CN115544903A - Battery data processing method, device and equipment based on big data and storage medium - Google Patents
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Abstract
The invention relates to the field of big data, and discloses a battery data processing method, device, equipment and storage medium based on big data, which are used for improving the accuracy of battery analysis. The method comprises the following steps: performing data screening on the initial operation data corresponding to each first battery to obtain standard operation data, and storing the standard operation data corresponding to each first battery to a first data source; constructing a battery analysis model set according to a first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models; selecting a second battery from the plurality of first batteries, acquiring target operation data from a first data source, and acquiring battery parameter data of the second battery from a preset second data source; and inputting the battery parameter data and the target operation data into a battery analysis model set, and carrying out battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result.
Description
Technical Field
The invention relates to the field of big data, in particular to a battery data processing method, device and equipment based on big data and a storage medium.
Background
The electric two-wheeled vehicle brings great convenience to the daily life of the common people. The lithium ion battery is used as a core component, the charge state information of the battery directly determines the endurance condition of the electric two-wheel vehicle, once the estimation error is made, the fault occurs to delay the journey, and the safety accident occurs due to the interruption of power. The existing state of charge prediction is completed by a management system of a battery, and the problem of continuous increase of errors is easy to occur because the working condition of the battery is not fixed. The battery is a chemical product, and many features are process quantities, and related to past use history data of the battery, and the understanding of the history of the battery is extremely important for the estimation of the residual capacity of the battery.
In the existing scheme, because the electromotive force of the battery has a hysteresis effect, the electromotive force of the battery at a certain moment depends on the charging and discharging operations for a long time before, and if the historical data are insufficient, the current electromotive force is difficult to evaluate, so that the state of charge of the battery is difficult to evaluate through the working voltage or the open-circuit voltage, namely, the accuracy of the existing scheme is low.
Disclosure of Invention
The invention provides a battery data processing method, a battery data processing device, battery data processing equipment and a battery data processing storage medium based on big data, which are used for improving the accuracy of battery analysis.
The invention provides a battery data processing method based on big data in a first aspect, which comprises the following steps: the method comprises the steps of obtaining operation data of a plurality of first batteries, obtaining initial operation data corresponding to each first battery, and sending the initial operation data corresponding to each first battery to a preset battery management platform; performing data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and storing the standard operation data corresponding to each first battery to a first data source; constructing a battery analysis model set according to the first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models; selecting a battery to be analyzed from the plurality of first batteries as a second battery, acquiring target operation data corresponding to the second battery from the first data source, and acquiring battery parameter data of the second battery from a preset second data source; inputting the battery parameter data and the target operation data into the battery analysis model set, and performing battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result; and sending the target battery state analysis result to the battery management platform.
According to the battery data processing method based on big data, provided by the invention, the running data of the plurality of first batteries is subjected to data screening, and then the battery analysis model set is constructed to carry out the calculation and analysis of the battery state, so that the analysis of the analysis result of the target battery state is more accurate.
With reference to the first aspect, in a first implementation manner of the first aspect of the present invention, the performing data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and storing the standard operation data corresponding to each first battery in a first data source includes: performing data cleaning on the initial operation data corresponding to each first battery to obtain cleaned initial operation data; carrying out repeated value removal and missing value filling on the cleaned initial operation data to obtain preprocessed initial operation data; performing characteristic screening on the preprocessed initial operation data to obtain standard operation data corresponding to each first battery; and storing the standard operation data corresponding to each first battery to a first data source.
In the scheme, the server performs data screening, data cleaning, repeated value clearing, missing value filling and feature screening on the initial operation data respectively, so that the accuracy of the standard operation data generated in the step is higher, and the accuracy of battery state analysis can be further improved.
With reference to the first aspect, in a second implementation manner of the first aspect of the present invention, the building a battery analysis model set according to the first data source, wherein the battery analysis model set includes a plurality of sub-battery analysis models, includes: extracting a plurality of training data sets from the first data source and acquiring a plurality of training models, wherein the training data sets correspond to the training models one to one; performing model training on each training model according to each training data set to obtain a sub-battery analysis model corresponding to each training model; and carrying out model integration on the plurality of sub-battery analysis models to generate a battery analysis model set.
In the scheme, the server firstly conducts model training on each training model according to each training data set to obtain the sub-battery analysis model corresponding to each training model, and then conducts model integration on the plurality of sub-battery analysis models, so that the plurality of sub-battery analysis models can be integrated into one battery analysis model set, and further the calculation accuracy of the battery analysis result is improved.
With reference to the first aspect, in a third implementation manner of the first aspect of the present invention, the inputting the battery parameter data and the target operation data into the battery analysis model set, and performing battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result includes: inputting the battery parameter data and the target operating data into the battery analysis model set; performing model matching on the battery parameter data and the battery analysis model set to obtain a first sub-battery analysis model, wherein the battery parameter data comprises: the battery cell factory internal resistance, the battery cell factory capacity and the battery cell factory material; carrying out data classification on the target operation data to obtain first operation data and second operation data; performing model matching on the first operation data to obtain a second sub-battery analysis model, and performing model matching on the second operation data to obtain a third sub-battery analysis model; inputting the battery parameter data into the first sub-analysis model to predict the standard state of the battery, so as to obtain standard state data; inputting the first operation data and the standard state data into the second sub-battery analysis model to analyze the battery charging and discharging habits to obtain a charging and discharging analysis result; inputting the second operation data and the standard state data into the third sub-battery analysis model to perform user use habit analysis to obtain a use habit analysis result; and generating a target battery state analysis result corresponding to the second battery according to the charging and discharging analysis result and the use habit analysis result.
According to the scheme, the battery parameter data are subjected to model matching to generate standard state data for standard comparison, then the target operation data are subjected to data classification, then the corresponding sub-battery analysis models are adopted for analysis, and finally the analysis results are subjected to result fusion, so that the accuracy of battery state analysis is improved.
With reference to the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect of the present invention, the inputting the first operation data and the standard state data into the second sub-battery analysis model to perform battery charging and discharging habit analysis to obtain a charging and discharging analysis result includes: performing feature extraction on the first operation data to obtain a plurality of first feature data corresponding to the first operation data, wherein the plurality of first feature data include: charging and discharging cycle segments, charging duration, charging times and kilometers of weekly driving; generating first input data according to the plurality of first characteristic data and the standard state data; and inputting the first input data into the second sub-battery analysis model, and performing battery charging and discharging habit analysis on the first input data through the second sub-battery analysis model to obtain a charging and discharging analysis result.
With reference to the third implementation manner of the first aspect, in a fifth implementation manner of the first aspect of the present invention, the inputting the second operation data and the standard state data into the third sub-battery analysis model to perform a usage habit analysis of a user to obtain a usage habit analysis result includes: performing feature extraction on the second operating data to obtain a plurality of second feature data corresponding to the second operating data, wherein the plurality of second feature data include: charge start time, energy consumption, rapid acceleration and mileage; generating second input data according to the plurality of second characteristic data and the standard state data; and inputting the second input data into the third sub-battery analysis model, and carrying out user use habit analysis on the second input data through the third sub-battery analysis model to obtain a use habit analysis result.
With reference to the first aspect, in a sixth implementation manner of the first aspect of the present invention, the big-data-based battery data processing method further includes: matching a target battery maintenance scheme from a plurality of candidate battery maintenance schemes according to the target battery state analysis result; and monitoring the full life cycle of the target battery according to the target battery maintenance scheme.
The second aspect of the present invention provides a big-data-based battery data processing apparatus, including:
the acquisition module is used for acquiring the operation data of the plurality of first batteries, acquiring initial operation data corresponding to each first battery, and sending the initial operation data corresponding to each first battery to a preset battery management platform;
the screening module is used for screening the initial operating data corresponding to each first battery to obtain standard operating data corresponding to each first battery, and storing the standard operating data corresponding to each first battery to a first data source;
a building module, configured to build a battery analysis model set according to the first data source, where the battery analysis model set includes a plurality of sub-battery analysis models;
the selection module is used for selecting a battery to be analyzed from the plurality of first batteries as a second battery, acquiring target operation data corresponding to the second battery from the first data source, and acquiring battery parameter data of the second battery from a preset second data source;
the analysis module is used for inputting the battery parameter data and the target operation data into the battery analysis model set and analyzing the battery state of the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result;
and the sending module is used for sending the target battery state analysis result to the battery management platform.
With reference to the second aspect, in a first embodiment of the second aspect of the present invention, the screening module is specifically configured to: performing data cleaning on the initial operation data corresponding to each first battery to obtain cleaned initial operation data; carrying out repeated value removal and missing value filling on the cleaned initial operation data to obtain preprocessed initial operation data; performing characteristic screening on the preprocessed initial operation data to obtain standard operation data corresponding to each first battery; and storing the standard operation data corresponding to each first battery to a first data source.
With reference to the second aspect, in a second embodiment of the second aspect of the present invention, the building module is specifically configured to: extracting a plurality of training data sets from the first data source and acquiring a plurality of training models, wherein the training data sets correspond to the training models one to one; performing model training on each training model according to each training data set to obtain a sub-battery analysis model corresponding to each training model; and carrying out model integration on the plurality of sub-battery analysis models to generate a battery analysis model set.
With reference to the second aspect, in a third implementation of the second aspect of the present invention, the analysis module further includes:
an input unit for inputting the battery parameter data and the target operation data into the battery analysis model set; performing model matching on the battery parameter data and the battery analysis model set to obtain a first sub-battery analysis model, wherein the battery parameter data comprises: the battery cell factory internal resistance, the battery cell factory capacity and the battery cell factory material; performing data classification on the target operation data to obtain first operation data and second operation data; performing model matching on the first operation data to obtain a second sub-battery analysis model, and performing model matching on the second operation data to obtain a third sub-battery analysis model; inputting the battery parameter data into the first sub-analysis model to predict the standard state of the battery, so as to obtain standard state data;
the first analysis unit is used for inputting the first operation data and the standard state data into the second sub-battery analysis model to analyze the battery charging and discharging habits, and obtaining a charging and discharging analysis result;
the second analysis unit is used for inputting the second operation data and the standard state data into the third sub-battery analysis model to perform user use habit analysis to obtain a use habit analysis result;
and the generating unit is used for generating a target battery state analysis result corresponding to the second battery according to the charging and discharging analysis result and the using habit analysis result.
With reference to the third embodiment of the second aspect, in a fourth embodiment of the second aspect of the present invention, the first analysis unit is specifically configured to: performing feature extraction on the first operation data to obtain a plurality of first feature data corresponding to the first operation data, wherein the plurality of first feature data include: charging and discharging cycle segments, charging time, charging times and weekly driving kilometers; generating first input data according to the plurality of first characteristic data and the standard state data; and inputting the first input data into the second sub-battery analysis model, and carrying out battery charging and discharging habit analysis on the first input data through the second sub-battery analysis model to obtain a charging and discharging analysis result.
With reference to the third embodiment of the second aspect, in a fifth embodiment of the second aspect of the present invention, the second analysis unit is specifically configured to: performing feature extraction on the second operating data to obtain a plurality of second feature data corresponding to the second operating data, wherein the plurality of second feature data include: charge start time, energy consumption, rapid acceleration and mileage; generating second input data according to the plurality of second characteristic data and the standard state data; and inputting the second input data into the third sub-battery analysis model, and performing user use habit analysis on the second input data through the third sub-battery analysis model to obtain a use habit analysis result.
With reference to the second aspect, in a sixth embodiment of the second aspect of the present invention, the big-data based battery data processing apparatus further includes: the matching module is used for matching a target battery maintenance scheme from a plurality of candidate battery maintenance schemes according to the target battery state analysis result; and monitoring the full life cycle of the target battery according to the target battery maintenance scheme.
A third aspect of the present invention provides a big-data-based battery data processing apparatus, comprising: a memory and at least one processor, the memory having instructions stored therein; the at least one processor calls the instructions in the memory to cause the big-data based battery data processing device to execute the big-data based battery data processing method.
A fourth aspect of the present invention provides a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to execute the above-described big-data-based battery data processing method.
In the technical scheme provided by the invention, data screening is carried out on the initial operation data corresponding to each first battery to obtain standard operation data, and the standard operation data corresponding to each first battery is stored to a first data source; constructing a battery analysis model set according to a first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models; selecting a second battery from the plurality of first batteries, acquiring target operation data from a first data source, and acquiring battery parameter data of the second battery from a preset second data source; the method comprises the steps of inputting battery parameter data and target operation data into a battery analysis model set, and analyzing the battery state of a second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result.
Drawings
FIG. 1 is a diagram of an embodiment of a big data-based battery data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a battery status analysis process according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a battery charging/discharging habit analyzing process according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a process of analyzing a user's usage habits according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of a big data based battery data processing apparatus according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of another embodiment of a big data based battery data processing apparatus according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an embodiment of a big data based battery data processing device in the embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a battery data processing method, device and equipment based on big data and a storage medium, which are used for improving the accuracy of battery analysis. The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," or "having," and any variations thereof, are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
For convenience of understanding, a specific flow of an embodiment of the present invention is described below, and referring to fig. 1, an embodiment of a big data based battery data processing method according to an embodiment of the present invention includes:
s101, obtaining operation data of a plurality of first batteries, obtaining initial operation data corresponding to each first battery, and sending the initial operation data corresponding to each first battery to a preset battery management platform;
it is understood that the execution subject of the present invention may be a battery data processing device based on big data, and may also be a terminal or a server, which is not limited herein. The embodiment of the present invention is described by taking a server as an execution subject.
Specifically, in the solution of the present application, the operation data mainly includes data information such as ohmic resistance, a peak value of a capacity increment, a inflection point of a differential voltage, an equal-voltage charge-discharge time difference, an equal-time charge-discharge voltage difference, and battery charge-discharge times of the battery, where the server obtains operation data of the plurality of first batteries, obtains a change curve between a battery voltage and a charge amount during a battery charging process from the operation data of the plurality of first batteries, performs polynomial fitting on the change curve between the battery voltage and the charge amount to obtain a polynomial corresponding to the change curve between the battery voltage and the charge amount, derives the obtained polynomial to obtain an IC curve representing the capacity increment of the battery, and finally obtains initial operation data corresponding to each first battery according to the IC curve, and sends the initial operation data corresponding to each first battery to a preset battery management platform.
S102, performing data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and storing the standard operation data corresponding to each first battery to a first data source;
specifically, the server performs data screening on initial operation data corresponding to each first battery, where the server performs data screening according to data rules in a data cleaning rule base, and it should be noted that the data cleaning rule base is established for data diversity, and includes a merged cleaning rule, where the merged cleaning rule is used to process similar repeated data and a missing data processing rule, and further, the server obtains standard operation data corresponding to each first battery, and stores the standard operation data corresponding to each first battery in a first data source.
S103, constructing a battery analysis model set according to the first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models;
it should be noted that the server preselects model parameters for battery analysis, sets and optimizes parameter value ranges, constructs different battery analysis models, updates model weights of different models by using a bayesian multi-model set in combination with historical operation monitoring data and prediction data of different batteries, and finally constructs a battery analysis model set according to the updated model weights of different models.
S104, selecting a battery to be analyzed from the plurality of first batteries as a second battery, acquiring target operation data corresponding to the second battery from a first data source, and acquiring battery parameter data of the second battery from a preset second data source;
specifically, the server selects a battery to be analyzed from the multiple first batteries as a second battery, and further, it should be noted that operation data and parameter data corresponding to the battery to be analyzed are respectively stored in different data sources, wherein after the server determines the second battery, the server performs identifier confirmation on the second battery, determines a battery identifier corresponding to the second battery, and determines a corresponding first data source and a second data source according to the battery identifier, and further, the server acquires target operation data corresponding to the second battery from the first data source, and acquires battery parameter data of the second battery from a preset second data source.
S105, inputting the battery parameter data and the target operation data into a battery analysis model set, and performing battery state analysis on a second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result;
specifically, the server inputs battery parameter data and target operation data into a battery analysis model set, further, the server performs analysis model matching through the battery parameter data and the target operation data to determine a plurality of corresponding sub-battery analysis models, further, the server performs battery state analysis on the battery parameter data and the target operation data through the plurality of sub-battery analysis models respectively to obtain a charging and discharging analysis result and a use habit analysis result, and finally the server determines a corresponding target battery state analysis result according to the charging and discharging analysis result and the use habit analysis result.
And S106, sending the target battery state analysis result to a battery management platform.
In the embodiment of the invention, the initial operation data corresponding to each first battery is subjected to data screening to obtain standard operation data, and the standard operation data corresponding to each first battery is stored to a first data source; constructing a battery analysis model set according to a first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models; selecting a second battery from the plurality of first batteries, acquiring target operation data from a first data source, and acquiring battery parameter data of the second battery from a preset second data source; the method comprises the steps of inputting battery parameter data and target operation data into a battery analysis model set, and analyzing the battery state of a second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result.
In a specific embodiment, the process of executing step S102 may specifically include the following steps:
(1) Performing data cleaning on the initial operation data corresponding to each first battery to obtain cleaned initial operation data;
(2) Carrying out repeated value removal and missing value filling on the cleaned initial operation data to obtain preprocessed initial operation data;
(3) Performing characteristic screening on the preprocessed initial operation data to obtain standard operation data corresponding to each first battery;
(4) And storing the standard operation data corresponding to each first battery to a first data source.
Specifically, the server performs data cleaning on initial operation data corresponding to each first battery to obtain cleaned initial operation data, wherein the integrity of the obtained initial operation data is detected according to a predetermined data cleaning rule base to obtain the cleaned initial operation data, and further, the server performs repeated value removal and missing value filling on the cleaned initial operation data to obtain the preprocessed initial operation data, obtains the preprocessed initial operation data, performs feature screening processing on the basis of the preprocessed initial operation data, screens out the preprocessed initial operation data of which the importance meets a preset condition, uses the initial operation data as interesting feature data to obtain standard operation data corresponding to each first battery, and stores the standard operation data corresponding to each first battery in a first data source. According to the scheme, the accuracy of missing data prediction in the initial operation data can be improved, and the missing data can be filled quickly.
In a specific embodiment, the process of executing step S103 may specifically include the following steps:
(1) Extracting a plurality of training data sets from a first data source and acquiring a plurality of training models, wherein the training data sets correspond to the training models one to one;
(2) Performing model training on each training model according to each training data set to obtain a sub-battery analysis model corresponding to each training model;
(3) And carrying out model integration on the plurality of sub-battery analysis models to generate a battery analysis model set.
Specifically, the server performs rotation self-supervision processing on a plurality of first training data sets to form a plurality of processed first training data sets, determines a plurality of first training data sets corresponding to a second training data set, determines gradient parameters matched with the plurality of first training data sets through a training data processing network according to the plurality of first training data sets, updates the plurality of first training data sets, and determines a target training data set matched with a target neural network model, so that the server performs model training on each training model according to the training data sets to obtain a sub-battery analysis model corresponding to each training model, performs model integration on the plurality of sub-battery analysis models, and generates a battery analysis model set.
In a specific embodiment, as shown in fig. 2, the process of executing step S105 may specifically include the following steps:
s201, inputting battery parameter data and target operation data into a battery analysis model set;
s202, carrying out model matching on the battery parameter data and the battery analysis model set to obtain a first sub-battery analysis model, wherein the battery parameter data comprise: the battery cell factory internal resistance, the battery cell factory capacity and the battery cell factory material;
s203, carrying out data classification on the target operation data to obtain first operation data and second operation data;
s204, performing model matching on the first operation data to obtain a second sub-battery analysis model, and performing model matching on the second operation data to obtain a third sub-battery analysis model;
s205, inputting the battery parameter data into a first sub-analysis model to predict the standard state of the battery, and obtaining standard state data;
s206, inputting the first operation data and the standard state data into a second sub-battery analysis model to perform battery charging and discharging habit analysis to obtain a charging and discharging analysis result;
s207, inputting the second operation data and the standard state data into a third sub-battery analysis model for user use habit analysis to obtain a use habit analysis result;
and S208, generating a target battery state analysis result corresponding to the second battery according to the charging and discharging analysis result and the use habit analysis result.
Specifically, the server inputs battery parameter data and target operation data into a battery analysis model set, and performs model matching on the battery parameter data and the battery analysis model set to obtain a first sub-battery analysis model, wherein the battery parameter data comprises: the battery cell factory internal resistance, the battery cell factory capacity and the battery cell factory material, data classification is carried out on target operation data to obtain first operation data and second operation data, wherein, the server acquires target operation data, performs word segmentation processing on the target operation data to obtain word segmentation results corresponding to the target operation data, matches the word segmentation results corresponding to the target operation data with keywords in a preset keyword library, and determining a label of the target operation data according to the matching result, wherein the label is used for representing a category corresponding to the target operation data, the keyword library comprises at least one keyword related to at least one category to obtain first operation data and second operation data, performing model matching on the first operation data to obtain a second sub-battery analysis model, performing model matching on the second operation data to obtain a third sub-battery analysis model, inputting the first operation data and the standard state data into the second sub-battery analysis model to perform battery charging and discharging habit analysis to obtain a charging and discharging analysis result, specifically, performing battery charging and discharging performance tests on the battery at different temperatures and different currents by the server, and the first operation data and the standard state data are input into a second sub-battery analysis model for battery charging and discharging habit analysis, wherein, the server presets the cut-off point of the battery charging and discharging process, according to the reaction mechanism equation of different cut-off points, thereby inferring the electrode reaction mechanism, obtaining a charging and discharging analysis result according to the electrode reaction mechanism, inputting the second operation data and the standard state data into a third sub-battery analysis model for user use habit analysis to obtain a use habit analysis result, and generating a target battery state analysis result corresponding to the second battery according to the charging and discharging analysis result and the use habit analysis result.
In a specific embodiment, as shown in fig. 3, the process of executing step S206 may specifically include the following steps:
s301, performing feature extraction on the first operation data to obtain a plurality of first feature data corresponding to the first operation data, wherein the plurality of first feature data comprise: charging and discharging cycle segments, charging time, charging times and weekly driving kilometers;
s302, generating first input data according to the plurality of first characteristic data and the standard state data;
and S303, inputting the first input data into a second sub-battery analysis model, and carrying out battery charging and discharging habit analysis on the first input data through the second sub-battery analysis model to obtain a charging and discharging analysis result.
Specifically, the server obtains first operation data, determines a current first operation data spatial feature library by using a data spatial feature extraction method, obtains a historical data spatial feature library, determines the sensitive data spatial feature library by using preset sensitive data features and the historical data spatial feature library, performs modular operation on the current first operation data spatial feature library and the sensitive data spatial feature library to obtain a plurality of first feature data corresponding to the first operation data, generates first input data according to the plurality of first feature data and standard state data, inputs the first input data into a second sub-battery analysis model, and performs battery charging and discharging habit analysis on the first input data through the second sub-battery analysis model to obtain a charging and discharging analysis result.
In a specific embodiment, as shown in fig. 4, the process of executing step S207 may specifically include the following steps:
s401, performing feature extraction on the second operation data to obtain a plurality of second feature data corresponding to the second operation data, wherein the plurality of second feature data comprise: charge start time, energy consumption, rapid acceleration and mileage;
s402, generating second input data according to the plurality of second characteristic data and the standard state data;
and S403, inputting the second input data into a third sub-battery analysis model, and performing user use habit analysis on the second input data through the third sub-battery analysis model to obtain a use habit analysis result.
Specifically, the server performs feature extraction on the second operation data to obtain a plurality of second feature data corresponding to the second operation data, where the plurality of second feature data includes: and when the server analyzes the use habits of the user on the second input data through a third sub-battery analysis model, the server sends the second input data to the third sub-battery analysis model, and the third sub-battery analysis model analyzes the second input data, establishes a use habit model, analyzes the use habits of the user, and obtains a use habit analysis result.
In a specific embodiment, after step S106, the method for processing battery data based on big data further includes the following steps:
(1) Matching a target battery maintenance scheme from a plurality of candidate battery maintenance schemes according to the target battery state analysis result;
(2) And monitoring the full life cycle of the target battery according to the maintenance scheme of the target battery.
Specifically, the server performs scheme matching on the target battery state analysis result, matches a target battery maintenance scheme from a plurality of candidate battery maintenance schemes, finally determines cycle time according to the target battery maintenance scheme, and finally performs full-life cycle monitoring on the target battery through the cycle time.
In the above description of the method for processing battery data based on big data in the embodiment of the present invention, referring to fig. 5, a device for processing battery data based on big data in the embodiment of the present invention is described below, where an embodiment of the device for processing battery data based on big data in the embodiment of the present invention includes:
the acquiring module 501 is configured to acquire operation data of a plurality of first batteries, obtain initial operation data corresponding to each first battery, and send the initial operation data corresponding to each first battery to a preset battery management platform;
a screening module 502, configured to perform data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and store the standard operation data corresponding to each first battery to a first data source;
a building module 503, configured to build a battery analysis model set according to the first data source, where the battery analysis model set includes a plurality of sub-battery analysis models;
a selecting module 504, configured to select a battery to be analyzed from the multiple first batteries as a second battery, obtain target operation data corresponding to the second battery from the first data source, and obtain battery parameter data of the second battery from a preset second data source;
the analysis module 505 is configured to input the battery parameter data and the target operation data into the battery analysis model set, and perform battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result;
a sending module 506, configured to send the target battery state analysis result to the battery management platform.
Performing data screening on the initial operation data corresponding to each first battery through the cooperative cooperation of the components to obtain standard operation data, and storing the standard operation data corresponding to each first battery to a first data source; constructing a battery analysis model set according to a first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models; selecting a second battery from the plurality of first batteries, acquiring target operation data from a first data source, and acquiring battery parameter data of the second battery from a preset second data source; the method comprises the steps of inputting battery parameter data and target operation data into a battery analysis model set, and analyzing the battery state of a second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result.
Referring to fig. 6, another embodiment of the device for processing battery data based on big data according to the embodiment of the present invention includes:
the acquiring module 501 is configured to acquire operation data of a plurality of first batteries, obtain initial operation data corresponding to each first battery, and send the initial operation data corresponding to each first battery to a preset battery management platform;
a screening module 502, configured to perform data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and store the standard operation data corresponding to each first battery to a first data source;
a building module 503, configured to build a battery analysis model set according to the first data source, where the battery analysis model set includes a plurality of sub-battery analysis models;
a selecting module 504, configured to select a battery to be analyzed from the multiple first batteries as a second battery, obtain target operation data corresponding to the second battery from the first data source, and obtain battery parameter data of the second battery from a preset second data source;
the analysis module 505 is configured to input the battery parameter data and the target operation data into the battery analysis model set, and perform battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result;
a sending module 506, configured to send the target battery state analysis result to the battery management platform.
Optionally, the screening module 502 is specifically configured to:
performing data cleaning on the initial operation data corresponding to each first battery to obtain cleaned initial operation data; carrying out repeated value removal and missing value filling on the cleaned initial operation data to obtain preprocessed initial operation data; performing characteristic screening on the preprocessed initial operation data to obtain standard operation data corresponding to each first battery; and storing the standard operation data corresponding to each first battery to a first data source.
Optionally, the building module 503 is specifically configured to:
extracting a plurality of training data sets from the first data source and obtaining a plurality of training models, wherein the training data sets correspond to the training models one to one; performing model training on each training model according to each training data set to obtain a sub-battery analysis model corresponding to each training model; and carrying out model integration on the plurality of sub-battery analysis models to generate a battery analysis model set.
Optionally, the analyzing module 504 further includes:
the input unit is used for inputting the battery parameter data and the target operation data into the battery analysis model set; performing model matching on the battery parameter data and the battery analysis model set to obtain a first sub-battery analysis model, wherein the battery parameter data comprises: the battery cell factory internal resistance, the battery cell factory capacity and the battery cell factory material; carrying out data classification on the target operation data to obtain first operation data and second operation data; performing model matching on the first operation data to obtain a second sub-battery analysis model, and performing model matching on the second operation data to obtain a third sub-battery analysis model; inputting the battery parameter data into the first sub-analysis model to predict the standard state of the battery, so as to obtain standard state data;
the first analysis unit is used for inputting the first operation data and the standard state data into the second sub-battery analysis model to analyze the battery charging and discharging habits to obtain a charging and discharging analysis result;
the second analysis unit is used for inputting the second operation data and the standard state data into the third sub-battery analysis model to perform user use habit analysis to obtain a use habit analysis result;
and the generating unit is used for generating a target battery state analysis result corresponding to the second battery according to the charging and discharging analysis result and the use habit analysis result.
Optionally, the first analysis unit is specifically configured to:
performing feature extraction on the first operation data to obtain a plurality of first feature data corresponding to the first operation data, wherein the plurality of first feature data include: charging and discharging cycle segments, charging time, charging times and weekly driving kilometers; generating first input data according to the plurality of first characteristic data and the standard state data; and inputting the first input data into the second sub-battery analysis model, and carrying out battery charging and discharging habit analysis on the first input data through the second sub-battery analysis model to obtain a charging and discharging analysis result.
Optionally, the second analysis unit is specifically configured to:
performing feature extraction on the second operation data to obtain a plurality of second feature data corresponding to the second operation data, wherein the plurality of second feature data include: charge start time, energy consumption, rapid acceleration and mileage; generating second input data according to the plurality of second characteristic data and the standard state data; and inputting the second input data into the third sub-battery analysis model, and carrying out user use habit analysis on the second input data through the third sub-battery analysis model to obtain a use habit analysis result.
Optionally, the big data-based battery data processing apparatus further includes:
a matching module 507, configured to match a target battery maintenance scheme from multiple candidate battery maintenance schemes according to the target battery state analysis result; and monitoring the full life cycle of the target battery according to the target battery maintenance scheme.
In the embodiment of the invention, data screening is carried out on the initial operation data corresponding to each first battery to obtain standard operation data, and the standard operation data corresponding to each first battery is stored to a first data source; constructing a battery analysis model set according to a first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models; selecting a second battery from the plurality of first batteries, acquiring target operation data from a first data source, and acquiring battery parameter data of the second battery from a preset second data source; the method comprises the steps of inputting battery parameter data and target operation data into a battery analysis model set, and analyzing the battery state of a second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result.
Fig. 5 and fig. 6 above describe the battery data processing device based on big data in the embodiment of the present invention in detail from the perspective of a modular functional entity, and the battery data processing device based on big data in the embodiment of the present invention is described in detail below from the perspective of hardware processing.
Fig. 7 is a schematic structural diagram of a big data based battery data processing apparatus 600 according to an embodiment of the present invention, which may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) for storing applications 633 or data 632. Memory 620 and storage medium 630 may be, among other things, transient or persistent storage. The program stored in the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations in the big data based battery data processing apparatus 600. Still further, the processor 610 may be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the big data based battery data processing apparatus 600.
The big-data based battery data processing apparatus 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input-output interfaces 660, and/or one or more operating systems 631, such as Windows Server, mac OS X, unix, linux, freeBSD, and the like. Those skilled in the art will appreciate that the configuration of the big-data based battery data processing apparatus shown in fig. 7 does not constitute a limitation of the big-data based battery data processing apparatus, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
The invention also provides a big data-based battery data processing device, which comprises a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps of the big data-based battery data processing method in the above embodiments.
The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium, and which may also be a volatile computer-readable storage medium, having stored therein instructions, which, when executed on a computer, cause the computer to perform the steps of the big-data based battery data processing method.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media that can store program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A battery data processing method based on big data is characterized by comprising the following steps:
the method comprises the steps of obtaining operation data of a plurality of first batteries, obtaining initial operation data corresponding to each first battery, and sending the initial operation data corresponding to each first battery to a preset battery management platform;
performing data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and storing the standard operation data corresponding to each first battery to a first data source;
constructing a battery analysis model set according to the first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models;
selecting a battery to be analyzed from the plurality of first batteries as a second battery, acquiring target operation data corresponding to the second battery from the first data source, and acquiring battery parameter data of the second battery from a preset second data source;
inputting the battery parameter data and the target operation data into the battery analysis model set, and performing battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result;
and sending the target battery state analysis result to the battery management platform.
2. The big-data-based battery data processing method according to claim 1, wherein the step of performing data screening on the initial operation data corresponding to each first battery to obtain standard operation data corresponding to each first battery, and storing the standard operation data corresponding to each first battery to a first data source comprises:
performing data cleaning on the initial operation data corresponding to each first battery to obtain cleaned initial operation data;
carrying out repeated value removal and missing value filling on the cleaned initial operation data to obtain preprocessed initial operation data;
performing characteristic screening on the preprocessed initial operation data to obtain standard operation data corresponding to each first battery;
and storing the standard operation data corresponding to each first battery to a first data source.
3. The big-data-based battery data processing method according to claim 1, wherein the building of the battery analysis model set according to the first data source, wherein the battery analysis model set comprises a plurality of sub-battery analysis models, comprises:
extracting a plurality of training data sets from the first data source and obtaining a plurality of training models, wherein the training data sets correspond to the training models one to one;
performing model training on each training model according to each training data set to obtain a sub-battery analysis model corresponding to each training model;
and carrying out model integration on the plurality of sub-battery analysis models to generate a battery analysis model set.
4. The big data based battery data processing method according to claim 1, wherein the inputting the battery parameter data and the target operation data into the battery analysis model set, and performing battery state analysis on the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result comprises:
inputting the battery parameter data and the target operating data into the battery analysis model set;
performing model matching on the battery parameter data and the battery analysis model set to obtain a first sub-battery analysis model, wherein the battery parameter data comprises: the battery cell factory internal resistance, the battery cell factory capacity and the battery cell factory material;
performing data classification on the target operation data to obtain first operation data and second operation data;
performing model matching on the first operation data to obtain a second sub-battery analysis model, and performing model matching on the second operation data to obtain a third sub-battery analysis model;
inputting the battery parameter data into the first sub-analysis model to predict the standard state of the battery, so as to obtain standard state data;
inputting the first operation data and the standard state data into the second sub-battery analysis model to analyze the charging and discharging habits of the battery, and obtaining a charging and discharging analysis result;
inputting the second operation data and the standard state data into the third sub-battery analysis model to perform user use habit analysis to obtain a use habit analysis result;
and generating a target battery state analysis result corresponding to the second battery according to the charging and discharging analysis result and the use habit analysis result.
5. The big data-based battery data processing method according to claim 4, wherein the step of inputting the first operation data and the standard state data into the second sub-battery analysis model to perform battery charging and discharging habit analysis to obtain a charging and discharging analysis result comprises:
performing feature extraction on the first operating data to obtain a plurality of first feature data corresponding to the first operating data, wherein the plurality of first feature data include: charging and discharging cycle segments, charging time, charging times and weekly driving kilometers;
generating first input data according to the plurality of first characteristic data and the standard state data;
and inputting the first input data into the second sub-battery analysis model, and carrying out battery charging and discharging habit analysis on the first input data through the second sub-battery analysis model to obtain a charging and discharging analysis result.
6. The big-data-based battery data processing method according to claim 4, wherein the step of inputting the second operating data and the standard state data into the third sub-battery analysis model to perform user usage habit analysis to obtain a usage habit analysis result comprises:
performing feature extraction on the second operation data to obtain a plurality of second feature data corresponding to the second operation data, wherein the plurality of second feature data include: charge start time, energy consumption, rapid acceleration and mileage;
generating second input data according to the plurality of second characteristic data and the standard state data;
and inputting the second input data into the third sub-battery analysis model, and carrying out user use habit analysis on the second input data through the third sub-battery analysis model to obtain a use habit analysis result.
7. The big-data-based battery data processing method according to claim 1, further comprising:
matching a target battery maintenance scheme from a plurality of candidate battery maintenance schemes according to the target battery state analysis result;
and monitoring the full life cycle of the target battery according to the target battery maintenance scheme.
8. A big-data-based battery data processing apparatus, comprising:
the acquisition module is used for acquiring the operation data of the plurality of first batteries, acquiring initial operation data corresponding to each first battery, and sending the initial operation data corresponding to each first battery to a preset battery management platform;
the screening module is used for screening the initial operating data corresponding to each first battery to obtain standard operating data corresponding to each first battery, and storing the standard operating data corresponding to each first battery to a first data source;
a building module configured to build a battery analysis model set according to the first data source, wherein the battery analysis model set includes a plurality of sub-battery analysis models;
the selection module is used for selecting a battery to be analyzed from the plurality of first batteries as a second battery, acquiring target operation data corresponding to the second battery from the first data source, and acquiring battery parameter data of the second battery from a preset second data source;
the analysis module is used for inputting the battery parameter data and the target operation data into the battery analysis model set and analyzing the battery state of the second battery through a plurality of sub-battery analysis models in the battery analysis model set to obtain a target battery state analysis result;
and the sending module is used for sending the target battery state analysis result to the battery management platform.
9. A big-data-based battery data processing apparatus, characterized in that the big-data-based battery data processing apparatus comprises: a memory and at least one processor, the memory having instructions stored therein;
the at least one processor invoking the instructions in the memory to cause the big-data based battery data processing apparatus to perform the big-data based battery data processing method of any of claims 1-7.
10. A computer-readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, implement the big data based battery data processing method according to any of claims 1 to 7.
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