Online static power supply switching method, system and device and storage medium

Abstract

The invention provides an online static power supply switching method, system and device and a storage medium, and relates to the technical field of power supply switching, and the method comprises the steps: determining the operation parameters of a main power supply at all moments in an experiment duration according to the experiment process of a target experiment; determining a first fault probability of the main power supply according to the operation parameters; adjusting the first fault probability according to the environmental parameters of the laboratory corresponding to the target experiment to generate a second fault probability; adjusting the second fault probability according to historical fault data of the main power supply to generate a target fault probability; and if the target fault probability at any moment is greater than the preset fault probability within the experiment duration, switching the main power supply to the standby power supply at the corresponding moment. The method has the technical effects that the fault probability of the main power supply is predicted, the main power supply and the standby power supply are timely switched before the fault occurs, and the adverse effect of the power supply fault on the experiment is timely prevented.

Description

Technical Field

[0001] The present application relates to the technical field of power switching, and in particular to an online static power switching method, system, device and storage medium. Background Art

[0002] In modern scientific research experiments, the accuracy of experimental results and the continuity of the experimental process are crucial. However, the problem of power supply stability during the experiment has always been an important problem that plagues laboratories, especially in some precision experiments that need to be carried out for a long time or have extremely high requirements for power supply quality. The instability of the power supply may cause distortion of experimental data, interruption of the experiment, and even damage to the experimental equipment, which will not only waste experimental resources, but also affect the acquisition of important research results.

[0003] At present, laboratories usually adopt a power supply solution with a main and backup power supply configuration, which monitors the operating status of the main power supply in real time and switches to the backup power supply when abnormal parameters such as voltage and frequency are detected. However, this passive power switching solution has obvious lag, and often switches only after the main power supply has failed, resulting in the inability to prevent the adverse effects of power failure on the experiment in time. Summary of the invention

[0004] The present application provides an online static power supply switching method for predicting the failure probability of the main power supply, realizing timely switching of the main and standby power supplies before the failure occurs, and timely preventing the adverse effects of the power supply failure on the experiment.

[0005] In a first aspect, the present application provides an online static power supply switching method, the method comprising: obtaining an experimental procedure of a target experiment and an experimental duration for completing the target experiment, and determining the operating parameters of a main power supply at each moment within the experimental duration according to the experimental procedure; determining a first failure probability of the main power supply at each moment within the experimental duration according to the operating parameters; obtaining environmental parameters of a laboratory corresponding to the target experiment, adjusting the first failure probability according to the environmental parameters, and generating a second failure probability; obtaining historical failure data of the main power supply, adjusting the second failure probability according to the historical failure data, and generating a target failure probability; if the target failure probability at any moment within the experimental duration is greater than a preset failure probability, switching the main power supply to a backup power supply at the corresponding moment.

[0006] By adopting the above technical solution, the operating parameters of the main power supply are determined by obtaining the experimental process and duration, and the first fault probability is calculated based on the operating parameters. The first fault probability is adjusted in combination with the laboratory environment parameters to obtain the second fault probability. Finally, the target fault probability is further adjusted and generated according to the historical fault data, thereby realizing the multi-dimensional prediction of the main power supply failure. When the target fault probability exceeds the preset fault probability, the failure probability of the main power supply is predicted in advance, and the main and standby power supplies are switched in time before the fault occurs, so as to prevent the adverse effects of the power failure on the experiment in time.

[0007] Optionally, determining the operating parameters of the main power supply at each moment during the experimental duration according to the experimental procedure includes: determining, according to the experimental procedure, multiple power supply devices required to complete the target experiment, as well as device parameters of each of the power supply devices and a usage time period of each of the power supply devices during the experimental duration; generating a device usage timing table according to the usage time period of each of the power supply devices during the experimental duration; calculating the total power and total current of the main power supply at each moment during the experimental duration in combination with the device parameters and the device usage timing table; and using the total power and total current of the main power supply at each moment during the experimental duration as the operating parameters of the main power supply at each moment.

[0008] By adopting the above technical solution, the power supply equipment and its parameters and usage time period required for the experiment are determined, the equipment usage time sequence table is generated, and the total power and total current of the main power supply at each moment during the experiment are calculated in combination with the equipment parameters, thereby achieving accurate acquisition of the main power supply operation parameters. This time-sequential parameter calculation method based on the experimental process not only takes into account the usage sequence of each power supply equipment, but also realizes the dynamic prediction of the main power supply load, providing a reliable data basis for the accurate calculation of the subsequent failure probability.

[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 the power influence coefficient based on the ratio of the total power of the main power supply at each moment to the rated power; determining the current influence coefficient based on the ratio of the total current of the main power supply at each moment to the rated current; arithmetically multiplying the power influence coefficient by a preset first weight to obtain a first weighted value; arithmetically multiplying the current influence coefficient by a preset second weight 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 adopting the above technical solution, by calculating the ratio of the total power of the main power supply to the rated power and the ratio of the total current to the rated current, the power influence coefficient and the current influence coefficient are obtained respectively, and the two coefficients are weighted and calculated using different weights to finally determine the first fault probability. This weighted calculation method based on the dual indicators of power and current not only takes into account the differentiated impact of different operating parameters on the fault probability, but also realizes the reasonable quantification of various influencing factors through weight allocation, thereby improving the accuracy and scientificity of fault probability prediction.

[0011] Optionally, the environmental parameters include ambient temperature and ambient humidity, and the first failure probability is adjusted according to the environmental parameters to generate a second failure probability, including: determining a temperature influence coefficient according to a ratio of the ambient temperature to a preset temperature threshold; determining a humidity influence coefficient according to a 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.

[0012] By adopting the above technical solution, the temperature influence coefficient and humidity influence coefficient are obtained by calculating the ratio of the ambient temperature to the preset temperature threshold and the ratio of the ambient humidity to the preset humidity threshold, respectively, and the two are superimposed to obtain the environmental influence factor, and finally the second fault probability is generated by arithmetic operation with the first fault probability. This probability adjustment method that considers the dual environmental factors of temperature and humidity realizes the quantitative evaluation of the impact of laboratory environmental conditions on power supply failures, making the fault probability prediction more in line with the actual operating environment, and further improving the accuracy of the prediction results.

[0013] Optionally, the second fault probability is adjusted according to the historical fault data to generate a target fault probability, including: obtaining the number of failures of the main power supply within a preset time period; if the number of failures is greater than the preset number of failures, then according to a first difference between the number of failures and the preset number of failures, the second fault probability is increased by a first preset fault probability to generate a target fault probability, wherein the first preset fault probability increases with an increase in the first difference; if the number of failures is not greater than the preset number of failures, then according to a second difference between the preset number of failures and the number of failures, the second fault probability is reduced by a second preset fault probability to generate a target fault probability, wherein the second preset fault probability increases with an increase in the second difference.

[0014] By adopting the above technical solution, by comparing the actual number of failures of the main power supply within the preset time with the preset number of failures, the second failure probability is dynamically adjusted according to the difference, increasing the failure probability when the number of failures is large, and reducing the failure probability when the number of failures is small, and the adjustment range increases accordingly with the increase of the difference. This adaptive adjustment mechanism based on historical fault data not only takes into account the historical operating conditions of the power supply, but also realizes self-optimization of the fault probability prediction through dynamic adjustment, so that the target fault probability generated in the end has more predictive value.

[0015] Optionally, switching the main power supply to the backup power supply at the corresponding moment includes: obtaining the output voltage and output frequency of the main power supply and the backup power supply; judging 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, 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, adjusting the output voltage and output frequency of the backup power supply until switching is performed after the switching conditions are met.

[0016] By adopting the above technical solution, by comparing the output voltage difference and output frequency difference of the main and standby power supplies with the preset threshold, it is ensured that switching is only performed when the voltage and frequency parameters of the two are close. If the parameter difference is too large, the standby power supply is adjusted first. This static switching method based on voltage and frequency dual parameter control avoids electrical shocks caused by excessive parameter differences during power switching, achieves a smooth transition of the main and standby 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 moment, it also includes: obtaining the output parameters of the backup power supply after the switching; judging whether the output parameters meet the preset parameter requirements; if the output parameters do not meet the preset parameter requirements, generating a switching exception signal, and sending the switching exception signal to a preset terminal; if the output parameters meet the preset parameter requirements, recording the switching time and switching result of this switching.

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

[0019] In a second aspect, the present application provides an online static power supply 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 process of the target experiment and the experimental duration to complete the target experiment, and determine the operating parameters of the main power supply at each moment within the experimental duration according to the experimental process; the determination module is used to determine the first failure probability of the main power supply at each moment within the experimental duration according to the operating parameters; the first adjustment module is used to acquire the environmental parameters of the laboratory corresponding to the target experiment, adjust the first failure probability according to the environmental parameters, and generate a second failure probability; the second adjustment module is used to acquire the historical failure data of the main power supply, adjust the second failure probability according to the historical failure data, and generate a target failure probability; the switching module is used to switch the main power supply to a backup power supply at the corresponding moment if the target failure probability at any moment within the experimental duration is greater than the preset failure probability.

[0020] In a third aspect, the present application provides an electronic device, which 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 so that the electronic device executes a computer program such as any one of the above-mentioned online static power switching methods.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: storing a computer program that can be loaded by a processor and execute any of the above-mentioned online static power switching methods.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: By obtaining the experimental process and duration to determine the operating parameters of the main power supply, and calculating the first failure probability based on the operating parameters, the first failure probability is adjusted in combination with the laboratory environment parameters to obtain the second failure probability, and finally the target failure probability is further adjusted and generated based on the historical failure data, thereby realizing the multi-dimensional prediction of the main power failure. When the target failure probability exceeds the preset failure probability, the failure probability of the main power supply is predicted in advance, and the main and backup power supplies are switched in time before the failure occurs, so as to prevent the adverse effects of the power failure on the experiment in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of an online static power supply switching method provided in an embodiment of the present application; Figure 2 is a circuit diagram provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an online static power supply switching system provided in an embodiment of the present application; Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0024] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0025] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0026] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0027] The online static power switching method of this application is mainly used in precision laboratories with high requirements for power stability, important scientific research projects that cannot withstand the risk of power outages, special experimental environments with large fluctuations in environmental parameters, and automated experimental platforms that require multiple devices to work together. These application scenarios usually have the characteristics of high requirements for the continuity of the experimental process, the need for continuous collection of experimental data, and high value of experimental equipment.

[0028] This application uses multi-dimensional fault prediction, combined with environmental factors and historical data, to achieve active preventive switching of the power supply system, ensuring the safety of laboratory electricity use while ensuring the stable and reliable operation of the experimental process. This method is particularly suitable for experimental environments involving expensive instruments and equipment, requiring long-term continuous operation, uninterrupted experimental processes, and requiring precise control of electrical equipment, providing effective technical support for the safe and stable operation of the laboratory.

[0029] Figure 1 1 is a flow chart of an online static power supply switching method provided by an embodiment of the present application. Figure 1 As shown, the method includes S101-S105: S101, obtaining the experimental procedures of the target experiment and the experimental duration for completing the target experiment, and determining the operating parameters of the main power supply at each moment within the experimental duration according to the experimental procedures.

[0030] In this embodiment, the experimental process includes information such as the experimental operation steps, the required equipment and its operation sequence, and the experimental duration refers to the total time required from the start to the end of the experiment. The 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.

[0031] In the specific implementation, the experimental process information of the target experiment is first obtained through the laboratory management system. This information records the type of power supply equipment required to complete the experiment, the order in which the equipment is used, and the usage time of each device. For example, in a biological sample culture experiment, the experimental process may include: using a centrifuge to process the sample for 30 minutes, followed by continuous culture in a constant temperature incubator for 48 hours, during which time a microscope is required for three observations, each observation time is 20 minutes. Based on the experimental process information, the system determines that the total duration of this experiment is 48 hours and 50 minutes.

[0032] Next, the system determines the multiple power supply devices and their equipment parameters required to complete the target experiment based on the experimental process. In the above example, the equipment involved includes a centrifuge (power 1500W, rated current 6.8A), a constant temperature incubator (power 800W, rated current 3.6A) and a microscope (power 200W, rated current 0.9A). The system generates a detailed equipment usage schedule, recording the specific usage time period of each device during the experiment. According to the schedule, combined with the power and current parameters of each device, the system calculates the total power and total current values ​​of the main power supply at each moment during the experiment as the operating parameters of the main power supply.

[0033] Based on the above embodiment, as an optional implementation, in S101, according to the experimental process, determining the operating parameters of the main power supply at each moment during the experimental duration specifically includes S11-S14: S11, according to the test process, determining multiple power supply devices required to complete the target experiment, as well as device parameters of each power supply device and the use time period of each power supply device within the experiment duration.

[0034] S12, generating a device usage time sequence table according to the usage time period of each power device within the experiment duration.

[0035] S13, combining the equipment parameters and the equipment usage schedule, calculate the total power and total current of the main power supply at each moment during the experiment duration.

[0036] S14, taking the total power and total current of the main power supply at each moment during the experiment duration as the operating parameters of the main power supply at each moment.

[0037] In the actual experiment, the start-up time, power requirements and operation duration of different experimental equipment are different, and these differences will directly affect the load condition of the main power supply. In order to accurately grasp the operating status of the main power supply, it is necessary to first clarify all the electrical equipment involved in the experiment and their operating characteristics.

[0038] According to the experimental procedures, the system determines the multiple power supply devices required for the experiment, such as the vacuum pump, heating furnace, and detector. Each device has its specific equipment parameters, such as the rated power of the vacuum pump is 2.5kW and the working current is 12A; the rated power of the heating furnace is 5kW and the working current is 25A; the rated power of the detector is 1kW and the working current is 5A. At the same time, the system also records the specific use time periods of these devices during the experiment, such as the vacuum pump running at 0-30 minutes and 50-80 minutes, the heating furnace running at 20-60 minutes, and the detector running continuously throughout the experiment.

[0039] In order to intuitively display the operating sequence relationship of each device, the system organizes this time information into a device usage time table. The time table uses time as the horizontal axis and the device as the vertical axis, clearly marking the start and stop time points of each device, which is convenient for subsequent calculation of the total load at each moment. For example, at 25 minutes into the experiment, the vacuum pump and the heating furnace are running at the same time, while at 55 minutes, only the heating furnace and the detector are running.

[0040] The system calculates the total power and current of the main power supply at each moment during the experiment according to the equipment usage schedule and the parameters of each device. The calculation adopts the cumulative method, adding up the power and current of all devices running at the same time.

[0041] Finally, the system uses the calculated total power and total current data as the operating parameters of the main power supply at each moment. These parameter data can intuitively reflect the load changes of the main power supply during the experiment and provide a reliable data basis for subsequent fault probability calculations.

[0042] S102, determining the first failure probability of the main power supply at each moment within the experiment duration according to the operating parameters.

[0043] In this embodiment, in order to accurately evaluate the failure risk of the main power supply during the experiment, it is necessary to calculate the first failure probability 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 evaluation value.

[0044] During the specific implementation, first obtain the rated power and rated current values ​​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 the main power supply of a laboratory is 5000W, and the rated current is 22.7A. Then, calculate the ratio of the total power of the main power supply to the rated power at each moment during the experiment to obtain the power influence coefficient. Similarly, calculate the ratio of the total current to the rated current to obtain the current influence coefficient. In the above example, if the total power at a certain moment is 2500W and the total current is 11.3A, the power influence coefficient at that moment is 0.5 and the current influence coefficient is 0.498.

[0045] Considering the different degrees of influence of power and current on the probability of main power failure, the system sets corresponding weight coefficients respectively. For example, the first weight of power influence is 0.6, and the second weight of current influence is 0.4. Multiply the power influence coefficient by the first weight to get the first weighted value 0.3, and multiply the current influence coefficient by the second weight to get the second weighted value 0.199. Finally, add the two weighted values ​​to get the first failure probability at that moment 0.499.

[0046] Based on the above embodiment, 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: S21, obtain the rated power and rated current of the main power supply.

[0047] S22, determining a power influence coefficient according to a ratio of a total power of the main power supply at each moment to a rated power.

[0048] In the specific implementation, first obtain the rated power value of the main power supply, which is 15kW in this example. The system monitors the total power of the main power supply at each moment during the experiment in real time, and calculates the ratio between it and the rated power. For example, the total power is 8.5kW at 25 minutes into the experiment, and the power ratio at this time is 0.567 (i.e. 8.5kW / 15kW). Based on a large amount of operating data analysis and electrical equipment reliability theory, 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; when the power ratio is greater than 0.9, the power influence coefficient is 1.5.

[0049] In the above example, when the total power is 8.5kW, the power ratio of 0.567 falls within the range of 0.4 to 0.7, so the corresponding power influence coefficient is 1.0. The design of this piecewise function takes into account the working characteristics of the power supply equipment at different load levels: the risk of failure is relatively small when running at low load, while the risk of failure increases significantly when running at high load. At the same time, by controlling the 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.

[0050] S23, determining a current influence coefficient according to a ratio of a total current of the main power supply at each moment to a rated current.

[0051] During the specific implementation, the system obtains the rated current value of the main power supply, 60A, and continuously monitors the actual total current during the experiment. When the experiment was carried out for 25 minutes, the total current generated by the vacuum pump, heating furnace and detector was 42A. The system calculated that the current ratio at this time was 0.7 (42A / 60A). Based on the use specifications of electrical equipment and laboratory safety standards, the system uses a segmented evaluation method to determine the current influence coefficient: when the current ratio is below 0.5, the current influence coefficient is 0.85; when the current ratio is between 0.5 and 0.75, the current influence coefficient is 1.0; when the current ratio is between 0.75 and 0.9, the current influence coefficient is 1.15; when the current ratio exceeds 0.9, the current influence coefficient is 1.35. This segmented design reflects the nonlinear influence characteristics of current load on equipment reliability.

[0052] In this example, when the current ratio of 0.7 corresponding to the total current of 42 A falls within the range of 0.5 to 0.75, the system determines that the current influence coefficient is 1.0, indicating that the current load at this time is within the normal operating range of the device.

[0053] S24, arithmetically multiplying the power influence coefficient by a preset first weight to obtain a first weighted value.

[0054] S25, arithmetically multiplying the current influence coefficient by a preset second weight to obtain a second weighted value.

[0055] S26, arithmetically adding the first weighted value and the second weighted value to determine a first failure probability of the main power supply at each moment within the experimental duration.

[0056] 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 distribution reflects the characteristic that the impact of power fluctuation on power failure is slightly greater than that of current fluctuation. In S24, the system multiplies the power impact coefficient by the first weight. For example, when the power impact coefficient is 1.0, the first weight value is 0.6 (1.0×0.6). Similarly, when the current impact coefficient is 1.0, it is multiplied by the second weight to obtain a second weight value of 0.4 (1.0×0.4).

[0057] The system then adds the two weighted values ​​together to obtain the first failure probability at each moment during the experiment. For example, when the experiment is 25 minutes in, the first failure probability is 1.0 (0.6+0.4). This weighted calculation method ensures that the final failure probability can comprehensively reflect the impact of both power and current while remaining within a reasonable numerical range.

[0058] S103, obtaining environmental parameters of a laboratory corresponding to the target experiment, adjusting the first failure probability according to the environmental parameters, and generating a second failure probability.

[0059] In this embodiment, the failure probability of the power supply device is not only related to its operating load, but the environmental conditions also affect the operating reliability of the device. Especially in a precision laboratory environment, fluctuations in temperature and humidity may cause the performance of the power supply device to degrade or increase the risk of failure. For example, a higher ambient temperature will accelerate the aging of electronic components, and excessive humidity may cause the insulation performance to degrade. Therefore, this application uses environmental parameters as an important basis for correcting the failure probability to improve the accuracy and reliability of the prediction.

[0060] In the specific implementation, the intelligent temperature and humidity sensing system installed in the laboratory continuously collects environmental data. The system pre-sets the temperature threshold of 25°C and the humidity threshold of 60% as the reference values ​​of the standard working environment. These parameters are determined based on the optimal working conditions of the power supply equipment. When the system detects that the laboratory ambient temperature is 30°C, the temperature influence coefficient is calculated to be 0.12 (i.e. (30°C-25°C) / 25°C×0.5); 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.

[0061] Add the temperature influence coefficient 0.12 and the humidity influence coefficient 0.125 to get the environmental influence factor 0.245 that reflects the comprehensive environmental influence. Then, multiply the first failure probability by (1+environmental influence factor) to generate the second failure probability after considering the environmental influence. For example, when the first failure probability at a certain moment is 0.499, the second failure probability after considering the environmental influence is 0.621 (i.e. 0.499×(1+0.245)).

[0062] Based on the above embodiment, as an optional implementation, in S103, the environmental parameters include environmental temperature and environmental humidity, and adjusting the first fault probability according to the environmental parameters to generate the second fault probability specifically includes S31-S34: S31, determining a temperature influence coefficient according to a ratio of the ambient temperature to a preset temperature threshold.

[0063] S32, determining a humidity influence coefficient according to a ratio of the ambient humidity to a preset humidity threshold.

[0064] S33, arithmetically adding the temperature influence coefficient and the humidity influence coefficient to obtain the environmental influence factor.

[0065] S34, arithmetically multiplying the first failure probability by the environmental impact factor to generate a second failure probability.

[0066] In specific implementation, the system continuously collects environmental data through the intelligent temperature and humidity sensing system installed in the laboratory. The system pre-sets the temperature threshold of 25°C and the humidity threshold of 60% as the reference values ​​of the standard working environment. These thresholds are determined based on the optimal working conditions of the power supply equipment. During the experiment, when the system detected that the laboratory ambient temperature was 28°C, the temperature influence coefficient was calculated to be 0.06 (i.e. (28°C-25°C) / 25°C×0.5); when the ambient humidity was detected to be 70%, the humidity influence coefficient was calculated to be 0.083 (i.e. (70%-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.

[0067] The system adds the temperature influence coefficient 0.06 and the humidity influence coefficient 0.083 to obtain the environmental influence factor 0.143. Then, the first failure probability 0.75 at a certain moment is multiplied 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 impact of environmental factors on the failure probability remains within a reasonable range, and also reflects the actual impact of temperature and humidity changes on equipment reliability.

[0068] S104, obtaining historical fault data of the main power supply, adjusting the second fault probability according to the historical fault data, and generating a target fault probability.

[0069] In specific implementation, the system will record and count the main power supply failures in the past three months, including key information such as the time of failure, type of failure, and duration of failure. The system sets the preset number of failures to three as the evaluation benchmark. This benchmark is determined based on the average failure interval and acceptable failure rate of the power supply equipment. When statistics show that the main power supply has failed four times within the preset time, it means that the actual failure frequency of the equipment is higher than expected. The system will calculate the historical failure impact factor according to the rule of adding a correction factor of 0.05 for each additional failure. In this example, the historical failure impact factor corresponding to the failure exceeding one time is 0.05.

[0070] Multiply the second failure probability by (1+historical failure impact factor) to get the final target failure probability. For example, if the second failure probability at a certain moment is 0.621, the target failure probability after considering the impact of historical failures is 0.652 (i.e. 0.621×(1+0.05)). On the contrary, if the main power supply only fails twice within the preset time, which is less than the preset number of failures, the correction factor is reduced by 0.03 for each failure less. At this time, the historical failure impact factor is -0.03, and the final target failure probability is 0.602 (i.e. 0.621×(1-0.03)).

[0071] Based on the above embodiment, as an optional implementation, in S104, adjusting the second fault probability according to the historical fault data to generate the target fault probability specifically includes S41-S43: S41, obtaining the number of failures of the main power supply within a preset time period.

[0072] S42, if the number of failures is greater than the preset number of failures, then according to a 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 a target failure probability, wherein the first preset failure probability increases with an increase in the first difference.

[0073] S43, if the number of failures is not greater than the preset number of failures, then according to a second difference between the preset number of failures and the number of failures, the second failure probability is reduced by the second preset failure probability to generate a target failure probability, wherein the second preset failure probability increases with an increase in the second difference.

[0074] In the specific implementation process, the system first sets the observation period to 30 days as the preset duration, and sets the preset number of failures to 2. The setting of these parameters is determined based on the average failure interval and maintenance requirements of the laboratory power supply equipment. For example, the main power supply of a laboratory has failed 3 times in the last 30 days, and the system obtains this information by querying the fault 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).

[0075] 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 to 0.08 accordingly; 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 that the second failure probability at a certain moment is 0.75, the generated target failure probability is 0.8 (0.75+0.05).

[0076] On the contrary, if the main power supply has only one failure within 30 days, which is lower than the preset number of failures, the system will process according to the logic of S43. At this time, the second difference is calculated to be 1 (2-1=1), and the corresponding second preset failure probability is 0.04. The same progressive adjustment is adopted. 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 the equipment's good operating record on its 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).

[0077] S105, if the target failure probability at any time within the experimental duration is greater than the preset failure probability, the main power supply is switched to the backup power supply at the corresponding time.

[0078] In this embodiment, the preset failure probability is an important decision threshold for the system to determine whether to perform a power switching operation. The purpose of setting this threshold is to ensure the continuity and stability of experimental power supply through predictive switching before the failure actually occurs. Based on a large amount of experimental data analysis and expert experience, the system sets the preset failure probability to 0.65, which can ensure that the system has sufficient sensitivity to capture potential failure risks and avoid frequent switching due to excessive sensitivity.

[0079] During the specific implementation, the system continuously monitors the changes in the target failure probability. When it is detected that the target failure probability at a certain moment t1 is 0.67, which exceeds the preset failure probability of 0.65, the system immediately starts 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 the best working state; second, the system detects 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 state, the system implements power switching through a high-speed switch. The entire switching process is completed within 20 milliseconds, which is much lower than the power outage time that most precision instruments and equipment can tolerate.

[0080] Based on the above embodiment, as an optional implementation, in S105, switching the main power supply to the backup power supply at the corresponding time specifically includes S51-S54: S51, obtaining the output voltage and output frequency of the main power supply and the backup power supply.

[0081] S52, 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.

[0082] 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, the main power supply is switched to the backup power supply.

[0083] 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, adjust the output voltage and output frequency of the backup power supply until the switching condition is met and then switch.

[0084] In the specific implementation process, the system first monitors the output parameters of the main power supply and the backup power supply in real time through a high-precision sampling circuit. For example, in a certain switching operation, the system detects that the output voltage of the main power supply is 220V and the output frequency is 50Hz, and the output voltage of the backup power supply is 223V and the output frequency is 49.8Hz. The system pre-sets the first preset threshold (voltage difference threshold) to 5V and the second preset threshold (frequency difference threshold) to 0.3Hz. The setting of these thresholds is based on the tolerance of the experimental equipment and the experimental requirements, which ensures the safety of switching without excessively restricting the switching conditions.

[0085] In this example, the voltage difference between the main and standby 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 the voltage and frequency matching requirements are met at the same time, the system directly performs the switching operation. The switching process uses fast static switching technology to control the switching time within 10ms, effectively avoiding voltage drops at the switching moment.

[0086] However, in another case, when the system detects that the output voltage of the backup power supply is 227V and the output frequency is 50.5Hz, the voltage difference of 7V exceeds the preset threshold of 5V, and the system will start the parameter adjustment program of the backup power supply. The adjustment process uses the PID control algorithm to gradually adjust the output parameters of the backup power supply to gradually approach the parameters of the main power supply.

[0087] 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; at the same time, it adjusts the output frequency from 50.5Hz to 50.2Hz, reducing the frequency difference to within 0.2Hz. When both parameters meet the switching conditions, the system performs the switching operation.

[0088] After the main power supply is switched to the backup power supply at the corresponding moment, the following steps are also included: 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 a preset terminal; if the output parameters meet the preset parameter requirements, record the switching time and switching result of this switching.

[0089] In the specific implementation process, the system uses a high-precision sampling circuit to collect the output parameters of the backup power supply after switching in real time, 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 working requirements of the experimental equipment, the preset parameter requirements set by the system are: the output voltage allowable range is 220V±3V, the output frequency allowable range is 50Hz±0.1Hz, and the voltage fluctuation rate does not exceed 2%.

[0090] When the output parameters after switching meet the preset requirements, the system will record the switching information in the database. The record content includes the switching time, power parameters before and after switching, switching results (success), etc. These records are not only used for retrospective analysis of switching operations, but also provide data support for subsequent system maintenance and optimization.

[0091] However, in another switching operation, the system detected that the output voltage of the backup power supply after switching was 225V, which exceeded the preset range; at the same time, the voltage fluctuation rate reached 2.5%, which also exceeded the allowable range. At this time, the system immediately generated a switching abnormality 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 through the network, so that relevant personnel can respond and handle the problem in a timely manner.

[0092] like Figure 2 As shown, Figure 2 This is a circuit diagram provided in an embodiment of the present application. When the main power supply fails, it quickly switches to the backup power supply to reduce power outage accidents.

[0093] Figure 2 It is a circuit system for realizing automatic switching between main and backup power sources. The whole system adopts dual power input to ensure power reliability. The main power input and backup power input are connected to the system through circuit breakers QF1 and QF2 respectively. These two circuit breakers not only play the role of electrical isolation, but also can cut off the power supply in time to protect the system safety when short circuit or overload occurs.

[0094] V1 and V2 in the circuit are voltage transformers, which respectively collect the voltage signals of the main power supply and the backup power supply in real time. The collected AC signals are converted into DC signals by the V4 and V5 rectifier diodes and then sent to the V3 comparator. The V3 comparator is responsible for real-time comparison and monitoring of the status of the two power supplies. When the main power supply voltage is detected to be abnormal, the comparator will output the corresponding control signal. E1 at the end of the system is the load power supply device. It is the power supply end of the entire system and is responsible for providing electrical energy to the load power supply device. The U3 component represents a three-phase input power supply device, which has three input ports (Out1, Out2, Out3) for receiving the switched power input.

[0095] Under normal working conditions, the main power supply is supplied by QF1, and the voltage signal collected by V1 is sent to V3 for monitoring after being rectified by V4; when the main power supply fails, V3 detects the abnormality and triggers the switch, and the system automatically switches to the backup power supply. At this time, V2 collects the voltage signal of the backup power supply and continues to monitor the status after being rectified by V5. The entire circuit design is simple and efficient, using mutual inductors to achieve electrical isolation, and cooperating with rectification circuits to ensure signal stability. The dual-circuit circuit breaker provides reliable protection functions, which can effectively ensure the continuous and stable power supply of the load equipment, and is convenient for daily maintenance and troubleshooting. This design can not only achieve fast and reliable switching of power supplies, but also has strong anti-interference ability and system stability, and is particularly suitable for occasions with high requirements for power supply reliability.

[0096] Based on the above method, the present application also discloses an online static power switching system, such as Figure 3As shown, Figure 3 is a structural diagram of an online static power switching system provided by an embodiment of the present application, the system comprises: an acquisition module, a determination module, a first adjustment module, a second adjustment module and a switching module; wherein, An 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 according to the experimental procedures; a determination module is used to determine the first failure probability of the main power supply at each moment within the experimental duration according to the operating parameters; a first adjustment module is used to acquire the environmental parameters of the laboratory corresponding to the target experiment, adjust the first failure probability according to the environmental parameters, and generate a second failure probability; a second adjustment module is used to acquire the historical failure data of the main power supply, adjust the second failure probability according to the historical failure data, and generate a target failure probability; a switching module is used to switch the main power supply to the backup power supply at the corresponding moment if the target failure probability at any moment within the experimental duration is greater than the preset failure probability.

[0097] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0098] See also Figure 4 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present 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 .

[0099] The communication bus 1002 is used to realize the connection and communication between these components.

[0100] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0101] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0102] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0103] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 4 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of an online static power switching method.

[0104] exist Figure 4In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program for an online static power switching method stored in the memory 1005. When executed by one or more processors, the electronic device executes one or more methods described in the above embodiments.

[0105] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0106] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0107] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0108] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0112] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An online static power switching method, characterized in that: The method comprises: Obtaining an experimental procedure of a target experiment and an experimental duration for completing the target experiment, and determining operating parameters of a main power supply at each moment within the experimental duration according to the experimental procedure; Determining, according to the operating parameters, a first failure probability of the main power supply at each moment within the experimental duration; Acquire environmental parameters of a laboratory corresponding to the target experiment, adjust the first failure probability according to the environmental parameters, and generate a second failure probability; 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; If the target failure probability at any time during the experiment duration is greater than the preset failure probability, the main power supply is switched to the backup power supply at the corresponding time.

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 each moment during the experimental duration according to the experimental procedure includes: According to the test procedure, determining a plurality of power supply devices required to complete the target experiment, as well as device parameters of each of the power supply devices and a usage time period of each of the power supply devices within the duration of the experiment; Generate a device usage time sequence table according to the usage time period of each power supply device within the experimental duration; Combined with the equipment parameters and the equipment usage time table, calculate the total power and total current of the main power supply at each time during the experimental duration; The total power and total current of the main power supply at each moment during the experimental duration are taken as the operating parameters of the main power supply at each moment.

3. The online static power switching method according to claim 2, characterized in that: Determining the first failure probability of the main power supply at each moment within the experimental duration according to the operating parameters includes: Obtaining the rated power and rated current of the main power supply; Determining a power influence coefficient according to a ratio of the total power of the main power supply at each moment to the rated power; Determining a current influence coefficient according to a ratio of the total current of the main power supply at each moment to the rated current; Arithmetically multiplying the power influence coefficient by a preset first weight to obtain a first weighted value; Arithmetically multiplying the current influence coefficient by a preset second weight to obtain a second weighted value; The first weighted value and the second weighted value are arithmetically added to determine a first failure probability of the main power supply at each moment within the experimental duration.

4. The online static power switching method according to claim 1, characterized in that: The environmental parameters include environmental temperature and environmental humidity, and the adjusting the first failure probability according to the environmental parameters to generate the second failure probability includes: Determining a temperature influence coefficient according to a ratio of the ambient temperature to a preset temperature threshold; Determining a humidity influence coefficient according to a 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; The first failure probability is arithmetically multiplied by the environmental impact factor to generate the second failure probability.

5. The online static power switching method according to claim 1, characterized in that: The adjusting the second failure probability according to the historical failure data to generate a target failure probability includes: Obtaining the number of failures of the main power supply within a preset time period; If the number of failures is greater than the preset number of failures, then according to a first difference between the number of failures and the preset number of failures, the second failure probability is increased by a first preset failure probability to generate a target failure probability, wherein the first preset failure probability increases with an increase in the first difference; If the number of failures is not greater than the preset number of failures, the second failure probability is reduced by a second preset failure probability according to a second difference between the preset number of failures and the number of failures to generate a target failure probability, wherein the second preset failure probability increases with an increase in the second difference.

6. 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 a corresponding time includes: Obtaining 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, 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, the output voltage and output frequency of the backup power supply are adjusted until the switching condition is met and then switching is performed.

7. The online static power switching method according to claim 1, characterized in that: After the main power supply is switched to the backup power supply at the corresponding time, the method further includes: Obtaining output parameters of the backup power supply after switching; Determining whether the output parameter meets the preset parameter requirements; If the output parameter does not meet the preset parameter requirement, a switching abnormality signal is generated and sent to a preset terminal; if the output parameter meets the preset parameter requirement, the switching time and switching result of this switching are recorded.

8. 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 process 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 according to the experimental process; The determination module is used to determine the first failure probability of the main power supply at each moment within the experimental duration according to the operating parameters; The first adjustment module is used to obtain 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 second adjustment module is used to obtain 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; The switching module is used to switch the main power supply to the backup power supply at the corresponding moment if the target failure probability at any moment within the experimental duration is greater than the preset failure probability.

9. An electronic device, characterized in that: 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 so that the electronic device executes the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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