CN110398375A - Monitoring method, device, equipment and the medium of cooling system of vehicle working condition - Google Patents
Monitoring method, device, equipment and the medium of cooling system of vehicle working condition Download PDFInfo
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- CN110398375A CN110398375A CN201910639266.3A CN201910639266A CN110398375A CN 110398375 A CN110398375 A CN 110398375A CN 201910639266 A CN201910639266 A CN 201910639266A CN 110398375 A CN110398375 A CN 110398375A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
- B60K11/00—Arrangement in connection with cooling of propulsion units
- B60K11/02—Arrangement in connection with cooling of propulsion units with liquid cooling
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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
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Abstract
The present invention relates to monitoring method, device, equipment and the media of a kind of cooling system of vehicle working condition, this method comprises: obtaining the coolant temperature data of vehicle within a preset time interval；Obtain the characteristic value of coolant temperature data within a preset time interval；Characteristic value is inputted in machine learning model, determines the operating status of the cooling system of vehicle；Machine learning model is that the characteristic value training of the coolant temperature data based on different type vehicle within a preset time interval obtains.In the method, the reliability to the judgement of cooling system of vehicle operating status is improved using machine learning model, and then improves the accuracy of determining cooling system of vehicle operating status；In addition, being assured that out the operating status of the cooling system of vehicle according to the coolant temperature data of vehicle characteristic value within a preset time interval and machine learning model, the efficiency of the operating status of the cooling system of determining vehicle is improved.
Description
Technical field
The present invention relates to vehicular field, more particularly to a kind of monitoring method of cooling system of vehicle working condition, device,
Equipment and medium.
Background technique
In the process of moving, the heat that the heat generating components of vehicle generates can be dispersed into air to vehicle by cooling system of vehicle
In, so that the heat generating components of vehicle is maintained preferable working condition.Therefore, operation of the operating status of cooling system of vehicle to vehicle
State has important influence.
In traditional technology, to cooling system of vehicle method for determining running state mainly by by the coolant liquid temperature of vehicle
Degree with the normal range value of preset vehicle's coolant temperature data according to being compared, if the coolant temperature data of vehicle are super
Cross the normal range value of vehicle's coolant temperature data, it is determined that the operating status of the cooling system of vehicle is abnormality, instead
It, it is determined that the operating status of the cooling system of vehicle is normal condition.
But to cooling system of vehicle method for determining running state only by the coolant liquid temperature of the vehicle in traditional technology
Degree is analyzed according to the normal range value with preset vehicle's coolant temperature data, not to the coolant temperature number of vehicle
According to multiple features between relationship account for, to the operating status judgment method of the cooling system of vehicle, that there are accuracy is lower
The problem of.
Summary of the invention
Based on this, it is necessary to accurate for existing in traditional technology to the operating status judgment method of the cooling system of vehicle
Lower problem is spent, monitoring method, device, equipment and the medium of a kind of cooling system of vehicle working condition are provided.
In a first aspect, the embodiment of the present invention provides a kind of monitoring method of cooling system of vehicle working condition, the method
Include:
Obtain the coolant temperature data of vehicle within a preset time interval；
Obtain characteristic value of the coolant temperature data in the prefixed time interval；
The characteristic value is inputted in machine learning model, determines the operating status of the cooling system of the vehicle；It is described
Machine learning model is the characteristic value instruction of the coolant temperature data based on different type vehicle in the prefixed time interval
It gets.
The spy that the coolant temperature data are obtained in the prefixed time interval in one of the embodiments,
Value indicative, comprising:
According to the type of vehicle of the vehicle, from the corresponding relationship of preset type of vehicle and standard coolant temperature threshold value
In, obtain the corresponding standard coolant temperature threshold value of the vehicle；The standard coolant temperature threshold value includes temperature upper limit
And temperature upper limit value；
According to the coolant temperature data and the corresponding standard coolant temperature threshold value of the vehicle, the cooling is obtained
The characteristic value of the liquid temperature data in the prefixed time interval.
In one of the embodiments, the characteristic value include the coolant temperature data within a preset time interval
Very poor, median, standard deviation, standard coolant temperature threshold value corresponding greater than the vehicle temperature upper limit ratio value,
And the ratio value of the temperature upper limit value of standard coolant temperature threshold value corresponding less than the vehicle.
In one of the embodiments, the method also includes:
Count the temperature curve distribution map of different vehicle type；
Determine the corresponding temperature of preset cut-point upper limit ratio in the temperature curve distribution map of the different vehicle type
Upper limit value and the corresponding temperature upper limit value of preset cut-point lower proportion ratio；
According to the corresponding temperature upper limit of the different vehicle type and temperature upper limit value, the preset vehicle class is determined
The corresponding relationship of type and standard coolant temperature threshold value.
The machine learning model is unsupervised abnormal point mining model in one of the embodiments,.
The abnormal point mining model is isolated forest model in one of the embodiments, the method also includes:
Obtain the coolant temperature data of different vehicle type；
Obtain the characteristic value of the coolant temperature data of the different vehicle type in the prefixed time interval；
According to the characteristic value of the coolant temperature data of the different vehicle type within a preset time interval and preset
Abnormal point ratio is trained initial isolated forest model, obtains the abnormal point mining model.
The preset abnormal point ratio is 0.5% in one of the embodiments,.
Second aspect, the embodiment of the present invention provide a kind of monitoring device of cooling system of vehicle working condition, described device
Include:
First obtains module, for obtaining the coolant temperature data of vehicle within a preset time interval；
Second obtains module, for obtaining characteristic value of the coolant temperature data in the prefixed time interval；
First determining module determines the cooling system of the vehicle for inputting the characteristic value in machine learning model
The operating status of system；The machine learning model is the coolant liquid temperature based on different type vehicle in the prefixed time interval
What the characteristic value training of degree evidence obtained.
The third aspect, the embodiment of the present invention provide a kind of computer equipment, including memory and processor, the memory
It is stored with computer program, the processor performs the steps of when executing the computer program
Obtain the coolant temperature data of vehicle within a preset time interval；
Obtain characteristic value of the coolant temperature data in the prefixed time interval；
The characteristic value is inputted in machine learning model, determines the operating status of the cooling system of the vehicle；It is described
Machine learning model is the characteristic value instruction of the coolant temperature data based on different type vehicle in the prefixed time interval
It gets.
Fourth aspect, the embodiment of the present invention provide a kind of computer readable storage medium, are stored thereon with computer program,
The computer program performs the steps of when being executed by processor
Obtain the coolant temperature data of vehicle within a preset time interval；
Obtain characteristic value of the coolant temperature data in the prefixed time interval；
The characteristic value is inputted in machine learning model, determines the operating status of the cooling system of the vehicle；It is described
Machine learning model is the characteristic value instruction of the coolant temperature data based on different type vehicle in the prefixed time interval
It gets.
In the monitoring method of cooling system of vehicle working condition provided by the above embodiment, device, equipment and medium, calculate
Machine equipment obtains the coolant temperature data of vehicle within a preset time interval, obtains coolant temperature data between preset time
Every interior characteristic value, characteristic value is inputted in machine learning model, determines the operating status of the cooling system of vehicle, machine learning
Model is that the characteristic value training of the coolant temperature data based on different type vehicle within a preset time interval obtains, at this
In method, computer equipment obtains the coolant liquid by obtaining the coolant temperature data of vehicle within a preset time interval
Characteristic value is inputted in machine learning model, determines the cooling system of vehicle by the characteristic value of temperature data within a preset time interval
The operating status of system, since the machine learning model is the coolant liquid temperature based on different type vehicle within a preset time interval
The characteristic value training of degree evidence obtains, when getting feature of the coolant temperature data of vehicle in the prefixed time interval
When value, so that it may which the operating status for determining the cooling system of the vehicle using the machine learning model utilizes machine learning model
The reliability to the judgement of cooling system of vehicle operating status is improved, and then improves determining cooling system of vehicle operating status
Accuracy；In addition, just according to the coolant temperature data of vehicle characteristic value within a preset time interval and machine learning model
The operating status that can determine the cooling system of vehicle improves the efficiency of the operating status of the cooling system of determining vehicle.
Detailed description of the invention
Fig. 1 is the applied environment figure of the monitoring method for the cooling system of vehicle working condition that one embodiment provides；
Fig. 2 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that one embodiment provides；
Fig. 3 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that another embodiment provides；
Fig. 4 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that another embodiment provides；
Fig. 5 is single day coolant temperature curve distribution figure of certain type of vehicle that one embodiment provides；
Fig. 6 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that another embodiment provides；
Fig. 7 is the corresponding coolant temperature distribution map of different abnormal point ratios that one embodiment provides；
Fig. 8 is the monitoring device structural schematic diagram for the cooling system of vehicle working condition that one embodiment provides；
Fig. 9 is the monitoring device structural schematic diagram for the cooling system of vehicle working condition that one embodiment provides；
Figure 10 is the schematic diagram of internal structure for the computer equipment that one embodiment provides.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the application, not
For limiting the application.
The monitoring method of cooling system working condition provided by the embodiments of the present application can be applied to as shown in Figure 1 answer
With in environment.Wherein, terminal 102 is communicated with server 104 by network by network.Wherein, terminal 102 can with but not
It is limited to be various vehicle intelligent equipments, server 104 can use the service of the either multiple server compositions of independent server
Device cluster is realized.
How technical solution of the present invention and technical solution of the present invention are solved with specific embodiment below above-mentioned
Technical problem is described in detail.These specific embodiments can be combined with each other below, for the same or similar concept
Or process may repeat no more in certain embodiments.
Fig. 2 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that one embodiment provides.This reality
Applying example, what is involved is computer equipments to obtain the coolant temperature data of vehicle within a preset time interval, obtains coolant temperature
Characteristic value is inputted in machine learning model, determines the cooling system of vehicle by the characteristic value of data within a preset time interval
The specific implementation process of operating status.As shown in Fig. 2, this method may include:
S201 obtains the coolant temperature data of vehicle within a preset time interval.
Specifically, computer equipment obtains the coolant temperature data of vehicle within a preset time interval.Wherein, computer
Equipment is communicated with vehicle intelligent equipment by network, and vehicle intelligent equipment can be in real time by vehicle's coolant temperature data
It is uploaded to computer equipment, computer equipment can be according to certain day vehicle coolant temperature data that vehicle intelligent equipment uploads
Average value obtains vehicle in the coolant temperature data of this day, to obtain the coolant liquid temperature of vehicle within a preset time interval
Degree evidence.Optionally, prefixed time interval can be seven days, or eight days or can set according to actual needs and specifically
It is fixed.Optionally, vehicle intelligent equipment can read the coolant temperature data of automobile by bus.Optionally, vehicle intelligent is set
It is standby to provide to upload vehicle's coolant temperature data to computer equipment according to preset agreement, for example, vehicle intelligent equipment
It can provide to upload to computer equipment according to the instant parameter protocol of onboard diagnostic system (0N-Board Diagnostic, OBD)
Vehicle's coolant temperature data.
S202 obtains the characteristic value of coolant temperature data within a preset time interval.
Specifically, being obtained cold after computer equipment gets the coolant temperature data of vehicle within a preset time interval
But characteristic value of the liquid temperature data in above-mentioned prefixed time interval.Optionally, coolant temperature data are in prefixed time interval
Interior characteristic value may include in coolant temperature data very poor, median within a preset time interval and standard deviation extremely
It is two kinds few.Illustratively, for example, computer equipment has obtained coolant temperature data of certain vehicle in seven days, computer is set
Very poor, median and standard deviation of the coolant temperature data of the standby available vehicle in seven days, the vehicle that will be obtained
Very poor, median of the coolant temperature data in seven days and standard deviation as the vehicle coolant temperature data at seven days
Interior characteristic value.
Characteristic value is inputted in machine learning model, determines the operating status of the cooling system of vehicle by S203；Machine learning
Model is that the characteristic value training of the coolant temperature data based on different type vehicle within a preset time interval obtains.
Specifically, computer equipment inputs features described above value in machine learning model, the cooling system of vehicle is determined
Operating status.Wherein, machine learning model is the coolant temperature based on different type vehicle in above-mentioned prefixed time interval
What the characteristic value training of data obtained.Illustratively, computer equipment can be by the coolant temperature data of vehicle in seven days
Very poor, median and standard deviation as characteristic value, input in machine learning model, determine the operation shape of the cooling system of vehicle
State.Optionally, it when computer equipment determines that the operating status of the cooling system of vehicle is abnormal, can be sent to car owner abnormal
Prompt information prompts the cooling system of host vehicle to exist abnormal.Optionally, which can be neural network mould
Type can be input with the characteristic value of the coolant temperature data of different type vehicle within a preset time interval, with the difference vehicle
The operating status of the cooling system of the vehicle of type is output, is trained to initial neural network model, obtains nerve
Network model judges the operating status of the cooling system of vehicle.
In the present embodiment, coolant temperature data of the computer equipment by acquisition vehicle within a preset time interval,
And the characteristic value of coolant temperature data within a preset time interval is obtained, characteristic value is inputted in machine learning model, really
The operating status of the cooling system of vehicle is determined, since the machine learning model is to be based on different type vehicle between preset time
It is obtained every the characteristic value training of interior coolant temperature data, when getting the coolant temperature data of vehicle when this is preset
Between interval in characteristic value when, so that it may the operating status of the cooling system of the vehicle is determined using the machine learning model, benefit
The reliability judged cooling system of vehicle operating status is improved with machine learning model, and then it is cooling to improve determining vehicle
The accuracy of system running state；In addition, according to the characteristic value of the coolant temperature data of vehicle within a preset time interval and
Machine learning model is assured that out the operating status of the cooling system of vehicle, improves the fortune of the cooling system of determining vehicle
The efficiency of row state.
In another embodiment, above-mentioned computer equipment can be various vehicle intelligent equipments, in vehicle intelligent equipment
It is previously stored with above-mentioned machine learning model, vehicle intelligent equipment obtains the coolant temperature number of vehicle within a preset time interval
According to, and the characteristic value of coolant temperature data within a preset time interval is obtained, vehicle intelligent equipment loads the machine learning
Features described above value is inputted in machine learning model, determines the operating status of the cooling system of vehicle by model.
Fig. 3 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that another embodiment provides.This
What is involved is the specific implementations that computer equipment obtains the characteristic value of coolant temperature data within a preset time interval for embodiment
Process.As shown in figure 3, on the basis of the above embodiments, as an alternative embodiment, above-mentioned S202, comprising:
S301 is closed according to the type of vehicle of vehicle from preset type of vehicle is corresponding with standard coolant temperature threshold value
In system, the corresponding standard coolant temperature threshold value of vehicle is obtained；Standard coolant temperature threshold value includes temperature upper limit and temperature
Lower limit value.
Specifically, type of vehicle of the computer equipment according to above-mentioned vehicle, from preset type of vehicle and standard coolant liquid
In the corresponding relationship of temperature threshold, the corresponding standard coolant temperature threshold value of the vehicle is obtained.Wherein, standard coolant temperature threshold
Value includes temperature upper limit and temperature upper limit value.For example, when the cooling system working condition of the vehicle determined is xx type of vehicle,
The corresponding temperature upper limit of the vehicle can be obtained from preset type of vehicle and the corresponding relationship of standard coolant temperature threshold value
Value and temperature upper limit value, obtain the corresponding standard coolant temperature threshold value of the vehicle.
S302 obtains coolant temperature according to coolant temperature data and the corresponding standard coolant temperature threshold value of vehicle
The characteristic value of data within a preset time interval.
Specifically, computer equipment is according to above-mentioned coolant temperature data and the corresponding standard coolant temperature threshold of vehicle
Value obtains characteristic value of the coolant temperature data in above-mentioned prefixed time interval.Optionally, characteristic value includes above-mentioned coolant liquid
Temperature data very poor, median within a preset time interval, is greater than the corresponding standard coolant temperature threshold of the vehicle at standard deviation
The ratio of the temperature upper limit value of the ratio value of the temperature upper limit of value and standard coolant temperature threshold value corresponding less than the vehicle
Example value.Optionally, computer equipment can obtain the coolant temperature data above-mentioned pre- according to above-mentioned coolant temperature data
If very poor, median and standard deviation in time interval, according to coolant temperature data standard coolant liquid corresponding with the vehicle
The coolant temperature data that temperature threshold obtains the vehicle are greater than in the temperature of the corresponding standard coolant temperature threshold value of the vehicle
The coolant temperature data of the ratio value of limit value and the vehicle are less than the temperature of the corresponding standard coolant temperature threshold value of the vehicle
Spend the ratio value of lower limit value.
In the present embodiment, computer equipment is cooled down according to the type of vehicle of vehicle from preset type of vehicle and standard
In the corresponding relationship of liquid temperature threshold, the corresponding standard coolant temperature threshold value of vehicle is obtained, according to the coolant temperature of vehicle
Data and the corresponding standard coolant temperature threshold value of vehicle obtain the coolant temperature data of vehicle within a preset time interval
Characteristic value, since the coolant temperature data characteristic value within a preset time interval of vehicle is the coolant temperature according to vehicle
What data and the corresponding standard coolant temperature threshold value of vehicle obtained, and the corresponding standard coolant temperature threshold value of vehicle being capable of table
The maximum probability distribution for showing the coolant temperature of the type vehicle, in this way according to the coolant temperature of vehicle and the corresponding standard of vehicle
Coolant temperature threshold value, the characteristic value of the coolant temperature data of the vehicle of acquisition within a preset time interval is more accurate, mentions
The accuracy of the characteristic value of the coolant temperature data of the high vehicle obtained within a preset time interval.
In the above-mentioned type of vehicle according to vehicle, closed from preset type of vehicle is corresponding with standard coolant temperature threshold value
In system, in the scene of the corresponding standard coolant temperature threshold value of acquisition vehicle, need to obtain preset type of vehicle and mark first
The corresponding relationship of quasi- coolant temperature threshold value.Fig. 4 is the monitoring for the cooling system of vehicle working condition that another embodiment provides
The flow diagram of method.Fig. 5 is single day coolant temperature curve distribution figure of certain type of vehicle that one embodiment provides.This
What is involved is the tools that computer equipment determines preset type of vehicle with the corresponding relationship of standard coolant temperature threshold value for embodiment
Body realizes process.As shown in figure 4, on the basis of the above embodiments, as an alternative embodiment, the above method also wraps
It includes:
S401 counts the temperature curve distribution map of different vehicle type.
Specifically, the temperature curve distribution map of computer equipment statistics different vehicle type.As shown in figure 5, with vehicle class
Single day coolant temperature value of type is horizontal axis, is vertical with the vehicle number that different coolant temperatures is worth the corresponding type of vehicle
Axis counts the temperature curve distribution map of different vehicle type.
S402 determines the corresponding temperature of preset cut-point upper limit ratio in the temperature curve distribution map of different vehicle type
Upper limit value and the corresponding temperature upper limit value of preset cut-point lower proportion ratio.
Specifically, computer equipment determines preset cut-point upper limit ratio in the temperature curve distribution map of different vehicle type
The corresponding temperature upper limit of example and the corresponding temperature upper limit value of preset cut-point lower proportion ratio.Optionally, computer equipment
It can be used as preset cut-point lower proportion ratio by 5%, be used as preset cut-point upper limit ratio for 95%, that is, will be different
The temperature of type of vehicle accounts for 5% quantile as preset cut-point upper limit ratio in temperature curve distribution map, will be different
The temperature of type of vehicle accounts for 95% quantile as preset cut-point lower proportion ratio in temperature curve distribution map.
S403 determines preset type of vehicle according to the corresponding temperature upper limit of different vehicle type and temperature upper limit value
With the corresponding relationship of standard coolant temperature threshold value.
Specifically, computer equipment determines vehicle according to the corresponding temperature upper limit of different vehicle type and temperature upper limit value
The corresponding relationship of type and standard coolant temperature threshold value.Optionally, computer equipment can be corresponding by different vehicle type
Temperature upper limit be determined as the temperature upper limit of standard coolant temperature threshold value, by the corresponding lowest temperature of different vehicle type
Value is determined as the temperature upper limit value of standard coolant temperature threshold value, determines preset type of vehicle and standard coolant temperature threshold value
Corresponding relationship.
In the present embodiment, the temperature curve distribution map of computer equipment statistics different vehicle type, determines different vehicle
In the temperature curve distribution map of type under the preset corresponding temperature upper limit of cut-point upper limit ratio and preset cut-point
The corresponding temperature upper limit value of limit ratio, due to preset cut-point upper limit ratio in the temperature curve distribution map of different vehicle type
And preset cut-point lower proportion ratio is to be obtained based on a large amount of data statistics value, and then can accurately determine preset
The corresponding temperature upper limit of cut-point upper limit ratio and the corresponding temperature upper limit value of preset cut-point lower proportion ratio, and it is pre-
If type of vehicle and the corresponding relationship of standard coolant temperature threshold value be according to the corresponding temperature upper limit of different vehicle type
It is determined with temperature upper limit value, due to the accuracy of determining different vehicle type corresponding temperature upper limit and temperature upper limit value
It is improved, and then improves the accurate of determining preset type of vehicle and the corresponding relationship of standard coolant temperature threshold value
Degree.
In the above-mentioned characteristic value input machine learning model by vehicle's coolant temperature data within a preset time interval
In scene, on the basis of the above embodiments, as an alternative embodiment, machine learning model is unsupervised exception
Point mining model.
Specifically, computer equipment can input the characteristic value of vehicle's coolant temperature data within a preset time interval
In unsupervised abnormal point mining model.Optionally, unsupervised abnormal point mining model may include moving average method
(Moving average, MA) model, 3-sigma model, local outlier factor (Local Outlier Factor, LOF) mould
Type, K-Means Clustering Model, OneClassSvm abnormality detection model, isolated forest model, principal component analysis-mahalanobis distance mould
Type and self-encoding encoder (AutoEncoder, AE) model.In the present embodiment, vehicle's coolant temperature data is in preset time
The machine learning model of characteristic value input in interval is unsupervised abnormal point mining model, without carrying out preparatory mark,
According to the characteristic value of the vehicle's coolant temperature data of input within a preset time interval, so that it may determine cooling system of vehicle
Operating status improves the efficiency of the operating status of determining cooling system of vehicle.
Fig. 6 is the flow diagram of the monitoring method for the cooling system of vehicle working condition that another embodiment provides.Fig. 7
The corresponding coolant temperature distribution map of different abnormal point ratios provided for one embodiment.What is involved is computers for the present embodiment
Equipment obtains the specific implementation process of abnormal point mining model.As shown in fig. 6, on the basis of the above embodiments, as one kind
Optional embodiment, when abnormal point mining model is isolated forest model, the above method further include:
S601 obtains the coolant temperature data of different vehicle type.
Specifically, computer equipment obtains the coolant temperature data of different vehicle type.Optionally, computer equipment can
To obtain the coolant temperature of different vehicle type by the network communication between the vehicle intelligent equipment of different vehicle type
Data.Optionally, vehicle intelligent equipment can read the coolant temperature data of automobile by bus.Optionally, vehicle intelligent
Equipment can provide to upload vehicle's coolant temperature data to computer equipment according to preset agreement, for example, vehicle intelligent is set
It is standby to be provided in computer equipment according to the instant parameter protocol of onboard diagnostic system (0N-Board Diagnostic, OBD)
Pass vehicle's coolant temperature data.
S602 obtains the characteristic value of the coolant temperature data of different vehicle type within a preset time interval.
Specifically, computer equipment obtains the spy of the coolant temperature data of different vehicle type within a preset time interval
Value indicative, wherein the characteristic value of the coolant temperature data of different vehicle type within a preset time interval includes different vehicle class
The coolant temperature data of type very poor, median within a preset time interval, is greater than the corresponding mark of same types of vehicles at standard deviation
The ratio value of the temperature upper limit of quasi- coolant temperature threshold value and be less than the corresponding standard coolant temperature threshold of same types of vehicles
The ratio value of the temperature upper limit value of value.Optionally, prefixed time interval can be seven days, or eight days or can be according to reality
Border demand and specifically set.
S603, according to the characteristic value of the coolant temperature data of different vehicle type within a preset time interval and preset
Abnormal point ratio is trained initial isolated forest model, obtains abnormal point mining model.
Specifically, the spy of coolant temperature data of the computer equipment according to different vehicle type within a preset time interval
Value indicative and preset abnormal point ratio, are trained initial isolated forest model, obtain abnormal point mining model.It is optional
, the process that computer equipment is trained initial isolated forest model may include: from different type of vehicle default
Subsample is randomly selected in the characteristic value of coolant temperature data in time interval as the root node in isolated tree, then with
Machine specifies a dimension, a cut point p is generated in present node data, cut point p is the specified dimension in present node
Maximum value and minimum value between generate at random, present node data space is divided into 2 sub-spaces by cut point p: specified
Data of the dimension less than cut point p are placed in the left subtree of present node, and the data more than or equal to cut point p are placed in currently
The right subtree of node repeats the above steps then in child node and constructs new child node, until only one in child node counts
According to or child node reach the height of definition, so far isolated tree establishment process is completed, then by different vehicle type in preset time
The characteristic value of coolant temperature data in interval brings every isolated tree into and test simultaneously record path length, then according to pre-
If abnormal point ratio-dependent different vehicle type cooling system operating status, obtain abnormal point mining model.Optionally, in advance
If abnormal point ratio be 0.5%., it should be noted that preset abnormal point ratio be 0.5% be by PCA dimensionality reduction technology,
By above-mentioned characteristic value within a preset time interval by five dimension data dimensionality reductions to 2-D data, and it is real by a large amount of paired observation
Determining reasonable abnormal point ratio value is tested, as shown in fig. 7, abnormal point ratio is right for 0.5% in a large amount of experimental result
The definitive result answered is more accurate relative to definitive result corresponding to other abnormal point ratios.
In the present embodiment, when abnormal point mining model is isolated forest model, computer equipment obtains different vehicle class
The coolant temperature data of type, and obtain the feature of the coolant temperature data of different vehicle type within a preset time interval
It is worth, then the characteristic value and preset abnormal point of the coolant temperature data according to different vehicle type within a preset time interval
Ratio is trained initial isolated forest model, obtains abnormal point mining model, due to the coolant liquid of different vehicle type
The characteristic value of temperature data within a preset time interval has different characteristics, according to the coolant temperature number of different vehicle type
According to characteristic value and preset abnormal point ratio within a preset time interval, initial isolated forest model is trained, energy
The accuracy of obtained abnormal point mining model is enough improved, and then can be improved the operating status of the cooling system of determining vehicle
Accuracy.
It should be understood that although each step in the flow chart of Fig. 2-6 is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, these steps can execute in other order.Moreover, at least one in Fig. 2-6
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, the execution sequence in these sub-steps or stage is also not necessarily successively
It carries out, but can be at least part of the sub-step or stage of other steps or other steps in turn or alternately
It executes.
Fig. 8 is the monitoring device structural schematic diagram for the cooling system of vehicle working condition that one embodiment provides.Such as Fig. 8 institute
Show, the apparatus may include: first, which obtains module 10, second, obtains module 11 and the first determining module 12.
Specifically, first obtains module 10, for obtaining the coolant temperature data of vehicle within a preset time interval；
Second obtains module 11, for obtaining the characteristic value of coolant temperature data within a preset time interval；
First determining module 12 determines the fortune of the cooling system of vehicle for inputting characteristic value in machine learning model
Row state；Machine learning model is the characteristic value of the coolant temperature data based on different type vehicle within a preset time interval
What training obtained.
The monitoring device of cooling system of vehicle working condition provided in this embodiment, can execute above method embodiment,
That the realization principle and technical effect are similar is similar for it, and details are not described herein.
Fig. 9 is the monitoring device structural schematic diagram for the cooling system of vehicle working condition that one embodiment provides.Such as Fig. 9 institute
Show, above-mentioned second acquisition module 11 includes: temperature threshold acquiring unit 111 and characteristic value acquiring unit 112.
Specifically, temperature threshold acquiring unit 111, for the type of vehicle according to vehicle, from preset type of vehicle with
In the corresponding relationship of standard coolant temperature threshold value, the corresponding standard coolant temperature threshold value of vehicle is obtained；Standard coolant liquid temperature
Spending threshold value includes temperature upper limit and temperature upper limit value；
Characteristic value acquiring unit 112, for according to coolant temperature data and the corresponding standard coolant temperature threshold of vehicle
Value obtains the characteristic value of coolant temperature data within a preset time interval.
Optionally, features described above value includes very poor, the median, standard of coolant temperature data within a preset time interval
The ratio value of the temperature upper limit of difference, corresponding greater than vehicle standard coolant temperature threshold value and it is less than the corresponding mark of vehicle
The ratio value of the temperature upper limit value of quasi- coolant temperature threshold value.
The monitoring device of cooling system of vehicle working condition provided in this embodiment, can execute above method embodiment,
That the realization principle and technical effect are similar is similar for it, and details are not described herein.
Continuing with referring to Fig. 9, on the basis of the above embodiments, optionally, as shown in figure 9, above-mentioned apparatus further include: system
Count module 13, the second determining module 14 and third determining module 15.
Specifically, statistical module 13, for counting the temperature curve distribution map of different vehicle type；
Second determining module 14, the preset cut-point upper limit in the temperature curve distribution map for determining different vehicle type
The corresponding temperature upper limit of ratio and the corresponding temperature upper limit value of preset cut-point lower proportion ratio；
Third determining module 15, for determining according to the corresponding temperature upper limit of different vehicle type and temperature upper limit value
The corresponding relationship of preset type of vehicle and standard coolant temperature threshold value.
The monitoring device of cooling system of vehicle working condition provided in this embodiment, can execute above method embodiment,
That the realization principle and technical effect are similar is similar for it, and details are not described herein.
Optionally, above-mentioned machine learning model is unsupervised abnormal point mining model.
The monitoring device of cooling system of vehicle working condition provided in this embodiment, can execute above method embodiment,
That the realization principle and technical effect are similar is similar for it, and details are not described herein.
Continuing with referring to Fig. 9, on the basis of the above embodiments, optionally, as shown in figure 9, above-mentioned apparatus further include:
Three, which obtain module the 16, the 4th, obtains module 17 and training module 18.
Specifically, third obtains module 16, for obtaining the coolant temperature data of different vehicle type；
4th obtains module 17, for obtaining the coolant temperature data of different vehicle type within a preset time interval
Characteristic value；
Training module 18, the feature for the coolant temperature data according to different vehicle type within a preset time interval
Value and preset abnormal point ratio, are trained initial isolated forest model, obtain abnormal point mining model.
Optionally, preset abnormal point ratio is 0.5%.
The monitoring device of cooling system of vehicle working condition provided in this embodiment, can execute above method embodiment,
That the realization principle and technical effect are similar is similar for it, and details are not described herein.
The specific restriction of monitoring device about cooling system of vehicle working condition may refer to cold above for vehicle
But the restriction of the monitoring method of working state of system, details are not described herein.The monitoring of above-mentioned cooling system of vehicle working condition fills
Modules in setting can be realized fully or partially through software, hardware and combinations thereof.Above-mentioned each module can be in the form of hardware
It is embedded in or independently of the storage that in the processor in computer equipment, can also be stored in a software form in computer equipment
In device, the corresponding operation of the above modules is executed in order to which processor calls.
The monitoring method of cooling system of vehicle working condition provided by the embodiments of the present application can be adapted for as shown in Figure 10
Computer equipment, internal structure chart can be as shown in Figure 10.The computer equipment includes the place connected by system bus
Manage device, memory and network interface.Wherein, the processor of the computer equipment is for providing calculating and control ability.The calculating
The memory of machine equipment includes non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system
And computer program.The database of the computer equipment is used to store the number in the monitoring method of above-mentioned cooling system working condition
According to.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The network interface of machine equipment is used to communicate with external terminal by network connection.When the computer program is executed by processor with
Realize a kind of monitoring method of cooling system of vehicle working condition.
In one embodiment, a kind of computer equipment, including memory and processor are provided, is stored in memory
Computer program, the processor perform the steps of when executing computer program
Obtain the coolant temperature data of vehicle within a preset time interval；
Obtain the characteristic value of coolant temperature data within a preset time interval；
Characteristic value is inputted in machine learning model, determines the operating status of the cooling system of vehicle；Machine learning model
Be the coolant temperature data based on different type vehicle within a preset time interval characteristic value training obtain.
Computer equipment provided by the above embodiment, implementing principle and technical effect are similar with above method embodiment,
Details are not described herein.
In one embodiment, a kind of readable storage medium storing program for executing is provided, computer program, computer program are stored thereon with
It is performed the steps of when being executed by processor
Obtain the coolant temperature data of vehicle within a preset time interval；
Obtain the characteristic value of coolant temperature data within a preset time interval；
Characteristic value is inputted in machine learning model, determines the operating status of the cooling system of vehicle；Machine learning model
Be the coolant temperature data based on different type vehicle within a preset time interval characteristic value training obtain.
Readable storage medium storing program for executing provided by the above embodiment, implementing principle and technical effect and above method embodiment class
Seemingly, details are not described herein.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of monitoring method of cooling system of vehicle working condition, which is characterized in that the described method includes:
Obtain the coolant temperature data of vehicle within a preset time interval；
Obtain characteristic value of the coolant temperature data in the prefixed time interval；
The characteristic value is inputted in machine learning model, determines the operating status of the cooling system of the vehicle；The machine
Learning model is that the characteristic values of the coolant temperature data based on different type vehicle in the prefixed time interval is trained
It arrives.
2. the method according to claim 1, wherein described obtain the coolant temperature data described default
Characteristic value in time interval, comprising:
According to the type of vehicle of the vehicle, from preset type of vehicle and the corresponding relationship of standard coolant temperature threshold value,
Obtain the corresponding standard coolant temperature threshold value of the vehicle；The standard coolant temperature threshold value includes temperature upper limit and temperature
Spend lower limit value；
According to the coolant temperature data and the corresponding standard coolant temperature threshold value of the vehicle, the coolant liquid temperature is obtained
Degree is according to the characteristic value in the prefixed time interval.
3. according to the method described in claim 2, it is characterized in that, the characteristic value includes the coolant temperature data in institute
State very poor, the median, standard deviation, the temperature of standard coolant temperature threshold value corresponding greater than the vehicle in prefixed time interval
Spend the ratio of the ratio value of upper limit value and the temperature upper limit value of standard coolant temperature threshold value corresponding less than the vehicle
Value.
4. according to the method described in claim 2, it is characterized in that, the method also includes:
Count the temperature curve distribution map of different vehicle type；
Determine the corresponding temperature upper limit of preset cut-point upper limit ratio in the temperature curve distribution map of the different vehicle type
Value and the corresponding temperature upper limit value of preset cut-point lower proportion ratio；
According to the corresponding temperature upper limit of the different vehicle type and temperature upper limit value, determine the preset type of vehicle with
The corresponding relationship of standard coolant temperature threshold value.
5. method according to claim 1-4, which is characterized in that the machine learning model is unsupervised different
Often point mining model.
6. according to the method described in claim 5, it is characterized in that, the abnormal point mining model is isolated forest model, institute
State method further include:
Obtain the coolant temperature data of different vehicle type；
Obtain the characteristic value of the coolant temperature data of the different vehicle type in the prefixed time interval；
According to the characteristic value and preset exception of the coolant temperature data of the different vehicle type within a preset time interval
Point ratio, is trained initial isolated forest model, obtains the abnormal point mining model.
7. according to the method described in claim 6, it is characterized in that, the preset abnormal point ratio is 0.5%.
8. a kind of monitoring device of cooling system of vehicle working condition, which is characterized in that described device includes:
First obtains module, for obtaining the coolant temperature data of vehicle within a preset time interval；
Second obtains module, for obtaining characteristic value of the coolant temperature data in the prefixed time interval；
First determining module determines the cooling system of the vehicle for inputting the characteristic value in machine learning model
Operating status；The machine learning model is the coolant temperature number based on different type vehicle in the prefixed time interval
According to characteristic value training obtain.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In when the processor executes the computer program the step of any one of realization claim 1-7 the method.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of any one of claim 1-7 the method is realized when being executed by processor.
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