CN116306284A - Power supply service life prediction method and device, electronic equipment and storage medium - Google Patents
Power supply service life prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for predicting service life of a power supply, electronic equipment and a storage medium. The method is characterized by comprising the following steps: acquiring power supply working data of a power supply to be predicted; simulating the power supply working data according to a preset data simulation method, and determining a plurality of power supply prediction data; determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model; the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data. The method and the device realize accurate prediction of the target service life information of the power supply to be predicted, can find the power supply with serious service life attenuation, and reduce the safety risk of the power supply.
Description
Technical Field
The present invention relates to the field of power supply regulation, and in particular, to a method and apparatus for predicting a service life of a power supply, an electronic device, and a storage medium.
Background
As one of the important living facilities of the present resident living, the business organization of the bank generally uses the uninterruptible power supply UPS (Uninterruptible Power Supply) equipment to assist in supplying power to the electronic devices in the business organization in order to ensure the all-weather normal service of the business organization. Along with the use of uninterruptible power supply equipment, the health degree of the uninterruptible power supply can be prolonged in service life and influenced by other factors, so that the health degree of the uninterruptible power supply is influenced, and a certain potential safety hazard exists. In the prior art, the service life of the uninterruptible power supply is predicted, usually, the working time of the uninterruptible power supply is recorded, the residual service life is predicted according to the working time of the uninterruptible power supply through a monitoring system, and the residual service life of the uninterruptible power supply is predicted by only considering the working time of the power supply without considering the service condition of the power supply in the prior art.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for predicting the service life of a power supply, so as to accurately and rapidly predict the service life of the power supply.
According to an aspect of the present invention, there is provided a power supply life prediction method, including:
acquiring power supply working data of a power supply to be predicted;
simulating the power supply working data according to a preset data simulation method, and determining a plurality of power supply prediction data;
determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model;
the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
According to another aspect of the present invention, there is provided a power supply life prediction apparatus including:
the external memory module is used for acquiring power supply working data of the power supply to be predicted;
the data simulation module is used for simulating the power supply working data according to a preset data simulation method and determining a plurality of power supply prediction data;
the target service life prediction module is used for determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model;
the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of predicting the lifetime of a power supply according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute a method for predicting the lifetime of a power supply according to any one of the embodiments of the present invention.
According to the technical scheme, the power supply working data of the power supply to be predicted are obtained, and the power supply working data can feed back the real-time state of the power supply, so that the accuracy of prediction is improved; simulating the power supply working data according to a preset data simulation method, determining a plurality of power supply prediction data, expanding the data through the simulation method, and performing data prediction by using the plurality of data, so that the randomness of the prediction is improved, and the accuracy of the prediction is further improved; according to the power supply prediction data and the power supply prediction model, the target service life information of the power supply to be predicted is determined, the target service life of the power supply to be predicted can be rapidly output through the power supply prediction model, the accurate and rapid prediction of the service life of the power supply is realized, the efficiency and accuracy of predicting the service life of the power supply are effectively improved, the technical problem that the service life of the uninterruptible power supply cannot be accurately predicted in the prior art is solved, the safe power supply of service equipment is effectively ensured, the potential safety hazard is reduced, and data support is provided for power supply maintenance.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting service life of a power supply according to an embodiment of the present invention;
FIG. 2 is a flowchart of another method for predicting service life of a power supply according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a power supply life prediction device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a method for predicting a service life of a power supply according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Fig. 1 is a flowchart of a method for predicting service life of a power supply according to an embodiment of the present invention, where the method may be performed by a power supply service life predicting device, and the power supply service life predicting device may be implemented in hardware and/or software, and the power supply service life predicting device may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, acquiring power supply working data of a power supply to be predicted.
The power supply to be predicted can be a power supply with the service life to be determined, the power supply to be predicted can be used for supplying power to the equipment to be powered for multiple times, and the power supply to be predicted can be an uninterruptible power supply. It should be noted that, when the input of the mains supply is normal, the uninterruptible power supply can stabilize the voltage of the mains supply and charge the mains supply; when the mains supply input is abnormal, the mains supply cannot supply power, and the uninterruptible power supply supplies power.
The power supply operation data may be basic data for predicting the service life of the power supply to be predicted.
Optionally, when the power supply works, the working information is uploaded to a monitoring system corresponding to the power supply at regular intervals, and after the monitoring system receives the working information reported by the power supply, the reported working information is stored according to a storage position corresponding to the power supply.
Optionally, in the embodiment of the present invention, an External Memory (External Memory) may be configured to store the working information reported by the power supply, the power supply uploads the working information to the External Memory, and neurons in the External Memory store the working information uploaded by the power supply. The external memory can be an external memory unit in the neural network, the neuron can store a certain amount of information, and when the external memory unit is used for storing information, the capacity of the neural network for storing information can be improved, and the complexity of the neural network is reduced.
Specifically, power supply working data stored in the power supply to be predicted is requested, and the power supply working data of the power supply to be predicted is obtained.
Optionally, in another optional embodiment of the present invention, the acquiring power supply operation data of the power supply to be predicted includes:
and acquiring historical working data of the power supply to be predicted, and determining the power supply working data of the power supply to be predicted according to the historical working data.
The historical working data can be the historical data of the power supply to be predicted to work. Optionally, the historical operating data includes at least one of power supply discharge information, power supply internal resistance information, and historical lifetime information.
Optionally, the historical working data of the power supply to be predicted is stored in an external memory, when the user determines the power supply to be predicted, the corresponding historical working data is requested to the external memory, and the obtained historical working data comprises power supply discharge information, power supply internal resistance information and historical service life information. The power supply discharge information is the discharge information of the power supply to be predicted when the power supply to be predicted works in a calendar, and the power supply to be predicted correspondingly has one power supply discharge information at each discharge, so that the power supply working data of the power supply to be predicted is determined according to the power supply discharge information, the power supply internal resistance information and the historical service life information at each time.
Specifically, the stored historical working data of the power supply to be predicted is requested, and after the historical working data of the power supply to be predicted is obtained, the power supply working data of the power supply to be predicted is determined according to the historical working data.
Optionally, in another optional embodiment of the present invention, the determining power supply operation data of the power supply to be predicted according to the historical operation data includes:
summing the power supply discharge information of the power supply to be predicted when the power supply to be predicted is deeply discharged each time, and determining the superposition discharge information of the power supply to be predicted; and determining the power supply working data of the power supply to be predicted according to the superimposed discharge information, the power supply internal resistance information and the historical life information.
The superimposed discharge information may be a sum of power discharge information of the power supply to be predicted each time the power supply is deeply discharged. Optionally, when the power supply to be predicted is subjected to deep discharge, primary power supply discharge information of the power supply to be predicted is recorded, and further power supply discharge information of the power supply to be predicted when the power supply to be predicted is subjected to deep discharge each time exists in historical working data of the power supply to be predicted, and the power supply discharge information of the power supply to be predicted when the power supply to be predicted is subjected to deep discharge each time is subjected to superposition summation, so that superposition discharge information of the power supply to be predicted is obtained.
Specifically, historical working data of the power supply to be predicted is obtained, power supply discharge information of the power supply to be predicted when the power supply to be predicted discharges deeply is determined, the power supply discharge information of the power supply to be predicted when the power supply to be predicted discharges deeply each time is subjected to superposition summation, superposition discharge information of the power supply to be predicted is obtained, and the power supply working data of the power supply to be predicted is determined according to the superposition discharge information, the power supply internal resistance information and the historical service life information.
S120, simulating the power supply working data according to a preset data simulation method, and determining a plurality of power supply prediction data.
The data simulation method may be a method preset for performing random simulation according to power supply operation data. The data simulation method can simulate the characteristics of the power supply working data according to the data characteristics of the power supply working data, and randomly generate a plurality of variables which are the same as the characteristics of the power supply working data. The predetermined data simulation method may be a stochastic simulation algorithm, for example.
The power supply prediction data can be data generated through simulation by a preset data simulation method; the data characteristics of the power supply prediction data are consistent with those of the power supply working data, and the power supply prediction data are used for predicting the service life of the power supply to be predicted.
Specifically, power supply working data of a power supply to be predicted is obtained, and the power supply working data is simulated by a preset data simulation method to obtain a plurality of random power supply prediction data.
S130, determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model.
The target service life information may be remaining service life information of the power supply to be predicted; the target life information may be used to determine a time of use for which the power supply to be predicted is able to continue to operate.
The power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
Alternatively, the power supply prediction model may be at least one of a long-short-term memory recurrent neural network, a recurrent neural network, and a gated recurrent unit. Taking a long-term memory cyclic neural network as an example, the training process of the power supply prediction model is as follows:
initializing a long-term and short-term memory cyclic neural network, determining input parameters and output parameters of neurons in an initial prediction model, setting the number of the neurons in the initial prediction model, and setting the number of hidden layers and an activation function in the initial prediction model. The input parameters can be set as power training data, and the output parameters are training service life information. And connecting the initial prediction model with an external memory unit, acquiring historical working data of a plurality of uninterruptible power supplies from the external memory unit, and determining power supply training data according to the historical working data of the plurality of uninterruptible power supplies.
And taking the acquired power training data of the uninterrupted power supplies as a training set and a testing set, inputting the power training data in the training set into an initial prediction model for training to obtain training service life information output by the initial prediction model, determining training service life information and training errors of the training set through a loss function, adjusting neuron parameters in the initial prediction model through the training errors in a back propagation mode, and stopping training the initial prediction model under the condition that the output of the initial prediction model meets an error threshold value to obtain the power prediction model.
Specifically, a plurality of power supply prediction data are obtained, the power supply prediction data are input into a power supply prediction model, and target service life information output by the power supply prediction model is obtained.
According to the technical scheme, the power supply working data of the power supply to be predicted are obtained, and the power supply working data can feed back the real-time state of the power supply, so that the accuracy of prediction is improved; simulating the power supply working data according to a preset data simulation method, determining a plurality of power supply prediction data, expanding the data through the simulation method, and performing data prediction by using the plurality of data, so that the randomness of the prediction is improved, and the accuracy of the prediction is further improved; according to the power supply prediction data and the power supply prediction model, the target service life information of the power supply to be predicted is determined, the target service life of the power supply to be predicted can be rapidly output through the power supply prediction model, the accurate and rapid prediction of the service life of the power supply is realized, the efficiency and accuracy of predicting the service life of the power supply are effectively improved, the technical problem that the service life of the uninterruptible power supply cannot be accurately predicted in the prior art is solved, the safe power supply of service equipment is effectively ensured, the potential safety hazard is reduced, and data support is provided for power supply maintenance.
Example two
Fig. 2 is a flowchart of another method for predicting service life of a power supply according to the second embodiment of the present invention, where the relationship between the present embodiment and the above embodiment is a specific method for predicting target service life information of a power supply to be predicted by a power supply prediction model. As shown in fig. 2, the method for predicting the service life of the power supply includes:
s210, acquiring power supply working data of a power supply to be predicted.
S220, determining statistical characteristic information of the power supply to be predicted according to the power supply internal resistance information of the power supply to be predicted; and determining a plurality of power supply prediction data according to the power supply working data, the statistical characteristic information and a preset data simulation method.
The internal resistance information of the power supply can be the blocking effect of the internal resistance information of the power supply on the current. It should be noted that, when the power supply works, a part of internal resistance of the power supply consumes a certain amount of electricity, and the internal resistance of the power supply increases along with the increase of the service time of the battery, so that the quality of the output electric energy of the power supply is affected; further, as the internal resistance of the power supply increases, the heating value of the power supply is affected, and a certain safety risk is caused.
The statistical characteristic information may be a regularity characteristic of the latest internal resistance information of the power supply in the historical working data. The statistical characteristic information may be used to reflect the law of internal resistance of the power supply to be predicted.
Specifically, historical working information of a power supply to be predicted is obtained, power internal resistance information of a node of the power supply to be predicted at the latest time is determined according to the historical working information of the power supply to be predicted, statistical characteristic information of the power supply to be predicted is determined, and data simulation is performed according to power working data and the statistical characteristic information of the power supply to be predicted according to a preset data simulation method, so that a plurality of power supply prediction data are obtained.
S230, inputting the power supply prediction data into the power supply prediction model to obtain a plurality of residual service lives of the power supply to be predicted, and determining target service life information of the power supply to be predicted according to the residual service lives and a preset probability density calculation method.
The remaining service life may be the time during which the power supply to be predicted can be safely and normally used.
The probability density calculating method may be a preset method for calculating a remaining service life distribution rule and a confidence region.
Optionally, acquiring a plurality of power supply prediction data, inputting the power supply prediction data into a power supply prediction model to obtain a plurality of residual service lives output by the power supply prediction model, and performing probability density calculation on the plurality of residual service lives through a preset probability density calculation method to determine target service life information of the power supply to be predicted.
Specifically, a plurality of power supply prediction data are obtained, each power supply prediction data is sequentially input into a power supply prediction model to be predicted, a plurality of residual service lives of the power supply to be predicted are obtained, probability density calculation is carried out on the plurality of residual service lives through a probability density calculation method, and target service life information of the power supply to be predicted is determined.
Optionally, in another optional embodiment of the present invention, the determining, according to a plurality of remaining service lives and a preset probability density calculating method, target service life information of the power source to be predicted includes:
determining a distribution rule and a confidence interval of the residual service lives according to the residual service lives and a preset probability density calculation method; and determining target service life information of the power supply to be predicted according to the distribution rule of the residual service life and the confidence interval.
Wherein, the distribution rule can be used for representing the distribution situation of a plurality of residual service lives; by way of example, the distribution law may be a positive-ethernet distribution.
The confidence interval can be an interval range where a plurality of residual service lives are located; the confidence interval may be used to represent the error range of a plurality of remaining life distributions.
Specifically, according to a plurality of residual service lives and a preset probability density calculation method, a distribution rule and a confidence interval of the residual service lives are determined, the distribution condition and the error range of the residual service lives are determined through the distribution rule and the confidence interval, and then target service life information of the power supply to be predicted is determined according to the distribution rule and the confidence interval of the residual service lives.
According to the technical scheme, the power supply working data of the power supply to be predicted are obtained, and the power supply working data can feed back the real-time state of the power supply, so that the accuracy of prediction is improved; simulating the power supply working data according to a preset data simulation method, determining a plurality of power supply prediction data, expanding the data through the simulation method, and performing data prediction by using the plurality of data, so that the randomness of the prediction is improved, and the accuracy of the prediction is further improved; according to the power supply prediction data and the power supply prediction model, the target service life information of the power supply to be predicted is determined, the target service life of the power supply to be predicted can be rapidly output through the power supply prediction model, the accurate and rapid prediction of the service life of the power supply is realized, the efficiency and accuracy of predicting the service life of the power supply are effectively improved, the technical problem that the service life of the uninterruptible power supply cannot be accurately predicted in the prior art is solved, the safe power supply of service equipment is effectively ensured, the potential safety hazard is reduced, and data support is provided for power supply maintenance.
Optionally, the embodiment of the invention discloses another method for predicting the service life of a power supply, wherein the method comprises the following steps:
s1, initializing and setting LSTMRNN (Long Short-Term Memory Recurrent Neural Network, long-term memory cyclic neural network). Initializing the LSTM RNN, determining the input parameters, the number of neurons, the number of hidden layers, an activation function, normalization setting and output parameters of the LSTM RNN.
S2, acquiring external memory data. And reading the historical working data of the power supply to be predicted from the external memory.
Optionally, the historical working data of the power supply to be predicted is collected through a monitoring system of the uninterruptible power supply, and the collected historical working data is stored in an external memory. Wherein the historical operating data includes at least one of power discharge information, power internal resistance information, and historical lifetime information.
Specifically, after the historical working data of the power supply to be predicted is obtained, the historical power supply discharging information in the historical working data is overlapped to obtain overlapped discharging information, and the power supply working data of the power supply to be predicted is determined according to the overlapped discharging information, the power supply internal resistance information and the historical service life information.
S3, LSTMRNN training. And training the LSTMRNN through a preset training tool. Wherein the training tool may be a TensorFlow open source framework.
By way of example, the process of training an LSTMRNN may be as follows:
and taking the acquired power training data of the plurality of uninterruptible power supplies as a training set and a testing set, inputting the power training data in the training set into the LSTMRNN for training to obtain training service life information output by the LSTMRNN, determining training service life information and training errors of the training set through a loss function, adjusting neuron parameters in the LSTMRNN through the training errors in a counter-propagation mode, and stopping training the LSTMRNN under the condition that the output of the LSTMRNN meets an error threshold value to obtain the LSTMRNN model.
S4, predicting the residual service life of the power supply forwards in a circulating way. And inputting the power supply prediction data to be predicted into an LSTMRNN model to obtain the residual service life of the power supply to be predicted.
S5, monte Carlo simulation. And acquiring the statistical characteristics of power supply internal resistance information adjacent to a prediction starting point according to the historical working data of the power supply to be predicted, randomly generating n power supply prediction data according to the statistical characteristics of the power supply internal resistance information, and sequentially inputting each power supply prediction data into an LSTMRNN model to obtain a plurality of residual service lives.
S6, comprehensive treatment. According to the probability density calculation method, distribution rules and confidence intervals of a plurality of residual service lives can be obtained, comprehensive analysis is carried out on the residual service lives, target service life information of the power supply to be predicted is determined, and meanwhile, the target service life information is written into an external memory.
The technical scheme of the embodiment of the invention can accurately and rapidly predict the service life of the power supply, effectively improves the efficiency and accuracy of predicting the service life of the power supply, solves the technical problem that the service life of the uninterruptible power supply cannot be accurately predicted in the prior art, effectively ensures the safe power supply of service equipment, reduces potential safety hazard and provides data support for the maintenance of the power supply.
Example III
Fig. 3 is a schematic structural diagram of a power supply life prediction device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: an external memory module 310, data simulation modules 320 and 330,
an external memory module 310, configured to obtain power supply operation data of a power supply to be predicted;
the data simulation module 320 is configured to simulate the power supply working data according to a preset data simulation method, and determine a plurality of power supply prediction data;
a target service life prediction module 330, configured to determine target service life information of a power supply to be predicted according to a plurality of the power supply prediction data and a power supply prediction model;
the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
According to the technical scheme, the power supply working data of the power supply to be predicted are obtained, and the power supply working data can feed back the real-time state of the power supply, so that the accuracy of prediction is improved; simulating the power supply working data according to a preset data simulation method, determining a plurality of power supply prediction data, expanding the data through the simulation method, and performing data prediction by using the plurality of data, so that the randomness of the prediction is improved, and the accuracy of the prediction is further improved; according to the power supply prediction data and the power supply prediction model, the target service life information of the power supply to be predicted is determined, the target service life of the power supply to be predicted can be rapidly output through the power supply prediction model, the accurate and rapid prediction of the service life of the power supply is realized, the efficiency and accuracy of predicting the service life of the power supply are effectively improved, the technical problem that the service life of the uninterruptible power supply cannot be accurately predicted in the prior art is solved, the safe power supply of service equipment is effectively ensured, the potential safety hazard is reduced, and data support is provided for power supply maintenance.
Optionally, the external memory module is specifically configured to:
and acquiring historical working data of the power supply to be predicted, and determining the power supply working data of the power supply to be predicted according to the historical working data.
Optionally, the external memory module is specifically further configured to:
the historical operating data includes at least one of power discharge information, power internal resistance information, and historical lifetime information.
Optionally, the external memory module is specifically further configured to:
summing the power supply discharge information of the power supply to be predicted when the power supply to be predicted is deeply discharged each time, and determining the superposition discharge information of the power supply to be predicted;
and determining the power supply working data of the power supply to be predicted according to the superimposed discharge information, the power supply internal resistance information and the historical life information.
Optionally, the data simulation module is specifically configured to:
determining statistical characteristic information of the power supply to be predicted according to the power supply internal resistance information of the power supply to be predicted;
and determining a plurality of power supply prediction data according to the power supply working data, the statistical characteristic information and a preset data simulation method.
Optionally, the target service life prediction module is specifically configured to:
inputting a plurality of power supply prediction data into the power supply prediction model to obtain a plurality of residual service lives of a power supply to be predicted, wherein the residual service lives are available time from an internal resistance value of the power supply to a power supply failure critical threshold value at the current moment;
and determining target service life information of the power supply to be predicted according to the residual service lives and a preset probability density calculation method.
Optionally, the target service life prediction module is specifically further configured to:
determining a distribution rule and a confidence interval of the residual service lives according to the residual service lives and a preset probability density calculation method;
and determining target service life information of the power supply to be predicted according to the distribution rule of the residual service life and the confidence interval.
The power supply service life prediction device provided by the embodiment of the invention can execute the power supply service life prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 14 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a method of predicting the life of the power supply.
In some embodiments, the method of predicting the life of a power source may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above-described method of predicting the lifetime of a power supply may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method of predicting the power supply lifetime in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
Example five
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method for predicting a lifetime of a power supply as provided in any embodiment of the present invention, the method comprising:
acquiring power supply working data of a power supply to be predicted;
simulating the power supply working data according to a preset data simulation method, and determining a plurality of power supply prediction data;
determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model;
the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It will be appreciated by those of ordinary skill in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method for predicting the service life of a power supply, comprising:
acquiring power supply working data of a power supply to be predicted;
simulating the power supply working data according to a preset data simulation method, and determining a plurality of power supply prediction data;
determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model;
the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
2. The method of claim 1, wherein the obtaining power supply operation data of the power supply to be predicted comprises:
and acquiring historical working data of the power supply to be predicted, and determining the power supply working data of the power supply to be predicted according to the historical working data.
3. The method of claim 2, wherein the historical operating data includes at least one of power supply discharge information, power supply internal resistance information, and historical life information.
4. A method according to claim 3, wherein said determining power supply operation data of the power supply to be predicted from the historical operation data comprises:
summing the power supply discharge information of the power supply to be predicted when the power supply to be predicted is deeply discharged each time, and determining the superposition discharge information of the power supply to be predicted;
and determining the power supply working data of the power supply to be predicted according to the superimposed discharge information, the power supply internal resistance information and the historical life information.
5. The method of claim 2, wherein simulating the power operation data according to a predetermined data simulation method to determine a plurality of power prediction data comprises:
determining statistical characteristic information of the power supply to be predicted according to the power supply internal resistance information of the power supply to be predicted;
and determining a plurality of power supply prediction data according to the power supply working data, the statistical characteristic information and a preset data simulation method.
6. The method of claim 1, wherein determining target life information for a power source to be predicted based on a plurality of the power source prediction data and a power source prediction model comprises:
inputting a plurality of power supply prediction data into the power supply prediction model to obtain a plurality of residual service lives of a power supply to be predicted, wherein the residual service lives are available time from an internal resistance value of the power supply to a power supply failure critical threshold value at the current moment;
and determining target service life information of the power supply to be predicted according to the residual service lives and a preset probability density calculation method.
7. The method of claim 6, wherein determining the target lifetime information of the power source to be predicted according to a plurality of remaining lifetimes and a preset probability density calculation method comprises:
determining a distribution rule and a confidence interval of the residual service lives according to the residual service lives and a preset probability density calculation method;
and determining target service life information of the power supply to be predicted according to the distribution rule of the residual service life and the confidence interval.
8. A power supply life prediction apparatus, comprising:
the external memory module is used for acquiring power supply working data of the power supply to be predicted;
the data simulation module is used for simulating the power supply working data according to a preset data simulation method and determining a plurality of power supply prediction data;
the target service life prediction module is used for determining target service life information of the power supply to be predicted according to the power supply prediction data and the power supply prediction model;
the power supply prediction model is obtained by training a pre-established initial prediction model based on power supply training data.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of predicting the lifetime of a power supply of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of predicting the lifetime of a power supply of any one of claims 1-7.
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| CN116882303A (en) * | 2023-09-06 | 2023-10-13 | 深圳市联明电源有限公司 | Laser power supply life prediction method, system and storage medium |
| CN116882303B (en) * | 2023-09-06 | 2023-12-22 | 深圳市联明电源有限公司 | Laser power supply life prediction method, system and storage medium |
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