Detection method and detection system for starting power supply cell

Abstract

The invention relates to the technical field of electrical variable measurement, in particular to a detection method and a detection system for a starting power supply cell. The method comprises the steps of obtaining a local abnormal trend of a data point according to a difference between an instantaneous amplitude of the data point and an instantaneous amplitude mean value of a window of the data point, an instantaneous amplitude standard deviation and a window fitting slope mean value of the data point and adjacent data points on two sides of the data point, so as to obtain a proportion of a suspicious data point in a current time sequence, constructing a slope value sequence based on instantaneous amplitude slope values between adjacent data points to obtain a global abnormal trend of the current time sequence; calculating the box separation width of the current sequential sequence, wherein the box separation width is in negative correlation with the global abnormal trend; in the HBOS algorithm, the box separation width of the current sequential sequence is used to obtain the abnormal detection result of each data point, so that the accuracy of the detection result of the starting power supply cell is effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric variable measurement, and in particular to a detection method and a detection system for a starting power cell. BACKGROUND

[0002] The starting power is a core power supply unit of a vehicle, and its performance directly affects the reliability of engine starting, the stability of the electrical system and the safety of driving. The current data of the starting power cell is a key indicator reflecting its performance state. Abnormal fluctuations in the current (such as sudden increase, sudden decrease, irregular oscillation, etc.) may indicate that there are process defects or potential faults in the cell, thereby increasing the risk of thermal runaway. The HBOS (Histogram-based Outlier Score, HBOS for short) algorithm is an unsupervised algorithm based on a histogram. The core idea is to regard each feature of the current data as an independent variable. By constructing a histogram to calculate the abnormal score, the abnormal detection of the starting power cell can be realized. The HBOS algorithm has the characteristics of high calculation efficiency and can quickly process the current data of the starting power cell generated in real time, so it is suitable for abnormal detection when testing real-time vehicle systems. When the current data is skewed, the abnormal current data will be surrounded by a large number of normal current data, and its neighborhood points are mostly normal current data. At this time, if the conventional HBOS algorithm is used to process and detect abnormal current data, the abnormal current data will be submerged in a large number of normal data, which will ultimately reduce the detection accuracy of the abnormal current data of the starting power cell and cannot meet the demand for high-precision detection.

[0003] In summary, how to accurately obtain the detection result of the starting power cell is a problem to be solved at present. SUMMARY

[0004] In order to solve the technical problem of how to accurately obtain the detection result of the starting power cell, the present application provides a detection method and a detection system for a starting power cell.

[0005] In the first aspect, the present application provides a detection method for a starting power cell, which adopts the following technical scheme: A detection method for a starting power cell, comprising the steps of: Obtain the instantaneous amplitude of each data point in the current time series of the starting power cell; construct a window for each data point based on a preset window size, and fit the instantaneous amplitude in the window to obtain the window fitting slope of the data point; obtain the local abnormal trend of the data point based on the difference between the instantaneous amplitude of the data point and the instantaneous amplitude mean of its window, the instantaneous amplitude standard deviation, and the mean of the window fitting slopes of the data point and the adjacent data points on both sides; determine the suspicious data point based on the comparison result of the local abnormal trend of the data point and the local threshold, obtain the proportion of the suspicious data point in the current time series, and construct a slope value sequence based on the instantaneous amplitude slope values ​​between adjacent data points; multiply the ratio of the standard deviation and the mean of the slope value sequence by the proportion of the suspicious data point in the current time series to obtain the global abnormal trend of the current time series; calculate the bin width of the current time series, and the bin width is negatively correlated with the global abnormal trend; use the bin width of the current time series in the HBOS algorithm to obtain the abnormality detection result of each data point.

[0006] The present invention processes the current data sequence of the starting power supply cell through the HBOS algorithm, and can accurately obtain the detection results of the starting power supply cell. In the process of HBOS algorithm processing, the present invention takes into account that the default bin width in the algorithm cannot adapt to data skew, resulting in a high bin width when the abnormal data points are surrounded by normal data points and cannot accurately perform abnormal detection; based on this, the present invention analyzes the local trend change of the instantaneous amplitude of each data point in the current data sequence and the global instantaneous amplitude trend change, and combines the two to adjust the default bin width, effectively avoiding misjudgment caused by local fluctuations and improving the accuracy of abnormal detection. When obtaining local trend changes, the present invention captures the abnormal trend in the local window through the offset between the instantaneous amplitude of the data point and the average level and the instantaneous amplitude slope change in the window, so that the local abnormal trend can be accurately obtained, effectively improving the accuracy of starting power supply cell detection.

[0007] According to a method for detecting a starting power cell provided by the present invention, obtaining the instantaneous amplitude of each data point in a current time series sequence of the starting power cell includes: collecting the current time series data of the starting power cell during the detection process and performing preprocessing to obtain a current time series sequence; and processing the current time series sequence using a Hilbert transform to obtain the instantaneous amplitude of each data point in the current time series sequence.

[0008] The present invention takes into account that the sensor may collect noise when collecting data, and such noise may interfere with the identification of abnormal trends. Therefore, the present invention can effectively avoid the influence of noise data on subsequent detection results by preprocessing the collected current data.

[0009] The application provides a detection method for starting a power supply cell, and the method comprises the following steps: constructing a window of each data point based on a preset window size, fitting the instantaneous amplitude in the window to obtain a window fitting slope of the data point, and the preset window size is ; a continuous data points are obtained as the window of the data point, the instantaneous amplitude in the window is fitted by using a least square method to obtain the window fitting slope of the data point.

[0010] The application considers that the local abnormal trend can reflect the local abnormal fluctuation of the data point when the default bin width is adjusted, the window of the data point is constructed, and the abnormal trend in the window is analyzed, so that the bin width can be accurately reduced near the abnormal point in the subsequent process, and the resolution is improved.

[0011] The application provides a detection method for starting a power supply cell, and the method comprises the following steps: obtaining the local abnormal trend of the data point, and the method comprises the following steps: ; 、 respectively, the local abnormal trend of the first data point, the instantaneous amplitude, 、 respectively, the instantaneous amplitude mean and the instantaneous amplitude standard deviation of the window of the first data point, the window fitting slope of the first data point, the window fitting slope mean of the first data point and the two adjacent data points on the two sides, the zero prevention coefficient, the normalization function, and the absolute value symbol.

[0012] The application provides a detection method for starting a power supply cell, and the method comprises the following steps: determining the suspicious data point according to the comparison result of the local abnormal trend of the data point and the local threshold value, and the method comprises the following steps: if the local abnormal trend of the data point is greater than the local threshold value, the data point is a suspicious data point.

[0013] The application provides a detection method for starting a power supply cell, and the method comprises the following steps: calculating the bin width of the current time sequence, and the method comprises the following steps: taking the negative number of the global abnormal trend of the current time sequence as the exponential input of the exponential function with e as the base to obtain a correction factor; and taking the product of the correction factor and the default bin width of the HBOS algorithm as the bin width of the current time sequence.

[0014] The application adjusts the default bin width by the local abnormal trend and the global abnormal trend, can effectively avoid the misjudgment caused by the local fluctuation, and improves the abnormal detection accuracy.

[0015] According to the application, a detection method for starting power supply cells is provided, and a default bin width of an HBOS algorithm is obtained, including: obtaining a difference between a maximum current value and a minimum current value in a current time sequence, and obtaining a default bin width according to a ratio of the difference to a preset bin number.

[0016] According to the application, a detection method for starting power supply cells is provided, and a default bin width of an HBOS algorithm is obtained, including: obtaining a difference between a maximum current value and a minimum current value in a current time sequence, and obtaining a default bin width according to a ratio of the difference to a preset bin number.

[0017] According to the application, a detection method for starting power supply cells is provided, and a default bin width of an HBOS algorithm is obtained, including: obtaining a difference between a maximum current value and a minimum current value in a current time sequence, and obtaining a default bin width according to a ratio of the difference to a preset bin number.

[0018] The application considers that the abnormal test detection result of the starting power supply cell obtained by collection is important for research and development improvement of the starting power supply, and therefore, the application generates a test result after each test is completed, so as to facilitate staff to process and check.

[0019] In a second aspect, the application provides a detection system for starting power supply cells, which adopts the following technical scheme: The detection system for starting power supply cells includes a processor and a memory, and the memory stores computer program instructions, which are executed by the processor to realize the detection method for starting power supply cells.

[0020] By adopting the above technical scheme, the detection method for starting power supply cells is generated into a computer program and stored in the memory to be loaded and executed by the processor, so that a terminal device is manufactured according to the memory and the processor, and use is facilitated.

[0021] The application has the following technical effects: Based on the technical scheme, the detection method and the detection system of the starting power cell are provided, the current data sequence of the starting power cell is processed by the HBOS algorithm, and the detection result of the starting power cell can be accurately obtained. In the process of processing by the HBOS algorithm, it is considered that the default bin width in the algorithm cannot adapt to data skew, so that when the abnormal data points are surrounded by normal data points, the bin width is too high to accurately detect the abnormality. Based on this, the local trend change and the global instantaneous amplitude trend change of the instantaneous amplitude of each data point in the current data sequence are analyzed, and the default bin width is adjusted in combination with the two, so as to effectively avoid the misjudgment caused by local fluctuation and improve the accuracy of abnormal detection. When the local trend change is obtained, the abnormal trend in the local window is captured by the offset between the instantaneous amplitude of the data point and the average level and the change of the instantaneous amplitude slope in the window, so that the local abnormal trend can be accurately obtained, and the accuracy of the detection of the starting power cell is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A flowchart of a detection method of a starting power cell is provided in the embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all the embodiments.

[0024] The detection method of the starting power cell provided by the present application can accurately adjust the default bin width of the HBOS algorithm by analyzing the local abnormal trend of each current data and the global abnormal trend of the current data as a whole when detecting the starting power cell, so as to effectively improve the detection accuracy of the abnormal current data of the starting power cell.

[0025] For details, please refer to Figure 1 as shown, Figure 1 A flowchart of a detection method of a starting power cell is provided in the embodiment of the present application, which specifically includes the following steps: S1: Obtain the current value of each data point in the current time sequence of the starting power cell.

[0026] For example, in the embodiment of the present application, the current time sequence data of the starting power cell in the detection process is collected and preprocessed to obtain the current time sequence.

[0027] Specifically, when simulating the starting working condition of the vehicle, the test environment can be set to an environment of-30°C to 85°C, and then the vehicle can be subjected to a charging and discharging cycle process. This process can be in turn a first static state, constant current charging, constant voltage charging, a second static state, and pulse discharging, wherein the duration of the first static state is less than that of the second static state, and the duration of the first static state can be set to 10 seconds, and the duration of the second static state can be set to 30 seconds.

[0028] At each collection time, the current sensor is used to collect the current value of the starting power cell in the detection process, and one collection time and the corresponding current value thereof are taken as one data point to obtain current time sequence data; wherein the current value collection frequency can be set to 500Hz, and the collection frequency can be set according to actual needs, which is not limited herein.

[0029] For example, when the current time sequence data is preprocessed, an adaptive filtering algorithm can be used to remove the noise in the collected current time sequence data. The interpolation method is used to fill in the missing values. The preprocessing method and the specific implementation method can be set according to actual needs, which is not repeated herein.

[0030] It should be noted that the abnormal current data is manifested as a microscopic peak in the current time sequence, and the default bin width in the HBOS algorithm can smooth the abnormal value, so that the peak is submerged in the normal data. Therefore, the present embodiment provides a new bin width adjustment method, which can optimize the bin width by analyzing the abnormal trend of the current data, use finer bins in the abnormal area, thereby magnify the difference between the abnormal points and the normal points, and improve the accuracy of the abnormal detection, that is, the following steps are performed.

[0031] S2: Obtain the instantaneous amplitude of each data point in the current time sequence of the starting power cell; construct a window of each data point based on a preset window size, fit the instantaneous amplitudes in the window to obtain a window fitting slope of the data point; and calculate the local abnormal trend of each data point.

[0032] It should be noted that when adjusting the bin width, the local abnormality and the global abnormality of the data points need to be considered at the same time, so that the bin width can be reduced near the abnormal points, and the bin width can be generally reduced when the overall abnormal level is increased. The Hilbert transform is used to extract the envelope line of the current time sequence, so as to obtain the instantaneous amplitude of each data point. This value reflects the trend change of the current signal, and the signal remains stable in the local time. The slope of the instantaneous amplitude should be consistent with the overall trend of the previous and subsequent time.

[0033] Based on this, embodiments of the present invention can construct a local window of a data point. Within the window, the local abnormal trend of the data point is obtained based on the difference between the instantaneous amplitude of the data point and the instantaneous amplitude mean of its window, as well as the mean of the window-fitted slopes of the data point and its adjacent data points on both sides. The mean instantaneous amplitude of the window represents the average instantaneous amplitude level in the local range, the standard deviation of the instantaneous amplitude represents the degree of local instantaneous amplitude fluctuation, and the mean of the window-fitted slope represents the difference between the overall trend formed by the data point and its adjacent data points.

[0034] For example, in an embodiment of the present invention, obtaining the instantaneous amplitude of each data point in the current time series of the starting power cell includes: processing the current time series using Hilbert transform to obtain the instantaneous amplitude of each data point in the current time series.

[0035] The specific steps of using Hilbert transform to process the current time series sequence and obtain the instantaneous amplitude of each data point in the current time series sequence can be achieved by existing technologies, and will not be described in detail in the embodiment of the present invention.

[0036] For example, in an embodiment of the present invention, a window of each data point is constructed based on a preset window size, and the instantaneous amplitude in the window is fitted to obtain the window fitting slope of the data point, including: the preset window size is ; Get continuous data with one data point as the center The data point is used as the window of the data point; the instantaneous amplitude in the window is fitted using the least squares method to obtain the window fitting slope of the data point.

[0037] in, It can be set to 5; the window size can be set according to actual needs.

[0038] For example, the sampling time can be used as the independent variable and the instantaneous amplitude corresponding to the sampling time can be used as the dependent variable for fitting. The fitting step can be implemented by existing technologies and will not be described in detail in the embodiment of the present invention.

[0039] For example, in an embodiment of the present invention, calculating the local abnormal trend of a data point includes: ; For the The local abnormal trend of the data points, For the The instantaneous amplitude of a data point, For the The instantaneous amplitude mean of a window of data points, For the The standard deviation of the instantaneous amplitude of a window of data points, For the The slope of the window fit of data points, For the The mean of the slopes of the window fitting of a data point and its two adjacent data points, To prevent zero coefficient, is the normalization function, is the absolute value symbol.

[0040] An example to illustrate how to obtain the mean value of the window fitting slope: If , then the current data point is the 4th data point, and the window fitting slopes of the 3rd, 4th, and 5th data points are obtained respectively, and the average of the window fitting slopes of these three data points is taken as the average of the window fitting slopes of the 4th data point and the adjacent data points on both sides of it.

[0041] In this calculation method, the anti-zero coefficient is an extremely small positive number and can be set to 0.01. The specific setting can be made according to actual needs.

[0042] It is The absolute deviation of the instantaneous amplitude of a data point from the mean instantaneous amplitude of its window, It indicates the degree of deviation between the instantaneous amplitude and the average level of the instantaneous amplitude. The smaller the deviation, the more stable the instantaneous amplitude of the current remains in a short period of time; the larger the deviation, the more significant the fluctuation intensity at the current moment deviates from the recent normal level. It is used to standardize the numerator. The instantaneous amplitude of the current time series data may show different magnitude levels in different time periods. Direct comparison of the absolute deviation will produce errors. Through standardization, it is possible to uniformly evaluate whether the deviation of the data point is significant under the current fluctuation level.

[0043] It is The window fitting slope of the data points and the The absolute difference between a data point and the mean of the window-fit slopes of its two adjacent data points is calculated by dividing the absolute difference by This difference can be converted into a relative difference. In the stationary region of the signal, The value of approaches 0, so even small instantaneous amplitude abnormal trends can be sensitively captured; on the contrary, in areas where the signal changes drastically, anomalies are triggered only when the window fitting slope of the current data point deviates, avoiding mistaking normal fluctuations for abnormalities.

[0044] In summary, in The greater the absolute difference between the instantaneous amplitude of a data point and the instantaneous amplitude mean of its window, the greater the absolute difference between the instantaneous amplitude of the data point and the instantaneous amplitude mean of its window. The window fitting slope of the data points and the The greater the absolute difference between the data point and the average of the window fitting slopes of the adjacent data points on both sides, the greater the local abnormal trend of the data point, and the greater the possibility of the data point being an abnormal suspicious data point.

[0045] According to the above steps, the local abnormal trend of each data point in the current time sequence of the starting power cell can be obtained, and the suspicious data points can be preliminarily screened out. The more the suspicious data points, the greater the global trend abnormality degree of the current time sequence.

[0046] S3: determining the suspicious data points according to the comparison result of the local abnormal trend of the data points and the local threshold value, obtaining the proportion of the suspicious data points in the current time sequence, and constructing a slope value sequence based on the instantaneous amplitude slope values between the adjacent data points; and calculating the global abnormal trend of the current time sequence.

[0047] The local threshold value can be set to 0.5, and the local threshold value can be set according to actual needs.

[0048] It should be noted that in the case that the global abnormal trend of the current time sequence is relatively high, it indicates that the starting power cell is in an unstable state as a whole. At this time, even if the local abnormal trend of the data point is temporarily low, a smaller bin width also needs to be set to maintain the detection sensitivity. By calculating the overall abnormal trend based on the local abnormal trend, the combination of the two can be realized, thereby effectively avoiding misjudgment caused by local fluctuations and forming a more stable binning strategy.

[0049] For example, in the embodiment of the application, determining the suspicious data points according to the comparison result of the local abnormal trend of the data points and the local threshold value comprises: if the local abnormal trend of the data point is greater than the local threshold value, the data point is a suspicious data point.

[0050] For example, the ratio of the standard deviation and the average value of the slope value sequence is multiplied by the proportion of the suspicious data points in the current time sequence to obtain the global abnormal trend of the current time sequence.

[0051] In this calculation method, the ratio of the standard deviation and the average value of the slope value sequence is the coefficient of variation of the slope value sequence, which can measure the relative fluctuation intensity of the slope sequence. The greater the value, the more intense the slope fluctuation, and the more intense the instantaneous amplitude change of the current, which may correspond to a sudden change in the local abnormal trend.

[0052] The greater the proportion of the suspicious data points in the current time sequence, the more times the local mutation trend appears in the current time sequence, and the greater the global abnormal trend of the corresponding instantaneous amplitude value.

[0053] ​After obtaining the global abnormal trend of the current time series based on the above steps, the default bin width of the current time series can be adjusted according to the global abnormal trend of the current time series, so as to accurately detect abnormal values.

[0054] S4: Calculate the bin width of the current time series, and the bin width is negatively correlated with the global abnormal trend; use the bin width of the current time series in the HBOS algorithm to obtain the abnormal detection result of each data point.

[0055] For example, in the embodiment of the application, the calculation of the bin width of the current time series comprises: taking the negative number of the global abnormal trend of the current time series as the exponential input of the exponential function with e as the base to obtain a correction factor; and taking the product of the correction factor and the default bin width of the HBOS algorithm as the bin width of the current time series.

[0056] It can be understood that the greater the global abnormal trend of the current time series is, the default bin width of the traditional HBOS algorithm should be appropriately reduced to magnify the difference between abnormal points and normal points, so as to improve the detection sensitivity, therefore, the bin width is negatively correlated with the global abnormal trend.

[0057] For example, in the embodiment of the application, the method for obtaining the default bin width of the HBOS algorithm comprises: obtaining the difference between the maximum current value and the minimum current value in the current time series, and obtaining the default bin width according to the ratio of the difference to the preset bin number.

[0058] The preset bin number can be set to 30; the preset bin number can be set according to actual needs, and the embodiment of the application does not make too many limitations here.

[0059] For example, in the embodiment of the application, the use of the bin width of the current time series in the HBOS algorithm to obtain the abnormal detection result of each data point comprises: constructing a histogram of the current time series by using the bin width of the current time series, counting the data point distribution frequency in each bin interval to obtain the probability density of each bin interval; taking the negative logarithm of the probability density of the bin interval as the HBOS abnormal score of each data point in the bin interval; and obtaining the detection result of the starting power cell according to the HBOS abnormal score of the data point.

[0060] The specific steps of obtaining the probability density of each bin interval according to the data point distribution frequency can be obtained by the specific steps in the HBOS algorithm, and the embodiment of the application does not make too many limitations here.

[0061] For example, when obtaining the detection result of the starting power cell according to the HBOS abnormal score of the data point, an abnormal score threshold can be preset, and the data point greater than the abnormal score threshold is taken as abnormal current data.

[0062] The abnormal score threshold can be set according to actual needs.

[0063] It can be understood that the starting power supply can be continuously optimized and adjusted based on the repeated testing of the starting power supply cell, so as to improve the product performance. In order to facilitate subsequent processing of the staff, a report can be generated after each detection result of the current time sequence is obtained.

[0064] For example, in the embodiment of the application, the abnormal detection result of each data point is obtained, and then the detection report is generated according to the detection result of the current time sequence of the starting power supply cell.

[0065] The detection report can be generated online, and the form of the detection report can be in pdf format. The way of generating the detection report and the form of the detection report can be set according to actual needs, and the embodiment of the application does not make too many limitations here.

[0066] As can be seen, in the embodiment of the application, the instantaneous amplitude of each data point in the current time sequence of the starting power supply cell is obtained; a window of each data point is constructed based on a preset window size, and the instantaneous amplitude in the window is fitted to obtain the window fitting slope of the data point; the local abnormal trend of the data point is obtained according to the difference between the instantaneous amplitude of the data point and the mean value of the instantaneous amplitude of its window, the standard deviation of the instantaneous amplitude, and the mean value of the window fitting slopes of the data point and its two adjacent data points; the suspicious data point is determined according to the comparison result of the local abnormal trend of the data point and the local threshold, the proportion of the suspicious data point in the current time sequence is obtained, and the slope value sequence is constructed based on the instantaneous amplitude slope values between adjacent data points; the ratio of the standard deviation and the average value of the slope value sequence is multiplied by the proportion of the suspicious data point in the current time sequence, to obtain the global abnormal trend of the current time sequence; the bin width of the current time sequence is calculated, and the bin width is negatively correlated with the global abnormal trend; the bin width of the current time sequence is used in the HBOS algorithm to obtain the abnormal detection result of each data point, which effectively improves the detection result accuracy of the starting power supply cell.

[0067] The embodiment of the application also discloses a detection system for a starting power supply cell, which comprises a processor and a memory, and the memory stores computer program instructions.

[0068] The above system also includes a communication bus and a communication interface and other components familiar to those skilled in the art, the settings and functions of which are known in the art, and therefore will not be described here.

[0069] In this application, the aforementioned memory can be any tangible media that includes or stores a program or programs that can be used by or in connection with an instruction execution system, apparatus, or device.

[0070] The above are preferred embodiments of the present application, not to limit the protection scope of the present application, therefore: all equivalent changes made according to the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method for detecting a starting power cell, characterized in that: include: Obtain the instantaneous amplitude of each data point in the current time series of the starting power cell; A window is constructed for each data point based on a preset window size, and the instantaneous amplitude in the window is fitted to obtain the window fitting slope of the data point. The local abnormal trend of the data point is obtained based on the difference between the instantaneous amplitude of the data point and the mean instantaneous amplitude of its window, the standard deviation of the instantaneous amplitude, and the mean of the window fitting slopes of the data point and its adjacent data points on both sides. Suspicious data points are determined based on the comparison results of the local abnormal trend of the data points and the local threshold, the proportion of suspicious data points in the current time series is obtained, and a slope value sequence is constructed based on the instantaneous amplitude slope values ​​between adjacent data points; Multiply the ratio of the standard deviation and the mean of the slope value series by the proportion of suspicious data points in the current time series to obtain the global abnormal trend of the current time series; Calculate the bin width of the current time series. The bin width is negatively correlated with the global abnormal trend. The bin width of the current time series is used in the HBOS algorithm to obtain the anomaly detection results of each data point.

2. A method for detecting a starting power cell according to claim 1, characterized in that: The step of obtaining the instantaneous amplitude of each data point in the current time series of the starting power cell includes: The current time series data of the starting power cell during the detection process is collected and preprocessed to obtain a current time series sequence. The current time series sequence is processed using Hilbert transform to obtain the instantaneous amplitude of each data point in the current time series sequence.

3. The method for detecting a starting power cell according to claim 1, wherein: The step of constructing a window for each data point based on a preset window size and fitting the instantaneous amplitude in the window to obtain the window fitting slope of the data point includes: The default window size is ; Get continuous data with one data point as the center The data point is used as the window of the data point; the instantaneous amplitude in the window is fitted using the least squares method to obtain the window fitting slope of the data point.

4. The method for detecting a starting power cell according to claim 1, wherein: Obtaining the local abnormal trend of the data point includes: ； 、 Respectively The local abnormal trend and instantaneous amplitude of each data point, 、 Respectively The instantaneous amplitude mean and instantaneous amplitude standard deviation of the data point window, For the The slope of the window fit of data points, For the The mean of the slopes of the window fitting of a data point and its two adjacent data points, To prevent zero coefficient, is the normalization function, is the absolute value symbol.

5. The method for detecting a starting power cell according to claim 1, wherein: Determining suspicious data points based on a comparison result of a local abnormal trend of a data point with a local threshold value includes: If the local abnormal trend of a data point is greater than the local threshold, the data point is considered a suspicious data point.

6. The method for detecting a starting power cell according to claim 1, wherein: The calculating the bin width of the current timing sequence includes: The negative number of the global abnormal trend of the current time series is used as the exponential input of the exponential function with base e to obtain the correction factor; the product of the correction factor and the default bin width of the HBOS algorithm is used as the bin width of the current time series.

7. A method for detecting a starting power cell according to claim 6, characterized in that: The default bin width of the HBOS algorithm is obtained by: The difference between the maximum current and the minimum current in the current time series is obtained, and the default bin width is obtained according to the ratio of the difference to the preset bin number.

8. The method for detecting a starting power cell according to claim 1, wherein: The HBOS algorithm uses the bin width of the current time series to obtain the anomaly detection results of each data point, including: The histogram of the current time series is constructed using the bin width of the current time series, and the distribution frequency of the data points in each bin interval is counted to obtain the probability density of each bin interval; The negative logarithm of the probability density of the binning interval is used as the HBOS anomaly score of each data point in the binning interval; the detection result of the starting power cell is obtained according to the HBOS anomaly score of the data point.

9. The method for detecting a starting power cell according to claim 1, wherein: The abnormality detection results of each data point are obtained, and then the following steps are further included: Generate a test report based on the test results of the current timing sequence of the starting power cell.

10. A detection system for a starting power cell, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for detecting a starting power cell according to any one of claims 1 to 9 is implemented.

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