CN115825752A - Method and device for predicting residual life of ship power battery and electronic equipment - Google Patents
Method and device for predicting residual life of ship power battery and electronic equipment Download PDFInfo
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Abstract
The invention provides a method and a device for predicting the residual life of a ship power battery and electronic equipment, wherein the method comprises the following steps: acquiring current operation data of a ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery; inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training. The method predicts the battery life of the current operation data of the ship power battery through a pre-trained battery residual life prediction model, and the type of the battery residual life prediction model is a one-dimensional convolution neural network, so that the residual life of the ship power battery can be accurately predicted.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for predicting the residual life of a ship power battery and electronic equipment.
Background
At present, the demand of ships engaged in transportation of hazardous chemicals is increased year by year, and if the ships collide or leak in the transportation process, serious disaster accidents such as fire, explosion, environmental pollution and the like can be caused. The dangerous chemical emergency rescue ship is very important for ships engaged in dangerous chemical transportation, and the residual service life of the power battery of the dangerous chemical rescue ship influences the performance of the rescue ship. The traditional method for remaining life of the power battery of the hazardous chemical emergency rescue ship mainly comprises an experience-based method, but the method has obvious defects: the method based on experience is low in precision, depends on experience knowledge and is only suitable for specific technical scenes.
Therefore, the existing method for predicting the residual life of the power battery of the ship has the problem of low precision.
Disclosure of Invention
The invention aims to provide a method and a device for predicting the residual life of a ship power battery and electronic equipment, so as to improve the prediction accuracy of the residual life of the ship power battery.
In a first aspect, an embodiment of the present invention provides a method for predicting a remaining life of a power battery of a ship, where the method includes: acquiring current operation data of a ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery; inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the pool remaining life prediction model is obtained by training in the following manner: acquiring indirect health factor data of a ship power battery and ship power battery capacity corresponding to the indirect health factor data; the indirect health factor data comprises the average discharge voltage, the equal-time discharge voltage, the equal-pressure drop discharge time, the equal-pressure rise charge time and the discharge voltage lowest point moment of the ship power battery; and training a preset initial one-dimensional convolution neural network by taking the indirect health factor data of the ship power battery as input and the ship power battery capacity corresponding to the indirect health factor data as output until a preset training requirement is met to obtain a trained residual life prediction model of the battery.
With reference to the first possible implementation manner of the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where the step of training a preset initial one-dimensional convolutional neural network by using indirect health factor data of the ship power battery as an input and ship power battery capacity corresponding to the indirect health factor data as an output until a preset training requirement is met to obtain a trained battery remaining life prediction model includes: preprocessing the indirect health factor data of the ship power battery to obtain indirect health factor preprocessing data; and taking the indirect health factor preprocessing data as input, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output, and training a preset initial one-dimensional convolution neural network until a preset training requirement is met to obtain a trained battery residual life prediction model.
With reference to the second possible implementation manner of the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the step of preprocessing the indirect health factor data of the ship power battery to obtain preprocessed indirect health factor data includes: carrying out data cleaning on the indirect health factor data of the ship power battery based on a first preset rule to obtain indirect health factor data; and rejecting abnormal values in the indirect health factor data based on a second preset rule to obtain indirect health factor preprocessing data.
With reference to the second possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the structure of the initial one-dimensional convolutional neural network includes an input layer, a feature extraction layer, and an output layer; taking the indirect health factor preprocessing data as input, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output, training a preset initial one-dimensional convolution neural network until a preset training requirement is met, and obtaining a trained battery residual life prediction model, wherein the method comprises the following steps of: inputting the indirect health factor preprocessing data into the feature extraction layer through the input layer, and extracting feature data in the indirect health factor preprocessing data; taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as the output data of the output layer; and training the initial one-dimensional convolution neural network according to the characteristic data and the output data until a preset training requirement is met, and obtaining a trained battery residual life prediction model.
With reference to the third possible implementation manner of the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the step of removing abnormal values in the indirect health factor data based on a second preset rule to obtain indirect health factor preprocessed data includes: and based on a second preset rule, removing abnormal values in the indirect health factor data through a boxplots function to obtain indirect health factor preprocessing data.
With reference to the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where after the step of acquiring current operation data of the ship power battery, the method further includes: preprocessing the current operation data based on a third preset rule to obtain data to be predicted; inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery, wherein the step comprises the following steps of: and inputting the data to be predicted into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery.
In a second aspect, an embodiment of the present invention provides an apparatus for predicting a remaining life of a power battery of a ship, where the apparatus includes: the data acquisition module is used for acquiring the current operation data of the ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery; the battery life prediction module is used for inputting the current operation data into a pre-trained battery residual life prediction model and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training.
In a third aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes a processor and a memory, where the memory stores machine-executable instructions executable by the processor, and the processor executes the machine-executable instructions to implement the method for predicting the remaining life of a ship power battery according to any one of the first to sixth possible embodiments of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium, where the computer storage medium stores a computer program, where the computer program includes program instructions, and the program instructions, when executed by a processor, cause the processor to execute the method for predicting the remaining life of a ship power battery according to any one of the first to sixth possible embodiments of the first aspect.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a method and a device for predicting the residual life of a ship power battery and electronic equipment, wherein the method comprises the following steps: acquiring current operation data of a ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery; inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training. The method predicts the battery life of the current operation data of the ship power battery through a pre-trained battery residual life prediction model, and the type of the battery residual life prediction model is a one-dimensional convolution neural network, so that the residual life of the ship power battery can be accurately predicted.
Additional features and advantages of the present disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the above-described techniques of the present disclosure.
In order to make the aforementioned objects, features and advantages of the present disclosure more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a method for predicting the remaining life of a power battery of a ship according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a training method of a battery remaining life prediction model according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for predicting the remaining life of a power battery of a ship according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Icon: 31-a data acquisition module; 32-a battery life prediction module; 41-a memory; 42-a processor; 43-bus; 44-communication interface.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, the demand of ships engaged in transportation of hazardous chemicals is increased year by year, and if the ships collide or leak in the transportation process, serious disaster accidents such as fire, explosion, environmental pollution and the like can be caused. The dangerous chemical emergency rescue ship is very important for ships engaged in dangerous chemical transportation, and the residual service life of the power battery of the dangerous chemical rescue ship influences the performance of the rescue ship. The traditional method for remaining life of the power battery of the hazardous chemical emergency rescue ship mainly comprises an experience-based method, but the method has obvious defects: the method based on experience is low in precision, depends on experience knowledge and is only suitable for specific technical scenes. Therefore, the existing method for predicting the residual life of the power battery of the ship has the problem of low precision.
Based on this, the embodiment of the invention provides a method and a device for predicting the residual life of a ship power battery, and an electronic device. In order to facilitate understanding of the embodiment of the present invention, a method for predicting the remaining life of a power battery of a ship, disclosed in the embodiment of the present invention, is first described in detail.
Example 1
Fig. 1 is a schematic flow chart of a method for predicting the remaining life of a power battery of a ship according to an embodiment of the present invention. As seen in fig. 1, the method comprises the steps of:
step S101: acquiring current operation data of a ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery.
In this embodiment, the current operation data includes: the average discharge voltage, the equal-time discharge voltage, the equal-pressure drop discharge time, the equal-pressure rise charge time and the discharge voltage minimum point time of the current ship power battery.
In one embodiment, after step S101, the current operation data is preprocessed based on a third preset rule to obtain data to be predicted.
Step S102: inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training.
In actual operation, the battery residual life prediction model is obtained based on indirect health factor data of a ship power battery and ship power battery capacity training corresponding to the indirect health factor data; the indirect health factor data comprises average discharge voltage, equal-time discharge voltage, equal-pressure drop discharge time, equal-pressure rise charge time and discharge voltage lowest point time of the historical ship power battery. Furthermore, the residual life of the power battery of the ship can be predicted through the residual life prediction model of the battery.
In one embodiment, the step S102 includes: and inputting the data to be predicted into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery.
The embodiment of the invention provides a method for predicting the residual life of a ship power battery, which comprises the following steps: acquiring current operation data of a ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery; inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training. The method predicts the battery life of the current operation data of the ship power battery through a pre-trained battery residual life prediction model, and the type of the battery residual life prediction model is a one-dimensional convolution neural network, so that the residual life of the ship power battery can be accurately predicted.
Example 2
On the basis of the method shown in fig. 1, the invention further provides another method for predicting the remaining life of the power battery of the ship, which focuses on describing the training process of the battery remaining life prediction model in step S102 in embodiment 1. As shown in fig. 2, fig. 2 is a schematic flow chart of a training method of a battery remaining life prediction model according to an embodiment of the present invention, and as shown in fig. 2, the battery remaining life prediction model is obtained through the following training steps:
step S201: acquiring indirect health factor data of a ship power battery and ship power battery capacity corresponding to the indirect health factor data; the indirect health factor data comprises the average discharge voltage, the equal-time discharge voltage, the equal-pressure drop discharge time, the equal-pressure rise charge time and the discharge voltage lowest point moment of the ship power battery.
Step S202: and taking the indirect health factor data of the ship power battery as input, taking the capacity of the ship power battery corresponding to the indirect health factor data as output, and training a preset initial one-dimensional convolution neural network until the preset training requirement is met to obtain a trained residual life prediction model of the battery.
In one embodiment, the step S202 includes the following steps A1-A2:
step A1: and preprocessing the indirect health factor data of the ship power battery to obtain the indirect health factor preprocessing data.
Here, the above step A1 includes: firstly, carrying out data cleaning on indirect health factor data of the ship power battery based on a first preset rule to obtain the indirect health factor data. And secondly, rejecting abnormal values in the indirect health factor data based on a second preset rule to obtain indirect health factor preprocessing data.
Here, the step of removing abnormal values in the indirect health factor data based on a second preset rule to obtain indirect health factor pre-processed data includes: based on a second preset rule, removing abnormal values in the indirect health factor data through a boxplots function to obtain indirect health factor preprocessing data.
Step A2: and taking the indirect health factor preprocessing data as input, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output, and training a preset initial one-dimensional convolution neural network until a preset training requirement is met to obtain a trained battery residual life prediction model.
In actual operation, the structure of the initial one-dimensional convolutional neural network includes an input layer, a feature extraction layer, and an output layer. The step A2 includes: firstly, inputting the indirect health factor preprocessing data into the feature extraction layer through the input layer, and extracting feature data in the indirect health factor preprocessing data. And then, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as the output data of the output layer. And finally, training the initial one-dimensional convolution neural network according to the characteristic data and the output data until a preset training requirement is met, and obtaining a trained residual life prediction model of the battery.
The embodiment of the invention provides a method for predicting the residual life of a ship power battery, which comprises the following steps: acquiring current operation data of a ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery; inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training. The battery residual life prediction model is obtained by training in the following way: firstly, acquiring indirect health factor data of a ship power battery and ship power battery capacity corresponding to the indirect health factor data; the indirect health factor data comprise the average discharge voltage, the equal-time discharge voltage, the equal-pressure drop discharge time, the equal-pressure rise charge time and the discharge voltage lowest point moment of the ship power battery. And then, taking the indirect health factor data of the ship power battery as input, taking the capacity of the ship power battery corresponding to the indirect health factor data as output, and training a preset initial one-dimensional convolution neural network until the preset training requirement is met to obtain a trained residual life prediction model of the battery. According to the method, the initial one-dimensional convolution neural network is trained through the indirect health factor data of the ship power battery and the capacity of the ship power battery corresponding to the indirect health factor data, and the prediction precision of the service life of the ship power battery is further improved.
Example 3
The embodiment of the invention also provides a device for predicting the residual life of the power battery of the ship, and as shown in fig. 3, the embodiment of the invention provides a schematic structural diagram of the device for predicting the residual life of the power battery of the ship. As can be seen from fig. 3, the apparatus comprises:
the data acquisition module 31 is used for acquiring current operation data of the ship power battery; wherein the current operation data is used for indicating the current operation state of the ship power battery.
A battery life prediction module 32, configured to input the current operation data into a pre-trained battery remaining life prediction model, and output a predicted remaining life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training.
The data acquiring module 31 is connected to the battery life predicting module 32.
In one embodiment, the apparatus further includes: and a model building module. The model building module is used for obtaining indirect health factor data of a ship power battery and ship power battery capacity corresponding to the indirect health factor data; the indirect health factor data comprises the average discharge voltage, the equal-time discharge voltage, the equal-pressure drop discharge time, the equal-pressure rise charge time and the discharge voltage lowest point moment of the ship power battery; and taking the indirect health factor data of the ship power battery as input, taking the capacity of the ship power battery corresponding to the indirect health factor data as output, and training a preset initial one-dimensional convolution neural network until the preset training requirement is met to obtain a trained residual life prediction model of the battery.
In one embodiment, the battery life prediction module 32 is further configured to preprocess the indirect health factor data of the ship power battery to obtain indirect health factor preprocessing data; and taking the indirect health factor preprocessing data as input, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output, and training a preset initial one-dimensional convolution neural network until a preset training requirement is met to obtain a trained battery residual life prediction model.
In one embodiment, the model building module is further configured to perform data cleaning on indirect health factor data of the ship power battery based on a first preset rule to obtain indirect health factor data; and rejecting abnormal values in the indirect health factor data based on a second preset rule to obtain indirect health factor preprocessing data.
In one embodiment, the structure of the initial one-dimensional convolutional neural network includes an input layer, a feature extraction layer, and an output layer; the model building module is also used for inputting the indirect health factor preprocessing data into the feature extraction layer through the input layer and extracting feature data in the indirect health factor preprocessing data; taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as the output data of the output layer; and training the initial one-dimensional convolution neural network according to the characteristic data and the output data until a preset training requirement is met, and obtaining a trained residual life prediction model of the battery.
In one embodiment, the model building module is further configured to remove abnormal values in the indirect health factor data by a boxplots method based on a second preset rule to obtain the indirect health factor preprocessing data.
In one embodiment, the data obtaining module 31 is further configured to perform preprocessing on the current operating data based on a third preset rule to obtain data to be predicted; the battery life prediction module 32 is further configured to input the data to be predicted into a pre-trained battery remaining life prediction model, and output the predicted remaining life of the ship power battery.
The device for predicting the residual life of the ship power battery provided by the embodiment of the invention has the same technical characteristics as the method for predicting the residual life of the ship power battery provided by the embodiment, so that the same technical problems can be solved, and the same technical effects are achieved. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the apparatus described above may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Example 4
The embodiment provides an electronic device, which comprises a processor and a memory, wherein the memory stores computer executable instructions capable of being executed by the processor, and the processor executes the computer executable instructions to realize the steps of the method for predicting the residual life of the power battery of the ship.
Referring to fig. 4, a schematic structural diagram of an electronic device is shown, where the electronic device includes: the memory 41 and the processor 42, wherein the memory stores a computer program capable of running on the processor 42, and the processor implements the steps provided by the method for predicting the residual life of the ship power battery when executing the computer program.
As shown in fig. 4, the apparatus further includes: a bus 43 and a communication interface 44, the processor 42, the communication interface 44 and the memory 41 being connected by the bus 43; the processor 42 is for executing executable modules, such as computer programs, stored in the memory 41.
The Memory 41 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 44 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 43 may be an ISA bus, a PCI bus, an EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
The memory 41 is used for storing a program, and the processor 42 executes the program after receiving an execution instruction, and the method executed by the prediction apparatus for revealing the remaining life of the power battery of the ship according to any of the foregoing embodiments of the present invention may be applied to the processor 42, or implemented by the processor 42. The processor 42 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by instructions in the form of hardware integrated logic circuits or software in the processor 42. The Processor 42 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 41, and a processor 42 reads information in the memory 41 and performs the steps of the method in combination with hardware thereof.
Further, an embodiment of the present invention further provides a computer storage medium, where a computer program is stored, where the computer program includes program instructions, and the program instructions, when executed by the processor 42, cause the processor 42 to execute a method for predicting the remaining life of the ship power battery.
The device for predicting the residual life of the ship power battery and the verification device of the method for predicting the residual life of the ship power battery provided by the embodiment of the invention have the same technical characteristics, so that the same technical problems can be solved, and the same technical effect can be achieved.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Claims (10)
1. A method for predicting the residual life of a ship power battery is characterized by comprising the following steps:
acquiring current operation data of a ship power battery; wherein the current operating data is indicative of a current operating state of the marine power battery;
inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training.
2. The method for predicting the residual life of the power battery of the ship according to claim 1, wherein the pool residual life prediction model is trained by the following steps:
acquiring indirect health factor data of a ship power battery and ship power battery capacity corresponding to the indirect health factor data; wherein the indirect health factor data comprises an average discharge voltage, an equal time discharge voltage, an equal voltage drop discharge time, an equal voltage rise charge time, and a discharge voltage minimum point time of the marine power battery;
and taking the indirect health factor data of the ship power battery as input, taking the ship power battery capacity corresponding to the indirect health factor data as output, training a preset initial one-dimensional convolution neural network until a preset training requirement is met, and obtaining a trained residual life prediction model of the battery.
3. The method for predicting the residual life of the ship power battery according to claim 2, wherein the step of training a preset initial one-dimensional convolutional neural network by taking indirect health factor data of the ship power battery as input and ship power battery capacity corresponding to the indirect health factor data as output until a preset training requirement is met to obtain a trained model for predicting the residual life of the battery comprises the following steps of:
preprocessing the indirect health factor data of the ship power battery to obtain indirect health factor preprocessing data;
and taking the indirect health factor preprocessing data as input, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output, and training a preset initial one-dimensional convolution neural network until the preset training requirement is met to obtain a trained battery residual life prediction model.
4. The method for predicting the residual life of the ship power battery according to claim 3, wherein the step of preprocessing the indirect health factor data of the ship power battery to obtain the indirect health factor preprocessed data comprises the following steps:
carrying out data cleaning on indirect health factor data of the ship power battery based on a first preset rule to obtain indirect health factor data;
and rejecting abnormal values in the indirect health factor data based on a second preset rule to obtain indirect health factor preprocessing data.
5. The method for predicting the residual life of the marine power battery according to claim 3, wherein the structure of the initial one-dimensional convolutional neural network comprises an input layer, a feature extraction layer and an output layer;
taking the indirect health factor preprocessing data as input, taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output, training a preset initial one-dimensional convolution neural network until a preset training requirement is met, and obtaining a trained battery residual life prediction model, wherein the method comprises the following steps of:
inputting the indirect health factor preprocessing data into the feature extraction layer through the input layer, and extracting feature data in the indirect health factor preprocessing data;
taking the ship power battery capacity corresponding to the indirect health factor preprocessing data as output data of the output layer;
and training the initial one-dimensional convolution neural network according to the characteristic data and the output data until a preset training requirement is met, and obtaining a trained battery residual life prediction model.
6. The method for predicting the residual life of the power battery of the ship according to claim 4, wherein the step of removing abnormal values in the indirect health factor data based on a second preset rule to obtain the indirect health factor preprocessing data comprises the following steps:
based on a second preset rule, removing abnormal values in the indirect health factor data through a boxplots method to obtain indirect health factor preprocessing data.
7. The method for predicting the remaining life of a marine power battery as claimed in claim 1, wherein after the step of obtaining current operation data of the marine power battery, the method further comprises:
preprocessing the current operation data based on a third preset rule to obtain data to be predicted;
inputting the current operation data into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery, wherein the step comprises the following steps:
and inputting the data to be predicted into a pre-trained residual life prediction model of the battery, and outputting the predicted residual life of the ship power battery.
8. A prediction device of a residual life of a ship power battery is characterized by comprising:
the data acquisition module is used for acquiring the current operation data of the ship power battery; wherein the current operating data is indicative of a current operating state of the marine power battery;
the battery life prediction module is used for inputting the current operation data into a pre-trained battery residual life prediction model and outputting the predicted residual life of the ship power battery; the battery residual life prediction model is obtained based on one-dimensional convolutional neural network training.
9. An electronic device, comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of predicting the remaining life of a marine vessel power battery according to any one of claims 1 to 7.
10. A computer storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method of predicting the remaining life of a marine vessel power battery according to any one of claims 1 to 7.
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