CN112198824A - Vehicle-mounted data processing method and system - Google Patents
Vehicle-mounted data processing method and system Download PDFInfo
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- CN112198824A CN112198824A CN202011089334.2A CN202011089334A CN112198824A CN 112198824 A CN112198824 A CN 112198824A CN 202011089334 A CN202011089334 A CN 202011089334A CN 112198824 A CN112198824 A CN 112198824A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0428—Safety, monitoring
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention provides a vehicle-mounted data processing method and a system, wherein the method comprises the steps of obtaining an ECU high-frequency signal corresponding to a vehicle monitoring requirement; converting the ECU high-frequency signal into various low-frequency performance indexes corresponding to the model according to the monitoring model; filling the various low-frequency performance indexes into expansion bytes in a bus data frame respectively according to the category of the low-frequency performance indexes; and sending the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, wherein the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform, so that the cloud platform can monitor the vehicle state according to the vehicle-mounted monitoring signal. The invention solves the problems that the existing vehicle controllers are increased more and more, interactive signals are numerous, and bus load needs to be controlled due to vehicle safety, so that the bus monitoring ECU electric control signals are difficult to realize.
Description
Technical Field
The invention relates to the technical field of vehicle monitoring, in particular to a vehicle-mounted data processing method and system.
Background
With the trend of electric, intelligent and interconnected automobile development, the automobile industry is advancing into the big data era. The number of parts such as an automobile control unit, a sensor and an actuator is increased, various electric control signals of the automobile need to be monitored in real time for improving the robustness of a vehicle system, and the monitoring signals need to have the capability of uploading to a cloud platform for big data analysis. At present, a TBOX (tunnel boring machine) has the capability of acquiring and uploading bus interaction signals to a large data platform, but cannot monitor more Electronic Control signals inside an ECU (Electronic Control Unit); in addition, referring to the hybrid vehicle network topology shown in fig. 1, the vehicle bus transmission baud rate is 500Kbit/s, the transmission frequency is required to be within 10ms for signals with high precision requiring transmission rate, such as signals of engine speed, torque and the like, and the signal requirement length is long, such as the engine speed signal occupies 16 bits, as shown in table 1, a bus frame signal deployment example is shown.
TABLE 1
Aiming at signals of the engine speed, the bus only allows 5000/16-312 signals to be transmitted at most, at present, vehicle controllers are increased more and more, interactive signals are numerous, and the CAN network related to new energy vehicle models is more complex; considering the safety of the vehicle, the bus load needs to be controlled within 70%, and the ECU electric control signal is difficult to realize through bus monitoring.
Disclosure of Invention
The invention aims to solve the technical problem that the vehicle-mounted data processing method and the vehicle-mounted data processing system are used for solving the problems that the number of the existing vehicle controllers is increased, the number of interactive signals is large, and the ECU electric control signals cannot be monitored through a bus.
The invention provides a vehicle-mounted data processing method, which comprises the following steps:
step S11, acquiring an ECU high-frequency signal corresponding to the vehicle monitoring requirement;
step S12, converting the ECU high-frequency signal into various low-frequency performance indexes corresponding to the model according to the monitoring model;
step S13, filling the low-frequency performance indexes into expansion bytes in a bus data frame according to the types of the low-frequency performance indexes;
and S14, sending the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, wherein the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform, so that the cloud platform can monitor the vehicle state according to the vehicle-mounted monitoring signal.
Further, the monitoring model is obtained by training through a corresponding functional algorithm according to historical ECU high-frequency signals, and comprises one or more of a user driving model, a dynamic general working condition model and a component performance model;
the functional algorithms include one or more of the following algorithms: linear regression algorithms, decision tree algorithms, neural network algorithms, integration algorithms, hierarchical clustering algorithms, and spectral clustering algorithms.
Furthermore, each monitoring model correspondingly generates a corresponding category low-frequency performance index, and a capacity expansion byte is set for each category of low-frequency performance index data on a bus data frame;
step S13 specifically includes: and filling the low-frequency performance index data of the corresponding category into the corresponding bytes of the bus data frame.
Further, the step S11 specifically includes: acquiring a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a gear signal and an altitude signal corresponding to the monitoring requirement from the ECU electric control signal on the ECU;
the step S12 specifically includes: according to a pre-established user driving model, extracting user portrait indexes corresponding to a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal, wherein the user portrait indexes comprise four sub indexes: a dynamic index, an economic index, a driving style index and a driver classification index; the user driving model is established according to a neural network;
the step S13 specifically includes: expanding four bytes on a bus data frame, and respectively filling the dynamic index, the economic index, the driving style index and the driver classification index into the four expanded bytes on the bus data frame.
Further, the step S11 specifically includes: acquiring an engine speed signal, an engine torque signal and a transmission gear signal corresponding to a monitoring requirement from the ECU electric control signal on the ECU, and acquiring an engine oil injection signal and an engine ignition signal corresponding to the monitoring requirement from the actuator signal;
the step S12 specifically includes: according to a pre-established dynamic total working condition model, power assembly performance indexes corresponding to an engine rotating speed signal, an engine torque signal, an engine oil injection signal, an engine ignition signal and a transmission gear signal are extracted, wherein the power assembly performance indexes comprise two sub indexes: an engine fuel saving index and a transmission gear shifting frequency index; the dynamic total working condition model is established according to a regression algorithm and a fitting algorithm;
the step S13 specifically includes: and expanding two bytes on the bus data frame, and respectively filling the engine oil saving index and the transmission gear shifting frequency index into the expanded byte on the bus data frame according to the sequence.
Further, the step S11 specifically includes: acquiring an oil tank liquid level signal, an oil tank pressure signal, an oil rail pressure signal and an oil pump current signal corresponding to a monitoring requirement from the ECU electric control signal on the ECU;
the step S12 specifically includes: extracting fuel system performance indexes corresponding to a fuel tank liquid level signal, a fuel tank pressure signal, a fuel rail pressure signal and an oil pump current signal according to a pre-established component performance model, wherein the fuel system performance indexes comprise two sub-indexes: fuel tank pressure indicators and fueling system safety indicators; the component performance model is established according to a regression algorithm and a fitting algorithm;
the step S13 specifically includes: and expanding two bytes on the bus data frame, and respectively filling the oil tank pressure index and the refueling system safety index into the expanded byte on the bus data frame according to the sequence.
The invention provides a vehicle-mounted data processing system, which comprises:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring an ECU high-frequency signal corresponding to a vehicle monitoring demand;
the conversion unit is used for converting the ECU high-frequency signal into various low-frequency performance indexes corresponding to the model according to the monitoring model;
the capacity expansion unit is used for respectively filling the various low-frequency performance indexes into capacity expansion bytes in a bus data frame according to the category of the low-frequency performance indexes;
and the monitoring and transmission unit is used for sending the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, and the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform so that the cloud platform can monitor the vehicle state according to the vehicle-mounted monitoring signal.
Further, the monitoring model is obtained by training through a corresponding functional algorithm according to historical ECU high-frequency signals, and comprises one or more of a user driving model, a dynamic general working condition model and a component performance model;
the functional algorithms include one or more of the following algorithms: linear regression algorithms, decision tree algorithms, neural network algorithms, integration algorithms, hierarchical clustering algorithms, and spectral clustering algorithms.
Furthermore, each monitoring model correspondingly generates a corresponding category low-frequency performance index, and a capacity expansion byte is set for each category of low-frequency performance index data on a bus data frame;
the capacity expansion unit is specifically configured to: and filling the low-frequency performance index data of the corresponding category into the corresponding bytes of the bus data frame.
Further, the obtaining unit is specifically configured to: acquiring a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a gear signal and an altitude signal corresponding to the monitoring requirement from the ECU electric control signal on the ECU;
the conversion unit is specifically configured to: according to a pre-established user driving model, extracting user portrait indexes corresponding to a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal, wherein the user portrait indexes comprise four sub indexes: a dynamic index, an economic index, a driving style index and a driver classification index; the user driving model is established according to a neural network;
the capacity expansion unit is specifically configured to: expanding four bytes on a bus data frame, and respectively filling the dynamic index, the economic index, the driving style index and the driver classification index into the four expanded bytes on the bus data frame.
The implementation of the invention has the following beneficial effects:
according to the invention, the ECU controller converts the high-frequency electric control signal in the ECU into the low-frequency performance index, the low-frequency performance index can be filled into a small number of bytes by expanding or changing the number of bytes of the bus signal, the expanded bus signal is sent to the vehicle-mounted communication module TBOX, the TBOX acquires the expanded or changed bus signal and transmits the expanded or changed bus signal to the cloud server, so that the cloud server can obtain the low-frequency performance index; the problem of current vehicle controller increase more and more, interactive signal is numerous, can't pass through bus monitoring ECU electrical control signal is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a block diagram of a hybrid network topology provided by the background art.
Fig. 2 is a flowchart of a vehicle-mounted data processing method according to an embodiment of the present invention.
Fig. 3 is a flowchart of a vehicle-mounted data processing method according to an embodiment of the present invention.
Fig. 4 is a flowchart of a vehicle-mounted data processing method according to an embodiment of the present invention.
Fig. 5 is a block diagram of an in-vehicle data processing system according to an embodiment of the present invention.
Detailed Description
In this patent, the following description will be given with reference to the accompanying drawings and examples.
As shown in fig. 2, an embodiment of the present invention provides a vehicle-mounted data processing method, where the method includes:
and step S11, the ECU acquires an ECU high-frequency signal corresponding to the vehicle monitoring requirement from the ECU electric control signal.
In the embodiment, the ECU stores ECU electric control signals which comprise signals of sensors of the ECU, signals of an actuator and signals inside an ECU chip and are characterized by high-frequency ECU signals; as can be seen from fig. 3, the sensor signals include a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a shift signal, an altitude signal, etc., the actuator signals include an engine fuel injection signal, an engine ignition signal, a fuel injection frequency signal, a fuel injection pulse width signal, a fuel injection pressure signal, an ignition angle signal, an ignition energy signal, an ignition frequency signal, an oil pump current signal, etc., and the ECU chip signals include an ECU current temperature signal, a memory ratio signal, an ECU current signal, an ECU voltage signal, etc.
Specifically, the signals are collected and have a corresponding relation with the vehicle monitoring requirement, for example, an oil injection system of the engine needs to be monitored, and an oil injection frequency signal, an oil injection pulse width signal and an oil injection pressure signal are collected from an ECU electric control signal; in other embodiments, it is desirable to monitor the engine ignition system, collect the ignition angle signal, the ignition energy signal and the ignition frequency signal from the ECU electrical control signal, and the ECU high frequency signal corresponding to the engine ignition system monitoring includes the ignition angle signal, the ignition energy signal and the ignition frequency signal.
With reference to fig. 3, the important monitoring electric control signal is the ECU high frequency signal corresponding to the vehicle monitoring requirement.
And step S12, the ECU converts the high-frequency signal of the ECU into various low-frequency performance indexes corresponding to the model.
With reference to fig. 3, training the historical ECU high-frequency signals through corresponding functional algorithms to form a monitoring model, where the monitoring model includes one or more of a user driving model, a dynamic general working condition model, and a component performance model; the functional algorithm comprises a classification algorithm and a clustering algorithm, wherein the classification algorithm comprises linear regression, a decision tree, a neural network and an integration algorithm, and the clustering algorithm comprises hierarchical clustering and spectral clustering algorithms; the functional algorithm thus utilizes one or more of the algorithms described above.
And step S13, filling the low-frequency performance indexes into expansion bytes in a bus data frame respectively according to the types of the low-frequency performance indexes.
The expansion byte may be one byte or a plurality of bytes.
It should be further noted that each monitoring model correspondingly generates a corresponding category low-frequency performance index, and a capacity expansion byte is set for each category low-frequency performance index data on a bus data frame;
step S13 specifically includes: and filling the low-frequency performance index data of the corresponding category into the corresponding bytes of the bus data frame.
Referring to table 2, each category indicator (or sub-indicator) is expanded by one byte, and includes 8 bits; table 2 shows that a byte interval is expanded, and the byte interval includes three bytes, which are byte 0, byte 1, and byte 2, respectively, and finally these expanded bytes are unified into a bus data frame.
TABLE 2
And step S14, the ECU sends the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, and the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform so that the cloud platform can monitor the vehicle state according to the vehicle-mounted monitoring signal.
In an embodiment of the present invention, step S11 specifically includes: acquiring a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a gear signal and an altitude signal corresponding to the vehicle monitoring requirement from the ECU electric control signal on the ECU;
the step S12 specifically includes: according to a pre-established user driving model, extracting user portrait indexes corresponding to a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal, wherein the user portrait indexes comprise four sub indexes: the driving method comprises the following steps that a dynamic index, an economic index, a driving style index and a driver classification index are set up, and a user driving model is built according to a neural network;
the step S13 specifically includes: expanding four bytes on a bus data frame, and respectively filling the dynamic index, the economic index, the driving style index and the driver classification index into the expanded four bytes;
referring to table 3, a large amount of high-frequency data, currently, the bus cannot be monitored and converted into a power performance index, an economic index, a driving style index and a driver classification index, four bytes are expanded on the bus to represent the data respectively, the interactive signal is EMS _ User, the length of each byte is 8 bits, the physical value is 0-255, wherein 0 represents a default value, 255 represents an invalid value, 1-10 represents the power index in the User portrait, 11-20 represents the economic index in the User portrait, 21-30 represents the driving style index in the User portrait, and 251-254 represents the driver classification index in the User portrait; a series of high-frequency data of a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal are reduced into low-frequency data for monitoring the ECU electric control signal from different angles, a subsequent cloud platform can acquire the expression mode of another angle of the ECU electric control signal according to the expanded bytes, although the data acquired by the cloud platform is not accurate as that of directly acquiring the vehicle speed signal, the accelerator signal, the brake signal, the acceleration signal, the steering wheel corner signal, the gear signal, the altitude signal and the like, and the aim of reducing the cost and the bandwidth of a data transmission channel is fulfilled by reducing the accuracy.
TABLE 3
If high-frequency signals such as a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal, an altitude signal and the like need to be monitored, because data change is frequent, occupied network resources are very much, and only 4 bytes need to be expanded on a bus monitoring signal through the method in the embodiment, so that the effect is achieved, and the problem existing in the prior art is solved.
In a specific embodiment of the present invention, when the monitoring demand is monitoring a powertrain performance index of the vehicle;
the step S11 specifically includes: acquiring an engine speed signal, an engine torque signal and a transmission gear signal corresponding to a vehicle monitoring requirement from the ECU electric control signal on the ECU, and acquiring an engine oil injection signal and an engine ignition signal corresponding to the vehicle monitoring requirement from the actuator signal;
the step S12 specifically includes: extracting power assembly performance indexes corresponding to the engine speed, the engine torque, the engine oil injection, the engine ignition and the transmission gear according to the pre-established dynamic total working condition model, wherein the power assembly performance indexes comprise two sub-indexes: an engine fuel saving index and a transmission gear shifting frequency index; the dynamic total working condition model is established according to a regression algorithm and a fitting algorithm;
the step S13 specifically includes: and expanding two bytes on a bus data frame, and respectively filling the engine oil saving index and the transmission gear shifting frequency index into one byte according to the sequence.
In a specific embodiment of the present invention, when the vehicle monitoring requirement is to monitor a fuel system performance index of the vehicle;
the step S11 specifically includes: acquiring an oil tank liquid level signal, an oil tank pressure signal, an oil rail pressure signal and an oil pump current signal corresponding to a vehicle monitoring requirement from the ECU electric control signal on the ECU;
the step S12 specifically includes: extracting fuel system performance indexes corresponding to a fuel tank liquid level signal, a fuel tank pressure signal, a fuel rail pressure signal and an oil pump current signal according to a pre-established component performance model, wherein the fuel system performance indexes comprise two sub-indexes: fuel tank pressure indicators and fueling system safety indicators; the component performance model is established according to a regression algorithm and a fitting algorithm;
the step S13 specifically includes: and expanding two bytes on a bus data frame, and respectively filling the oil tank pressure index and the refueling system safety index into one byte according to the sequence.
Referring to fig. 4, after the ECU processes the ECU electrical control signal, converts the ECU electrical control signal from a high frequency signal into a low frequency signal, expands the volume of the low frequency signal, deploys the expanded bus data frame to the CAN network, and the TBOX acquires the expanded bus data frame through the CAN network, generates a vehicle-mounted monitoring signal, and uploads the vehicle-mounted monitoring signal to the cloud platform.
As shown in fig. 5, an embodiment of the present invention provides an in-vehicle data processing system, including:
an acquisition unit 21 configured to acquire an ECU high-frequency signal corresponding to a vehicle monitoring demand;
the conversion unit 22 is used for converting the ECU high-frequency signals into various low-frequency performance indexes corresponding to models according to the monitoring models;
the capacity expansion unit 23 is configured to fill the various low-frequency performance indexes into capacity expansion bytes in a bus data frame according to the category of the low-frequency performance index;
and the monitoring and transmission unit 24 is configured to send the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, and the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform, so that the cloud platform monitors a vehicle state according to the vehicle-mounted monitoring signal.
Further, the monitoring model is obtained by training through a corresponding functional algorithm according to historical ECU high-frequency signals, and comprises one or more of a user driving model, a dynamic general working condition model and a component performance model;
the functional algorithms include one or more of the following algorithms: linear regression algorithms, decision tree algorithms, neural network algorithms, integration algorithms, hierarchical clustering algorithms, and spectral clustering algorithms.
Furthermore, each monitoring model correspondingly generates a corresponding category low-frequency performance index, and a capacity expansion byte is set for each category of low-frequency performance index data on a bus data frame;
the capacity expansion unit 23 is specifically configured to: and filling the low-frequency performance index data of the corresponding category into the corresponding bytes of the bus data frame.
Further, the obtaining unit 21 is specifically configured to: acquiring a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a gear signal and an altitude signal corresponding to the monitoring requirement from the ECU electric control signal on the ECU;
the conversion unit 22 is specifically configured to: according to a pre-established user driving model, extracting user portrait indexes corresponding to a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal, wherein the user portrait indexes comprise four sub indexes: a dynamic index, an economic index, a driving style index and a driver classification index; the user driving model is established according to a neural network;
the capacity expansion unit 23 is specifically configured to: expanding four bytes on a bus data frame, and respectively filling the dynamic index, the economic index, the driving style index and the driver classification index into the four expanded bytes on the bus data frame.
The implementation of the invention has the following beneficial effects:
according to the invention, the ECU converts the high-frequency electric control signal in the ECU into the low-frequency performance index, the low-frequency performance index can be filled into a small number of bytes by expanding or changing the bus signal into a small number of bytes, the expanded bus signal is sent to the vehicle-mounted communication module TBOX, and the TBOX acquires the expanded or changed bus signal and transmits the expanded or changed bus signal to the cloud server, so that the cloud server can obtain the low-frequency performance index; the problem of current vehicle controller increase more and more, interactive signal is numerous, can't pass through bus monitoring ECU electrical control signal is solved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A vehicle-mounted data processing method is characterized by comprising the following steps:
step S11, acquiring an ECU high-frequency signal corresponding to the vehicle monitoring requirement;
step S12, converting the ECU high-frequency signal into various low-frequency performance indexes corresponding to the model according to the monitoring model;
step S13, filling the low-frequency performance indexes into expansion bytes in a bus data frame according to the types of the low-frequency performance indexes;
and S14, sending the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, wherein the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform, so that the cloud platform can monitor the vehicle state according to the vehicle-mounted monitoring signal.
2. The method of claim 1, wherein the monitoring model is obtained by training a corresponding functional algorithm according to historical ECU high-frequency signals, and the monitoring model comprises one or more of a user driving model, a dynamic general working condition model and a component performance model;
the functional algorithms include one or more of the following algorithms: linear regression algorithms, decision tree algorithms, neural network algorithms, integration algorithms, hierarchical clustering algorithms, and spectral clustering algorithms.
3. The method of claim 2, wherein each monitoring model generates a corresponding class of low frequency performance indicators, and a capacity byte is set on the bus data frame for each class of low frequency performance indicator data;
step S13 specifically includes: and filling the low-frequency performance index data of the corresponding category into the corresponding bytes of the bus data frame.
4. The method according to claim 3, wherein the step S11 specifically includes: acquiring a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a gear signal and an altitude signal corresponding to the vehicle monitoring requirement from the ECU electric control signal on the ECU;
the step S12 specifically includes: according to a pre-established user driving model, extracting user portrait indexes corresponding to a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal, wherein the user portrait indexes comprise four sub indexes: a dynamic index, an economic index, a driving style index and a driver classification index; the user driving model is established according to a neural network;
the step S13 specifically includes: expanding four bytes on a bus data frame, and respectively filling the dynamic index, the economic index, the driving style index and the driver classification index into the four expanded bytes on the bus data frame.
5. The method according to claim 3, wherein the step S11 specifically includes: acquiring an engine speed signal, an engine torque signal and a transmission gear signal corresponding to a vehicle monitoring requirement from the ECU electric control signal on the ECU, and acquiring an engine oil injection signal and an engine ignition signal corresponding to the vehicle monitoring requirement from the actuator signal;
the step S12 specifically includes: according to a pre-established dynamic total working condition model, power assembly performance indexes corresponding to an engine rotating speed signal, an engine torque signal, an engine oil injection signal, an engine ignition signal and a transmission gear signal are extracted, wherein the power assembly performance indexes comprise two sub indexes: an engine fuel saving index and a transmission gear shifting frequency index; the dynamic total working condition model is established according to a regression algorithm and a fitting algorithm;
the step S13 specifically includes: and expanding two bytes on the bus data frame, and respectively filling the engine oil saving index and the transmission gear shifting frequency index into the expanded byte on the bus data frame according to the sequence.
6. The method according to claim 3, wherein the step S11 specifically includes: acquiring an oil tank liquid level signal, an oil tank pressure signal, an oil rail pressure signal and an oil pump current signal corresponding to a vehicle monitoring requirement from the ECU electric control signal on the ECU;
the step S12 specifically includes: extracting fuel system performance indexes corresponding to a fuel tank liquid level signal, a fuel tank pressure signal, a fuel rail pressure signal and an oil pump current signal according to a pre-established component performance model, wherein the fuel system performance indexes comprise two sub-indexes: fuel tank pressure indicators and fueling system safety indicators; the component performance model is established according to a regression algorithm and a fitting algorithm;
the step S13 specifically includes: and expanding two bytes on the bus data frame, and respectively filling the oil tank pressure index and the refueling system safety index into the expanded byte on the bus data frame according to the sequence.
7. An in-vehicle data processing system, the system comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring an ECU high-frequency signal corresponding to a vehicle monitoring demand;
the conversion unit is used for converting the ECU high-frequency signal into various low-frequency performance indexes corresponding to the model according to the monitoring model;
the capacity expansion unit is used for respectively filling the various low-frequency performance indexes into capacity expansion bytes in a bus data frame according to the category of the low-frequency performance indexes;
and the monitoring and transmission unit is used for sending the bus data frame to a vehicle-mounted communication module through a vehicle-mounted bus, and the vehicle-mounted communication module generates a vehicle-mounted monitoring signal according to the bus data frame and uploads the vehicle-mounted monitoring signal to a cloud platform so that the cloud platform can monitor the vehicle state according to the vehicle-mounted monitoring signal.
8. The system of claim 7, wherein the monitoring model is obtained by training a corresponding functional algorithm according to historical ECU high-frequency signals, and the monitoring model comprises one or more of a user driving model, a dynamic general working condition model and a component performance model;
the functional algorithms include one or more of the following algorithms: linear regression algorithms, decision tree algorithms, neural network algorithms, integration algorithms, hierarchical clustering algorithms, and spectral clustering algorithms.
9. The system of claim 8, wherein each monitoring model generates a corresponding class of low frequency performance indicators, and a capacity expansion byte is set on the bus data frame for each class of low frequency performance indicator data;
the capacity expansion unit is specifically configured to: and filling the low-frequency performance index data of the corresponding category into the corresponding bytes of the bus data frame.
10. The system of claim 9, wherein the obtaining unit is specifically configured to: acquiring a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel angle signal, a gear signal and an altitude signal corresponding to the monitoring requirement from the ECU electric control signal on the ECU;
the conversion unit is specifically configured to: according to a pre-established user driving model, extracting user portrait indexes corresponding to a vehicle speed signal, an accelerator signal, a brake signal, an acceleration signal, a steering wheel corner signal, a gear signal and an altitude signal, wherein the user portrait indexes comprise four sub indexes: a dynamic index, an economic index, a driving style index and a driver classification index; the user driving model is established according to a neural network;
the capacity expansion unit is specifically configured to: expanding four bytes on a bus data frame, and respectively filling the dynamic index, the economic index, the driving style index and the driver classification index into the four expanded bytes on the bus data frame.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114333099A (en) * | 2021-12-10 | 2022-04-12 | 华人运通(上海)云计算科技有限公司 | Vehicle use condition monitoring method and device and vehicle |
| CN114401286A (en) * | 2021-12-21 | 2022-04-26 | 江西五十铃汽车有限公司 | Data uploading method and system based on vehicle-mounted terminal, storage medium and vehicle |
| CN114861516A (en) * | 2021-02-04 | 2022-08-05 | 广州汽车集团股份有限公司 | Vehicle dynamic performance determination method and device and storage medium |
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