The Method of Remote Vehicle Diagnosis and Spare Parts Retrieval Based on Decision Tree Classification and Trouble Code Classification

A technology of decision tree classification and remote diagnosis, which is applied in the direction of electrical testing/monitoring, etc., can solve problems such as maintenance labor costs that cannot be solved in detail, and achieve the effect of solving the problem of experience limitations

Active Publication Date: 2019-09-27
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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  • The Method of Remote Vehicle Diagnosis and Spare Parts Retrieval Based on Decision Tree Classification and Trouble Code Classification
  • The Method of Remote Vehicle Diagnosis and Spare Parts Retrieval Based on Decision Tree Classification and Trouble Code Classification
  • The Method of Remote Vehicle Diagnosis and Spare Parts Retrieval Based on Decision Tree Classification and Trouble Code Classification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0014] Example 1 : A method for remote vehicle 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 fault codes;

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

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

[0019] Step 5. Create a language model, establish a cell vocabulary, search for cell words in the cell vocabulary, arrange the cell words, and use the decision-making classification of the decision tree model to form a diagnostic database of fault code corresponding work items;

[0020] Step 6. Associa...

Embodiment 2

[0022] Example 2: It has the same technical solution as the embodiment 1. More specifically, for step four of the embodiment 1,

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

[0024] Table I

[0025] VIN123 VIN4 VIN6 VIN78 BJDM LFV514B 06J115403J LFV328K LN052167A21 LFV424F LN052167A24

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

[0027] First of all: Determine the entropy of the 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] Among them, I=1:N (N-type results, such as 1 in this example, that is, the spare part belongs to this model, so the probability P(I)=1)

[0030] Then E(5)=-(1 / 1)Log2(1 / 1)-(0 / 1)Log2(0 / 1)=0+0=0

[0031] E(3)=-(1 / 1)Log2(1 / 1)-(0 / 1)Log2(0 / 1)=0+0=0

...

Embodiment 3

[0046] Example 3: It has the same technical solution as Embodiment 1 or 2, and more specifically, for step 5 of Embodiment 1,

[0047] The establishment of the language model and the establishment of the cell vocabulary in the fifth step includes 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 appearance of the n-th cell word is only related to the preceding n-1 cell words; the calculation formula for the weight of the occurrence 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] Among them, P(T) is the weight of the fault description sentence T, P(w n |w 1 ,w 2 ,...,W n-1 ) Is the weight of the n-th ce...

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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 solely on the feelings and practical experience of the inspector 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 aging of maintenance workers' skills. , It is often impossible to quickly and economically use all aspects of technical forces to solve faults; with the increasing number of cars, various services in the automotive aftermarket have sprung up like mushrooms. So from the perspective of the car owner, how can we better and more comprehensively understand the car's condition, when a breakdown occurs, how to quickly obtain the car's pending solution and the...

Claims

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Application Information

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