CN105094119A - Vehicle-mounted expert diagnosis method and system - Google Patents
Vehicle-mounted expert diagnosis method and system Download PDFInfo
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- CN105094119A CN105094119A CN201510518603.5A CN201510518603A CN105094119A CN 105094119 A CN105094119 A CN 105094119A CN 201510518603 A CN201510518603 A CN 201510518603A CN 105094119 A CN105094119 A CN 105094119A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/0227—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions
- G05B23/0229—Qualitative history assessment, whereby the type of data acted upon, e.g. waveforms, images or patterns, is not relevant, e.g. rule based assessment; if-then decisions knowledge based, e.g. expert systems; genetic algorithms
Abstract
The invention provides a vehicle-mounted expert diagnosis method and system. The method includes the following steps that: fault monitoring is performed on a vehicle in real time; when a fault is monitored, fault data are obtained, and a timer is started, and the fault data and a fault occurrence time point are stored in a fault list in the form of a list; and fault diagnosis is performed on failure equipment to be diagnosed according to the fault list. According to the method, fault monitoring is performed on the vehicle in real time, and when the fault is monitored, the fault data are stored the form of a list, so that an engineer can directly perform diagnosis remotely, and therefore, the root of the problem can be located quickly, and manpower cost and time cost can be greatly decreased, and requirements of users for intellectualization can be satisfied.
Description
Technical field
The application relates to expert diagnosis technical field, particularly relates to a kind of vehicle-mounted expert diagnosing method and system.
Background technology
Along with the fast development of motive objects networking technology, large data cloud backstage technology, the information industry continuous potentiality to existing industry is excavated, newly traditional industries are promoted by the thinking duplicate removal of information industry, new situation will be opened up for traditional industries, and the automobile in the future new forms of energy wisdom vehicle that to be electronic information, software leading, be mainly reflected in: the aspects such as self monitoring and management and control, Information share and exchange, unified command and scheduling, automatic Pilot, new forms of energy.
At present, the information of vehicles for automobile is all by obtaining some basic automobile CAN-bus data simply and showing, and does not comprehensively analyze automobile CAN-bus data and the degree of depth is excavated.In addition, existing vehicle is only generally have the conventional func such as display, amusement, navigation, when vehicle breaks down time, slip-stick artist scene is needed to check data, analyze data, then debatable root could be located, so both waste human cost and time cost, user cannot be met again to intelligentized demand.
Summary of the invention
In view of this, this application provides a kind of vehicle-mounted expert diagnosing method and system, to overcome in prior art, both waste of manpower cost and time cost are located to vehicle failure, the problem of user to intelligentized demand cannot be met again.
For achieving the above object, this application provides following technical scheme:
A kind of vehicle-mounted expert diagnosing method, the method comprises:
Carry out fault to vehicle in real time to intercept;
Obtain fault data when listening to fault and start timer, described fault data and fault generation moment are stored in error listing with tabular form;
The faulty equipment treating diagnosis according to described error listing carries out fault diagnosis.
Preferably, describedly described fault data and fault be there is the moment and are stored in after in error listing with tabular form, also comprise:
The data source meeting preset time range in described fault data is carried out remote storage and local storage.
Preferably, described remote storage of the data source meeting preset time range in described fault data being carried out comprises with local storage:
Described data source is copied in the first queue, and is uploaded to cloud backstage to carry out remote storage by wireless network;
Described data source is copied in the second queue, saving scene data source, and described field data source write into Databasce is stored to carry out this locality.
Preferably, describedly in real time fault is carried out to vehicle and intercepts and specifically comprise:
By the data upload of CAN in the buffer cycles queue of the preset length built in advance;
Detecting thread in employing dominant symbols position intercepts the data in described buffer cycles queue, judges whether one of them ID sequence of current time breaks down;
When listening to fault and occurring, start timer and carry out timing, otherwise, continue to intercept next ID sequence.
Preferably, describedly described fault data and fault be there is the moment and are stored in error listing with tabular form and comprise:
Be there is moment one_to_one corresponding and be stored in described error listing in the device id of the fault name in described fault data, guilty culprit equipment, fault occurrence frequency, fault.
Preferably, the described fault data according to storing in described error listing is selected faulty equipment to be diagnosed to carry out fault diagnosis to comprise:
All faults of described faulty equipment are parsed according to described error listing;
Choose fault to be analyzed in described all faults as required;
Fault data corresponding to described fault to be analyzed is found out according to described error listing;
According to described fault data, fault diagnosis is carried out to faulty equipment described to be diagnosed.
A kind of vehicle-mounted expert diagnostic system, this system comprises:
Intercepting module, intercepting for carrying out fault to vehicle in real time;
First memory module, for when described in intercept and obtain fault data when module listens to fault and start timer, described fault data is stored in error listing with tabular form;
Diagnostic module, the faulty equipment that diagnosis treated by the error listing for uploading according to described first memory module carries out fault diagnosis.
Preferably, this system also comprises:
Second memory module, for carrying out remote storage and local storage by the data source meeting preset time range in described fault data.
Preferably, described second memory module comprises:
Remote storage modules, for described data source being copied in the first queue, and is uploaded to cloud backstage to carry out remote storage by wireless network;
Local memory module, for described data source is copied in the second queue, saving scene data source, and described field data source write into Databasce is stored to carry out this locality.
Preferably, intercept module described in comprise:
Buffer cycles module, for by the data upload of CAN in the buffer cycles queue of preset length built in advance;
Fault intercepts module, detecting thread and intercepting data in described buffer cycles queue, judging whether one of them ID sequence of current time breaks down for adopting dominant symbols position;
Judge module, for intercept when described fault module listen to fault occur time, start timer carry out timing, otherwise, continue to intercept next ID sequence.
Preferably, specifically for there is moment one_to_one corresponding be stored in the device id of the fault name in described fault data, guilty culprit equipment, fault occurrence frequency, fault in described error listing in described first memory module.
Preferably, described diagnostic module comprises:
Parsing module, for parsing all faults of described faulty equipment according to described error listing;
Choose module, for choosing fault to be analyzed in all faults that described parsing module parses as required;
Search module, for choosing fault data corresponding to fault to be analyzed that module chooses described in finding out according to described error listing;
Fault diagnosis module, for search described in basis module searches to fault data fault diagnosis is carried out to faulty equipment described to be diagnosed.
From above technical scheme, this application provides a kind of vehicle-mounted expert diagnosing method and system, the method comprises: carry out fault to vehicle in real time and intercept; Obtain fault data when listening to fault and start timer, described fault data and fault generation moment are stored in error listing with tabular form; The faulty equipment treating diagnosis according to described error listing carries out fault diagnosis.The method is intercepted by carrying out fault to vehicle in real time, when a failure occurs it, fault data is carried out list storage so that slip-stick artist is directly diagnosed by long-range, the root that quick position goes wrong, considerably reduce human cost and time cost, meet user to intelligentized demand.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to the accompanying drawing provided.
The process flow diagram of a kind of vehicle-mounted expert diagnosing method that Fig. 1 provides for the embodiment of the present application one;
A kind of process flow diagram in real time vehicle being carried out to the method that fault is intercepted that Fig. 2 provides for the embodiment of the present application one;
A kind of process flow diagram carrying out the method for fault diagnosis that Fig. 3 provides for the embodiment of the present application one;
The process flow diagram of a kind of vehicle-mounted expert diagnosing method that Fig. 4 provides for the embodiment of the present application two;
The structural representation of a kind of vehicle-mounted expert diagnostic system that Fig. 5 provides for the embodiment of the present application three;
A kind of structural representation of intercepting module that Fig. 6 provides for the embodiment of the present application three;
The structural representation of a kind of diagnostic module that Fig. 7 provides for the embodiment of the present application three;
The structural representation of a kind of vehicle-mounted expert diagnostic system that Fig. 8 provides for the embodiment of the present application four.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Locating both waste of manpower cost and time cost for overcoming in prior art to vehicle failure, cannot meet again the problem of user to intelligentized demand, this application provides a kind of vehicle-mounted expert diagnosing method and system, concrete scheme is as described below:
Embodiment one
The embodiment of the present application one provides a kind of vehicle-mounted expert diagnosing method, as shown in Figure 1, and the process flow diagram of a kind of vehicle-mounted expert diagnosing method that Fig. 1 provides for the embodiment of the present application one.The method comprises:
S101: in real time fault is carried out to vehicle and intercept.
Concrete, as shown in Figure 2, a kind of process flow diagram in real time vehicle being carried out to the method that fault is intercepted that Fig. 2 provides for the embodiment of the present application one.Specifically comprise:
S201: by the data upload of CAN in the buffer cycles queue of the preset length built in advance.
In this application, need to build the buffer cycles queue that length is N in advance, for saving scene.Concrete length does not limit in this application, can set according to actual needs.
S202: detecting thread in employing dominant symbols position intercepts the data in buffer cycles queue, judges whether one of them ID sequence of current time breaks down.
Start dominant symbols position detecting thread, for judging current time t, whether certain ID sequence breaks down.Concrete, whether CAN agreement, when formulating, has some fault position information, breaks down for decision node equipment.But depending the prompting of these fault positions alone, is the real causes that bad analysis of failure occurs; And for those in-and-out faults, more have no way of doing it, even can neglect these hidden danger.And common OBD (On-BoardDiagnostic, onboard diagnostic system) diagnosis, be not only data source limited, the more important thing is, its data source is result data, do not have step attribute be also difficult to extract correlative character, slip-stick artist according to OBD diagnostic result also very important person for go analyze, time-consuming, be sometimes also not easy the root orienting fault generation.
S203: when listening to fault and occurring, starts timer and carries out timing, otherwise, continue to intercept next ID sequence.
In the application, by starting timer, both have recorded the moment that fault occurs, and basis of time can be provided for the acquisition of fault data again.
S102: obtain fault data when listening to fault and start timer, is stored in fault data and fault generation moment in error listing with tabular form.
Wherein, in this application, fault data and fault generation moment are stored in error listing with tabular form to comprise:
Be there is moment one_to_one corresponding and be stored in error listing in the device id of the fault name in fault data, guilty culprit equipment, fault occurrence frequency, fault.
Particularly, once lighting a fire in power up, the node device ID that bus breaks down can learn device name; The frequency that under this equipment, various fault occurs can assess the performance of node device; Moment occurs fault can quick position source of reference data to be analyzed, has saved the time that the data source in moment occurs traversal coupling fault.
Above-mentioned data are set up a malfunction index table, can realize the data source that quick position is to be analyzed, without the need to all data sources of traversal coupling, efficiency is high.
S103: the faulty equipment treating diagnosis according to error listing carries out fault diagnosis.
Wherein, in this application, as shown in Figure 3, a kind of process flow diagram carrying out the method for fault diagnosis of providing for the embodiment of the present application one of Fig. 3.Concrete, select faulty equipment to be diagnosed to carry out fault diagnosis according to the fault data stored in error listing and comprise:
S301: all faults parsing faulty equipment according to error listing.
Select faulty equipment to be diagnosed, parse all faults of faulty equipment according to error listing.
S302: choose fault to be analyzed in all faults as required.
S303: find out fault data corresponding to fault to be analyzed according to error listing.
According to the moment t that equipment failure list can find this fault to occur fast, and then quick position is to the data source in (t-Δ T, t+ Δ T) in database:
Wherein, dominant symbols reason investigation comprises:
I) the step feature of this ID sequence in (t-Δ T, t+ Δ T) is extracted;
Ii) dominant symbols that may occur is investigated one by one.
Hidden failure reason investigation comprises:
I) other high ID sequence of the degree of correlation is mated;
Ii) judge whether this ID sequence has Spline smoothing;
Iii) to this ID sequence with the high degree of correlation of step attribute, iterative analysis is carried out.
S304: the faulty equipment treating diagnosis according to fault data carries out fault diagnosis.
According to the result of above-mentioned steps,
I) find the reason that fault most probable occurs, and provide corresponding instruction;
Ii) in conjunction with the equipment etc. of fault occurrence frequency, fault level, guilty culprit, analyze vehicle performance, and provide Motor Maintenance suggestion.
This diagnostic method that the embodiment of the present application one provides, the node device historical failure on supporting bus can not only be had to inquire about, statistics and prompting are had for the normal property sent out fault, for slip-stick artist assesses the reliability of node device, stability, antijamming capability, and equipment work optimum environmental demand etc. provides guidance foundation, and data are checked at Replacing engineering teacher scene, analyze data, the root that quick position goes wrong, support local diagnosis and remote diagnosis, powerful database platform, considerably reduce human cost and time cost, meet the demand of user to Vehicular intelligent.
Embodiment two
On the basis of embodiment one, the embodiment of the present application two provides another kind of vehicle-mounted expert diagnosing method, and fault data and fault generation moment, except the step comprised as shown in Figure 1, are being stored in after in error listing with tabular form, are also comprising by the method:
The data source meeting preset time range in fault data is carried out remote storage and local storage.
Concrete, as shown in Figure 4, the process flow diagram of a kind of vehicle-mounted expert diagnosing method that Fig. 4 provides for the embodiment of the present application two, Fig. 4 replaces with by the step S102 in Fig. 1:
S102: obtain fault data when listening to fault and start timer, is stored in fault data and fault generation moment in error listing with tabular form, and the data source meeting preset time range in fault data is carried out remote storage and local storage.
Carry out in timing course at timer, if there is new fault to occur, timer will be postponed delay second, records the data source in delay second.
Other appropriate sections see the description of embodiment one, can repeat no more in the present embodiment.
Concrete, the data source meeting preset time range in fault data is carried out remote storage and comprises with local storage:
Data source is copied in the first queue, and be uploaded to cloud backstage to carry out remote storage by wireless network.
In data source to the first queue in the queue of copy buffer cycles in (t-Δ T, t+ Δ T), the data source in the first queue is sent to backstage by 3G or other wireless transmission methods, to carry out remote storage.
Data source is copied in the second queue, saving scene data source, and field data source write into Databasce is stored to carry out this locality.
In data source to the second queue in the queue of copy buffer cycles in (t-Δ T, t+ Δ T), and the data source write into Databasce in the second queue is stored to carry out this locality.
In the application, the fault data source occurred before and after the moment in bus is the data source with step attribute, precisely analyze, and correlation analysis is helpful for fault.The storage that data source is allowed a choice, because the data of bus send always, add up for a long time to get off and can bring no small expense to storage, and some data is also of little use for diagnosis, and the data that the application stores significantly reduce storage overhead, and for failure cause diagnosis, be very important, because if data source can not extract step attribute, correlative character, the accuracy for diagnosis is also influential.
The present invention, by these key messages under data-base recording, is especially difficult to the data reproduced.This kind of data are more special, disappear due to the time of generation when some fault is.This kind of fault, for fault recurrence, more difficult, also very time-consuming, is not easy to be captured, but brings no small harm sometimes.This kind of fault, such as, BMS and battery management system, because cell also exists difference, what cause the threshold value threshold sets of some battery is not mate very much, these cells may be caused to overcharge or cross putting, affect the performance and used life of battery, and then have influence on the course continuation mileage of the overall performance of whole electric battery, serviceable life, car load.This kind of fault sometimes the duration very short, be not easy to be found, but long-term accumulation continues, is a chronic malignant tumor.
This diagnostic method, carries out mining analysis to bus data, extracts data steps feature, can quick position to trouble spot, especially disguised stronger fault, is equivalent to join expert's level diagnosis house keeper to automobile, a road convoy.
In addition, this solution, except supporting local data library storage, also supports cloud backstage remote storage.There is provided professional data to store and inquiry to mass data, gathered slip-stick artist for many years to the analysis logic broken down and algorithm, data can be checked, analyze data in Replacing engineering teacher scene, vouch the root gone wrong in position fast.
Expert diagnosis can be diagnosed in this locality, can the fault of artificial selection Water demand, also can carry out remote diagnosis on cloud backstage and provide remote technology support.By data mining and analysis, analyze vehicle performance, driving habits, for safe driving, green driving provide expert instruction.
From above technical scheme, the method that the embodiment of the present application two provides, not only achieve the technique effect achieved by embodiment one, and automobile bus data local data library storage and teledata library storage, in the situations such as emergency episode, be convenient to in-situ FTIR spectroelectrochemitry, its intellectuality can touch terminal, and user can select fault, and analyze, man-machine interaction effect is good.
Embodiment three
On the basis of embodiment one, the embodiment of the present application three provides a kind of vehicle-mounted expert diagnostic system, as shown in Figure 5, and the structural representation of a kind of vehicle-mounted expert diagnostic system that Fig. 5 provides for the embodiment of the present application three.This system comprises: intercept module 401, first memory module 402 and diagnostic module 403, wherein,
Intercepting module 401, intercepting for carrying out fault to vehicle in real time.
As shown in Figure 6, a kind of structural representation of intercepting module that Fig. 6 provides for the embodiment of the present application three, this is intercepted module and comprises: buffer cycles module 501, fault intercept module 502 and judge module 503, wherein,
Buffer cycles module 501, for by the data upload of CAN in the buffer cycles queue of preset length built in advance.
Fault intercepts module 502, detecting thread and intercepting data in buffer cycles queue, judging whether one of them ID sequence of current time breaks down for adopting dominant symbols position.
Judge module 503, for intercept when fault module listen to fault occur time, start timer carry out timing, otherwise, continue to intercept next ID sequence.
First memory module 402, for when intercepting acquisition fault data when module 401 listens to fault and starting timer, is stored in fault data in error listing with tabular form.
Specifically for moment one_to_one corresponding is there is and is stored in described error listing in the first memory module by the device id of the fault name in fault data, guilty culprit equipment, fault occurrence frequency, fault.
Diagnostic module 403, the faulty equipment that diagnosis treated by the error listing for uploading according to the first memory module 402 carries out fault diagnosis.
Concrete, as shown in Figure 7, the structural representation of a kind of diagnostic module that Fig. 7 provides for the embodiment of the present application three, this diagnostic module comprises: parsing module 601, choose module 602, search module 603 and fault diagnosis module 604, wherein,
Parsing module 601, for parsing all faults of faulty equipment according to error listing;
Choose module 602, for choosing fault to be analyzed in all faults that parsing module 601 parses as required;
Searching module 603, choosing fault data corresponding to fault to be analyzed that module 602 chooses for finding out according to error listing;
Fault diagnosis module 604, for carrying out fault diagnosis according to the faulty equipment searching fault data that module 603 finds and treat diagnosis.
Concrete repeats no more in this enforcement, in detail can see the description of embodiment one appropriate section.
Embodiment four
On the basis of embodiment three, present application example four provides another kind of vehicle-mounted expert diagnostic system, as shown in Figure 8, and the structural representation of a kind of vehicle-mounted expert diagnostic system that Fig. 8 provides for the embodiment of the present application four.This system, except comprising the structure shown in Fig. 5, also comprises:
Second memory module 404, for carrying out remote storage and local storage by the data source meeting preset time range in fault data.
Concrete, the second memory module comprises:
Remote storage modules, for data source being copied in the first queue, and is uploaded to cloud backstage to carry out remote storage by wireless network;
Local memory module, for data source being copied in the second queue, saving scene data source, and field data source write into Databasce is stored to carry out this locality.
Specifically repeat no more in the present embodiment, can see the description of other embodiment relevant portions.
From above technical scheme, the vehicle-mounted expert diagnostic system that the embodiment of the present application three and embodiment four provide, the node device historical failure on supporting bus can not only be had to inquire about, statistics and prompting are had for the normal property sent out fault, for slip-stick artist assesses the reliability of node device, stability, antijamming capability, and equipment work optimum environmental demand etc. provides guidance foundation, and data are checked at Replacing engineering teacher scene, analyze data, the root that quick position goes wrong, support local diagnosis and remote diagnosis, powerful database platform, considerably reduce human cost and time cost, meet the demand of user to Vehicular intelligent.
Finally, also it should be noted that, in this article, the such as relational terms of first and second grades and so on is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply the relation that there is any this reality between these entities or operation or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar portion mutually see.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the application.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein when not departing from the spirit or scope of the application, can realize in other embodiments.Therefore, the application can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.
Claims (12)
1. a vehicle-mounted expert diagnosing method, is characterized in that, the method comprises:
Carry out fault to vehicle in real time to intercept;
Obtain fault data when listening to fault and start timer, described fault data and fault generation moment are stored in error listing with tabular form;
The faulty equipment treating diagnosis according to described error listing carries out fault diagnosis.
2. method according to claim 1, is characterized in that, describedly described fault data and fault are occurred the moment is stored in after in error listing with tabular form, also comprises:
The data source meeting preset time range in described fault data is carried out remote storage and local storage.
3. method according to claim 2, is characterized in that, described remote storage of the data source meeting preset time range in described fault data being carried out comprises with local storage:
Described data source is copied in the first queue, and is uploaded to cloud backstage to carry out remote storage by wireless network;
Described data source is copied in the second queue, saving scene data source, and described field data source write into Databasce is stored to carry out this locality.
4. method according to claim 1, is characterized in that, describedly in real time carries out fault to vehicle and intercepts and specifically comprise:
By the data upload of CAN in the buffer cycles queue of the preset length built in advance;
Detecting thread in employing dominant symbols position intercepts the data in described buffer cycles queue, judges whether one of them ID sequence of current time breaks down;
When listening to fault and occurring, start timer and carry out timing, otherwise, continue to intercept next ID sequence.
5. method according to claim 1, is characterized in that, describedly described fault data and fault are occurred the moment is stored in error listing with tabular form and comprises:
Be there is moment one_to_one corresponding and be stored in described error listing in the device id of the fault name in described fault data, guilty culprit equipment, fault occurrence frequency, fault.
6. method according to claim 1, is characterized in that, the described fault data according to storing in described error listing is selected faulty equipment to be diagnosed to carry out fault diagnosis to comprise:
All faults of described faulty equipment are parsed according to described error listing;
Choose fault to be analyzed in described all faults as required;
Fault data corresponding to described fault to be analyzed is found out according to described error listing;
According to described fault data, fault diagnosis is carried out to faulty equipment described to be diagnosed.
7. a vehicle-mounted expert diagnostic system, is characterized in that, this system comprises:
Intercepting module, intercepting for carrying out fault to vehicle in real time;
First memory module, for when described in intercept and obtain fault data when module listens to fault and start timer, described fault data is stored in error listing with tabular form;
Diagnostic module, the faulty equipment that diagnosis treated by the error listing for uploading according to described first memory module carries out fault diagnosis.
8. system according to claim 7, is characterized in that, this system also comprises:
Second memory module, for carrying out remote storage and local storage by the data source meeting preset time range in described fault data.
9. system according to claim 8, is characterized in that, described second memory module comprises:
Remote storage modules, for described data source being copied in the first queue, and is uploaded to cloud backstage to carry out remote storage by wireless network;
Local memory module, for described data source is copied in the second queue, saving scene data source, and described field data source write into Databasce is stored to carry out this locality.
10. system according to claim 7, is characterized in that, described in intercept module and comprise:
Buffer cycles module, for by the data upload of CAN in the buffer cycles queue of preset length built in advance;
Fault intercepts module, detecting thread and intercepting data in described buffer cycles queue, judging whether one of them ID sequence of current time breaks down for adopting dominant symbols position;
Judge module, for intercept when described fault module listen to fault occur time, start timer carry out timing, otherwise, continue to intercept next ID sequence.
11. systems according to claim 7, it is characterized in that, specifically for there is moment one_to_one corresponding be stored in the device id of the fault name in described fault data, guilty culprit equipment, fault occurrence frequency, fault in described error listing in described first memory module.
12. systems according to claim 7, is characterized in that, described diagnostic module comprises:
Parsing module, for parsing all faults of described faulty equipment according to described error listing;
Choose module, for choosing fault to be analyzed in all faults that described parsing module parses as required;
Search module, for choosing fault data corresponding to fault to be analyzed that module chooses described in finding out according to described error listing;
Fault diagnosis module, for search described in basis module searches to fault data fault diagnosis is carried out to faulty equipment described to be diagnosed.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105867351A (en) * | 2016-04-29 | 2016-08-17 | 大连楼兰科技股份有限公司 | Vehicle fault code real-time acquisition and historical data analysis and diagnosis method and device |
| CN109213113A (en) * | 2017-07-04 | 2019-01-15 | 百度在线网络技术（北京）有限公司 | Vehicular diagnostic method and system |
| CN111815803A (en) * | 2020-06-19 | 2020-10-23 | 安徽安凯汽车股份有限公司 | New energy automobile fault storage system and detection method |
| CN112054843A (en) * | 2020-08-21 | 2020-12-08 | 武汉光迅信息技术有限公司 | Optical signal transmission system and method |
| WO2021217626A1 (en) * | 2020-04-30 | 2021-11-04 | 上海华东汽车信息技术有限公司 | Vehicle data processing method and apparatus, computer device, and storage medium |
| CN114090130A (en) * | 2021-11-26 | 2022-02-25 | 上海星融汽车科技有限公司 | Method and system for preloading execution logic |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1783161A (en) * | 2004-12-04 | 2006-06-07 | 现代奥途纳特株式会社 | System and method for controlling remote distance vehicle using remote distance information processing system |
| CN102830690A (en) * | 2012-05-04 | 2012-12-19 | 王蓉 | Data processing system of automobile fault data |
| CN102945042A (en) * | 2012-11-14 | 2013-02-27 | 深圳市元征科技股份有限公司 | Standard fault code extracting method based on professional diagnosis |
| CN102981496A (en) * | 2012-11-14 | 2013-03-20 | 深圳市元征科技股份有限公司 | Remote control and data storage method based on vehicle professional diagnosis |
| CN103226065A (en) * | 2013-04-10 | 2013-07-31 | 苏州市职业大学 | Intelligent automobile performance monitoring system based on onboard automatic diagnosis system |
-
2015
- 2015-08-21 CN CN201510518603.5A patent/CN105094119B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1783161A (en) * | 2004-12-04 | 2006-06-07 | 现代奥途纳特株式会社 | System and method for controlling remote distance vehicle using remote distance information processing system |
| CN102830690A (en) * | 2012-05-04 | 2012-12-19 | 王蓉 | Data processing system of automobile fault data |
| CN102945042A (en) * | 2012-11-14 | 2013-02-27 | 深圳市元征科技股份有限公司 | Standard fault code extracting method based on professional diagnosis |
| CN102981496A (en) * | 2012-11-14 | 2013-03-20 | 深圳市元征科技股份有限公司 | Remote control and data storage method based on vehicle professional diagnosis |
| CN103226065A (en) * | 2013-04-10 | 2013-07-31 | 苏州市职业大学 | Intelligent automobile performance monitoring system based on onboard automatic diagnosis system |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105867351A (en) * | 2016-04-29 | 2016-08-17 | 大连楼兰科技股份有限公司 | Vehicle fault code real-time acquisition and historical data analysis and diagnosis method and device |
| CN105867351B (en) * | 2016-04-29 | 2019-06-28 | 大连楼兰科技股份有限公司 | Vehicle trouble code acquires the method and device with historical data analysis diagnosis in real time |
| CN109213113A (en) * | 2017-07-04 | 2019-01-15 | 百度在线网络技术（北京）有限公司 | Vehicular diagnostic method and system |
| WO2021217626A1 (en) * | 2020-04-30 | 2021-11-04 | 上海华东汽车信息技术有限公司 | Vehicle data processing method and apparatus, computer device, and storage medium |
| CN111815803A (en) * | 2020-06-19 | 2020-10-23 | 安徽安凯汽车股份有限公司 | New energy automobile fault storage system and detection method |
| CN112054843A (en) * | 2020-08-21 | 2020-12-08 | 武汉光迅信息技术有限公司 | Optical signal transmission system and method |
| CN114090130A (en) * | 2021-11-26 | 2022-02-25 | 上海星融汽车科技有限公司 | Method and system for preloading execution logic |
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