response curve of its output voltage. This active excitation is crucial for diagnosing whether a fault has been eliminated because it allows the system to "tentatively" recover some functions in a controlled environment, rather than blindly restarting. For example, a fixed diagnostic time interval (e.g., every 500 milliseconds or 1 second) can be set using a timer module within a microcontroller (MCU) or digital signal processor (DSP), triggering a pulse width modulation (PWM) signal generator at that time point to generate a turn-on drive signal with a preset first duty cycle. This signal is sent directly to the power switch driver circuit, causing it to conduct briefly and thus energizing the power output. Alternatively, it can be implemented using a programmable logic device (FPGA) or an application-specific integrated circuit (ASIC). By configuring a state machine in the FPGA, when the system enters the lockout control state, the state machine outputs a pulse signal with a specific duty cycle as a turn-on drive signal when a preset diagnostic time interval is reached, based on an internal counter or an external clock signal, controlling the power switch driver circuit to conduct briefly.

[0057] Subsequently, the transient voltage response curve is acquired, and a sequence of characteristic parameters, including rise time, peak response voltage, decay time, voltage change rate, and residual voltage, is extracted to construct the current fault fingerprint vector. This step aims to quantify and extract key features characterizing the operating state from the transient voltage response generated after the power supply is excited. The transient voltage response curve contains rich information such as the state of internal components and load characteristics of the power supply. By extracting characteristic parameters such as rise time, peak response voltage, decay time, voltage change rate, and residual voltage, a multi-dimensional "fault fingerprint vector" can be formed, which can comprehensively and precisely describe the current health status and potential fault modes of the power supply. For example, a high-speed analog-to-digital converter (ADC) can be used to continuously sample the transient voltage response curve, converting the analog voltage signal into a digital signal. Subsequently, the sampled data is analyzed by an algorithm running on an embedded processor (such as an MCU or DSP). For example, rise time can be calculated by identifying the time required for the voltage to rise from one benchmark value (e.g., 10%) to another benchmark value (e.g., 90%); peak voltage can be found directly from the sampled data; decay time can be calculated by identifying the time required for the voltage to drop from its peak value to a stable value; the rate of change of voltage can be obtained by performing differential operations or fitting slopes on the sampled data; and residual voltage can be the value at which the voltage stabilizes after the transient response ends. These parameters combine to form a fault fingerprint vector. Alternatively, a dedicated transient event recorder or oscilloscope can be used for high-precision data acquisition, and the acquired waveform data can be transmitted to a host computer or cloud server for processing. On the host computer or cloud, advanced signal processing algorithms (such as wavelet transform, Fourier transform, or machine learning feature extraction algorithms) can be used to automatically identify and extract the above feature parameters from the transient response waveform. For example, curve fitting techniques can be used to accurately calculate the slopes of the rise and fall edges, thereby obtaining the rate of change of voltage and decay time.

[0058] Based on this, historical normal fingerprint vectors matching the current ambient temperature and load state are retrieved from the preset rule base. The feature difference degree between the current fault fingerprint vector and the historical normal fingerprint vector is calculated, and a similarity score is generated based on the feature difference degree. This step aims to quantify the deviation of the current power supply state from the normal state by comparing it with reference data under known normal conditions. The preset rule base stores the transient response characteristics (historical normal fingerprint vectors) of the power supply during normal operation under different ambient temperatures and load states. By retrieving the historical data that best matches the current operating conditions and calculating the feature difference degree between the current fault fingerprint vector and the historical normal fingerprint vector, the influence of environmental and load changes on the diagnostic results can be eliminated, thereby more accurately assessing the true health status of the power supply. A similarity score is generated based on the difference degree, providing a quantitative basis for subsequent recovery decisions. For example, the preset rule base can be a lookup table or database stored in non-volatile memory (such as EEPROM, Flash). The database pre-records a sequence of characteristic parameters for the normal transient response of the power supply under different temperature ranges (e.g., -20℃, 0℃, 25℃, 50℃, etc.) and different load levels (e.g., no load, 25% load, 50% load, full load, etc.). During diagnosis, the system first acquires the current ambient temperature sensor data and load current data, and then searches the rule base for the closest or matching historical normal fingerprint vector based on this data. Feature difference can be obtained by calculating the Euclidean distance, Manhattan distance, or weighted distance between the corresponding feature parameters of the two vectors. The similarity score can be generated based on the difference using an inverse mapping function (e.g., similarity = 1 / (1 + difference) or similarity = MaxScore - difference). Alternatively, the rule base can also be built based on a machine learning model. Before the system is put into use, the power supply is subjected to extensive testing under various ambient temperature and load conditions to collect transient response data during normal operation, and a model (e.g., Support Vector Machine (SVM), Neural Network (NN), or Decision Tree) is trained. This model can predict the corresponding "normal" fingerprint vector based on the input ambient temperature and load state. During diagnosis, the current ambient temperature and load status are input into the model to obtain predicted historical normal fingerprint vectors. Feature dissimilarity can be measured by calculating the cosine similarity or the complement of the Pearson correlation coefficient between two vectors. The similarity score can be directly output by the model or obtained by inputting the dissimilarity score into a preset scoring function.

[0059] Finally, the similarity score is compared with a preset similarity threshold to determine whether the system recovery process can be initiated. This step is the core of the system recovery decision-making process. It compares the quantified similarity score with preset judgment criteria to determine whether it is safe to attempt to restore the power supply to normal operation. By setting a reasonable similarity threshold, it can be ensured that the recovery process is only allowed to start when the transient response behavior of the power supply is sufficiently close to the normal state, thereby avoiding blind recovery before the fault is completely eliminated and reducing the risk of secondary damage. For example, in a microcontroller or processor, the calculated similarity score is compared with a floating-point or integer threshold pre-stored in the program memory. If the similarity score is greater than or equal to the preset similarity threshold, the logic determines that the recovery condition is met; otherwise, it is not met. This threshold can be calibrated and optimized through a large amount of experimental and empirical data to balance the timeliness and safety of recovery. Alternatively, it can be implemented using a fuzzy logic controller. The similarity score is used as a fuzzy input, and a fuzzy set is defined (such as "very low", "low", "medium", "high", "very high"). At the same time, a fuzzy output is defined (such as "do not recover", "recover cautiously", "recover"). By using fuzzy rules (e.g., "restore if similarity is high") combined with fuzzy inference mechanisms, a clear restoration decision is ultimately obtained. This approach can handle the uncertainty between the score and the threshold, making the decision more robust.

[0060] Through the above technical solution, after an electronic device experiences overvoltage and enters a locked control state, the system no longer blindly waits or relies on manual intervention. Instead, it actively and periodically applies weak excitation to the power supply, generating a transient voltage response curve. By extracting refined features from this curve, a fault fingerprint vector containing multi-dimensional information is constructed, comprehensively reflecting the current state of the power supply. More importantly, this solution introduces a matching mechanism for ambient temperature and load status. It retrieves the historical normal fingerprint vector that best matches the current operating condition from a preset rule base for comparison, effectively eliminating the interference of external environmental factors on the diagnostic results, making fault diagnosis more accurate and robust. The similarity score generated based on feature differences provides a quantitative and objective basis for system recovery, avoiding secondary faults or unnecessary downtime caused by misjudgments. This intelligent diagnosis and recovery mechanism significantly improves the adaptability and reliability of the electronic device overvoltage protection system, ensuring timely and safe restoration of system operation after fault resolution, thereby improving equipment availability and maintenance efficiency.

[0061] In this regard, such as Figure 4As shown, this application further proposes step S330, which involves retrieving historical normal fingerprint vectors that match the current ambient temperature and load status from the preset rule base, calculating the feature difference degree between the current fault fingerprint vector and the historical normal fingerprint vector, and generating a similarity score based on the feature difference degree. Step S331: Calculate the normalized difference between each feature parameter in the current fault fingerprint vector and the corresponding feature parameter in the historical normal fingerprint vector; Step S332: Multiply each of the normalized differences by the corresponding preset weight coefficients and sum them to obtain the comprehensive feature difference degree; Step S333: Subtract the comprehensive feature difference from the preset full score, or substitute the comprehensive feature difference into a preset inverse mapping function to calculate the similarity score.

[0062] To ensure comparability of feature parameters with different dimensions, this application employs a normalized difference calculation method. Specifically, the normalized difference involves comparing each feature parameter in the current fault fingerprint vector (e.g., rise time, peak response voltage, decay time, voltage change rate, and residual voltage) with the corresponding feature parameter in the historical normal fingerprint vector, and standardizing the difference. For example, a relative difference method can be used, which involves subtracting the historical normal feature parameter value from the current feature parameter value and then dividing by the historical normal feature parameter value (or its absolute value) to eliminate the influence of dimensions; alternatively, a maximum-minimum normalization method can be used, mapping the difference to a fixed interval of [0, 1] or [-1, 1], ensuring that the difference of all features is measured on a uniform scale. This approach avoids certain feature parameters with larger values ​​dominating the calculation of difference, thus ensuring that the contribution of all feature parameters to the final similarity score is fair and effective.

[0063] After obtaining the normalized differences of each feature parameter, this application further multiplies each normalized difference by a preset corresponding weight coefficient and then sums them to obtain the comprehensive feature difference degree. The introduction of weight coefficients aims to reflect the importance of different feature parameters in fault diagnosis. For example, for certain features that are highly sensitive to overvoltage faults and have obvious changing trends (such as peak response voltage and voltage change rate), higher weights can be assigned; while for features with low sensitivity or insignificant changes, lower weights are assigned. These weight coefficients can be preset and optimized based on expert experience, historical fault data analysis, or machine learning algorithms (such as through feature selection or regression analysis). By weighted summation, the overall difference between the current fault fingerprint vector and the historical normal fingerprint vector can be more accurately reflected, highlighting the influence of key fault features, thereby enabling the comprehensive feature difference degree to more comprehensively and accurately characterize the health status of the system.

[0064] To transform the comprehensive feature difference into an intuitive and easily judged similarity score, this application provides two calculation methods. One method is to subtract the comprehensive feature difference from a preset maximum score. For example, if the maximum score is set to 100 points, a larger comprehensive feature difference results in a lower similarity score, and vice versa, providing a linear inverse mapping relationship. The other method is to substitute the comprehensive feature difference into a preset inverse mapping function. For example, an exponential decay function (such as Score=A*e^(-B*Difference)) or a reciprocal function (such as Score=A / (1+B*Difference)) can be used, where A and B are adjustable parameters to ensure that a larger difference results in a lower score, and that the score is within a reasonable range. Both methods can convert the numerical value representing the degree of difference into a score representing the degree of similarity, making the score inversely proportional to the difference. This facilitates subsequent comparison with a preset similarity threshold, thereby accurately determining whether the fault has been eliminated and providing a reliable basis for system recovery.

[0065] Through the above technical solution, this application provides a standardized and refined similarity scoring mechanism. By normalizing each feature parameter, the influence of differences in the dimensions of different feature parameters is eliminated, ensuring the fairness of the comparison. Furthermore, weighting coefficients are introduced to perform a weighted summation of the normalized differences, making key feature parameters sensitive to faults play a more important role in the comprehensive difference score, thereby improving the accuracy and reliability of fault diagnosis. Finally, the comprehensive feature difference score is transformed into an intuitive similarity score, enabling the system to determine whether the fault has been eliminated based on quantitative and standardized indicators. This avoids misjudgments or omissions caused by inaccurate scoring, significantly improving the intelligence and reliability of the system recovery process, effectively reducing the risk of blind restarts, and thus ensuring the stable operation of electronic equipment and business continuity after overvoltage protection.

[0066] In some of the embodiments described above in this application, a similarity score is compared with a preset similarity threshold to determine whether the recovery conditions are met. However, in this process, a single successful diagnosis may only reflect the transient state and cannot ensure that the fault has been completely eliminated, which may lead to a risk of blind restart due to accidental factors.

[0067] In this regard, such as Figure 5 As shown, this application further proposes step S340, the step of comparing the similarity score with a preset similarity threshold to determine whether the recovery conditions are met, including: Step S341: When the similarity score is greater than or equal to the preset similarity threshold, a single diagnosis is determined to be passed and the consecutive pass count is accumulated; or when the similarity score is less than the preset similarity threshold, the consecutive pass count is cleared to zero. Step S342: If the consecutive pass count reaches the preset consecutive pass count threshold, it is determined that the fault troubleshooting condition is met, and the system recovery process is started.

[0068] Specifically, the similarity score refers to a quantitative indicator obtained by comparing the current fault fingerprint vector with historical normal fingerprint vectors, used to measure how close the current system state is to a normal state. This score is typically a numerical value, such as between 0 and 1 or between 0 and 100; a higher value indicates a higher similarity and a system state closer to normal. The preset similarity threshold is a pre-set critical value used to determine whether a single diagnosis passes. This threshold is set according to the system's reliability requirements and the actual application scenario, for example, it can be set to 0.8 or 80%. When the similarity score reaches or exceeds this threshold, it indicates that the system exhibits sufficient normal characteristics within the current diagnostic cycle, and the single diagnosis is considered passed. The consecutive pass count is a counter used to record the number of consecutive successful diagnoses; the counter increments by 1 each time a single diagnosis passes. For example, after each diagnostic cycle, the system obtains the similarity score and compares it with the preset similarity threshold. If the score is greater than or equal to the threshold, an internal variable is incremented by 1; alternatively, this can be achieved through a logic judgment module that receives the similarity score and a preset similarity threshold as input and outputs a boolean value indicating whether a single diagnosis passes. If true, a counter is triggered to perform an accumulation operation.

[0069] Conversely, if the similarity score is less than the preset similarity threshold, the consecutive pass count is reset to zero. Resetting the consecutive pass count to zero means that when a single diagnosis fails, the consecutive pass counter is reset to its initial value (usually 0). This ensures that the recovery process is only triggered when the system consistently and stably performs normally, avoiding misjudgments caused by occasional normal states. For example, if the similarity score is lower than the preset similarity threshold, the aforementioned internal variable is directly assigned a value of 0; or, in the logic judgment module, if a single diagnosis result is false, a reset signal is sent to the counter to reset it to zero.

[0070] Furthermore, if the continuous pass count reaches a preset continuous pass count threshold, the fault troubleshooting condition is deemed met, and the system recovery process is initiated. The continuous pass count threshold is a pre-set integer value representing the minimum number of consecutive pass diagnostics the system needs to perform. This threshold is used to further improve the reliability of system recovery and prevent false recovery due to short-term fluctuations or accidental factors. For example, it can be set to 3, 5, or more times. Meeting the fault troubleshooting condition means that the system has been diagnosed as normal multiple times consecutively, thus indicating that the fault causing the overvoltage has been stably eliminated. The system recovery process refers to a series of operations to unlock the control state and restore the electronic equipment to normal operation. This may include re-enabling the main power path, closing the fast discharge circuit, resetting the protection relay, etc. For example, the system continuously monitors the value of internal variables; when it reaches or exceeds the preset continuous pass count threshold, the system's internal state machine switches from "locked diagnostics" to "recovery preparation" and issues a command to initiate the system recovery process; alternatively, it can be implemented using a comparator module that compares the continuous pass count with the preset continuous pass count threshold. When the count reaches the threshold, the comparator outputs a trigger signal to initiate the system recovery process.

[0071] Through the above technical solution, this application, under locked control, not only periodically extracts the fault fingerprint vector of the output voltage response curve and compares it with a preset rule base for similarity, but also further introduces a continuous diagnostic confirmation mechanism. Specifically, by setting a continuous pass count and a continuous pass count threshold, the randomness and uncertainty that may exist in a single diagnostic result are avoided. When the similarity score reaches the preset threshold, only the continuous pass count is accumulated, rather than immediate recovery; only when the system is diagnosed as normal multiple times consecutively (reaching the preset pass count threshold) is it determined that the fault has been stably eliminated and the system recovery process is initiated. Conversely, any failure in diagnosis will immediately clear the continuous pass count, thereby effectively preventing the risk of blindly restarting before the fault is completely eliminated. This mechanism significantly improves the accuracy, reliability, and safety of system recovery, ensuring that electronic equipment can recover to normal operation in a more robust and intelligent manner after experiencing a voltage surge, avoiding secondary faults or service interruptions caused by misjudgment, thus meeting the needs of intelligent and refined operation and maintenance of power systems in high-reliability scenarios.

[0072] In some of the solutions described above in this application, the number of overpressure levels at each level is counted and a health index is calculated to assess the equipment status and issue warnings. However, in this process, due to the lack of specific calculation methods and weighting mechanisms, the calculation of the health index may not be accurate enough and may not effectively reflect the cumulative risk differences of different overpressure levels, resulting in insufficient timeliness and accuracy of maintenance warnings.

[0073] In this regard, such as Figure 6 As shown, this application further proposes step S400, which involves counting the number of times each overpressure level is reached and calculating a health index based on the weighting coefficients of each overpressure level, so as to issue a maintenance warning signal when the health index is lower than a preset health threshold. Step S410: Read the cumulative number of times each overvoltage level is recorded in the historical overvoltage level database; Step S420: Multiply the cumulative number of times at each level by the corresponding weight coefficient and sum them to obtain the cumulative risk value; Step S430: Subtract the accumulated risk value from the preset health baseline value to obtain the current health index, so as to issue the maintenance warning signal when the current health index is lower than the preset health threshold, prompting the user to maintain the electronic device.

[0074] First, the cumulative counts for each overvoltage level are read from the historical overvoltage level database. This database stores the frequency of various overvoltage level events experienced by the electronic device during operation. The cumulative count refers to the total number of times each specific overvoltage level event has been detected and recorded since the device was put into operation or last maintenance. For example, this historical overvoltage level database can be a database or structured data table stored in the device's non-volatile memory (such as EEPROM or flash memory), and the cumulative counts for each overvoltage level can be obtained by executing database query commands or direct memory address access. Alternatively, for resource-constrained embedded systems, the database can be a predefined memory array, and the cumulative counts for the corresponding overvoltage level can be read through index access. This ensures that the health assessment is based on actual historical operating data, rather than subjective estimates, providing a reliable data foundation for subsequent risk quantification.

[0075] Secondly, the cumulative number of occurrences at each level is multiplied by the corresponding weighting coefficient and summed to obtain the cumulative risk value. Given that different overvoltage levels cause varying degrees of potential damage to electronic devices, this application introduces weighting coefficients to quantify this difference. These weighting coefficients are pre-set values ​​used to reflect the severity of each overvoltage level; typically, higher-level overvoltage events correspond to larger weighting coefficients. For example, these weighting coefficients can be stored in a fixed lookup table. After reading the cumulative number of occurrences, the processor retrieves the corresponding weighting coefficient from the table based on the overvoltage level and performs a multiplication operation. Alternatively, these weighting coefficients can be configurable parameters, allowing adjustment based on specific device models or application scenarios, while maintaining the same calculation logic. This weighted summation method more accurately reflects the cumulative impact of different overvoltage level events on the device's health status, avoiding the drawback of treating all overvoltage events the same, and enabling the cumulative risk value to effectively distinguish the greater harm of high-risk overvoltage events.

[0076] Next, the current health index is obtained by subtracting the accumulated risk value from the preset health baseline value. When the current health index falls below the preset threshold, a maintenance warning signal is issued to prompt the user to maintain the electronic device. The health baseline value represents the health level of the electronic device in an ideal or brand-new state, while the accumulated risk value reflects the damage caused by overvoltage events. By subtracting the accumulated risk value from the baseline value, the degree of decline in the device's health status can be intuitively quantified, forming an easily understandable and dynamically monitorable health index. For example, when the calculated current health index falls below the preset health threshold, the system will trigger the maintenance warning mechanism. This warning signal can take various forms, such as illuminating indicator lights on the device, displaying a warning message on the device screen, sending a notification to a remote monitoring system (e.g., via network protocol or SMS), or recording a detailed maintenance log. This mechanism achieves automated warnings based on objective quantitative indicators, ensuring that when the device's health status deteriorates to a certain extent, it can promptly remind the user to perform preventative maintenance, thereby effectively avoiding potential failures and extending the device's lifespan.

[0077] Through the above technical solution, this application overcomes the shortcomings of traditional health assessments, which lack specific calculation methods and weighting mechanisms. By accurately reading historical overvoltage data, introducing differentiated weighting coefficients to calculate cumulative risk, and dynamically generating a health index based on a health benchmark value, this application achieves a refined and quantitative assessment of the health status of electronic equipment. This assessment method not only accurately reflects the cumulative impact of different overvoltage levels on the equipment, but also, by setting early warning thresholds, can promptly issue maintenance warning signals when the equipment health drops to a critical point, prompting users to intervene. This significantly improves the timeliness and accuracy of maintenance warnings, enabling the operation and maintenance of electronic equipment to shift from passive fault response to proactive preventative maintenance, thereby effectively reducing equipment failure rates, ensuring business continuity, and extending equipment lifespan. In particular, combined with the aforementioned hierarchical locking control and fault diagnosis and recovery mechanism, the health assessment and early warning functions of this application provide electronic equipment with a comprehensive intelligent operation and maintenance system from real-time protection and fault self-diagnosis to long-term health management, further improving the system's reliability and intelligence level under complex dynamic operating conditions.

[0078] like Figure 7 As shown in the embodiment of this application, an overvoltage protection electronic device is also disclosed. The electronic device includes: a memory 10, a processor 20, and a computer program stored on the memory 10 and executable on the processor 20. The computer program is configured to implement the steps of the control method for overvoltage protection of the above-mentioned electronic device.

[0079] The core innovation of this embodiment lies in the fact that by combining the memory 10, processor 20 and computer program in a collaborative manner, a graded response mechanism for different overvoltage amplitudes is realized, the fault elimination status is actively diagnosed to avoid blind restarts is avoided, and the health of the equipment is evaluated by quantifying historical overvoltage data, thereby improving the intelligence level of overvoltage protection and preventive maintenance capabilities of electronic equipment.

[0080] Specifically, the memory 10 stores computer programs and related data, including preset overvoltage judgment thresholds, historical overvoltage level records, and preset rule bases. This provides a stable data foundation for the execution of the processor 20, ensuring that the parameters and rules required for the method steps can be called at any time, avoiding protection failures caused by data loss in traditional solutions. The processor 20 runs the computer program stored in the memory 10, executing a complete method flow including sampling preprocessing, hierarchical locking control, fault diagnosis and recovery, and health calculation. By processing the power supply output voltage data in real time, it generates a filtered voltage and compares it with multi-level thresholds to achieve adaptive protection responses for different overvoltage levels, solving the problems of overprotection or underprotection under a single threshold mechanism. The computer program is configured to implement a specific control method, instructing the processor 20 to periodically extract fault fingerprint vectors for similarity comparison in the locked state, actively diagnosing fault elimination status and reducing the risk of blind restarts; simultaneously, it counts the number of overvoltage levels and calculates a weighted health index, issuing timely maintenance warnings and supporting preventive maintenance.

[0081] Through the above technical solution, the overvoltage protection electronic device effectively addresses the adaptability challenges under dynamic operating conditions, improves equipment reliability and operation and maintenance efficiency, and meets the needs of intelligent and refined operation and maintenance of power systems in high reliability scenarios.

[0082] With the increasing integration and complexity of electronic devices, overvoltage protection technology faces challenges such as adaptability to dynamic operating conditions, multi-dimensional fault identification, and environmental compatibility. Existing overvoltage protection technologies for electronic devices mostly employ a single fixed threshold-triggered shutdown protection strategy, lacking a graded response mechanism for different overvoltage amplitudes. This leads to overprotection affecting business continuity during minor disturbances or insufficient protection measures during severe faults. Furthermore, traditional solutions often rely on manual reset or fixed-delay recovery after locking, failing to proactively diagnose whether the fault has been eliminated, thus posing a risk of blind restarting. They also neglect the cumulative analysis of historical overvoltage frequency and severity, making it difficult to achieve early preventative maintenance warnings through quantitative assessment of equipment health, and thus failing to meet the demands for intelligent and refined operation and maintenance of power systems in high-reliability scenarios.

[0083] To address this, this application proposes a storage medium, which is a computer-readable storage medium storing a computer program. When executed by the processor 20, the computer program implements the steps of the control method for overvoltage protection of the aforementioned electronic device. Specifically, by providing an executable computer program, the storage medium enables the processor 20 to achieve intelligent overvoltage protection control. During implementation, the computer program includes instructions for sampling and preprocessing the output voltage of the power supply to obtain a filtered voltage; presetting multiple overvoltage judgment thresholds, comparing the filtered voltage with each level of overvoltage judgment threshold to determine and accumulate the current overvoltage level, and performing graded locking control based on the current overvoltage level; periodically extracting fault fingerprint vectors from the response curve under the output voltage in the locking control state, and comparing the fault fingerprint vectors with a preset rule base for similarity, so as to initiate a system recovery process when the similarity meets preset recovery conditions; counting the number of overvoltage levels at each level, and calculating a health index based on the weight coefficients of each overvoltage level, so as to issue a maintenance warning signal when the health index is lower than a preset threshold.

[0084] The core innovation of this embodiment lies in combining a computer-readable storage medium with a stored computer program in a specific manner, enabling the processor 20 to execute a graded response mechanism, proactive fault diagnosis, and quantitative health assessment. This solves the problems of lack of graded response, inability to proactively diagnose and eliminate faults, and difficulty in quantifying health status in overvoltage protection of electronic equipment. Because the computer program stored on the storage medium, when executed by the processor 20, implements graded locking control based on comparisons between the filtered voltage and multiple thresholds, it avoids insufficient or excessive protection caused by a single threshold. Furthermore, by extracting fault fingerprint vectors from the response curve and comparing them with a rule base, recovery conditions are proactively determined, reducing the risk of blind restarts. In addition, based on the number of overvoltage levels and weighting coefficients, a quantitative assessment of equipment health is achieved, enabling timely issuance of maintenance warning signals.

[0085] Through the above technical solutions, this application not only provides a refined response to overvoltages of varying degrees, but also achieves proactive fault diagnosis and quantitative management of equipment health. Specifically, the graded locking control mechanism dynamically adjusts protection measures based on the comparison between the filtered voltage and the overvoltage judgment threshold, ensuring business continuity during minor disturbances and providing sufficient protection during severe faults; the similarity comparison mechanism between the fault fingerprint vector and the preset rule base periodically evaluates the fault clearance status under the locked state, avoiding blind restarts relying on manual resets or fixed delays; the weighted calculation of the health index provides data support for preventative maintenance by quantifying the cumulative impact of historical overvoltage events. In summary, this storage medium effectively meets the needs of intelligent and refined operation and maintenance of power systems in high-reliability scenarios by programmatically executing the above steps.

[0086] With the increasing integration and complexity of electronic devices, overvoltage protection technology faces challenges such as adaptability to dynamic operating conditions, multi-dimensional fault identification, and environmental compatibility. Existing overvoltage protection technologies for electronic devices mostly adopt a single fixed threshold-triggered shutdown protection strategy, lacking a graded response mechanism for different overvoltage amplitudes. This leads to overprotection affecting business continuity during minor disturbances or insufficient protection measures during serious faults. At the same time, traditional solutions often rely on manual reset or fixed-delay recovery after locking, failing to proactively diagnose whether the fault has been eliminated and thus posing a risk of blind restart. Furthermore, they neglect the cumulative analysis of historical overvoltage frequency and level, making it difficult to achieve early preventative maintenance warnings for faults through quantitative assessment of equipment health. This fails to meet the needs of intelligent and refined operation and maintenance of power systems in high-reliability scenarios.

[0087] In response, this application proposes a computer program product, which includes a computer program that, when executed by a processor 20, implements the steps of the control method for overvoltage protection of the aforementioned electronic equipment. The core innovation of this embodiment lies in combining an overvoltage grading judgment mechanism with dynamic fault diagnosis technology and introducing a health measurement and evaluation model. This achieves adaptive response to different overvoltage amplitudes, proactive identification of fault states, and preventative early warning of equipment health status, thereby improving the intelligent operation and maintenance level and reliability of the power system.

[0088] Specifically, the computer program product executes control logic through processor 20. First, it samples and preprocesses the power supply's output voltage to obtain a filtered voltage. During implementation, an analog-to-digital converter can be used to periodically sample the output voltage, converting the analog signal into a digital signal. A digital filtering algorithm is then used to process the sampled data, effectively eliminating noise and interference components to obtain a smooth and stable filtered voltage signal, providing an accurate basis for subsequent overvoltage detection. Furthermore, the system pre-sets multiple overvoltage judgment thresholds. These thresholds are divided according to the withstand voltage capability and fault severity of the electronic equipment, corresponding to different overvoltage levels. Processor 20 compares the real-time acquired filtered voltage with each overvoltage judgment threshold to determine the current overvoltage level and accumulates its occurrence count. Based on this level, it executes graded locking control. For example, when a slight overvoltage is detected, processor 20 executes soft shutdown measures to reduce output power; when a severe overvoltage is detected, hard shutdown measures are triggered to completely cut off the main power input, thereby avoiding insufficient or excessive protection caused by a single threshold.

[0089] In the locked control state, the processor 20 periodically extracts the fault fingerprint vector from the response curve under the output voltage and compares the similarity of this vector with a preset rule base. Specifically, the processor 20 applies a specific excitation signal to stimulate the power output, collects the transient voltage response curve, and extracts parameter sequences such as shape features and time features to form the fault fingerprint vector. By calculating the matching degree between this vector and the reference vector in the rule base, when the similarity meets the preset recovery conditions, the processor 20 initiates the system recovery process, effectively avoiding the risk of blind restarts. Furthermore, the processor 20 continuously counts the occurrence frequency of each overvoltage level and performs weighted calculations based on preset weight coefficients to obtain a health index that quantitatively represents the health status of the equipment. When this index is lower than a preset threshold, the processor 20 issues a maintenance warning signal, prompting the user to perform preventative maintenance, thus achieving a refined assessment of the equipment's health status.

[0090] Through the above technical solutions, this application effectively solves the key problems of the lack of a graded response mechanism, passive fault diagnosis, and insufficient health assessment in the prior art. The processor 20, based on the comprehensive processing of filter voltage, overvoltage level determination results, fault fingerprint vector similarity, and health index, constructs an overvoltage protection system with strong dynamic adaptability and high diagnostic accuracy. This not only ensures the stable operation of the power system under various operating conditions but also achieves early preventative maintenance warnings through quantitative data analysis, significantly improving the intelligent operation and maintenance level and long-term reliability of electronic equipment in high-reliability scenarios.

[0091] The following example will provide a more detailed explanation of the above technical solution: In an industrial control electronic device, the power management unit is responsible for supplying power to the device and implementing overvoltage protection. During operation, the device may encounter voltage anomalies caused by power grid fluctuations or internal faults.

[0092] First, the electronic device continuously monitors the output voltage of the power supply. The output voltage is sampled using a resistor divider network to obtain a sampled voltage. Then, this sampled voltage passes through a low-pass filter to effectively remove high-frequency noise, thereby obtaining a stable and reliable filtered voltage.

[0093] The device is pre-set with multiple overvoltage thresholds, such as a first overvoltage threshold, a second overvoltage threshold, and a third overvoltage threshold, and these thresholds are in an increasing order. These thresholds correspond to different overvoltage levels to achieve graded protection.

[0094] When the equipment is running, if a slight fluctuation in the power grid causes the filtered voltage to exceed the first overvoltage threshold but not the second overvoltage threshold, the system will determine the current overvoltage level to be the first overvoltage level. At this time, the power switch drive circuit will be set to a low-level flag, controlling the switch drive circuit to stop working to prevent the voltage from rising further, but without immediately cutting off the main power supply, thus maintaining the equipment's business continuity. Simultaneously, the system will accumulate and record the number of times the first overvoltage level has occurred in the historical overvoltage level database.

[0095] If grid fluctuations intensify and the filtered voltage exceeds the second overvoltage threshold but does not reach the third overvoltage threshold, the system will determine the current overvoltage level to be the second overvoltage level. At this time, the system will generate a hard shutdown command, controlling the fast discharge circuit to quickly clamp the bus voltage to a safe range and disconnect the enable signal of the main power path, implementing stronger protection. The number of times the second overvoltage level has occurred will be accumulated in the historical overvoltage level database.

[0096] In extreme cases, if the filtered voltage exceeds the third overvoltage threshold, the system will determine it to be a third-level overvoltage. At this point, the system will immediately trigger the overvoltage protection relay to completely cut off the power supply to the entire unit, preventing serious damage to the equipment. The number of third-level overvoltage instances will be accumulated in the historical overvoltage level database.

[0097] When the equipment enters a locked control state due to an overvoltage event, unlike traditional solutions that rely on manual reset or fixed-delay recovery, this solution initiates an intelligent diagnostic process. In the locked control state, the system generates a conduction drive signal according to a preset diagnostic time interval, controlling the power switch drive circuit to briefly conduct according to a first duty cycle, thereby generating a transient voltage response curve for the power supply's output voltage. Subsequently, the system acquires this transient voltage response curve and extracts a sequence of characteristic parameters including rise time, peak response voltage, decay time, voltage change rate, and residual voltage, thereby constructing the current fault fingerprint vector.

[0098] Next, the system retrieves historical normal fingerprint vectors that match the current ambient temperature and load status from a preset rule base. By calculating the normalized difference between each feature parameter in the current fault fingerprint vector and the corresponding feature parameter in the historical normal fingerprint vector, and then multiplying each normalized difference by a preset weight coefficient and summing the results, a comprehensive feature difference score is obtained. Finally, the similarity score is calculated by subtracting this comprehensive feature difference score from a preset maximum score, or by substituting the comprehensive feature difference score into a preset inverse proportional mapping function.

[0099] The similarity score is compared with a preset similarity threshold. If the similarity score is greater than or equal to the preset threshold, the single diagnosis is considered successful, and the consecutive success count is incremented; if the score is less than the threshold, the consecutive success count is reset to zero. If the consecutive success count reaches a preset consecutive success number threshold (e.g., three consecutive successful diagnoses), the system determines that the fault-solving conditions are met and initiates the system recovery process, safely restoring the equipment to normal operation and avoiding the risk of blind restarts.

[0100] Furthermore, this electronic device also features a preventative maintenance early warning function. The system periodically reads the cumulative number of overvoltage events for each level in the historical overvoltage level database. It multiplies the cumulative number of events for each level by its corresponding weighting coefficient (e.g., the weighting coefficient for the third overvoltage level is higher than that for the first overvoltage level) and sums them to obtain the cumulative risk value. Then, it subtracts this cumulative risk value from a preset health baseline value to obtain the current health index. When the current health index falls below a preset threshold, the system issues a maintenance early warning signal, prompting user A to perform maintenance on the electronic device. This health assessment based on the cumulative impact of historical overvoltage events allows for timely intervention before potential failures occur, significantly improving equipment reliability and operational efficiency, and solving the problem of traditional solutions neglecting the cumulative analysis of historical overvoltage data.

[0101] The above descriptions are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. The above descriptions are only some embodiments of this application and do not limit the patent scope of this application. Any equivalent structural transformations made based on the technical concept of this application and utilizing the content of this application's specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A control method for overvoltage protection of electronic equipment, characterized in that, The control method for overvoltage protection of the electronic device includes: The output voltage of the power supply is sampled and preprocessed to obtain the filtered voltage; Multiple overvoltage judgment thresholds are preset, each corresponding to a different overvoltage level. The filtered voltage is compared with each of the overvoltage judgment thresholds to determine and accumulate the current overvoltage level. Based on the current overvoltage level, graded locking control is performed. In the locked control state, the fault fingerprint vector of the response curve under the output voltage is periodically extracted, and the fault fingerprint vector is compared with the similarity of the preset rule base. When the similarity meets the preset recovery conditions, the system recovery process is started. The number of overpressure levels at each level is counted, and a health index is calculated by weighting the overpressure levels at each level according to their weighting coefficients. A maintenance warning signal is issued when the health index is lower than a preset health threshold.

2. The control method for overvoltage protection of electronic equipment as described in claim 1, characterized in that, The step of sampling and preprocessing the output voltage of the power supply to obtain the filtered voltage includes: The output voltage of the power supply is sampled by a resistor divider network to obtain a sampled voltage, and the sampled voltage is then filtered by a low-pass filter to remove high-frequency noise, thus obtaining the filtered voltage.

3. The control method for overvoltage protection of electronic equipment as described in claim 1, characterized in that, The multiple overvoltage determination thresholds include a first overvoltage threshold, a second overvoltage threshold, and a third overvoltage threshold. The step of comparing the filtered voltage with each of the overvoltage determination thresholds to determine and accumulate the current overvoltage level, and performing graded locking control based on the current overvoltage level, includes: If the filtered voltage is greater than the first overvoltage threshold and less than the second overvoltage threshold, the current overvoltage level is determined to be the first overvoltage level. The power switch drive circuit is set to a low-level flag, the switch drive circuit is controlled to stop working, and the number of times the first overvoltage level is recorded in the historical overvoltage level database is accumulated. If the filtered voltage is greater than the second overvoltage threshold and less than the third overvoltage threshold, the current overvoltage level is determined to be the second overvoltage level, and a hard shutdown command is generated to control the fast discharge circuit to conduct in order to clamp the bus voltage to a safe range, cut off the enable signal of the main power path, and accumulate and record the number of times the second overvoltage level is recorded in the historical overvoltage level database. If the filtered voltage is greater than the third overvoltage threshold, the current overvoltage level is determined to be the third overvoltage level, the overvoltage protection relay is triggered to cut off the power input to the whole machine, and the number of times the third overvoltage level is recorded in the historical overvoltage level database is accumulated. Wherein, the first overvoltage threshold is less than the second overvoltage threshold, the second overvoltage threshold is less than the third overvoltage threshold, and the historical overvoltage level database stores the frequency of occurrence of various overvoltage level events experienced by electronic devices during operation.

4. The control method for overvoltage protection of electronic equipment as described in claim 3, characterized in that, The step of periodically extracting the fault fingerprint vector of the response curve under the output voltage in the locked control state, and comparing the fault fingerprint vector with a preset rule base for similarity, so as to start the system recovery process when the similarity meets the preset recovery conditions, includes: In the locked control state, a turn-on drive signal is generated according to a preset diagnostic time interval to control the power switch drive circuit to turn on according to a first duty cycle, so as to generate a transient voltage response curve for the output voltage of the power supply. The response curve of the transient voltage is acquired, and a sequence of characteristic parameters including rise time, peak response voltage, decay time, voltage change rate, and residual voltage is extracted to construct the current fault fingerprint vector. Retrieve historical normal fingerprint vectors that match the current ambient temperature and load status from the preset rule base, calculate the feature difference degree between the current faulty fingerprint vector and the historical normal fingerprint vector, and generate the similarity score based on the feature difference degree. The similarity score is compared with a preset similarity threshold to determine whether the system recovery process can be initiated.

5. The control method for overvoltage protection of electronic equipment as described in claim 4, characterized in that, The steps of retrieving historical normal fingerprint vectors that match the current ambient temperature and load status from the preset rule base, calculating the feature difference between the current faulty fingerprint vector and the historical normal fingerprint vector, and generating the similarity score based on the feature difference include: Calculate the normalized difference between each feature parameter in the current fault fingerprint vector and the corresponding feature parameter in the historical normal fingerprint vector; The comprehensive feature difference is obtained by multiplying each of the normalized differences by the corresponding preset weight coefficient and then summing them. The similarity score is calculated by subtracting the comprehensive feature difference from the preset full score, or by substituting the comprehensive feature difference into a preset inverse mapping function.

6. The control method for overvoltage protection of electronic equipment as described in claim 4, characterized in that, The step of comparing the similarity score with a preset similarity threshold to determine whether the recovery conditions are met includes: When the similarity score is greater than or equal to the preset similarity threshold, the single diagnosis is determined to be passed and the consecutive pass count is accumulated; or when the similarity score is less than the preset similarity threshold, the consecutive pass count is cleared to zero. If the consecutive pass count reaches a preset consecutive pass count threshold, the fault troubleshooting condition is determined to be met, and the system recovery process is initiated.

7. The control method for overvoltage protection of electronic equipment as described in claim 3, characterized in that, The step of counting the number of times each overpressure level is reached, and calculating a health index based on the weighting coefficients of each overpressure level, and issuing a maintenance warning signal when the health index is lower than a preset health threshold includes: Read the cumulative number of times each overvoltage level is recorded in the historical overvoltage level database; Multiply the cumulative number of occurrences at each level by their respective weighting coefficients and sum them to obtain the cumulative risk value. The current health index is obtained by subtracting the accumulated risk value from the preset health baseline value. When the current health index is lower than the preset health threshold, the maintenance warning signal is issued to prompt the user to maintain the electronic device.

8. An overvoltage protection electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for overvoltage protection of the electronic device as claimed in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the control method for overvoltage protection of electronic equipment as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the control method for overvoltage protection of an electronic device as described in any one of claims 1 to 7.

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