CN112819031A - Vehicle-mounted weight prediction method and system, electronic device and medium - Google Patents

Vehicle-mounted weight prediction method and system, electronic device and medium Download PDF

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CN112819031A
CN112819031A CN202110001692.1A CN202110001692A CN112819031A CN 112819031 A CN112819031 A CN 112819031A CN 202110001692 A CN202110001692 A CN 202110001692A CN 112819031 A CN112819031 A CN 112819031A
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vehicle
speed
load
segments
engine
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CN112819031B (en
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刘昱
李菁元
安晓盼
于晗正男
马琨其
付铁强
颜燕
李孟良
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China Automotive Technology and Research Center Co Ltd
CATARC Automotive Test Center Tianjin Co Ltd
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CATARC Automotive Test Center Tianjin Co Ltd
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Abstract

The invention relates to a vehicle-mounted weight prediction method and system, electronic equipment and a medium. The vehicle-mounted weight prediction method comprises the following steps: (a) vehicle driving data acquisition and parameter calculation: acquiring the engine speed, the engine load percentage and the ECU speed of the vehicle in the running process under different load conditions; calculating to obtain the current torque and the current transmission ratio of the transmission; (b) cutting segments and screening constant-speed segments; (c) training a relevant vector machine model; (d) load prediction: and for the vehicle with unknown load, inputting the average transmission gear ratio, the average speed and the average torque of the screened segments into the correlation vector machine model to obtain the load prediction result of each segment, and further obtaining the prediction result of the vehicle load. The method is simple and easy to implement, low in cost, high in efficiency, free from the limitation of site conditions, and capable of effectively monitoring the actual running load of the vehicle.

Description

Vehicle-mounted weight prediction method and system, electronic device and medium
Technical Field
The invention relates to the field of transportation, in particular to a vehicle-mounted weight prediction method and system, electronic equipment and a medium.
Background
With the continuous progress of science and technology and society of China, the road traffic transportation industry also realizes rapid development, but as the supervision of traffic control departments lacks powerful means and the safety consciousness of the national people is relatively weak, the traffic safety situation of China is increasingly severe. In recent years, although the degree of investment and attention on highway construction and maintenance of the country is increased year by year, particularly on expressways, the overload phenomenon is still forbidden, the overload of the commercial vehicle not only damages the national highway construction, but also increases the driving danger, and a lot of serious traffic accidents are caused by overload.
The existing vehicle load testing method is limited by factors such as technology, cost and field, and effective monitoring of the actual running load of the vehicle cannot be achieved. For the whole vehicle enterprises, the development and matching of the vehicle need a large amount of load data in the actual running process of the vehicle, and if the load estimation is unreasonable, the matching calibration of a vehicle type power system can be greatly influenced, so that the oil consumption, the emission and the power performance in the actual running of the vehicle are influenced. In addition, if the actual load of the vehicle can be monitored in real time, the method has important significance for reasonably distributing the load of different vehicles, improving the transport efficiency of a fleet and reducing the transport cost.
In view of the above, the present invention is particularly proposed.
Disclosure of Invention
In a first aspect, the present invention is directed to a vehicle-mounted weight prediction method, which is simple, convenient, low-cost, high-efficiency, and free from the limitation of site conditions, and can effectively monitor the actual running load of a vehicle.
In a second aspect, the present invention is directed to an electronic device.
In a third aspect, the present invention is directed to a medium.
In a fourth aspect, the present invention is directed to a vehicle-mounted weight prediction system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a vehicle-mounted weight prediction method, which comprises the following steps:
(a) vehicle driving data acquisition and parameter calculation:
a1, acquiring the engine speed, the engine load percentage and the ECU speed of the vehicle in the running process under different load conditions;
a2, obtaining the maximum torque of the engine under the engine speed according to the external characteristic curve of the engine and the engine speed, and obtaining the current torque according to the maximum torque of the engine and the load percentage of the engine;
a3, obtaining the transmission ratio of the current transmission according to the wheel radius, the final gear ratio, the engine speed and the ECU speed;
(b) cutting and screening uniform speed fragments: dividing time segments, removing idle time segments to obtain continuous motion segments, and screening constant-speed segments from the continuous motion segments;
(c) training a relevant vector machine model: taking the average transmission ratio, the average speed and the average torque of each constant speed segment as input, taking the load as output, and training a relevant vector machine to obtain a relevant vector machine model;
(d) load prediction: for a vehicle with unknown load, collecting the engine speed, the engine load percentage and the ECU speed, then obtaining the current torque according to the step a2, and obtaining the current transmission ratio according to the step a 3; and (c) carrying out segment cutting and uniform-speed segment screening according to the step (b), and inputting the average transmission gear ratio, the average speed and the average torque of the screened segments into the related vector machine model to obtain the load prediction result of each segment, thereby obtaining the prediction result of the vehicle load.
As a further preferable technical solution, in step a3, the current transmission gear ratio is calculated by the following method:
Figure BDA0002881569970000031
where v is the ECU speed, n is the engine speed, r is the wheel radius, i0Is the transmission ratio of the transmission; i.e. igIs a main reduction ratio.
As a further preferable technical solution, in the step (b), the method of dividing time slices includes: and dividing the vehicle speed per second into different time segments by adopting a moving window method.
As a further preferred technical solution, in the step (b), the method for screening uniform velocity fragments comprises: and calculating the speed variation coefficient of the continuous motion segment, sequencing the speed variation coefficients from large to small, and reserving the last 20 percent of segments as uniform-speed segments.
As a further preferable technical solution, the vehicle speed variation coefficient is calculated by the following method:
Figure BDA0002881569970000033
wherein cv is the coefficient of variation of vehicle speed, std (v) is the standard deviation of vehicle speed,
Figure BDA0002881569970000032
is the average vehicle speed.
As a further preferred technical scheme, in the step (c), the parameters of the correlation vector machine are obtained by adopting an artificial bee colony algorithm;
preferably, the total number of bees of the artificial bee colony algorithm is 20-50, the number of honey-collected bees is 10-25, the maximum search frequency is 50-100, and the maximum iteration frequency is 50-150;
preferably, the total number of bees is 30-50, and more preferably 40;
preferably, the number of the honey bees is 15-25, and more preferably 20;
preferably, the maximum number of searches is 50-80, and more preferably 50;
preferably, the maximum number of iterations is 80-120, and more preferably 100.
As a further preferable mode, in the step (d), after the load prediction results of the respective segments are obtained, the load prediction results of the respective segments are ranked, and the average value of the load results between 5% and 95% of the branch lines is calculated as the prediction result of the vehicle load.
In a second aspect, the present invention provides an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described vehicle weight prediction method.
In a third aspect, the present invention provides a medium having stored thereon computer instructions for causing the computer to execute the above-described vehicle-mounted weight prediction method.
In a fourth aspect, the invention provides a vehicle-mounted weight prediction system, which comprises a vehicle driving data acquisition module, a parameter calculation module, a segment cutting module, a constant-speed segment screening module, a correlation vector machine training module and a load prediction module;
the vehicle driving data acquisition module, the parameter calculation module, the segment cutting module, the constant-speed segment screening module and the related vector machine training module are sequentially connected;
the load forecasting module is also connected with the uniform speed segment screening module.
Compared with the prior art, the invention has the beneficial effects that:
the vehicle-mounted weight prediction method provided by the invention is used for acquiring and calculating parameters of the running data of the known load-carrying vehicle, performing segment cutting and uniform-speed segment screening, obtaining a related vector machine model through training, and then performing load prediction on the vehicle with unknown load by adopting the related vector machine model, thereby realizing dynamic monitoring on the load of the vehicle.
The method is simple, convenient and easy to implement, low in cost, high in efficiency and not limited by site conditions, can effectively test the load of the vehicle in actual operation, is convenient for effective supervision of the heavy commercial vehicle load by the government, provides support for dynamic design and matching optimization of vehicle types of whole vehicle enterprises, and improves the carrying efficiency of a fleet.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a basic flow diagram of a vehicle weight prediction method provided by the present invention;
FIG. 2 is a schematic diagram of the vehicle weight prediction system provided by the present invention;
FIG. 3 is a schematic view of a vehicle driving data collection method according to embodiment 1 of the present invention;
FIG. 4 is an engine external characteristic curve according to embodiment 1 of the present invention;
FIG. 5 is a fitness curve in example 1 of the present invention;
fig. 6 is a result of predicting the vehicle load in embodiment 1 of the present invention.
Icon: 1-vehicle driving data acquisition module; 2-a parameter calculation module; 3-a fragment cutting module; 4-uniform fragment screening module; 5-a correlation vector machine training module; 6-load prediction module; 7-the driver; 8-load information; 9-vehicle terminal; 10-vehicle ODB interface; 11-vehicle travel data; 12-a mobile base station; 13-remote platform.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
According to an aspect of the present invention, as shown in fig. 1, there is provided a vehicle-mounted weight prediction method including the steps of:
(a) vehicle driving data acquisition and parameter calculation:
a1, acquiring the engine speed, the engine load percentage and the ECU speed of the vehicle in the running process under different load conditions;
a2, obtaining the maximum torque of the engine under the engine speed according to the external characteristic curve of the engine and the engine speed, and obtaining the current torque according to the maximum torque of the engine and the load percentage of the engine;
a3, obtaining the transmission ratio of the current transmission according to the wheel radius, the final gear ratio, the engine speed and the ECU speed;
(b) cutting and screening uniform speed fragments: dividing time segments, removing idle time segments to obtain continuous motion segments, and screening constant-speed segments from the continuous motion segments;
(c) training a relevant vector machine model: taking the average transmission ratio, the average speed and the average torque of each constant speed segment as input, taking the load as output, and training a relevant vector machine to obtain a relevant vector machine model;
(d) load prediction: for a vehicle with unknown load, collecting the engine speed, the engine load percentage and the ECU speed, then obtaining the current torque according to the step a2, and obtaining the current transmission ratio according to the step a 3; and (c) carrying out segment cutting and uniform-speed segment screening according to the step (b), and inputting the average transmission gear ratio, the average speed and the average torque of the screened segments into the related vector machine model to obtain the load prediction result of each segment, thereby obtaining the prediction result of the vehicle load.
The vehicle-mounted weight prediction method is used for acquiring the running data of the known load-carrying vehicle and calculating parameters, performing segment cutting and uniform-speed segment screening, obtaining a relevant vector machine model through training, and then adopting the relevant vector machine model to predict the load of the vehicle with unknown load, thereby realizing the dynamic monitoring of the vehicle load.
The method is simple, convenient and easy to implement, low in cost, high in efficiency and not limited by site conditions, can effectively test the load of the vehicle in actual operation, is convenient for effective supervision of the heavy commercial vehicle load by the government, provides support for dynamic design and matching optimization of vehicle types of whole vehicle enterprises, and improves the carrying efficiency of a fleet.
It should be noted that:
the "average transmission gear ratio" mentioned above refers to the arithmetic mean of the respective current transmission gear ratios in the constant speed segment.
The above-mentioned "average vehicle speed" refers to an arithmetic average of the vehicle speeds of the respective ECUs in the uniform speed segment.
The above-mentioned "average torque" refers to the arithmetic average of the respective current torques in the constant speed segment.
In a preferred embodiment, in step a3, the current transmission gear ratio is calculated using the following method:
Figure BDA0002881569970000071
where v is the ECU speed, n is the engine speed, r is the wheel radius, i0Is the transmission ratio of the transmission; i.e. igIs a main reduction ratio.
I above0The (transmission gear ratio) is determined by the output gear.
In a preferred embodiment, in step (b), the method of dividing the time slices comprises: and dividing the vehicle speed per second into different time segments by adopting a moving window method.
In a preferred embodiment, in step (b), the method for screening uniform velocity fragments comprises: calculating the speed variation coefficient of the continuous motion segment, sequencing the speed variation coefficients from large to small, and reserving the last 20 percent of segments as uniform-speed segments;
or, calculating the speed variation coefficient of the continuous motion segment, sequencing the speed variation coefficients from small to large, and reserving the first 20 percent of segments as uniform-speed segments.
Preferably, the vehicle speed variation coefficient is calculated by the following method:
Figure BDA0002881569970000072
wherein cv is the coefficient of variation of vehicle speed, std (v) is the standard deviation of speed,
Figure BDA0002881569970000073
is the average vehicle speed.
In a preferred embodiment, in step (c), the parameters of the correlation vector machine are obtained by using an artificial bee colony algorithm. The artificial bee colony algorithm can select optimal parameters in a self-adaptive mode, is high in convergence speed and is not prone to falling into local optimal solutions.
Preferably, the total number of bees of the artificial bee colony algorithm is 20-50, the number of honey-collected bees is 10-25, the maximum search frequency is 50-100, and the maximum iteration frequency is 50-150. The total number of the bees is, for example, 20, 25, 30, 35, 40, 45 or 50; the number of the honey bees is, for example, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24 or 25; the maximum number of searches is, for example, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100; the maximum number of iterations is, for example, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, or 150.
Preferably, the total number of bees is 30-50, and more preferably 40.
Preferably, the number of the honey bees is 15-25, and more preferably 20.
Preferably, the maximum number of searches is 50 to 80, and more preferably 50.
Preferably, the maximum number of iterations is 80-120, and more preferably 100.
In a preferred embodiment, in the step (d), after the load prediction results of the segments are obtained, the load prediction results of the segments are ranked, and the average value of the load results between 5% and 95% of the branch lines is calculated as the prediction result of the vehicle load. The error of directly taking the average value of the load prediction results of all the segments as the prediction result of the vehicle load is relatively large, and the average value of the load results between 5% and 95% of the branch lines is taken as the prediction result of the vehicle load, so that the error can be greatly reduced, and the prediction accuracy is enhanced.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described method.
The at least one processor in the electronic device may perform the above method and thus have at least the same advantages as the above method.
According to another aspect of the present invention, there is provided a medium having stored thereon computer instructions for causing the computer to perform the above-described method. The computer instructions in the medium can be used to cause a computer to perform the above-described method, and thus have at least the same advantages as the above-described method.
According to another aspect of the present invention, as shown in fig. 2, a vehicle-mounted weight prediction system is provided, which includes a vehicle driving data acquisition module 1, a parameter calculation module 2, a segment cutting module 3, a constant speed segment screening module 4, a correlation vector machine training module 5, and a load prediction module 6;
the vehicle driving data acquisition module 1, the parameter calculation module 2, the segment cutting module 3, the constant-speed segment screening module 4 and the related vector machine training module 5 are sequentially connected;
the load forecasting module 6 is also connected with the uniform speed segment screening module 4.
In the above vehicle-mounted weight prediction system:
(1) the vehicle running data acquisition module is used for acquiring the engine speed, the engine load percentage and the ECU speed of the vehicle in the running process under different load conditions, and the engine speed, the engine load percentage and the ECU speed of the vehicle with unknown load in the running process.
(2) The parameter calculation module is used for obtaining the current torque and the current transmission ratio of the known load-carrying vehicle and the unknown load-carrying vehicle, specifically, obtaining the maximum torque of the engine under the engine speed according to the collected engine speed and the combination of an external characteristic curve of the engine, obtaining the current torque according to the maximum torque of the engine and the load percentage of the engine, and obtaining the current transmission ratio according to the wheel radius, the main reduction ratio, the engine speed and the ECU vehicle speed.
(3) And the segment cutting module is used for dividing time segments and removing idle time segments to obtain continuous motion segments.
(4) The constant-speed segment screening module is used for screening constant-speed segments from continuous motion segments of known load-carrying vehicles and unknown load-carrying vehicles.
(5) And the related vector machine training module is used for training the related vector machine according to the average transmission gear ratio, the average speed, the average torque and the load of each constant speed segment to obtain a related vector machine model.
(6) And the load prediction module is used for inputting the average transmission gear ratio, the average speed and the average torque of the constant speed segment obtained by the constant speed segment screening module into a related vector machine model obtained by the related vector machine training module to obtain the load prediction result of each segment, and further obtaining the prediction result of the vehicle load.
The vehicle-mounted weight prediction system corresponds to the vehicle-mounted weight prediction method, and the method is executed, so that the vehicle-mounted weight prediction system at least has the same advantages as the method, can realize dynamic monitoring of the vehicle load, has strong system integrity, can automatically perform dynamic monitoring on the vehicle load, and has high efficiency and low cost.
The present invention will be described in further detail with reference to examples.
Example 1
A vehicle-mounted weight prediction method comprises the following steps:
data acquisition
The test adopts an autonomous driving method to collect the actual operation data of 20 tractors of a certain type, the collection time is from 11/1/2016 to 5/31/2017, and the accumulated driving mileage is 160 kilometers. The test system consists of a vehicle-mounted data acquisition terminal (the sampling frequency is 1Hz) and a data management platform. The vehicle-mounted data acquisition terminal encodes the acquired information according to a uniform data protocol and transmits the encoded information to the working condition data management platform in real time through a GPRS network. The vehicle-mounted data acquisition terminal adopts a collection mode as shown in fig. 3, a driver 7 manually inputs load information 8 into a vehicle-mounted terminal 9, a vehicle ODB interface 10 collects vehicle driving data 11 to the vehicle-mounted terminal 9, the vehicle-mounted terminal 9 sends the two information to a mobile base station 12 through GPRS, and the mobile base station 12 sends the data to a remote platform 13 through the Internet. The parameters for the on-board weight prediction include ECU vehicle speed, engine load factor, and the like.
Data processing
According to the current rotation speed of the engine, the maximum torque of the engine at the rotation speed is obtained by using an external characteristic curve (shown in figure 4) of the engine. And multiplying the maximum torque of the engine at the rotating speed by the load percentage of the engine to obtain the current torque of the engine.
Calculating the transmission ratio of the current transmission according to the rotating speed of the engine, the radius of wheels, the final reduction ratio and the speed of the ECU:
Figure BDA0002881569970000111
wherein v is the current vehicle speed (km/h), n is the engine speed (r/min), r is the wheel radius (mm), i0The transmission ratio of the transmission is determined by an output gear; i.e. igIs a main reduction ratio;
setting the window duration as 100 seconds, and dividing the vehicle speed from second to second into different time segments by using a moving window method. Removing time slices containing idling, and calculating the vehicle speed variation coefficient of each time slice:
Figure BDA0002881569970000112
wherein cv is a variation lineAnd std (v) is a standard deviation of vehicle speed,
Figure BDA0002881569970000113
is the average vehicle speed.
And sequencing the fragments from large to small according to the variation coefficient, and reserving the fragments 20% behind the fluctuation value as uniform-speed fragments.
Model training
And training a correlation vector machine model by taking the average transmission gear ratio, the average speed and the average torque of each constant speed section as input and the load as output. Wherein, the kernel function parameter optimization of the correlation vector machine adopts an artificial bee colony algorithm. The total number of bees of the artificial bee colony algorithm is set to be 40, the number of honey-collected bees is set to be 20, the maximum search frequency is set to be 50, the maximum iteration frequency is set to be 100, and the fitness function is the prediction error percentage. The fitness curve of the optimization process is shown in fig. 5. And obtaining the optimal kernel function parameter of 1.7 after optimization.
Model validation
100 groups of samples are selected under each load, and the average transmission gear ratio, the average vehicle speed and the average torque are used as input to obtain load output of a plurality of segments. The results of the predictions of the load of the vehicle were ranked from large to small for each load, and the average of the results of the load between 5% and 95% of the split lines was calculated as the result of the prediction of the load, as shown in fig. 6. It can be seen that the predicted load and the actual load of the method provided by the embodiment have good correlation, and the average error is only 6.7%.
Model prediction
For vehicles with unknown loads, collecting the data of the engine speed, the ECU speed and the engine load percentage, and carrying out segment cutting and uniform-speed segment screening. And taking the average transmission gear ratio, the average speed and the average torque of the screened segments as input to obtain the prediction results of 100 uniform velocity segments. The predicted results are ranked from large to small, and the average value of the load results between 5% and 95% of the branch lines is calculated as the predicted result of the vehicle load.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

1. A vehicle-mounted weight prediction method is characterized by comprising the following steps:
(a) vehicle driving data acquisition and parameter calculation:
a1, acquiring the engine speed, the engine load percentage and the ECU speed of the vehicle in the running process under different load conditions;
a2, obtaining the maximum torque of the engine under the engine speed according to the external characteristic curve of the engine and the engine speed, and obtaining the current torque according to the maximum torque of the engine and the load percentage of the engine;
a3, obtaining the transmission ratio of the current transmission according to the wheel radius, the final gear ratio, the engine speed and the ECU speed;
(b) cutting and screening uniform speed fragments: dividing time segments, removing idle time segments to obtain continuous motion segments, and screening constant-speed segments from the continuous motion segments;
(c) training a relevant vector machine model: taking the average transmission ratio, the average speed and the average torque of each constant speed segment as input, taking the load as output, and training a relevant vector machine to obtain a relevant vector machine model;
(d) load prediction: for a vehicle with unknown load, collecting the engine speed, the engine load percentage and the ECU speed, then obtaining the current torque according to the step a2, and obtaining the current transmission ratio according to the step a 3; and (c) carrying out segment cutting and uniform-speed segment screening according to the step (b), and inputting the average transmission gear ratio, the average speed and the average torque of the screened segments into the related vector machine model to obtain the load prediction result of each segment, thereby obtaining the prediction result of the vehicle load.
2. The on-board weight prediction method of claim 1, wherein in step a3, the current transmission gear ratio is calculated using the following method:
Figure FDA0002881569960000011
where v is the ECU speed, n is the engine speed, r is the wheel radius, i0Is the transmission ratio of the transmission; i.e. igIs a main reduction ratio.
3. The vehicle-mounted weight prediction method according to claim 1, wherein in the step (b), the method of dividing the time slice includes: and dividing the vehicle speed per second into different time segments by adopting a moving window method.
4. The vehicle-mounted weight prediction method according to claim 1, wherein in the step (b), the method for screening the uniform speed segments comprises the following steps: and calculating the speed variation coefficient of the continuous motion segment, sequencing the speed variation coefficients from large to small, and reserving the last 20 percent of segments as uniform-speed segments.
5. The on-vehicle weight prediction method according to claim 4, wherein the vehicle speed variation coefficient is calculated by:
Figure FDA0002881569960000021
wherein cv is the coefficient of variation of vehicle speed, std (v) is the standard deviation of vehicle speed,
Figure FDA0002881569960000022
is the average vehicle speed.
6. The vehicle-mounted weight prediction method according to claim 1, wherein in the step (c), the parameters of the correlation vector machine are obtained by using an artificial bee colony algorithm;
preferably, the total number of bees of the artificial bee colony algorithm is 20-50, the number of honey-collected bees is 10-25, the maximum search frequency is 50-100, and the maximum iteration frequency is 50-150;
preferably, the total number of bees is 30-50, and more preferably 40;
preferably, the number of the honey bees is 15-25, and more preferably 20;
preferably, the maximum number of searches is 50-80, and more preferably 50;
preferably, the maximum number of iterations is 80-120, and more preferably 100.
7. The on-board vehicle weight prediction method according to any one of claims 1 to 6, wherein in the step (d), after the load prediction results of the respective segments are obtained, the load prediction results of the respective segments are ranked, and an average value of the load results between 5% and 95% of the branch lines is calculated as the prediction result of the vehicle load.
8. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the on-board weight prediction method of any of claims 1-6.
9. A medium having stored thereon computer instructions for causing a computer to execute the vehicle weight prediction method of any one of claims 1-6.
10. A vehicle-mounted weight prediction system is characterized by comprising a vehicle driving data acquisition module, a parameter calculation module, a segment cutting module, a constant-speed segment screening module, a correlation vector machine training module and a load prediction module;
the vehicle driving data acquisition module, the parameter calculation module, the segment cutting module, the constant-speed segment screening module and the related vector machine training module are sequentially connected;
the load forecasting module is also connected with the uniform speed segment screening module.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114036830A (en) * 2021-11-03 2022-02-11 厦门大学 Bus load prediction method and device based on multi-view ensemble learning
CN114386892A (en) * 2021-11-30 2022-04-22 江铃汽车股份有限公司 Transportation vehicle load management method, system, storage medium and equipment
CN114735015A (en) * 2022-04-14 2022-07-12 广西玉柴机器股份有限公司 Diesel engine commercial vehicle load assessment method
CN116662897A (en) * 2023-08-01 2023-08-29 中汽研汽车检验中心（天津）有限公司 Vehicle load prediction method based on hub data
CN116821775A (en) * 2023-08-29 2023-09-29 陕西重型汽车有限公司 Load estimation method based on machine learning
CN117494585A (en) * 2023-12-29 2024-02-02 中汽研汽车检验中心（天津）有限公司 Commercial vehicle actual load prediction method based on mutual information and data blurring
US20240109548A1 (en) * 2022-10-04 2024-04-04 Geotab Inc. Systems and methods for determining an estimated weight of a vehicle

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114676648B (en) * 2022-05-30 2022-08-05 岚图汽车科技有限公司 Vehicle load spectrum prediction method and device based on machine learning

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750422A (en) * 2012-07-03 2012-10-24 中国东方电气集团有限公司 Design method of electric car driving system
CN107564276A (en) * 2017-07-06 2018-01-09 重庆大学 A kind of traffic incidents detection method based on traffic behavior mutation
CN107680375A (en) * 2017-09-29 2018-02-09 深圳市易成自动驾驶技术有限公司 A kind of vehicle load computational methods, device and storage medium
CN108446562A (en) * 2018-03-27 2018-08-24 桂林电子科技大学 Intrusion detection method based on taboo with artificial bee colony bidirectional optimistic support vector machines
CN110044451A (en) * 2019-04-30 2019-07-23 湖南行必达网联科技有限公司 Weighing method, device and the electronic equipment of vehicle load
CN110148230A (en) * 2019-05-20 2019-08-20 兴民智通(武汉)汽车技术有限公司 A kind of vehicle load-carrying prediction technique based on LSTM neural network
CN111340280A (en) * 2020-02-19 2020-06-26 宜通世纪物联网研究院(广州)有限公司 System, method, device and medium for monitoring, preventing and controlling pollutant discharge amount
CN111579037A (en) * 2020-04-29 2020-08-25 北理新源(佛山)信息科技有限公司 Method and system for detecting vehicle overload

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750422A (en) * 2012-07-03 2012-10-24 中国东方电气集团有限公司 Design method of electric car driving system
CN107564276A (en) * 2017-07-06 2018-01-09 重庆大学 A kind of traffic incidents detection method based on traffic behavior mutation
CN107680375A (en) * 2017-09-29 2018-02-09 深圳市易成自动驾驶技术有限公司 A kind of vehicle load computational methods, device and storage medium
CN108446562A (en) * 2018-03-27 2018-08-24 桂林电子科技大学 Intrusion detection method based on taboo with artificial bee colony bidirectional optimistic support vector machines
CN110044451A (en) * 2019-04-30 2019-07-23 湖南行必达网联科技有限公司 Weighing method, device and the electronic equipment of vehicle load
CN110148230A (en) * 2019-05-20 2019-08-20 兴民智通(武汉)汽车技术有限公司 A kind of vehicle load-carrying prediction technique based on LSTM neural network
CN111340280A (en) * 2020-02-19 2020-06-26 宜通世纪物联网研究院(广州)有限公司 System, method, device and medium for monitoring, preventing and controlling pollutant discharge amount
CN111579037A (en) * 2020-04-29 2020-08-25 北理新源(佛山)信息科技有限公司 Method and system for detecting vehicle overload

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114036830A (en) * 2021-11-03 2022-02-11 厦门大学 Bus load prediction method and device based on multi-view ensemble learning
CN114036830B (en) * 2021-11-03 2024-06-11 厦门大学 Bus load prediction method and device based on multi-view integrated learning
CN114386892A (en) * 2021-11-30 2022-04-22 江铃汽车股份有限公司 Transportation vehicle load management method, system, storage medium and equipment
CN114735015A (en) * 2022-04-14 2022-07-12 广西玉柴机器股份有限公司 Diesel engine commercial vehicle load assessment method
CN114735015B (en) * 2022-04-14 2024-03-29 广西玉柴机器股份有限公司 Load assessment method for commercial diesel vehicle
US20240109548A1 (en) * 2022-10-04 2024-04-04 Geotab Inc. Systems and methods for determining an estimated weight of a vehicle
US12036998B2 (en) * 2022-10-04 2024-07-16 Geotab Inc. Systems and methods for determining an estimated weight of a vehicle
CN116662897A (en) * 2023-08-01 2023-08-29 中汽研汽车检验中心（天津）有限公司 Vehicle load prediction method based on hub data
CN116662897B (en) * 2023-08-01 2023-10-20 中汽研汽车检验中心（天津）有限公司 Vehicle load prediction method based on hub data
CN116821775A (en) * 2023-08-29 2023-09-29 陕西重型汽车有限公司 Load estimation method based on machine learning
CN117494585A (en) * 2023-12-29 2024-02-02 中汽研汽车检验中心（天津）有限公司 Commercial vehicle actual load prediction method based on mutual information and data blurring
CN117494585B (en) * 2023-12-29 2024-04-02 中汽研汽车检验中心（天津）有限公司 Commercial vehicle actual load prediction method based on mutual information and data blurring

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