Data analysis method and device, electronic equipment, storage medium and program product

Abstract

The invention provides a data analysis method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of data analysis. According to the method, mass spectrum data of a gas product generated in the charging and discharging process of a battery device and voltage change data of voltage along with time are obtained, and then the mass spectrum data and the voltage change data are analyzed, so that the incidence relation between the voltage and the mass spectrum data of the gas product in the time dimension can be analyzed; therefore, the performance of the battery can be evaluated more comprehensively and accurately, and data support can be provided for optimization of battery design and the like.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and more specifically, to a data analysis method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] Differential electrochemical mass spectrometry (DEMS) is an in-situ gas generation characterization technique based on microreactors and mass spectrometry analysis. It enables online detection and analysis of gases generated during in-situ chemical reactions. DEMS technology is widely used in research in chemistry, environment, and materials science. For example, in the field of electrochemical battery gas generation detection, it can analyze trace amounts of gases produced or consumed by the battery in situ, as well as the side reaction gases generated during the charging and discharging processes of energy storage devices such as lithium-ion batteries, lithium metal batteries, and sodium-ion batteries. It can analyze and detect gas consumption or generation during battery operation.

[0003] While analyzing battery gas production can help identify potential problems in battery design, thereby optimizing the design and improving battery performance, relying solely on DEMS technology makes it difficult to comprehensively and accurately evaluate battery performance. Summary of the Invention

[0004] The purpose of this application is to provide a data analysis method, apparatus, electronic device, storage medium, and program product to improve upon existing methods that rely solely on DEMS technology for analysis, making it difficult to comprehensively and accurately evaluate battery performance.

[0005] In a first aspect, embodiments of this application provide a data analysis method, the method comprising:

[0006] Acquire mass spectrometry data and voltage change data, wherein the mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and the voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process.

[0007] The mass spectrometry data and the voltage change data are analyzed to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

[0008] In the above implementation process, by acquiring the mass spectrometry data of the gaseous products generated by the battery device during charging and discharging, as well as the voltage change data over time, and then analyzing the mass spectrometry data and voltage change data, the correlation between voltage and the mass spectrometry data of gaseous products in the time dimension can be analyzed. This allows for a more comprehensive and accurate evaluation of battery performance, which is beneficial for providing data support for optimizing battery design and other aspects.

[0009] Optionally, the step of analyzing the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension includes:

[0010] The generation rate of gaseous products during the charge-discharge process was analyzed based on the mass spectrometry data.

[0011] The generation rate and voltage change data are analyzed to obtain the correlation between the generation rate and voltage change data in the time dimension.

[0012] In the above-mentioned process, the generation rate of gaseous products during charging and discharging can indicate possible side reactions inside the battery, which can help identify battery safety risks. Battery change data can assess the health status of the battery during charging and discharging. Therefore, by jointly analyzing the generation rate and voltage change data, battery performance can be evaluated more comprehensively and accurately.

[0013] Optionally, the step of analyzing the generation rate of gaseous products during the charge-discharge process based on the mass spectrometry data includes:

[0014] The concentration data of the gaseous products during the charge-discharge process are determined based on the mass spectrometry data.

[0015] Based on the carrier gas rate, carrier gas concentration, and the concentration data, the generation rate of gaseous products during the charge-discharge process is determined.

[0016] In the above process, since the gaseous products are carried out by the carrier gas, the carrier gas rate and carrier gas concentration can be used as reference standards to more accurately quantify the generation rate of the gaseous products.

[0017] Optionally, determining the generation rate of gaseous products during the charge-discharge process based on the carrier gas rate, carrier gas concentration, and the concentration data includes:

[0018] The scaling factor is determined based on the carrier gas rate and the carrier gas concentration;

[0019] The generation rate of gaseous products during the charge-discharge process is calculated based on the scaling factor and the concentration data.

[0020] In the above implementation process, since the gaseous products are carried out by the carrier gas, it can be assumed that the carrier gas rate is proportional to the gaseous product generation rate, and the carrier gas concentration is proportional to the gaseous product concentration data. Therefore, the gaseous product concentration data can be obtained quickly directly based on the scaling factor and concentration data.

[0021] Optionally, the mass spectrometry data includes the signal intensity of the gaseous products, and determining the concentration data of the gaseous products during the charge-discharge process based on the mass spectrometry data includes:

[0022] The concentration data of the gaseous products during the charging and discharging process are determined based on the signal strength.

[0023] In the above implementation process, the signal intensity of the gaseous product is obtained, and it can be converted into concentration data to facilitate the subsequent calculation of the generation rate.

[0024] Optionally, determining the concentration data of the gaseous products during the charge-discharge process based on the signal intensity includes:

[0025] The correspondence between signal intensity and concentration data is obtained in advance by fitting the signal intensity and corresponding concentration data of gaseous products generated by multiple battery devices during charging and discharging.

[0026] The concentration data of the gaseous products during the charging and discharging process are determined based on the aforementioned correspondence.

[0027] In the above implementation process, by pre-fitting the correspondence between signal intensity and concentration data, the concentration data of gaseous products can be quickly determined during actual analysis.

[0028] Optionally, the generation rate includes data on the change in the rate of gaseous products over time during the charge-discharge process. Analyzing the generation rate and the voltage change data to obtain the correlation between the generation rate and the voltage change data over time includes:

[0029] Generate a time-time correlation curve between the voltage change data and the generation rate;

[0030] The correlation curve is used to determine the relationship between the generation rate and the voltage change data in the time dimension.

[0031] In the above implementation process, generating correlation curves makes it easier to directly analyze the correlation between the generation rate and voltage change data, making the analysis more convenient and efficient.

[0032] Optionally, after generating the correlation curve between the voltage change data and the generation rate over time, the method further includes:

[0033] Output the correlation curve to allow analysts to visually observe the correlation between the generation rate and voltage change data.

[0034] Optionally, the step of analyzing the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension includes:

[0035] A preset Excel macro is invoked to analyze the mass spectrometry data and the voltage change data, thereby obtaining the correlation between the mass spectrometry data and the voltage change data in the time dimension.

[0036] In the above implementation process, since Excel macros are an automation tool, analysis and calculation can be performed quickly using Excel macros, which is highly efficient.

[0037] Secondly, embodiments of this application provide a data analysis apparatus, the apparatus comprising:

[0038] The data acquisition module is used to acquire mass spectrometry data and voltage change data. The mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and the voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process.

[0039] The joint analysis module is used to analyze the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

[0040] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the method provided in the first aspect above are performed.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0042] Fifthly, embodiments of this application provide a computer program product, including computer program instructions, which, when read and executed by a processor, perform the steps of the method provided in the first aspect above.

[0043] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 A flowchart illustrating a data analysis method provided in an embodiment of this application;

[0046] Figure 2 A structural block diagram of a mass spectrometry detection system provided in an embodiment of this application;

[0047] Figure 3 A schematic diagram of the correlation curve between voltage change data and generation rate provided in an embodiment of this application;

[0048] Figure 4 A schematic diagram of an Excel processing interface provided in an embodiment of this application;

[0049] Figure 5 A structural block diagram of a data analysis device provided in an embodiment of this application;

[0050] Figure 6 This is a schematic diagram of the structure of an electronic device for performing a data analysis method, provided as an embodiment of this application. Detailed Implementation

[0051] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0052] It should be noted that the terms "system" and "network" in the embodiments of this invention can be used interchangeably. "Multiple" refers to two or more; therefore, in the embodiments of this invention, "multiple" can also be understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / ", unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0053] Although DEMS technology is applied in the field of battery gas generation detection, it can help identify potential problems in battery design by analyzing the gas generation of the battery, thereby optimizing the battery design and improving battery performance. However, the current solution is based solely on DEMS analysis data, which makes it difficult to evaluate battery performance more comprehensively and accurately.

[0054] Generally, analyzing voltage changes during battery charging and discharging can be used to assess battery health. However, currently, there is no method to jointly analyze mass spectrometry data of various gaseous products and voltage change data during battery charging and discharging, because these two types of data are essentially data from different dimensions, making it difficult to conceive of combining them for analysis. The inventors of this application discovered this problem during long-term research, finding a correlation between mass spectrometry data and voltage change data in certain situations. For example, during charging, as the voltage increases, the mass spectrometry data also changes rapidly. By discovering this potential correlation, it is possible to analyze the correspondence between voltage and mass spectrometry data, and thus analyze battery performance.

[0055] The defects in the above-mentioned prior art solutions are all results obtained by the inventors after practice and careful research. Therefore, the discovery process of the above problems and the solutions proposed by the embodiments of the present invention in the following text should be considered as contributions made by the inventors to the present invention.

[0056] To address the aforementioned issues, this application provides a data analysis method. This method acquires mass spectrometry data of gaseous products generated during the charging and discharging process of a battery device, as well as voltage change data over time. Then, it performs joint analysis of the mass spectrometry data and voltage change data. This allows for the analysis of the temporal correlation between voltage and the mass spectrometry data of gaseous products, thereby enabling a more comprehensive and accurate evaluation of battery performance and providing data support for optimizing battery design.

[0057] The embodiments of this application can be applied to various scenarios for analyzing battery gas production in battery production, research and development, and performance analysis.

[0058] Please refer to Figure 1 , Figure 1 A flowchart of a data analysis method provided in this application embodiment, the method including the following steps:

[0059] Step S110: Acquire mass spectrometry data and voltage change data.

[0060] Among them, mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process.

[0061] The battery device mentioned in the embodiments of this application may refer to a battery pack, battery module, battery cell, or battery cell, etc.

[0062] The data analysis method of this application embodiment can be executed on a host computer. The host computer can acquire mass spectrometry data and voltage change data from external sources. For example, the host computer can be connected to a mass spectrometer, and the mass spectrometry data obtained by the mass spectrometer analysis can be directly transmitted to the host computer. The host computer can also be connected to a voltage acquisition device, and the voltage acquisition device transmits the voltage change data of the battery device during the charging and discharging process to the host computer.

[0063] In some implementations, a DEMS mass spectrometer can be used to detect the mass spectrometric data of gaseous products generated during the charging and discharging process of a battery device. DEMS is a technique that combines electrochemical processes with mass spectrometry analysis to monitor gaseous or volatile intermediate and final products generated during electrochemical reactions in real time.

[0064] In the specific implementation process, the mass spectrometry detection system, such as Figure 2 As shown, the system may include a carrier gas device, a flow meter, a filtration system, a vacuum system, and a mass spectrometer. The carrier gas device, used to hold the carrier gas, may include a gas cylinder. The device may also include a gas purification unit, from which the carrier gas enters to remove impurities (such as H₂O and CO₂), and then enters the flow meter. The flow meter controls the flow rate of the carrier gas, typically set between 0.1 and 2 ml / min. In some embodiments, the carrier gas may be argon, but other carrier gases such as helium, nitrogen, and hydrogen can also be used.

[0065] The carrier gas enters the housing cavity of the battery device after passing through a flow meter. Then, the gaseous products generated during the charging and discharging process of the battery device are carried out by the carrier gas to the filtration system. The battery device can be placed inside an electrochemical reaction cell. The gaseous products generated during the charging and discharging process undergo an electrochemical reaction and are then carried out by the carrier gas. The filtration system includes dry ice and a cold trap for condensing and removing the carried-out electrolyte. Finally, the gas enters the vacuum system. The vacuum system maintains the vacuum environment required by the mass spectrometer. The mass spectrometer can accurately identify the type and relative content of gases by measuring their mass-to-charge ratio, enabling online monitoring of the gaseous products.

[0066] In some implementations, the mass spectrometer can be directly connected to a host computer, allowing the acquired mass spectrometry data to be directly transmitted to the host computer. Alternatively, the mass spectrometry data output by the mass spectrometer can be transmitted to the host computer manually.

[0067] Voltage change data can be acquired using an external data acquisition device, such as a HIOKI data acquisition device, which can measure and record the voltage of the battery device at various time points during the charging and discharging process to obtain voltage change data. Alternatively, it can be recorded by the battery management system inside the battery device, which records the voltage at various time points during the charging and discharging process and generates voltage change data. The voltage change data can be directly transmitted to the host computer by the external data acquisition device or the battery management system, or it can be manually transmitted to the host computer.

[0068] Understandably, the charging and discharging process refers to the charging process and the discharging process. The charging process refers to the stage from the start of charging to the end of charging, and the discharging process refers to the stage from the start of discharging to the end of discharging.

[0069] Step S120: Analyze the mass spectrometry data and voltage change data to obtain the correlation between the mass spectrometry data and voltage change data in the time dimension.

[0070] Mass spectrometry data can include the relative abundance of gaseous products during charge and discharge processes. Therefore, combining this relative abundance with voltage change data can provide a more accurate and comprehensive feedback on battery performance. For example, by correlating voltage and gas abundance, we can gain a deeper understanding of the electrochemical reaction mechanisms within the battery, which helps optimize battery design, such as selecting more stable electrode materials or improving electrolyte formulations to reduce side reactions and enhance safety.

[0071] Voltage variation data provides direct information about the operating status of the battery device. When combined with mass spectrometry data, it can provide more complete battery behavior data, thereby increasing the reliability of the analysis results.

[0072] This analysis can involve examining the temporal correlation between voltage variation data and mass spectrometry data. For example, correlating voltage and mass spectrometry data at the same time point allows for the analysis of the relative content of voltage and gaseous products at that same time point. This can determine the relative content of gaseous products at the time of maximum voltage, minimum voltage, maximum relative content of gaseous products at voltage, or minimum relative content of gaseous products at voltage, and so on. This correlation can provide data support for improvements in battery materials, such as optimizing cell materials to reduce gas production or gas production rate.

[0073] In addition, the cumulative data of gaseous product content and voltage change data can be analyzed together, for example, the total content of each gaseous product and voltage change data can be analyzed together.

[0074] Understandably, the gaseous products mentioned in this scheme may refer to various gaseous products generated during the charging and discharging process, or to one or more specific gaseous products.

[0075] In the above implementation process, by acquiring the mass spectrometry data of the gaseous products generated by the battery device during charging and discharging, as well as the voltage change data over time, and then analyzing the mass spectrometry data and voltage change data, the correlation between voltage and the mass spectrometry data of gaseous products in the time dimension can be analyzed. This allows for a more comprehensive and accurate evaluation of battery performance, which is beneficial for providing data support for optimizing battery design and other aspects.

[0076] Based on the above embodiments, in order to more accurately evaluate the battery performance, the generation rate of gaseous products during the charging and discharging process can be analyzed based on mass spectrometry data. Then, the generation rate and voltage change data can be analyzed to obtain the correlation between the generation rate and voltage change data in the time dimension.

[0077] Since mass spectrometers can accurately detect the types and relative contents of gaseous products, the mass spectrometry data mentioned above can include the relative contents of gaseous products. Therefore, by obtaining the relative contents of gaseous products at various time points during the charging and discharging process, the generation rate of gaseous products during the charging and discharging process can be obtained.

[0078] The generation rate can be understood as the change in the relative content of gaseous products over time. Voltage change data can include the voltage at each time point during the charging and discharging process of the battery device. After obtaining the generation rate and voltage at each time point, the generation rate and voltage at each time point can be correlated. For example, the voltage and generation rate at each time point can be statistically analyzed, and then the correlation between the voltage and generation rate in the time dimension can be analyzed by fitting or correlation analysis.

[0079] For example, by analyzing the voltage and generation rate at the same time point, we can determine the generation rate of gaseous products when the voltage is at its maximum, or the generation rate of gaseous products when the voltage is at its minimum, or the voltage at which the generation rate of gaseous products is at its maximum, or the voltage at which the generation rate of gaseous products is at its minimum, and so on.

[0080] In the above-mentioned process, the generation rate of gaseous products during charging and discharging can indicate possible side reactions inside the battery, which can help identify battery safety risks. Battery change data can assess the health status of the battery during charging and discharging. Therefore, by jointly analyzing the generation rate and voltage change data, battery performance can be evaluated more comprehensively and accurately.

[0081] Based on the above embodiments, in the method of determining the generation rate of gaseous products during the charging and discharging process based on mass spectrometry data, the concentration of gaseous products during the charging and discharging process can be determined first based on mass spectrometry data, and then the generation rate of gaseous products during the charging and discharging process can be determined based on carrier gas rate, carrier gas concentration and concentration data.

[0082] Understandably, mass spectrometry data includes the relative content of gaseous products. Relative content refers to the proportion of gaseous products in the total composition, while concentration data indicates the proportion or content of each gaseous product. Relative content is a relative figure, while concentration data is an absolute figure, representing the actual content of the gaseous products. Therefore, after determining the relative content of each gaseous product, it can be converted into the corresponding concentration data through a certain conversion relationship, thus revealing the concentration data of each gaseous product.

[0083] The carrier gas rate can be obtained through a flow meter, and the carrier gas concentration can be obtained through mass spectrometry analysis. That is, the mass spectrometry data can also include the relative content of the carrier gas, which can be converted into the corresponding carrier gas concentration. Therefore, the carrier gas concentration at each time point during the charging and discharging process can be known.

[0084] Understandably, the carrier gas rate and carrier gas concentration can be used as reference standards. Since the gaseous products are carried out by the carrier gas, the generation rate of the gaseous products is directly proportional to the carrier gas rate, and the concentration data of the gaseous products is also directly proportional to the carrier gas concentration. Therefore, the generation rate of the gaseous products can be determined based on this relationship.

[0085] In the above process, since the gaseous products are carried out by the carrier gas, the carrier gas rate and carrier gas concentration can be used as reference standards to more accurately quantify the generation rate of the gaseous products.

[0086] Based on the above embodiments, in determining the generation rate of gaseous products, a scaling factor can be determined first based on the carrier gas rate and carrier gas concentration, and then the generation rate of gaseous products during the charging and discharging process can be calculated based on the scaling factor and concentration quantity.

[0087] The scaling factor can be the ratio of carrier gas rate to carrier gas concentration. Multiplying the scaling factor by the concentration data yields the generation rate; for example, the generation rate of gaseous products can be expressed as F. i , where i can represent the number of the gaseous product, and its calculation formula is expressed as F. i =aC i , where C i This represents the concentration data of the gaseous product numbered i, where 'a' represents the scaling factor. F Ar C represents the carrier gas rate. Ar This indicates the carrier gas concentration.

[0088] Of course, this calculation formula can be modified to some extent. For example, if the scaling factor is the ratio of carrier gas concentration to carrier gas rate, the calculation formula can be expressed as F. i =C i / a.

[0089] In some implementations, the above calculation formula can be obtained through the internal standard method. The principle of the internal standard method is to add a certain weight of pure substance as an internal standard to a certain amount of the sample mixture to be analyzed, and then analyze the sample containing the internal standard to obtain the generation rate of other gaseous products.

[0090] In this scheme, the carrier gas serves as an internal standard. The inventors discovered experimentally that the gas generation rate is directly proportional to the concentration; therefore, the generation rate of the gaseous product is directly proportional to its concentration and inversely proportional to the concentration of the internal standard. Thus, the following relationship can be established: C i This represents the concentration data of the gaseous product numbered i, F. Ar C represents the carrier gas rate. Ar Let represent the carrier gas concentration, and k represent the proportionality constant. k can be determined experimentally. If the ratio of carrier gas rate to carrier gas concentration changes in the same way as the ratio of the rate to concentration of other gaseous products, then k is 1, and the above calculation formula can be derived. Of course, in practical applications, the value of k can be determined through specific experiments.

[0091] In the above implementation process, since the gaseous products are carried out by the carrier gas, it can be assumed that the carrier gas rate is proportional to the gaseous product generation rate, and the carrier gas concentration is proportional to the gaseous product concentration data. Therefore, the gaseous product concentration data can be obtained quickly directly based on the scaling factor and concentration data.

[0092] Based on the above embodiments, in some cases, the mass spectrometry data includes the signal intensity of the gas products. When determining the concentration data of the gas products based on the mass spectrometry data, the concentration data of the gas products during the charging and discharging process can be determined based on the signal intensity.

[0093] The signal strength can be obtained from DEMS software analysis. The signal strength output by DEMS software can characterize the ion current intensity; the greater the ion current intensity, the higher the signal strength. In some implementations, the correspondence between the signal strength and concentration data of the gaseous product can be pre-established experimentally. For example, if the signal strength and concentration data have a linear relationship, it can be expressed by the following formula: I = mC, where I represents the signal strength, C represents the concentration data, and m represents the proportionality constant, which is obtained from pre-experimentation and may differ for different gaseous products.

[0094] Therefore, when determining the concentration data of the gaseous product during the charging and discharging process, the signal intensity of the gaseous product at each time point during the charging and discharging process can be substituted into the calculation formula for the corresponding gaseous product to obtain the concentration data of the gaseous product at each time point during the charging and discharging process.

[0095] In the above implementation process, by obtaining the signal intensity of the gaseous products, it can be converted into concentration data to facilitate the subsequent calculation of the generation rate.

[0096] Based on the above embodiments, in the method of obtaining concentration data, the correspondence between signal intensity and concentration data can be obtained first. This correspondence is obtained in advance by fitting the signal intensity of gas products generated by multiple battery devices during charging and discharging with the corresponding concentration data. Then, the concentration data of gas products during charging and discharging is determined according to the correspondence.

[0097] The correspondence can be obtained by fitting using the external standard method. The principle of the external standard method is to establish the correspondence between signal intensity and concentration by measuring the signal intensity of a series of standard samples with known concentrations, thereby calculating the concentration of unknown samples.

[0098] Understandably, if the relationship between signal intensity and concentration data is linear, it can be represented by a linear regression equation, such as I = mC + b, where I represents signal intensity, m is a proportionality constant, b is the intercept, and C is the concentration data. m and b are derived from prior experiments, and m and b may differ for different gaseous products. In mass spectrometry analysis, the intercept b can represent background signal or instrument noise, i.e., the signal intensity that the mass spectrometer can still detect when there are no gaseous products or the concentration is 0.

[0099] Of course, if the relationship between signal intensity and concentration data obtained by fitting the external standard method is non-linear, then non-linear regression can be used to analyze the relationship between the two, which may involve more complex mathematical models, such as polynomial regression, exponential regression, etc.

[0100] In the above implementation process, by pre-fitting the correspondence between signal intensity and concentration data, the concentration data of gaseous products can be quickly determined during actual analysis.

[0101] In some other ways, the concentration data of gaseous products can be predicted by a pre-trained first neural network model, and / or the generation rate of gaseous products can be predicted by a pre-trained second neural network model.

[0102] For example, during the training process of the first neural network model, a large amount of gas signal intensity and concentration data can be collected and then input into the first neural network model for training. This allows the first neural network model to learn the correlation between signal intensity and concentration data. In actual prediction, the signal intensity of the gas products detected by the mass spectrometer can be directly input into the first neural network model for prediction, thereby obtaining accurate concentration data of the gas products.

[0103] During training, the second neural network model can be trained by collecting a large amount of gas rate and concentration data, including carrier gas rate and concentration data, and then inputting this data into the model. This allows the second neural network model to learn the correlations and achieve accurate predictions. During prediction, the obtained carrier gas rate, carrier gas concentration, and gaseous product concentration data can be input into the second neural network model, which can then predict the formation rate of the gaseous products.

[0104] Understandably, when deploying a neural network model, the output layer of the first neural network model can be connected to the input layer of the second neural network model, so that the concentration data predicted by the first neural network model can be directly input into the second neural network model for prediction.

[0105] In some implementations, the first and second neural network models can employ Long Short-Term Memory (LSTM) networks. Since the signal strength, concentration data, and rate information input to the model are time series, and LSTM models have higher prediction accuracy in time series forecasting, this is beneficial. Of course, the first and second neural network models can also use different models, or other models such as recurrent neural networks, convolutional neural networks, generative adversarial networks, or combinations of these network models.

[0106] Based on the above embodiments, the generation rate may include data on the change of the rate of gaseous products during the charging and discharging process over time. For ease of analysis, a correlation curve between the generation rate and the generation rate in the time dimension can be generated, and then the correlation relationship between the generation rate and the voltage change data in the time dimension can be determined based on the correlation curve.

[0107] Understandably, voltage change data reflects how voltage changes over time, and the generation rate also reflects how the rate changes over time. Therefore, voltage change data and generation rate can be correlated over time. For example, by obtaining the voltage and generation rate of gaseous products at the same time point, the resulting correlation curve could be as follows: Figure 3As shown in the correlation curve graph, the left vertical axis represents voltage, the right vertical axis represents the generation rate, and the horizontal axis represents the various time points of the charging process. This graph allows for a direct analysis of the voltage and the generation rate of gaseous products at each time point.

[0108] In some implementations, the correlation curve can be automatically generated by Excel macros, and Excel macros can also be called to analyze the correlation between voltage change data and generation rate based on the correlation curve, such as the generation rate of gaseous products when the voltage is maximum, or the voltage when the generation rate of gaseous products is maximum. After determining the correlation, the Excel macro can output these correlations to the analysts so that the analysts can quickly understand the correlation analysis results.

[0109] In the above implementation process, generating correlation curves makes it easier to directly analyze the correlation between the generation rate and voltage change data, making the analysis more convenient and efficient.

[0110] Based on the above embodiments, in order to make it easier for analysts to intuitively see the correlation, a correlation curve graph can also be output.

[0111] One approach is to generate a correlation curve using an Excel macro and then output and display it on the Excel processing interface. Alternatively, if the Excel interface is not available, the correlation curve can be output to other devices for display. This allows analysts to directly observe the correlation between the generation rate of gaseous products and voltage change data through the correlation curve.

[0112] Based on the above embodiments, in order to improve the analysis efficiency, a preset Excel macro can be called to analyze the mass spectrometry data and voltage change data in the way the correlation between mass spectrometry data and voltage change data is analyzed to obtain the correlation between mass spectrometry data and voltage change data in the time dimension. For example, the generation rate of gas products during the charging and discharging process can be analyzed, as well as the correlation between the generation rate and voltage change data can be analyzed.

[0113] In Excel, a macro is an automation tool that can record and repeatedly execute a series of operations, thereby improving work efficiency.

[0114] In the implementation process, Excel macros can be pre-configured to enable functions such as generation rate analysis, data import, and concentration data calculation. For example, the aforementioned concentration data calculation formulas and generation rate calculation formulas can be pre-imported into the macros, so that the macros can be invoked to perform the calculations during specific applications.

[0115] In some implementations, mass spectrometry data and voltage change data can be automatically imported into Excel by calling Excel macros, and then the generation rate can be calculated based on the mass spectrometry data. For analysts, simply clicking the "Macro" button in Excel is enough to call up the relevant macro functions and execute the corresponding calculation tasks, making the operation simpler and more efficient.

[0116] Among them, the Excel processing interface can be as follows: Figure 4 As shown in the interface, m and b are first set for the gaseous products. m refers to the proportionality constant in the above generation rate calculation formula, and b refers to the intercept in the generation rate calculation formula. The m and b for each gas shown here are different. m and b can be extracted and entered by the analyst. The mass fraction can be used to indicate the corresponding gas.

[0117] Mass spectrometry data and voltage change data can be pre-stored in the host computer, such as in an Excel file. Analysts can import the mass spectrometry data and voltage change data from the background by clicking the "Select .dat file" button on the processing interface. Based on the signal concentration in the mass spectrometry data, the concentration data of the gaseous products is first calculated. Then, the generation rate of the gaseous products is calculated using k and b, as well as the concentration data, carrier gas rate, and carrier gas concentration. The final generation rate of the gaseous products can be displayed on the processing interface, allowing analysts to intuitively see the final results.

[0118] In the above implementation process, since Excel macros are an automation tool, analysis and calculation can be performed quickly using Excel macros, which is highly efficient.

[0119] Please refer to Figure 5 , Figure 5 This is a structural block diagram of a data analysis device 200 provided in an embodiment of this application. The data analysis device 200 may be a module, program segment, or code on an electronic device. It should be understood that this data analysis device 200 is similar to the one described above. Figure 1 The method implementation corresponds to this and can be executed. Figure 1 The various steps involved in the method embodiment, and the specific functions of the data analysis device 200, can be found in the description above. To avoid repetition, detailed descriptions are omitted here.

[0120] Optionally, the data analysis device 200 includes:

[0121] The data acquisition module 210 is used to acquire mass spectrometry data and voltage change data. The mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and the voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process.

[0122] The joint analysis module 220 is used to analyze the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

[0123] Optionally, the joint analysis module 220 is used to analyze the generation rate of gaseous products during the charge-discharge process based on the mass spectrometry data; and to analyze the generation rate and the voltage change data to obtain the correlation between the generation rate and the voltage change data in the time dimension.

[0124] Optionally, the joint analysis module 220 is used to determine the concentration data of the gaseous products during the charge-discharge process based on the mass spectrometry data; and to determine the generation rate of the gaseous products during the charge-discharge process based on the carrier gas rate, carrier gas concentration, and the concentration data.

[0125] Optionally, the joint analysis module 220 is used to determine a scaling factor based on the carrier gas rate and the carrier gas concentration; and to calculate the generation rate of gaseous products during the charge-discharge process based on the scaling factor and the concentration data.

[0126] Optionally, the mass spectrometry data includes the signal intensity of the gaseous products, and the joint analysis module 220 is used to determine the concentration data of the gaseous products during the charge-discharge process based on the signal intensity.

[0127] Optionally, the joint analysis module 220 is used to obtain the correspondence between signal intensity and concentration data, wherein the correspondence is obtained in advance by fitting the signal intensity and corresponding concentration data of gaseous products generated by multiple battery devices during charging and discharging; and the concentration data of gaseous products during the charging and discharging process is determined according to the correspondence.

[0128] Optionally, the generation rate includes data on the change in the rate of gaseous products over time during the charge-discharge process. The joint analysis module 220 is used to generate a correlation curve between the voltage change data and the generation rate in the time dimension; and to determine the correlation relationship between the generation rate and the voltage change data in the time dimension based on the correlation curve.

[0129] Optionally, the joint analysis module 220 is also used to output the correlation curve.

[0130] Optionally, the joint analysis module 220 is used to call a preset Excel macro to analyze the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

[0131] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0132] Please refer to Figure 6 , Figure 6 This is a schematic diagram of an electronic device for performing a data analysis method, provided in an embodiment of this application. The electronic device may include: at least one processor 310, such as a CPU; at least one communication interface 320; at least one memory 330; and at least one communication bus 340. The communication bus 340 is used to establish communication between these components. In this embodiment, the communication interface 320 is used for signaling or data communication with other node devices. The memory 330 may be a high-speed RAM or a non-volatile memory, such as at least one disk storage device. Optionally, the memory 330 may also be at least one storage device located remotely from the aforementioned processor. The memory 330 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 310, the electronic device performs the aforementioned... Figure 1 The method and process are shown.

[0133] Understandable. Figure 6 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown. Figure 6 The components shown can be implemented using hardware, software, or a combination thereof.

[0134] This application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following... Figure 1 The method process executed by the electronic device in the illustrated method embodiment.

[0135] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can perform the methods provided in the above-described method embodiments, such as including:

[0136] Acquire mass spectrometry data and voltage change data, wherein the mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and the voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process.

[0137] The mass spectrometry data and the voltage change data are analyzed to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

[0138] In summary, the embodiments of this application provide a data analysis method, apparatus, electronic device, storage medium, and program product. The method acquires mass spectrometry data of gaseous products generated by a battery device during charging and discharging, as well as voltage change data over time. Then, it analyzes the mass spectrometry data and voltage change data. This allows for the analysis of the temporal correlation between voltage and the mass spectrometry data of gaseous products, thereby enabling a more comprehensive and accurate evaluation of battery performance and providing data support for optimizing battery design.

[0139] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0140] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0141] Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0142] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0143] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A data analysis method, characterized in that, The method includes: Acquire mass spectrometry data and voltage change data, wherein the mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and the voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process. The mass spectrometry data and the voltage change data are analyzed to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

2. The method according to claim 1, characterized in that, The analysis of the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension includes: The generation rate of gaseous products during the charge-discharge process was analyzed based on the mass spectrometry data. The generation rate and voltage change data are analyzed to obtain the correlation between the generation rate and voltage change data in the time dimension.

3. The method according to claim 2, characterized in that, The analysis of the generation rate of gaseous products during the charge-discharge process based on the mass spectrometry data includes: The concentration data of the gaseous products during the charge-discharge process are determined based on the mass spectrometry data. Based on the carrier gas rate, carrier gas concentration, and the concentration data, the generation rate of gaseous products during the charge-discharge process is determined.

4. The method according to claim 3, characterized in that, Determining the generation rate of gaseous products during the charge-discharge process based on the carrier gas rate, carrier gas concentration, and the concentration data includes: The scaling factor is determined based on the carrier gas rate and the carrier gas concentration; The generation rate of gaseous products during the charge-discharge process is calculated based on the scaling factor and the concentration data.

5. The method according to claim 3, characterized in that, The mass spectrometry data includes the signal intensity of the gaseous products, and determining the concentration data of the gaseous products during the charge-discharge process based on the mass spectrometry data includes: The concentration data of the gaseous products during the charging and discharging process are determined based on the signal strength.

6. The method according to claim 5, characterized in that, Determining the concentration data of the gaseous products during the charging and discharging process based on the signal intensity includes: The correspondence between signal intensity and concentration data is obtained in advance by fitting the signal intensity and corresponding concentration data of gaseous products generated by multiple battery devices during charging and discharging. The concentration data of the gaseous products during the charging and discharging process are determined based on the aforementioned correspondence.

7. The method according to claim 2, characterized in that, The generation rate includes data on the change in the rate of gaseous products over time during the charge-discharge process. Analyzing the generation rate and the voltage change data to obtain the correlation between the generation rate and the voltage change data over time includes: Generate a time-time correlation curve between the voltage change data and the generation rate; The correlation curve is used to determine the relationship between the generation rate and the voltage change data in the time dimension.

8. The method according to claim 7, characterized in that, After generating the correlation curve between the voltage change data and the generation rate over time, the method further includes: Output the correlation curve.

9. The method according to any one of claims 1-8, characterized in that, The analysis of the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension includes: A preset Excel macro is invoked to analyze the mass spectrometry data and the voltage change data, thereby obtaining the correlation between the mass spectrometry data and the voltage change data in the time dimension.

10. A data analysis device, characterized in that, The device includes: The data acquisition module is used to acquire mass spectrometry data and voltage change data. The mass spectrometry data refers to the mass spectrometry data of the gaseous products generated by the battery device during the charging and discharging process, and the voltage change data refers to the voltage change data of the battery device over time during the charging and discharging process. The joint analysis module is used to analyze the mass spectrometry data and the voltage change data to obtain the correlation between the mass spectrometry data and the voltage change data in the time dimension.

11. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the method as described in any one of claims 1-9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it performs the method as described in any one of claims 1-9.

13. A computer program product, characterized in that, It includes computer program instructions, which, when read and executed by a processor, perform the method as described in any one of claims 1-9.

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