CN115184029A - Intelligent engine product based on Internet of things - Google Patents
Intelligent engine product based on Internet of things Download PDFInfo
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- CN115184029A CN115184029A CN202210765109.9A CN202210765109A CN115184029A CN 115184029 A CN115184029 A CN 115184029A CN 202210765109 A CN202210765109 A CN 202210765109A CN 115184029 A CN115184029 A CN 115184029A
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- 238000012423 maintenance Methods 0.000 claims abstract description 29
- 238000005457 optimization Methods 0.000 claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 7
- 230000008569 process Effects 0.000 claims abstract description 6
- 230000008929 regeneration Effects 0.000 claims description 22
- 238000011069 regeneration method Methods 0.000 claims description 22
- 239000010705 motor oil Substances 0.000 claims description 19
- 239000002826 coolant Substances 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000009423 ventilation Methods 0.000 claims description 3
- 239000002245 particle Substances 0.000 description 8
- 239000004071 soot Substances 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000001680 brushing effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001590 oxidative effect Effects 0.000 description 2
- 230000002028 premature Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000002283 diesel fuel Substances 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
- F02B77/00—Component parts, details or accessories, not otherwise provided for
- F02B77/08—Safety, indicating, or supervising devices
- F02B77/083—Safety, indicating, or supervising devices relating to maintenance, e.g. diagnostic device
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
- H04L2012/40208—Bus networks characterized by the use of a particular bus standard
- H04L2012/40215—Controller Area Network CAN
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Vehicle Cleaning, Maintenance, Repair, Refitting, And Outriggers (AREA)
Abstract
The invention relates to an intelligent engine product based on Internet of things, wherein a vehicle-mounted terminal is communicated with an ECU (electronic control unit) through a CAN (controller area network) bus, the vehicle-mounted terminal is communicated with a big data platform, and the vehicle-mounted terminal sends engine operation data collected from the ECU to the big data platform through the Internet of things; the large data platform analyzes real-time data of the engine, counts the data from different dimensions, learns a machine algorithm, identifies and judges the current service performance of the engine and the hidden faults of the engine, realizes pre-judgment analysis, optimizes the existing calibration strategy of the engine according to the pre-judgment analysis and sends the optimized calibration strategy back to the ECU through the vehicle-mounted terminal, and the ECU updates the optimized calibration strategy through an upgrading instruction of the large data platform; and constructing user maintenance information for the analyzed fault pre-judgment analysis and sending the user maintenance information to the user. The remote and batch safe upgrading can be realized. The upgrading process is simplified, and the product performance optimization iteration efficiency is improved; the influence on the vehicle utilization of the user is reduced while the service cost is greatly reduced.
Description
Technical Field
The invention relates to an engine performance maintenance technology, in particular to an intelligent engine product based on Internet of things.
Background
The traditional engine has no function of the internet of things, the engine is mainly controlled by an ECU (electronic control unit), and manufacturers and users are difficult to know real-time running states and use conditions of vehicles and engines. The market performance of the product is difficult to obtain, the service efficiency is low, and the iteration period of the product performance is long. And the product advantage is difficult to be reflected in the intense market competition.
The market performance of new products after marketing is mainly analyzed based on user repair; the period from the occurrence of problems and the reporting of the problems to the solution of the problems is long, the cases are dispersed, the causes of the problems cannot be quickly positioned, and the improvement iteration is carried out.
In the use process of the existing vehicle, a user does not know whether the driving habit of the user causes the common working condition of the engine to be out of the economic oil consumption area of the engine or not.
At present, the replacement of an air filter, an engine oil filter, a diesel oil filter and engine oil of a vehicle can only be operated according to the timing and the fixed mileage on a maintenance specification, and a maintenance instruction on the vehicle is only obtained by simply accumulating the time and the mileage and is not obtained after the actual state of the vehicle is analyzed.
If the calibration data of the existing engine and the functions of the whole vehicle need to be upgraded, one service person needs to go to the site, the service cost is high, and the working efficiency of a user is influenced.
Disclosure of Invention
Aiming at the problem of maintaining the performance of the vehicle engine, an intelligent engine product based on Internet of things is provided.
The technical scheme of the invention is as follows: an intelligent engine product based on Internet of things is characterized in that an ECU acquires engine operation data in real time through each intelligent sensor of an engine, and the ECU controls the operation state of the engine; the vehicle-mounted terminal is communicated with the ECU through a CAN bus to collect real-time running data of the engine, the vehicle-mounted terminal is communicated with the big data platform, and the vehicle-mounted terminal sends the running data of the engine to the big data platform through the Internet of things; the large data platform analyzes real-time data of the engine, counts the data from different dimensions, learns a machine algorithm, identifies and judges the current service performance of the engine and the existence of invisible faults of the engine, realizes pre-judgment analysis, optimizes the existing calibration strategy of the engine according to the pre-judgment analysis, sends the optimized calibration strategy obtained by the large data platform back to the ECU through the vehicle-mounted terminal, and the ECU updates the optimized calibration strategy through an upgrading instruction of the large data platform; and constructing user maintenance information for the analyzed fault pre-judgment analysis and sending the user maintenance information to the user.
Furthermore, the big data platform counts the running data of the engine, when the set ECU upgrading condition is met, the big data platform sends an upgrading instruction through the vehicle-mounted terminal, a user enters an upgrading state after confirming and finishes upgrading calibration data, and meanwhile the ECU state, the flashing time and the flashing result in the upgrading process are transmitted back to the big data platform.
Further, fault code data false alarm fault optimization in the calibration strategy optimization: the vehicle-mounted terminal collects engine operation data and sends the data to a big data platform, the number, proportion, type and specific fault content of fault codes in the data are analyzed, the obtained fault is judged to be a false alarm fault through analysis of technicians, the fault calibration strategy is modified, and the calibration strategy is optimized or the corresponding parameter data of the fault are updated.
Further, the actual fault improvement of fault code data in the calibration strategy optimization is as follows: the vehicle-mounted terminal collects engine operation data and sends the data to the big data platform, the number, proportion, type and specific fault content of fault codes in the data are analyzed, fault judgment that the pressure of a crankcase exceeds a reasonable range is obtained, technicians analyze whether an actual fault is the leakage of a ventilation pipeline of the crankcase and is caused by sensor pollution, the fault codes are updated, and a maintenance instruction for changing the arrangement position of the sensor to avoid pollution is sent out.
Further, the engine aftertreatment regeneration strategy optimization iteration during ECU upgrading: the driving mileage and the driving time are used as the input of a regeneration strategy, a large amount of regeneration data is obtained by utilizing platform data, and the relationship among the regeneration interval, the tail gas pressure difference and the flow rate during regeneration in the data is analyzed, so that the regeneration strategy is optimized and the remote upgrading optimization calibration is utilized.
Further, the maintenance information: the large data platform collects data of engine speed, engine oil temperature, coolant temperature and engine oil pressure in a user use state at regular time, a user use performance training database is established, user use performance calibration is obtained through database data learning, standard maintenance mileage and time corresponding to the vehicle type of the user is calibrated, optimized and corrected according to performance calibration in a user use scene, and information needing maintenance is pushed to a driver user.
Further, the maintenance information: the vehicle-mounted terminal collects engine operation data and sends the data to the big data platform, the big data platform determines the engine calibration rotating speed according to the engine type, judges whether the engine exceeds the rotating speed according to the rotating speed data size and the duration, records the time point, the place, the duration and the highest rotating speed of the engine exceeding the rotating speed, and sends the data to a user in a short message mode.
Further, the maintenance information: the vehicle-mounted terminal collects engine operation data and sends the data to the big data platform, the big data platform judges whether the engine oil pressure is too low according to the engine oil pressure data in a rated rotating speed interval, and records the time point, the place, the duration and the lowest engine oil pressure and sends the data to a user in a short message mode.
The invention has the beneficial effects that: the invention relates to an intelligent engine product based on Internet of things, which obtains real-time operation data of an engine by means of intelligent sensing, vehicle-mounted terminal TBOX, internet of things, 4G communication and the like; based on technologies such as intelligent network connection, big data and machine learning, the engine real-time state monitoring, the predictive health examination, the remote service and the fast iteration of product performance are realized, and the product value and the market competitiveness are improved.
Drawings
FIG. 1 is a schematic diagram of a product structure of an intelligent engine based on Internet of things;
FIG. 2 is a schematic diagram of 10 faults before the reporting times of each fault are arrayed, which are obtained by the product of the invention;
FIG. 3 is a schematic diagram of the top 10 failures of the ET series weekly ranking obtained by the product of the present invention;
FIG. 4 is a sample graph of the premature regeneration of the product of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
As shown in a schematic structural diagram of an intelligent engine product based on internet of things shown in fig. 1, an ECU collects engine operation data in real time through various intelligent sensors of the engine, and controls the operation state of the engine; the vehicle-mounted Terminal (TBOX) is communicated with the ECU through a CAN bus to collect real-time running data of the engine, the vehicle-mounted terminal is communicated with the big data platform, and the vehicle-mounted terminal sends the running data of the engine to the big data platform through the Internet of things. The large data platform analyzes real-time data of the engine, counts the data from different dimensions, learns a machine algorithm, identifies and judges the current service performance of the engine and the invisible faults of the engine, realizes pre-judgment analysis, optimizes the existing calibration strategy of the engine according to the pre-judgment analysis, sends the optimized calibration strategy obtained by the large data platform back to the ECU through the vehicle-mounted terminal, and the ECU updates the optimized calibration strategy through an upgrading instruction of the large data platform; and constructing user maintenance information for the analyzed fault pre-judgment analysis and sending the user maintenance information to a user, and realizing intelligent functions of intelligent maintenance reminding, part health early warning, part optimization suggestion, remote service, active iteration upgrade of product performance and the like of the engine. The related information directly reaches the user through short messages or micro services and the like, and timely and attentive service is provided for the user.
The method comprises the steps of collecting data of engine speed, engine oil temperature, coolant temperature and engine oil pressure in a user use state at regular time, constructing a user use performance training database, obtaining user use performance calibration through database data learning, optimizing and correcting standard maintenance mileage and time corresponding to a vehicle type according to performance calibration in a user use scene, achieving the purpose of maintenance as required, pushing information needing maintenance to a driver user, and ensuring that an engine is always in a good working state. The problems of cost increase caused by early maintenance and engine damage caused by late maintenance are avoided.
Remote upgrading of engine calibration data: and establishing an upgrading instruction through a big data platform based on the requirement of upgrading the engine calibration data. When the big data platform monitors that the ECU upgrading condition is met, the ECU enters an upgrading state and finishes upgrading the calibration data after the driver confirms the action. Meanwhile, the ECU state, the brushing time and the brushing result in the upgrading process are returned to the big data platform. The display of the upgradeable unit and the display of the historical instructions are shown in tables 1 and 2:
TABLE 1
TABLE 2
Fault code data false alarm fault optimization: TBOX collects engine operation data, and sends the data to a big data platform, the number, proportion, type and specific fault content of fault codes in the data are analyzed, and aiming at the fault code P04007CEGR valve response slow fault, technicians analyze that the fault is a false alarm fault and is a calibration strategy problem, and the problem can be solved by optimizing the calibration strategy or updating and upgrading corresponding parameter data of the fault. The failure 10 before each failure report number is ranked as shown in fig. 2.
Failure code data actual failure improved generation: TBOX collects engine operation data, and sends the data to a big data platform, the number, proportion, type and specific fault content of fault codes in the data are analyzed, and for faults that the pressure of a crankcase exceeds a reasonable range, technicians analyze whether an actual fault is the leakage of a ventilation pipeline of the crankcase, which is caused by sensor pollution, update the fault codes, and avoid pollution by changing the arrangement position of the sensors, so that the problem can be solved. The ET series failed top 10 week rank as shown in figure 3.
And (3) fast optimizing and iterating a post-treatment regeneration strategy of the national six-engine: the exhaust aftertreatment module of the national six-engine has the advantages that the purpose of reducing the emission of particles in exhaust is achieved by heating and oxidizing the soot particles in the exhaust, the action of heating and oxidizing the soot particles in the exhaust is called regeneration, the aftertreatment module is provided with a particle catcher to capture the soot particles of the engine, the efficiency of the engine is reduced due to too much soot particles, the fuel consumption is high and uneconomic due to frequent regeneration when the soot particles are few, and the driving mileage and the driving time are used as the input of a regeneration strategy in the strategy because the soot particles in the prior art can not be measured. And acquiring a large amount of regeneration data by using the platform data, and analyzing the relationship among regeneration intervals, tail gas pressure difference during regeneration and flow in the data, thereby optimizing a regeneration strategy and optimizing calibration by using a remote upgrading function. The product regeneration is shown in figure 4 as a premature sample graph.
Engine overspeed reminding: the TBOX acquires engine operation data and sends the data to a big data platform, the big data platform determines the engine calibration rotating speed according to the engine type, judges whether the engine exceeds the rotating speed according to the rotating speed data size and the duration, records the time point, the place, the duration and the highest rotating speed of the engine exceeding the rotating speed and sends the data to a user in a short message mode, and some sensitive information is hidden.
Engine oil pressure is low to remind: the TBOX acquires engine operation data and sends the data to a big data platform, the big data platform judges whether the engine oil pressure is too low according to the engine oil pressure data in a rated rotating speed interval, and records the time point, the place, the duration and the lowest engine oil pressure and sends the data to a user in a short message mode, so that sensitive information is hidden.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. An intelligent engine product based on Internet of things is characterized in that an ECU acquires engine operation data in real time through each intelligent sensor of an engine, and the ECU controls the operation state of the engine; the vehicle-mounted terminal is communicated with the ECU through a CAN bus to collect real-time running data of the engine, the vehicle-mounted terminal is communicated with the big data platform, and the vehicle-mounted terminal sends the running data of the engine to the big data platform through the Internet of things; the large data platform analyzes real-time data of the engine, counts the data from different dimensions, learns a machine algorithm, identifies and judges the current service performance of the engine and the invisible faults of the engine, realizes pre-judgment analysis, optimizes the existing calibration strategy of the engine according to the pre-judgment analysis, sends the optimized calibration strategy obtained by the large data platform back to the ECU through the vehicle-mounted terminal, and the ECU updates the optimized calibration strategy through an upgrading instruction of the large data platform; and (4) constructing user maintenance information for the analyzed fault pre-judgment analysis and sending the user maintenance information to a user.
2. The intelligent engine product based on the internet of things as claimed in claim 1, wherein the big data platform counts engine operating data, when the set ECU upgrading condition is met, the big data platform sends an upgrading instruction through the vehicle-mounted terminal, a user enters an upgrading state after confirming and finishes upgrading of calibration data, and meanwhile, the ECU state, the flashing time and the flashing result in the upgrading process are transmitted back to the big data platform.
3. The intelligent internet-of-things-based engine product of claim 1, wherein fault code data false alarm fault optimization in the calibration strategy optimization is as follows: the vehicle-mounted terminal collects engine operation data and sends the data to a big data platform, the number, proportion, type and specific fault content of fault codes in the data are analyzed, the obtained fault is judged to be a false alarm fault through analysis of technicians, the fault calibration strategy is modified, and the calibration strategy is optimized or the corresponding parameter data of the fault are updated.
4. The intelligent internet-of-things-based engine product of claim 1, wherein fault code data actual fault improvement in the calibration strategy optimization is as follows: the vehicle-mounted terminal collects engine operation data and sends the data to the big data platform, the number, proportion, type and specific fault content of fault codes in the data are analyzed, fault judgment that the pressure of a crankcase exceeds a reasonable range is obtained, technicians analyze whether an actual fault is the leakage of a ventilation pipeline of the crankcase and is caused by sensor pollution, the fault codes are updated, and a maintenance instruction for changing the arrangement position of the sensor to avoid pollution is sent out.
5. The internet-of-things-based smart engine product of claim 2, wherein the engine aftertreatment regeneration strategy optimization iteration in the ECU upgrade: the driving mileage and the driving time are used as the input of a regeneration strategy, a large amount of regeneration data is obtained by utilizing platform data, and the relationship among the regeneration interval, the tail gas pressure difference and the flow rate during regeneration in the data is analyzed, so that the regeneration strategy is optimized and the remote upgrading optimization calibration is utilized.
6. The internet-of-things-based smart engine product of claim 1, wherein the maintenance information: the large data platform collects data of engine speed, engine oil temperature, coolant temperature and engine oil pressure in a user use state at regular time, a user use performance training database is established, user use performance calibration is obtained through database data learning, standard maintenance mileage and time corresponding to the vehicle type of the user is calibrated, optimized and corrected according to performance calibration in a user use scene, and information needing maintenance is pushed to a driver user.
7. The internet-of-things-based smart engine product of claim 1, wherein the maintenance information: the vehicle-mounted terminal collects engine operation data and sends the data to the big data platform, the big data platform determines the engine calibration rotating speed according to the engine type, judges whether the engine exceeds the rotating speed according to the rotating speed data size and the duration, records the time point, the place, the duration and the highest rotating speed of the engine exceeding the rotating speed, and sends the data to a user in a short message mode.
8. The smart internet-based engine product of claim 1, wherein the maintenance information: the vehicle-mounted terminal collects engine operation data and sends the engine operation data to the big data platform, the big data platform judges whether the engine oil pressure is too low according to the engine oil pressure data in a rated rotating speed interval, and records the occurrence time point, place, duration and lowest engine oil pressure and sends the engine oil pressure to a user in a short message mode.
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2022
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