An online static power switching method, system, device and storage medium

Abstract

This application provides an online static power switching method, system, device, and storage medium, relating to the field of power switching technology. The method includes: determining the operating parameters of the main power supply at various times within the experimental duration according to the experimental procedures of the target experiment; determining a first failure probability of the main power supply based on the operating parameters; adjusting the first failure probability according to the environmental parameters of the laboratory corresponding to the target experiment to generate a second failure probability; adjusting the second failure probability according to the historical failure data of the main power supply to generate a target failure probability; and if the target failure probability is greater than a preset failure probability at any time within the experimental duration, switching the main power supply to a backup power supply at the corresponding time. The technical effect of this application is: predicting the failure probability of the main power supply, realizing timely switching between the main and backup power supplies before a failure occurs, and preventing the adverse effects of power failure on the experiment in a timely manner.

Description

Technical Field

[0001] This application relates to the field of power switching technology, specifically to an online static power switching method, system, device, and storage medium. Background Technology

[0002] In modern scientific research experiments, the accuracy of experimental results and the continuity of the experimental process are crucial. However, the stability of power supply during experiments has always been a major problem that plagues laboratories, especially in some precision experiments that require long-term continuous operation or have extremely high requirements for power quality. Instability of the power supply may lead to distorted experimental data, experimental interruption, or even damage to experimental equipment. This not only wastes experimental resources but may also affect the acquisition of important research results.

[0003] Currently, laboratories typically employ a primary and backup power supply configuration. By monitoring the operating status of the primary power supply in real time, the system switches to the backup power supply when abnormalities are detected in parameters such as voltage or frequency. However, this passive power switching scheme has a significant lag, often only switching occurs after the primary power supply has already failed, making it impossible to promptly prevent adverse effects of power failures on experiments. Summary of the Invention

[0004] This application provides an online static power supply switching method to predict the failure probability of the main power supply and realize timely switching between the main and backup power supplies before the failure occurs, so as to prevent the adverse effects of power supply failure on the experiment in a timely manner.

[0005] In a first aspect, this application provides an online static power supply switching method, the method comprising: acquiring the experimental procedures of a target experiment and the experimental duration for completing the target experiment; determining the operating parameters of the main power supply at various times within the experimental duration based on the experimental procedures; determining a first failure probability of the main power supply at various times within the experimental duration based on the operating parameters; acquiring the environmental parameters of the laboratory corresponding to the target experiment; adjusting the first failure probability based on the environmental parameters to generate a second failure probability; acquiring historical failure data of the main power supply; adjusting the second failure probability based on the historical failure data to generate a target failure probability; and if the target failure probability is greater than a preset failure probability at any time within the experimental duration, switching the main power supply to a standby power supply at the corresponding time.

[0006] By adopting the above technical solution, the operating parameters of the main power supply are determined by acquiring the experimental procedures and duration. A first failure probability is calculated based on these parameters, and then adjusted according to laboratory environmental parameters to obtain a second failure probability. Finally, a target failure probability is generated by further adjusting the probability based on historical failure data, thus achieving multi-dimensional prediction of main power supply failures. When the target failure probability exceeds the preset failure probability, the failure probability of the main power supply is predicted in advance, enabling timely switching between main and backup power supplies before a failure occurs, and preventing adverse effects of power supply failures on the experiment.

[0007] Optionally, determining the operating parameters of the main power supply at various times within the experimental duration according to the experimental procedures includes: determining, according to the experimental procedures, multiple power supply devices required to complete the target experiment, the device parameters of each power supply device, and the usage time period of each power supply device within the experimental duration; generating a device usage time sequence table based on the usage time period of each power supply device within the experimental duration; calculating the total power and total current of the main power supply at various times within the experimental duration by combining the device parameters and the device usage time sequence table; and using the total power and total current of the main power supply at various times within the experimental duration as the operating parameters of the main power supply at each time.

[0008] By adopting the above technical solution, a usage sequence table is generated by determining the power supply equipment, its parameters, and the usage period required for the experiment. Combined with the equipment parameters, the total power and current of the main power supply at each moment during the experiment are calculated, thus achieving accurate acquisition of the main power supply's operating parameters. This time-sequential parameter calculation method based on experimental procedures not only considers the usage sequence of each power supply device but also enables dynamic prediction of the main power supply load, providing a reliable data foundation for accurate calculation of subsequent failure probabilities.

[0009] Optionally, determining the first failure probability of the main power supply at each moment within the experimental duration based on the operating parameters includes: obtaining the rated power and rated current of the main power supply; determining a power influence coefficient based on the ratio of the total power to the rated power of the main power supply at each moment; determining a current influence coefficient based on the ratio of the total current to the rated current of the main power supply at each moment; multiplying the power influence coefficient by a preset first weight arithmetic to obtain a first weighted value; multiplying the current influence coefficient by a preset second weight arithmetic to obtain a second weighted value; and arithmetically adding the first weighted value and the second weighted value to determine the first failure probability of the main power supply at each moment within the experimental duration.

[0010] By employing the aforementioned technical solution, the power influence coefficient and current influence coefficient are obtained by calculating the ratio of the total main power supply to the rated power and the ratio of the total current to the rated current, respectively. These two coefficients are then weighted using different weights to determine the final probability of the first fault. This weighted calculation method based on both power and current indicators considers the differentiated impact of different operating parameters on the fault probability and achieves reasonable quantification of various influencing factors through weight allocation, thereby improving the accuracy and scientific rigor of fault probability prediction.

[0011] Optionally, the environmental parameters include ambient temperature and ambient humidity. Adjusting the first fault probability based on the environmental parameters to generate a second fault probability includes: determining a temperature influence coefficient based on the ratio of the ambient temperature to a preset temperature threshold; determining a humidity influence coefficient based on the ratio of the ambient humidity to a preset humidity threshold; arithmetically adding the temperature influence coefficient and the humidity influence coefficient to obtain an environmental influence factor; and arithmetically multiplying the first fault probability by the environmental influence factor to generate the second fault probability.

[0012] By employing the aforementioned technical solution, the temperature influence coefficient and humidity influence coefficient are obtained by calculating the ratio of ambient temperature to a preset temperature threshold and the ratio of ambient humidity to a preset humidity threshold, respectively. These two coefficients are then superimposed to obtain an environmental influence factor. Finally, a second fault probability is generated through arithmetic operations with the first fault probability. This probability adjustment method, which considers both temperature and humidity environmental factors, enables a quantitative assessment of the impact of laboratory environmental conditions on power supply failures. This makes the fault probability prediction more closely aligned with the actual operating environment, further improving the accuracy of the prediction results.

[0013] Optionally, adjusting the second fault probability based on the historical fault data to generate a target fault probability includes: obtaining the number of faults of the main power supply within a preset time period; if the number of faults is greater than a preset number of faults, increasing the second fault probability by a first preset fault probability based on a first difference between the number of faults and the preset number of faults to generate a target fault probability, wherein the first preset fault probability increases with the increase of the first difference; if the number of faults is not greater than the preset number of faults, decreasing the second fault probability by a second preset fault probability based on a second difference between the preset number of faults and the number of faults to generate a target fault probability, wherein the second preset fault probability increases with the increase of the second difference.

[0014] By adopting the above technical solution, the second fault probability is dynamically adjusted based on the difference between the actual number of faults in the main power supply within a preset time period and the preset number of faults. The fault probability is increased when the number of faults is high and decreased when the number of faults is low, with the adjustment range increasing accordingly as the difference increases. This adaptive adjustment mechanism based on historical fault data not only considers the historical operating conditions of the power supply but also achieves self-optimization of fault probability prediction through dynamic adjustment, making the final generated target fault probability more predictive.

[0015] Optionally, switching the main power supply to the backup power supply at the corresponding time includes: acquiring the output voltage and output frequency of the main power supply and the backup power supply; determining whether the output voltage difference between the main power supply and the backup power supply is less than a first preset threshold and whether the output frequency difference is less than a second preset threshold; if the output voltage difference is less than the first preset threshold and the output frequency difference is less than the second preset threshold, then switching the main power supply to the backup power supply; if the output voltage difference is not less than the first preset threshold or the output frequency difference is not less than the second preset threshold, then adjusting the output voltage and output frequency of the backup power supply until the switching conditions are met before switching.

[0016] By employing the above technical solution, and comparing the output voltage and frequency differences between the primary and backup power supplies with preset thresholds, switching is ensured only when the voltage and frequency parameters of the two power supplies are close. If the parameter differences are too large, the backup power supply is adjusted first. This static switching method based on dual voltage and frequency parameter control avoids electrical shocks caused by excessive parameter differences during power switching, achieves a smooth transition between primary and backup power supplies, effectively protects the experimental equipment, and ensures the stability of the experimental process.

[0017] Optionally, after switching the main power supply to the backup power supply at the corresponding time, the method further includes: obtaining the output parameters of the backup power supply after the switch; determining whether the output parameters meet the preset parameter requirements; if the output parameters do not meet the preset parameter requirements, generating a switching abnormality signal and sending the switching abnormality signal to a preset terminal; if the output parameters meet the preset parameter requirements, recording the switching time and switching result of this switch.

[0018] By adopting the above technical solution, and through real-time monitoring and evaluation of the output parameters of the backup power supply after switching, a switching anomaly signal is promptly generated and sent to a preset terminal when an anomaly is detected, and the switching time and result are recorded when the switching is normal. This feedback monitoring mechanism after switching not only achieves the traceability of the power switching process, but also establishes a rapid response channel for switching anomalies, thereby ensuring the reliability of the experimental power supply and providing data support for the optimization of subsequent power switching strategies.

[0019] Secondly, this application provides an online static power switching system, the system comprising: an acquisition module, a determination module, a first adjustment module, a second adjustment module, and a switching module; wherein, the acquisition module is used to acquire the experimental procedures of the target experiment and the experimental duration for completing the target experiment, and determine the operating parameters of the main power supply at each moment within the experimental duration based on the experimental procedures; the determination module is used to determine a first failure probability of the main power supply at each moment within the experimental duration based on the operating parameters; the first adjustment module is used to acquire the environmental parameters of the laboratory corresponding to the target experiment, and adjust the first failure probability based on the environmental parameters to generate a second failure probability; the second adjustment module is used to acquire historical failure data of the main power supply, and adjust the second failure probability based on the historical failure data to generate a target failure probability; the switching module is used to switch the main power supply to a standby power supply at the corresponding moment if the target failure probability is greater than a preset failure probability at any moment within the experimental duration.

[0020] Thirdly, this application provides an electronic device that adopts the following technical solution: it includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to make the electronic device execute a computer program of any of the above-described online static power switching methods.

[0021] Fourthly, this application provides a computer-readable storage medium that stores a computer program capable of being loaded by a processor and executing any of the above-mentioned online static power switching methods.

[0022] In summary, this application includes at least one of the following beneficial technical effects:

[0023] By obtaining experimental procedures and duration, the operating parameters of the main power supply are determined. Based on these parameters, a first failure probability is calculated. This first failure probability is then adjusted using laboratory environmental parameters to obtain a second failure probability. Finally, a target failure probability is generated through further adjustments based on historical failure data. This achieves multi-dimensional prediction of main power supply failures. When the target failure probability exceeds the preset failure probability, the failure probability of the main power supply is predicted in advance, enabling timely switching between main and backup power supplies before a failure occurs, thus preventing adverse effects of power supply failures on the experiment. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating an online static power switching method provided in an embodiment of this application;

[0025] Figure 2This is a circuit diagram provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of an online static power switching system provided in an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0028] Explanation of reference numerals in the attached figures: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. Detailed Implementation

[0029] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0030] In the description of the embodiments in this application, words such as "illustrative," "for example," or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "illustrative," "for example," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Rather, the use of words such as "illustrative," "for example," or "for example" is intended to present the relevant concepts in a specific manner.

[0031] The online static power switching method of this application is mainly applied to scenarios such as precision laboratories with high requirements for power stability, important scientific research projects that cannot tolerate the risk of power outages, special experimental environments with large fluctuations in environmental parameters, and automated experimental platforms that require the collaborative operation of multiple devices. These application scenarios are typically characterized by high requirements for the continuity of the experimental process, the need for continuous acquisition of experimental data, and the high value of the experimental equipment.

[0032] This application utilizes multi-dimensional fault prediction, combined with environmental factors and historical data, to achieve proactive preventative switching of the power system. This ensures both laboratory power safety and the stable and reliable operation of the experimental process. This method is particularly suitable for experimental environments involving valuable instruments and equipment, requiring long-term continuous operation, uninterrupted experimental processes, and precise control of electrical equipment, providing effective technical support for the safe and stable operation of laboratories.

[0033] Figure 1 This is a flowchart illustrating an online static power switching method provided in an embodiment of this application. Figure 1 As shown, the method includes S101-S105:

[0034] S101, obtain the experimental procedures and the experimental duration of the target experiment, and determine the operating parameters of the main power supply at each moment within the experimental duration based on the experimental procedures.

[0035] In this embodiment, the experimental procedure includes the operational steps, required equipment, and their operating sequence. The experimental duration refers to the total time required from the start to the end of the experiment. Operating parameters include, but are not limited to, key electrical parameters such as the total power and total current of the main power supply during the experiment.

[0036] In practice, the experimental procedure information for the target experiment is first obtained through the laboratory management system. This information records the types of power equipment required to complete the experiment, the order in which the equipment is used, and the usage time of each piece of equipment. For example, in a biological sample culture experiment, the experimental procedure might include: processing the sample in a centrifuge for 30 minutes, followed by continuous incubation in a constant temperature incubator for 48 hours, during which three observations are required using a microscope, each lasting 20 minutes. Based on the experimental procedure information, the system determines the total duration of this experiment to be 48 hours and 50 minutes.

[0037] Next, the system determines the multiple power supply devices and their parameters required to complete the target experiment based on the experimental procedures. In the example above, the devices involved include a centrifuge (1500W power, 6.8A rated current), a constant temperature incubator (800W power, 3.6A rated current), and a microscope (200W power, 0.9A rated current). The system generates a detailed device usage timetable, recording the specific usage time periods of each device within the experimental duration. Based on the timetable and the power and current parameters of each device, the system calculates the total power and total current values ​​of the main power supply at various times within the experimental duration, serving as the operating parameters of the main power supply.

[0038] Based on the above embodiments, as an optional implementation method, in S101, determining the operating parameters of the main power supply at various times within the experimental duration according to the experimental procedure specifically includes S11-S14:

[0039] S11, Based on the test procedure, determine the multiple power supply devices required to complete the target experiment, as well as the equipment parameters of each power supply device and the usage time of each power supply device within the test duration.

[0040] S12, Generate a device usage sequence table based on the usage time periods of each power supply device within the experimental duration.

[0041] S13, combining the equipment parameters and the equipment usage timing table, calculate the total power and total current of the main power supply at each moment during the experimental duration.

[0042] S14, the total power and total current of the main power supply at each moment during the experimental duration are used as the operating parameters of the main power supply at each moment.

[0043] In actual experiments, the start-up time, power requirements, and operating duration of different experimental equipment vary, and these differences directly affect the load status of the main power supply. In order to accurately grasp the operating status of the main power supply, it is necessary to first identify all electrical equipment involved in the experiment and their operating characteristics.

[0044] The system determined the required power supply equipment for the experiment, including a vacuum pump, a heating furnace, and a detector, based on the experimental procedures. Each piece of equipment has specific parameters; for example, the vacuum pump has a rated power of 2.5kW and an operating current of 12A; the heating furnace has a rated power of 5kW and an operating current of 25A; and the detector has a rated power of 1kW and an operating current of 5A. The system also recorded the specific usage time periods of these devices during the experiment, such as the vacuum pump running from 0-30 minutes and 50-80 minutes, the heating furnace running from 20-60 minutes, and the detector running continuously throughout the entire experiment.

[0045] To visually demonstrate the operational sequence of each device, the system compiles this time information into a device usage time sequence table. This table uses time as the horizontal axis and device as the vertical axis, clearly indicating the start and stop times of each device, facilitating subsequent calculations of the total load at each moment. For example, at 25 minutes into the experiment, the vacuum pump and heating furnace are running simultaneously, while at 55 minutes, only the heating furnace and the detector are operating.

[0046] The system calculates the total power and total current of the main power supply at each moment during the experiment, based on the equipment usage schedule and the parameters of each device. The calculation uses an additive method, summing the power and current of all devices operating at the same moment.

[0047] Finally, the system uses the calculated total power and total current data as the operating parameters of the main power supply at various times. These parameters can intuitively reflect the load changes of the main power supply during the experiment, providing a reliable data foundation for subsequent fault probability calculations.

[0048] S102, based on the operating parameters, determine the probability of the first failure of the main power supply at each moment within the experimental duration.

[0049] In this embodiment, to accurately assess the failure risk of the main power supply during the experiment, a first failure probability needs to be calculated based on the operating parameters. Since the failure risk of the main power supply is closely related to its actual load level, it is necessary to compare and analyze the actual operating state of the main power supply with its rated operating parameters to obtain a relatively objective failure probability assessment value.

[0050] In practice, the first step is to obtain the rated power and rated current of the main power supply. These parameters are the standard operating parameters of the main power supply under normal working conditions. For example, the rated power of a laboratory main power supply is 5000W, and the rated current is 22.7A. Then, the ratio of the total power to the rated power of the main power supply at various times during the experiment is calculated to obtain the power influence coefficient. Similarly, the ratio of the total current to the rated current is calculated to obtain the current influence coefficient. In the above example, if the total power at a certain time is 2500W and the total current is 11.3A, then the power influence coefficient at that time is 0.5, and the current influence coefficient is 0.498.

[0051] Considering the different degrees to which power and current affect the probability of main power supply failure, the system sets corresponding weighting coefficients for each. For example, the first weight for the power effect is 0.6, and the second weight for the current effect is 0.4. Multiplying the power effect coefficient by the first weight yields a first weighted value of 0.3, and multiplying the current effect coefficient by the second weight yields a second weighted value of 0.199. Finally, the two weighted values ​​are added together to obtain the first failure probability at that moment, which is 0.499.

[0052] Based on the above embodiments, as an optional implementation, in S102, determining the first failure probability of the main power supply at each moment within the experimental duration according to the operating parameters specifically includes S21-S26:

[0053] S21, obtain the rated power and rated current of the main power supply.

[0054] S22, determine the power influence coefficient based on the ratio of the total power of the main power supply to the rated power at each moment.

[0055] In practice, the rated power of the main power supply is first obtained, which is 15kW in this example. The system monitors the total power of the main power supply at various times during the experiment and calculates its ratio with the rated power. For example, if the total power is 8.5kW after 25 minutes of the experiment, the power ratio is 0.567 (i.e., 8.5kW / 15kW). Based on the analysis of a large amount of operational data and the theory of electrical equipment reliability, the system uses a piecewise function to determine the power influence coefficient: when the power ratio is less than 0.4, the power influence coefficient is 0.8; when the power ratio is between 0.4 and 0.7, the power influence coefficient is 1.0; when the power ratio is between 0.7 and 0.9, the power influence coefficient is 1.2; and when the power ratio is greater than 0.9, the power influence coefficient is 1.5.

[0056] In the example above, when the total power is 8.5kW, the power ratio of 0.567 falls within the range of 0.4 to 0.7, therefore the corresponding power influence coefficient is 1.0. This piecewise function design considers the operating characteristics of the power supply equipment under different load levels: the failure risk is relatively small when operating at low loads, while the failure risk increases significantly when operating at high loads. At the same time, by controlling the variation range of the influence coefficient within a reasonable range (0.8 to 1.5), the excessive influence of a single factor on the final failure probability is avoided.

[0057] S23. Determine the current influence coefficient based on the ratio of the total current to the rated current of the main power supply at each moment.

[0058] In practice, the system acquires the rated current of the main power supply (60A) and continuously monitors the actual total current during the experiment. After 25 minutes of operation, the total current generated by the vacuum pump, heating furnace, and detector is 42A. The system calculates the current ratio at this point to be 0.7 (42A / 60A). Based on electrical equipment usage specifications and laboratory safety standards, the system employs a segmented evaluation method to determine the current influence coefficient: 0.85 for current ratios below 0.5; 1.0 for current ratios between 0.5 and 0.75; 1.15 for current ratios between 0.75 and 0.9; and 1.35 for current ratios exceeding 0.9. This segmented design reflects the nonlinear influence of current load on equipment reliability.

[0059] In this example, when the current ratio of 0.7 corresponding to the total current of 42A falls within the range of 0.5 to 0.75, the system determines the current influence coefficient to be 1.0, indicating that the current load is within the normal operating range of the equipment.

[0060] S24, multiply the power influence coefficient by the preset first weight arithmetic to obtain the first weighted value.

[0061] S25, multiply the current influence coefficient by the preset second weight arithmetic to obtain the second weight value.

[0062] S26, arithmetically add the first weighted value and the second weighted value to determine the first failure probability of the main power supply at each moment during the experimental duration.

[0063] In this embodiment, the system sets the first weight (power weight) to 0.6 and the second weight (current weight) to 0.4. This weight allocation reflects the characteristic that power fluctuations have a slightly greater impact on power supply failures than current fluctuations. In S24, the system multiplies the power influence coefficient by the first weight; for example, when the power influence coefficient is 1.0, the first weighted value is 0.6 (1.0 × 0.6). Similarly, when the current influence coefficient is 1.0, it is multiplied by the second weight to obtain a second weighted value of 0.4 (1.0 × 0.4).

[0064] The system then adds the two weighted values ​​to obtain the first failure probability at each time point within the experimental duration. For example, at 25 minutes into the experiment, the first failure probability is 1.0 (0.6 + 0.4). This weighted calculation method ensures that the final failure probability comprehensively reflects the influence of both power and current, while remaining within a reasonable numerical range.

[0065] S103, obtain the environmental parameters of the laboratory corresponding to the target experiment, adjust the first failure probability according to the environmental parameters, and generate the second failure probability.

[0066] In this embodiment, the failure probability of the power supply equipment is not only related to its operating load, but environmental conditions also affect the equipment's operational reliability. Especially in a precision laboratory environment, fluctuations in temperature and humidity can lead to performance degradation or increased failure risk of the power supply equipment. For example, higher ambient temperatures accelerate the aging of electronic components, while excessive humidity can cause a decrease in insulation performance. Therefore, this application uses environmental parameters as an important basis for failure probability correction to improve the accuracy and reliability of predictions.

[0067] In practice, an intelligent temperature and humidity sensing system installed in the laboratory continuously collects environmental data. The system is pre-set with a temperature threshold of 25℃ and a humidity threshold of 60% as reference values ​​for the standard operating environment. These parameters are determined based on the optimal operating conditions of the power supply equipment. When the system detects an ambient temperature of 30℃, the temperature influence coefficient is calculated to be 0.12 (i.e., (30℃-25℃) / 25℃×0.5); simultaneously, when the ambient humidity is detected to be 75%, the humidity influence coefficient is calculated to be 0.125 (i.e., (75%-60%) / 60%×0.5). The attenuation coefficient of 0.5 is introduced here to avoid excessive influence of environmental factors on the probability of failure.

[0068] Adding the temperature influence coefficient (0.12) and the humidity influence coefficient (0.125) yields the environmental impact factor (0.245), reflecting the overall environmental impact. Then, the first failure probability is multiplied by (1 + environmental impact factor) to generate the second failure probability considering environmental impact. For example, when the first failure probability at a certain moment is 0.499, the second failure probability considering environmental impact is 0.621 (i.e., 0.499 × (1 + 0.245)).

[0069] Based on the above embodiments, as an optional implementation, in S103, the environmental parameters include ambient temperature and ambient humidity. Adjusting the first fault probability according to the environmental parameters to generate the second fault probability specifically includes S31-S34:

[0070] S31. Determine the temperature influence coefficient based on the ratio of ambient temperature to a preset temperature threshold.

[0071] S32, determine the humidity influence coefficient based on the ratio of ambient humidity to a preset humidity threshold.

[0072] S33, the environmental impact factor is obtained by arithmetically adding the temperature influence coefficient and the humidity influence coefficient.

[0073] S34. The first failure probability is arithmetically multiplied by the environmental impact factor to generate the second failure probability.

[0074] In practice, the system continuously collects environmental data through an intelligent temperature and humidity sensor system installed in the laboratory. The system pre-sets a temperature threshold of 25℃ and a humidity threshold of 60% as standard operating environment reference values; these thresholds are determined based on the optimal operating conditions of the power supply equipment. During the experiment, when the system detected an ambient temperature of 28℃, the temperature influence coefficient was calculated to be 0.06 (i.e., (28℃-25℃) / 25℃×0.5); simultaneously, when the system detected an ambient humidity of 70%, the humidity influence coefficient was calculated to be 0.083 (i.e., (70%-60%) / 60%×0.5). An attenuation coefficient of 0.5 is introduced here to avoid excessive influence of environmental factors on the probability of failure.

[0075] The system adds the temperature influence coefficient (0.06) and humidity influence coefficient (0.083) to obtain the environmental influence factor (0.143). Then, it multiplies the first failure probability (0.75) at a given time by (1 + environmental influence factor) to obtain the second failure probability (0.858) (i.e., 0.75 × (1 + 0.143)). This calculation method ensures that the influence of environmental factors on the failure probability remains within a reasonable range, while also reflecting the actual impact of temperature and humidity changes on equipment reliability.

[0076] S104: Obtain historical fault data of the main power supply, adjust the second fault probability based on the historical fault data, and generate the target fault probability.

[0077] In practice, the system records and analyzes the main power supply's fault information over the past three months, including key details such as fault occurrence time, fault type, and fault duration. The system sets a preset fault count of three as the evaluation benchmark, determined based on the power supply's average fault interval and acceptable fault rate. When the statistics show that the main power supply has experienced four faults within the preset timeframe, it indicates that the actual fault frequency of the equipment is higher than expected. The system calculates the historical fault impact factor according to a correction factor increasing by 0.05 for each additional fault. In this example, the historical fault impact factor is 0.05 for each additional fault.

[0078] The second failure probability is multiplied by (1 + historical failure impact factor) to obtain the final target failure probability. For example, if the second failure probability at a certain moment is 0.621, then the target failure probability after considering the impact of historical failures is 0.652 (i.e., 0.621 × (1 + 0.05)). Conversely, if the main power supply experiences only 2 failures within a preset time period, which is less than the preset number of failures by 1, then a correction factor of 0.03 is applied for each less failure. In this case, the historical failure impact factor is -0.03, and the final target failure probability is 0.602 (i.e., 0.621 × (1 - 0.03)).

[0079] Based on the above embodiments, as an optional implementation, in S104, adjusting the second fault probability according to historical fault data to generate the target fault probability specifically includes S41-S43:

[0080] S41, obtain the number of failures of the main power supply within a preset time period.

[0081] S42, if the number of failures is greater than the preset number of failures, then based on the first difference between the number of failures and the preset number of failures, the second failure probability is increased by the first preset failure probability to generate the target failure probability, wherein the first preset failure probability increases with the increase of the first difference.

[0082] S43, if the number of failures is not greater than the preset number of failures, then the second failure probability is reduced by the second preset failure probability according to the second difference between the preset number of failures and the number of failures to generate the target failure probability, wherein the second preset failure probability increases with the increase of the second difference.

[0083] In the specific implementation process, the system first sets the observation period to 30 days as the preset duration and the preset number of failures to 2. These parameters are set based on the mean time between failures (MTBF) and maintenance requirements of the laboratory power supply equipment. For example, if the main power supply of a laboratory experiences 3 failures in the last 30 days, the system obtains this information by querying the failure record database. Since the actual number of failures (3 times) is greater than the preset number of failures (2 times), the system calculates the first difference as 1 (3-2=1).

[0084] For the case where the first difference is 1, the system sets the first preset failure probability to 0.05. If the first difference increases to 2, the first preset failure probability increases accordingly to 0.08; when the first difference reaches 3, the first preset failure probability further increases to 0.12. This progressive probability adjustment mechanism reflects the cumulative impact of frequent failures on equipment reliability. In this example, the first preset failure probability of 0.05 corresponding to the first difference of 1 will be added to the second failure probability. Assuming the second failure probability at a certain moment is 0.75, the generated target failure probability is 0.8 (0.75 + 0.05).

[0085] Conversely, if the main power supply fails only once within 30 days, below the preset failure count, the system processes the failure according to logic S43. In this case, the calculated second difference is 1 (2-1=1), corresponding to a second preset failure probability of 0.04. A progressive adjustment is also used: when the second difference is 2, the second preset failure probability is 0.06; when the second difference is 3, it increases to 0.09. This reduction mechanism reflects the positive impact of a good operating record on the equipment's reliability. In this case, if the second failure probability is 0.75, the final target failure probability will be reduced to 0.71 (0.75-0.04).

[0086] S105, if the target failure probability is greater than the preset failure probability at any moment during the experiment duration, the main power supply will be switched to the backup power supply at the corresponding moment.

[0087] In this embodiment, the preset fault probability is a crucial decision threshold for the system to determine whether a power switching operation is necessary. The purpose of setting this threshold is to ensure the continuity and stability of experimental power supply through predictive switching before a fault actually occurs. Based on extensive experimental data analysis and expert experience, the system sets the preset fault probability to 0.65. This value ensures that the system has sufficient sensitivity to detect potential fault risks while avoiding frequent switching due to oversensitivity.

[0088] In practice, the system continuously monitors changes in the target failure probability. When the target failure probability at a certain moment t1 is detected to be 0.67, exceeding the preset failure probability of 0.65, the system immediately initiates the power switching procedure. The switching process includes three key steps: First, the system sends a start signal to the backup power supply to ensure that the backup power supply is in optimal working condition; second, the system checks whether the output parameters of the backup power supply are stable, including key indicators such as voltage and frequency; finally, after confirming that the backup power supply is in normal condition, the system achieves power switching through a high-speed switch. The entire switching process is completed within 20 milliseconds, far less than the power outage time that most precision instruments and equipment can tolerate.

[0089] Based on the above embodiments, as an optional implementation, in S105, switching the main power supply to the backup power supply at the corresponding time specifically includes S51-S54:

[0090] S51 obtains the output voltage and output frequency of the main power supply and the backup power supply.

[0091] S52, determine whether the output voltage difference between the main power supply and the backup power supply is less than a first preset threshold and whether the output frequency difference is less than a second preset threshold.

[0092] S53, if the output voltage difference is less than the first preset threshold and the output frequency difference is less than the second preset threshold, then the main power supply will be switched to the backup power supply.

[0093] S54, if the output voltage difference is not less than the first preset threshold or the output frequency difference is not less than the second preset threshold, then adjust the output voltage and output frequency of the backup power supply until the switching conditions are met and then switch.

[0094] In practice, the system first monitors the output parameters of the main power supply and the backup power supply in real time using a high-precision sampling circuit. For example, during a switching operation, the system detects that the main power supply output voltage is 220V and the output frequency is 50Hz, while the backup power supply output voltage is 223V and the output frequency is 49.8Hz. The system presets a first threshold (voltage difference threshold) of 5V and a second threshold (frequency difference threshold) of 0.3Hz. These thresholds are set based on the tolerance of the experimental equipment and experimental requirements, ensuring switching safety without unduly restricting switching conditions.

[0095] In this example, the voltage difference between the primary and backup power supplies is 3V (|223V-220V|=3V), which is less than the first preset threshold of 5V; the frequency difference is 0.2Hz (|50Hz-49.8Hz|=0.2Hz), which is less than the second preset threshold of 0.3Hz. Since both voltage and frequency matching requirements are met, the system directly performs the switching operation. The switching process employs fast static switching technology, controlling the switching time to within 10ms, effectively avoiding voltage drops during switching.

[0096] However, in another scenario, when the system detects that the backup power supply's output voltage is 227V and its output frequency is 50.5Hz, the voltage difference of 7V exceeds the preset threshold of 5V. In this case, the system will initiate the backup power supply parameter adjustment program. The adjustment process employs a PID control algorithm, gradually adjusting the backup power supply's output parameters to bring them closer to the main power supply's parameters.

[0097] Specifically, the system first gradually adjusts the output voltage of the backup power supply from 227V to 224V, reducing the voltage difference to within 4V; simultaneously, it adjusts the output frequency from 50.5Hz to 50.2Hz, reducing the frequency difference to within 0.2Hz. Only when both parameters meet the switching conditions will the system execute the switching operation.

[0098] After switching the main power supply to the backup power supply at the corresponding time, the following is also included:

[0099] Obtain the output parameters of the backup power supply after switching; determine whether the output parameters meet the preset parameter requirements; if the output parameters do not meet the preset parameter requirements, generate a switching abnormality signal and send the switching abnormality signal to the preset terminal; if the output parameters meet the preset parameter requirements, record the switching time and switching result of this switch.

[0100] In the specific implementation process, the system uses a high-precision sampling circuit to collect the output parameters of the backup power supply in real time after switching, including key indicators such as output voltage, output frequency, and voltage fluctuation rate. For example, after a power switch is completed, the system detects that the output voltage of the backup power supply is 220V, the output frequency is 50Hz, and the voltage fluctuation rate is 1%. According to the operating requirements of the experimental equipment, the preset parameters set by the system are: the allowable range of output voltage is 220V±3V, the allowable range of output frequency is 50Hz±0.1Hz, and the voltage fluctuation rate does not exceed 2%.

[0101] When the output parameters after the switch meet the preset requirements, the system will record the switching information in the database. The record includes the switching time, power parameters before and after the switch, and the switching result (success). These records are not only used for traceability analysis of the switching operation, but also provide data support for subsequent system maintenance and optimization.

[0102] However, during another switching operation, the system detected that the output voltage of the backup power supply after the switch was 225V, exceeding the preset range; at the same time, the voltage fluctuation rate reached 2.5%, also exceeding the allowable range. At this time, the system immediately generated a switching anomaly signal, which contained key information such as abnormal parameter values ​​and switching time, and pushed the signal to the monitoring terminal in the laboratory control room and the mobile terminal of the on-duty personnel via the network, enabling relevant personnel to respond and handle the problem in a timely manner.

[0103] like Figure 2 As shown, Figure 2 This is a circuit diagram provided in an embodiment of the present application, which allows for rapid switching to backup power when the main power supply fails, thereby reducing power outage accidents.

[0104] Figure 2 This is a circuit system designed for automatic switching between primary and backup power supplies. The entire system employs dual power inputs to ensure power reliability. The primary power input and backup power input are connected to the system via circuit breakers QF1 and QF2, respectively. These two circuit breakers provide electrical isolation and can also promptly disconnect the power supply to protect the system in the event of a short circuit or overload fault.

[0105] In the circuit, V1 and V2 are voltage transformers, which collect the voltage signals of the main power supply and the backup power supply in real time, respectively. The collected AC signals are converted into DC signals by rectifier diodes V4 and V5 and then sent to comparator V3. Comparator V3 is responsible for comparing and monitoring the status of the two power supplies in real time. When an abnormality is detected in the main power supply voltage, the comparator will output a corresponding control signal. E1 at the end of the system is the load power supply device, which is the power supply terminal of the entire system and is responsible for providing power to the load power supply device. The U3 component represents the three-phase input power supply device, which has three input ports (Out1, Out2, Out3) for receiving the switched power input.

[0106] Under normal operating conditions, the main power supply is provided through QF1. The voltage signal acquired by V1 is rectified by V4 and then sent to V3 for monitoring. When the main power supply fails, V3 detects the anomaly and triggers a switchover, automatically switching to the backup power supply. At this time, V2 acquires the backup power supply voltage signal, which is rectified by V5 to continue status monitoring. The entire circuit design is simple and efficient, using current transformers for electrical isolation, and a rectifier circuit to ensure signal stability. Dual circuit breakers provide reliable protection, effectively ensuring a continuous and stable power supply to the load equipment, while facilitating daily maintenance and troubleshooting. This design not only enables rapid and reliable power switching but also has strong anti-interference capabilities and system stability, making it particularly suitable for applications with high power supply reliability requirements.

[0107] Based on the above method, this application also discloses an online static power switching system, such as... Figure 3As shown, Figure 3 This is a schematic diagram of an online static power switching system provided in an embodiment of this application. The system includes: an acquisition module, a determination module, a first adjustment module, a second adjustment module, and a switching module; wherein,

[0108] The system comprises the following modules: an acquisition module for acquiring the experimental procedures and duration of the target experiment, and determining the operating parameters of the main power supply at various times within the experimental duration based on the experimental procedures; a determination module for determining the first failure probability of the main power supply at various times within the experimental duration based on the operating parameters; a first adjustment module for acquiring the environmental parameters of the laboratory corresponding to the target experiment, adjusting the first failure probability based on the environmental parameters, and generating a second failure probability; a second adjustment module for acquiring historical failure data of the main power supply, adjusting the second failure probability based on the historical failure data, and generating a target failure probability; and a switching module for switching the main power supply to a backup power supply at the corresponding time if the target failure probability is greater than the preset failure probability at any time within the experimental duration.

[0109] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0110] Please see Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0111] The communication bus 1002 is used to realize the connection and communication between these components.

[0112] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0113] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0114] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 1001 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 1001 and may be implemented as a separate chip.

[0115] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage device located remotely from the aforementioned processor 1001. Figure 4 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for an online static power switching method.

[0116] exist Figure 4In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 1001 can be used to call an application program stored in the memory 1005 for an online static power switching method. When executed by one or more processors, the electronic device performs one or more of the methods described in the above embodiments.

[0117] An electronic device readable storage medium stores instructions that, when executed by one or more processors, cause the electronic device to perform one or more of the methods described in the above embodiments.

[0118] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0119] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0120] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some service interfaces; indirect couplings or communication connections between devices or units may be electrical or other forms.

[0121] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0123] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0124] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. An online static power switching method, characterized in that, The method includes: Obtain the experimental procedures and the experimental duration of the target experiment. Based on the experimental procedures, determine the operating parameters of the main power supply at each moment within the experimental duration. The total power and total current of the main power supply at each moment within the experimental duration are the operating parameters of the main power supply. Based on the operating parameters, determining the first failure probability of the main power supply at each moment within the experimental duration includes: obtaining the rated power and rated current of the main power supply; determining a power influence coefficient based on the ratio of the total power to the rated power of the main power supply at each moment; determining a current influence coefficient based on the ratio of the total current to the rated current of the main power supply at each moment; multiplying the power influence coefficient by a preset first weight arithmetic to obtain a first weighted value; multiplying the current influence coefficient by a preset second weight arithmetic to obtain a second weighted value; and arithmetically adding the first weighted value and the second weighted value to determine the first failure probability of the main power supply at each moment within the experimental duration. The environmental parameters of the laboratory corresponding to the target experiment are obtained, and the first failure probability is adjusted according to the environmental parameters to generate a second failure probability. The environmental parameters include ambient temperature and ambient humidity. The step of adjusting the first failure probability according to the environmental parameters to generate the second failure probability includes: determining a temperature influence coefficient based on the ratio of the ambient temperature to a preset temperature threshold; determining a humidity influence coefficient based on the ratio of the ambient humidity to a preset humidity threshold; arithmetically adding the temperature influence coefficient and the humidity influence coefficient to obtain an environmental influence factor; and arithmetically multiplying the first failure probability by the environmental influence factor to generate the second failure probability. The process involves: acquiring historical fault data of the main power supply; adjusting the second fault probability based on the historical fault data to generate a target fault probability; acquiring the number of faults of the main power supply within a preset time period; if the number of faults is greater than a preset number of faults, increasing the second fault probability by a first preset fault probability based on a first difference between the number of faults and the preset number of faults to generate a target fault probability, wherein the first preset fault probability increases with the increase of the first difference; if the number of faults is not greater than the preset number of faults, decreasing the second fault probability by a second preset fault probability based on a second difference between the preset number of faults and the number of faults to generate a target fault probability, wherein the second preset fault probability increases with the increase of the second difference; and if the target fault probability is greater than the preset fault probability at any moment within the experimental time period, switching the main power supply to a backup power supply at the corresponding moment.

2. The online static power switching method according to claim 1, characterized in that, The step of determining the operating parameters of the main power supply at various times within the experimental duration according to the experimental procedures includes: Based on the experimental procedure, determine the multiple power supply devices required to complete the target experiment, the equipment parameters of each power supply device, and the usage time period of each power supply device within the experimental duration; Based on the usage time periods of each power supply device within the experimental duration, a device usage sequence table is generated. Based on the device parameters and the device usage timing table, calculate the total power and total current of the main power supply at each moment during the experimental duration; The total power and total current of the main power supply at each moment during the experimental period are taken as the operating parameters of the main power supply at each moment.

3. The online static power switching method according to claim 1, characterized in that, The step of switching the main power supply to the backup power supply at the corresponding time includes: Obtain the output voltage and output frequency of the main power supply and the backup power supply; Determine whether the output voltage difference between the main power supply and the backup power supply is less than a first preset threshold, and whether the output frequency difference is less than a second preset threshold; If the output voltage difference is less than the first preset threshold and the output frequency difference is less than the second preset threshold, then the main power supply is switched to the backup power supply. If the output voltage difference is not less than the first preset threshold or the output frequency difference is not less than the second preset threshold, then the output voltage and output frequency of the backup power supply are adjusted until the switching conditions are met before switching.

4. The online static power switching method according to claim 1, characterized in that, After switching the main power supply to the backup power supply at the corresponding time, the method further includes: Obtain the output parameters of the backup power supply after switching; Determine whether the output parameters meet the preset parameter requirements; If the output parameters do not meet the preset parameter requirements, a switching error signal is generated and sent to a preset terminal; if the output parameters meet the preset parameter requirements, the switching time and switching result of this switching are recorded.

5. An online static power switching system, characterized in that, The system includes: an acquisition module, a determination module, a first adjustment module, a second adjustment module, and a switching module; wherein... The acquisition module is used to acquire the experimental procedures of the target experiment and the experimental duration of completing the target experiment. Based on the experimental procedures, it determines the operating parameters of the main power supply at each moment within the experimental duration. The total power and total current values ​​of the main power supply at each moment within the experimental duration are the operating parameters of the main power supply. The determining module is used to determine the first failure probability of the main power supply at each moment within the experimental duration based on the operating parameters, including: obtaining the rated power and rated current of the main power supply; determining a power influence coefficient based on the ratio of the total power to the rated power of the main power supply at each moment; determining a current influence coefficient based on the ratio of the total current to the rated current of the main power supply at each moment; multiplying the power influence coefficient by a preset first weight arithmetic to obtain a first weighted value; multiplying the current influence coefficient by a preset second weight arithmetic to obtain a second weighted value; and arithmetically adding the first weighted value and the second weighted value to determine the first failure probability of the main power supply at each moment within the experimental duration. The first adjustment module is used to acquire environmental parameters of the laboratory corresponding to the target experiment, and adjust the first failure probability according to the environmental parameters to generate a second failure probability. The environmental parameters include ambient temperature and ambient humidity. The step of adjusting the first failure probability according to the environmental parameters to generate the second failure probability includes: determining a temperature influence coefficient based on the ratio of the ambient temperature to a preset temperature threshold; determining a humidity influence coefficient based on the ratio of the ambient humidity to a preset humidity threshold; arithmetically adding the temperature influence coefficient and the humidity influence coefficient to obtain an environmental influence factor; and arithmetically multiplying the first failure probability by the environmental influence factor to generate the second failure probability. The second adjustment module is used to acquire historical fault data of the main power supply, adjust the second fault probability according to the historical fault data, and generate a target fault probability, including: acquiring the number of faults of the main power supply within a preset time period; if the number of faults is greater than a preset number of faults, then increasing the second fault probability by a first preset fault probability according to a first difference between the number of faults and the preset number of faults to generate a target fault probability, wherein the first preset fault probability increases with the increase of the first difference; if the number of faults is not greater than the preset number of faults, then decreasing the second fault probability by a second preset fault probability according to a second difference between the preset number of faults and the number of faults to generate a target fault probability, wherein the second preset fault probability increases with the increase of the second difference; The switching module is used to switch the main power supply to the backup power supply at the corresponding moment if the probability of the target failure is greater than the preset failure probability at any moment during the experimental period.

6. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1-4.

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