A power supply early warning method, apparatus, device and storage medium

Abstract

This invention discloses a power warning method, apparatus, device, and storage medium, relating to the field of computer technology. The method includes: selecting a target device with the highest current power consumption from a group of local devices and switching the target device's power mode; collecting current power status information of the target device during the power mode switching process and performing deep learning based on the current power status information to obtain a current power lifespan model curve for the target device; analyzing the current power status information based on the current power lifespan model curve to determine the power lifespan assessment value for the target device, and determining whether to generate a power warning message based on the power lifespan assessment value. By performing warning analysis based on real-time data collected during the power mode switching process, the accuracy of power warnings is effectively improved, thereby avoiding accidents caused by power operations performed due to unstable current power status.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a power warning method, apparatus, device, and storage medium. Background Technology

[0002] Currently, power supplies for data center equipment typically employ a 1+1 redundancy configuration. To meet the ever-increasing power consumption demands of the equipment, the rated power of the power supplies has also increased. During the maintenance of power supply terminal lines and the scheduled replacement of power supplies in data center computer rooms, it is necessary to switch between primary and backup power supplies for the operating equipment. The primary power supply needs to take over the entire system workload. If the output current of the primary power supply experiences a momentary jump, the output voltage of the primary power supply may fluctuate beyond the specified value due to the sudden change in output current, or even cause hardware damage to the power supply. If the actual status of the power supply cannot be monitored in real time during the alternating primary and backup switching process, it may lead to the complete power failure of the redundant power supplies, causing system crashes, equipment interruptions, or even data loss, seriously affecting the security and stability of the equipment system.

[0003] It is evident that how to provide accurate power supply warnings is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a power warning method, device, equipment, and storage medium. This method can perform early warning analysis based on data collected in real time during power mode switching, effectively improving the accuracy of power warnings and thus preventing accidents caused by power operations performed due to unstable power conditions. The specific solution is as follows:

[0005] In a first aspect, this application discloses a power supply early warning method, comprising:

[0006] The target device with the highest current power consumption among several local devices is selected, and the power mode of the target device is switched.

[0007] The current power status information of the target device is collected during the power mode switching process, and deep learning is performed based on the current power status information to obtain the current power lifetime model curve of the target device.

[0008] The current power status information is analyzed based on the current power life model curve to determine the power life assessment value corresponding to the target device, and based on the power life assessment value, it is determined whether to generate power warning information to issue a power warning.

[0009] Optionally, the step of filtering out the target device with the highest current power consumption among several local devices and switching the power mode of the target device includes:

[0010] The input power consumption of several local devices is statistically analyzed, and the target device with the highest current power consumption among the several devices is determined based on the input power consumption;

[0011] A power mode switching command is issued to switch the power modes of each power supply of the target device, and the input power and output power of the current working power supply of the target device are read.

[0012] If the current working power supply is in standby mode and the output power is greater than the preset output power threshold, then the power mode of the current working power supply is switched to working mode, and each power supply corresponding to the target device is enabled, and then the input power is set to zero.

[0013] If a mode switching failure occurs during the execution of the power mode switching command, the process jumps to the step of issuing the power mode switching command to switch the power modes of each power supply of the target device, and reading the input power and output power of the current working power supply of the target device.

[0014] Optionally, the step of issuing a power mode switching command to switch the power modes of each power supply of the target device includes:

[0015] Based on the first power mode switching command, the second power mode switching command, the third power mode switching command and the fourth power mode switching command issued in sequence, the corresponding power switching plan is executed in sequence to switch the power modes of each power supply of the target device.

[0016] The first power mode switching instruction is used to switch the first power supply of the target device to the working state and the second power supply to the standby state, and to record the first input power and the first output power of the first power supply and the second power supply respectively.

[0017] The second power mode switching command is used to switch both the first power supply and the second power supply to the working state, and to record the second input power and the second output power of the first power supply and the second power supply respectively.

[0018] The third power mode switching command is used to switch the first power supply to standby mode and the second power supply to working mode, and to record the third input power and the third output power of the first power supply and the second power supply respectively.

[0019] The fourth power mode switching command is used to switch both the first power supply and the second power supply back to the working state, and to record the fourth input power and the fourth output power of the first power supply and the second power supply respectively.

[0020] Optionally, the step of collecting the current power status information of the target device during the power mode switching process and performing deep learning based on the current power status information to obtain the current power lifetime model curve of the target device includes:

[0021] A timestamp verification code is generated, and the current power status information of the target device during the power mode switching process is collected; the current power status information includes the current power temperature information, current output voltage information, and current output current information of the target device.

[0022] The timestamp verification code is used as verification information for the current power status information;

[0023] The current power state information and the verification information are input into the deep learning module so that the deep learning module can verify the current power state information based on the verification information, and generate the current power lifetime model curve corresponding to the target device based on the current power state information after the verification is passed.

[0024] Optionally, generating the current power lifetime model curve corresponding to the target device based on the current power state information includes:

[0025] The output voltage during the power mode switching process is determined based on the current output voltage information in the current power state information, and the average current sharing rate and instantaneous current sharing rate during the power mode switching process are determined based on the current output current information in the target power state information.

[0026] Based on the output voltage, the average current-averaging current, and the instantaneous current-averaging current, construct the current power life model curve corresponding to the target device.

[0027] Optionally, the step of analyzing the current power state information based on the current power life model curve to determine the power life assessment value corresponding to the target device, and determining whether to generate power warning information based on the power life assessment value to issue a power warning, includes:

[0028] The power life limit range of the target device is determined based on the current power life model curve.

[0029] Calculate the sum of the products of the output voltage and the preset influence parameter at each moment in the output voltage to obtain the output voltage lifetime influence value;

[0030] Calculate the sum of the products of the average flow rate at each time step and the preset influence parameter to obtain the lifetime influence value of the average flow rate.

[0031] Calculate the sum of the products of the instantaneous average flow rate at each moment and the preset influence parameter in the instantaneous average flow rate to obtain the lifetime influence value of the instantaneous average flow rate;

[0032] Based on the preset weights corresponding to the target device, corresponding weight values ​​are assigned to the output voltage lifetime impact value, the average flow rate lifetime impact value, and the instantaneous flow rate lifetime impact value.

[0033] The weighted sum of the output voltage lifetime impact value, the average current-averaging lifetime impact value, and the instantaneous current-averaging lifetime impact value is calculated based on the weighted values, and the weighted sum is determined as the power supply lifetime assessment value corresponding to the power supply of the target device.

[0034] Determine whether the power supply life assessment value is within the power supply life limit range. If the power supply life assessment value is within the power supply life limit range, generate a power supply warning message to issue a power supply warning.

[0035] Optionally, determining whether to generate a power warning message based on the power life assessment value includes:

[0036] Based on the power life assessment value, it is determined whether to generate a power warning message. If so, a power warning message for the target device is generated and transmitted to the front-end visualization page of the power warning system for display.

[0037] Secondly, this application discloses a power warning device, comprising:

[0038] The mode switching module is used to filter out the target device with the highest current power consumption among several local devices and switch the power mode of the target device.

[0039] The curve construction module is used to collect the current power status information of the target device during the power mode switching process, and perform deep learning based on the current power status information to obtain the current power lifetime model curve of the target device.

[0040] The lifespan warning module is used to analyze the current power status information based on the current power lifespan model curve to determine the power lifespan assessment value corresponding to the target device, and to determine whether to generate power warning information based on the power lifespan assessment value to provide power warning.

[0041] Thirdly, this application discloses an electronic device, including:

[0042] Memory, used to store computer programs;

[0043] A processor for executing the computer program to implement the steps of the aforementioned power warning method.

[0044] Fourthly, this application discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned power warning method.

[0045] In this application, the first step is to select the target device with the highest current power consumption among several local devices and switch the power mode of the target device. Then, the current power status information of the target device is collected during the power mode switching process, and deep learning is performed based on the current power status information to obtain the current power lifetime model curve of the target device. Finally, the current power status information is analyzed based on the current power lifetime model curve to determine the power lifetime assessment value of the target device, and the power lifetime assessment value is used to determine whether to generate power warning information for power warning.

[0046] Beneficial Effects: As can be seen, the method of this application requires, after selecting the target device with the highest power consumption among several local devices, switching the power mode of the target device, collecting real-time power status information of the target device during the power mode switching process, constructing a power lifespan model curve based on the collected power status information, and finally determining the current power lifespan assessment value of the target device based on the power model lifespan curve, and issuing a power warning based on the power lifespan assessment value. This allows for the collection of power status information during the power mode switching process, the construction of a power lifespan model curve based on the collected power status information, the determination of the power lifespan assessment value based on the constructed power lifespan model curve, and the determination of whether to issue a power warning based on the obtained power lifespan assessment value. Because the warning analysis is based on real-time data collected during the power mode switching process, the accuracy of the power warning is effectively improved, thereby avoiding situations where power operations are performed due to unstable power status, leading to accidents. Attached Figure Description

[0047] To more clearly illustrate the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart of a power supply early warning method provided in an embodiment of the present invention;

[0049] Figure 2 A power warning system architecture diagram provided in an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of a power warning device provided in an embodiment of the present invention;

[0051] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0053] In existing technologies, during scheduled power supply maintenance, it is necessary to switch the power supply of the operating equipment to a primary or backup power supply. The primary power supply needs to take over the entire workload of the system. If the output current of the primary power supply experiences a momentary jump, the output voltage of the primary power supply may fluctuate beyond the specified value due to the sudden change in output current, or even cause damage to the power supply hardware. If the actual status of the power supply cannot be monitored in real time during the alternating primary and backup switching process, it may cause the entire redundant power supply of the equipment to lose power, resulting in system crash, equipment interruption, or even data loss, which seriously affects the safety and stability of the equipment system.

[0054] To overcome the aforementioned technical problems, this application discloses a power warning method, device, equipment, and storage medium, which can perform early warning analysis based on data collected in real time during power mode switching, effectively improving the accuracy of power warning and thus avoiding accidents caused by power operation due to unstable current power status.

[0055] The terms "comprising" and "having," and any variations thereof, in the specification and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may include steps or units not listed.

[0056] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] This application utilizes a server power supply lifespan deep learning and early warning system for power supply early warning. This system includes an intelligent control module for server power supply master / standby mode switching, a server power supply lifespan model deep learning module, and a server power supply status monitoring and early warning module. The server power supply lifespan deep learning and early warning system is deployed on the control computer in the data center, and the front-end visual interface software is installed on the operation and maintenance mobile terminal and the data center control computer.

[0058] See Figure 1 As shown in the figure, this application discloses a power warning method, including:

[0059] Step S11: Select the target device with the highest current power consumption among several local devices, and switch the power mode of the target device.

[0060] In this embodiment, after the server device successfully powers on and connects to the server power lifespan deep learning and early warning system, the server power primary / standby mode switching intelligent control module, the server power lifespan model deep learning module, and the server power status monitoring and early warning module are automatically installed on the server device. Then, the system needs to collect the input power consumption of several local devices to determine the target device with the highest current power consumption. A power mode switching command is then issued to switch the power modes of each power supply of the target device, and the input and output power of the target device's current operating power supply are read. Specifically, the server power lifespan deep learning and early warning system issues a working command to the server power lifespan model deep learning module. After receiving the working command, the server power lifespan model deep learning module identifies the devices with high power consumption in the current system as target devices. Then, the server power lifespan model deep learning module, through the server power status monitoring and early warning module, issues a power mode switching command to the server power primary / standby mode switching intelligent control module, executes the corresponding power switching plan, and intelligently switches the power modes of the target device's power supply to primary / standby mode according to the power switching plan, reading the input and output power of the target device's power supply during the switching process.

[0061] It should be noted that, taking a server device with redundant power supply from two power supplies, a first power supply PSU0 and a second power supply PSU1, as an example, the process of issuing power mode switching commands to switch the power modes of each power supply of the target device is as follows: based on the sequentially issued first power mode switching command, second power mode switching command, third power mode switching command, and fourth power mode switching command, the corresponding power switching plan is executed sequentially to switch the power modes of each power supply of the target device; wherein, the first power mode switching command is to issue a command to switch the first power supply to the Active working state and the second power supply to the Standby standby state, and then the first power supply and the second power supply are recorded respectively. The first power mode switching command is to set the first input power and the first output power; the second power mode switching command is to switch both the first and second power supplies to normal operating state, and then record the second input power and the second output power of the first and second power supplies respectively; the third power mode switching command is to switch the first power supply to standby state and the second power supply to active operating state, and record the third input power and the third output power of the first and second power supplies respectively; the fourth power mode switching command is to switch both the first and second power supplies to normal operating state again, and record the fourth input power and the fourth output power of the first and second power supplies respectively. All of the above power mode switching commands are issued through the ipmitool command of the BMC (baseboard management controller).

[0062] Furthermore, when the server power supply primary / standby mode switching intelligent control module issues Active and Standby commands via the BMC's ipmitool command, the server power supply status monitoring and early warning module will read the power supply's input and output power values ​​in real time. If the current working power supply is in standby mode and the output power is greater than a preset output power threshold, such as exceeding the 20W limit, the power supply mode of the current working power supply will be switched to working mode. Then, after the reset mode is started, the Active and Standby commands will be reissued via the ipmitool command until the server power supply status monitoring and early warning module reports that the actual difference in output power of the Standby mode power supply does not exceed the 20W limit. Then, the apparent input power of the Standby mode power supply will be forcibly reset to zero. It should be noted that since the server power supplies in the data center may use models from different manufacturers, the adjustment method for switching the load from standby mode power supply to working mode power supply will be different, and the time required will also be different. Because the standby power supply is essentially in a standby state and can be woken up at any time, it has relatively low standby power consumption. To ensure consistent apparent input power, if the power supply fails to complete load switching within a specified time, the apparent input power of the standby power supply will be forcibly reset to zero. This ensures that the load of the Standby power supply is completely switched to the Active power supply, thereby ensuring that the load of the data center power supply terminal line powered by the Active power supply reaches the expected maximum training value through the server power supply master / standby mode switching action. This avoids the discrepancy between the actual load jump value and the monitored theoretical value during the switching process due to incomplete switching of the power supply load in the Standby state to the Active power supply. This would lead to a large deviation between the actual state of the server power supply and the life assessment curve, resulting in the power supply failing to accurately monitor and automatically warn of non-compliant operating conditions. Consequently, the server may experience power failure and shutdown due to power supply aging in single power supply mode, causing server operation interruption or even data loss.

[0063] Furthermore, a reset mode can be set. If a mode switching failure occurs during the execution of the power mode switching command, the process can jump to the step of issuing the power mode switching command to switch the power modes of each power supply of the target device, and reading the input power and output power of the current working power supply of the target device, so as to reissue the mode switching command.

[0064] Step S12: Collect the current power status information of the target device during the power mode switching process, and perform deep learning based on the current power status information to obtain the current power lifetime model curve of the target device.

[0065] In this embodiment, the current power status information of the target device during the power mode switching process can be collected so as to construct the current power life model curve of the target device based on the collected information. Specifically, the server power status monitoring and early warning module can be used to monitor the current power status information of the target device through the server CPLD communication bus, that is, the current power temperature information, the current output voltage information, and the current output current information, and generate a timestamp check code. Then, the timestamp check code is added to the time axis of the current power status information as verification information. Then, the current power status information and the verification information are input to the deep learning module, that is, the server power life model deep learning module.

[0066] After receiving the current power status information and the verification information, the deep learning module for the server power supply lifetime model verifies the current power status information based on the verification information. Upon successful verification, it generates the current power supply lifetime model curve corresponding to the target device based on the current power status information. Specifically, it needs to determine the output voltage during the power mode switching process based on the current output voltage information in the current power status information. It should be noted that the server device's power mode switching process is approximately a 50% load transition. The lower limit of the output voltage during the mode switching process is set to 11.6V, and the upper limit is set to 12.8V. Further, it needs to determine the average current sharing and instantaneous current sharing during the power mode switching process based on the current output current information in the target power status information. The average current sharing should be less than 5%, and the instantaneous current sharing should be less than 50%. The calculation formula is as follows: Current sharing = (PSU0 output current - average output current of PSU0 and PSU1) / average output current of PSU0 and PSU1. Then, it can generate the current power supply lifetime model curve corresponding to the target device based on the current power status information. In this way, real-time data can be used to construct power supply lifespan model curves, thereby improving the accuracy of power supply early warning.

[0067] Step S13: Analyze the current power status information based on the current power life model curve to determine the power life assessment value corresponding to the target device, and determine whether to generate power warning information based on the power life assessment value to issue a power warning.

[0068] In this embodiment, it is necessary to analyze the current power state information based on the current power life model curve to determine the power life assessment value corresponding to the target device. Specifically, it is necessary to determine the power life limit range of the target device based on the current power life model curve, and then calculate the sum of the products of the output voltage and preset influence parameters at each moment of the output voltage to obtain the output voltage life influence value. Specifically, based on the previous power life model curve, the power life assessment value of the server device power supply can be defined within the range of 0 to 1. Power supplies with a lifespan assessment value between 0 and 0.9 can be used normally. Power supplies with a lifespan assessment value between 0.9 and 1 require warning handling. Furthermore, the rated design output of the server power supply is 12.2V. Taking 12.2V as the midpoint, the lower limit interval of 11.6V and 12.2V is divided into 100 equal parts, with a preset influence parameter of 0.01 for each part, and a preset influence parameter of 1 for 11.6V. Similarly, the upper limit interval of 12.2V and 12.8V is divided into 100 equal parts, with a preset influence parameter of 0.01 for each part, and a preset influence parameter of 1 for 12.8V.

[0069] Furthermore, it is necessary to calculate the sum of the products of the average current at each moment and the preset influence parameter in the average current attenuation to obtain the lifetime influence value of the average current attenuation. The average current attenuation of the server power supply is 0% and 5% as the judgment interval, which is divided into 100 parts. The preset influence parameter for each part is 0.01, and the preset influence parameter corresponding to the average current attenuation of 5% is 1.

[0070] The next step is to calculate the sum of the products of the instantaneous average current and the preset influence parameter at each moment in the instantaneous average current, so as to obtain the lifetime influence value of the instantaneous average current. The instantaneous average current of the server power supply is divided into 100 equal parts, with the 0% and 50% instantaneous average current as the judgment intervals. The preset influence parameter for each part is 0.01, and the preset influence parameter corresponding to the average current of 50% is 1.

[0071] Finally, the power supply lifetime assessment value for the target device needs to be calculated based on the calculated output voltage lifetime impact value, average current-average lifetime impact value, and instantaneous current-average lifetime impact value. Specifically, corresponding weights need to be assigned to the output voltage lifetime impact value, average current-average lifetime impact value, and instantaneous current-average lifetime impact value: the weight for the output voltage lifetime impact value is 0.4, the weight for the average current-average lifetime impact value is 0.3, and the weight for the instantaneous current-average lifetime impact value is 0.3. Then, the power supply lifetime assessment value is calculated according to the following formula: Power supply lifetime assessment value = Voltage lifetime impact value * 0.4 + Average current-average lifetime impact value * 0.3 + Instantaneous current-average lifetime impact value * 0.3. If the power supply lifetime assessment value is within the range of 0.9 to 1, an early warning is issued.

[0072] It should be noted that if it is confirmed that an early warning is required, a power warning message is generated and transmitted to the front-end visualization page of the power warning system for display. Furthermore, since the power life model curve is constructed based on the real-time collected power information, the accuracy and reliability of the power warning are effectively improved. Therefore, it also effectively solves the problem of data deviation displayed on the front-end visualization interface.

[0073] In this embodiment, after first identifying the target device with the highest power consumption among several local devices, the target device undergoes a power mode switch. Then, real-time power status information of the target device is collected during the power mode switch, and a power lifespan model curve is constructed based on the collected power status information. Finally, the current power lifespan assessment value of the target device is determined based on the power model lifespan curve, and a power warning is issued based on this assessment value. This approach allows for the collection of power status information during the power mode switch, the construction of a power lifespan model curve, the determination of a power lifespan assessment value, and the assessment of whether to issue a power warning. Because the warning analysis is based on real-time data collected during the power mode switch, the accuracy of the power warning is effectively improved, thus avoiding situations where power operations are performed due to unstable power conditions, potentially leading to accidents.

[0074] See Figure 2 As shown, this application discloses a power supply early warning system, including:

[0075] like Figure 2 As shown, after the server node, i.e., the server device, is powered on, the server power lifespan deep learning and early warning system configured on the data center control unit receives and processes the server device's network request. The server power lifespan deep learning and early warning system then interconnects with the server power status monitoring and early warning module and the server power lifespan model deep learning module configured on the server device. The server power status monitoring and early warning module on the server device also interconnects with the server power primary / standby mode switching intelligent control module. Once the server power lifespan deep learning and early warning system is successfully interconnected with each module, it begins operation.

[0076] During the commissioning phase after the server installation in the data center, maintenance personnel need to issue a command to activate the server power lifespan deep learning and early warning system through the visual interface. After the system is activated, it first monitors and prepares for early warning of the server's power status in real time through the server power status monitoring and early warning module. Before the server power lifespan model deep learning module trains the power lifespan curve, it first issues a power mode switching command to the server power status monitoring and early warning module. The server power status monitoring and early warning module receives the command and issues a power mode switching command to the server power primary / standby mode switching intelligent control module. The server power primary / standby mode switching intelligent control module then processes the power lifespan model according to the power lifespan model training plan transmitted by the server power status monitoring and early warning module. The server equipment's power supply undergoes intelligent switching between primary and backup modes. During this process, the server power status monitoring and early warning module acquires real-time power status information according to the power lifespan model training plan and promptly transmits it to the server power lifespan model deep learning module via the CPLD communication bus. The server power lifespan model deep learning module receives real-time power status information from the server power status monitoring and early warning module during the intelligent switching process, automatically trains and performs deep learning on the server equipment's power lifespan during the switching process, automatically fits the server power lifespan model curve, and uploads it to the server power lifespan deep learning and early warning system visualization interface repository and the server power status monitoring and early warning module repository for archiving and future use. Furthermore, during server power mode switching, the server power lifespan deep learning and early warning system monitors the power supply in real time during the primary / standby mode switch through the server power status monitoring and early warning module. The monitored power status information is then fed into the server power lifespan model curve fitted by the server power lifespan model deep learning module during the server racking and debugging phase in the data center for calculation and analysis. The server power status monitoring and early warning module identifies and locates power supplies that exceed the limits of the server power lifespan model curve and transmits power lifespan limit warning information to the server power lifespan deep learning and early warning system. Data center maintenance personnel can view the server power warning information during the primary / standby switch process in real time through a mobile terminal visual interface, so as to quickly locate and deal with the warning power supply problems in a timely manner.

[0077] See Figure 3 As shown in the figure, this application discloses a power warning device, including:

[0078] The mode switching module 11 is used to filter out the target device with the highest current power consumption among several local devices, and to switch the power mode of the target device.

[0079] The curve construction module 12 is used to collect the current power status information of the target device during the power mode switching process, and perform deep learning based on the current power status information to obtain the current power lifetime model curve of the target device.

[0080] The lifespan warning module 13 is used to analyze the current power status information based on the current power lifespan model curve to determine the power lifespan assessment value corresponding to the target device, and to determine whether to generate power warning information based on the power lifespan assessment value to provide power warning.

[0081] In this embodiment, firstly, the target device with the highest current power consumption among several local devices needs to be selected, and the power mode of the target device is switched. Then, the current power status information of the target device during the power mode switching process is collected, and deep learning is performed based on the current power status information to obtain the current power lifetime model curve corresponding to the target device. Finally, the current power status information is analyzed based on the current power lifetime model curve to determine the power lifetime assessment value corresponding to the target device, and based on the power lifetime assessment value, it is determined whether to generate power warning information to issue a power warning. Therefore, the method of this application requires selecting the target device with the highest power consumption among several local devices, switching the power mode of the target device, collecting real-time power status information of the target device during the power mode switching process, constructing a power lifetime model curve based on the collected power status information, determining the current power lifetime assessment value of the target device based on the power model lifetime curve, and issuing a power warning based on the power lifetime assessment value. In this way, power status information during power mode switching can be collected, and a power life model curve can be constructed based on the collected power status information. Then, the power life assessment value can be determined based on the constructed power life model curve, and then a power warning can be determined based on the obtained power life assessment value. Since the warning analysis is performed based on the data collected in real time during power mode switching, the accuracy of power warning is effectively improved, thereby avoiding the situation where power operation is performed due to the current power instability, which could lead to accidents.

[0082] In some embodiments, the mode switching module 11 may specifically include:

[0083] The device determination submodule is used to count the input power consumption of several local devices, so as to determine the target device with the highest current power consumption among the several devices based on the input power consumption;

[0084] The first mode switching submodule is used to issue power mode switching instructions to switch the power modes of each power supply of the target device, and to read the input power and output power of the current working power supply of the target device.

[0085] The second mode switching submodule is used to switch the power mode of the current working power supply to the working state and enable each power supply corresponding to the target device if the current working power supply is in standby mode and the output power is greater than a preset output power threshold, and then set the input power to zero.

[0086] The step-by-step module is used to, if a mode switching failure occurs during the execution of the power mode switching command corresponding to the power mode switching command, jump to the step of issuing the power mode switching command to switch the power modes of each power supply of the target device, and reading the input power and output power of the current working power supply of the target device.

[0087] In some embodiments, the first mode switching submodule may specifically include:

[0088] The switching plan execution unit is used to execute the corresponding power switching plan in sequence based on the first power mode switching instruction, the second power mode switching instruction, the third power mode switching instruction and the fourth power mode switching instruction issued in sequence, so as to switch the power mode of each power supply of the target device.

[0089] The first power mode switching instruction is used to switch the first power supply of the target device to the working state and the second power supply to the standby state, and to record the first input power and the first output power of the first power supply and the second power supply respectively.

[0090] The second power mode switching command is used to switch both the first power supply and the second power supply to the working state, and to record the second input power and the second output power of the first power supply and the second power supply respectively.

[0091] The third power mode switching command is used to switch the first power supply to standby mode and the second power supply to working mode, and to record the third input power and the third output power of the first power supply and the second power supply respectively.

[0092] The fourth power mode switching command is used to switch both the first power supply and the second power supply back to the working state, and to record the fourth input power and the fourth output power of the first power supply and the second power supply respectively.

[0093] In some embodiments, the curve construction module 12 may specifically include:

[0094] The data acquisition submodule is used to generate timestamp verification codes and acquire the current power status information of the target device during the power mode switching process; the current power status information includes the current power temperature information, current output voltage information and current output current information of the target device.

[0095] The verification information confirmation submodule is used to use the timestamp verification code as verification information for the current power status information.

[0096] The curve generation submodule is used to input the current power state information and the verification information into the deep learning module, so that the deep learning module can verify the current power state information based on the verification information, and generate the current power lifetime model curve corresponding to the target device based on the current power state information after the verification is passed.

[0097] In some embodiments, the curve generation submodule may specifically include:

[0098] An information determination unit is used to determine the output voltage during the power mode switching process based on the current output voltage information in the current power state information, and to determine the average current sharing rate and instantaneous current sharing rate during the power mode switching process based on the current output current information in the target power state information.

[0099] The curve construction unit is used to construct the current power life model curve corresponding to the target device based on the output voltage, the average current sharing, and the instantaneous current sharing.

[0100] In some embodiments, the lifespan warning module 13 may specifically include:

[0101] A power lifespan determination unit is used to determine the power lifespan limit range of the target device based on the current power lifespan model curve.

[0102] The first influence value calculation unit is used to calculate the sum of the products of the output voltage and the preset influence parameter at each moment in the output voltage, so as to obtain the output voltage lifetime influence value.

[0103] The second influence value calculation unit is used to calculate the sum of the products of the average flow rate at each moment and the preset influence parameter in the average flow rate, so as to obtain the average flow rate lifetime influence value.

[0104] The third influence value calculation unit is used to calculate the sum of the products of the instantaneous average flow rate and the preset influence parameter at each moment in the instantaneous average flow rate, so as to obtain the lifetime influence value of the instantaneous average flow rate.

[0105] The weight allocation unit is used to allocate corresponding weight values ​​to the output voltage lifetime impact value, the average flow rate lifetime impact value, and the instantaneous flow rate lifetime impact value based on the preset weights corresponding to the target device.

[0106] The power supply lifetime assessment value calculation unit is used to perform a weighted summation of the output voltage lifetime impact value, the average current-average lifetime impact value, and the instantaneous current-average lifetime impact value based on the weight value, and determine the weighted summation result as the power supply lifetime assessment value corresponding to the power supply of the target device.

[0107] A power warning unit is used to determine whether the power life assessment value is within the power life limit range. If the power life assessment value is within the power life limit range, a power warning message is generated to provide a power warning.

[0108] In some embodiments, the lifespan warning module 13 may specifically include:

[0109] The warning information display unit is used to determine whether to generate power warning information based on the power life assessment value. If so, it generates power warning information for the target device and transmits the power warning information to the front-end visualization page of the power warning system for warning display through the front-end visualization page.

[0110] Furthermore, embodiments of this application also disclose an electronic device, Figure 4 This is a structural diagram of an electronic device according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. Specifically, the electronic device may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the power warning method disclosed in any of the foregoing embodiments. Furthermore, the electronic device in this embodiment may specifically be a computer.

[0111] In this embodiment, the power supply 23 is used to provide operating voltage for various hardware devices on the electronic device; the communication interface 24 can create a data transmission channel between the electronic device and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0112] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0113] The operating system 221 is used to manage and control the various hardware devices on the electronic device and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the power warning method executed by the electronic device as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0114] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned power warning method. The specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0115] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0116] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0118] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0119] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A power supply early warning method, characterized in that, include: The target device with the highest current power consumption among several local devices is selected, and the power mode of the target device is switched. The current power status information of the target device is collected during the power mode switching process, and deep learning is performed based on the current power status information to obtain the current power lifetime model curve of the target device. The current power status information is analyzed based on the current power life model curve to determine the power life assessment value corresponding to the target device, and based on the power life assessment value, it is determined whether to generate power warning information to issue a power warning. Specifically, based on the first power mode switching command, the second power mode switching command, the third power mode switching command and the fourth power mode switching command issued in sequence, the corresponding power switching plan is executed in sequence to switch the power modes of each power supply of the target device. The first power mode switching instruction is used to switch the first power supply of the target device to the working state and the second power supply to the standby state, and to record the first input power and the first output power of the first power supply and the second power supply respectively. The second power mode switching command is used to switch both the first power supply and the second power supply to the working state, and to record the second input power and the second output power of the first power supply and the second power supply respectively. The third power mode switching command is used to switch the first power supply to standby mode and the second power supply to working mode, and to record the third input power and the third output power of the first power supply and the second power supply respectively. The fourth power mode switching command is used to switch both the first power supply and the second power supply back to the working state, and to record the fourth input power and the fourth output power of the first power supply and the second power supply respectively.

2. The power supply early warning method according to claim 1, characterized in that, The step of selecting the target device with the highest current power consumption among several local devices and switching the power mode of the target device includes: The input power consumption of several local devices is statistically analyzed, and the target device with the highest current power consumption among the several devices is determined based on the input power consumption; A power mode switching command is issued to switch the power modes of each power supply of the target device, and the input power and output power of the current working power supply of the target device are read. If the current working power supply is in standby mode and the output power is greater than the preset output power threshold, then the power mode of the current working power supply is switched to working mode, and each power supply corresponding to the target device is enabled, and then the input power is set to zero. If a mode switching failure occurs during the execution of the power mode switching command, the process jumps to the step of issuing the power mode switching command to switch the power modes of each power supply of the target device, and reading the input power and output power of the current working power supply of the target device.

3. The power supply early warning method according to claim 1, characterized in that, The process of collecting current power status information of the target device during power mode switching, and performing deep learning based on the current power status information to obtain the current power lifetime model curve of the target device, includes: A timestamp verification code is generated, and the current power status information of the target device during the power mode switching process is collected; the current power status information includes the current power temperature information, current output voltage information, and current output current information of the target device. The timestamp verification code is used as verification information for the current power status information; The current power state information and the verification information are input into the deep learning module so that the deep learning module can verify the current power state information based on the verification information, and generate the current power lifetime model curve corresponding to the target device based on the current power state information after the verification is passed.

4. The power supply early warning method according to claim 3, characterized in that, The step of generating the current power lifetime model curve corresponding to the target device based on the current power state information includes: The output voltage during the power mode switching process is determined based on the current output voltage information in the current power state information, and the average current sharing rate and instantaneous current sharing rate during the power mode switching process are determined based on the current output current information in the target power state information. Based on the output voltage, the average current-averaging current, and the instantaneous current-averaging current, construct the current power life model curve corresponding to the target device.

5. The power supply early warning method according to claim 4, characterized in that, The step of analyzing the current power status information based on the current power life model curve to determine the power life assessment value corresponding to the target device, and determining whether to generate power warning information based on the power life assessment value to issue a power warning, includes: The power life limit range of the target device is determined based on the current power life model curve. Calculate the sum of the products of the output voltage and the preset influence parameter at each moment in the output voltage to obtain the output voltage lifetime influence value; Calculate the sum of the products of the average flow rate at each time step and the preset influence parameter to obtain the lifetime influence value of the average flow rate. Calculate the sum of the products of the instantaneous average flow rate at each moment and the preset influence parameter in the instantaneous average flow rate to obtain the lifetime influence value of the instantaneous average flow rate; Based on the preset weights corresponding to the target device, corresponding weight values ​​are assigned to the output voltage lifetime impact value, the average flow rate lifetime impact value, and the instantaneous flow rate lifetime impact value. The output voltage lifetime impact value, the average current-average lifetime impact value, and the instantaneous current-average lifetime impact value are weighted and summed based on the weight values, and the weighted summation result is determined as the power supply lifetime assessment value corresponding to the power supply of the target device. Determine whether the power supply life assessment value is within the power supply life limit range. If the power supply life assessment value is within the power supply life limit range, generate a power supply warning message to issue a power supply warning.

6. The power supply early warning method according to any one of claims 1 to 5, characterized in that, The step of determining whether to generate a power warning message based on the power life assessment value includes: Based on the power life assessment value, it is determined whether to generate a power warning message. If so, a power warning message for the target device is generated and transmitted to the front-end visualization page of the power warning system for display.

7. A power supply early warning device, characterized in that, include: The mode switching module is used to filter out the target device with the highest current power consumption among several local devices and switch the power mode of the target device. The curve construction module is used to collect the current power status information of the target device during the power mode switching process, and perform deep learning based on the current power status information to obtain the current power lifetime model curve of the target device. The lifespan warning module is used to analyze the current power status information based on the current power lifespan model curve to determine the power lifespan assessment value corresponding to the target device, and to determine whether to generate power warning information based on the power lifespan assessment value to provide power warning. The power warning device includes: The switching plan execution unit is used to execute the corresponding power switching plan in sequence based on the first power mode switching instruction, the second power mode switching instruction, the third power mode switching instruction and the fourth power mode switching instruction issued in sequence, so as to switch the power mode of each power supply of the target device. The first power mode switching instruction is used to switch the first power supply of the target device to the working state and the second power supply to the standby state, and to record the first input power and the first output power of the first power supply and the second power supply respectively. The second power mode switching command is used to switch both the first power supply and the second power supply to the working state, and to record the second input power and the second output power of the first power supply and the second power supply respectively. The third power mode switching command is used to switch the first power supply to standby mode and the second power supply to working mode, and to record the third input power and the third output power of the first power supply and the second power supply respectively. The fourth power mode switching command is used to switch both the first power supply and the second power supply back to the working state, and to record the fourth input power and the fourth output power of the first power supply and the second power supply respectively.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the power warning method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the power warning method as described in any one of claims 1 to 6.

Images