Battery fault warning methods, devices, computer equipment, readable storage media, and program products

Abstract

This application relates to a battery fault early warning method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: denoising impedance data of multiple batteries to obtain denoised impedance data; using each denoised impedance data point as a data point and calculating the local density corresponding to each data point; dynamically adjusting a density threshold based on the local density of each data point to obtain a density threshold for each data point; classifying all data points into multiple groups based on the density thresholds corresponding to different data points; calculating the distance between any two groups; and determining that a battery fault exists among the multiple batteries if the distance between any two groups is greater than a preset distance. This method can promptly detect battery faults, issue early warnings, and facilitate further processing.

Description

Technical Field

[0001] This application relates to the field of fault warning technology, and in particular to a battery fault warning method, device, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] With the widespread application of batteries in new energy, electric vehicles, smart grids, and other fields, monitoring their health status has become increasingly important. Electrochemical impedance spectroscopy, as a widely used non-destructive detection method, can analyze the internal state of batteries through electrochemical responses at different frequencies.

[0003] Traditional electrochemical impedance spectroscopy mainly extracts feature parameters based on equivalent circuit models. Cluster analysis, as an unsupervised learning method, can automatically discover potential patterns in data, identify battery anomalies or inconsistencies, and improve the efficiency and accuracy of electrochemical impedance spectroscopy analysis.

[0004] However, traditional technologies cannot effectively handle highly complex data and cannot provide timely warnings of battery failures. Summary of the Invention

[0005] Therefore, it is necessary to provide a battery fault early warning method, device, computer equipment, computer-readable storage medium, and computer program product that can handle highly complex battery data to address the aforementioned technical problems.

[0006] In a first aspect, this application provides a battery fault early warning method, including:

[0007] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0008] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0009] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0010] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0011] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0012] In one embodiment, the step of denoising the impedance data of multiple batteries to obtain denoised impedance data includes:

[0013] Multiple batteries were tested to obtain impedance data for the multiple batteries;

[0014] The impedance data of the multiple batteries are filtered to obtain denoised impedance data.

[0015] In one embodiment, the step of taking each denoised impedance data point as a data point and calculating the local density corresponding to each data point includes:

[0016] For each data point, calculate the distance between that data point and the remaining data points;

[0017] The local density corresponding to the data point is calculated based on the distance and the preset smoothing parameters.

[0018] In one embodiment, the step of dynamically adjusting the density threshold based on the local density corresponding to each data point to obtain the density threshold corresponding to each data point includes:

[0019] Based on the local density corresponding to each data point, determine the adjustment factor corresponding to each data point.

[0020] Divide the adjustment factor by the local density to obtain the density threshold corresponding to each data point.

[0021] In one embodiment, the step of dividing all data points into multiple groups based on density thresholds corresponding to different data points includes:

[0022] Calculate the distance between every two data points;

[0023] If the distance between the two data points is greater than a preset value, then the distance between the two data points is compared with the smallest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the smallest density threshold, then the two data points belong to the same group; otherwise, the two data points do not belong to the same group.

[0024] If the distance between the two data points is less than a preset value, then the distance between the two data points is compared with the largest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the largest density threshold, then the two data points belong to the same group; otherwise, the two data points do not belong to the same group.

[0025] In one embodiment, calculating the distance between any two groups in the plurality of groups, and determining that a battery fault exists among the plurality of batteries when the distance between any two groups is greater than a preset distance, includes:

[0026] Calculate the center of each of the multiple groups, and calculate the distance between the centers of every two groups.

[0027] If the distance between the centers of two groups is greater than a preset distance, it is determined that there is a battery fault among the plurality of batteries.

[0028] Secondly, this application also provides a battery fault warning device, comprising:

[0029] The acquisition module is used to denoise the impedance data of multiple batteries separately to obtain the denoised impedance data.

[0030] The calculation module is used to take each denoised impedance data as a data point and calculate the local density corresponding to each data point; the local density is used to measure the distance between the data point and the remaining data points.

[0031] The calculation module is also used to dynamically adjust the density threshold according to the local density corresponding to each data point to obtain the density threshold corresponding to each data point.

[0032] The segmentation module is used to divide all data points into multiple groups based on the density thresholds corresponding to the different data points.

[0033] The determination module is used to calculate the distance between every two groups in multiple groups, and if the distance between two groups is greater than a preset distance, it is determined that there is a battery fault among the multiple batteries.

[0034] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0035] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0036] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0037] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0038] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0039] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0041] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0042] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0043] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0044] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0045] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0046] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0047] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0048] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0049] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0050] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0051] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0052] The aforementioned battery fault early warning method, device, computer equipment, computer-readable storage medium, and computer program product first perform denoising processing on the impedance data of multiple batteries to obtain denoised impedance data. This eliminates noise interference during data processing, ensuring data validity. Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated. Local density is used to measure the distance between each data point and the remaining data points. Quantifying the distance between each data point and the remaining data points into a corresponding local density makes subsequent data processing more convenient. Based on the local density corresponding to each data point, a density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point. Based on the density thresholds corresponding to different data points, the data points are divided into multiple groups. The distance between any two groups is calculated. If the distance between any two groups is greater than a preset distance, a battery fault is determined among the multiple batteries. In this way, by analyzing the impedance data of multiple batteries, battery faults can be detected in a timely manner, an early warning can be issued, and further processing can be carried out. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart illustrating a battery fault warning method in one embodiment;

[0055] Figure 2 This is a flowchart illustrating a battery fault warning method in another embodiment;

[0056] Figure 3 This is a structural block diagram of a battery fault warning device in one embodiment;

[0057] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] In one embodiment, such as Figure 1As shown, a battery fault early warning method is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps 102 to 110:

[0060] Step 102: Denoise the impedance data of multiple batteries separately to obtain the denoised impedance data.

[0061] The battery impedance data is a complex number consisting of a real part and an imaginary part. Noise reduction can be achieved through filtering.

[0062] Specifically, the terminal can filter the impedance data of multiple batteries separately to obtain the denoised impedance data of multiple batteries.

[0063] Step 104: Take each denoised impedance data as a data point and calculate the local density corresponding to each data point; the local density is used to measure the distance between the data point and the remaining data points.

[0064] Here, the distance is the Euclidean distance, which is the true distance between two data points.

[0065] In some embodiments, the terminal treats each denoised impedance data point as a data point and calculates the distance between each data point and the remaining data points. Based on the distance between each data point and the remaining data points and a preset smoothing parameter, the local density corresponding to each data point is calculated. The smoothing parameter controls the smoothness of the data points.

[0066] Step 106: Dynamically adjust the density threshold based on the local density corresponding to each data point to obtain the density threshold corresponding to each data point.

[0067] In some embodiments, the terminal determines an adjustment factor for each data point based on the local density corresponding to each data point. The adjustment factor for each data point is then divided by the local density of that data point to obtain a density threshold for that data point.

[0068] Step 108: Divide all data points into multiple groups based on the density thresholds corresponding to different data points;

[0069] In some embodiments, the terminal calculates the distance between every two data points, and based on the relationship between the distance between the two data points and a preset value, further determines the relationship between the distance between the two data points and a density threshold, thereby determining whether the two data points belong to the same group.

[0070] Step 110: Calculate the distance between any two groups in the multiple groups. If the distance between two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0071] In some embodiments, the terminal calculates the center of each class group, and then calculates the distance between the centers of every two classes in the multiple classes. When there is a case where the distance between the centers of two classes is greater than a preset distance, it is determined that there is a battery fault in the battery.

[0072] In the aforementioned battery fault early warning method, the impedance data of multiple batteries are denoised separately to obtain denoised impedance data. This eliminates noise interference during data processing and ensures data validity. Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated. Local density is used to measure the distance between each data point and the remaining data points. Quantifying the distance between each data point and the remaining data points into a corresponding local density makes subsequent data processing more convenient. Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point. Based on the density thresholds corresponding to different data points, the data points are divided into multiple groups. The distance between any two groups is calculated. If the distance between any two groups is greater than a preset distance, a battery fault is identified among the multiple batteries. In this way, by analyzing the impedance data of multiple batteries, battery faults can be detected in a timely manner, an early warning can be issued, and further processing can be carried out.

[0073] In one exemplary embodiment, the impedance data of multiple batteries are denoised to obtain denoised impedance data, including: testing multiple batteries to obtain impedance data of multiple batteries; and filtering the impedance data of multiple batteries to obtain denoised impedance data.

[0074] In practice, the terminal performs EIS (electrochemical impedance spectroscopy) tests on the battery under different operating conditions to obtain battery impedance data within a preset frequency range. The obtained battery impedance data is then filtered to obtain battery impedance data after noise reduction.

[0075] The noise can be any type of noise, and this application embodiment does not limit this. The filtering method can be Gaussian filtering, or other filtering methods that can remove noise, and this application embodiment does not limit this.

[0076] In the above embodiments, the terminal uses a filtering method to eliminate noise interference in the battery impedance data, thus ensuring the validity of the battery impedance data.

[0077] In some exemplary embodiments, each denoised impedance data is used as a data point, and the local density corresponding to each data point is calculated, including: for each data point, calculating the distance between the data point and the remaining data points; and calculating the local density corresponding to the data point based on the distance and a preset smoothing parameter.

[0078] In some embodiments, the terminal will use each denoised impedance data as a data point and calculate the Euclidean distance between each data point and the remaining data points. The specific calculation formula is shown in (1). Here, each data point represents the impedance data of a battery.

[0079]

[0080] in, Let i be the real part of the battery impedance data represented by data point i. Let i represent the imaginary part of the battery impedance data. Let j be the real part of the battery impedance data represented by data point j. Let j be the imaginary part of the battery impedance data represented by data point j.

[0081] Based on the Euclidean distance of each data point and the preset smoothing parameters, the local density corresponding to each data point is calculated. The specific calculation formula is shown in (2).

[0082]

[0083] in, It is the Euclidean distance between data point i and data point j. These are the preset smoothing parameters.

[0084] In the above embodiments, the terminal controls the smoothness of the data by customizing smoothing parameters, which improves the readability and interpretability of the data and enhances the accuracy of the final result.

[0085] In some exemplary embodiments, the density threshold is dynamically adjusted according to the local density corresponding to each data point to obtain the density threshold corresponding to each data point, including: determining the adjustment factor corresponding to each data point according to the local density corresponding to each data point; dividing the adjustment factor by the local density to obtain the density threshold corresponding to each data point.

[0086] In some embodiments, the terminal determines the corresponding adjustment factor based on the different local densities of each data point. The density threshold corresponding to each data point is obtained by dividing the adjustment factor corresponding to each data point by the local density corresponding to that data point. The specific calculation formula is shown in (3).

[0087]

[0088] in, To adjust the factor, This represents the local density.

[0089] In the above embodiments, the terminal dynamically sets the density threshold by dynamically adjusting the adjustment factor. Using a smaller density threshold in areas of high local density can capture subtle changes in characteristics; using a larger density threshold in areas of low local density can avoid misidentifying noise points as anomalies.

[0090] In some exemplary embodiments, all data points are divided into multiple groups based on density thresholds corresponding to different data points, including: calculating the distance between any two data points; if the distance between two data points is greater than a preset value, comparing the distance between the two data points with the smallest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the smallest density threshold, then the two data points belong to the same group; otherwise, the two data points do not belong to the same group; if the distance between two data points is less than a preset value, comparing the distance between the two data points with the largest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the largest density threshold, then the two data points belong to the same group; otherwise, the two data points do not belong to the same group.

[0091] In practice, the terminal calculates the distance between every two data points. When the distance between two data points is greater than a preset value, it compares the distance between the two data points with the smallest density threshold among the density thresholds corresponding to the two data points. If the distance between the two data points is greater than the smallest density threshold, then the two data points belong to the same group; otherwise, the two data points do not belong to the same group.

[0092] When the distance between two data points is less than a preset value, the distance between the two data points is compared with the largest density threshold among the density thresholds corresponding to the two data points. If the distance between the two data points is greater than the largest density threshold, the two data points belong to the same group; otherwise, the two data points do not belong to the same group. The preset value is determined by the specific battery impedance data during the actual experiment.

[0093] In the above embodiments, the terminal divides all data points into multiple groups, making the abnormal behavior more obvious when anomalies occur during subsequent monitoring.

[0094] In some exemplary embodiments, calculating the distance between every two groups in a plurality of groups, and determining that a battery fault exists in a plurality of batteries when the distance between two groups is greater than a preset distance, includes: calculating the center of each group in a plurality of groups, and calculating the distance between the centers of every two groups in a plurality of groups; determining that a battery fault exists in a plurality of batteries when the distance between the centers of two groups is greater than a preset distance.

[0095] In some embodiments, for multiple groups, the terminal calculates the center of each group, and the specific calculation formula is shown in (4).

[0096]

[0097] in, Let k be the number of data points in class k. Here is the impedance data for the i-th data point.

[0098] Furthermore, the distance between the centers of every two groups in the multiple groups is calculated. When the distance between the centers of two groups is greater than a preset distance, it is determined that there is a battery fault among the multiple batteries. The specific calculation formula is shown in (5).

[0099]

[0100] in, For the preset distance, Let be the distance between the centers of class k and class l. As the center of group k, It is the center of group l.

[0101] In the above embodiments, the terminal quantifies the state of multiple batteries, and when inconsistencies occur, it can promptly detect battery failures among multiple batteries, thereby providing further early warning.

[0102] To illustrate the battery fault warning method in this application in detail, an embodiment is described below, and the specific flowchart is as follows. Figure 2 As illustrated, this application provides an example of a specific lithium battery fault warning method.

[0103] First, the terminal performs electrochemical impedance spectroscopy (EIS) tests on the battery under different states of charge to obtain battery impedance data in the frequency range of 10 MHz to 100 kHz. Then, wavelet transform is used to denoise the obtained battery impedance data, resulting in battery impedance data free from noise.

[0104] The terminal will treat each denoised impedance data as a data point and calculate the Euclidean distance between each data point and the remaining data points, as shown in formula (1). Each data point represents the impedance data of a battery. Based on the Euclidean distance of each data point and the preset smoothing parameters, the local density corresponding to each data point will be calculated, as shown in formula (2). The terminal will determine the corresponding adjustment factor according to the different local densities of each data point. The density threshold corresponding to each data point will be obtained by dividing the adjustment factor corresponding to each data point by the local density corresponding to that data point, as shown in formula (3).

[0105] The terminal calculates the distance between any two data points. If the distance between two data points is greater than a preset value, it compares the distance between the two data points with the smallest density threshold among the density thresholds corresponding to the two data points. If the distance between the two data points is greater than the smallest density threshold, then the two data points belong to the same class; otherwise, the two data points do not belong to the same class.

[0106] When the distance between two data points is less than a preset value, the distance between the two data points is compared with the largest density threshold among the density thresholds corresponding to the two data points. If the distance between the two data points is greater than the largest density threshold, the two data points belong to the same group; otherwise, the two data points do not belong to the same group. The preset value is determined by the specific battery impedance data during the actual experiment.

[0107] Specifically, let's take a set of impedance data as an example.

[0108] Data point 1: ;

[0109] Data point 2: ;

[0110] Data point 3: ;

[0111] The local density of each data point can be calculated using the formula above. .

[0112] Based on the local density of the data points and the corresponding adjustment factors, calculate the density threshold for the data points. Then, adjust the factors... Set to 0.05.

[0113] Data point 1: ;

[0114] Data point 2: ;

[0115] Data point 3: ;

[0116] Because among the local densities of the three data points, data point 2 has a relatively high local density and belongs to a high-density region, the smallest density threshold is used for comparison when comparing density thresholds; data point 3 has a relatively low local density and belongs to a low-density region, the largest density threshold is used for comparison when comparing density thresholds.

[0117] The Euclidean distance between data point 1 and data point 2 is It is compared with the smallest density threshold between the two, i.e., the density threshold of data point 2, and is less than Therefore, data point 1 and data point 2 belong to the same class.

[0118] The Euclidean distance between data point 1 and data point 3 is It is compared with the density threshold of the largest of the two, namely the density threshold of data point 3, and is greater than Therefore, data point 1 and data point 3 do not belong to the same class.

[0119] According to the above rules, all data points are divided into multiple groups. For each group, the terminal calculates the center of each group, and the specific calculation formula is shown in (4).

[0120] Furthermore, the distance between the centers of every two groups in the multiple groups is calculated. When the distance between the centers of two groups is greater than a preset distance, it is determined that there is a battery fault among the multiple batteries. The specific calculation formula is shown in (5).

[0121] This application can monitor the impedance data of multiple batteries after electrochemical impedance spectroscopy processing, calculate the local density of each data point, dynamically adjust the density threshold, and automatically identify outliers in the data points, thereby providing timely fault warnings and improving the accuracy and real-time performance of battery health status monitoring.

[0122] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0123] Based on the same inventive concept, this application also provides a battery fault warning device for implementing the battery fault warning method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more battery fault warning device embodiments provided below can be found in the limitations of the battery fault warning method described above, and will not be repeated here.

[0124] In one exemplary embodiment, such as Figure 3 As shown, a battery fault early warning device is provided, including: an acquisition module 301, a calculation module 302, a division module 303, and a determination module 304, wherein:

[0125] The acquisition module is used to denoise the impedance data of multiple batteries separately to obtain the denoised impedance data.

[0126] The calculation module is used to take each denoised impedance data as a data point and calculate the local density corresponding to each data point; the local density is used to measure the distance between the data point and the remaining data points.

[0127] The calculation module is also used to dynamically adjust the density threshold according to the local density corresponding to each data point, so as to obtain the density threshold corresponding to each data point.

[0128] The segmentation module is used to divide all data points into multiple groups based on the density thresholds corresponding to the different data points.

[0129] The determination module is used to calculate the distance between every two groups in multiple groups, and if the distance between two groups is greater than a preset distance, it is determined that there is a battery fault among the multiple batteries.

[0130] In some embodiments, the acquisition module is further configured to test multiple batteries to obtain impedance data of the multiple batteries; and to filter the impedance data of the multiple batteries to obtain noise-reduced impedance data.

[0131] In some embodiments, the above-described calculation module is further configured to calculate the distance between the data point and the remaining data points for each data point; and to calculate the local density corresponding to the data point based on the distance and a preset smoothing parameter.

[0132] In some embodiments, the above calculation module is further configured to determine the adjustment factor corresponding to each data point based on the local density corresponding to each data point; and divide the adjustment factor by the local density to obtain the density threshold corresponding to each data point.

[0133] In some embodiments, the segmentation module is further configured to calculate the distance between any two data points; if the distance between two data points is greater than a preset value, then the distance between the two data points is compared with the smallest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the smallest density threshold, then the two data points belong to the same class; otherwise, the two data points do not belong to the same class; if the distance between two data points is less than a preset value, then the distance between the two data points is compared with the largest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the largest density threshold, then the two data points belong to the same class; otherwise, the two data points do not belong to the same class.

[0134] In some embodiments, the determining module is further configured to calculate the center of each of the multiple groups and the distance between the centers of every two groups; if the distance between the centers of two groups is greater than a preset distance, it is determined that there is a battery fault among the multiple batteries.

[0135] Each module in the aforementioned battery fault warning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0136] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a battery failure warning method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0137] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0138] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0139] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0140] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0141] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0142] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0143] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0144] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0145] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0146] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0147] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0148] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0149] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0150] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0151] The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data.

[0152] Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points.

[0153] Based on the local density corresponding to each data point, the density threshold is dynamically adjusted to obtain the density threshold corresponding to each data point.

[0154] Based on the density thresholds corresponding to the different data points, all data points are divided into multiple groups;

[0155] Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

[0156] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0157] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0158] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0159] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A battery fault early warning method, characterized in that, The method includes: The impedance data of multiple batteries are denoised separately to obtain the denoised impedance data. Each denoised impedance data point is used as a data point, and the local density corresponding to each data point is calculated; the local density is used to measure the distance between the data point and the remaining data points. Based on the local density corresponding to each data point, determine the adjustment factor corresponding to each data point. Divide the adjustment factor by the local density to obtain the density threshold corresponding to each data point; Calculate the distance between every two data points; If the distance between two data points is greater than a preset value, then the distance between the two data points is compared with the smallest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the smallest density threshold, then the two data points belong to the same group; otherwise, the two data points do not belong to the same group. If the distance between two data points is less than a preset value, then the distance between the two data points is compared with the largest density threshold among the density thresholds corresponding to the two data points; if the distance between the two data points is greater than the largest density threshold, then the two data points belong to the same class; otherwise, the two data points do not belong to the same class. Calculate the distance between any two groups in the multiple groups. If the distance between any two groups is greater than a preset distance, determine that there is a battery fault among the multiple batteries.

2. The method according to claim 1, characterized in that, The process of denoising the impedance data of multiple batteries to obtain denoised impedance data includes: Multiple batteries were tested to obtain impedance data for the multiple batteries; The impedance data of the multiple batteries are filtered to obtain denoised impedance data.

3. The method according to claim 1, characterized in that, The step of taking each denoised impedance data point as a data point and calculating the local density corresponding to each data point includes: For each data point, calculate the distance between that data point and the remaining data points; The local density corresponding to the data point is calculated based on the distance and the preset smoothing parameters.

4. The method according to claim 1, characterized in that, The step of calculating the distance between any two groups in the multiple battery classes, and determining that a battery fault exists among the multiple batteries when the distance between any two groups is greater than a preset distance, includes: Calculate the center of each of the multiple groups, and calculate the distance between the centers of every two groups. If the distance between the centers of two groups is greater than a preset distance, it is determined that there is a battery fault among the plurality of batteries.

5. A battery fault early warning device, characterized in that, The device includes: The acquisition module is used to denoise the impedance data of multiple batteries separately to obtain the denoised impedance data. The calculation module is used to take each denoised impedance data as a data point and calculate the local density corresponding to each data point; the local density is used to measure the distance between the data point and the remaining data points. The calculation module is further configured to determine the adjustment factor corresponding to each data point based on the local density corresponding to each data point; and divide the adjustment factor by the local density to obtain the density threshold corresponding to each data point. The segmentation module calculates the distance between any two data points. If the distance between two data points is greater than a preset value, it compares the distance between the two data points with the smallest density threshold among the density thresholds corresponding to the two data points. If the distance between the two data points is greater than the smallest density threshold, the two data points belong to the same group; otherwise, the two data points do not belong to the same group. If the distance between two data points is less than a preset value, it compares the distance between the two data points with the largest density threshold among the density thresholds corresponding to the two data points. If the distance between the two data points is greater than the largest density threshold, the two data points belong to the same group; otherwise, the two data points do not belong to the same group. The determination module is used to calculate the distance between every two groups in multiple groups, and if the distance between two groups is greater than a preset distance, it is determined that there is a battery fault among the multiple batteries.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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