CN113329063A - Method for judging possible sulfur poisoning of SCR (selective catalytic reduction) based on big data - Google Patents
Method for judging possible sulfur poisoning of SCR (selective catalytic reduction) based on big data Download PDFInfo
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- CN113329063A CN113329063A CN202110511367.XA CN202110511367A CN113329063A CN 113329063 A CN113329063 A CN 113329063A CN 202110511367 A CN202110511367 A CN 202110511367A CN 113329063 A CN113329063 A CN 113329063A
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 231100000572 poisoning Toxicity 0.000 title claims abstract description 25
- 230000000607 poisoning effect Effects 0.000 title claims abstract description 25
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 title claims abstract description 24
- 229910052717 sulfur Inorganic materials 0.000 title claims abstract description 24
- 239000011593 sulfur Substances 0.000 title claims abstract description 24
- 238000010531 catalytic reduction reaction Methods 0.000 title description 2
- 239000007788 liquid Substances 0.000 claims abstract description 10
- 238000004891 communication Methods 0.000 claims description 9
- 230000008439 repair process Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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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
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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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
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
The invention discloses a method for judging possible sulfur poisoning of SCR based on big data, which comprises the following steps: and the data platform acquires the position of the vehicle in real time through the vehicle-mounted terminal. The data platform obtains the standard gas station location in real time. When the ECU identifies that the liquid level of the vehicle oil tank is suddenly increased, a liquid level sudden increase signal is sent to the data platform through the vehicle-mounted terminal. The data platform determines whether the current vehicle location is near a standard gas station location. And if the current vehicle position is not near the standard gas station position, the data platform sends an alarm signal to the vehicle-mounted terminal. Therefore, the method for judging possible sulfur poisoning of SCR based on big data has high judgment accuracy and does not increase cost.
Description
Technical Field
The invention relates to the technical field of diesel engine aftertreatment SCR, in particular to a method for judging possible sulfur poisoning of SCR based on big data.
Background
There is no effective pre-determination method for sulfur poisoning of pre-diesel aftertreatment SCR. Most of the methods monitor that the SCR conversion efficiency is obviously reduced, and judge and process the SCR after the SCR is invalid, so that the accuracy is poor and the efficiency is low.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention aims to provide a method for judging possible sulfur poisoning of SCR based on big data, which has high judgment accuracy and does not increase the cost.
In order to achieve the above object, the present invention provides a method for determining possible sulfur poisoning of an SCR based on big data, comprising: and the data platform acquires the position of the vehicle in real time through the vehicle-mounted terminal. The data platform obtains the standard gas station location in real time. When the ECU identifies that the liquid level of the vehicle oil tank is suddenly increased, a liquid level sudden increase signal is sent to the data platform through the vehicle-mounted terminal. The data platform determines whether the current vehicle location is near a standard gas station location. And if the current vehicle position is not near the standard gas station position, the data platform sends an alarm signal to the vehicle-mounted terminal.
In one embodiment of the invention, the data platform is in communication connection with the in-vehicle terminal, and the in-vehicle terminal is in communication connection with the ECU.
In one embodiment of the invention, the data platform is in wireless communication connection with the vehicle-mounted terminal.
In one embodiment of the present invention, the in-vehicle terminal incorporates a GPS.
In an embodiment of the invention, the data platform comprises a map server, a message server, a first-aid repair scheduling server and a communication server.
Compared with the prior art, the method for judging possible sulfur poisoning of SCR based on big data has high judgment accuracy and does not increase cost.
Drawings
FIG. 1 is a schematic flow diagram of a method for determining possible sulfur poisoning of an SCR based on big data according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a system architecture in which a method for determining possible sulfur poisoning of an SCR based on big data is actually applied according to an embodiment of the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
Fig. 1 is a schematic flow chart of a method for determining possible sulfur poisoning of an SCR based on big data according to an embodiment of the present invention. Fig. 2 is a schematic diagram of a system architecture in which a method for determining possible sulfur poisoning of an SCR based on big data is actually applied according to an embodiment of the present invention.
As shown in fig. 1, a method for determining possible sulfur poisoning of an SCR based on big data according to a preferred embodiment of the present invention includes: and S1, the data platform 3 acquires the vehicle position in real time through the vehicle-mounted terminal 2. S2, the data platform 3 obtains the standard gas station location in real time. And S3, when the ECU identifies that the liquid level of the oil tank of the vehicle 1 is suddenly increased, the ECU sends a liquid level sudden increase signal to the data platform 3 through the vehicle-mounted terminal 2. S4, the data platform 3 determines whether the current vehicle location is near a standard gas station location. And S5, if the current vehicle position is not near the standard gas station position, the data platform 3 sends out alarm signal to the vehicle-mounted terminal 2.
As shown in fig. 2, in one embodiment of the present invention, the data platform 3 is communicatively connected to the in-vehicle terminal 2, and the in-vehicle terminal 2 is communicatively connected to the ECU. The data platform 3 is connected with the vehicle-mounted terminal 2 in a wireless communication mode. The in-vehicle terminal 2 incorporates a GPS. The data platform 3 comprises a map server, a message server, an emergency repair scheduling server and a communication server.
In practical application, the method for judging possible sulfur poisoning of SCR based on big data comprises the steps that the data platform 3 obtains real-time positioning of a vehicle through the vehicle-mounted terminal 2, meanwhile, the data platform 3 monitors the position of a related gas station in a vehicle operation area, when the ECU identifies that the liquid level of an oil tank is suddenly increased and the vehicle is not located near the position of the gas station in the vehicle operation area monitored by the data platform 3 in a time period before and after refueling, illegal refueling can be identified, the possibility of SCR sulfur poisoning is triggered to be post-processed by a diesel engine, and a judgment result is output to SCR related control logic (through the vehicle-mounted terminal and the ECU) to be subjected to logic prevention processing in advance. The mobile base station sends information to the vehicle-mounted terminal 2(GPS), and the vehicle-mounted terminal 2 and the data platform 3 can realize information interaction. In this way, the vehicle positioning and the starting control command can be acquired and sent to the vehicle-mounted terminal 2, and the like. The data platform 3 may also be in communication connection with a dealer, a complete vehicle factory, or a used vehicle sales, etc. (by way of example only), and may also transmit an abnormal warning signal of the vehicle 1 to persons associated with the current vehicle 1.
In summary, the method for judging possible sulfur poisoning of SCR based on big data of the present invention has the following beneficial effects:
1. the identification is accurate: the information of the big data platform 3 and the ECU are utilized to identify the liquid level change of the oil tank, whether the normal refueling behavior is adopted or not is comprehensively judged, and the possibility of triggering the post-treatment SCR sulfur poisoning of the diesel engine due to abnormal refueling is caused.
2. The cost is not increased: the software strategy can be updated without increasing the hardware cost.
3. The judgment accuracy is high: the ECU is solely used for judging whether S poisoning occurs or not, the accuracy is poor, the SCR sulfur poisoning is judged after abnormal refueling behavior is judged in advance, and the accuracy is higher.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (5)
1. A method for determining possible sulfur poisoning of an SCR based on big data, comprising:
the data platform acquires the position of the vehicle in real time through the vehicle-mounted terminal;
the data platform acquires the position of a standard gas station in real time;
when the ECU identifies that the liquid level of a vehicle oil tank is suddenly increased, a liquid level sudden-increase signal is sent to the data platform through the vehicle-mounted terminal;
the data platform judges whether the current vehicle position is near the standard gas station position; and
and if the current vehicle position is not near the standard gas station position, the data platform sends an alarm signal to the vehicle-mounted terminal.
2. The method for determining possible sulfur poisoning of an SCR according to claim 1, wherein said data platform is communicatively coupled to said vehicle terminal, and said vehicle terminal is communicatively coupled to said ECU.
3. The method for determining possible sulfur poisoning of an SCR based on big data as claimed in claim 2, wherein said data platform is in wireless communication with said vehicle terminal.
4. The method for determining possible sulfur poisoning of an SCR based on big data as claimed in claim 1, wherein the vehicle-mounted terminal has a GPS built therein.
5. The method for determining possible sulfur poisoning of an SCR based on big data as claimed in claim 1, wherein said data platform comprises a map server, a message server, a rush repair dispatch server and a communication server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110511367.XA CN113329063A (en) | 2021-05-11 | 2021-05-11 | Method for judging possible sulfur poisoning of SCR (selective catalytic reduction) based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110511367.XA CN113329063A (en) | 2021-05-11 | 2021-05-11 | Method for judging possible sulfur poisoning of SCR (selective catalytic reduction) based on big data |
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| Publication Number | Publication Date |
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| CN113329063A true CN113329063A (en) | 2021-08-31 |
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| CN202110511367.XA Pending CN113329063A (en) | 2021-05-11 | 2021-05-11 | Method for judging possible sulfur poisoning of SCR (selective catalytic reduction) based on big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114890367A (en) * | 2022-03-25 | 2022-08-12 | 潍柴动力股份有限公司 | Method and device for determining refueling position of vehicle |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016135532A1 (en) * | 2015-02-26 | 2016-09-01 | Delfín-Cortés Pedro | System to verify the quantity and quality of fuel received in vehicles and automotors |
| CN111507864A (en) * | 2020-04-29 | 2020-08-07 | 北理新源(佛山)信息科技有限公司 | Gas station type determination method and system based on GuoLiu intelligent vehicle-mounted terminal |
| CN112333663A (en) * | 2020-10-30 | 2021-02-05 | 广州亚美信息科技有限公司 | Black oil filling point determining method and device |
-
2021
- 2021-05-11 CN CN202110511367.XA patent/CN113329063A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016135532A1 (en) * | 2015-02-26 | 2016-09-01 | Delfín-Cortés Pedro | System to verify the quantity and quality of fuel received in vehicles and automotors |
| CN111507864A (en) * | 2020-04-29 | 2020-08-07 | 北理新源(佛山)信息科技有限公司 | Gas station type determination method and system based on GuoLiu intelligent vehicle-mounted terminal |
| CN112333663A (en) * | 2020-10-30 | 2021-02-05 | 广州亚美信息科技有限公司 | Black oil filling point determining method and device |
Non-Patent Citations (1)
| Title |
|---|
| 殷振波等: "《汽车维修电工（技师）》", 31 July 2017 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114890367A (en) * | 2022-03-25 | 2022-08-12 | 潍柴动力股份有限公司 | Method and device for determining refueling position of vehicle |
| CN114890367B (en) * | 2022-03-25 | 2024-04-16 | 潍柴动力股份有限公司 | Method and device for determining refueling position of vehicle |
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Application publication date: 20210831 |