AU2021266924A1 - Intelligent vehicle diagnosis method and system, and diagnosis device - Google Patents
Intelligent vehicle diagnosis method and system, and diagnosis device Download PDFInfo
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title description 6
- 230000009191 jumping Effects 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 27
- 238000002405 diagnostic procedure Methods 0.000 claims abstract description 24
- 230000001960 triggered effect Effects 0.000 claims abstract description 15
- 238000003672 processing method Methods 0.000 claims description 19
- 238000012545 processing Methods 0.000 claims description 17
- 230000004044 response Effects 0.000 claims description 10
- 238000012216 screening Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 3
- 230000000875 corresponding effect Effects 0.000 description 31
- 238000005516 engineering process Methods 0.000 description 13
- 230000007935 neutral effect Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 4
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- 239000004202 carbamide Substances 0.000 description 3
- 238000011160 research Methods 0.000 description 3
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- 230000000694 effects Effects 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 239000000110 cooling liquid Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000010705 motor oil Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
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- 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
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- G06F9/451—Execution arrangements for user interfaces
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- G—PHYSICS
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
An intelligent vehicle diagnostic method, characterized in that, comprising:
responding to a current vehicle fault model selected by a user, and calling the vehicle
fault model for diagnosis; acquiring one or more data streams corresponding to the
current vehicle fault model; calling and showing in sequence a plurality of pages
corresponding to the current vehicle fault model according to a predetermined page
jumping sequence and a jumping condition; judging whether one or more
predetermined algorithm trigger conditions have been met according to the data
streams acquired and /or the page called currently, if yes, trigger corresponding
diagnostic conclusion analysis algorithm; and judging whether the corresponding
data streams satisfy one or more predetermined diagnostic conclusion conditions by
the triggered diagnostic conclusion analysis algorithm and getting a corresponding
preset diagnostic conclusion. In the present invention, corresponding predetermined
diagnostic conclusion analysis algorithm can be triggered by the predetermined
algorithm trigger conditions, and corresponding predetermined diagnostic
conclusion can be got by the diagnostic conclusion analysis algorithm, so as to ease
the maintenance staff in diagnosing vehicle faults, which is of high accuracy and can
reduce a misjudge rate.
Description
Intelligent vehicle diagnostic method, system and diagnostic device thereof
Technical field
The present invention relates to the vehicle diagnostic technical field, especially an
intelligent vehicle diagnostic method, system and diagnostic device.
Background technology
With the development of science and technology, automotive structures have
become more and more complicated, with more and more functions and higher
automation levels, not only different parts of the same device are correlated and
coupled tightly, and a close relationship exists among different devices, which
become an entirety during running. Once a fault happens with one part, a series of
ripple effects may be triggered, as a result the entire process cannot run as usual, or
even severe loss can be caused, therefore, requirements on fault diagnosis is higher
and higher.
In a preliminary stage of vehicle diagnosis, the diagnostic results relied primarily on
senses and professional experience of technical specialists, usually only simple
treatment is done to the diagnostic information, and the diagnostic levels are subject
to limitation of personal technical abilities and working experience.
In the second stage is conventional diagnostic technology with sensor technology
and dynamic testing technology as a medium, and signal processing and model
building treatment as a basis.
The third stage is an intelligent diagnostic technology stage, wherein a diagnostic
system can acquire, transmit, process, regenerate and utilize the diagnostic
information effectively, and is provided with a capacity to recognize and predict
status of a diagnostic object in a predetermined environment.
Currently study on automobile vehicle inspection and diagnostic technology focuses on the following aspects: sensor study, researches on signal analysis and processing technology, and researches on artificial intelligence and specialist systems, wherein researches on the intelligent vehicle diagnostic technology has become a mainstream of diagnostic technology development, by the intelligent diagnostic technology, computers can imitate intelligent activities of humans, and are provided with abilities of knowledge application, logical inference, and actual problem solving. Vehicle repairing relates to many special fields and jobs, wherein fault diagnosis is a critical node, application of the intelligent vehicle diagnostic technology in vehicle repairing need cooperation of an internal intelligent automobile system, so that when a fault occurs, a position of the fault can be identified quickly, one or more causes of the fault can be found, and inspection efficiency can be improved. Currently vehicle diagnosis and maintenance is more and more intelligent and convenient, however, there is no intelligent vehicle diagnostic technology that can reach a diagnostic conclusion by analyzing the vehicle data stream automatically.
Summary of the invention
Based on the foregoing circumstance, and targeting at the foregoing technical
problem, the present invention provides an intelligent vehicle diagnostic method,
system and diagnostic device.
To address the foregoing problems, the present invention adopts a following
technical solution:
The present technical solution provides an intelligent vehicle diagnostic method,
comprising:
Responding to a current vehicle fault model selected by a user, and calling the vehicle
fault model for diagnosis;
Acquiring one or more data streams corresponding to the current vehicle fault
model;
Calling and showing in sequence a plurality of pages corresponding to the current
vehicle fault model according to a predetermined page jumping sequence and a
jumping condition;
Judging whether one or more predetermined algorithm trigger conditions have been
met according to the data streams acquired and /or the page called currently, if yes,
trigger corresponding diagnostic conclusion analysis algorithm; and
Judging whether the corresponding data streams satisfy one or more predetermined
diagnostic conclusion conditions by the triggered diagnostic conclusion analysis
algorithm and getting a corresponding preset diagnostic conclusion.
In an embodiment of the intelligent vehicle diagnostic method, responding to the
current vehicle fault model selected by the user, and calling the vehicle fault model
for diagnosis further comprises:
Receiving vehicle information input by the user; and
Responding to a type of the vehicle fault model selected by the user, calling a vehicle
fault model with the type and the vehicle information consistent with the type and
the vehicle information selected by the user.
An embodiment of the intelligent vehicle diagnostic method further comprises:
Acquiring the data streams needed for vehicle diagnosis from an OBD port of the
vehicle;
Acquiring the data streams corresponding to the vehicle fault model further
comprises:
Screening the data streams corresponding to the current vehicle fault model from
the data streams needed for vehicle diagnosis.
In an embodiment of the intelligent vehicle diagnostic method, the jumping
condition comprises:
Jumping upon clicking next: jumping from the current page to the next page in
response to clicking next by the user;
Timing jumping: jumping from the current page to the next page after timing of the
timer finishes;
Jumping by clicking confirm: jumping automatically from the current page to the next
page in response to clicking a confirm button by the user;
Jumping upon clicking confirm and timing: jumping automatically from the current
page to the next page in response to clicking the confirm button by the user and
when timing of the timer finishes; and
Jumping upon data stream conformity: jumping automatically from the current page
to the next page when the corresponding data streams satisfy one or more
predetermined conditions.
In an embodiment of the intelligent vehicle diagnostic method, the algorithm trigger
conditions are any combination of one or more of an extreme value condition, a
threshold value condition, a status condition and a page condition.
Wherein the extreme value condition is a condition that an extreme value of the one
or more data streams shall satisfy, wherein the extreme value is a maximum value or
minimum value;
Wherein the threshold value condition is a threshold value condition that the one or
more data streams shall satisfy; and
Wherein the page condition is that a predetermined page has been called currently.
In an embodiment of the intelligent vehicle diagnostic method, judging whether
corresponding data streams satisfy the one or more predetermined diagnostic
conclusion conditions further comprises:
Processing the corresponding data streams with a preset processing method, getting
processing values, and judging whether the processing values satisfy the
predetermined diagnostic conclusion conditions;
Wherein the processing method comprises taking the maximum value, the minimum
value, an average value, the most frequent value and the least frequent value, assigning the values according to one or more value assignment conditions or operating the plurality of data streams.
In an embodiment of the intelligent vehicle diagnostic method, the algorithm trigger
conditions can be preset in following steps:
Providing a configuration interface, for the user to input algorithm trigger condition
statement written according to predetermined laws;
Reading the algorithm trigger condition statement input by the user from the
configuration interface; and
Interpreting the algorithm trigger conditions by the predetermined laws.
In an embodiment of the intelligent vehicle diagnostic method, the processing
method can be preset in a following manner:
Providing a configuration interface for the user to input processing method
statement written according to predetermined laws;
Reading the processing method statement input by the user from the configuration
interface; and
Interpreting the processing method by the foregoing predetermined configuration
interface.
The present technical solution further provides an intelligent vehicle diagnostic
system, comprising a storage module, wherein the storage module includes
instructions to be loaded and executed by the processor, and the instructions when
executed by the processor will have the processor execute the intelligent vehicle
diagnostic method in the foregoing technical solution.
The present technical solution further provides a diagnostic device, and the
diagnostic device is provided with the intelligent vehicle diagnostic system provided
in the foregoing technical solution.
In the present invention, corresponding predetermined diagnostic conclusion
analysis algorithm can be triggered by the predetermined algorithm trigger conditions, and corresponding predetermined diagnostic conclusion can be got by the diagnostic conclusion analysis algorithm, so as to ease the maintenance staff in diagnosing vehicle faults, which is of high accuracy and can reduce a misjudge rate.
Embodiments
An embodiment of the present invention provides an intelligent vehicle diagnostic
method, comprising:
Responding to a vehicle fault model selected by the user, and calling the vehicle fault
model for diagnosis.
In actual application, connect a virtual channel identifier VCI to an OBD port of a
vehicle, connect the upper computer with the VCI, input the vehicle information in
the upper computer, upon receiving the vehicle information from the user, respond
by popping up a list of vehicle fault model types for the user to select from the list a
desired type, such as starting failure, insufficient power or urea solution
non-consumption, and in response to the type selected by the user, call the vehicle
diagnostic model corresponding to the type and the vehicle information selected by
the user, wherein, the vehicle information is used to distinguish vehicles, as
configuration of the same type of fault models may be different when the vehicle
information is different, it is necessary to call the vehicle fault model with consistent
type and vehicle information as those selected by the user.
Different model IDs are provided for different models, and when calling the vehicle
fault model, call by the model ID.
The vehicle fault model comprises a data stream acquisition module, a page
management module, a condition analysis model and an analysis algorithm model,
specifically as per the following diagnostic process:
1. Acquiring one or more data streams corresponding to the current vehicle
diagnostic model with the data stream acquisition module.
In actual application situations, the user connects the VCI with the OBD port on the vehicle, connects the VCI with the upper computer, and acquires the data streams required for vehicle diagnosis by the OBD port on the vehicle, correspondingly, step 1 further comprises:
Screening the data streams corresponding to the current vehicle fault model from
the data streams required for vehicle diagnosis. For example, when the current
model is a model for insufficient power, collect data streams such as engine water
temperature, temperature of inlet air, temperature of engine oil, motor torque
limiting status, torque limiting status of the after-treatment system, and signals and
voltage of the accelerator pedal etc..
2. Calling and showing in sequence a plurality of pages corresponding to the current
vehicle fault model according to a predetermined page jumping order and a
jumping condition by the page management module, such as "starting
preparation tips" page and "guiding the client to complete vehicle starting
operation as required" page, so as to guide operation of the user.
In one embodiment, the jumping condition comprises:
Jumping by clicking next: jumping automatically from the current page to the next
page in response to the user's clicking the next button.
Timing jumping: jumping automatically from the current page to the next page after
timing of the timer finishes.
Jumping by clicking confirm: jumping automatically from the current page to the next
page in response to the user's clicking confirm button;
Jumping by clicking confirm and timing: jumping automatically from the current page
to the next page in response to the user's clicking confirm button and timing of the
timer finishes.
Jumping when the data streams satisfy conditions: jumping automatically from the
current page to the next page when the corresponding data streams satisfy
predeterminedconditions.
The jumping conditions can be preset by the user in the page management interface.
3. Judging whether the predetermined algorithm trigger conditions have been
satisfied by the acquired data streams and /or the page currently called with the
condition analysis module, if yes, triggering the corresponding diagnostic
conclusion analysis algorithm.
In the present embodiment, the algorithm trigger condition is any one or
combination of extreme values, a threshold value, a status condition and a page
condition.
Wherein, the extreme values are conditions that the one or more data streams shall
satisfy, the extreme values are a maximum value or a minimum value, the threshold
value condition is one that the threshold value of the one or more data streams shall
satisfy, the status condition is a condition that the one or more data streams shall
satisfy, and the page condition is that a predetermined page has been called at
present.
Specifically, the algorithm trigger condition can be preset in a following manner:
Providing a configuration interface, for the user to input an algorithm trigger
condition statement according to predetermined laws.
Reading the algorithm trigger condition statement input by the user from the
configuration interface.
And interpreting the algorithm trigger condition from the predetermined laws.
In the predetermined laws, connectors: && (and) and (or) and judgment symbols >,
<, >=, <=, ==, !=, and priority of the judgment symbols is higher than priority of the
connectors.
Hereinafter examples are used to explain samples of the algorithm trigger condition,
which are not limited to the following samples:
Sample one:
Data stream code: [{data stream code}< a] {data stream code}< a: acquiring times: selecting one or more segments of the data streams acquired (symbols are those in
English input mode).
Wherein, ":" is a separation symbol, "data stream code" means a data stream, which
can be matched by a data stream table, "{}"is to select the data stream enclosed in
the bracket, "[{data stream code}< a]" means a maximum value in the data stream is
smaller than a, and similarly, "[{data stream code}> a]" means that a minimum value
of the data stream is bigger than a.
Statement in sample one means that in the selected data stream segment, the
maximum value in the data stream is smaller than a value a.
Hereinafter some specific application examples will be given based on the sample
one:
Example 1: S000089: [{S000089}> 5] {S000089}< 5:1$most
In the statement, S000089 represents a motor rotation velocity, most means to take
a segment with the most data, 1 means collection for one time, $ means using bit
arithmetic, "[{S000089}<5]" means the maximum value of the motor rotation
velocity is less than 5, and the statement means that the algorithm trigger condition
is: acquiring a segment with the most data of the motor rotation velocity for one
time, and reading values of the motor rotation velocity are less than 5, in case such
trigger condition is satisfied, corresponding diagnostic conclusion analysis algorithm
is triggered.
Example 2: S000089: [{S000089}< 150] {S000089}> 5 && {S000089}< 150: 2$most
In the statement, S000089 represents rotation velocity of the motor, && represents
simultaneously true, 2 represents collecting for two times, and most means taking a
time period with the most data. And the statement means that the trigger condition
is acquiring the time period with the most data of the motor rotation velocity for two
times, and values of the motor rotation velocity are less than 150 however bigger
than 5 for consecutive two times, and in case the foregoing trigger conditions have
been satisfied, the corresponding diagnostic conclusion analysis algorithm can be triggered.
Sample two:
Data stream code 1, data stream code 2, data stream code 3: ({data stream code 1}>
a && {data stream code 1} <= b) && eq ({data stream code}, status 1) && eq ({data
stream code 3}, status 2): collection times: selecting a collection data stream period
Wherein, the plurality of data streams are connected by connectors, eq ({data stream
code 2}, status 1) means that {data stream code 2} =status 1.
Hereinafter the sample two will be explained with actual application examples:
Example 1:
S000035, S000049, S000031: ({S000035} > = 22 && {S000035} <= 28) &&
eq({S000049}, disconnected) && eq({S000031}, neutral gear):2$most
In the statement, S000035 is battery voltage, S000049 is an under-vehicle shutdown
switch, S000031 is a neutral switch, && means simultaneously true, "eq({S000049},
disconnected) means the under-vehicle shutdown switch is disconnected, "eq({S000031}, neutral gear) means the neutral switch is in the neutral position, 2
means collection times, and most means to collect a time period with the most data.
The statement means that the trigger conditions are: collecting the time period with
the most data, when read values of S000035 are bigger than or equal 22 and smaller
than or equal 28, a read value of the under-vehicle shutdown switch is disconnected,
a read value of the neutral switch is in the neutral position, and all the foregoing
conditions happen for two times simultaneously, and in case such conditions have
been satisfied, corresponding diagnostic conclusion analysis algorithm can be
triggered.
Example 2:
S000089
S000126: [{S000089}<600] {S000126}> 200 && {S000089}> 150 && {S000089}<
500: 1$most
In the statement, S000089 is motor rotation speed, S000126 is actual rail pressure,
"[{S000089} < 600]" means a maximum value of the motor rotation speed is less
than 600, most means to take a time period with the most data, and the statement
means the trigger conditions are: taking the time period with the most data, and
when read values of the motor rotation velocity are bigger than 150 however smaller
than 500, and read values of the actual rail pressure is bigger than 200, in case the
foregoing trigger conditions are satisfied, corresponding diagnostic conclusion
analysis algorithm is triggered.
Sample three:
Data stream code, DM; [{data stream code}< a] {data stream code}>a && {DM}==
b-c
Wherein, {DM}==b-c means that a page c of a model b is called at present, and other
conditions can be called simultaneously by connectors. In actual application
conditions, some diagnostic conclusion analysis algorithm can only be triggered at a
designated page.
Hereinafter the sample three will be illustrated by some specific examples:
Example one: S000089, DM: [{S000089} < 5] {S000089} < 5 && {DM} == 17-147:
1$new
In the present statement, S000089 represents motor rotation velocity, 1 represents
collection times, new represents taking data from a latest time period, and the
statement shows that the trigger conditions are: taking data from the latest time
period, calling a page 147 of the model 17, and read values of the motor rotation
velocity smaller than 5, in case all the foregoing conditions have been satisfied,
corresponding diagnostic conclusion analysis algorithm can be triggered.
Example 2: S000115, S000130: {DM} == 18-220 && ({S000115}< 1380
|| {S000115}> 1460) && ({S000130}< 200 && {S000130} >- 200:5
In the present statement, S000135 is a trigger current for a metering unit,S000130 is a deviation of the rail pressure, and the statement means that the trigger conditions are: when the page 220 of the model 18 is called at present, reading values of the trigger current for the metering unit are bigger than 1460 or smaller than 1380, and reading values of the deviation of the rail pressure are bigger than -200 or smaller than 200 for five times, and in case the foregoing conditions are met, corresponding diagnostic conclusion analysis algorithm can be triggered.
Sample four:
Data stream code: conditions for {data stream code}: collection times: choosing a
data stream segment to collect
Hereinafter the sample four will be illustrated with actual application examples:
Example 1: S000089: {S000089}<5:3$most
In the statement, S000089 represents rotation velocity of the motor, and the
statement means that the trigger condition is that taking a time period with the most
data, when reading values of the motor rotation velocity are smaller than 5 for three
consecutive times, and in case the foregoing conditions are met, corresponding
diagnostic conclusion analysis algorithm can be triggered.
4. Judging whether the corresponding data streams satisfy the predetermined
diagnostic conclusion conditions by the triggered diagnostic conclusion analysis
algorithm by the algorithm analysis module and getting corresponding
predetermined diagnostic conclusions
For example, when a minimum value of urea pressure is less than -1, the conclusion
is that: the urea pressure is normal.
In the present embodiment, when predetermined diagnostic conclusion conditions
have been satisfied, corresponding predetermined conclusion ID can be got, and by
the conclusion ID, specific conclusion can be matched in the conclusion management
sheet, with every analysis conclusion corresponding to a unique conclusion ID, and
can be called in the analysis model.
In the present embodiment, judging whether the corresponding data streams satisfy
the predetermined diagnostic conclusion conditions further comprises:
Processing the corresponding data streams with a predetermined processing method,
getting processing values and judging whether the processing values satisfy the
predetermined diagnostic conclusion conditions.
Wherein, the processing manner includes taking a maximum value (max), a minimum
value (min), an average value (avg), a most frequent value (mos), a least frequent
value (least) and assigning according to the assigning conditions and operating the
data streams.
Specifically, the processing manner can be preset in a following manner:
Providing a configuration interface, for the user to input processing manner
statements according to predetermined rules, and the configuration interface can be
the same configuration interface as the predetermined algorithm trigger conditions.
Reading from the configuration interface the processing manner statement input by
the user.
Analyzing the processing method from the predetermined rules, and the
predetermined rules can be the same as those in the predetermined algorithm
trigger condition.
Sample one:
Max $ data stream code l{data stream code}
For a single data stream, taking a maximum value max $ data stream code, taking a
minimum value min $ data stream code, average value avg $ data stream code, a
most frequent value most $ data stream code, a least frequent value least $ data
stream code.
Hereinafter some actual application instances will be given to the sample 1:
Example 1: max$ S000089 {S000089}
S000089 is a motor rotation velocity, max $ means taking the maximum value, and
the processing method statement means to take the maximum value of the motor
rotation velocity.
Example 2: min$ S0000351 {S000035}
S000035 is a battery voltage, min $ means taking the minimum value, and the
processing method statement means to take the minimum value of the battery
voltage.
Example 3: most $S000035| {S000035}
S000035 is the battery voltage, most $ means taking the most frequent value, and
the processing maethod statement means to take the most frequent value of the
battery voltage.
Sample two:
Most $ data stream code l if ({ data stream code}==status)?O:1
The processing method statements support judging, and when it is necessary to
judge a status-type data stream, if function can be used, and when the reading value
of the data stream code is a designated state, assign a value 0 to the data stream,
otherwise assign 1, and the if function can be used together with other functions.
Hereinafter some actual application examples will be given to the sample two:
Example 1: most $S000049| if ({S000049}==connected)?O:1
S000049 is an under-vehicle shutdown switch, and the processing method statement
means that when the most frequent reading value of the under-vehicle shutdown
switch is connected, assign a value 0 to the present data stream, otherwise assign 1.
Sample three:
RTF& data stream code 1, data stream code 2 {data stream code 1} / {data stream
code 2}
Wherein, the RTF function means a rate of the data stream code 1 and the data stream code 2.
Hereinafter some concrete application examples will be given to the sample 3:
Example 1: RTF&S000007, S00008{S000007} /{S000008}
S000007 is signal voltage of the accelerator pedal 1,S000008 is a signal voltage of
the accelerator pedal 2, and the statement means a ratio of the signal voltage of the
accelerator pedal l and the signal voltage of the accelerator pedal 2.
Sample four:
Max$ data stream code 1, most $ data stream code 2 {data stream code 2} - {data
stream code 1}
The processing method statement supports four arithmetic operations of the data
streams.
Hereinafter concrete application examples will be given to the sample four:
Example 1: max $S000089, most $S000495|{S000495}- {S000089}
S000089 is the motor rotation velocity, S000495 is a highest theoretical rotation
velocity, and the processing method statement means to calculate a difference
between the highest theoretical rotation velocity and the maximum motor rotation
velocity.
Similarly, the page jumping conditions that jump to the next page when the data
streams satisfy the conditions can be configured by the statement, for example:
Example 1: S000096: {S000096} >70:2
The statement supports >, <, >=, <=, == and !=, wherein, S000096 means the cooling
liquid temperature, and the statement means that when the temperature of the
cooling liquid is bigger than 70 for consecutive two times, and when the condition is
met, the page will jump automatically to the next page.
Example 2: S000007, S000008, S000011: {S000007}>3.6 && {S000008}> 1.8
&& {S000011} > 90:1
The statement supports && and || connection, English commas can be used to
separate the data streams, wherein S000007 means an initial value of voltage of the
accelerator pedal, S000008 means an initial value of voltage of the accelerator pedal,
S000008 means a position of the accelerator pedal, and the statement means that
when the initial value of the voltage of the accelerator pedal 1 is higher than 3.6V,
initial value of the voltage of the accelerator pedal 2 is higher than 1.8V, and opening
degree of the accelerator pedal is bigger than 90%, when the foregoing conditions
are met, the page will jump automatically to the next page.
Based on the same invention concept, embodiments of the present invention further
provide an intelligent vehicle diagnostic system, comprising a storage module, and
the storage module stores instructions (program codes) to be loaded and executed
by a processor, and the instructions when executed by the processor will have the
processor to execute steps in exemplary embodiments described in an intelligent
vehicle diagnostic method part of the present invention.
Wherein, the storage module can include readable media in a form of volatile
storage unit, such as random access memory (RAM) and / or high speed cache
storage unit, and can further include read-only memory (ROM).
Any combination of one or more programming languages can be used to write the
program codes to execute operations of the present invention, and programming
languages include object-oriented programming languages such as Java, C++ etc.,
and conventional process programming languages such as "C" language and similar
programming languages. Program codes can be completely executed in a user
computing device, partly executed on a user device, executed as an independent
software packet, partly executed on a user device and partly executed on a remote
computing device, or completely executed on a remote computing device. When
using the remote computing devices, the remote computing devices can be
connected to external computing devices (for example connected via Internet from
Internet service providers) by any types of network, such as local area network (LAN)
or wide area network (WAN).
Based on the same invention concept, embodiments of the present invention further
provides a diagnostic device, and the diagnostic devices is provided with the
foregoing intelligent diagnostic system, which will not be further elaborated here.
However, those of ordinary skill in the art shall recognize that, the foregoing
embodiments are only used to explain the present invention, instead of restricting
the present invention, and all changes and variations of the foregoing embodiments
falling within substantial spirits of the present invention fall into protection scope of
the present invention defined by the claims.
Claims (10)
1. An intelligent vehicle diagnostic method, comprising:
Responding to a current vehicle fault model selected by a user, and calling the
vehicle fault model for diagnosis;
Acquiring one or more data streams corresponding to the current vehicle fault
model;
Calling and showing in sequence a plurality of pages corresponding to the current
vehicle fault model according to a predetermined page jumping sequence and a
jumping condition;
Judging whether one or more predetermined algorithm trigger conditions have
been met according to the data streams acquired and /or the page called
currently, if yes, trigger corresponding diagnostic conclusion analysis algorithm;
and
Judging whether the corresponding data streams satisfy one or more
predetermined diagnostic conclusion conditions by the triggered diagnostic
conclusion analysis algorithm and getting a corresponding preset diagnostic
conclusion.
2. The intelligent vehicle diagnostic method according to claim 1, wherein
responding to the current vehicle fault model selected by the user, and calling the
vehicle fault model for diagnosis further comprises:
Receiving vehicle information input by the user; and
Responding to a type of the vehicle fault model selected by the user, calling a
vehicle fault model with the type and the vehicle information consistent with the
type and the vehicle information selected by the user.
3. The intelligent vehicle diagnostic method according to claim 2, wherein further
comprising
Acquiring the data streams needed for vehicle diagnosis from an OBD port of the
vehicle;
Acquiring the data streams corresponding to the vehicle fault model further
comprises:
Screening the data streams corresponding to the current vehicle fault model from
the data streams needed for vehicle diagnosis.
4. The intelligent vehicle diagnostic method according to claim 3, wherein the
jumping condition comprises:
Jumping upon clicking next: jumping from the current page to the next page in
response to clicking next by the user;
Timing jumping: jumping from the current page to the next page after timing of
the timer finishes;
Jumping by clicking confirm: jumping automatically from the current page to the
next page in response to clicking a confirm button by the user;
Jumping upon clicking confirm and timing: jumping automatically from the
current page to the next page in response to clicking the confirm button by the
user and when timing of the timer finishes; and
Jumping upon data stream conformity: jumping automatically from the current
page to the next page when the corresponding data streams satisfy one or more
predetermined conditions.
5. The intelligent vehicle diagnostic method according to claim 4, wherein the
algorithm trigger conditions are any combination of one or more of an extreme
value condition, a threshold value condition, a status condition and a page
condition;
Wherein the extreme value condition is a condition that an extreme value of the
one or more data streams shall satisfy, wherein the extreme value is a maximum
value or minimum value;
Wherein the threshold value condition is a threshold value condition that the one
or more data streams shall satisfy; and
Wherein the page condition is that a predetermined page has been called
currently.
6. The intelligent vehicle diagnostic method according to claim 5, wherein judging
whether corresponding data streams satisfy the one or more predetermined
diagnostic conclusion conditions further comprises:
Processing the corresponding data streams with a preset processing method,
getting processing values, and judging whether the processing values satisfy the
predetermined diagnostic conclusion conditions;
Wherein the processing method comprises taking the maximum value, the
minimum value, an average value, the most frequent value and the least frequent
value, assigning the values according to one or more value assignment conditions
or operating the plurality of data streams.
7. The intelligent vehicle diagnostic method according to claim 6, wherein the
algorithm trigger conditions can be preset in following steps:
Providing a configuration interface, for the user to input algorithm trigger
condition statement written according to predetermined laws;
Reading the algorithm trigger condition statement input by the user from the
configuration interface; and
Interpreting the algorithm trigger conditions by the predetermined laws.
8. The intelligent vehicle diagnostic method according to claim 7, wherein the
processing method can be preset in a following manner:
Providing a configuration interface for the user to input processing method
statement written according to predetermined laws;
Reading the processing method statement input by the user from the
configuration interface; and
Interpreting the processing method by the foregoing predetermined
configuration interface.
9. An intelligent vehicle diagnostic system, characterized in that comprising a
storage module, wherein the storage module includes instructions to be loaded
and executed by the processor, and the instructions when executed by the
processor will have the processor execute the intelligent vehicle diagnostic
method as claimed in any of claims 1-8.
10. A diagnostic device, wherein the diagnostic device is provided with the intelligent
vehicle diagnostic system as claimed in claim 9.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011079545.8A CN112199145A (en) | 2020-10-10 | 2020-10-10 | Intelligent diagnosis method, system and diagnosis equipment for vehicle |
| CN202011079545.8 | 2020-10-10 | ||
| PCT/CN2021/081601 WO2021223522A1 (en) | 2020-10-10 | 2021-03-18 | Intelligent vehicle diagnosis method and system, and diagnosis device |
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| AU2021266924A1 true AU2021266924A1 (en) | 2022-04-28 |
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| CN (1) | CN112199145A (en) |
| AU (1) | AU2021266924A1 (en) |
| CA (1) | CA3141186A1 (en) |
| WO (1) | WO2021223522A1 (en) |
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| CN112199145A (en) * | 2020-10-10 | 2021-01-08 | 上海星融汽车科技有限公司 | Intelligent diagnosis method, system and diagnosis equipment for vehicle |
| 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 |
| 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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