CN106066642B

CN106066642B - Error code diagnosis vehicle work item and spare part search method based on FP-Tree sequential mode mining

Info

Publication number: CN106066642B
Application number: CN201610364333.1A
Authority: CN
Inventors: 田雨农; 刘亮
Original assignee: Dalian Roiland Technology Co Ltd
Current assignee: Dalian Roiland Technology Co Ltd
Priority date: 2016-05-27
Filing date: 2016-05-27
Publication date: 2019-04-19
Anticipated expiration: 2036-05-27
Also published as: CN106066642A

Abstract

Error code diagnosis vehicle work item and spare part search method based on FP-Tree sequential mode mining, belong to information retrieval field, have technical point that parsing vehicle VIN code obtains variable, the variable includes parsing obtained engine displacement, body style, engine mission type by VIN code；Decision tree analysis is done to spare part code corresponding to variable, variable data is completed and sorts out to form spare part information, and establish index, form diagnostic knowledge base；Step 4 creates error code by FP-Tree algorithm and replaces the frequent item set of spare part corresponding relationship according to transaction database；Using the topological relation between spare part position and the position ECU of guilty culprit, topology search is carried out, frequent item set is selected；The corresponding relationship of spare part and maintenance mans is constructed, the diagnostic data base that error code corresponds to work item is formed；Diagnostic data base is associated with diagnostic knowledge base, and establishes major key；Effect is: after obtaining error code, can be quickly found out the solution of most common failure and correspond to spare part, work item.

Description

Error code diagnosis vehicle work item and spare part based on FP-Tree sequential mode mining are examined Suo Fangfa

Technical field

The invention belongs to information retrieval fields, are related to a kind of method for vehicle remote diagnosis and spare part retrieval

Background technique

The rank that China's automobile maintenance industry has been diagnosed from the feeling and practical experience that fully rely on examiner at present Section has developed to and has carried out comprehensive detection diagnostic phases using special equipment, but generally existing in orthodox car maintenance industry Many problems, such as service worker's technology aging, often can not quickly, economically be solved using the technical force of various aspects therefore Barrier；Increasing with car ownership, market respective services emerge in multitude like the mushrooms after rain after automobile.So from car owner How angle more preferably could more fully understand vehicle condition, when breaking down, how quick obtaining love vehicle scheme to be solved and required Working hour and spare part relevant information, accurately automobile wearable device is completely necessary to car owner's real-time requirement is met.It is general OBD mobile unit can only read associated vehicle fault message, and detailed settlement project and related maintenance people cannot be made to failure Expenses of labour, spare part take, so that car owner be caused blindly into shop, blindly to consume.

Summary of the invention

It, can be accurately and fast by error code when in order to solve for breaking down without the error code vehicle of identification and classification Speed matches work item and spare part corresponding to the error code, and the following technical solutions are proposed by the present invention: one kind being based on FP-Tree sequence mould The error code diagnosis vehicle work item and spare part search method that formula is excavated, including

Step 1 acquires vehicle information data；

Step 2 parsing vehicle VIN code obtains variable, the variable include parsed by VIN code obtained engine displacement, Body style, engine mission type；

Step 3 does decision tree analysis to spare part code corresponding to variable, completes variable data and sorts out to form spare part letter Breath, and index is established, form diagnostic knowledge base；

Step 4 creates error code by FP-Tree algorithm and replaces the frequency of spare part corresponding relationship according to transaction database Numerous item collection；Using the topological relation between spare part position and the position ECU of guilty culprit, topology search is carried out, frequent episode is selected Collection；The corresponding relationship of spare part and maintenance mans is constructed, the diagnostic data base that error code corresponds to work item is formed；

Diagnostic data base is associated with by step 5 with diagnostic knowledge base, and establishes major key.

The utility model has the advantages that the present invention after obtaining error code, can be quickly found out the solution of most common failure and correspond to standby Part, work item.The experience confinement problems for effectively solving technician and partsman obtain the solution of failure from big data. The present invention finds corresponding relationship by frequent item set algorithm FP-Tree and sequential mode mining.It is used, is mentioned using two algorithm fusions It has supplied to know the high error code of accuracy based on big data and replace the corresponding relationship of spare part, be suitable in addition to single failure A possibility that solving parallel there are also multiple faults, the long-range error code for estimating vehicle judge the spare part and work item that need repairing, provide Total solution provides reference for the maintenance of vehicle.

Detailed description of the invention

Fig. 1 is flow chart of the present invention for vehicle remote diagnosis and the method for spare part retrieval；

Fig. 2 is the VIN number code translation schematic diagram for the vehicle that chassis number is LFV5A14B8Y3000001；

Fig. 3 is the FP-Tree structural schematic diagram that the embodiment of the present invention is inserted into first error code and spare part corresponding relationship；

Fig. 4 is the FP-Tree structural schematic diagram that the embodiment of the present invention is inserted into Article 2 error code and spare part corresponding relationship；

Fig. 5 is the FP-Tree structural schematic diagram that the embodiment of the present invention is inserted into Article 3 error code and spare part corresponding relationship；

Fig. 6 is the FP-Tree structural schematic diagram of the error code that the embodiment of the present invention generates and spare part corresponding relationship；

Topological relation figure of the Fig. 7 between the position ECU where spare part position of the present invention and vehicle trouble.

Specific embodiment

Embodiment 1:A kind of error code diagnosis vehicle work item and spare part retrieval side based on FP-Tree sequential mode mining Method, including

Step 1 acquires vehicle information data；

By above scheme, when collecting vehicle trouble code, the error code that vehicle trouble generates is identified, and pass through key The variable that word parsing vehicle VIN code obtains obtains work item and spare part information to carry out systematic searching.

Embodiment 2:With technical solution same as Example 1, more specifically, three the step of for embodiment 1 It says,

Using the historical record of maintenance and repair parts table as data basis in the step 3, spare part is divided by decision-tree model Class, maintenance and repair parts table sample is for example shown in table one:

Table one

VIN123	VIN4	VIN6	VIN78	BJDM
					LFV	5	1	4B	06J 115 403 J
LFV	3	2	8K	LN 052 167 A21
					LFV	4	2	4F	LN 052 167 A24

The basic principle of decision-tree model is as follows:

First: determining the entropy of every dimension spare part different classifications, by taking VIN4 as an example, entropy is defined as

E=sum (- p (I) * log (p (I)))

Wherein I=1:N (N class as a result, such as a kind of this example, i.e. the spare part belongs to this vehicle, therefore probability P (I)=1)

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

E (3)=- (1/1) Log2 (1/1)-(0/1) Log2 (0/1)=0+0=0

E (4)=- (1/1) Log2 (1/1)-(0/1) Log2 (0/1)=0+0=0

If entropy is 0, show that discrimination is higher；Entropy is 1, then shows no discrimination；

Therefore these three different spare part codes can be distinguished by VIN4.

After having determined how each dimension classifies, the priority level between different dimensions is distinguished by information gain

Gain (Sample, Action)=E (sample)-sum (| Sample (v) |/Sample*E (Sample (v)))

Then Gain (VIN4)=E (S)-(1/3) * E (5)-(1/3) * E (3)-(1/3) * E (4)=1-0=1

Gain (VIN6)=E (S)-(1/3) * E (1)-(2/3) * E (2)=1-0-2/3=1/3

Gain (VIN78)=E (S)-(1/3) * E (4B)-(1/3) * E (8K)-(1/3) * E (4F)=1-0=1

If information gain is bigger, show that classification priority is higher；Conversely, priority is lower.

So chassis number the 4th (VIN4) is identical with chassis number 78 (VIN78) classification priority, followed by chassis Number the 6th (VIN6).

It, can be by spare part code according to chassis number the 4th (VIN4), chassis number the 6th (VIN6) by the above committed step It is distinguished with chassis number 78 (VIN78).

In conclusion the basic step of spare part search method is:

Maintenance and repair parts table is distinguished with dimension by comentropy；

Maintenance and repair parts table different dimensions are divided into priority by information gain；

The priority and differentiation degree divided according to 1,2 steps draws decision tree；

A regular chassis number is inputted, system is defeated according to VIN123, VIN4, VIN6, VIN78 and the decision tree obtained Spare part code under the vehicle out.

The spare part code by association price of spare parts table, obtain Chinese, price and the present use state of spare part with And the applicable vehicle model information of spare part.

The acquisition of above-mentioned technical proposal, be will different automobile types, different displacements, corresponding to different engine mission type Spare part code sort out carry out analysis comparison one by one after, find under the premise of identical main group number, the information such as vehicle discharge capacity are different, institute Corresponding spare part code is also not quite similar, wherein regular in order to look for, and has used the above method, more complete and comprehensive to be formed Theoretical knowledge information bank.

Embodiment 3:With technical solution identical with embodiment 1 or 2, more specifically, four the step of for embodiment 1 For, in the step 4, according to transaction database, error code and replacement spare part corresponding relationship are created by FP-Tree algorithm Frequent item set the step of, include

S1.1 inputs transaction database and minimum support threshold value min σ, scans transaction database, deletes frequency and is less than most The project of small support obtains whole frequent item set F1, arranges to obtain L by its support descending to the frequent episode in F1；

S1.2 creates the root node of FP-Tree, with " null " label, transaction database is scanned again, transaction database In each record according in L sequence arrange, generate FP-Tree；

S1.3 finds all frequent modes from FP-Tree.

In the step 4, using the topological relation between spare part position and the position ECU of guilty culprit, carries out topology and search Rope, the step of selecting frequent item set include:

S2.1 classifies spare part according to the construction rule of spare part code；

S2.2 constructs topological relation to the position ECU of spare part and guilty culprit, and the topological relation is identified, and obtains To the corresponding relationship of spare part and the position ECU.

In the step 4, the corresponding relationship of building spare part and maintenance mans forms the diagnosis number that error code corresponds to work item Include: according to the step of library

S3.1 scans the database of spare part and maintenance mans, obtains the frequent item set F2 of spare part and maintenance mans；To in F2 Frequent episode arrange to obtain L ' by its support descending；

S3.2 creates the root node of FP-Tree, and with " null " label, database, remembers each in database again Record is arranged according to the sequence in L ', generates FP-Tree；

S3.3 finds all frequent modes, the corresponding relationship of building spare part and maintenance mans from FP-Tree.

It is [p | P] according to the frequent episode table after S1.1 or S3.1 sequence, wherein p is first frequent episode, and P is remaining The list of frequent episode.It calls insert_tree ([p | P], T), insert_tree ([p | P], T) process executive condition is as follows: such as Fruit T has child node N to make N.item_name=p.item_name, then the counting of N increases by 1；Otherwise a new node N is created, it will Its counting is set as 1, is linked to its father node T, and be linked to same names item_ by node_link The node of name；If P non-empty, recursive call insert_tree (P, N).8, the case where being single for FP-Tree, directly Export the combination+postModel of all nodes on whole path.

The spare part is respectively according to attachment, entertainment information, engine, fuel oil, exhaust, air-conditioning, gearbox, and front axle turns To device, rear axle, wheel, brake, pedal gear, vehicle body, 0~9 classification of electronic equipment progress.

Embodiment 4: having technical solution same as Example 3, as the supplement of embodiment 3, also has following technology Scheme:

One, FP-Tree algorithm

Using FP-Tree, the data structure of deflation stores lookup frequent item set, Mining Association Rules, according to confidence level, The extractions such as support are out of order the possibility item collection with spare part.

Input: transaction database D (connection relationship of error code and replacement spare part) and minimum support threshold value min σ；

Output: FP-tree corresponding to transaction database D.

FP-tree is constructed according to the following steps:

1, transaction database D is scanned, whole frequent item set F1 and each included in transaction database D are obtained Support.L is obtained by its support descending sort to the frequent episode in F1.

2, the root node T of FP-tree is created, with " null " label, transaction database D is scanned again, for Transaction Information Frequent episode therein is selected and is sorted by the order in L by each affairs in the D of library.If the frequent episode table after sequence is [p | P], Wherein p is first frequent episode, and P is remaining frequent episode.Calling insert_tree ([p | P], T).insert_tree([p | P], T) process executive condition is as follows: if T has child node N to make N.item_name=p.item_name, the counting of N increases 1；Otherwise create a new node N, counted and be set as 1, be linked to its father node T, and pass through node_link by its It is linked to the node with identical item_name.If P non-empty, recursively insert_tree (P, N) is called.FP-tree is The structure of one high compression, it stores all information for Mining Frequent Itemsets Based.

The case where being single for FP-Tree, it is not necessary to which recursive call FPGrowth again is directly exported whole Various combination+the postModel of all nodes can on paths.

Transaction database is as follows, and every a line represents primary fault code and replaces the possibility relationship of spare part:

Failure A, failure B, spare part C, spare part D
	Failure B, failure E, spare part D, spare part F
Failure B, spare part C, spare part D
	Failure A, failure B, spare part C, failure E, spare part D, spare part F
Failure A, spare part C, spare part F
	Failure B, spare part C, spare part F
Failure A, spare part C, spare part D
	Failure A, failure B, spare part C, spare part G, spare part D
Failure A, failure B, spare part F
	Failure A, failure B, spare part G, spare part D

Purpose: finding out a kind of combination always occurred together, for example failure B and spare part D always occur together, then [failure B, Spare part D] it is a frequent mode.A part of rough relationship is obtained by FP-Tree, is then refined, is picked by topology search Except the combination for being unsatisfactory for topological relation.

(1) scan database, each presses frequency sort descending, and deletes the item that frequency is less than minimum support MinSup Mesh.

Failure A:7

Failure B:8

Spare part C:7

Spare part D:7

Spare part F:5

* present scan { Minsup=3 }

Then failure B, spare part C, spare part D, failure A, spare part F are frequent 1 item collection, are calculated as F1.

(2) it for the possibility relationship of each error code and replacement spare part, resequences according to the sequence in F1.

Failure B, spare part D, spare part C, failure A
	Failure B, spare part D, spare part F
Failure B, spare part D, spare part C
	Failure B, spare part D, spare part C, failure A, spare part F
Spare part C, failure A, spare part F
	Failure B, failure A, spare part F
Failure B, spare part C, spare part F
	Spare part D, spare part C, failure A
Failure B, spare part D, spare part C, failure A
	Failure B, spare part D, failure A

(3) each article of record obtained in (2) step is inserted into FP-Tree.Initial suffix pattern is sky, most throughout one's life As shown in figures 1-4 at FP-Tree.

Leftmost side is called header entry in Fig. 4, and the node of same names will be chained up in tree, and first of chained list Element is exactly the element in header entry.If FP-Tree is empty (containing only an empty root node), FP-Growth function It returns.Each single item+the postModel of header entry is exported at this time, and support is the counting of respective items in header entry.

(4) each single item in header entry (we take for " failure A:7 ") is carried out the following behaviour 1. arrived 5. for items Make:

1. finding all " failure A " nodes from FP-Tree, its ancestor node is traversed up, 4 paths are obtained:

Spare part D:6, failure B:8, failure A:1
	Spare part D:6, failure B:8, spare part C:4, failure A:3
Failure B:8, failure A:1
	Spare part C:2, failure A:2

2. count is both configured to the count of failure A for the node on each paths

Spare part D:1, failure B:1, failure A:1
	Spare part D:3, failure B:3, spare part C:3, failure A:3
Failure B:1, failure A:1
	Spare part C:2, failure A:2

3. failure A can be removed because each single item end is all failure A, obtain conditional pattern base (Conditional Pattern Base, CPB), suffix pattern at this time is: (failure A).

Spare part D:1, failure B:1
	Spare part D:3, failure B:3, spare part C:3
Failure B:1
	Spare part C:2

4. result above is returned to step 3, recursive iteration operation as original transaction database.

5. finally obtained frequent item set is (removal only has spare part or only faulty set of relations)

Two, topology search

The possibility item collection of the failure and spare parts (project) that obtain to association algorithm is further shunk, and vehicle structure is utilized It makes, the topological relation between the position ECU where limitation spare part position and vehicle trouble selects frequent episode in limited range Collection.

According to the construction of spare part code rule, Spare Parts Classification is carried out, specific as follows:

1 (engine): engine assembly, gray iron, piston, connecting rod, connecting component, engine bracker, bracket are suddenly tight Firmware, fuel oil spray such as air inlet pipe, air flow meter；

2 (fuel oil, exhaust, air-conditioning are cooling): fuel tank, exhaust pipe, air-conditioning refrigeration system etc.；

3 (gearboxes): transmission assembly and internal part；

4 (front axle, transfers): front-wheel drive differential mechanism, steering system (turning machine), front damper etc.；

5 (rear axles): rear axle, rear wheel drive differential mechanism, rear shock absorber, such as rear axle, rear-wheel bearing；

6 (wheel, brakes): wheel, wheel trim, brake system；

7 (pedal gears): trick braking system；

8 (vehicle bodies): vehicle body and decoration, air-conditioner housing, front/rear collision bumper, such as body assembly, air-conditioning ventilation system；

9 (electronic equipments): electric appliance, such as engine, starter, controller, lamps and lanterns, harness；

0 (attachment, Infotainment): attachment (jack, antenna, radio, engine bottom guard plates) and material of paint etc..

Corresponding relationship between spare part and maintenance items (work item) is constructed using FP-Free frequent item set algorithm, to obtain Obtain the total solution that error code corresponds to spare part and work item.

2, the number sorted corresponding vehicle ECU title topological relation of spare part

3, the number sorted corresponding work item code topological relation of spare part

It is combined by the above method and finds the corresponding relationship of failure and spare part, obtained error code and correspond to the complete of spare part and work item Whole solution.

The preferable specific embodiment of the above, only the invention, but the protection scope of the invention is not It is confined to this, anyone skilled in the art is in the technical scope that the invention discloses, according to the present invention The technical solution of creation and its inventive concept are subject to equivalent substitution or change, should all cover the invention protection scope it It is interior.

Claims

1. a kind of error code diagnosis vehicle work item and spare part search method, feature based on FP-Tree sequential mode mining exists In, including

Step 1 acquires vehicle information data；

Step 2 parsing vehicle VIN code obtains variable, and the variable includes the engine displacement obtained by the parsing of VIN code, vehicle body Type, engine mission type；

Step 3 does decision tree analysis to spare part code corresponding to variable, completes variable data and sorts out to form spare part information, and Index is established, diagnostic knowledge base is formed；

Step 4 creates error code by FP-Tree algorithm and replaces the frequent episode of spare part corresponding relationship according to transaction database Collection；Using the topological relation between spare part position and the position ECU of guilty culprit, topology search is carried out, frequent item set is selected；Structure The corresponding relationship of spare part and maintenance mans is built, the diagnostic data base that error code corresponds to work item is formed；

2. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are retrieved as described in claim 1 Method, which is characterized in that further include that step 6 identifies the error code that vehicle trouble generates, and parses vehicle by keyword The variable that VIN code obtains obtains work item and spare part information to carry out systematic searching.

3. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are examined as claimed in claim 1 or 2 Suo Fangfa, which is characterized in that using the historical record of maintenance and repair parts table as data basis in the step 4, pass through decision-tree model Spare part is done and is classified；

The step of spare part search method, is:

(1) maintenance and repair parts table is distinguished with dimension by comentropy；

(2) maintenance and repair parts table different dimensions are divided into priority by information gain；

(3) priority and differentiation degree divided according to step (1), (2) draws decision tree；

(4) a regular VIN code is inputted, according to VIN code and the spare part code of the decision tree obtained output vehicle.

4. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are examined as claimed in claim 1 or 2 Suo Fangfa, which is characterized in that the price list of the spare part associated codes spare part, the spare part information include the Chinese name of spare part Claim, the applicable vehicle model information of price and present use state and spare part.

5. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are retrieved as described in claim 1 Method, which is characterized in that in the step 4, according to transaction database, error code is created by FP-Tree algorithm and is replaced standby The step of frequent item set of part corresponding relationship, include

S1.1 inputs transaction database and minimum support threshold value min σ, scans transaction database, deletes frequency and is less than most ramuscule The project for degree of holding obtains whole frequent item set F1, arranges to obtain L by its support descending to the frequent episode in F1；

S1.2 creates the root node of FP-Tree, with " null " label, transaction database is scanned again, in transaction database Each record is arranged according to the sequence in L, generates FP-Tree；

S1.3 finds all frequent modes from FP-Tree.

6. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are retrieved as described in claim 1 Method, which is characterized in that in the step 4, using the topological relation between spare part position and the position ECU of guilty culprit, into Row topology search, the step of selecting frequent item set include:

S2.2 constructs topological relation to the position ECU of spare part and guilty culprit, and the topological relation is identified, and obtains standby The corresponding relationship of part and the position ECU.

7. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are retrieved as described in claim 1 Method, which is characterized in that in the step 4, the corresponding relationship of building spare part and maintenance mans forms error code and corresponds to work item Diagnostic data base the step of include:

S3.1 scans the database of spare part and maintenance mans, obtains the frequent item set F2 of spare part and maintenance mans；To the frequency in F2 Numerous item is arranged to obtain L ' by its support descending；

S3.2 creates the root node of FP-Tree, and with " null " label, database, presses each record in database again According to the sequence arrangement in L ', FP-Tree is generated；

8. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining as described in claim 5 or 7 is examined with spare part Suo Fangfa, which is characterized in that it according to the frequent episode table after S1.1 or S3.1 sequence is [p | P], wherein p is first frequent episode, And P is the list of remaining frequent episode.

9. the error code diagnosis vehicle work item based on FP-Tree sequential mode mining and spare part are retrieved as claimed in claim 8 Method, which is characterized in that calling insert_tree ([p | P], T), insert_tree ([p | P], T) and process executive condition is such as Under: if T has child node N to make N.item_name=p.item_name, the counting of N increases by 1；Otherwise a new node is created N is counted and is set as 1, its father node T is linked to, and is linked to by node_link with same names The node of item_name；If P non-empty, recursive call insert_tree (P, N).