阅读说明：本技术 车辆变速箱档位识别方法及计算机可读存储介质 (Vehicle transmission gear recognition method and computer-readable storage medium ) 是由 阮志毅 洪志新 于 2019-02-13 设计创作，主要内容包括：本发明公开了一种车辆变速箱档位识别方法及存储介质,方法包括：根据变速箱配置信息,对无离合信号样本数据的运转档位进行预标记；根据预标记结果,计算无离合信号情况下各档位的先验概率及概率分布；计算各档位的后验概率,选取最大值对应的档位作为无离合信号情况下的变速箱档位识别结果；根据无离合信号情况下的档位识别结果,对有离合信号样本数据的运转档位进行预标记；根据预标记结果,计算半联动下的概率分布；计算有离合信号情况下各档位及半联动下的先验概率；计算出各档位及半联动的后验概率,选取最大值对应的档位作为有离合信号情况下的变速箱档位识别结果。本发明可实现对运转档位的简单、高效、准确、可靠识别。(The invention discloses a method for identifying a gear of a vehicle gearbox and a storage medium, wherein the method comprises the following steps: pre-marking the operating gear of the sample data of the clutch-free signal according to the configuration information of the gearbox; calculating prior probability and probability distribution of each gear under the condition of no clutch signal according to the pre-marking result; calculating the posterior probability of each gear, and selecting the gear corresponding to the maximum value as a gear recognition result of the gearbox under the condition of no clutch signal; pre-marking the operating gear with clutch signal sample data according to a gear identification result under the condition of no clutch signal; calculating probability distribution under half linkage according to the pre-marking result; calculating prior probability of each gear and semi-linkage under the condition of clutch signals; and calculating the posterior probability of each gear and the semi-linkage, and selecting the gear corresponding to the maximum value as the gear recognition result of the gearbox under the condition of clutch signals. The invention can realize simple, efficient, accurate and reliable identification of the operating gears.)

1. A vehicle transmission gear identification method, comprising:

collecting sample data in a preset first time period, wherein the sample data comprises a timestamp, an engine rotating speed, a rotating speed of an output shaft of a gearbox and a trampling state of a clutch pedal;

obtaining sample data of a clutch pedal in a non-treading state to obtain sample data of a non-clutch signal;

pre-marking the operating gear of the clutch-free signal sample data according to gearbox configuration information, wherein the gearbox configuration information comprises the number of gears of a gearbox, the transmission ratio of each non-neutral gear and the engine speed at neutral idle speed;

respectively calculating to obtain a first prior probability of each gear and a first probability distribution of the engine rotating speed of each gear and the rotating speed of an output shaft of the gearbox according to the operation gears pre-marked by the clutch-free signal sample data;

dividing continuously acquired clutch-free signal sample data into the same group according to the timestamp to obtain a plurality of clutch-free signal sample data groups;

respectively calculating the posterior probability of each non-clutch signal sample data set corresponding to each gear according to the first prior probability of each gear and the first probability distribution of each gear, and respectively determining the operating gear of each non-clutch signal sample data set according to the posterior probability;

acquiring sample data of a clutch pedal in a trampling state to obtain sample data of a clutch signal;

acquiring continuous on-off signal sample data within a preset second time period to obtain a plurality of on-off signal sample data sets, and pre-marking the operating gears of the on-off signal sample data sets as the operating gears of the previous off-off signal sample data set respectively;

calculating to obtain the probability distribution of the engine rotating speed in a semi-linkage state and the rotating speed of the output shaft of the gearbox according to the operation gear pre-marked by the clutch signal sample data set;

calculating a second prior probability and a prior probability of a semi-linkage state of each gear according to the multiple sample data sets with the clutch signal and the sample data set without the clutch signal before the sample data sets;

acquiring a preset number of continuous newly-acquired clutch signal data to obtain a clutch signal data set;

respectively calculating the likelihood of the clutch signal data set corresponding to each gear and the semi-linkage state according to the first probability distribution of each gear and the probability distribution of the semi-linkage state;

according to the second prior probability of each gear, the prior probability of the semi-linkage state and the likelihood of the clutch signal data set corresponding to each gear and the semi-linkage state, respectively calculating the posterior probability of the clutch signal data set corresponding to each gear and the semi-linkage state through a Bayes formula;

and marking the operating gear with the clutch signal data set as a gear corresponding to the maximum posterior probability.

2. The method for identifying a gear position of a vehicle transmission according to claim 1, wherein the pre-marking the operating gear position of the sample data of the clutch-free signal according to the transmission configuration information specifically comprises:

according to the engine speed when the neutral gear is idle, pre-marking the running gear of the no-clutch signal sample data with the engine speed within a preset range as the neutral gear;

respectively calculating the ratio of the engine rotating speed of other sample data without clutch signals to the rotating speed of the output shaft of the gearbox, and comparing the ratio with the transmission ratio of each non-neutral gear;

and pre-marking the operating gears of other sample data without clutch signals as gears corresponding to the transmission ratio closest to the ratio of the operating gears.

3. The method for identifying the gear position of the vehicle gearbox according to claim 1, wherein the first prior probability of each gear position and the first probability distribution of the engine speed and the gearbox output shaft speed of each gear position are respectively calculated according to the operation gear position pre-marked by the clutch-free signal sample data and specifically comprise the following steps:

respectively counting the frequency of each gear shifted from other gears according to the operation gears pre-marked by the clutch-free signal sample data to obtain a first prior probability of each gear;

calculating statistics of sample data of the clutch-free signal of each gear respectively, and acquiring a first two-dimensional probability density function of the engine speed and the speed of an output shaft of the gearbox of each gear according to the statistics;

and respectively carrying out discretization and normalization processing on the first two-dimensional probability density function of each gear to obtain a first probability distribution of the engine rotating speed and the rotating speed of the output shaft of the gearbox of each gear.

4. The method for identifying the gear position of the transmission of the vehicle according to claim 1, wherein the step of calculating the posterior probability of each non-clutch signal sample data set corresponding to each gear position according to the first prior probability of each gear position and the first probability distribution of each gear position respectively, and determining the operating gear position of each non-clutch signal sample data set according to the posterior probability respectively comprises:

according to the first probability distribution of each gear, respectively acquiring the probability of each non-clutch signal sample data in a non-clutch signal sample data set corresponding to each gear;

multiplying the probability of each non-clutch signal sample data in the non-clutch signal sample data group corresponding to the same gear to obtain the likelihood of the non-clutch signal sample data group corresponding to the same gear;

according to the first prior probability of each gear and the likelihood of the sample data set of the clutchless signal corresponding to each gear, respectively calculating the posterior probability of the sample data set of the clutchless signal corresponding to each gear through a Bayes formula;

and marking the operating gear of the clutch-free signal sample data set as a gear corresponding to the maximum posterior probability.

5. The method for identifying the gear position of the transmission of the vehicle according to claim 1, wherein after the calculating the posterior probability of each sample data set of the clutch-free signal corresponding to each gear position according to the first prior probability of each gear position and the first probability distribution of each gear position respectively and determining the operating gear position of each sample data set of the clutch-free signal according to the posterior probability respectively, further comprises:

and updating the first prior probability of each gear and the first probability distribution of each gear according to the operating gears of the clutch-free signal sample data set.

6. The method for identifying the gear position of the vehicle gearbox according to claim 1, wherein the probability distribution of the engine rotating speed and the gearbox output shaft rotating speed in the semi-linkage state is calculated according to the pre-marked operating gear position with the clutch signal sample data set, and specifically comprises the following steps:

respectively calculating statistics of sample data of the clutch signal of each gear, and acquiring a second two-dimensional probability density function of the engine speed and the speed of the output shaft of the gearbox of each gear according to the statistics;

discretizing and normalizing the second two-dimensional probability density function of each gear respectively to obtain a second probability distribution of the engine rotating speed and the rotating speed of the output shaft of the gearbox of each gear;

respectively calculating the occupation ratio of the clutch signal sample data corresponding to each gear according to the operation gears pre-marked by the clutch signal sample data sets and the number of the sample data in the operation gears;

and taking the ratio as a weight, and carrying out weighted summation on the second probability distribution of each gear to obtain the probability distribution of the engine rotating speed and the rotating speed of the output shaft of the gearbox in a semi-linkage state.

7. The method for identifying a gear position of a transmission of a vehicle according to claim 1, wherein the calculating a second prior probability and a prior probability of the semi-linkage state of each gear according to the multiple sample data sets of clutch signals and the sample data set of previous clutch-free signals specifically comprises:

respectively acquiring a previous non-clutch signal sample data set of the multiple sample data sets with clutch signals, and calculating the total number of sample data corresponding to each gear and the total number of sample data of the non-clutch signals according to the operating gear of the previous non-clutch signal sample data set and the number of sample data in the previous non-clutch signal sample data set;

calculating the total number of sample data of the clutch signal according to the plurality of sample data sets of the clutch signal;

adding the total number of the sample data of the clutch-free signal and the total number of the sample data of the clutch signal to obtain a first total number;

dividing the total number of sample data corresponding to each gear by the first total number to obtain a second prior probability of each gear;

and dividing the total number of the sample data of the clutch signal by the first total number to obtain the prior probability of the semi-linkage state.

8. The method for identifying a gear position of a transmission of a vehicle according to claim 1, wherein the calculating the likelihood that the clutch signal data set corresponds to each gear position and the semi-linkage state respectively according to the first probability distribution of each gear position and the probability distribution of the semi-linkage state specifically comprises:

respectively acquiring the probability of each clutch signal data in the clutch signal data group corresponding to each gear and the semi-linkage state according to the first probability distribution of each gear and the probability distribution of the semi-linkage state;

multiplying the probability that each clutch signal data in the clutch signal data group corresponds to the same gear to obtain the likelihood that the clutch signal data group corresponds to the same gear;

and multiplying the probability of the semi-linkage state corresponding to each clutch signal data in the clutch signal data group to obtain the likelihood of the semi-linkage state corresponding to the clutch signal data group.

9. The method for identifying a gear position of a transmission of a vehicle according to claim 1, wherein the step of marking the operating gear position of the clutched signal data set as the gear position corresponding to the maximum a posteriori probability is specifically:

judging whether the maximum value of the posterior probability corresponds to a semi-linkage state or not;

if not, marking the operating gear with the clutch signal data set as a gear corresponding to the maximum value of the posterior probability;

and if so, marking the operating gear of the clutch signal data group as the operating gear of the clutch signal data group in the first non-semi-linkage state after the clutch signal data group.

10. The method of identifying a gear position in a transmission of a vehicle according to claim 1, wherein said marking the operating gear position in said clutch signal data set as the gear position corresponding to the maximum a posteriori probability further comprises:

and updating the second prior probability of each gear, the prior probability of the semi-linkage state, the second probability distribution of each gear and the probability distribution of the semi-linkage state according to the operating gears with the clutch signal data set.

11. The method for identifying the gear position of the transmission of the vehicle according to claim 1, wherein after the calculating the posterior probability of the clutch signal data set corresponding to each gear position and the semi-linkage state respectively through the Bayesian formula, the method further comprises:

if the maximum value of the posterior probability corresponds to a semi-linkage state, judging that the clutch state is a semi-linkage state;

if the gear corresponding to the maximum value of the posterior probability is a neutral gear, judging that the clutch state is an equivalent complete separation state;

if the gear corresponding to the maximum value of the posterior probability is not neutral, the clutch state is determined to be the almost complete engagement state.

12. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of any of claims 1-11.

Technical Field

The invention relates to the technical field of vehicle gear identification, in particular to a vehicle gearbox gear identification method and a computer readable storage medium.

Background

Disclosure of Invention

The technical problem to be solved by the invention is as follows: a method for recognizing the gear of gear box of vehicle features that the data collected by data bus is used to realize the simple, efficient, accurate and reliable recognition of running gear.

In order to solve the technical problems, the invention adopts the technical scheme that: a vehicle transmission gear identification method comprising:

collecting sample data in a preset first time period, wherein the sample data comprises a timestamp, an engine rotating speed, a rotating speed of an output shaft of a gearbox and a trampling state of a clutch pedal;

obtaining sample data of a clutch pedal in a non-treading state to obtain sample data of a non-clutch signal;

pre-marking the operating gear of the clutch-free signal sample data according to gearbox configuration information, wherein the gearbox configuration information comprises the number of gears of a gearbox, the transmission ratio of each non-neutral gear and the engine speed at neutral idle speed;

respectively calculating to obtain a first prior probability of each gear and a first probability distribution of the engine rotating speed of each gear and the rotating speed of an output shaft of the gearbox according to the operation gears pre-marked by the clutch-free signal sample data;

dividing continuously acquired clutch-free signal sample data into the same group according to the timestamp to obtain a plurality of clutch-free signal sample data groups;

respectively calculating the posterior probability of each non-clutch signal sample data set corresponding to each gear according to the first prior probability of each gear and the first probability distribution of each gear, and respectively determining the operating gear of each non-clutch signal sample data set according to the posterior probability;

acquiring sample data of a clutch pedal in a trampling state to obtain sample data of a clutch signal;

acquiring continuous on-off signal sample data within a preset second time period to obtain a plurality of on-off signal sample data sets, and pre-marking the operating gears of the on-off signal sample data sets as the operating gears of the previous off-off signal sample data set respectively;

calculating to obtain the probability distribution of the engine rotating speed in a semi-linkage state and the rotating speed of the output shaft of the gearbox according to the operation gear pre-marked by the clutch signal sample data set;

calculating a second prior probability and a prior probability of a semi-linkage state of each gear according to the multiple sample data sets with the clutch signal and the sample data set without the clutch signal before the sample data sets;

acquiring a preset number of continuous newly-acquired clutch signal data to obtain a clutch signal data set;

respectively calculating the likelihood of the clutch signal data set corresponding to each gear and the semi-linkage state according to the first probability distribution of each gear and the probability distribution of the semi-linkage state;

according to the second prior probability of each gear, the prior probability of the semi-linkage state and the likelihood of the clutch signal data set corresponding to each gear and the semi-linkage state, respectively calculating the posterior probability of the clutch signal data set corresponding to each gear and the semi-linkage state through a Bayes formula;

and marking the operating gear with the clutch signal data set as a gear corresponding to the maximum posterior probability.

The invention also relates to a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps as described above.

The invention has the beneficial effects that: according to the method, through a Bayes analysis method, the operating gears without clutch signal sample data are pre-marked according to configuration information of a gearbox, then according to a pre-marking result, the prior probability of each gear under the condition of no clutch signal and the probability distribution of the engine rotating speed of each gear and the rotating speed of an output shaft of the gearbox are obtained through calculation, then the posterior probability of each gear is calculated according to a Bayes formula, and the gear corresponding to the maximum value of the posterior probability is selected as the gear recognition result of the gearbox under the condition of no clutch signal. Similarly, the operating gear with clutch signal sample data is pre-marked according to a gear identification result of the clutch signal-free sample data through a Bayes analysis method, then probability distribution under a semi-linkage state is calculated according to the pre-marking result, then prior probability of each gear under the condition of clutch signal and prior probability under the semi-linkage state are calculated by combining the clutch signal-free sample data, then posterior probability of each gear and the semi-linkage state is calculated according to a Bayes formula by combining the probability distribution of each gear under the condition of clutch signal, and the gear corresponding to the maximum value of the posterior probability is selected as a gear identification result of the gearbox under the condition of clutch signal.

According to the invention, the gear of the gearbox can be identified and obtained only by means of data which can be directly acquired by a data bus, such as a timestamp, the rotating speed of an engine, the rotating speed of an output shaft of the gearbox and the treading state of a clutch pedal, and the simple, efficient, accurate and reliable identification of the operating gear is realized in a data driving mode.

Drawings

FIG. 1 is a flow chart of a method of identifying a gear position of a transmission of a vehicle according to the present invention;

FIG. 2 is a flowchart of a method for identifying a transmission gear based on clutch-less data according to a first embodiment of the present invention;

FIG. 3 is a two-dimensional scatter plot of engine speed versus transmission output shaft speed for a first embodiment of the present invention;

fig. 4 is a flowchart of a transmission gear identification method based on clutch data according to a first embodiment of the present invention.

Detailed Description

In order to explain technical contents, objects and effects of the present invention in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.

The most key concept of the invention is as follows: and analyzing the sample data of the clutch-free signal and the sample data of the clutch-containing signal in sequence by a Bayesian analysis method.

Referring to fig. 1, a method for identifying a gear position of a transmission of a vehicle includes:

collecting sample data in a preset first time period, wherein the sample data comprises a timestamp, an engine rotating speed, a rotating speed of an output shaft of a gearbox and a trampling state of a clutch pedal;

obtaining sample data of a clutch pedal in a non-treading state to obtain sample data of a non-clutch signal;

pre-marking the operating gear of the clutch-free signal sample data according to gearbox configuration information, wherein the gearbox configuration information comprises the number of gears of a gearbox, the transmission ratio of each non-neutral gear and the engine speed at neutral idle speed;

respectively calculating to obtain a first prior probability of each gear and a first probability distribution of the engine rotating speed of each gear and the rotating speed of an output shaft of the gearbox according to the operation gears pre-marked by the clutch-free signal sample data;

dividing continuously acquired clutch-free signal sample data into the same group according to the timestamp to obtain a plurality of clutch-free signal sample data groups;

respectively calculating the posterior probability of each non-clutch signal sample data set corresponding to each gear according to the first prior probability of each gear and the first probability distribution of each gear, and respectively determining the operating gear of each non-clutch signal sample data set according to the posterior probability;

acquiring sample data of a clutch pedal in a trampling state to obtain sample data of a clutch signal;

acquiring continuous on-off signal sample data within a preset second time period to obtain a plurality of on-off signal sample data sets, and pre-marking the operating gears of the on-off signal sample data sets as the operating gears of the previous off-off signal sample data set respectively;

calculating to obtain the probability distribution of the engine rotating speed in a semi-linkage state and the rotating speed of the output shaft of the gearbox according to the operation gear pre-marked by the clutch signal sample data set;

calculating a second prior probability and a prior probability of a semi-linkage state of each gear according to the multiple sample data sets with the clutch signal and the sample data set without the clutch signal before the sample data sets;

acquiring a preset number of continuous newly-acquired clutch signal data to obtain a clutch signal data set;

respectively calculating the likelihood of the clutch signal data set corresponding to each gear and the semi-linkage state according to the first probability distribution of each gear and the probability distribution of the semi-linkage state;

according to the second prior probability of each gear, the prior probability of the semi-linkage state and the likelihood of the clutch signal data set corresponding to each gear and the semi-linkage state, respectively calculating the posterior probability of the clutch signal data set corresponding to each gear and the semi-linkage state through a Bayes formula;

and marking the operating gear with the clutch signal data set as a gear corresponding to the maximum posterior probability.

From the above description, the beneficial effects of the present invention are: the simple, efficient, accurate and reliable identification of the operating gears can be realized.

Further, the pre-marking the operating gear of the clutch-free signal sample data according to the configuration information of the gearbox specifically comprises:

according to the engine speed when the neutral gear is idle, pre-marking the running gear of the no-clutch signal sample data with the engine speed within a preset range as the neutral gear;

respectively calculating the ratio of the engine rotating speed of other sample data without clutch signals to the rotating speed of the output shaft of the gearbox, and comparing the ratio with the transmission ratio of each non-neutral gear;

and pre-marking the operating gears of other sample data without clutch signals as gears corresponding to the transmission ratio closest to the ratio of the operating gears.

According to the description, the operation gear of the clutch-free signal sample data is pre-marked according to the configuration information of the gearbox, so that the accuracy of pre-marking is improved, and the accuracy of the subsequent posterior probability calculation is improved.

Further, the step of respectively calculating a first prior probability of each gear and a first probability distribution of the engine speed of each gear and the transmission output shaft speed according to the operation gear pre-marked by the clutch-free signal sample data specifically comprises:

respectively counting the frequency of each gear shifted from other gears according to the operation gears pre-marked by the clutch-free signal sample data to obtain a first prior probability of each gear;

calculating statistics of sample data of the clutch-free signal of each gear respectively, and acquiring a first two-dimensional probability density function of the engine speed and the speed of an output shaft of the gearbox of each gear according to the statistics;

and respectively carrying out discretization and normalization processing on the first two-dimensional probability density function of each gear to obtain a first probability distribution of the engine rotating speed and the rotating speed of the output shaft of the gearbox of each gear.

According to the above description, the probability of the sample data of the clutch-free signal appearing in each gear is calculated according to the result of the pre-marking, the probability is used as the prior probability, and the probability of the engine rotating speed in the sample data of the clutch-free signal and the probability of the rotating speed of the output shaft of the gearbox taking different values in each gear are calculated and obtained as the probability distribution.

Further, the step of respectively calculating posterior probabilities of the non-clutch signal sample data sets corresponding to the gears according to the first prior probabilities of the gears and the first probability distribution of the gears, and respectively determining the operating gears of the non-clutch signal sample data sets according to the posterior probabilities specifically includes:

according to the first probability distribution of each gear, respectively acquiring the probability of each non-clutch signal sample data in a non-clutch signal sample data set corresponding to each gear;

multiplying the probability of each non-clutch signal sample data in the non-clutch signal sample data group corresponding to the same gear to obtain the likelihood of the non-clutch signal sample data group corresponding to the same gear;

according to the first prior probability of each gear and the likelihood of the sample data set of the clutchless signal corresponding to each gear, respectively calculating the posterior probability of the sample data set of the clutchless signal corresponding to each gear through a Bayes formula;

and marking the operating gear of the clutch-free signal sample data set as a gear corresponding to the maximum posterior probability.

According to the above description, the posterior probability is obtained through calculation according to the prior probability and the likelihood, that is, the conditional probability of each gear is taken on the premise of the sample data set of the clutch-free signal, and the gear corresponding to the maximum value of the posterior probability is selected as the gear identification result.

Further, the calculating, according to the first prior probability of each gear and the first probability distribution of each gear, the posterior probability of each gear corresponding to each non-clutch signal sample data set, and determining, according to the posterior probability, the operating gear of each non-clutch signal sample data set, further includes:

and updating the first prior probability of each gear and the first probability distribution of each gear according to the operating gears of the clutch-free signal sample data set.

From the above description, the accuracy of the subsequent identification is improved by updating the prior probability and the probability distribution.

Further, the calculating the probability distribution of the engine rotation speed in the semi-linkage state and the transmission output shaft rotation speed according to the operation gear pre-marked by the clutch signal sample data set specifically comprises:

respectively calculating statistics of sample data of the clutch signal of each gear, and acquiring a second two-dimensional probability density function of the engine speed and the speed of the output shaft of the gearbox of each gear according to the statistics;

discretizing and normalizing the second two-dimensional probability density function of each gear respectively to obtain a second probability distribution of the engine rotating speed and the rotating speed of the output shaft of the gearbox of each gear;

respectively calculating the occupation ratio of the clutch signal sample data corresponding to each gear according to the operation gears pre-marked by the clutch signal sample data sets and the number of the sample data in the operation gears;

and taking the ratio as a weight, and carrying out weighted summation on the second probability distribution of each gear to obtain the probability distribution of the engine rotating speed and the rotating speed of the output shaft of the gearbox in a semi-linkage state.

As can be seen from the above description, the probability distribution of the engine speed in the semi-linkage state and the transmission output shaft speed is obtained by first obtaining the second probability distribution of the semi-linkage state after each gear, and then performing weighted summation according to the ratio.

Further, the calculating a second prior probability and a prior probability of the semi-linkage state of each gear according to the multiple sample data sets of the clutched signal and the sample data set of the previous clutchless signal specifically includes:

respectively acquiring a previous non-clutch signal sample data set of the multiple sample data sets with clutch signals, and calculating the total number of sample data corresponding to each gear and the total number of sample data of the non-clutch signals according to the operating gear of the previous non-clutch signal sample data set and the number of sample data in the previous non-clutch signal sample data set;

calculating the total number of sample data of the clutch signal according to the plurality of sample data sets of the clutch signal;

adding the total number of the sample data of the clutch-free signal and the total number of the sample data of the clutch signal to obtain a first total number;

dividing the total number of sample data corresponding to each gear by the first total number to obtain a second prior probability of each gear;

and dividing the total number of the sample data of the clutch signal by the first total number to obtain the prior probability of the semi-linkage state.

As can be seen from the above description, the non-clutch signal sample data corresponding to each gear is used as data in each gear, and the clutch signal sample data set with a short time difference between the beginning and the end is used as data in the semi-linkage state, so that the probability of occurrence of each gear and the semi-linkage state is calculated.

Further, the calculating, according to the first probability distribution of each gear and the probability distribution of the semi-linkage state, the likelihood that the clutch signal data set corresponds to each gear and the semi-linkage state specifically includes:

respectively acquiring the probability of each clutch signal data in the clutch signal data group corresponding to each gear and the semi-linkage state according to the first probability distribution of each gear and the probability distribution of the semi-linkage state;

multiplying the probability that each clutch signal data in the clutch signal data group corresponds to the same gear to obtain the likelihood that the clutch signal data group corresponds to the same gear;

and multiplying the probability of the semi-linkage state corresponding to each clutch signal data in the clutch signal data group to obtain the likelihood of the semi-linkage state corresponding to the clutch signal data group.

Further, the step of marking the operating gear of the clutch signal data set as the gear corresponding to the maximum posterior probability specifically is:

judging whether the maximum value of the posterior probability corresponds to a semi-linkage state or not;

if not, marking the operating gear with the clutch signal data set as a gear corresponding to the maximum value of the posterior probability;

and if so, marking the operating gear of the clutch signal data group as the operating gear of the clutch signal data group in the first non-semi-linkage state after the clutch signal data group.

As can be seen from the above description, when the maximum posterior probability corresponds to the half-linkage state, the operating gear of the clutch signal data set in the next non-half-linkage state (i.e., the newly acquired clutch signal-free data set or clutch signal data set in the non-half-linkage state) is acquired as the gear identification result of the clutch signal data set.

Further, after the operation gear of the clutch signal data set is marked as the gear corresponding to the maximum a posteriori probability, the method further comprises the following steps:

and updating the second prior probability of each gear, the prior probability of the semi-linkage state, the second probability distribution of each gear and the probability distribution of the semi-linkage state according to the operating gears with the clutch signal data set.

From the above description, the accuracy of the subsequent identification is improved by updating the prior probability and the probability distribution.

Further, after the posterior probabilities of the clutched signal data sets corresponding to the gears and the semi-linkage states are respectively calculated through a bayesian formula, the method further includes:

if the maximum value of the posterior probability corresponds to a semi-linkage state, judging that the clutch state is a semi-linkage state;

if the gear corresponding to the maximum value of the posterior probability is a neutral gear, judging that the clutch state is an equivalent complete separation state;

if the gear corresponding to the maximum value of the posterior probability is not neutral, the clutch state is determined to be the almost complete engagement state.

From the above description, it can be seen that the state of the clutch in the presence of the clutch signal can also be identified.

The invention also proposes a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps as described above.