An intelligent performance detection method for an oil drum brake device for electric vehicles

Abstract

The present invention relates to the field of data processing technology, and specifically to an intelligent performance detection method for an oil drum brake device for electric vehicles, comprising: obtaining pressure data, temperature data, speed, time and driving distance of each brake of the electric vehicle at the beginning of each brake, obtaining attribute values ​​of each brake according to the driving distance and pressure data, constructing a three-dimensional coordinate system with temperature, speed and attribute values ​​as coordinate axes, calculating the characteristic value of each data point, arranging all data points into a sequence in chronological order, obtaining the abnormality degree of the characteristic value according to the characteristic value of each data point, and then obtaining the abnormality degree of each data point, and combining the characteristic value to obtain the performance evaluation index of the drum brake device, and judging the qualification of the drum brake device. The present invention obtains the performance evaluation index of the drum brake device by analyzing and calculating multiple dimensional data during braking, and better visualizes the performance of the brake drum.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent performance detection method for an oil drum brake device for an electric vehicle. Background Art

[0002] The oil drum brake device for electric vehicles is a common braking system, usually composed of an oil pump, an oil drum, a brake hose, a brake cylinder, a brake drum, etc. Its working principle is to send brake fluid to the brake cylinder through the oil pump to generate pressure, push the brake pads in the brake drum to rub against the brake drum, thereby realizing the braking function of the vehicle. The performance test of the oil drum brake device for electric vehicles is very important, which can ensure the normal operation of the braking system and ensure driving safety. Its performance test usually includes test data such as braking force, braking distance, brake temperature, etc., so as to judge whether its performance meets the design requirements, so as to timely discover and solve potential problems and ensure the safety and reliability of the vehicle during braking.

[0003] When evaluating the performance of the oil drum brake device for electric vehicles, the performance analysis of the brake device will be affected by a variety of data such as the temperature of the brake drum when the brake device is in use, the speed of the electric vehicle, the braking distance, and the usage time, which will affect the complexity and accuracy of the brake performance evaluation and analysis. Summary of the invention

[0004] The invention provides an intelligent performance detection method for an oil drum brake device for an electric vehicle to solve the existing problems.

[0005] The intelligent performance detection method of the oil drum brake device for electric vehicles of the present invention adopts the following technical scheme:

[0006] An embodiment of the present invention provides a method for intelligently detecting the performance of an oil drum brake device for an electric vehicle, the method comprising the following steps:

[0007] Obtain the pressure data of the brake device at the beginning of each braking of the electric vehicle, the temperature data of the brake drum, the speed and time of the electric vehicle, and the travel distance between the beginning and the end of each braking;

[0008] According to the driving distance between the start and the end of each braking and the pressure data of the braking device, the attribute value of each braking is obtained;

[0009] The temperature data of the brake drum at the beginning of each braking of the electric vehicle is used as the horizontal axis, the speed of the electric vehicle at the beginning of each braking is used as the vertical axis, and the attribute value of each braking is used as the vertical axis to construct a three-dimensional coordinate system corresponding to all braking times; according to the attribute value of each data point in the three-dimensional coordinate system and the distance between each data point in the three-dimensional coordinate system and the coordinate origin, the characteristic value of each data point in the three-dimensional coordinate system is obtained;

[0010] Arrange all data points in the three-dimensional coordinate system in the order of the time when each braking starts to obtain a sequence; obtain the abnormality degree of the characteristic value of each data point in the sequence according to the characteristic value of each data point in the sequence; obtain the abnormality degree of each data point in the sequence according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value and the time when each data point corresponds to the start of braking;

[0011] According to the abnormality degree and characteristic value of each data point in the sequence, the performance evaluation index of the drum brake device is obtained; according to the performance evaluation index of the drum brake device, the qualification of the drum brake device is judged.

[0012] Furthermore, the method of obtaining the attribute value of each braking according to the driving distance between the start and the end of each braking and the pressure data of the braking device includes the following specific steps:

[0013] The ratio of the driving distance between the start and end of each braking to the pressure data of the braking device at the start of each braking is recorded as the attribute value of each braking of the electric vehicle.

[0014] Furthermore, the specific calculation formula for obtaining the characteristic value of each data point in the three-dimensional coordinate system according to the attribute value of each data point in the three-dimensional coordinate system and the distance between each data point in the three-dimensional coordinate system and the coordinate origin is:

[0015]

[0016] Among them, T l Represents the eigenvalue of the lth data point in the three-dimensional coordinate system; Z′ l Represents the attribute value corresponding to the lth data point in the three-dimensional coordinates; Z′ l,r represents the attribute value corresponding to the rth data point except the lth data point in the three-dimensional coordinate system; m represents the number of data points in the three-dimensional coordinate system; O l Represents the distance between the origin and the lth data point in the three-dimensional coordinate system; l,r Represents the distance between the origin of the three-dimensional coordinate system and the rth data point excluding the lth data point.

[0017] Furthermore, the specific calculation formula for obtaining the abnormality degree of the characteristic value of each data point in the sequence according to the characteristic value of each data point in the sequence is:

[0018]

[0019] Among them, C j Indicates the abnormality of the characteristic value of the jth data point in the sequence; T′ jrepresents the eigenvalue of the jth data point in the sequence; mid(T′) represents the eigenvalue of the data point corresponding to the median of the sequence; T′ j+1 represents the eigenvalue of the j+1th data point in the sequence; T′ j-1 represents the eigenvalue of the j-1th data point in the sequence; || is the absolute value function.

[0020] Furthermore, the abnormality degree of each data point in the sequence is obtained according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value and the time when each data point corresponds to the start of braking, and the specific steps include the following:

[0021] According to the characteristic value of each data point in the sequence and the time when the corresponding braking starts, the characteristic value difference between any two data points in the sequence due to the use time is obtained;

[0022] The abnormality degree of each data point in the sequence is obtained according to the abnormality degree of the characteristic value of each data point in the sequence and the difference of the characteristic values ​​between any two data points in the sequence due to the usage time.

[0023] Furthermore, the specific calculation formula for obtaining the abnormality degree of each data point in the sequence according to the abnormality degree of the characteristic value of each data point in the sequence and the difference of the characteristic values ​​between any two data points in the sequence due to the usage time is:

[0024]

[0025] Among them, Y j Indicates the degree of abnormality of the jth data point in the sequence; C j Indicates the abnormality of the characteristic value of the jth data point in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; T′ j,s represents the characteristic value of the sth data point in the sequence except the jth data point; m′ represents the number of all data points in the sequence; t j represents the time interval between the braking start time corresponding to the jth data point in the sequence and the braking start time corresponding to the first data point; t j,s It represents the time interval between the braking start time corresponding to the sth data point and the braking start time corresponding to the first data point in the sequence except the jth data point; norm[] is a linear normalization function; It is the difference of the characteristic value between the j-th data point and the s-th data point except the j-th data point in the sequence due to the usage time.

[0026] Furthermore, the performance evaluation index of the drum brake device is obtained according to the abnormality degree and characteristic value of each data point in the sequence, and the specific steps include the following:

[0027] According to the abnormality degree of each data point in the sequence, the overall abnormality degree of all data points in the sequence is obtained;

[0028] According to the overall abnormality degree of all data points in the sequence and the characteristic value of each data point, the performance evaluation index of the drum brake device is obtained.

[0029] Furthermore, the specific calculation formula for obtaining the overall abnormality degree of all data points in the sequence according to the abnormality degree of each data point in the sequence is:

[0030]

[0031] Among them, U represents the overall abnormality of all data points in the sequence; m′ represents the number of all data points in the sequence; represents the mean value of the abnormality of all data points in the sequence; min(Y) represents the minimum value of the abnormality of all data points in the sequence; Y j Indicates the degree of abnormality of the jth data point in the sequence; || is the absolute value function; norm() is the linear normalization function.

[0032] Furthermore, the specific calculation formula for obtaining the performance evaluation index of the drum brake device according to the overall abnormality degree of all data points in the sequence and the characteristic value of each data point is:

[0033]

[0034] Where W represents the performance evaluation index of the drum brake device; m′ represents the number of all data points in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; t represents the time interval between the first and last data points in the sequence when braking begins; U represents the overall abnormality of all data points in the sequence; exp[] is an exponential function with a natural constant as the base.

[0035] Furthermore, judging the eligibility of the drum brake device according to the performance evaluation index of the drum brake device includes the following specific steps:

[0036] When the performance evaluation index of the drum brake device is greater than a preset threshold, the drum brake device is determined to be qualified;

[0037] When the performance evaluation index of the drum brake device is less than or equal to a preset threshold, the drum brake device is determined to be unqualified.

[0038] The beneficial effects of the technical solution of the present invention are:

[0039] Obtain the pressure data of the brake device at the beginning of each braking of the electric vehicle, the temperature data of the brake drum, the speed of the electric vehicle, the time, and the driving distance between the beginning and the end of each braking, so as to provide basic data for subsequent performance testing; obtain the attribute value of each braking according to the driving distance between the beginning and the end of each braking and the pressure data of the brake device; construct a three-dimensional coordinate system corresponding to all braking times with the temperature data of the brake drum at the beginning of each braking of the electric vehicle as the horizontal axis, the speed of the electric vehicle at the beginning of each braking as the vertical axis, and the attribute value of each braking as the vertical axis; obtain the distance between each data point in the three-dimensional coordinate system and the origin of the coordinate system according to the attribute value of each data point in the three-dimensional coordinate system. The characteristic value of the electric vehicle is used to avoid the influence of the vehicle speed or brake drum temperature when the electric vehicle brakes; all data points in the three-dimensional coordinate system are arranged in the order of the time when each braking starts to obtain a sequence; according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value of each data point in the sequence is obtained; according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value and the time when each data point corresponds to the start of braking, the abnormality degree of each data point in the sequence is obtained; according to the abnormality degree and characteristic value of each data point in the sequence, a more accurate performance evaluation index of the drum brake device is obtained; according to the performance evaluation index of the drum brake device, the qualification of the drum brake device is judged. So far, the present invention makes an accurate evaluation of the performance evaluation index of the drum brake device, better visualizes the performance of the brake drum, and thus provides an accurate basis for whether the performance of the brake drum meets the production standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 The present invention is a flowchart of the steps of an intelligent performance detection method for an oil drum brake device for an electric vehicle. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent performance detection method of the oil drum brake device for electric vehicles proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] The specific scheme of the intelligent performance detection method of the oil drum brake device for electric vehicles provided by the present invention is described in detail below with reference to the accompanying drawings.

[0045] See also Figure 1 , which shows a flowchart of a method for intelligently detecting the performance of an oil drum brake device for an electric vehicle provided by an embodiment of the present invention, the method comprising the following steps:

[0046] Step S001: Obtain the pressure data of the brake device at the beginning of each braking of the electric vehicle, the temperature data of the brake drum, the speed of the electric vehicle, the time, and the driving distance between the beginning and the end of each braking.

[0047] For any drum brake device to be tested, the pressure data of the brake device at the beginning of each braking of the electric vehicle, the temperature data of the brake drum, the speed of the electric vehicle, the time and the driving distance between the beginning and the end of each braking are collected based on multiple sensors.

[0048] Step S002: Obtain the attribute value of each braking according to the driving distance between the start and the end of each braking and the pressure data of the braking device.

[0049] When the surface temperature of the brake drum and the speed of the electric vehicle at the beginning of each braking remain relatively uniform, the braking effect of each braking is the same. That is to say, under limited conditions, the driving distance between the start and the end of braking and the pressure applied by the braking device at the beginning of braking are in a certain inverse proportional relationship.

[0050] The ratio of the driving distance between the start and end of each braking to the pressure data of the braking device at the start of each braking is recorded as the attribute value of each braking of the electric vehicle.

[0051] Step S003: Use the temperature data of the brake drum at the beginning of each braking of the electric vehicle as the horizontal axis, the speed of the electric vehicle at the beginning of each braking as the vertical axis, and the attribute value of each braking as the vertical axis to construct a three-dimensional coordinate system corresponding to all braking times; obtain the characteristic value of each data point in the three-dimensional coordinate system according to the attribute value of each data point in the three-dimensional coordinate system and the distance between each data point in the three-dimensional coordinate system and the coordinate origin.

[0052] The temperature data of the brake drum at the beginning of each braking of the electric vehicle is used as the horizontal axis, the speed of the electric vehicle at the beginning of each braking is used as the vertical axis, and the attribute value of each braking is used as the vertical axis to construct a three-dimensional coordinate system corresponding to all braking times.

[0053] In a three-dimensional coordinate system, if the electric vehicle maintains the same speed and the brake drum surface temperature is the same, the attribute value of the electric vehicle should be the same each time it brakes. When there is unstable or abnormal braking, the attribute value of the brake will be different from the attribute value of the brake under similar conditions.

[0054] According to the attribute value of each data point in the three-dimensional coordinate system and the distance between each data point and the coordinate origin in the three-dimensional coordinate system, the characteristic value of each data point in the three-dimensional coordinate system is obtained. The calculation formula is as follows:

[0055]

[0056] Among them, T l Represents the eigenvalue of the lth data point in the three-dimensional coordinate system; Z′ l Represents the attribute value corresponding to the lth data point in the three-dimensional coordinates; Z′ l,r represents the attribute value corresponding to the rth data point except the lth data point in the three-dimensional coordinate system; m represents the number of data points in the three-dimensional coordinate system; O l Represents the distance between the origin and the lth data point in the three-dimensional coordinate system; l,r Represents the distance between the origin of the three-dimensional coordinate system and the rth data point excluding the lth data point.

[0057] It should be noted that It represents the ratio of the attribute value of the lth data point to the attribute value of the rth data point except the lth data point; It represents the ratio of the distance between the origin and the lth data point in the three-dimensional coordinate system to the distance between the origin and the rth data point except the lth data point; It indicates the difference between the lth data point and the rth data point except the lth data point in the three-dimensional coordinate system. The smaller the value, the closer the lth data point is to normal, and the smaller its eigenvalue is. On the contrary, the closer it is to abnormality, the larger its eigenvalue is. It represents the mean of the difference between the lth data point and each of the other data points in the three-dimensional coordinate system, and this value represents the eigenvalue of the lth data point. By calculating the eigenvalue of each data point, the influence of the vehicle speed or brake drum temperature on the electric vehicle during braking can be avoided, making the subsequent analysis of the braking performance lower in dimension, more convenient and accurate.

[0058] Step S004: Arrange all data points in the three-dimensional coordinate system in the order of the time when each braking starts to obtain a sequence; obtain the abnormality degree of the characteristic value of each data point in the sequence according to the characteristic value of each data point in the sequence; obtain the abnormality degree of each data point in the sequence according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value and the time when each data point corresponds to the start of braking.

[0059] As the use time of electric vehicles increases, the brake device will wear out and its performance may decline. The characteristic value of the data point corresponding to each brake may increase appropriately. If the performance of the electric vehicle brake declines more steadily, then its braking performance may be relatively good, and its performance stability may also be relatively good. When the brake drum brakes unstably, the characteristic value of the data point may differ greatly from the braking characteristic value of the similar time node.

[0060] Arrange all data points in the three-dimensional coordinate system in the order of the time when each braking starts to obtain a sequence. The calculation formula for the abnormality degree of the characteristic value of each data point in the sequence is as follows:

[0061]

[0062] Among them, C j Indicates the abnormality of the characteristic value of the jth data point in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; mid(T′) represents the eigenvalue of the data point corresponding to the median of the sequence; T′ j+1 represents the eigenvalue of the j+1th data point in the sequence; T′ j-1 represents the eigenvalue of the j-1th data point in the sequence; || is the absolute value function.

[0063] It should be noted that It represents the ratio of the eigenvalue of the jth data point in the sequence to the eigenvalue of the data point corresponding to the median of the sequence. Because the median is not affected by extreme values, it can better reflect the central position of the data. Therefore, the larger the ratio, the greater the abnormality of the eigenvalue of the jth data point in the sequence, and vice versa. It represents the mean of the difference between the eigenvalues ​​of the j-th data point in the sequence and its adjacent data points. When the braking performance of the data point is unstable, its eigenvalue is greatly different from the eigenvalues ​​of the adjacent data points before and after it, which makes the stability of the braking performance change poor. Therefore, the larger the value, the more abnormal the eigenvalue of the j-th data point in the sequence may be.

[0064] According to the abnormal degree of the characteristic value of each data point in the sequence, combined with the time node information of the brake collection corresponding to each data point, the calculation formula for the abnormal degree of each data point in the sequence is as follows:

[0065]

[0066] Among them, Y j Indicates the degree of abnormality of the jth data point in the sequence; C j Indicates the abnormality of the characteristic value of the jth data point in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; T′ j,s represents the characteristic value of the sth data point in the sequence except the jth data point; m′ represents the number of all data points in the sequence; t j represents the time interval between the braking start time corresponding to the jth data point in the sequence and the braking start time corresponding to the first data point; t j,s It represents the time interval between the braking start time corresponding to the sth data point and the braking start time corresponding to the first data point in the sequence except the jth data point; norm[] is a linear normalization function that normalizes the data value to the interval [0,1].

[0067] It should be noted that the greater the abnormality of the characteristic value of the jth data point in the sequence, the greater the abnormality of the data point; It is the difference of eigenvalues ​​due to the usage time between the jth data point in the sequence and the sth data point except the jth data point. The specific calculation process is the ratio of the eigenvalue of the jth data point in the sequence to the eigenvalue of the sth data point except the jth data point in the sequence divided by the time interval between the start of braking corresponding to the jth data point in the sequence and the start of braking corresponding to the first data point and the time interval between the start of braking corresponding to the sth data point except the jth data point in the sequence and the start of braking corresponding to the first data point. Because the brake drum will have a certain degree of wear as the electric vehicle is used, the attenuation of its braking performance shows a certain positive correlation with time. Therefore, this value represents the difference of eigenvalues ​​due to the usage time between the two data points. It represents the mean of the sum of the differences in the eigenvalues ​​of the j-th data point in the sequence and all the other data points in the sequence except the j-th data point due to the usage time. The larger the value is, the worse the correlation between the j-th data point in the sequence and the other data points in following the data changes is, and the more likely it is to be an abnormal data point, and the greater the degree of abnormality of the data point is.

[0068] Step S005: obtaining a performance evaluation index of the drum brake device according to the abnormality degree and characteristic value of each data point in the sequence; judging the eligibility of the drum brake device according to the performance evaluation index of the drum brake device.

[0069] According to the abnormal degree of all data points in the sequence, the overall abnormal degree of all data points in the sequence is obtained;

[0070]

[0071] Among them, U represents the overall abnormality of all data points in the sequence; m′ represents the number of all data points in the sequence; represents the mean value of the abnormality of all data points in the sequence; min(Y) represents the minimum value of the abnormality of all data points in the sequence; Y j Indicates the degree of abnormality of the jth data point in the sequence; || is the absolute value function; norm() is the linear normalization function, which normalizes the data value to the interval [0,1].

[0072] It should be noted that It is the ratio of the mean value of the abnormality of all data points in the sequence to the minimum value of the abnormality of all data points in the sequence. This ratio reflects the overall abnormality of all data points in the sequence. The smaller the ratio, the higher the performance evaluation index of the drum brake device. It is the mean of the difference between the abnormality degree of each data point in the sequence and the minimum abnormality degree of all data points in the sequence. The smaller the value, the smaller the overall abnormality degree of all data points in the sequence.

[0073] According to the overall abnormality degree of all data points in the sequence and the characteristic value of each data point, the performance evaluation index of the drum brake device is obtained, and its calculation formula is as follows:

[0074]

[0075] Where W represents the performance evaluation index of the drum brake device; m′ represents the number of all data points in the sequence; T′ jrepresents the eigenvalue of the jth data point in the sequence; t represents the time interval between the first data point and the last data point in the sequence when braking begins; U represents the overall abnormality of all data points in the sequence; exp[] is an exponential function with a natural constant as the base. In this embodiment, exp[-] is used to present the inverse proportional relationship and normalization processing. The implementer can set the inverse proportional function and normalization function according to the actual situation.

[0076] It should be noted that It is the ratio of the sum of the eigenvalues ​​of all data points in the sequence to the time interval between the first and last data points in the sequence when braking starts. This ratio indicates the rate of performance decay of the drum brake device. The smaller the ratio, the slower the braking performance decays, that is, the higher the performance evaluation index of the drum brake device. The smaller the overall abnormality of all data points in the sequence, the higher the performance evaluation index of the drum brake device.

[0077] According to the above method, the performance evaluation index W of the drum brake device is obtained. The larger the W value is, the better the performance of the drum brake device is.

[0078] The threshold a=0.7 is preset in this embodiment and is described as an example. Other values ​​may be set in other implementations and are not limited in this embodiment.

[0079] When the performance evaluation index of the drum brake device is greater than a preset threshold, the drum brake device is determined to be qualified;

[0080] When the performance evaluation index of the drum brake device is less than or equal to a preset threshold, the drum brake device is determined to be unqualified.

[0081] So far, the present invention is completed.

[0082] In summary, in an embodiment of the present invention, the pressure data, temperature data, speed, time and driving distance of each braking of the electric vehicle are obtained, and the attribute value of each braking is obtained according to the driving distance and pressure data, and a three-dimensional coordinate system with temperature, speed and attribute value as coordinate axes is constructed, and the characteristic value of each data point is calculated, and all data points are arranged into a sequence in chronological order. According to the characteristic value of each data point, the abnormality degree of its characteristic value is obtained, and then the abnormality degree of each data point is obtained, and combined with its characteristic value, the performance evaluation index of the drum brake device is obtained, and the qualification of the drum brake device is judged. The present invention obtains the performance evaluation index of the drum brake device by analyzing and calculating multiple dimensional data during braking, and better visualizes the performance of the brake drum.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent performance detection method for an oil drum brake device for an electric vehicle, characterized in that: The method comprises the following steps: Obtain the pressure data of the brake device at the beginning of each braking of the electric vehicle, the temperature data of the brake drum, the speed and time of the electric vehicle, and the travel distance between the beginning and the end of each braking; According to the driving distance between the start and the end of each braking and the pressure data of the braking device, the attribute value of each braking is obtained; The temperature data of the brake drum at the beginning of each braking of the electric vehicle is used as the horizontal axis, the speed of the electric vehicle at the beginning of each braking is used as the vertical axis, and the attribute value of each braking is used as the vertical axis to construct a three-dimensional coordinate system corresponding to all braking times; according to the attribute value of each data point in the three-dimensional coordinate system and the distance between each data point in the three-dimensional coordinate system and the coordinate origin, the characteristic value of each data point in the three-dimensional coordinate system is obtained; Arrange all data points in the three-dimensional coordinate system in the order of the time when each braking starts to obtain a sequence; obtain the abnormality degree of the characteristic value of each data point in the sequence according to the characteristic value of each data point in the sequence; obtain the abnormality degree of each data point in the sequence according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value and the time when each data point corresponds to the start of braking; According to the abnormality degree and characteristic value of each data point in the sequence, the performance evaluation index of the drum brake device is obtained; according to the performance evaluation index of the drum brake device, the qualification of the drum brake device is judged; The specific steps of obtaining the attribute value of each braking according to the driving distance between the start and the end of each braking and the pressure data of the braking device are as follows: The ratio of the driving distance between the start and end of each braking to the pressure data of the braking device at the start of each braking is recorded as the attribute value of each braking of the electric vehicle; The specific calculation formula for obtaining the characteristic value of each data point in the three-dimensional coordinate system according to the attribute value of each data point in the three-dimensional coordinate system and the distance between each data point in the three-dimensional coordinate system and the coordinate origin is: Among them, T l Represents the eigenvalue of the lth data point in the three-dimensional coordinate system; Z′ l Represents the attribute value corresponding to the lth data point in the three-dimensional coordinates; Z′ l,r represents the attribute value corresponding to the rth data point except the lth data point in the three-dimensional coordinate system; m represents the number of data points in the three-dimensional coordinate system; O l Represents the distance between the origin and the lth data point in the three-dimensional coordinate system; l,r Represents the distance between the origin of the three-dimensional coordinate system and the rth data point except the lth data point; The specific calculation formula for obtaining the abnormality degree of the characteristic value of each data point in the sequence according to the characteristic value of each data point in the sequence is: Among them, C j Indicates the abnormality of the characteristic value of the jth data point in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; mid(T′) represents the eigenvalue of the data point corresponding to the median of the sequence; T′ j+1 represents the eigenvalue of the j+1th data point in the sequence; T′ j-1 represents the eigenvalue of the j-1th data point in the sequence; || is the absolute value function; The specific steps of obtaining the abnormality degree of each data point in the sequence according to the characteristic value of each data point in the sequence, the abnormality degree of the characteristic value and the time when each data point corresponds to the start of braking include the following: According to the characteristic value of each data point in the sequence and the time when the corresponding braking starts, the characteristic value difference between any two data points in the sequence due to the use time is obtained; The abnormality degree of each data point in the sequence is obtained according to the abnormality degree of the characteristic value of each data point in the sequence and the difference of the characteristic value between any two data points in the sequence due to the use time; The specific calculation formula for obtaining the abnormality degree of each data point in the sequence according to the abnormality degree of the characteristic value of each data point in the sequence and the difference of the characteristic value between any two data points in the sequence due to the use time is: Among them, Y j Indicates the degree of abnormality of the jth data point in the sequence; C j Indicates the abnormality of the characteristic value of the jth data point in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; T′ j,s represents the characteristic value of the sth data point in the sequence except the jth data point; m′ represents the number of all data points in the sequence; t j represents the time interval between the braking start time corresponding to the jth data point in the sequence and the braking start time corresponding to the first data point; t j,s It represents the time interval between the braking start time corresponding to the sth data point and the braking start time corresponding to the first data point in the sequence except the jth data point; norm[] is a linear normalization function; It is the difference of the characteristic value between the j-th data point and the s-th data point except the j-th data point in the sequence due to the usage time.

2. According to claim 1, a method for intelligently detecting the performance of an oil drum brake device for an electric vehicle is characterized in that: The specific steps of obtaining the performance evaluation index of the drum brake device according to the abnormality degree and characteristic value of each data point in the sequence are as follows: According to the abnormality degree of each data point in the sequence, the overall abnormality degree of all data points in the sequence is obtained; According to the overall abnormality degree of all data points in the sequence and the characteristic value of each data point, the performance evaluation index of the drum brake device is obtained.

3. According to claim 2, a method for intelligently detecting the performance of an oil drum brake device for an electric vehicle is characterized in that: The specific calculation formula for obtaining the overall abnormality degree of all data points in the sequence according to the abnormality degree of each data point in the sequence is: Among them, U represents the overall abnormality of all data points in the sequence; m′ represents the number of all data points in the sequence; represents the mean value of the abnormality of all data points in the sequence; min(Y) represents the minimum value of the abnormality of all data points in the sequence; Y j Indicates the degree of abnormality of the jth data point in the sequence; || is the absolute value function; norm() is the linear normalization function.

4. According to claim 2, a method for intelligently detecting the performance of an oil drum brake device for an electric vehicle is characterized in that: The specific calculation formula for obtaining the performance evaluation index of the drum brake device according to the overall abnormality degree of all data points in the sequence and the characteristic value of each data point is: Where W represents the performance evaluation index of the drum brake device; m′ represents the number of all data points in the sequence; T′ j represents the eigenvalue of the jth data point in the sequence; t represents the time interval between the first and last data points in the sequence when braking begins; U represents the overall abnormality of all data points in the sequence; exp[] is an exponential function with a natural constant as the base.

5. According to claim 1, a method for intelligently detecting the performance of an oil drum brake device for an electric vehicle is characterized in that: The specific steps of judging the eligibility of the drum brake device according to the performance evaluation index of the drum brake device are as follows: When the performance evaluation index of the drum brake device is greater than a preset threshold, the drum brake device is determined to be qualified; When the performance evaluation index of the drum brake device is less than or equal to a preset threshold, the drum brake device is determined to be unqualified.

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