CN111546890A - Method for identifying truck overload behavior based on Internet of vehicles terminal - Google Patents
Method for identifying truck overload behavior based on Internet of vehicles terminal Download PDFInfo
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- CN111546890A CN111546890A CN202010426900.8A CN202010426900A CN111546890A CN 111546890 A CN111546890 A CN 111546890A CN 202010426900 A CN202010426900 A CN 202010426900A CN 111546890 A CN111546890 A CN 111546890A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
- B60K28/00—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions
- B60K28/08—Safety devices for propulsion-unit control, specially adapted for, or arranged in, vehicles, e.g. preventing fuel supply or ignition in the event of potentially dangerous conditions responsive to conditions relating to the cargo, e.g. overload
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
- H04B1/38—Transceivers, i.e. devices in which transmitter and receiver form a structural unit and in which at least one part is used for functions of transmitting and receiving
- H04B1/40—Circuits
Abstract
The invention provides a method for identifying overload behaviors of trucks based on a vehicle networking terminal, which comprises the following steps: 1) the hardware deployment is used for realizing the real-time monitoring of the truck information; 2) acquiring data information of the vehicle by using the internet of vehicles terminal, and transmitting the data information to the monitoring center database; 3) and the database combines the vehicle data information of the same model and utilizes a neural network model to intelligently identify whether the truck is overloaded or not. The method for identifying the overload behavior of the truck based on the Internet of vehicles terminal can detect whether the vehicle is overloaded or not in real time and monitor whether the vehicle is overspeed or not through the installation of the Internet of vehicles terminal, thereby achieving multiple purposes.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation, and particularly relates to a method for identifying overload behaviors of a truck based on a terminal of an internet of vehicles.
Background
According to the definition of China's school alliance of Internet of things, the Internet of Vehicles (Internet of Vehicles) is a huge interactive network formed by information such as vehicle position, speed and route. The vehicle can complete the collection of self environment and state information through devices such as a GPS, an RFID, a sensor, a camera image processing device and the like; through the internet technology, all vehicles can transmit and gather various information of the vehicles to the central processing unit; through computer technology, the information of a large number of vehicles can be analyzed and processed, so that the optimal routes of different vehicles can be calculated, road conditions can be reported in time, and signal lamp periods can be arranged.
With the rapid development of artificial intelligence and communication technology, it is not a fantasy to realize intelligent identification for truck overload by using a vehicle networking terminal, and the patent application provides a method for identifying truck overload behaviors by using the vehicle networking terminal.
Disclosure of Invention
In view of this, in order to overcome the problem that the existing truck cannot be monitored in real time and whether the truck is overloaded or not cannot be identified with high efficiency, the invention aims to provide a method for identifying truck overload behaviors based on a vehicle networking terminal.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a method for identifying truck overload behaviors based on a vehicle networking terminal comprises the following steps:
1) the hardware deployment is used for realizing the real-time monitoring of the truck information;
2) acquiring data information of the vehicle by using the internet of vehicles terminal, and transmitting the data information to the monitoring center database;
3) and the database combines the vehicle data information of the same model and utilizes a neural network model to intelligently identify whether the truck is overloaded or not.
Further, the specific implementation method of step 1 is as follows:
the method comprises the steps that a vehicle networking terminal is installed on a truck, the vehicle networking terminal is connected with a vehicle controller and used for obtaining data information of the vehicle, and the vehicle networking terminal sends the data information to a monitoring center database through a wireless communication device.
Further, the data information comprises the vehicle model, the service life of the vehicle, the unique identification of the truck and the fuel consumption of the vehicle.
Further, the fuel consumption of the vehicle comprises fuel consumption of nearly 10 kilometers, fuel consumption of nearly 20 kilometers and fuel consumption of nearly 30 kilometers.
Further, still be equipped with mike and megaphone on the car networking terminal, the surveillance center of being convenient for carries out real-time communication through car networking terminal and navigating mate.
Further, in step 3, the database is executed as follows:
301. the database acquires the oil consumption per ten kilometers in the form process of the nearest end, and then the data with larger difference at the two ends are omitted to obtain the average value of the oil consumption required per ten kilometers;
302. the database combines the neural network model to obtain the average oil consumption of the vehicle of the model used for similar time according to the model of the vehicle and the year of the vehicle;
303. comparing the fuel consumption of the vehicle with the average fuel consumption, and determining whether the difference exceeds a set threshold value, if so, executing step S304; if not, continuing to execute the step S301;
304. and if the overload of the delivery vehicle is identified, the unique identification number of the vehicle and the model of the Internet of vehicles terminal are sent to the monitoring center, and the monitoring center is communicated with the Internet of vehicles terminal.
Further, the unique identification number of the vehicle includes, but is not limited to, a frame number of the vehicle.
Compared with the prior art, the method for identifying the overload behavior of the truck based on the internet of vehicles terminal has the following advantages:
(1) the method for identifying the overload behavior of the truck based on the Internet of vehicles terminal can detect whether the vehicle is overloaded or not in real time and monitor whether the vehicle is overspeed or not through the installation of the Internet of vehicles terminal, thereby achieving multiple purposes.
(2) The method for identifying the truck overload behavior based on the internet of vehicles terminal combines the deep learning neural network model, and improves the detection accuracy.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a method for identifying a truck overload behavior based on a terminal in a car networking system according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. 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," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, a method for identifying truck overload behavior based on a terminal in a car networking system includes:
1) the hardware deployment is used for realizing the real-time monitoring of the truck information;
2) acquiring data information of the vehicle by using the internet of vehicles terminal, and transmitting the data information to the monitoring center database;
3) and the database combines the vehicle data information of the same model and utilizes a neural network model to intelligently identify whether the truck is overloaded or not.
The specific implementation method of the step 1 is as follows:
the method comprises the steps that a vehicle networking terminal is installed on a truck, the vehicle networking terminal is connected with a vehicle controller and used for obtaining data information of the vehicle, and the vehicle networking terminal sends the data information to a monitoring center database through a wireless communication device.
The data information comprises the vehicle model, the service life of the vehicle, the unique identification of the truck and the fuel consumption of the vehicle.
The oil consumption of the vehicle comprises oil consumption of nearly 10 kilometers, oil consumption of nearly 20 kilometers and oil consumption of nearly 30 kilometers.
Still be equipped with mike and megaphone on the car networking terminal, the surveillance center of being convenient for carries out real-time communication through car networking terminal and navigating mate.
In step 3, the execution method of the database is as follows:
301. the database acquires the oil consumption per ten kilometers in the form process of the nearest end, and then the data with larger difference at the two ends are omitted to obtain the average value of the oil consumption required per ten kilometers;
302. the database combines the neural network model to obtain the average oil consumption of the vehicle of the model used for similar time according to the model of the vehicle and the year of the vehicle;
303. comparing the fuel consumption of the vehicle with the average fuel consumption, and determining whether the difference exceeds a set threshold value, if so, executing step S304; if not, continuing to execute the step S301;
304. and if the overload of the delivery vehicle is identified, the unique identification number of the vehicle and the model of the Internet of vehicles terminal are sent to the monitoring center, and the monitoring center is communicated with the Internet of vehicles terminal.
The unique identification number of the vehicle includes, but is not limited to, the frame number of the vehicle.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A method for identifying truck overload behaviors based on a vehicle networking terminal is characterized by comprising the following steps:
1) the hardware deployment is used for realizing the real-time monitoring of the truck information;
2) acquiring data information of the vehicle by using the internet of vehicles terminal, and transmitting the data information to the monitoring center database;
3) and the database combines the vehicle data information of the same model and utilizes a neural network model to intelligently identify whether the truck is overloaded or not.
2. The method for identifying the overload behavior of the truck based on the terminal in the internet of vehicles according to claim 1, wherein the specific implementation method of the step 1 is as follows:
the method comprises the steps that a vehicle networking terminal is installed on a truck, the vehicle networking terminal is connected with a vehicle controller and used for obtaining data information of the vehicle, and the vehicle networking terminal sends the data information to a monitoring center database through a wireless communication device.
3. The method for identifying the overload behavior of the truck based on the terminal of the internet of vehicles according to claim 2, wherein: the data information comprises the vehicle model, the service life of the vehicle, the unique identification of the truck and the fuel consumption of the vehicle.
4. The method for identifying the overload behavior of the truck based on the terminal of the internet of vehicles according to claim 3, wherein: the oil consumption of the vehicle comprises oil consumption of nearly 10 kilometers, oil consumption of nearly 20 kilometers and oil consumption of nearly 30 kilometers.
5. The method for identifying the overload behavior of the truck based on the terminal of the internet of vehicles according to claim 2, wherein: still be equipped with mike and megaphone on the car networking terminal, the surveillance center of being convenient for carries out real-time communication through car networking terminal and navigating mate.
6. The method for identifying the overload behavior of the truck based on the terminal of the internet of vehicles according to claim 1, wherein: in step 3, the execution method of the database is as follows:
301. the database acquires the oil consumption per ten kilometers in the form process of the nearest end, and then the data with larger difference at the two ends are omitted to obtain the average value of the oil consumption required per ten kilometers;
302. the database combines the neural network model to obtain the average oil consumption of the vehicle of the model used for similar time according to the model of the vehicle and the year of the vehicle;
303. comparing the fuel consumption of the vehicle with the average fuel consumption, and determining whether the difference exceeds a set threshold value, if so, executing step S304; if not, continuing to execute the step S301;
304. and if the overload of the delivery vehicle is identified, the unique identification number of the vehicle and the model of the Internet of vehicles terminal are sent to the monitoring center, and the monitoring center is communicated with the Internet of vehicles terminal.
7. The method for identifying the overload behavior of the truck based on the terminal of the internet of vehicles according to claim 1 or 6, wherein: the unique identification number of the vehicle includes, but is not limited to, the frame number of the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010426900.8A CN111546890A (en) | 2020-05-19 | 2020-05-19 | Method for identifying truck overload behavior based on Internet of vehicles terminal |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010426900.8A CN111546890A (en) | 2020-05-19 | 2020-05-19 | Method for identifying truck overload behavior based on Internet of vehicles terminal |
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| JP2010185333A (en) * | 2009-02-12 | 2010-08-26 | Clarion Co Ltd | On vehicle information processor, method and program for controlling on vehicle information processor |
| CN103810852A (en) * | 2014-02-25 | 2014-05-21 | 李亚坤 | Remote monitoring system and method |
| CN104732787A (en) * | 2015-04-15 | 2015-06-24 | 福建古易信息科技有限公司 | Vehicle monitoring system and vehicle monitoring method |
| CN108564792A (en) * | 2018-06-21 | 2018-09-21 | 中国联合网络通信集团有限公司 | Overload of vehicle monitoring method and system |
| CN109377046A (en) * | 2018-10-18 | 2019-02-22 | 上海经达信息科技股份有限公司 | Overload of vehicle method of discrimination, system and device based on BP neural network |
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2020
- 2020-05-19 CN CN202010426900.8A patent/CN111546890A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6594579B1 (en) * | 2001-08-06 | 2003-07-15 | Networkcar | Internet-based method for determining a vehicle's fuel efficiency |
| JP2010185333A (en) * | 2009-02-12 | 2010-08-26 | Clarion Co Ltd | On vehicle information processor, method and program for controlling on vehicle information processor |
| CN103810852A (en) * | 2014-02-25 | 2014-05-21 | 李亚坤 | Remote monitoring system and method |
| CN104732787A (en) * | 2015-04-15 | 2015-06-24 | 福建古易信息科技有限公司 | Vehicle monitoring system and vehicle monitoring method |
| CN108564792A (en) * | 2018-06-21 | 2018-09-21 | 中国联合网络通信集团有限公司 | Overload of vehicle monitoring method and system |
| CN109377046A (en) * | 2018-10-18 | 2019-02-22 | 上海经达信息科技股份有限公司 | Overload of vehicle method of discrimination, system and device based on BP neural network |
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