Method of vehicle remote diagnosis and spare parts retrieval based on fp-tree sequence pattern mining and fault code classification

A technology of sequential pattern mining and remote diagnosis, applied in the field of information retrieval, it can solve problems such as maintenance labor costs for detailed solutions to failures, and achieve the effect of high accuracy

Active Publication Date: 2019-10-29
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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  • Method of vehicle remote diagnosis and spare parts retrieval based on fp-tree sequence pattern mining and fault code classification
  • Method of vehicle remote diagnosis and spare parts retrieval based on fp-tree sequence pattern mining and fault code classification
  • Method of vehicle remote diagnosis and spare parts retrieval based on fp-tree sequence pattern mining and fault code classification

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0019] Example 1: A method for vehicle remote diagnosis and spare parts retrieval based on FP-Tree sequence pattern mining and fault code classification, including

[0020] Step 1. Collect vehicle information data;

[0021] Step 2. Identify and classify the fault codes;

[0022] 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;

[0023] 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;

[0024] Step 5. 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 topology relationship between the position of the spare part and the ECU where the fa...

Embodiment 2

[0027] Example 2: Have the same technical scheme as embodiment 1, more specifically, for step 4 of embodiment 1,

[0028] 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:

[0029] Table I

[0030] 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

[0031] The basic principles of the decision tree model are as follows:

[0032] First: Determine the entropy of different categories of spare parts in each dimension. Taking VIN4 as an example, the entropy is defined as

[0033] E=sum(-p(I)*log(p(I)))

[0034] 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)

[0035] Then E(5)=-(1 / 1) Log2(1 / 1)-(0 / 1) Log2(0 / 1)=...

Embodiment 3

[0055] Example 3: Have the same technical scheme as embodiment 1 or 2, more specifically, for step five of embodiment 1,

[0056] In said step 5, according to the transaction database, the step of creating a frequent itemset of the corresponding relationship between the fault code and the replacement spare part through the FP-Tree algorithm includes

[0057]S1.1 Input the transaction database and the minimum support threshold minσ, scan the transaction database, delete 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;

[0058] 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;

[0059] S1.3 Find all frequent patterns from FP-Tree.

[0060] In the fifth step, using the topological relationship between the ...

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Abstract

The invention relates to an FP-Tree sequence pattern mining and fault code classification-based vehicle remote diagnosis and spare part retrieval method and belongs to the information retrieval field. The method includes the following steps that: fault codes are recognized and classified; according to a transactional database, a frequent item set of corresponding relations of fault codes and replacement spare parts is created by using an FP-Tree algorithm; topological searching is carried out according to the topological relations between the locations of the spare parts and the locations of ECUs where faults occur, the frequent item set is selected; corresponding relationships between the spare parts and maintenance items are constructed, and a diagnosis database of the work items corresponding to the fault codes is formed; and the diagnosis database is correlated with a diagnosis knowledge base, and primary keys are established. With the method adopted, after the fault codes are obtained, solutions of the 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 Patents(China)
IPC IPC(8): G06F16/24
CPCG06Q10/20
Inventor 田雨农刘亮
Owner DALIAN ROILAND SCI & TECH CO LTD
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