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Fault code diagnosis vehicle item and spare part retrieval method based on FP-Tree sequential pattern mining

A technology of sequential pattern mining and fault codes, applied in the field of information retrieval, can solve problems such as maintenance labor costs for detailed solutions to failures, and achieve the effect of solving the limitations of experience

Active Publication Date: 2016-11-02
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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  • Fault code diagnosis vehicle item and spare part retrieval method based on FP-Tree sequential pattern mining
  • Fault code diagnosis vehicle item and spare part retrieval method based on FP-Tree sequential pattern mining
  • Fault code diagnosis vehicle item and spare part retrieval method based on FP-Tree sequential pattern mining

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0017] Example 1: A fault code diagnosis vehicle work item and spare parts retrieval method based on FP-Tree sequence pattern mining, including

[0018] Step 1. Collect vehicle information data;

[0019] Step 2. Analyze the vehicle VIN code to obtain variables, and the variables include engine displacement, body type, and engine gearbox type obtained by VIN code analysis;

[0020] Step 3. 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;

[0021] Step 4. According to the transaction database, create a frequent item set of the corresponding relationship between the fault code and the replacement spare part through the FP-Tree algorithm; use the topological relationship between the position of the spare part and the ECU where the fault is located, perform a topology search, and select the frequent item s...

Embodiment 2

[0024] Example 2: It has the same technical solution as that of Example 1, and more specifically, for step 3 of Example 1,

[0025] In the step 3, 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, and the maintenance spare parts table sample is shown in Table 1:

[0026] Table I

[0027] VIN123

VIN4

VIN6

VIN78

BJDM

LFV

5

1

4B

06J 115 403J

LFV

3

2

8K

LN 052 167 A21

LFV

4

2

4F

LN 052 167 A24

[0028] The basic principles of the decision tree model are as follows:

[0029] First: Determine the entropy of different categories of spare parts in each dimension. Taking VIN4 as an example, the entropy is defined as

[0030] E=sum(-p(I)*log(p(I)))

[0031] 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) ...

Embodiment 3

[0052] Example 3: It has the same technical solution as that of embodiment 1 or 2, more specifically, for step 4 of embodiment 1, in said step 4, according to the transaction database, the corresponding relationship between fault codes and replacement spare parts is created by FP-Tree algorithm The steps of frequent itemsets of

[0053] S1.1 Input the transaction database and the minimum support threshold minσ, scan the transaction database, delete the items whose frequency is less than the minimum support, and obtain all frequent itemsets F1, and arrange the frequent items in F1 in descending order of their support to obtain L;

[0054] S1.2 Create the root node of FP-Tree, mark it with "null", scan the transaction database again, arrange each record in the transaction database according to the order in L, and generate FP-Tree;

[0055] S1.3 Find all frequent patterns from FP-Tree.

[0056] In the step 4, using the topological relationship between the position of the spare p...

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Abstract

The invention provides a fault code diagnosis vehicle item and spare part retrieval method based on FP-Tree sequential pattern mining, and belongs to the field of information retrieval. The technical key points are that vehicle VIN codes are analyzed so that variables are obtained, wherein the variables include engine displacement, the vehicle body type and the engine gearbox type which are obtained through analysis of the VIN codes; decision tree analysis is performed on spare part codes corresponding to the variables and variable data classification is completed to form spare part information, and indexes are established so that a diagnosis knowledge base is formed; step four, a frequent item set of the corresponding relation of the fault codes and the replacement spare parts is created through an FP-Tree algorithm according to an affair database; topological searching is performed by utilizing the topological relation between the position of the spare parts and the ECU position of the faults, and the frequent item set is selected; the corresponding relation between the spare parts and maintenance items is constructed so that a diagnosis database of the corresponding items of the fault codes is formed; and the diagnosis database is correlated with the diagnosis knowledge base, and a primary key is established. The effects are that the solutions for the common faults and the corresponding spare parts and the items can be quickly found after acquisition of the fault codes.

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
CPCG05B23/0208
Inventor 田雨农刘亮
Owner DALIAN ROILAND SCI & TECH CO LTD
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