CN112199145A - Intelligent diagnosis method, system and diagnosis equipment for vehicle - Google Patents
Intelligent diagnosis method, system and diagnosis equipment for vehicle Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000004458 analytical method Methods 0.000 claims abstract description 29
- 230000009191 jumping Effects 0.000 claims abstract description 20
- 230000001960 triggered effect Effects 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims description 37
- 238000012790 confirmation Methods 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 6
- 230000007935 neutral effect Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- XSQUKJJJFZCRTK-UHFFFAOYSA-N Urea Chemical compound NC(N)=O XSQUKJJJFZCRTK-UHFFFAOYSA-N 0.000 description 3
- 239000004202 carbamide Substances 0.000 description 3
- 239000002826 coolant Substances 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012631 diagnostic technique Methods 0.000 description 1
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- 239000010705 motor oil Substances 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
Abstract
A vehicle intelligent diagnosis method comprises the following steps: responding to the vehicle fault model selected by the user, calling the vehicle fault model to diagnose: acquiring a data stream corresponding to a current vehicle fault model; sequentially calling and displaying a plurality of pages corresponding to the current vehicle fault model according to a preset page jumping sequence and jumping conditions; judging whether a preset algorithm triggering condition is met or not according to the acquired data stream and/or the currently called page, and if so, triggering a corresponding diagnosis conclusion analysis algorithm; and judging whether the corresponding data stream meets the preset diagnosis conclusion condition or not through a triggered diagnosis conclusion analysis algorithm to obtain a corresponding preset diagnosis conclusion. According to the method and the device, the corresponding preset diagnosis conclusion analysis algorithm is triggered according to the preset algorithm triggering condition, the corresponding preset diagnosis conclusion is obtained through the diagnosis conclusion analysis algorithm, the vehicle fault diagnosis is convenient for maintenance personnel, the accuracy is high, and the misjudgment probability is reduced.
Description
Technical Field
The invention belongs to the technical field of vehicle diagnosis, and particularly relates to a vehicle intelligent diagnosis method, a vehicle intelligent diagnosis system and vehicle intelligent diagnosis equipment.
Background
With the development of science and technology, the structure of the automobile is more and more complex, the functions are more and more perfect, the automation degree is higher and higher, not only different parts of the same equipment are mutually related and tightly coupled, but also different equipment are tightly connected, and a whole is formed in the operation process. A fault may cause a series of chain reactions, which may result in the whole process not operating normally, and even cause significant loss, and thus the requirement for fault diagnosis is increasing.
In the preliminary stage of automobile diagnosis, the diagnosis result is mainly established on the basis of the sense and professional experience of field experts, only the diagnosis information is simply processed, and the diagnosis level is limited by the personal technical ability and the working experience.
The second stage is a conventional diagnostic technique based on signal processing and modeling processing by means of sensor technology and dynamic testing technology.
The third stage is an intelligent diagnosis technology stage, and the diagnosis system can effectively acquire, transmit, process, regenerate and utilize diagnosis information, so that the diagnosis system has the capability of performing successful state identification and state prediction on a diagnosis object in a given environment.
At present, the research of the domestic automobile detection and diagnosis technology mainly focuses on the following aspects: sensor research, research on signal analysis and processing technology, research on artificial intelligence and expert systems and the like, wherein the research on automobile intelligent diagnosis technology becomes the mainstream of the development of diagnosis technology, and a computer simulates human intelligent activities and has the capability of applying knowledge, logical reasoning and solving practical problems. The automobile maintenance relates to more professional fields and work contents, wherein fault diagnosis is the most critical link, and the application of the automobile intelligent diagnosis technology in the automobile maintenance needs the cooperation of an automobile intelligent system, so that the fault position can be quickly positioned after the fault occurs, the fault reason is determined, and the detection efficiency is improved. At present, the diagnosis and maintenance of vehicles are more intelligent and convenient, but at present, no vehicle intelligent diagnosis technology capable of automatically analyzing vehicle data streams to obtain corresponding diagnosis conclusions exists.
Disclosure of Invention
Based on the above, the invention provides a vehicle intelligent diagnosis method, system and diagnosis equipment aiming at the technical problems.
In order to solve the technical problems, the invention adopts the following technical scheme:
the scheme provides an intelligent vehicle diagnosis method, which comprises the following steps:
responding to the vehicle fault model selected by the user, calling the vehicle fault model to diagnose:
acquiring a data stream corresponding to a current vehicle fault model;
sequentially calling and displaying a plurality of pages corresponding to the current vehicle fault model according to a preset page jumping sequence and jumping conditions;
judging whether a preset algorithm triggering condition is met or not according to the acquired data stream and/or the currently called page, and if so, triggering a corresponding diagnosis conclusion analysis algorithm;
and judging whether the corresponding data stream meets the preset diagnosis conclusion condition or not through a triggered diagnosis conclusion analysis algorithm to obtain a corresponding preset diagnosis conclusion.
In one embodiment of the method aspect, said invoking the vehicle fault model for diagnosis in response to the user selected vehicle fault model further comprises:
receiving vehicle information input by a user;
and responding to the type of the vehicle fault model selected by the user, and calling the vehicle fault model of the type selected by the user and corresponding to the vehicle information for diagnosis.
In one embodiment of the method aspect, further comprising:
acquiring a data stream required by vehicle diagnosis through a vehicle OBD interface;
the acquiring of the data stream corresponding to the vehicle fault model further includes:
and screening the data stream corresponding to the current vehicle fault model from the data stream required by vehicle diagnosis.
In one embodiment of the method aspect, the jump condition comprises:
clicking the next step to jump: responding to the user clicking a next button, and automatically jumping from the current page to the next page;
timing jump: after the timer finishes timing, automatically jumping from the current page to the next page;
clicking to confirm skipping: automatically jumping from the current page to the next page in response to the user clicking a confirmation button;
click confirmation and timing jump: responding to a user click confirmation button, and automatically jumping from the current page to the next page after the timer finishes timing;
and (3) skipping the data stream according to the condition: and when the corresponding data stream meets the preset condition, automatically jumping from the current page to the next page.
In one embodiment of the method scheme, the algorithm triggering condition is any one or a combination of more than one of an extreme value condition, a threshold value condition, a state condition and a page condition;
the extreme value condition is a condition which is required to be met by the extreme value of one or more data streams, and the extreme value is a maximum value or a minimum value;
the threshold condition is a threshold condition which one or more data streams need to meet;
the state condition is a state condition which needs to be met by one or more data streams;
the page condition is that a specified page is currently called.
In an embodiment of the method, the determining whether the corresponding data stream satisfies a preset diagnosis conclusion condition further includes:
processing the corresponding data stream in a preset processing mode to obtain a processing value, and judging whether the processing value meets a preset diagnosis conclusion condition;
the processing mode comprises maximum value taking, minimum value taking, average value taking, most value taking, minimum value taking, assignment according to assignment conditions or operation on a plurality of data streams.
In one embodiment of the method variant, the algorithm trigger condition is preset by the following steps:
providing a configuration interface for a user to input an algorithm triggering condition statement written according to a preset rule;
reading in an algorithm triggering condition statement input by a user from the configuration interface;
and analyzing the algorithm triggering condition through the preset rule.
In one embodiment of the method variant, the processing mode is preset by the following steps:
providing a configuration interface for a user to input a processing mode statement written according to a preset rule;
reading a processing mode statement input by a user from the configuration interface;
and analyzing the processing mode according to the preset rule.
The scheme also provides a vehicle intelligent diagnosis system which comprises a storage module, wherein the storage module comprises instructions loaded and executed by a processor, and the instructions cause the processor to execute one vehicle intelligent diagnosis method in the method scheme.
The scheme also provides a diagnosis device which is provided with the intelligent vehicle diagnosis system in the system scheme.
According to the method and the device, the corresponding preset diagnosis conclusion analysis algorithm is triggered according to the preset algorithm triggering condition, the corresponding preset diagnosis conclusion is obtained through the diagnosis conclusion analysis algorithm, the vehicle fault diagnosis is convenient for maintenance personnel, the accuracy is high, and the misjudgment probability is reduced.
Detailed Description
The embodiment of the specification provides a vehicle intelligent diagnosis method, which comprises the following steps:
and responding to the vehicle fault model selected by the user, and calling the vehicle fault model for diagnosis.
In an actual application scene, a user connects a lower computer VCI of a diagnosis device with a vehicle OBD interface, connects an upper computer with the lower computer, inputs vehicle information in the upper computer, can pop up a type list of vehicle fault models after receiving the vehicle information input by the user, selects a required type through the type list, such as types which cannot be started, are insufficient in power or do not burn urea, and calls the vehicle fault model which is selected by the user and corresponds to the vehicle information to diagnose in response to the type selected by the user, wherein the vehicle information is used for distinguishing vehicles, and the vehicle fault models which are selected by the user and correspond to the vehicle information need to be called because the configuration of different vehicle information and fault models of the same type may be different.
Different models correspond to different model IDs, and when the vehicle fault model is called, calling can be carried out according to the model IDs.
The vehicle fault model comprises a data flow acquisition module, a page management module, a condition analysis module and an analysis algorithm module, and the specific diagnosis process comprises the following steps:
1. and acquiring the data stream corresponding to the current vehicle fault model by a data stream acquisition module.
In an actual application scenario, a user connects a lower computer VCI of a diagnostic device with a vehicle OBD interface, and after connecting an upper computer with the lower computer, the diagnostic device obtains a data stream required for vehicle diagnosis through the vehicle OBD interface, and accordingly, step 1 further includes:
and screening the data stream corresponding to the current vehicle fault model from the data stream required by vehicle diagnosis. If the current model is a model with insufficient power, data streams of engine water temperature, air inlet temperature, engine oil temperature, an engine torque limit state, a post-processing torque limit state, an accelerator pedal signal voltage and the like can be acquired.
2. The page management module calls and displays a plurality of pages corresponding to the current vehicle fault model in sequence according to a preset page jump sequence and jump conditions, and can perform operation guidance on a user, such as a page for preparing a start prompt and a page for prompting a client to finish vehicle starting operation according to requirements.
In one embodiment, the jump condition includes:
clicking the next step to jump: and automatically jumping from the current page to the next page in response to the user clicking the next button.
Timing jump: and after the timer finishes timing, automatically jumping from the current page to the next page.
Clicking to confirm skipping: in response to the user clicking the confirmation button, a jump is automatically made from the current page to the next page.
Click confirmation and timing jump: and responding to the user clicking a confirmation button, and automatically jumping from the current page to the next page after the timer finishes timing.
And (3) skipping the data stream according to the condition: and when the corresponding data stream meets the preset condition, automatically jumping from the current page to the next page.
The jump condition can be pre-configured by a user through a page management interface.
3. And judging whether a preset algorithm triggering condition is met or not by the condition analysis module according to the acquired data stream and/or the currently called page, and if so, triggering a corresponding diagnosis conclusion analysis algorithm.
In this embodiment, the algorithm triggering condition is any one or a combination of more than one of an extreme value condition, a threshold value condition, a state condition, and a page condition.
The extreme value condition is a condition that the extreme values of one or more data streams need to be met, the extreme values are maximum values or minimum values, the threshold value condition is a threshold value condition that one or more data streams need to be met, the state condition is a state condition that one or more data streams need to be met, and the page condition is that a specified page is called currently.
Specifically, the algorithm trigger condition is preset by the following steps:
and providing a configuration interface for a user to input an algorithm trigger condition statement written according to a preset rule.
And reading in an algorithm triggering condition statement input by a user from the configuration interface.
And analyzing the algorithm triggering condition through the preset rule.
In the predetermined rule, the support connector: and (or), the judger: a name of >! The judgment symbol has a higher priority than the connector.
The following illustrates the style of the algorithm trigger condition statement, but is not limited to the following styles:
the first mode is as follows:
data stream code: [ { data stream code } < a ] { data stream code } < a: collecting times: and selecting a collected data stream segment (symbols need to use English symbols).
Wherein, ": "is a delimiter," stream code "indicates a data stream that can be matched by the data stream, the data stream within the symbol will be selected," [ { stream code } < a ] "indicates that the maximum value of the data stream is less than a, and similarly," [ { stream code } > a ] "indicates that the minimum value of the data stream is greater than a.
The data stream section selected and collected has multiple selection modes, most represents the time period with the maximum data quantity, less represents the time period with the minimum data quantity, new represents the data of the latest time period, old represents the data collected in the initial time period, most, less, new and old correspond to corresponding functions and can be called from a preset function library.
Style one statements indicate that in the selected data stream segment, the maximum value of the data stream is less than a certain value a.
The following is a specific example of an application of the first embodiment:
example 1: s000089 [ { S000089} <5] { S000089} <5:1$ most
In this statement, S000089 represents the engine speed, most represents the period of time during which the data amount is the maximum, 1 represents the number of times of acquisition, $ represents the bit fetch operation, "[ { S000089} <5 ]" represents that the maximum value of the engine speed is less than 5, and this statement represents the trigger condition: and acquiring a time period with the maximum engine speed data amount for 1 time, wherein the engine speed reading value is less than 5, and if the trigger condition is met, triggering a corresponding diagnosis conclusion analysis algorithm.
Example 2: s000089 [ { S000089} <150] { S000089} >5& { S000089} <150:2$ most
In this sentence, S000089 represents the engine speed, & & represents the simultaneous establishment, 2 represents the number of times of acquisition, and most indicates the period of time in which the data amount is the maximum. This bar represents the trigger condition: and acquiring the time period with the maximum engine speed data amount for 2 times, continuously acquiring the engine speed reading value for 2 times, wherein the engine speed reading value is less than 150 and more than 5, and triggering a corresponding diagnosis conclusion analysis algorithm if the trigger condition is met.
And the second mode is as follows:
data stream code1, data stream code2, data stream code 3: ({ data stream code1} >, & { data stream code1} < ═ b) & & eq ({ data stream code2}, state 1) & & eq ({ data stream code3}, state 2): acquisition times: selecting a segment of a collected data stream
Where multiple data streams are connected by a connector, eq ({ data stream code2}, state 1) indicates { data stream code2} -, state 1.
The following is a specific example of the application of pattern two:
example 1:
s000035, S000049, S000031: ({ S000035} >, & & { S000035} < ═ 28) & & eq ({ S000049}, open) & & eq ({ S000031}, neutral): 2$ most
In this sentence, S000035 is a battery voltage, S000049 is an under-vehicle shutdown switch, S000031 is a neutral switch, & & indicates that "eq ({ S000049, off)" indicates that the under-vehicle shutdown switch is off, "eq ({ S000031, neutral)" indicates that the neutral switch is neutral, 2 indicates the number of acquisitions, and most indicates a period of time in which the data amount is maximum. This bar represents the trigger condition: and in the time period with the maximum data taking amount, the reading value of S000035 is greater than or equal to 22 and less than or equal to 28, the reading value of the shutdown switch under the vehicle is off, the reading value of the neutral switch is neutral, the reading values are continuously and simultaneously generated twice, and if the triggering condition is met, a corresponding diagnosis conclusion analysis algorithm is triggered.
Example 2:
S000089，
S000126:[{S000089}<600]{S000126}>200&&{S000089}>150&&{S000089}<500:1$most
in this statement, S000089 is the engine speed, S000126 is the actual rail pressure, "[ { S000089} <600 ]" indicates that the maximum value of the engine speed is less than 600, most indicates the period of time during which the data amount is maximum, and this statement indicates the trigger condition: and in the time period with the maximum data taking amount, the reading value of the engine speed is more than 150 and less than 500, the reading value of the actual rail pressure is more than 200, and if the triggering condition is met, a corresponding diagnosis conclusion analysis algorithm is triggered.
And (3) style three:
data stream code, DM: [ { data stream code } < a ] { data stream code } < a & { DM } -, b-c
Where, b-c indicates the c page currently calling the model b, and may be overlapped with other conditions by a connector. In practical application scenarios, some diagnostic conclusion analysis algorithms need to be triggered on a specific page.
The following is a specific application example of the style three:
example 1: s000089, DM [ { S000089} <5] { S000089} <5& { DM } ═ 17-147:1$ new &
In this statement, S000089 is the engine speed, 1 indicates the number of times of collection, new indicates that the latest time period data is taken, and this statement indicates the trigger condition: and taking data of the latest time period, calling 147 pages of the model 17 at present, enabling the engine speed reading value to be less than 5, and triggering a corresponding diagnosis conclusion analysis algorithm if the triggering condition is met.
Example 2: s000115, S000130: { DM } - { 18-220& ({ S000115} < 1380)
||{S000115}>1460)&&({S000130}<200&&{S000130}>-200):5
In this statement, S000115 is the cell trigger current, S000130 is the rail pressure deviation, and the bar represents the trigger condition: when the 220 page of the model 18 is currently invoked, the metering unit triggers a current reading greater than 1460 or less than 1380 and a rail pressure deviation reading greater than-200 or less than 200 meets 5 times, if the triggering condition is met, a corresponding diagnostic conclusion analysis algorithm is triggered.
And (4) pattern four:
the data stream code satisfies the condition of acquisition times of selecting the acquired data stream segment
The following gives examples of specific applications for the pattern four:
example 1: s000089: { S000089} <5:3$ most
In this statement, S000089 is the engine speed, and this statement represents the trigger condition: and (3) continuously reading the engine speed for 3 times less than 5 in the time period with the maximum data acquisition amount, and triggering a corresponding diagnosis conclusion analysis algorithm if the trigger condition is met.
4. And judging whether the corresponding data stream meets the preset diagnosis conclusion condition or not by the analysis algorithm module through the triggered diagnosis conclusion analysis algorithm to obtain the corresponding preset diagnosis conclusion.
If the minimum urea pressure is less than-1, the conclusion is that: the urea pressure was normal.
In this embodiment, when a preset diagnosis conclusion condition is satisfied, a corresponding preset conclusion ID is obtained, a specific conclusion can be matched from the conclusion management table through the conclusion ID, and each analysis conclusion corresponds to a unique conclusion ID and is called in the analysis model.
In this embodiment, the above determining whether the corresponding data stream satisfies the preset diagnosis conclusion condition further includes:
and processing the corresponding data stream in a preset processing mode to obtain a processing value, and judging whether the processing value meets a preset diagnosis conclusion condition.
The processing mode comprises the steps of taking a maximum value (max), taking a minimum value (min), taking an average value (avg), taking a most-valued value (mos), taking a least-valued value (least), and assigning values or operating a plurality of data streams according to assignment conditions.
Specifically, the processing method is preset through the following steps:
and providing a configuration interface for a user to input processing mode statements written according to a preset rule, wherein the configuration interface can be the same as the preset algorithm triggering condition.
And reading a processing mode statement input by a user from the configuration interface.
The processing mode is analyzed through the preset rule, and the same rule can be adopted with the preset algorithm triggering condition.
The following illustrates the style of the handling style statement, but is not limited to the following styles:
the first mode is as follows:
max $ data stream code | { data stream code }
The single data stream supports maximum max $ data stream code, minimum min $ data stream code, average avg $ data stream code, most value most $ data stream code, and minimum least value least $ data stream code.
The following is a specific example of an application of the first embodiment:
example 1: max $ S000089| { S000089}
S000089 represents the engine speed, max $ represents taking the maximum value thereof, and the processing mode statement represents taking the maximum value of the engine speed.
Example 2: min $ S000035| { S000035}
S000035 is the battery voltage, min $ represents the minimum value of the battery voltage, and the processing mode statement represents the minimum value of the battery voltage.
Example 3: most $ S000035| { S000035}
S000035 is a battery voltage, most $ represents the most significant value, and the processing mode statement represents the value at which the battery voltage occurs the most frequently.
And the second mode is as follows:
must $ data stream code | if ({ data stream code } -? 0:1
The processing mode statement supports judgment, if the state class data stream needs to be judged, an if function can be used, if the read value of the data stream code is in a specified state, the data stream is assigned with 0, otherwise, the data stream code is assigned with 1, and the data stream code can be overlapped with other functions.
The following is a specific example of the application of pattern two:
example 1: most $ S000049| if ({ S000049} ═ on)? 0:1
S000049 is an under-vehicle shutdown switch, and the processing mode statement represents that if the read value of the under-vehicle shutdown switch that occurs the most frequently is on, the data stream is assigned with 0, and otherwise, the data stream is assigned with 1.
And (3) style three:
RTF & data stream code1, data stream code2| { data stream code1}/{ data stream code2}
Wherein the RTF function represents the ratio of the data fetch stream code1 and the data stream code 2.
The following is a specific application example of the style three:
example 1: RTF & S000007, S000008| { S000007}/{ S000008}
S000007 is the signal voltage of the accelerator pedal 1, S000008 is the signal voltage of the accelerator pedal 2, and the processing mode statement represents the ratio of the signal voltage of the accelerator pedal 1 to the signal voltage of the accelerator pedal 2.
And (4) pattern four:
max $ data stream code1, most $ data stream code2| { data stream code2} - { data stream code1}
The processing mode statement supports a plurality of data streams to perform four arithmetic operations.
The following is a specific application example of the pattern four:
example 1: max $ S000089, most $ S000495| { S000495} - { S000089}
S000089 is the engine speed, and S000495 is the theoretical maximum speed, and the processing mode statement represents taking the difference between the theoretical maximum speed and the engine maximum speed.
Similarly, the data flow satisfying conditional jump in the page jump condition can also be configured by statements, such as:
example 1: s000096 { S000096} >70:2
Sentence support >, <, >, |! Where S000096 denotes the coolant temperature, the sentence denotes that the coolant temperature data stream is greater than 70 twice in succession, and when this condition is satisfied, the page will automatically jump to the next page.
Example 2: s000007, S000008, S000011: { S000007} >3.6& { S000008} >1.8
&&{S000011}>90:1
The sentence supports & & and | | connection, a plurality of data streams CODEs are separated by english commas, wherein S000007 denotes an accelerator pedal 1 voltage original value, S000008 denotes an accelerator pedal 2 voltage original value, and S000011 denotes an accelerator pedal position, the sentence denotes that the accelerator pedal 1 voltage original value is greater than 3.6V, the accelerator pedal 2 voltage original value is greater than 1.8V, and the accelerator pedal opening degree is greater than 90%, and when the condition is satisfied, the page will automatically jump to the next page.
According to the method and the device, the corresponding preset diagnosis conclusion analysis algorithm is triggered according to the preset algorithm triggering condition, the corresponding preset diagnosis conclusion is obtained through the diagnosis conclusion analysis algorithm, the vehicle fault diagnosis is convenient for maintenance personnel, the accuracy is high, and the misjudgment probability is reduced.
Based on the same inventive concept, the present specification also provides a vehicle intelligent diagnosis system, which comprises a storage module, wherein the storage module comprises instructions (program code) loaded and executed by a processor, and the instructions cause the processor to execute the steps according to the various exemplary embodiments of the present invention described in the vehicle intelligent diagnosis method part.
The memory module may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM) and/or a cache memory unit, and may further include a read only memory unit (ROM).
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Based on the same inventive concept, the embodiment of the present specification further provides a diagnostic device, where the diagnostic device has the above vehicle intelligent diagnostic system, and details are not repeated here.
However, those skilled in the art should realize that the above embodiments are illustrative only and not limiting to the present invention, and that changes and modifications to the above described embodiments are intended to fall within the scope of the appended claims, provided they fall within the true spirit of the present invention.
Claims (10)
1. A vehicle intelligent diagnosis method is characterized by comprising the following steps:
responding to the vehicle fault model selected by the user, calling the vehicle fault model to diagnose:
acquiring a data stream corresponding to a current vehicle fault model;
sequentially calling and displaying a plurality of pages corresponding to the current vehicle fault model according to a preset page jumping sequence and jumping conditions;
judging whether a preset algorithm triggering condition is met or not according to the acquired data stream and/or the currently called page, and if so, triggering a corresponding diagnosis conclusion analysis algorithm;
and judging whether the corresponding data stream meets the preset diagnosis conclusion condition or not through a triggered diagnosis conclusion analysis algorithm to obtain a corresponding preset diagnosis conclusion.
2. The intelligent vehicle diagnosis method according to claim 1, wherein the vehicle fault model is invoked for diagnosis in response to the vehicle fault model selected by the user, further comprising:
receiving vehicle information input by a user;
and responding to the type of the vehicle fault model selected by the user, and calling the vehicle fault model of the type selected by the user and corresponding to the vehicle information for diagnosis.
3. The intelligent diagnosis method for the vehicle according to claim 2, further comprising:
acquiring a data stream required by vehicle diagnosis through a vehicle OBD interface;
the acquiring of the data stream corresponding to the vehicle fault model further includes:
and screening the data stream corresponding to the current vehicle fault model from the data stream required by vehicle diagnosis.
4. The intelligent vehicle diagnosis method according to claim 3, wherein the skip condition includes:
clicking the next step to jump: responding to the user clicking a next button, and automatically jumping from the current page to the next page;
timing jump: after the timer finishes timing, automatically jumping from the current page to the next page;
clicking to confirm skipping: automatically jumping from the current page to the next page in response to the user clicking a confirmation button;
click confirmation and timing jump: responding to a user click confirmation button, and automatically jumping from the current page to the next page after the timer finishes timing;
and (3) skipping the data stream according to the condition: and when the corresponding data stream meets the preset condition, automatically jumping from the current page to the next page.
5. The intelligent vehicle diagnosis method according to claim 4, wherein the algorithm triggering condition is any one or more of an extreme value condition, a threshold value condition, a state condition and a page condition;
the extreme value condition is a condition which is required to be met by the extreme value of one or more data streams, and the extreme value is a maximum value or a minimum value;
the threshold condition is a threshold condition which one or more data streams need to meet;
the state condition is a state condition which needs to be met by one or more data streams;
the page condition is that a specified page is currently called.
6. The intelligent vehicle diagnosis method according to claim 5, wherein the determining whether the corresponding data stream satisfies a preset diagnosis conclusion condition further comprises:
processing the corresponding data stream in a preset processing mode to obtain a processing value, and judging whether the processing value meets a preset diagnosis conclusion condition;
the processing mode comprises maximum value taking, minimum value taking, average value taking, most value taking, minimum value taking, assignment according to assignment conditions or operation on a plurality of data streams.
7. The intelligent vehicle diagnosis method according to claim 6, wherein the algorithm triggering condition is preset by the following steps:
providing a configuration interface for a user to input an algorithm triggering condition statement written according to a preset rule;
reading in an algorithm triggering condition statement input by a user from the configuration interface;
and analyzing the algorithm triggering condition through the preset rule.
8. The intelligent diagnosis method for the vehicle according to claim 7, wherein the processing mode is preset by the following steps:
providing a configuration interface for a user to input a processing mode statement written according to a preset rule;
reading a processing mode statement input by a user from the configuration interface;
and analyzing the processing mode according to the preset rule.
9. A vehicle diagnostic intelligence system comprising a memory module including instructions loaded and executed by a processor, the instructions when executed causing the processor to perform a vehicle diagnostic intelligence method according to any one of claims 1-8.
10. A diagnostic apparatus characterized by having a vehicle intelligent diagnostic system according to claim 9.
Priority Applications (4)
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| CN202011079545.8A CN112199145A (en) | 2020-10-10 | 2020-10-10 | Intelligent diagnosis method, system and diagnosis equipment for vehicle |
| CA3141186A CA3141186A1 (en) | 2020-10-10 | 2021-03-18 | Intelligent vehicle diagnostic method, system and diagnostic device thereof |
| PCT/CN2021/081601 WO2021223522A1 (en) | 2020-10-10 | 2021-03-18 | Intelligent vehicle diagnosis method and system, and diagnosis device |
| AU2021266924A AU2021266924A1 (en) | 2020-10-10 | 2021-03-18 | Intelligent vehicle diagnosis method and system, and diagnosis device |
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Cited By (2)
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| CN113486221A (en) * | 2021-07-05 | 2021-10-08 | 上海星融汽车科技有限公司 | Vehicle diagnosis method based on inquiry and prompt, electronic equipment and vehicle |
| WO2021223522A1 (en) * | 2020-10-10 | 2021-11-11 | 上海星融汽车科技有限公司 | Intelligent vehicle diagnosis method and system, and diagnosis device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114169253B (en) * | 2021-12-29 | 2022-07-19 | 中国科学院空间应用工程与技术中心 | Data flow dynamic prediction method and system based on Flink and LSTM |
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Also Published As
| Publication number | Publication date |
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| WO2021223522A1 (en) | 2021-11-11 |
| CA3141186A1 (en) | 2021-11-11 |
| AU2021266924A1 (en) | 2022-04-28 |
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