CN113624291A - Oil consumption monitoring method, oil consumption monitoring device and engineering vehicle - Google Patents
Oil consumption monitoring method, oil consumption monitoring device and engineering vehicle Download PDFInfo
- Publication number
- CN113624291A CN113624291A CN202110853728.9A CN202110853728A CN113624291A CN 113624291 A CN113624291 A CN 113624291A CN 202110853728 A CN202110853728 A CN 202110853728A CN 113624291 A CN113624291 A CN 113624291A
- Authority
- CN
- China
- Prior art keywords
- oil consumption
- current vehicle
- oil
- historical
- consumption
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F9/00—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine
- G01F9/02—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine wherein the other variable is the speed of a vehicle
- G01F9/023—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine wherein the other variable is the speed of a vehicle with electric, electro-mechanic or electronic means
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F9/00—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F9/00—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine
- G01F9/008—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine where the other variable is the flight or running time
Landscapes
- Physics & Mathematics (AREA)
- Fluid Mechanics (AREA)
- General Physics & Mathematics (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
According to the oil consumption monitoring method, the oil consumption monitoring device and the engineering vehicle, the actual oil consumption of the current vehicle in the driving process is obtained, the reference oil consumption of the current vehicle is obtained according to the driving parameters and the oil consumption model of the current vehicle in the driving process, the oil consumption model is established according to historical oil consumption data, the historical oil consumption data comprise historical oil consumption data of the current vehicle and/or historical oil consumption data of a plurality of vehicles of the same type as the current vehicle, the difference between the actual oil consumption and the reference oil consumption is calculated, and when the difference is larger than a preset difference threshold value, the driving abnormity of the current vehicle is determined. According to the establishment of the first fuel consumption model and when the difference value is larger than the preset difference value threshold value, unreasonable fuel consumption of the vehicle caused by abnormal driving is determined, and the driver is timely warned, so that the aims of indirectly helping the driver to save fuel consumption, prolong the service life of an engine and reduce pollution emission are fulfilled.
Description
Technical Field
The application relates to the technical field of oil consumption monitoring, in particular to an oil consumption monitoring method, an oil consumption monitoring device and an engineering vehicle.
Background
With the development of economic society of China, vehicle equipment is gradually increased, but a plurality of problems, such as huge fuel consumption, also occur. However, in the current driving process of the vehicle, due to the driving behavior of the driver and other reasons, the problem of abnormal fuel consumption of the vehicle occurs, and the fuel consumption is huge. At present, an On-Board Diagnostic (OBD) system is installed On a plurality of vehicle devices, the OBD system monitors the running condition of an engine and the working state of an exhaust gas aftertreatment system at any time, and immediately sends out a warning once the situation that the emission exceeds the standard is found out, or a T-BOX (remote information processing BOX) Can deeply read the Can bus data and the private protocol of an automobile to detect the running condition of the vehicle in real time, a large amount of data of the internet of vehicles are accumulated, but the data are not fully utilized at present, and idle data and waste are also caused.
Disclosure of Invention
The present application is proposed to solve the above-mentioned technical problems. The embodiment of the application provides an oil consumption monitoring method, an oil consumption monitoring device and an engineering vehicle, and solves the problem of abnormal driving of the vehicle in the driving process.
According to an aspect of the present invention, there is provided a fuel consumption monitoring method, including: acquiring the actual oil consumption of the current vehicle in the driving process; acquiring reference oil consumption of the current vehicle according to the driving parameters and the first oil consumption model of the current vehicle in the driving process; the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle; calculating a difference between the actual fuel consumption and the reference fuel consumption; and when the difference value is larger than a preset difference value threshold value, determining that the current vehicle is abnormal in driving.
In an embodiment, before calculating the difference between the actual fuel consumption and the reference fuel consumption, the fuel consumption monitoring method further includes: acquiring the oil level change of the current vehicle within a preset time period; wherein the oil level variation represents an amount of oil consumed by the current vehicle over the preset time period; and determining the use state of the actual oil consumption according to the oil level change.
In an embodiment, the determining, according to the oil level variation, the usage state of the actual oil consumption includes: and if the oil level change is larger than a preset oil level change threshold value, determining that the use state of the actual oil consumption is abnormal.
In an embodiment, the preset time period includes a first time and a second time, and the second time is greater than the first time, wherein the obtaining of the oil level change of the current vehicle within the time period includes: acquiring a first oil level corresponding to the current vehicle at the first moment and a second oil level corresponding to the current vehicle at the second moment; wherein the second oil level is less than the first oil level; the determining the use state of the actual oil consumption according to the oil level change comprises: calculating a fuel consumption speed of the current vehicle in a time period according to a difference value between the first oil level and the second oil level; and if the oil consumption speed is greater than a preset oil consumption speed threshold value, determining that the use state of the actual oil consumption is abnormal.
In an embodiment, the obtaining the actual fuel consumption of the current vehicle during the driving process includes: acquiring the actual oil consumption of the current vehicle in the driving process according to the driving parameters of the current vehicle in the driving process and the second oil consumption model; the second oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle.
In an embodiment, the method for establishing the second fuel consumption model includes: obtaining historical oil consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical oil consumption data of the plurality of vehicles; and fitting the historical oil consumption data and the vehicle parameter data to obtain the second oil consumption model.
In an embodiment, the method for establishing the second fuel consumption model includes: obtaining historical oil consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical oil consumption data of the plurality of vehicles; and training a neural network model by taking the historical oil consumption data and the vehicle parameter data as training samples to obtain the second oil consumption model.
In an embodiment, the driving parameters are multiple, wherein when the difference is greater than a preset difference threshold, after determining that the actual fuel consumption is abnormal, the fuel consumption monitoring method further includes: counting the oil consumption value corresponding to each driving parameter; and reporting the reason of the abnormal oil consumption according to the comparison result of the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter.
In an embodiment, the reporting the cause of the abnormal oil consumption according to the comparison result between the oil consumption value corresponding to each of the driving parameters and the historical oil consumption data corresponding to each of the driving parameters includes: calculating a difference value between the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter; and selecting the running parameters corresponding to the difference value larger than the preset comparison result threshold value to report.
According to another aspect of the present invention, there is provided a fuel consumption monitoring apparatus comprising: the first acquisition module is used for acquiring the actual oil consumption of the current vehicle in the running process; the second obtaining module is used for obtaining the reference oil consumption of the current vehicle according to the running parameters of the current vehicle in the running process and the first oil consumption model; the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle; the calculation module is used for calculating the difference value between the actual oil consumption and the reference oil consumption; and the determining module is used for determining that the current vehicle is abnormal in driving when the difference value is larger than a preset difference value threshold value.
According to another aspect of the present invention, there is provided an engineering vehicle including: a vehicle body; a controller disposed on the vehicle body, the controller to: acquiring the actual oil consumption of the current vehicle in the driving process; acquiring reference oil consumption of the current vehicle according to the driving parameters and the first oil consumption model of the current vehicle in the driving process; the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle; calculating a difference between the actual fuel consumption and the reference fuel consumption; and when the difference value is larger than a preset difference value threshold value, determining that the current vehicle is abnormal in driving.
According to the oil consumption monitoring method, the oil consumption monitoring device and the engineering vehicle, the actual oil consumption of the current vehicle in the driving process is obtained, the reference oil consumption of the current vehicle is obtained according to the driving parameters of the current vehicle in the driving process and a first oil consumption model, wherein the first oil consumption model is established according to historical oil consumption data, the historical oil consumption data comprise historical oil consumption data of the current vehicle and/or historical oil consumption data of a plurality of vehicles of the same type as the current vehicle, the difference between the actual oil consumption and the reference oil consumption is calculated, and when the difference is larger than a preset difference threshold value, the driving abnormity of the current vehicle is determined. According to the establishment of the fuel consumption model and when the difference value is larger than the preset difference value threshold value, the unreasonable fuel consumption of the vehicle caused by abnormal driving is determined, and the driver is timely warned, so that the aims of indirectly helping the driver to save the fuel consumption, prolong the service life of the engine and reduce the pollution emission are fulfilled.
Drawings
The above and other objects, features and advantages of the present application will become more apparent by describing in more detail embodiments of the present application with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a schematic structural diagram of a work vehicle according to an exemplary embodiment of the present application.
Fig. 2 is a schematic structural diagram of a work vehicle according to another exemplary embodiment of the present application.
Fig. 3 is a schematic flow chart of a fuel consumption monitoring method according to an exemplary embodiment of the present application.
Fig. 4 is a flowchart illustrating a method for determining a usage state of actual fuel consumption according to an exemplary embodiment of the present application.
Fig. 5 is a flowchart illustrating a method for determining a usage state of actual fuel consumption according to another exemplary embodiment of the present application.
Fig. 6 is a flowchart illustrating a method for determining a usage state of actual fuel consumption according to another exemplary embodiment of the present application.
Fig. 7 is a schematic flow chart of a fuel consumption monitoring method according to another exemplary embodiment of the present application.
Fig. 8 is a flowchart illustrating a method for establishing a second fuel consumption model according to an exemplary embodiment of the present application.
Fig. 9 is a schematic flow chart of neural network fitting provided by an exemplary embodiment of the present application.
Fig. 10 is a schematic flow chart of a fuel consumption monitoring method according to another exemplary embodiment of the present application.
Fig. 11 is a schematic structural diagram of a fuel consumption monitoring device according to an exemplary embodiment of the present application.
Fig. 12 is a schematic structural diagram of a fuel consumption monitoring device according to another exemplary embodiment of the present application.
Fig. 13 is a block diagram of an electronic device provided in an exemplary embodiment of the present application.
Detailed Description
Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein.
Fig. 1 is a schematic structural diagram of a work vehicle according to an exemplary embodiment of the present application. As shown in fig. 1, the work vehicle 30 includes: a vehicle body 31 and a controller 32, the controller being provided on the vehicle body, the controller being configured to: acquiring the actual oil consumption of the current vehicle in the driving process; acquiring reference oil consumption of the current vehicle according to the driving parameters and the first oil consumption model of the current vehicle in the driving process; the first oil consumption model is established according to historical oil consumption data, the historical oil consumption data comprise historical oil consumption data of a current vehicle and/or historical oil consumption data of a plurality of vehicles of which the types are the same as that of the current vehicle, and a difference value between actual oil consumption and reference oil consumption is calculated; and determining that the current vehicle is abnormally driven when the difference value is greater than a preset difference value threshold.
Fig. 2 is a schematic structural diagram of a work vehicle according to another exemplary embodiment of the present application. As shown in fig. 2, the work vehicle 30 further includes: the oil level sensor 33. An oil level sensor 33 is provided on the vehicle body 31, the oil level sensor 33 is connected to the controller 32, and the oil level sensor 33 is used to monitor the oil level of the working vehicle 30.
Fig. 3 is a schematic flow chart of a fuel consumption monitoring method according to an exemplary embodiment of the present application. The embodiment can be applied to control equipment of an engineering vehicle, and as shown in fig. 3, the oil consumption monitoring method comprises the following steps:
step 110: and acquiring the actual oil consumption of the current vehicle in the running process.
Because whether the actual oil consumption is abnormal needs to be determined, the actual oil consumption of the current vehicle in the running process needs to be obtained first, so that the next calculation or judgment is carried out, wherein the actual oil consumption can be obtained by calculation according to the read mileage variation and the oil level variation. Or some parameters of the vehicle can be collected first, and the parameters are substituted into the model to calculate the actual oil consumption. For example, the parameters may include: vehicle parameters such as vehicle running duration, gear, engine speed, accelerator opening, vehicle speed, idle speed duration, neutral position duration and the like.
Step 120: and acquiring the reference oil consumption of the current vehicle according to the driving parameters of the current vehicle in the driving process and a first oil consumption model, wherein the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as that of the current vehicle.
The reference oil consumption is established according to historical oil consumption data of the current vehicle and/or historical oil consumption data of a plurality of vehicles of the same type as the current vehicle. The purpose of establishing the oil consumption model is to analyze theoretical oil consumption of the current vehicle in the current state, and then obtain reference oil consumption of the current vehicle based on the establishment of the oil consumption model. And then judging whether the actual oil consumption is normal or not according to the reference oil consumption, or judging whether the problem of oil leakage is generated in the running process of the vehicle or not.
Step 130: the difference between the actual fuel consumption and the reference fuel consumption is calculated.
And calculating the difference between the actual oil consumption and the reference oil consumption, namely D ═ A-B |, wherein B is the actual oil consumption, A is the reference oil consumption, and D is the difference. In addition, the percentage between the actual fuel consumption and the reference fuel consumption, i.e., D ═ a-B |/a, where D is the percentage value, B is the actual fuel consumption, and a is the reference fuel consumption, may also be calculated.
Step 140: and when the difference value is larger than a preset difference value threshold value, determining that the current vehicle is abnormal in driving.
After the difference is calculated, the difference is compared with a preset difference threshold, if the difference is larger than the preset difference threshold, it is indicated that the fuel consumption is abnormal due to the driving behavior of the driver, and then the driver is prompted to be abnormal in fuel consumption due to which driving behavior of the driver, and the driver needs to perform some processing to solve the problem of wrong driving behavior. In view of the above, the percentage value may also be compared, and when the percentage is greater than the preset percentage threshold, it is determined that the current vehicle is abnormal in driving, where the preset percentage threshold may be 20%.
According to the oil consumption monitoring method, the actual oil consumption of a current vehicle in the driving process is obtained, the reference oil consumption of the current vehicle is obtained according to the driving parameters of the current vehicle in the driving process and a first oil consumption model, wherein the first oil consumption model is established according to historical oil consumption data, the historical oil consumption data comprise historical oil consumption data of the current vehicle and/or historical oil consumption data of a plurality of vehicles of the same type as the current vehicle, the difference value between the actual oil consumption and the reference oil consumption is calculated, and when the difference value is larger than a preset difference value threshold value, the driving abnormity of the current vehicle is determined. According to the establishment of the first oil consumption model and when the difference value is larger than the preset difference value threshold value, the unreasonable oil consumption of the vehicle caused by abnormal driving is determined, and the alarm is given in time to prompt the driver, so that the aims of indirectly helping the driver to save the oil consumption, prolong the service life of the engine and reduce the pollution emission are fulfilled.
Fig. 4 is a flowchart illustrating a method for determining a usage state of actual fuel consumption according to an exemplary embodiment of the present application. As shown in fig. 4, before step 130, the method for monitoring oil consumption may further include:
step 150: the method comprises the steps of obtaining oil level change of a current vehicle in a preset time period, wherein the oil level change represents oil quantity consumed by the current vehicle in the preset time period.
And acquiring the oil level change of the current vehicle within a preset time period. The preset time period may be 1 minute or 2 minutes or 20 seconds (seconds). In minutes and seconds. The oil level change may look at a fuel gauge to determine how much fuel is currently being burned by the vehicle for a preset period of time.
Step 160: and determining the use state of the actual oil consumption according to the change of the oil level.
By the oil level variation, it is possible to determine whether the usage state of the actual oil consumption is normal or abnormal. And if the current vehicle running safety is normal, the current vehicle running safety is indicated. If the actual fuel consumption use state is an abnormal state, the current vehicle fuel consumption is abnormal, and corresponding measures need to be taken to deal with the problem of abnormal fuel consumption.
In the application, the using state of the actual oil consumption is detected firstly, namely whether the actual oil consumption is abnormal or not is detected, if the actual oil consumption is abnormal, the abnormal oil consumption is directly output, and whether the driving is abnormal or not does not need to be judged. In the prior art, whether the fuel consumption is abnormal is judged only when the driving is abnormal is judged, the sequence of technical means is different, so that the problems to be actually solved are different, and the generated effects are different. The problem actually solved by the application is how to improve driving safety. The prior art solves whether the actual fuel consumption is abnormal or not when the driving is not abnormal. The problems it solves are different. Whether driving is abnormal or not in the application, whether the actual oil consumption is abnormal or not is judged firstly, and therefore driving safety is improved. Because the severity of the actual fuel consumption abnormality is higher than that of the driving abnormality, for example, the fuel consumption is increased when the engine fails, and the safety of judging whether the driving is abnormal or not in the prior art is lower if the driving is abnormal or not. If it is determined that the driving abnormality may actually be caused by the actual fuel consumption abnormality, for example, the fuel consumption is increased due to sudden braking or sudden accelerator stepping of the driver, and the fuel consumption is actually caused by the engine failure, then it is not necessary to determine whether the actual fuel consumption is abnormal after the driving abnormality is determined in the prior art, and then the safety performance in the driving process is reduced. In addition, in the prior art, the driving reason cannot be reported, so that a driver cannot know which driving behavior causes abnormal driving.
In addition, whether the actual oil consumption is abnormal or not does not need to be analyzed by establishing a model, whether the actual oil consumption is abnormal or not can be analyzed only according to the change of the oil level or the oil consumption speed, and whether the actual oil consumption is abnormal or not can be detected more simply and conveniently.
Fig. 5 is a flowchart illustrating a method for determining a usage state of actual fuel consumption according to another exemplary embodiment of the present application. As shown in fig. 5, step 160 includes:
step 161: and if the oil level change is larger than a preset oil level change threshold value, determining that the use state of the actual oil consumption is abnormal.
If the oil level change is larger than the preset oil level change threshold value, namely the fuel quantity is changed violently, the abnormal use state of the actual fuel consumption is indicated. The abnormal cause may be oil theft, oil leakage, or oil tank not being covered well, resulting in oil volatilization. For example, if the fuel level drops by more than 5%, this indicates that the usage state of the actual fuel consumption is abnormal, because the fuel is excessive in the preset time period, and therefore, a part of the fuel amount of the current vehicle is not fully used during the running process.
Fig. 6 is a flowchart illustrating a method for determining a usage state of actual fuel consumption according to another exemplary embodiment of the present application. As shown in fig. 6, the preset time period includes a first time and a second time, and the second time is greater than the first time, wherein step 150 includes:
step 151: the method comprises the steps of obtaining a first oil level corresponding to a current vehicle at a first moment and a second oil level corresponding to a current vehicle at a second moment, wherein the second oil level is smaller than the first oil level.
The first time for starting fuel and the second time for ending fuel in the preset time period can be selected from the preset time period. A first level of starting fuel and a second level of ending fuel are obtained. The value of the actual fuel consumption of the current vehicle can be determined through the first oil level and the second oil level.
In one embodiment, as shown in FIG. 5, step 160 comprises:
step 162: and calculating the fuel consumption speed of the current vehicle in the time period according to the difference value between the first oil level and the second oil level.
And calculating the difference value between the first oil level and the second oil level, namely the oil level change of the current vehicle. The current vehicle fuel consumption rate in the preset time period can be calculated according to the difference. For example, if the preset time period is 1 minute, the oil consumption rate is the difference/1 minute or (first oil level-second oil level)/1 minute.
Step 163: and if the oil consumption speed is greater than the preset oil consumption speed threshold value, determining that the use state of the actual oil consumption is abnormal.
If the fuel consumption speed is larger than the preset fuel consumption speed threshold, the fact that the current vehicle fuel quantity is too large in the preset time period and exceeds the ideal fuel quantity indicates that the current vehicle actual fuel consumption use state is abnormal.
Fig. 7 is a schematic flow chart of a fuel consumption monitoring method according to another exemplary embodiment of the present application. As shown in fig. 7, step 110 may include:
step 111: and acquiring the actual oil consumption of the current vehicle in the driving process according to the driving parameters of the current vehicle in the driving process and a second oil consumption model, wherein the second oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle.
If the driving parameters of the vehicle in the driving process are obtained, the actual fuel consumption of the current vehicle can be obtained according to the driving parameters and the second fuel consumption model. The actual oil consumption can be obtained by conversion according to the driving parameters, and the corresponding actual oil consumption can be obtained by inputting the driving parameters into the second oil consumption model according to the second oil consumption model which is set in advance. Therefore, the calculation steps can be reduced by acquiring the actual fuel consumption of the current vehicle by using the second fuel consumption model, and the calculation accuracy is higher.
Fig. 8 is a flowchart illustrating a method for establishing a second fuel consumption model according to an exemplary embodiment of the present application. As shown in fig. 8, on the basis of the above embodiment, step 111 may include:
step 1111: and obtaining historical fuel consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical fuel consumption data of the plurality of vehicles.
The vehicle management system Can be used for deeply reading vehicle Can bus data and a private protocol through a T-BOX (Telematics BOX), wherein a T-BOX terminal is provided with an OBD module with dual-core processing and a CPU framework with dual-core processing, respectively collecting bus data and private protocol reverse control related to vehicle buses Dcan, Kcan and PTcan, and transmitting the data to a cloud server through a GPRS network so as to provide vehicle condition reports, driving reports, fuel consumption statistics, fault reminding, violation inquiry, position tracks, driving behaviors, safety theft prevention, reservation service, remote vehicle finding, control of vehicle doors, windows, lamps, locks, loudspeakers, double flashing, reflector folding, skylights, monitoring of central control warning, safety airbag states and the like) or TruckLink (three vehicle network platforms including key data of fuel consumption chassis data, pumping volume, pumping times, pumping operation of more than two hundreds of devices and the like) platform data real-time in the running process of the line-to-line vehicle And collecting data, and performing feature extraction on the vehicle parameter data by taking a single day as a time node. The main extracted feature values include: the method comprises the following steps of vehicle speed distribution, rotating speed distribution, torque distribution, gear distribution, kinetic energy variation, average vehicle speed, neutral gear sliding, idling, engine working conditions and other hundreds of characteristic values.
Due to the influence of various factors in the driving process of the vehicle, mutation of data and error collection of invalid data exist, and therefore the data must be preprocessed. Firstly, the acquired data is optimized, so that uncertain interference caused by data loss, data jumping and the like on the result is eliminated. Secondly, preprocessing the acquired vehicle parameter data, wherein the preprocessing mainly comprises the following steps: neutral coasting, idle timeout, overspeed statistics, acceleration information, deceleration information, cruise information, high torque low speed, low torque high speed, and the like. And finally, preprocessing the acquired vehicle parameter data, wherein the preprocessing mainly comprises the following steps: the system comprises a vehicle speed distribution, a rotating speed distribution, a torque distribution, mileage, accumulated oil and gas consumption, an engine working condition distribution, an average vehicle speed, positive and negative kinetic energy variation and the like.
The second oil consumption model is established according to vehicle parameter data corresponding to the historical oil consumption data of the vehicles and the historical oil consumption data of the vehicles with the same type as the current vehicle, namely the actual oil consumption of the current vehicle is obtained according to the driving parameters of the current vehicle in the driving process and the second oil consumption model based on the vehicles with the same type. The actual fuel consumption may be an average of historical fuel consumption data solutions of data of a plurality of vehicles of the same vehicle type, wherein the driving parameters may include: vehicle speed, engine speed, idle speed duration ratio, accelerator opening and the like.
Step 1112: and fitting the historical oil consumption data and the vehicle parameter data to obtain a second oil consumption model.
Historical fuel consumption data of a plurality of vehicles with the same type of the current vehicle and vehicle parameter data corresponding to the historical fuel consumption data of the plurality of vehicles. And (5) establishing a linear model by adopting a linear regression method, namely establishing a second oil consumption model. After determining vehicle parameter data influencing fuel consumption, a linear regression method is adopted to establish a linear model, namely the fuel consumption model is established, the scheme adopts a least square method to solve a regression equation, and the assumption is that a total k influence factor, x1.x2......xkTo linear relationship:
y＝β0+β1x1+β2x2+...+βkxk+ε
for y and x1,x2......xkMultiple independent observations are simultaneously made to obtain multiple groups of historical oil consumption data and vehicle parameters corresponding to the historical oil consumption dataThe regression solution can be performed by counting the data. The result of the regression equation is an equation that models fuel consumption. According to the equation obtained by the linear model, factors influencing oil consumption can be more intuitively seen.
Fig. 9 is a schematic flow chart of neural network fitting provided by an exemplary embodiment of the present application.
As shown in fig. 9, on the basis of the above embodiment, step 111 can be implemented as:
obtaining historical oil consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical oil consumption data of the plurality of vehicles, and training a neural network model by taking the historical oil consumption data and the vehicle parameter data as training samples to obtain a second oil consumption model.
The second fuel consumption model may be established using a non-linear model. And carrying out nonlinear fitting by using a multilayer BP neural network. The BP neural network consists of an input layer, a hidden layer and an output layer, wherein the hidden layer can be divided into a single hidden layer and a plurality of hidden layers according to the number of layers. The multi-hidden layer is composed of a plurality of single hidden layers, and compared with the single hidden layer, the multi-hidden layer has the advantages of strong generalization capability, high prediction precision and long training time. The selection of the number of hidden layers is comprehensively considered from the aspects of network precision and training time. In the second fuel consumption model prediction, no requirement is made on training time, so that multiple hidden layers are adopted to improve the network prediction accuracy.
In addition, because the initially acquired data has more features, great data redundancy is provided for searching for the optimal vehicle parameters, and the training of effective vehicle parameters is not facilitated later, the acquired and preprocessed data is subjected to strong relevant feature extraction. The method is characterized in that correlation analysis of vehicle parameter data characteristics corresponding to historical oil consumption and the historical oil consumption is searched on the premise of the same vehicle type, and a plurality of characteristics with strongest correlation degree with the oil consumption are found out. Firstly, initializing a network, namely training by taking historical oil consumption data in a database as target parameters and historical vehicle parameter data as input parameters. Then, a training sample and a test sample are extracted, namely, the historical oil consumption data and the historical vehicle parameter data are subjected to feature extraction, namely, random 90% of data in the historical vehicle parameter data are used as the training sample, and the rest 10% of data are used as the test sample. The randomly chosen 90% of the data samples are then normalized, i.e., the data samples are class mapped between [0, 1 ]. Based on the initial neural network model, the training parameters are assigned full values of '0' and '1' one by one. And counting the first N items of the change of each training parameter, which have the greatest influence on the prediction result and the target parameter, and considering that the N training parameters are strong relevant parameters having the strongest influence on the oil consumption. And finally, extracting N strongly-related features.
In an embodiment, the method for establishing the first fuel consumption model may be implemented as: and establishing a first oil consumption model according to the historical oil consumption data. And counting the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles with the same type of the current vehicle to obtain a statistical result. And calculating the average value corresponding to the statistical result. Or calculating a weighted average corresponding to the statistical result. And then fitting to obtain a first oil consumption model according to the average value corresponding to the statistical result, wherein the first oil consumption model comprises the corresponding relation between the model of the current vehicle and the average value corresponding to the statistical result. The first fuel consumption model may be established using a linear regression method.
In an embodiment, the driving parameters are multiple, and the fuel consumption monitoring method may be implemented as: and counting the oil consumption value corresponding to each driving parameter, and reporting the reason of the abnormal oil consumption according to the comparison result of the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter.
The fuel consumption value corresponding to each vehicle parameter data of the current vehicle is counted, for example, an actual fuel consumption value corresponding to the vehicle speed of the current vehicle, a fuel consumption value corresponding to the idle time length and the like can be counted, and the cause of the abnormal fuel consumption is reported according to the comparison result of the fuel consumption value corresponding to each driving parameter and the historical fuel consumption data corresponding to each driving parameter.
In an embodiment, the fuel consumption monitoring method may be implemented as: and calculating a difference value between the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter, and selecting the driving parameter with the difference value larger than the preset comparison result threshold value to report.
Calculating a difference value between the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter, selecting the driving parameter with the difference value larger than the preset comparison result threshold value to report, and showing that the actual oil consumption is influenced by the vehicle parameter data with the difference value larger than the preset comparison result threshold value, for example, the actual oil consumption is influenced by the vehicle speed if the difference value between the current vehicle speed and the historical vehicle speed is larger than the preset comparison result threshold value, so that the vehicle speed is reported too fast, and a driver can know which driving behaviors influence the current oil consumption.
Fig. 10 is a schematic flow chart of a fuel consumption monitoring method according to another exemplary embodiment of the present application. As shown in fig. 10, on the basis of the foregoing embodiment, the method for detecting oil consumption may include:
step 210: and acquiring the reference oil consumption of the current vehicle according to the running parameters of the current vehicle in the running process and the first oil consumption model.
Step 220: and acquiring the actual oil consumption of the current vehicle in the running process.
Step 230: and calculating the difference value between the actual oil consumption and the reference oil consumption and acquiring the oil level change of the current vehicle.
Step 240: and judging whether the oil level changes more than a preset oil level change threshold value or not. If yes, go to step 250, otherwise, go to step 260.
Step 250: and determining that the actual fuel consumption of the current vehicle is abnormal.
Step 260: and judging whether the difference value is larger than a preset difference value threshold value. If yes, go to step 270, otherwise, go to step 220.
Step 270: a driving abnormality is determined.
And acquiring the reference oil consumption of the current vehicle according to the running parameters of the current vehicle in the running process and the first oil consumption model. And acquiring the actual oil consumption of the current vehicle in the running process. And calculating the difference value between the actual oil consumption and the reference oil consumption and acquiring the oil level change of the current vehicle. And judging whether the oil level change is larger than a preset oil level change threshold value or not. And if the oil level change is larger than a preset oil level change threshold, determining that the actual oil consumption of the current vehicle is abnormal, and if the oil level change is smaller than or equal to the preset oil level change threshold, judging whether the difference value is larger than a preset difference value threshold. And if the difference is larger than a preset difference threshold, determining that the driving is abnormal, and if the difference is smaller than or equal to the preset difference threshold, turning to the step of obtaining the actual oil consumption of the current vehicle in the driving process.
Fig. 11 is a schematic structural diagram of a fuel consumption monitoring device according to an exemplary embodiment of the present application. As shown in fig. 11, the fuel consumption monitoring device 20 includes: the first obtaining module 201 is used for actual oil consumption of the current vehicle in the driving process. The second obtaining module 202 is configured to obtain a reference oil consumption of the current vehicle according to a driving parameter of the current vehicle in a driving process and a first oil consumption model, where the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data includes historical oil consumption data of the current vehicle and/or historical oil consumption data of multiple vehicles of the same type as the current vehicle. The fuel consumption monitoring system comprises a calculating module 203 and a determining module 204, wherein the calculating module is used for calculating a difference value between the actual fuel consumption and the reference fuel consumption, and the determining module is used for determining that the current vehicle is abnormal in driving when the difference value is larger than a preset difference threshold value.
This embodiment provides a fuel consumption monitoring devices, includes: the actual oil consumption of the current vehicle in the driving process is obtained through the first obtaining module 201, the reference oil consumption of the current vehicle is obtained through the second obtaining module 202 according to the driving parameters of the current vehicle in the driving process and the first oil consumption model, wherein the first oil consumption model is established according to historical oil consumption data, the historical oil consumption data comprise historical oil consumption data of the current vehicle and/or historical oil consumption data of a plurality of vehicles of the same type as the current vehicle, the calculating module 203 calculates a difference value between the actual oil consumption and the reference oil consumption, and the determining module 204 determines that the current vehicle is abnormal in driving when the difference value is larger than a preset difference value threshold value. According to the establishment of the first oil consumption model and when the difference value is larger than the preset difference value threshold value, the unreasonable oil consumption of the vehicle caused by abnormal driving is determined, and the alarm is given in time to prompt the driver, so that the aims of indirectly helping the driver to save the oil consumption, prolong the service life of the engine and reduce the pollution emission are fulfilled.
Fig. 12 is a schematic structural diagram of a fuel consumption monitoring device according to another exemplary embodiment of the present application. As shown in figure 12 of the drawings,
in an embodiment, before the calculating module 203, the oil consumption monitoring device 20 may further include
An oil level obtaining unit 205 for obtaining an oil level change of the current vehicle within a preset time period, wherein the oil level change represents an amount of oil consumed by the current vehicle within the preset time period; and the oil level determining unit 206 is used for determining the use state of the actual oil consumption according to the oil level change.
In an embodiment, the oil level determination unit 206 may include: an abnormality unit 2061 configured to determine that the usage state of the actual fuel consumption is abnormal if the oil level variation is larger than a preset oil level variation threshold.
In an embodiment, the preset time period includes a first time and a second time, and the second time is greater than the first time, wherein the oil level obtaining unit 205 may include: a time obtaining unit 2051 is configured to obtain a first oil level of the current vehicle at a first time and a second oil level corresponding to a second time, where the second oil level is smaller than the first oil level.
In an embodiment, the oil level determination module 206 may include: a fuel consumption rate unit 2062 for calculating a fuel consumption rate of the current vehicle in a time period based on a difference between the first oil level and the second oil level; the usage abnormality unit 2063 is configured to determine that the usage state of the actual fuel consumption is abnormal if the fuel consumption rate is greater than a preset fuel consumption rate threshold.
In an embodiment, the first obtaining module 201 may include: the second oil consumption model establishing unit 2011 is configured to obtain an actual oil consumption of the current vehicle in the driving process according to the driving parameters of the current vehicle in the driving process and a second oil consumption model, where the second oil consumption model is established according to historical oil consumption data, and the historical oil consumption data includes historical oil consumption data of the current vehicle and/or historical oil consumption data of multiple vehicles of the same type as the current vehicle.
In an embodiment, the second fuel consumption model establishing unit 2011 may include: the parameter acquiring unit 207 is configured to acquire historical fuel consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical fuel consumption data of the plurality of vehicles; and the oil consumption model unit 208 is configured to fit the historical oil consumption data and the vehicle parameter data to obtain a second oil consumption model.
In an embodiment, the second fuel consumption model establishing unit 2011 may be specifically configured to: obtaining historical oil consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical oil consumption data of the plurality of vehicles, and training a neural network model by taking the historical oil consumption data and the vehicle parameter data as training samples to obtain a second oil consumption model.
In an embodiment, the driving parameters are multiple, and the fuel consumption monitoring device 20 may be specifically configured to: and counting the oil consumption value corresponding to each driving parameter, and reporting the reason of the abnormal oil consumption according to the comparison result of the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter.
In an embodiment, the oil consumption monitoring device 20 may be specifically configured to: and calculating a difference value between the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter, and selecting the driving parameter with the difference value larger than the preset comparison result threshold value to report.
Next, an electronic apparatus according to an embodiment of the present application is described with reference to fig. 13. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom.
FIG. 13 illustrates a block diagram of an electronic device in accordance with an embodiment of the present application.
As shown in fig. 13, the electronic device 10 includes one or more processors 11 and a memory 12.
The processor 11 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.
Memory 12 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 11 to implement the fuel consumption monitoring methods of the various embodiments of the present application described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 10 may further include: an input device 13 and an output device 14, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
When the electronic device is a stand-alone device, the input means 13 may be a communication network connector for receiving the acquired input signals from the first device and the second device.
The input device 13 may also include, for example, a keyboard, a mouse, and the like.
The output device 14 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 14 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 10 relevant to the present application are shown in fig. 12, and components such as buses, input/output interfaces, and the like are omitted. In addition, the electronic device 10 may include any other suitable components depending on the particular application.
In addition to the above-described methods and apparatus, embodiments of the present application may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the fuel consumption monitoring method according to various embodiments of the present application described in the "exemplary methods" section above of this specification.
The computer program product may be written with program code for performing the operations of embodiments of the present application in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present application may also be a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps in the fuel consumption monitoring method according to various embodiments of the present application described in the "exemplary methods" section above in this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present application in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present application are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present application. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the foregoing disclosure is not intended to be exhaustive or to limit the disclosure to the precise details disclosed.
The block diagrams of devices, apparatuses, systems referred to in this application are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
It should also be noted that in the devices, apparatuses, and methods of the present application, the components or steps may be decomposed and/or recombined. These decompositions and/or recombinations are to be considered as equivalents of the present application.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the application. Thus, the present application is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the application to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (11)
1. A method for monitoring fuel consumption, comprising:
acquiring the actual oil consumption of the current vehicle in the driving process;
acquiring reference oil consumption of the current vehicle according to the driving parameters and the first oil consumption model of the current vehicle in the driving process; the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle;
calculating a difference between the actual fuel consumption and the reference fuel consumption; and
and when the difference value is larger than a preset difference value threshold value, determining that the current vehicle is abnormal in driving.
2. The fuel consumption monitoring method according to claim 1, wherein before calculating the difference between the actual fuel consumption and the reference fuel consumption, the method further comprises:
acquiring the oil level change of the current vehicle within a preset time period; wherein the oil level variation represents an amount of oil consumed by the current vehicle over the preset time period;
and determining the use state of the actual oil consumption according to the oil level change.
3. The fuel consumption monitoring method according to claim 2, wherein determining the usage state of the actual fuel consumption according to the fuel level variation comprises:
and if the oil level change is larger than a preset oil level change threshold value, determining that the use state of the actual oil consumption is abnormal.
4. The fuel consumption monitoring method according to claim 2, wherein the preset time period includes a first time and a second time, the second time being greater than the first time, wherein the obtaining of the oil level change of the current vehicle in the time period includes:
acquiring a first oil level corresponding to the current vehicle at the first moment and a second oil level corresponding to the current vehicle at the second moment; wherein the second oil level is less than the first oil level;
the determining the use state of the actual oil consumption according to the oil level change comprises:
calculating a fuel consumption speed of the current vehicle in a time period according to a difference value between the first oil level and the second oil level;
and if the oil consumption speed is greater than a preset oil consumption speed threshold value, determining that the use state of the actual oil consumption is abnormal.
5. The fuel consumption monitoring method according to claim 1, wherein the obtaining of the actual fuel consumption of the current vehicle during driving comprises:
acquiring the actual oil consumption of the current vehicle in the driving process according to the driving parameters of the current vehicle in the driving process and the second oil consumption model; the second oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of the same type as the current vehicle.
6. The fuel consumption monitoring method according to claim 5, wherein the second fuel consumption model is established by a method comprising:
obtaining historical oil consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical oil consumption data of the plurality of vehicles;
and fitting the historical oil consumption data and the vehicle parameter data to obtain the second oil consumption model.
7. The fuel consumption monitoring method according to claim 5, wherein the second fuel consumption model is established by a method comprising:
obtaining historical oil consumption data of a plurality of vehicles of the same type as the current vehicle and vehicle parameter data corresponding to the historical oil consumption data of the plurality of vehicles;
and training a neural network model by taking the historical oil consumption data and the vehicle parameter data as training samples to obtain the second oil consumption model.
8. The fuel consumption monitoring method according to claim 1, wherein the plurality of driving parameters are provided, and wherein when the difference is greater than a preset difference threshold value, after determining that the actual fuel consumption is abnormal, the method further comprises:
counting the oil consumption value corresponding to each driving parameter;
and reporting the reason of the abnormal oil consumption according to the comparison result of the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter.
9. The fuel consumption monitoring method according to claim 8, wherein the reporting of the cause of the abnormal fuel consumption according to the comparison result between the fuel consumption value corresponding to each of the driving parameters and the historical fuel consumption data corresponding to each of the driving parameters comprises:
calculating a difference value between the oil consumption value corresponding to each driving parameter and the historical oil consumption data corresponding to each driving parameter;
and selecting the running parameters corresponding to the difference value larger than the preset comparison result threshold value to report.
10. A fuel consumption monitoring device, comprising:
the first acquisition module is used for acquiring the actual oil consumption of the current vehicle in the running process;
the second obtaining module is used for obtaining the reference oil consumption of the current vehicle according to the running parameters of the current vehicle in the running process and the first oil consumption model; the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle;
the calculation module is used for calculating the difference value between the actual oil consumption and the reference oil consumption; and
and the determining module is used for determining that the current vehicle is abnormal in driving when the difference value is larger than a preset difference value threshold value.
11. A work vehicle, characterized by comprising:
a vehicle body;
a controller disposed on the vehicle body, the controller to:
acquiring the actual oil consumption of the current vehicle in the driving process;
acquiring reference oil consumption of the current vehicle according to the driving parameters and the first oil consumption model of the current vehicle in the driving process; the first oil consumption model is established according to historical oil consumption data, and the historical oil consumption data comprises the historical oil consumption data of the current vehicle and/or the historical oil consumption data of a plurality of vehicles of which the types are the same as the types of the current vehicle;
calculating a difference between the actual fuel consumption and the reference fuel consumption; and
and when the difference value is larger than a preset difference value threshold value, determining that the current vehicle is abnormal in driving.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110853728.9A CN113624291A (en) | 2021-07-27 | 2021-07-27 | Oil consumption monitoring method, oil consumption monitoring device and engineering vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110853728.9A CN113624291A (en) | 2021-07-27 | 2021-07-27 | Oil consumption monitoring method, oil consumption monitoring device and engineering vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113624291A true CN113624291A (en) | 2021-11-09 |
Family
ID=78381209
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110853728.9A Pending CN113624291A (en) | 2021-07-27 | 2021-07-27 | Oil consumption monitoring method, oil consumption monitoring device and engineering vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113624291A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114202313A (en) * | 2021-12-15 | 2022-03-18 | 中国电信股份有限公司 | Vehicle fuel consumption management method and device, computer storage medium and electronic equipment |
| CN114282707A (en) * | 2021-11-23 | 2022-04-05 | 盛景智能科技(嘉兴)有限公司 | Oil consumption prediction model training method, oil consumption prediction device and operation machine |
| CN114954299A (en) * | 2022-05-19 | 2022-08-30 | 李诣坤 | Fuel consumption early warning method and system for reducing fuel consumption of automobile |
| CN116398307A (en) * | 2021-12-28 | 2023-07-07 | 深圳联友科技有限公司 | Vehicle fuel-saving driving recommendation method and device and computer-readable storage medium |
| CN116620314A (en) * | 2023-04-14 | 2023-08-22 | 瑞修得信息科技(无锡)有限公司 | Driving auxiliary prompting method and device based on vehicle trip route |
| CN116644865A (en) * | 2023-07-27 | 2023-08-25 | 中汽信息科技(天津)有限公司 | Commercial vehicle fuel consumption prediction method, electronic equipment and storage medium |
| CN116858324A (en) * | 2023-06-27 | 2023-10-10 | 瑞修得信息科技(无锡)有限公司 | Fuel consumption abnormality detection method and device based on vehicle fueling bill |
| CN117168558A (en) * | 2023-11-03 | 2023-12-05 | 山东奥斯登房车有限公司 | High-end intelligent real-time monitoring method for fuel consumption of caravan |
| CN118254807A (en) * | 2024-05-31 | 2024-06-28 | 长城汽车股份有限公司 | Driving behavior optimization method and device, vehicle and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006118480A (en) * | 2004-10-25 | 2006-05-11 | Mitsubishi Fuso Truck & Bus Corp | Fuel consumption rate predicting device for vehicle |
| CN104833405A (en) * | 2014-02-06 | 2015-08-12 | Avl里斯脱有限公司 | Method for testing functionality of assembly for dynamic measuring of fuel consumption |
| CN109606286A (en) * | 2018-12-10 | 2019-04-12 | 英华达（上海）科技有限公司 | Vehicle oil consumption prediction technique, automobile navigation method and electronic equipment |
| CN110901645A (en) * | 2019-11-11 | 2020-03-24 | 东风航盛（武汉）汽车控制系统有限公司 | Method for calculating endurance mileage of extended-range hybrid electric vehicle |
| US20200158553A1 (en) * | 2018-11-20 | 2020-05-21 | GM Global Technology Operations LLC | Method to predict remaining time and expected consumption before a fluid sysytem reaches a low fluid level |
| CN111523701A (en) * | 2020-03-27 | 2020-08-11 | 广州亚美信息科技有限公司 | Method and device for evaluating vehicle running condition, computer equipment and storage medium |
| CN112883648A (en) * | 2021-02-23 | 2021-06-01 | 一汽解放汽车有限公司 | Training method and device for automobile fuel consumption prediction model and computer equipment |
-
2021
- 2021-07-27 CN CN202110853728.9A patent/CN113624291A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006118480A (en) * | 2004-10-25 | 2006-05-11 | Mitsubishi Fuso Truck & Bus Corp | Fuel consumption rate predicting device for vehicle |
| CN104833405A (en) * | 2014-02-06 | 2015-08-12 | Avl里斯脱有限公司 | Method for testing functionality of assembly for dynamic measuring of fuel consumption |
| US20200158553A1 (en) * | 2018-11-20 | 2020-05-21 | GM Global Technology Operations LLC | Method to predict remaining time and expected consumption before a fluid sysytem reaches a low fluid level |
| CN109606286A (en) * | 2018-12-10 | 2019-04-12 | 英华达（上海）科技有限公司 | Vehicle oil consumption prediction technique, automobile navigation method and electronic equipment |
| CN110901645A (en) * | 2019-11-11 | 2020-03-24 | 东风航盛（武汉）汽车控制系统有限公司 | Method for calculating endurance mileage of extended-range hybrid electric vehicle |
| CN111523701A (en) * | 2020-03-27 | 2020-08-11 | 广州亚美信息科技有限公司 | Method and device for evaluating vehicle running condition, computer equipment and storage medium |
| CN112883648A (en) * | 2021-02-23 | 2021-06-01 | 一汽解放汽车有限公司 | Training method and device for automobile fuel consumption prediction model and computer equipment |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114282707A (en) * | 2021-11-23 | 2022-04-05 | 盛景智能科技(嘉兴)有限公司 | Oil consumption prediction model training method, oil consumption prediction device and operation machine |
| CN114202313A (en) * | 2021-12-15 | 2022-03-18 | 中国电信股份有限公司 | Vehicle fuel consumption management method and device, computer storage medium and electronic equipment |
| CN116398307A (en) * | 2021-12-28 | 2023-07-07 | 深圳联友科技有限公司 | Vehicle fuel-saving driving recommendation method and device and computer-readable storage medium |
| CN114954299A (en) * | 2022-05-19 | 2022-08-30 | 李诣坤 | Fuel consumption early warning method and system for reducing fuel consumption of automobile |
| CN114954299B (en) * | 2022-05-19 | 2023-02-17 | 李诣坤 | Fuel consumption early warning method and system for reducing fuel consumption of automobile |
| CN116620314B (en) * | 2023-04-14 | 2024-02-02 | 瑞修得信息科技(无锡)有限公司 | Driving auxiliary prompting method and device based on vehicle trip route |
| CN116620314A (en) * | 2023-04-14 | 2023-08-22 | 瑞修得信息科技(无锡)有限公司 | Driving auxiliary prompting method and device based on vehicle trip route |
| CN116858324A (en) * | 2023-06-27 | 2023-10-10 | 瑞修得信息科技(无锡)有限公司 | Fuel consumption abnormality detection method and device based on vehicle fueling bill |
| CN116644865A (en) * | 2023-07-27 | 2023-08-25 | 中汽信息科技(天津)有限公司 | Commercial vehicle fuel consumption prediction method, electronic equipment and storage medium |
| CN116644865B (en) * | 2023-07-27 | 2023-10-20 | 中汽信息科技(天津)有限公司 | Commercial vehicle fuel consumption prediction method, electronic equipment and storage medium |
| CN117168558A (en) * | 2023-11-03 | 2023-12-05 | 山东奥斯登房车有限公司 | High-end intelligent real-time monitoring method for fuel consumption of caravan |
| CN117168558B (en) * | 2023-11-03 | 2024-01-16 | 山东奥斯登房车有限公司 | High-end intelligent real-time monitoring method for fuel consumption of caravan |
| CN118254807A (en) * | 2024-05-31 | 2024-06-28 | 长城汽车股份有限公司 | Driving behavior optimization method and device, vehicle and storage medium |
| CN118254807B (en) * | 2024-05-31 | 2024-09-27 | 长城汽车股份有限公司 | Driving behavior optimization method and device, vehicle and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113624291A (en) | Oil consumption monitoring method, oil consumption monitoring device and engineering vehicle | |
| JP4453764B2 (en) | Vehicle diagnostic device, vehicle diagnostic system, and diagnostic method | |
| CN111506048B (en) | Vehicle fault early warning method and related equipment | |
| WO2001086576A1 (en) | Monitoring of vehicle health based on historical information | |
| CN110826772A (en) | Vehicle exhaust emission prediction method, device, equipment and storage medium | |
| CN110097219B (en) | Electric vehicle operation and maintenance optimization method based on safety tree model | |
| CN105139648A (en) | Driving habit data generation method and system | |
| CN108398934A (en) | The system that a kind of equipment fault for rail traffic monitors | |
| CN111340280A (en) | System, method, device and medium for monitoring, preventing and controlling pollutant discharge amount | |
| CN110207996A (en) | Turbine engine failure method for early warning and device | |
| CN115599077B (en) | Vehicle fault delimiting method and device, electronic equipment and storage medium | |
| JP2022136039A (en) | Method for determining the operating state of vehicle components | |
| CN114200334A (en) | Storage battery early warning method and device, vehicle and medium | |
| CN110084500B (en) | Electric vehicle safety control method based on safety tree probability and safety importance degree and electric vehicle | |
| CN117994979A (en) | Multi-target fusion data processing method and device | |
| CN112699490B (en) | Vehicle maintenance result verification method and device | |
| Zorin et al. | Reliability and risk assessment of machine building items according to diagnostics results | |
| CN118013231B (en) | Intelligent automobile fault diagnosis method and device and intelligent automobile instrument panel system | |
| CN108415401A (en) | A kind of engineering truck Measuring error data managing method and system | |
| CN111145381B (en) | Safety state evaluation method of electric vehicle and electric vehicle | |
| CN116861199B (en) | Emission reduction evaluation method, equipment and storage medium for fuel oil cleaning synergistic agent | |
| CN115214700A (en) | Vehicle health management method and system | |
| CN118225983B (en) | Exhaust gas detection alarm system and detection method thereof | |
| CN118656594B (en) | Cloud security monitoring system and monitoring method based on single car-multi-car fusion | |
| CN114252195B (en) | Rail pressure credibility detection method, device, storage medium and equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |