A unimodal distribution vehicle detection index anomaly detection method and terminal

Abstract

The application discloses a unimodal distribution vehicle detection index abnormality detection method, obtains historical detection index data of a vehicle battery, eliminates abnormal values of the historical detection index data meeting a preset rule to obtain reference detection index data; calculates a unimodal trend value of the reference detection index data, obtains a difference value abnormal interval according to a difference value between the unimodal trend value and the reference detection index data; obtains current detection index data of the vehicle battery, calculates a current trend value of the vehicle battery according to the current detection index data; judges whether the difference value between the current trend value and the current detection index data falls into the difference value abnormal interval, if yes, outputs that the vehicle battery is abnormal, and if not, outputs that the vehicle battery is normal. The application compares and analyzes possible changes before and after detection index data of the vehicle battery, accurately judges whether the vehicle battery detection index is normal, and guarantees vehicle driving safety.

Description

Technical Field

[0001] The present invention relates to the field of electronic information technology, and in particular to a method and a terminal for detecting abnormality of vehicle detection indicators with unimodal distribution. Background Art

[0002] With the rapid development of new energy applications, the popularity of electric vehicles is increasing. As a core component of electric vehicles, automotive batteries require strict testing indicators during production and operation. Therefore, accurately determining whether vehicle battery test indicators are normal is particularly important. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to propose a method and terminal for detecting abnormality of vehicle detection indicators with unimodal distribution, so as to accurately judge whether the vehicle battery detection indicators are normal and ensure the driving safety of the vehicle.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for detecting abnormality of vehicle detection indicators with unimodal distribution, comprising the following steps:

[0006] S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data;

[0007] S2. Calculate the unimodal trend value of the reference detection index data, and obtain an abnormal difference interval based on the difference between the unimodal trend value and the reference detection index data;

[0008] S3. Acquire current detection index data of the vehicle battery, and calculate a current trend value of the vehicle battery based on the current detection index data;

[0009] S4. Determine whether the difference between the current trend value and the current detection index data falls within the abnormal difference interval. If so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] A terminal for detecting abnormality of vehicle detection indicators with unimodal distribution includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0012] S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data;

[0013] S2. Calculate the unimodal trend value of the reference detection index data, and obtain an abnormal difference interval based on the difference between the unimodal trend value and the reference detection index data;

[0014] S3. Acquire current detection index data of the vehicle battery, and calculate a current trend value of the vehicle battery based on the current detection index data;

[0015] S4. Determine whether the difference between the current trend value and the current detection index data falls within the abnormal difference interval. If so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal.

[0016] The beneficial effects of the present invention are: providing a method and terminal for detecting abnormalities in vehicle detection indicators with a unimodal distribution, obtaining a difference abnormality interval as comparison data based on the difference between the reference detection indicator data after the abnormal values ​​of the vehicle battery are eliminated and the corresponding unimodal trend value, and then determining whether the vehicle battery is abnormal based on whether the difference between the current trend value and the current detection indicator data falls within the difference abnormality interval, comparing and analyzing the possible changes before and after the detection indicator data of the vehicle battery, accurately judging whether the vehicle battery detection indicators are normal, and ensuring the driving safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the steps of a method for detecting abnormality of vehicle detection indicators with unimodal distribution according to the present invention;

[0018] Figure 2 A flowchart of online anomaly detection for a method for detecting anomalies of vehicle detection indicators with unimodal distribution according to the present invention;

[0019] Figure 3 The present invention provides a system block diagram of a terminal for detecting abnormal vehicle detection indicators with unimodal distribution.

[0020] Description of labels:

[0021] 1. A terminal for detecting abnormality of vehicle detection indicators with unimodal distribution; 2. A memory; 3. A processor. DETAILED DESCRIPTION

[0022] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0023] Please refer to Figure 1 and Figure 2 , a method for detecting abnormality of vehicle detection indicators with unimodal distribution, comprising the following steps:

[0024] S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data;

[0025] S2. Calculate the unimodal trend value of the reference detection index data, and obtain an abnormal difference interval based on the difference between the unimodal trend value and the reference detection index data;

[0026] S3. Acquire current detection index data of the vehicle battery, and calculate a current trend value of the vehicle battery based on the current detection index data;

[0027] S4. Determine whether the difference between the current trend value and the current detection index data falls within the abnormal difference interval. If so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal.

[0028] From the above description, it can be seen that the beneficial effect of the present invention is that: based on the difference between the reference detection index data after the abnormal values ​​of the vehicle battery are eliminated and the corresponding single-peak trend value, the difference abnormal interval is obtained as the comparison data, and then according to whether the difference between the current trend value and the current detection index data falls into the difference abnormal interval, it is determined whether the vehicle battery is abnormal, and the possible changes before and after the detection index data of the vehicle battery are compared and analyzed, and the vehicle battery detection indicators are accurately judged whether they are normal to ensure the safety of vehicle driving.

[0029] Furthermore, the calculation of the single peak trend value of the reference detection index data is specifically as follows:

[0030] The trend item of the reference detection index data is extracted according to a time series decomposition algorithm to obtain the single-peak trend value.

[0031] From the above description, it can be seen that by introducing time series analysis, the changes that occur in the vehicle battery during use can be accurately represented as trend items of the reference detection index data, thereby deriving a single-peak trend value for theoretical analysis.

[0032] Furthermore, the abnormal difference interval obtained based on the difference between the single peak trend value and the reference detection index data is specifically:

[0033] Calculating the difference between each item of the reference detection index data and the single peak trend value to obtain a difference sequence;

[0034] The abnormal points and the abnormal interval of the last point in the difference sequence are identified based on the SH-ESD algorithm to obtain the abnormal difference interval.

[0035] From the above description, we can see that we first calculate the difference between each data item in the reference detection index data and the single-peak trend value, and then identify the abnormal points in the difference sequence based on the high recognition ability of the SH-ESD algorithm, and at the same time select the abnormal interval of the last abnormal point as the difference abnormal interval.

[0036] Furthermore, the preset rules include:

[0037] Calculate the mean and standard deviation of the historical detection indicator data;

[0038] The difference between each item of the historical detection index data and the average value and the ratio of the difference to the standard deviation are calculated to determine and eliminate the abnormal values.

[0039] From the above description, it can be seen that by introducing the mean and standard deviation, the degree of difference between each data and the mean of the entire historical detection index data can be accurately evaluated, and then the outliers can be determined to ensure that the difference anomaly interval calculated subsequently is more accurate and reasonable.

[0040] Furthermore, the step S2 further includes:

[0041] Establishing a time series decomposition model for detection indicators of the vehicle battery according to the time series decomposition algorithm;

[0042] The current trend value of the vehicle battery currently predicted based on the current detection index data is specifically:

[0043] Substitute the current detection index data into the detection index time series decomposition model to calculate the current trend value.

[0044] From the above description, it can be seen that after using the time series decomposition algorithm to analyze the trend items of the reference detection index data, a detection index time series decomposition model based on the time series decomposition algorithm is established, so that when the current data is subsequently analyzed, the detection index time series decomposition model is used to calculate the current trend value to improve the speed and accuracy of data processing.

[0045] Please refer to Figure 3 A unimodal distribution vehicle detection index abnormality detection terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the following steps are implemented:

[0046] S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data;

[0047] S2. Calculate the unimodal trend value of the reference detection index data, and obtain an abnormal difference interval based on the difference between the unimodal trend value and the reference detection index data;

[0048] S3. Acquire current detection index data of the vehicle battery, and calculate a current trend value of the vehicle battery based on the current detection index data;

[0049] S4. Determine whether the difference between the current trend value and the current detection index data falls within the abnormal difference interval. If so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal.

[0050] From the above description, it can be seen that the beneficial effect of the present invention is that: based on the difference between the reference detection index data after the abnormal values ​​of the vehicle battery are eliminated and the corresponding single-peak trend value, the difference abnormal interval is obtained as the comparison data, and then according to whether the difference between the current trend value and the current detection index data falls into the difference abnormal interval, it is determined whether the vehicle battery is abnormal, and the possible changes before and after the detection index data of the vehicle battery are compared and analyzed, and the vehicle battery detection indicators are accurately judged whether they are normal to ensure the safety of vehicle driving.

[0051] Furthermore, the calculation of the single peak trend value of the reference detection index data is specifically as follows:

[0052] The trend item of the reference detection index data is extracted according to a time series decomposition algorithm to obtain the single-peak trend value.

[0053] From the above description, it can be seen that by introducing time series analysis, the changes that occur in the vehicle battery during use can be accurately represented as trend items of the reference detection index data, thereby deriving a single-peak trend value for theoretical analysis.

[0054] Furthermore, the abnormal difference interval obtained based on the difference between the single peak trend value and the reference detection index data is specifically:

[0055] Calculating the difference between each item of the reference detection index data and the single peak trend value to obtain a difference sequence;

[0056] The abnormal points and the abnormal interval of the last point in the difference sequence are identified based on the SH-ESD algorithm to obtain the abnormal difference interval.

[0057] From the above description, we can see that we first calculate the difference between each data item in the reference detection index data and the single-peak trend value, and then identify the abnormal points in the difference sequence based on the high recognition ability of the SH-ESD algorithm, and at the same time select the abnormal interval of the last abnormal point as the difference abnormal interval.

[0058] Furthermore, the preset rules include:

[0059] Calculate the mean and standard deviation of the historical detection indicator data;

[0060] The difference between each item of the historical detection index data and the average value and the ratio of the difference to the standard deviation are calculated to determine and eliminate the abnormal values.

[0061] From the above description, it can be seen that by introducing the mean and standard deviation, the degree of difference between each data and the mean of the entire historical detection index data can be accurately evaluated, and then the outliers can be determined to ensure that the difference anomaly interval calculated subsequently is more accurate and reasonable.

[0062] Furthermore, the step S2 further includes:

[0063] Establishing a time series decomposition model for detection indicators of the vehicle battery according to the time series decomposition algorithm;

[0064] The current trend value of the vehicle battery currently predicted based on the current detection index data is specifically:

[0065] Substitute the current detection index data into the detection index time series decomposition model to calculate the current trend value.

[0066] From the above description, it can be seen that after using the time series decomposition algorithm to analyze the trend items of the reference detection index data, a detection index time series decomposition model based on the time series decomposition algorithm is established, so that when the current data is subsequently analyzed, the detection index time series decomposition model is used to calculate the current trend value to improve the speed and accuracy of data processing.

[0067] Please refer to Figure 1 and Figure 2 , embodiment 1 of the present invention is:

[0068] A method for detecting abnormality of vehicle detection indicators with unimodal distribution, comprising the following steps:

[0069] S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data;

[0070] In this embodiment, the preset rules include: obtaining historical detection index data of the vehicle battery, calculating the average value and standard deviation of the historical detection index data; and eliminating abnormal values ​​of the historical detection index data that meet the preset rules based on the difference between each item of the historical detection index data and the average value and the standard deviation.

[0071] The above steps specifically use the z-score method and 3 times the standard deviation to eliminate outliers in historical data, as explained below:

[0072] The Z-score is a commonly used concept in statistics. It is a standardized score used to represent the position of a data point in its dataset. The Z-score measures how different a data point is from the mean of its dataset and how it performs compared to the standard deviation of the dataset. Generally, data points with a Z-score between -3 and +3 are considered to be within the normal range. The Z-score calculation formula is as follows:

[0073]

[0074] Where Z is the z-score, X is the value of the data point, u is the mean of the data points, and σ is the standard deviation.

[0075] In addition, it should be noted that, in addition to the indicator data of automobile batteries with a single peak distribution, this embodiment can also be applied to data objects such as capacity retention rate, maximum voltage of a single cell during charging, maximum temperature of a single cell during charging, maximum muzzle temperature during charging, charging SOC accuracy, maximum muzzle voltage during charging, battery DC internal resistance, temperature difference, temperature rise, etc.

[0076] S2. Calculate the unimodal trend value of the reference test index data, and obtain the difference abnormality interval based on the difference between the unimodal trend value and the reference test index data; wherein, extract the trend item of the reference test index data according to the time series decomposition algorithm to obtain the unimodal trend value, and then establish the test index time series decomposition model corresponding to the vehicle battery according to the time series decomposition algorithm;

[0077] In this embodiment, the time series decomposition algorithm is preferably STL (Seasonal-Trend Decomposition Procedure based on Loess): In time series analysis, the STL method is used to decompose time series data into three parts: seasonality, trend, and residual.

[0078] After obtaining the unimodal trend value according to the STL algorithm, the difference between each data item in the reference detection index data and the unimodal trend value is calculated to obtain the difference sequence; based on the SH-ESD algorithm, the abnormal points in the difference sequence and the abnormal interval of the last point are identified to obtain the difference abnormal interval.

[0079] Among them, SH-ESD is an anomaly detection method, the full name of which is "Seasonal Hybrid Extreme Studentized Deviate". This method is used to detect outliers in time series data, especially for seasonal data sets. Combined with the above content, SH-ESD processes the time series data through STL (Seasonal-Temporal decomposition using Loess) decomposition, first removing the seasonal term, and then using the median as the trend term to perform ESD test on the residual term. This processing method can not only detect global outliers, but also identify local outliers. As a preferred method, the anomaly interval of the last identified outlier is used as the difference anomaly interval.

[0080] S3. Obtain current detection index data of the vehicle battery, and calculate the current trend value of the vehicle battery based on the current detection index data;

[0081] Corresponding to the above content, the current detection index data is substituted into the detection index time series decomposition model to calculate the current trend value.

[0082] S4. Determine whether the difference between the current trend value and the current detection index data falls into the abnormal difference interval. If so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal.

[0083] Please refer to Figure 3 , the second embodiment of the present invention is:

[0084] A unimodal distribution vehicle detection index anomaly detection terminal 1 includes a memory 2, a processor 3 and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, a unimodal distribution vehicle detection index anomaly detection method of embodiment 1 is implemented.

[0085] In summary, the present invention provides a method and terminal for detecting abnormalities in vehicle detection indicators with a unimodal distribution, which combines the use of Z-score, STL algorithm and SH-ESD algorithm to calculate the difference between the reference detection index data of the vehicle battery after removing abnormal values ​​and the corresponding unimodal trend value, and obtain the difference abnormal interval as comparison data. Then, based on whether the difference between the current trend value and the current detection index data falls into the difference abnormal interval, it is determined whether the vehicle battery is abnormal, and the possible changes before and after the detection index data of the vehicle battery are compared and analyzed to accurately judge whether the vehicle battery detection indicators are normal, thereby ensuring the safety of vehicle driving.

[0086] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for detecting abnormality of vehicle detection indicators with unimodal distribution, characterized in that: The steps include: S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data; S2. Calculate the unimodal trend value of the reference detection index data, and obtain an abnormal difference interval based on the difference between the unimodal trend value and the reference detection index data; S3. Acquire current detection index data of the vehicle battery, and calculate a current trend value of the vehicle battery based on the current detection index data; S4. Determine whether the difference between the current trend value and the current detection index data falls within the abnormal difference interval; if so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal; The calculation of the single peak trend value of the reference detection index data is specifically as follows: Extracting the trend item of the reference detection index data according to a time series decomposition algorithm to obtain the single peak trend value; The step S2 further includes: Establishing a time series decomposition model for detection indicators of the vehicle battery according to the time series decomposition algorithm; The current trend value of the vehicle battery currently predicted based on the current detection index data is specifically: Substitute the current detection index data into the detection index time series decomposition model to calculate the current trend value.

2. The method for detecting abnormality of vehicle detection indicators with unimodal distribution according to claim 1, characterized in that: The abnormal difference interval obtained based on the difference between the single peak trend value and the reference detection index data is specifically: Calculating the difference between each item of the reference detection index data and the single peak trend value to obtain a difference sequence; The abnormal points and the abnormal interval of the last point in the difference sequence are identified based on the SH-ESD algorithm to obtain the abnormal difference interval.

3. The method for detecting abnormality of vehicle detection indicators with unimodal distribution according to claim 1, characterized in that: The preset rules include: Calculate the mean and standard deviation of the historical detection indicator data; The difference between each item of the historical detection index data and the average value and the ratio of the difference to the standard deviation are calculated to determine and eliminate the abnormal values.

4. A terminal for detecting abnormality of vehicle detection indicators with unimodal distribution, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the following steps are implemented: S1. Obtain historical test index data of the vehicle battery, remove abnormal values ​​in the historical test index data that meet preset rules, and obtain reference test index data; S2. Calculate the unimodal trend value of the reference detection index data, and obtain an abnormal difference interval based on the difference between the unimodal trend value and the reference detection index data; S3. Acquire current detection index data of the vehicle battery, and calculate a current trend value of the vehicle battery based on the current detection index data; S4. Determine whether the difference between the current trend value and the current detection index data falls within the abnormal difference interval; if so, output that the vehicle battery is abnormal; otherwise, output that the vehicle battery is normal; The calculation of the single peak trend value of the reference detection index data is specifically as follows: Extracting the trend item of the reference detection index data according to a time series decomposition algorithm to obtain the single peak trend value; The step S2 further includes: Establishing a time series decomposition model for detection indicators of the vehicle battery according to the time series decomposition algorithm; The current trend value of the vehicle battery currently predicted based on the current detection index data is specifically: Substitute the current detection index data into the detection index time series decomposition model to calculate the current trend value.

5. The terminal for detecting abnormality of vehicle detection indicators with unimodal distribution according to claim 4, characterized in that: The abnormal difference interval obtained based on the difference between the single peak trend value and the reference detection index data is specifically: Calculating the difference between each item of the reference detection index data and the single peak trend value to obtain a difference sequence; The abnormal points and the abnormal interval of the last point in the difference sequence are identified based on the SH-ESD algorithm to obtain the abnormal difference interval.

6. The terminal for detecting abnormality of vehicle detection indicators with unimodal distribution according to claim 4, characterized in that: The preset rules include: Calculate the mean and standard deviation of the historical detection indicator data; The difference between each item of the historical detection index data and the average value and the ratio of the difference to the standard deviation are calculated to determine and eliminate the abnormal values.

Images