CN109724812B - Vehicle fault early warning method and device, storage medium and terminal equipment - Google Patents
Vehicle fault early warning method and device, storage medium and terminal equipment Download PDFInfo
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- CN109724812B CN109724812B CN201811639390.1A CN201811639390A CN109724812B CN 109724812 B CN109724812 B CN 109724812B CN 201811639390 A CN201811639390 A CN 201811639390A CN 109724812 B CN109724812 B CN 109724812B
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Abstract
The invention provides a vehicle fault early warning method, a vehicle fault early warning device, a storage medium and terminal equipment, wherein the method comprises the following steps: determining the type and the detection time period of vehicle faults to be detected of the vehicle; acquiring vehicle data of the vehicle related to the vehicle fault type in the detection period; calculating the risk of vehicle failure of the vehicle in the detection period according to the vehicle data; wherein the vehicle fault is consistent with the vehicle fault type, and determining whether the vehicle needs to be warned according to the risk of the vehicle fault occurring within the detection period. By adopting the invention, the detection efficiency and the accuracy can be improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for early warning of vehicle faults, a storage medium and terminal equipment.
Background
With the increasing living standard and the continuous development of the technology level, automobiles become an indispensable part of the life of people. Vehicles typically require regular service and maintenance to improve performance. Generally speaking, a special Automobile Sales service 4S (automatic Sales service 4S) store is generally required, and the 4S store provides service functions of vehicle Sales (sale), spare and accessory parts (spare), after-Sales service (service), and information feedback (survey) in a whole. The 4S shop then checks the vehicle manually using special diagnostic instruments to clear potential failure risks.
However, the existing vehicle detection process has the following defects: on one hand, the condition of the vehicle is detected depending on data provided by an instrument of the vehicle, and the detection is influenced by the precision of the instrument; on the other hand, the method needs on-site detection and manual detection, and is high in cost and low in efficiency.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a storage medium, and a terminal device for vehicle fault early warning, so as to solve or alleviate one or more of the above technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a method for vehicle fault early warning, including:
determining the type and the detection time period of vehicle faults to be detected of the vehicle;
acquiring vehicle data of the vehicle related to the vehicle fault type in the detection period;
predicting the risk that the vehicle is possibly subjected to vehicle failure according to the change condition of the vehicle data of the vehicle in the detection period; wherein the vehicle fault is in accordance with the vehicle fault type, an
And determining whether the vehicle needs to be pre-warned or not according to the predicted risk.
In one embodiment, the vehicle fault types include a vehicle speed display fault, a meter charge display fault, an odometer display fault, a battery feed fault, and a vehicle start fault.
In one embodiment, if the vehicle fault type includes a vehicle speed display fault, the vehicle data includes a GPS location of the vehicle and a display vehicle speed of a vehicle speed meter, and the predicting the risk of the vehicle fault possibly occurring in the vehicle according to a change of the vehicle data of the vehicle in the detection period includes:
calculating the GPS speed of the vehicle at each moment in the detection time period according to the GPS position of the vehicle in the detection time period; and
comparing the GPS speed and the display speed of the vehicle at each moment in the detection time period, and determining the deviation degree of the GPS speed and the display speed at each moment;
and predicting the risk that the vehicle is likely to have vehicle speed display faults according to the GPS speed at each moment and the deviation degree of the displayed vehicle speed.
In one embodiment, if the vehicle fault type includes a meter charge display fault, the vehicle data includes a battery voltage of the vehicle, an output current of a battery, and a display remaining charge of a charge meter, and the predicting the risk of the vehicle fault possibly occurring in the vehicle according to a change of the vehicle data of the vehicle during the detection period includes:
calculating the actual residual capacity of the vehicle at each moment in the detection time period according to the battery voltage and the output current of the vehicle at each moment in the detection time period;
comparing the actual residual capacity and the display residual capacity of the vehicle at each moment in the detection time period, and determining the deviation degree of the actual residual capacity and the display residual capacity at each moment;
and predicting the risk that the vehicle is likely to have instrument electric quantity display faults according to the actual residual electric quantity at each moment and the deviation degree of the displayed residual electric quantity.
In one embodiment, if the vehicle fault type includes an odometer display fault, the vehicle data includes a GPS location of the vehicle and a displayed mileage of the odometer, and the predicting the risk of the vehicle fault being likely to occur to the vehicle based on a change in the vehicle data over the detection period includes:
calculating the actual mileage of the vehicle at each moment in the detection time period according to the GPS position of the vehicle at each moment in the detection time period; and
comparing the actual mileage and the displayed mileage of the vehicle at each moment in the detection period, and determining the deviation degree of the actual mileage and the displayed mileage at each moment;
and predicting the risk that the vehicle is likely to have an odometer display fault according to the actual mileage at each moment and the deviation degree of the displayed mileage.
In one embodiment, if the vehicle fault type includes a battery feeding fault, the vehicle data includes a battery remaining capacity, a battery voltage, a power consumption component state, a driving motor speed, a system voltage and a vehicle load level of the vehicle, and the predicting the vehicle fault risk may occur according to a change of the vehicle data in the detection period includes:
and predicting the risk of the vehicle possibly having a battery feed fault according to the change conditions of the six of the residual electric quantity of the storage battery, the voltage of the storage battery, the states of energy consumption parts, the rotating speed of a driving motor, the system voltage and the load grade of the vehicle at each moment in the detection period, and determining the reason of the risk.
In one embodiment, if the vehicle fault type includes a vehicle start fault, the vehicle data includes a high-voltage interlock state, a quick-charging gun state, an anti-theft state and a vehicle start state of the vehicle, and the predicting the vehicle fault risk is based on a change of the vehicle data of the vehicle in the detection period includes:
predicting the risk that the vehicle may have vehicle starting failure according to the change conditions of the high-voltage interlocking state, the quick-charging gun state, the anti-theft state and the vehicle starting state of the vehicle at each moment in the detection period, and determining the reason of the risk.
In one embodiment, the determining whether the vehicle needs to be warned based on the predicted risk includes:
determining a risk category for each of said risks based on the predicted risk;
and determining whether the vehicle needs to be pre-warned or not according to the risk category of the risk.
In a second aspect, an embodiment of the present invention provides an apparatus for vehicle fault early warning, including:
the type and time period determining module is used for determining the type and the detection time period of vehicle faults to be detected of the vehicle;
the vehicle data acquisition module is used for acquiring vehicle data related to the vehicle fault type in the detection period;
the risk calculation module is used for predicting the risk that the vehicle is possibly subjected to vehicle failure according to the change condition of the vehicle data in the detection period; wherein the vehicle fault is in accordance with the vehicle fault type, an
And the early warning determining module is used for determining whether the vehicle needs to be early warned according to the predicted risk.
In a third aspect, an embodiment of the present invention provides a device for vehicle fault early warning, where functions of the device may be implemented by hardware, or may be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the vehicle fault warning structure includes a processor and a memory, the memory is used for the vehicle fault warning device to execute the vehicle fault warning program, and the processor is configured to execute the program stored in the memory. The vehicle fault early warning device may further include a communication interface for communicating with other devices or a communication network.
In a fourth aspect, the embodiment of the present invention further provides a computer readable storage medium, which is used for computer software instructions for a vehicle fault early warning apparatus, and includes a program for executing the vehicle fault early warning method.
Any one of the above technical solutions has the following advantages or beneficial effects:
according to the embodiment of the invention, the risks of possible vehicle faults in the set detection time period can be respectively predicted according to different vehicle fault types, and then whether the vehicle needs to be pre-warned or not is determined according to the corresponding vehicle fault type and the determined risk condition, so that the detection efficiency is effectively improved.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 is a schematic flow chart of a method for vehicle fault warning according to an embodiment of the present invention.
FIG. 2 is a time-series line graph of GPS speed and display vehicle speed according to one embodiment of the present invention.
Fig. 3 is a timing line diagram of an embodiment of the actual remaining power and the display remaining power provided by the present invention.
FIG. 4 is a time-series line graph of an embodiment of actual mileage and displayed mileage as provided by the present invention.
5-1 through 5-6 are timing line diagrams of one embodiment of battery-fed vehicle data states provided by the present invention.
Fig. 6 is a flowchart illustrating an embodiment of a process for determining whether to warn.
FIG. 7 is a bar graph of one embodiment of the risk level classification statistics provided by the present invention.
FIG. 8 is a schematic diagram of one embodiment of a system architecture for a server provided by the present invention.
FIG. 9 is a schematic diagram of one embodiment of the interaction between platform systems provided by the present invention.
FIG. 10 is a framework diagram of one embodiment of logical relationships within a data platform provided by the present invention.
Fig. 11 is a schematic structural diagram of an embodiment of the vehicle fault warning apparatus provided in the present invention.
Fig. 12 is a schematic structural diagram of an embodiment of a terminal device provided by the present invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
Referring to fig. 1, an embodiment of the invention provides a method for early warning of a vehicle fault. The present embodiment may include steps S100 to S400 as follows:
and S100, determining the type and the detection time period of the vehicle fault to be detected by the vehicle.
In the present embodiment, the vehicle may include a motorcycle, an automobile, a ship, an airplane, and the like. The vehicle fault types may include a vehicle speed display fault, a meter charge display fault, an odometer display fault, a battery feed fault, and a vehicle start fault. The detection period may be a period of time in the past, e.g., within seven days, ten days, a month, etc. of the past.
S200, vehicle data related to the vehicle fault type in the detection period are obtained.
In some embodiments, the vehicle may upload data such as the state and the driving parameters of the vehicle during driving or during standby to the server for storage. The vehicle data may include GPS data, travel speed, mileage, location, engine status, theft status, battery powered status, etc.
In some embodiments, the method of vehicle fault warning may be performed by a server. And the server extracts corresponding vehicle data from the server according to the identification of the vehicle, the detection time interval and the vehicle fault type. Then, a risk situation that a vehicle malfunction may occur is determined according to the method of step S300.
S300, predicting the risk that the vehicle is possibly in vehicle failure according to the change condition of the vehicle data in the detection period. Wherein the vehicle fault is consistent with the vehicle fault type.
And S400, determining whether the vehicle needs to be pre-warned or not according to the predicted risk.
And for each vehicle fault type, determining whether the corresponding vehicle fault needs to be early warned. If the risk of a certain type of vehicle fault is high, it is determined that this type of vehicle fault requires a warning to the owner of the vehicle. Of course, when the early warning is given to the owner, whether multiple types of vehicle faults exist at the same time or not needs to be considered, and the risk is high, so that the situations of the multiple types of vehicle faults can be given early warning to the owner.
In some embodiments, if the same type of vehicle fault has been pre-warned to the owner multiple times in a short time, but the owner has not made a maintenance record during that time, it may report to the system personnel.
In some embodiments, if the vehicle fault type is a vehicle speed display fault, the vehicle data may include a GPS location of the vehicle and a displayed vehicle speed of a vehicle speed meter. The calculating of the risk of vehicle failure in the detection period in step 300 may include: calculating the GPS speed of the vehicle at each moment in the detection period according to the GPS position of the vehicle in the detection period; comparing the GPS speed and the display speed of the vehicle at each moment in the detection period, and determining the deviation degree of the GPS speed and the display speed at each moment; and predicting the risk that the vehicle is possibly subjected to vehicle speed display failure according to the GPS speed at each moment and the deviation degree of the displayed vehicle speed.
For example, the risk of vehicle speed display failure at each time within approximately 30 seconds may be calculated at a granularity of 100 milliseconds. The GPS velocity at each time may be determined from the ratio of the change in GSP position at the adjacent time to the difference in duration at the adjacent time.
In some embodiments, as shown in FIG. 2, a time-series line graph is used to display the difference between GPS speed and displayed vehicle speed (i.e., display speed). And comparing whether the difference value between the GPS speed and the display vehicle speed at each moment exceeds a set error range such as 5% or 10%, and if the number of the moments exceeding the error range is large, determining that the risk of vehicle speed display failure at the moment is high. The level of deviation of the GPS speed from the displayed vehicle speed at that time may also be determined by the amount of the error exceeding the error range. For example, a degree of primary deviation, a degree of secondary deviation, a degree of tertiary deviation, etc. Then, the risk that the vehicle is likely to have a vehicle speed display fault is predicted according to the number of times of each level of deviation degree. If the out-of-error range is larger, the higher the level of the degree of deviation, and if the number of three levels of deviation is larger, the higher the risk that the vehicle may malfunction in the vehicle speed display is.
In some embodiments, if the vehicle fault type is a meter charge display fault, the vehicle data may include a battery voltage of the vehicle, an output current of the battery, and a display remaining charge of the charge meter. The risk prediction process of step 300 may include: and calculating the actual residual capacity of the vehicle at each moment in the detection period according to the battery voltage and the output current of the vehicle at each moment in the detection period. And comparing the actual residual capacity of the vehicle at each moment in the detection period with the displayed residual capacity, and determining the deviation degree of the actual residual capacity and the displayed residual capacity at each moment. And predicting the risk that the vehicle is likely to have instrument electric quantity display faults according to the actual residual electric quantity at each moment and the deviation degree of the displayed residual electric quantity.
The calculation of the actual remaining capacity at a certain moment in the detection period may be as follows: and integrating the acquired battery voltage and output current at the moment and the previous moment in the detection period by adopting an integration mode to obtain the actual used electric quantity between the moment and the previous moment. And then, subtracting the actual used electric quantity between the moment and the previous moment from the actual residual electric quantity at the previous moment to obtain the actual residual electric quantity at the moment. The actual remaining capacity at the initial time in the detection period may use the display remaining capacity as a reference standard.
As shown in fig. 3, in some embodiments, a time line graph may be used to display the difference between the actual remaining capacity (actual capacity in the graph) and the remaining capacity (meter capacity in the graph) and the time variation. And comparing whether the difference value between the actual residual capacity and the display residual capacity at each moment exceeds a set error range such as 5% or 10%, and if the number of moments exceeding the error range is large, determining that the risk of instrument electric quantity display failure at the moment is high. The level of the degree of deviation of the actual remaining capacity from the display remaining capacity at this time may be determined in accordance with the magnitude of the out-of-error range. For example, a degree of primary deviation, a degree of secondary deviation, a degree of tertiary deviation, etc. And then, predicting the risk that the vehicle is likely to have instrument electric quantity display faults according to the number of the deviation degrees of each stage. If the out-of-error range is larger, the higher the level of the degree of deviation, and if the number of three levels of deviation is larger, the higher the risk that the vehicle may malfunction in the meter charge display is.
In some embodiments, if the vehicle fault type is an odometer display fault, the vehicle data may include a GPS location of the vehicle and a displayed mileage of the odometer. The risk prediction process in step 300 may include: calculating the actual mileage of the vehicle at each moment in the detection period according to the GPS position of the vehicle at each moment in the detection period; comparing the actual mileage and the displayed mileage of the vehicle at each moment in the detection period, and determining the deviation degree of the actual mileage and the displayed mileage at each moment; and predicting the risk that the vehicle is likely to have an odometer display fault according to the actual mileage at each moment and the deviation degree of the displayed mileage.
As shown in fig. 4, in some embodiments, a time of day line graph may be used to display the actual mileage (GPS mileage in the graph) and to display the difference in mileage (mileage in the graph) and the variation over time. And comparing whether the difference value between the actual mileage at each moment and the displayed mileage exceeds a set error range of 5% or 10%, and the like, wherein if the more moments exceeding the error range, the higher the risk that the display fault of the odometer is possible to occur in the future can be determined. The level of the degree of deviation of the actual mileage at that time from the displayed mileage can be determined according to the magnitude of the exceeding of the error range. For example, a degree of primary deviation, a degree of secondary deviation, a degree of tertiary deviation, etc. And then predicting the risk that the vehicle is likely to have the fault of the display of the odometer according to the number of the deviation degrees of each stage. If the out-of-error range is larger, the higher the level of the degree of deviation, and if the number of three levels of deviation is larger, the higher the risk that the vehicle may have a malfunction of the odometer display. .
In the above-described embodiment, if the vehicle failure condition is detected using the GPS data, it is possible to detect whether there is an abnormality in the GPS data or whether the GPS system is abnormal before determining the risk that the vehicle failure may occur. For example, in the case of determining that there is no fault of the vehicle itself, the data of the displayed mileage within a certain detection period is compared with the data of the actual mileage determined by the GPS system. If the abnormal condition does not exist, the GPS data is not abnormal or the GPS system works normally. If the abnormal condition exists, the abnormal condition indicates that the GPS data exists or the GPS system works abnormally. At this time, the above-mentioned scheme may be executed only by performing operations such as correcting the GPS data in advance, acquiring new GPS data, or correcting the GPS system to operate normally.
In some embodiments, if the vehicle fault type is a battery feed fault, the vehicle data may include a battery remaining capacity, a battery voltage, a power consuming component status, a drive motor speed, a system voltage, and a vehicle load level of the vehicle. The risk prediction process in step S300 may include: and determining whether the battery feeding fault is possible to occur at each moment of the vehicle in the detection period according to the residual electric quantity of the storage battery, the voltage of the storage battery, the states of energy consumption parts, the rotating speed of the driving motor, the system voltage and the load grade of the vehicle at each moment of the vehicle in the detection period, and determining the reason of the battery feeding fault. Then, according to the situation that the electric feeding fault can occur at each moment, the risk that the battery feeding fault can occur in the future of the vehicle is predicted.
In the present embodiment, the battery remaining amount and the battery voltage decrease with the use period of the vehicle. The energy consumption parts comprise an air conditioner, a car lamp and the like. The system voltage may include whether the vehicle is powered up and a voltage value after the power up. The vehicle load level may be determined in a graded manner according to the magnitude of the vehicle load, for example: zero order, 1 order, 2 order, etc.
If the residual capacity of the storage battery and the voltage of the storage battery are reduced greatly in a certain period of time, but the change of energy consumption parts, the rotating speed of a driving motor and the like is not large, the situation of battery feeding faults possibly occurring in the period of time is shown, and the risk that the battery feeding is possibly generated in the future of the vehicle can be predicted to be large. If the residual capacity of the storage battery and the reduction band of the voltage of the storage battery are matched with the data of the states of energy-consuming parts, the rotating speed of the driving motor, the system voltage and the load grade of the vehicle, the probability that the battery feeding fault is possible is low, and the risk that the battery feeding is possible in the future of the vehicle can be predicted to be low. In this embodiment, the risk level (i.e., the warning state in fig. 5-6) to which the risk belongs may be determined according to the magnitude of the risk. See in particular fig. 5-1 to 5-6.
In some embodiments, if the vehicle fault type includes a vehicle start fault, the vehicle data may include a high-voltage interlock status, a quick-charge gun status, a theft-prevention status, and a vehicle start status of the vehicle. Of course, the vehicle data also includes the state of a relay that controls the start of the vehicle, the state of a battery management system that supplies power to the vehicle, and the like. The risk prediction process in step S300 may include: according to the high-voltage interlocking state, the quick charging gun state, the anti-theft state and the vehicle starting state of the vehicle at each moment in the detection period, whether the vehicle possibly has a vehicle starting fault at each moment in the detection period is determined, and the reason of the battery feeding fault is determined. Then, according to the situation that the battery feeding fault can occur at each moment, the risk that the battery feeding fault can occur in the future of the vehicle is predicted.
For example, if the vehicle is in an activated state at time a, but the theft-protected state of the vehicle is theft-protected, then activation of the vehicle at this time is an abnormal situation. At this time, if the vehicle is in a state of being started at other times and the theft prevention state of the vehicle is in a non-theft prevention state. In this case, it is considered that the possibility that the vehicle has a vehicle start failure at time a is not high, and the antitheft system may be too sensitive. However, if the vehicle is in the activated state before and after the time a, and the theft-proof state of the vehicle is also in the non-theft-proof state. In this case, then, it can be considered that the possibility of the vehicle start failure occurring at the time a is high, and the cause of the occurrence of the risk can be determined to be the antitheft system. And predicting the risk of the same fault in the future of the vehicle based on the determined high and low possibility of the vehicle starting fault at each moment.
The high-pressure interlocking state means that: when the motor requires positive and negative rotation, the high-voltage side can be connected into two circuits to realize the positive and negative rotation of the motor. However, when the motor rotates forward, the flipping circuit cannot be turned on, and vice versa. If the high-voltage interlocking state is normal when the vehicle is started, the risk that the vehicle may have a vehicle starting fault is not high.
The state of the quick charge gun is as follows: whether the electric gun for charging the battery is charged or not. If the quick charging gun is in a charging state when the vehicle is started, the risk that the vehicle may have a vehicle starting fault is high.
In some embodiments, as shown in fig. 6, the process of determining whether to warn in step S400 may include steps S410 to S420 as follows:
and S410, determining the risk category of the risk according to the predicted risk.
And S420, determining whether the vehicle needs to be pre-warned or not according to the risk category of the risk.
As shown in fig. 7, the detection period was taken as the last seven days. The risk categories include first level risk, second level risk, and third level risk. And respectively accumulating the number of the first-level risk, the second-level risk and the third-level risk in the seven days. The higher the risk, the higher the risk level. If the three-level risk occurs, the vehicle needs to be warned even if the number of the three-level risk is small. If the number of the first-level risks is too large, the vehicle can be warned. If no three-level risk exists and the number of the first-level risk and the second-level risk is not large, the vehicle can not be warned.
In an implementation manner, the method provided in the embodiment of the present invention may further arrange the fault data into a display chart, and send the display chart to the display terminal of the vehicle owner. Such as an on-board display screen on the vehicle, the owner's cell phone, the IPAD, etc. Specific arranged charts include, but are not limited to, fig. 2, fig. 3, fig. 4, fig. 5-1 to fig. 5-6, fig. 7.
Referring to fig. 8 and fig. 9, an embodiment of the present invention further provides a server for executing the method of the foregoing embodiment, and the working principle inside the server may be as follows:
1. from the TSP (Telematics Service Provider) platform, the TSP platform receives Can (Controller Area network) network data and DTC (Diagnostic Trouble Code) data from tbox (Telematics BOX) for communicating with a server or a mobile client of a vehicle owner, and implementing vehicle information display and control).
2. Vehicle data CAN be divided into automatic diagnostic (OWNDTC) and passive Diagnostic (DTC) data and distributed into different topoics of a Kafka cluster by data source type, e.g. CAN, DTC, OWNDTC. Meanwhile, the TSP may serve as a relay platform and may send a control instruction and a Data request instruction of an RDS (Radio Data System) platform to the TSP. Request results for instruction requests with longer response times (e.g., request freeze frames and request DTC data) may be sent to the Kafka cluster. The returned request results are then written by a separate program into the Mysql database and the RDS may asynchronously query the returned request results.
3. From the aspect of the RDS, the RDS can inquire original data in a data platform, basic data, diagnosis data, summarized data and the like of a vehicle in Mysql data, and can also inquire video data through external services and acquire a control script through services of an after-sale platform. Meanwhile, the RDS can send remote control commands and data request commands through the TSP platform.
4. From the data platform, the data platform acquires the CAN data of the automobile in real time and stores the original data into the hbase database. Moreover, some statistical analysis can be performed based on the original data, and the analysis result is written into the hbase database.
5. From a database perspective, the hbase database stores raw vehicle data and statistical analysis results. Mysql timing synchronization hbase partial raw data and statistical analysis results. And the Mysql also stores basic information of the vehicle, DTC data, freeze frames and other data.
6. For external services, the external service system can provide functions of querying the hbase database and the Mysql database, and the like.
Referring to fig. 10, a block diagram of a data platform according to an embodiment of the present invention is shown.
The big data platform is logically divided into seven parts, which are respectively: the system comprises data access, a distributed file system, a distributed database, a computing engine, an authority safety system, an operation and maintenance monitoring and development test platform. The logical relationship of these parts can be seen in fig. 10. The big data platform adopts mainstream open source big data technology and components, wherein the technical components mainly used by the real-time computing part are Spark streaming, the batch computing part is component tools such as Spark and hive, and the data storage mainly comprises a hbase database, redis data and a mysql database.
Referring to fig. 11, an embodiment of the present invention provides a vehicle fault warning device, including:
the type and time period determining module 100 is used for determining the type and the detection time period of vehicle faults to be detected of the vehicle;
a vehicle data acquisition module 200, configured to acquire vehicle data of the vehicle related to the vehicle fault type in the detection period;
the risk calculation module 300 is used for predicting the risk that the vehicle is possibly in vehicle failure according to the change situation of the vehicle data in the detection period; wherein the vehicle fault is in accordance with the vehicle fault type, an
And an early warning determination module 400, configured to determine whether an early warning needs to be performed on the vehicle according to the predicted risk.
In some embodiments, the vehicle fault types include a vehicle speed display fault, a meter charge display fault, an odometer display fault, a battery feed fault, and a vehicle start fault.
In some embodiments, if the vehicle fault type includes a vehicle speed display fault, the vehicle data includes a GPS location of the vehicle and a displayed vehicle speed of a vehicle speed meter, and the risk calculation module 300 includes:
the GPS speed calculation unit is used for calculating the GPS speed of the vehicle at each moment in the detection time period according to the GPS position of the vehicle in the detection time period; and
the vehicle speed display fault risk determination unit is used for comparing the GPS speed and the display vehicle speed of the vehicle at each moment in the detection time period and determining the deviation degree of the GPS speed and the display vehicle speed at each moment; and predicting the risk that the vehicle is likely to have vehicle speed display faults according to the GPS speed at each moment and the deviation degree of the displayed vehicle speed.
In some embodiments, if the vehicle fault type includes a meter charge display fault, the vehicle data includes a battery voltage of the vehicle, an output current of the battery, and a display remaining charge of a charge meter, and the risk calculation module 300 includes:
the actual residual capacity calculating unit is used for calculating the actual residual capacity of the vehicle at each moment in the detection time period according to the battery voltage and the output current of the vehicle at each moment in the detection time period;
the instrument electric quantity display fault risk determining unit is used for comparing the actual residual electric quantity and the display residual electric quantity of the vehicle at each moment in the detection time period, and determining the deviation degree of the actual residual electric quantity and the display residual electric quantity; and predicting the risk that the vehicle is likely to have instrument electric quantity display faults according to the actual residual electric quantity at each moment and the deviation degree of the displayed residual electric quantity.
In some embodiments, if the vehicle fault type includes an odometer display fault, the vehicle data includes a GPS location of the vehicle and a displayed mileage of the odometer, and the risk calculation module 300 includes:
the actual mileage calculation unit is used for calculating the actual mileage of the vehicle at each moment in the detection time period according to the GPS position of the vehicle at each moment in the detection time period; and
the odometer display fault risk determination unit is used for comparing the actual mileage and the display mileage of the vehicle at each moment in the detection time period and determining the deviation degree of the actual mileage and the display mileage at each moment; and predicting the risk that the vehicle is likely to have an odometer display fault according to the actual mileage at each moment and the deviation degree of the displayed mileage. .
In some embodiments, if the vehicle fault type includes a battery feeding fault, the vehicle data includes a battery remaining capacity, a battery voltage, an energy consuming part status, a driving motor speed, a system voltage, and a vehicle load level of the vehicle, and the risk calculation module 300 includes:
and the battery feeding fault risk determining unit is used for predicting the risk that the vehicle is possible to generate the battery feeding fault according to the change conditions of the six of the residual electric quantity of the storage battery, the voltage of the storage battery, the states of energy consumption parts, the rotating speed of the driving motor, the system voltage and the load level of the vehicle at each moment in the detection period, and determining the reason of the risk.
In some embodiments, if the vehicle fault type includes a vehicle start fault, the vehicle data includes a high-voltage interlock status, a quick-charge gun status, an anti-theft status, and a vehicle start status of the vehicle, and the risk calculation module 300 includes:
and the vehicle starting fault determining unit is used for predicting the risk that the vehicle may have vehicle starting faults according to the change conditions of the high-voltage interlocking state, the quick charging gun state, the anti-theft state and the vehicle starting state of the vehicle at each moment in the detection period and determining the reason of the risk.
In some embodiments, the early warning determination module 400 may include:
a risk category determination unit for determining a risk category for each of the risks based on the predicted risk;
and the early warning judgment unit is used for determining whether the vehicle needs to be early warned according to the risk category of the risk.
The functions of the device can be realized by hardware, and can also be realized by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-described functions.
In one possible design, the vehicle fault warning apparatus includes a processor and a memory, the memory is used for the vehicle fault warning apparatus to execute the program for vehicle fault warning in the first aspect, and the processor is configured to execute the program stored in the memory. The vehicle fault early warning device may further include a communication interface for communicating with other devices or a communication network.
An embodiment of the present invention further provides a terminal device for vehicle fault early warning, as shown in fig. 12, the device includes: a memory 21 and a processor 22, the memory 21 having stored therein computer programs that may be executed on the processor 22. The processor 22, when executing the computer program, implements the method of vehicle fault warning in the above-described embodiments. The number of the memory 21 and the processor 22 may be one or more.
The apparatus further comprises:
a communication interface 23 for communication between the processor 22 and an external device.
The memory 21 may comprise a high-speed RAM memory and may also comprise a non-volatile memory, such as at least one disk memory.
If the memory 21, the processor 22 and the communication interface 23 are implemented independently, the memory 21, the processor 22 and the communication interface 23 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 12, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 21, the processor 22 and the communication interface 23 are integrated on a chip, the memory 21, the processor 22 and the communication interface 23 may complete mutual communication through an internal interface.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The computer readable media of embodiments of the present invention may be computer readable signal media or computer readable storage media or any combination of the two. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable read-only memory (CDROM). Additionally, the computer-readable storage medium may even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In embodiments of the present invention, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, input method, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, Radio Frequency (RF), etc., or any suitable combination of the preceding.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments are programs that can be executed by associated hardware through instructions of the programs, and the programs can be stored in a computer readable storage medium, and when executed, comprise one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (11)
1. A method for vehicle fault early warning, which is used for predicting the risk of vehicle fault, comprising:
determining the type of vehicle fault to be detected of a vehicle and a detection time period, wherein the detection time period is a past period of time;
acquiring vehicle data of the vehicle related to the vehicle fault type in the detection period;
predicting the risk that the vehicle is possibly subjected to vehicle failure according to the change condition of the vehicle data of the vehicle in the detection period; wherein the vehicle fault is in accordance with the vehicle fault type, an
Determining whether the vehicle needs to be pre-warned according to the predicted risk;
wherein the vehicle fault type comprises a vehicle starting fault, when the vehicle fault type comprises the vehicle starting fault, the vehicle data comprises an anti-theft state and a vehicle starting state, and the predicting the risk of the vehicle fault possibly occurring to the vehicle according to the change situation of the vehicle data of the vehicle in the detection period comprises: if the vehicle starting state is in a starting state at a specified moment, the anti-theft state is in an anti-theft state, but the vehicle starting state is in a starting state before and after the specified moment, and the anti-theft state is in a non-anti-theft state, the vehicle has a high possibility of having the vehicle starting fault at the specified moment; predicting a risk that the vehicle may have a vehicle start failure based on a likelihood that the vehicle will have the vehicle start failure, and determining a cause of the risk.
2. The method of claim 1, wherein the vehicle fault types further include vehicle speed display fault, meter charge display fault, odometer display fault, battery feed fault.
3. The method of claim 1, wherein if the vehicle fault type further includes a vehicle speed display fault, the vehicle data includes a GPS location of the vehicle and a displayed vehicle speed of a vehicle speed meter, and the predicting the risk of the vehicle being likely to have a vehicle fault based on a change in the vehicle data of the vehicle over the detection period comprises:
calculating the GPS speed of the vehicle at each moment in the detection time period according to the GPS position of the vehicle in the detection time period; and
comparing the GPS speed and the display speed of the vehicle at each moment in the detection time period, and determining the deviation degree of the GPS speed and the display speed at each moment;
and predicting the risk that the vehicle is likely to have vehicle speed display faults according to the GPS speed at each moment and the deviation degree of the displayed vehicle speed.
4. The method of claim 1, wherein if the vehicle fault type further includes a meter charge display fault, the vehicle data includes a battery voltage of the vehicle, an output current of a battery, and a display remaining charge of a charge meter, and the predicting the risk of the vehicle fault likely to occur according to a change in the vehicle data of the vehicle during the detection period comprises:
calculating the actual residual capacity of the vehicle at each moment in the detection time period according to the battery voltage and the output current of the vehicle at each moment in the detection time period;
comparing the actual residual capacity and the display residual capacity of the vehicle at each moment in the detection time period, and determining the deviation degree of the actual residual capacity and the display residual capacity at each moment;
and predicting the risk that the vehicle is likely to have instrument electric quantity display faults according to the actual residual electric quantity at each moment and the deviation degree of the displayed residual electric quantity.
5. The method of claim 1, wherein if the vehicle fault type further includes an odometer display fault, the vehicle data includes a GPS location of the vehicle and a displayed mileage of an odometer, and the predicting the risk of the vehicle being likely to have a vehicle fault based on a change in the vehicle data over the detection period of the vehicle comprises:
calculating the actual mileage of the vehicle at each moment in the detection time period according to the GPS position of the vehicle at each moment in the detection time period; and
comparing the actual mileage and the displayed mileage of the vehicle at each moment in the detection period, and determining the deviation degree of the actual mileage and the displayed mileage at each moment;
and predicting the risk that the vehicle is likely to have an odometer display fault according to the actual mileage at each moment and the deviation degree of the displayed mileage.
6. The method of claim 1, wherein if the vehicle fault type further comprises a battery feed fault, the vehicle data comprises a battery remaining capacity, a battery voltage, a state of energy consuming components, a driving motor speed, a system voltage and a vehicle load level of the vehicle, and the predicting the vehicle risk of the vehicle fault is possible according to a change of the vehicle data of the vehicle in the detection period comprises:
and predicting the risk of the vehicle possibly having a battery feed fault according to the change conditions of the six of the residual electric quantity of the storage battery, the voltage of the storage battery, the states of energy consumption parts, the rotating speed of a driving motor, the system voltage and the load grade of the vehicle at each moment in the detection period, and determining the reason of the risk.
7. The method of claim 1, wherein when the vehicle fault type comprises a vehicle start fault, the vehicle data further comprises a high-voltage interlock status, a fast-charge gun status of the vehicle, and the predicting the risk of the vehicle fault possibly occurring to the vehicle according to a change of the vehicle data of the vehicle within the detection period comprises:
and predicting the risk that the vehicle is possibly subjected to vehicle starting failure according to the change conditions of the high-voltage interlocking state and the quick-charging gun state of the vehicle at each moment in the detection period, and determining the reason of the risk.
8. The method of claim 1, wherein determining whether the vehicle needs to be pre-warned based on the predicted risk comprises:
determining a risk category for each of said risks based on the predicted risk;
and determining whether the vehicle needs to be pre-warned or not according to the risk category of the risk.
9. An apparatus for vehicle fault early warning, for predicting a risk of vehicle fault, comprising:
the type and time period determining module is used for determining the type and the detection time period of vehicle faults to be detected of the vehicle, wherein the detection time period is a past period of time;
the vehicle data acquisition module is used for acquiring vehicle data related to the vehicle fault type in the detection period;
the risk calculation module is used for predicting the risk that the vehicle is possibly subjected to vehicle failure according to the change condition of the vehicle data in the detection period; wherein the vehicle fault is in accordance with the vehicle fault type, an
The early warning determining module is used for determining whether the vehicle needs to be early warned according to the predicted risk;
wherein the vehicle fault type includes a vehicle start fault, when the vehicle fault type includes the vehicle start fault, the vehicle data includes an anti-theft state and a vehicle start state, and the risk calculation module 300 includes:
a vehicle start failure determination unit configured to, if the vehicle start state is a start state at a specified time, the antitheft state is antitheft, but the vehicle start state is a start state before and after the specified time, and the antitheft state is non-antitheft, a possibility that the vehicle start failure occurs at the specified time is high; predicting a risk that the vehicle may have a vehicle start failure based on a likelihood that the vehicle will have the vehicle start failure, and determining a cause of the risk.
10. The utility model provides a realize terminal equipment of vehicle trouble early warning which characterized in that, terminal equipment includes:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-8.
11. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 8.
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| CN110031874B (en) * | 2019-05-29 | 2021-10-01 | 吉旗（成都）科技有限公司 | Method for remotely and automatically testing antenna installation quality of GPS (Global positioning System) equipment |
| CN111832974A (en) * | 2020-07-28 | 2020-10-27 | 重庆长安新能源汽车科技有限公司 | Vehicle fault early warning method and storage medium |
| CN111731070B (en) * | 2020-08-01 | 2020-11-20 | 深圳小木科技有限公司 | Vehicle-mounted air conditioner and carriage air conditioning method |
| CN113343633B (en) * | 2021-06-10 | 2022-04-26 | 上海交通大学 | Thermal runaway fault classification and risk prediction method and system for power lithium battery |
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