Decision tree classification and fault code classification-based vehicle remote diagnosis and spare part retrieval method

A technology of decision -making tree classification and remote diagnosis, which is applied in the direction of electrical test/monitoring, which can solve problems such as failure to fail to fail to fail to fail to fail to fail to fail.

Active Publication Date: 2016-10-26
DALIAN ROILAND SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

General OBD on-board equipment can only read relevant vehicle fault information, but cannot make detailed solutions to the fault and related maintenance labor costs and spare parts costs, thus causing car owners to enter the store blindly and consume blindly

Method used

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  • Decision tree classification and fault code classification-based vehicle remote diagnosis and spare part retrieval method
  • Decision tree classification and fault code classification-based vehicle remote diagnosis and spare part retrieval method
  • Decision tree classification and fault code classification-based vehicle remote diagnosis and spare part retrieval method

Examples

Experimental program
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Embodiment 1

[0014] Example 1 : A method for vehicle remote diagnosis and spare parts retrieval based on decision tree classification and fault code classification, including

[0015] Step 1. Collect vehicle information data;

[0016] Step 2. Identify and classify the fault codes;

[0017] Step 3. Analyze the vehicle VIN code to obtain variables, and the variables include the engine displacement, body type, and engine gearbox type analyzed by the VIN code;

[0018] Step 4. Perform a decision tree analysis on the spare part codes corresponding to the variables, complete the classification of variable data to form spare part information, and establish an index to form a diagnostic knowledge base;

[0019] Step 5. Create a language model, set up a cell thesaurus, search the cell words by cutting words in the cell thesaurus, and arrange the cell words, and use the decision classification of the decision tree model to form a diagnostic database for the corresponding work item of the fault co...

Embodiment 2

[0022] Example 2: Have the same technical scheme as embodiment 1, more specifically, for step 4 of embodiment 1,

[0023] In the step 4, the historical records of the maintenance spare parts table are used as the data basis, and the spare parts are classified through the decision tree model. The maintenance spare parts table sample is shown in Table 1:

[0024] Table I

[0025] VIN123

VIN4

VIN6

VIN78

BJDM

LFV

5

1

4B

06J115403J

LFV

3

2

8K

LN052167A21

LFV

4

2

4F

LN052167A24

[0026] The basic principles of the decision tree model are as follows:

[0027] First: Determine the entropy of different categories of spare parts in each dimension. Taking VIN4 as an example, the entropy is defined as

[0028] E=sum(-p(I)*log(p(I)))

[0029] Wherein I=1:N (N category results, such as this example 1, that is, the spare part belongs to this model, so the probability P(I)=1)

[0030] Then...

Embodiment 3

[0046] Example 3: Have the same technical scheme as embodiment 1 or 2, more specifically, for step five of embodiment 1,

[0047] The creation of the language model in the step five and the establishment of the cell thesaurus include the following steps:

[0048] S1.1 Collect professional fault description language;

[0049] S1.2 Perform word vector decomposition on the professional fault description language.

[0050] The creation of the language model is based on the assumption that the occurrence of the nth cell word is only related to the previous n-1 cell words; the calculation formula for the occurrence weight of the fault description sentence T is:

[0051] P(T)=P(w 1 ,w 2 ,w 3 ,...,w n )

[0052] =P(w 1 )×P(w 2 |w 1 )×P(w 3 |w 1 ,w 2 )×…×P(w n |w 1 ,w 2 ,...,w n-1 )

[0053] ≈P(w 1 )×P(w 2 |w 1 )×P(w 3 |w 2 )…P(w n |w n-1 );

[0054] Wherein, P(T) is the weight of the fault description sentence T, P(w n |w 1 ,w 2,...,w n-1 ) is the weight...

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Abstract

The invention relates to a decision tree classification and fault code classification-based vehicle remote diagnosis and spare part retrieval method and belongs to the information retrieval field. Since the branch group numbers of different spare parts are still different even under a condition that main group numbers of the spare parts are identical, the objective of the invention is to realize exact matching of the spare parts. According to the technical schemes of the invention, fault codes are identified and classified; vehicle VIN codes are analyzed, so that variables can be obtained, wherein the variables include engine displacement, vehicle body type and engine transmission type which are obtained through VIN code analysis; decision tree analysis is carried out on spare part codes corresponding to the variables, variable data classification is completed, and spare part information is formed, indexes are established, and a diagnosis knowledge library is formed; a linguistic model is created, a cell word library is built, word switched retrieval is carried out on cell words are in the cell word library, and the cell words are arranged, a diagnosis database of work items corresponding to the fault codes is formed through the decision classification of a decision tree model; and the diagnosis database is associated with the diagnosis knowledge library, primary keys are built. With the method and device of the invention adopted, after the fault codes are obtained, solutions of common faults and corresponding spare parts and work items can be found out fast.

Description

technical field [0001] The invention belongs to the field of information retrieval and relates to a method for vehicle remote diagnosis and spare parts retrieval Background technique [0002] At present, my country's automobile maintenance industry has developed from the stage of diagnosis based entirely on the feeling and practical experience of inspectors to the stage of comprehensive detection and diagnosis using special equipment. However, there are many problems in the traditional automobile maintenance industry, such as the technical aging of maintenance workers. , It is often impossible to quickly and economically use various technical forces to solve the fault; with the increasing number of automobiles, various services in the automobile aftermarket have sprung up like mushrooms after rain. So from the perspective of the car owner, how can we better and more comprehensively understand the car condition, how to quickly obtain the car’s pending solution and the required...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
Inventor 田雨农刘亮
Owner DALIAN ROILAND SCI & TECH CO LTD
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