CN112964345A - Freight car weighing system and weighing method thereof - Google Patents
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- G01G19/086—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for incorporation in vehicles wherein the vehicle mass is dynamically estimated
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Abstract
The invention discloses a freight car weighing system and a weighing method thereof, wherein the weighing system comprises the following steps: s1: acquiring data to obtain a calibration data set; s2: training a deep neural network model through a calibration data set; obtaining a vehicle load model; s3: and deducing and outputting the vehicle cargo mass according to the vehicle load model. Aiming at the defects of low precision and poor stability of the conventional vehicle-mounted load detection, a vehicle load model is obtained by utilizing a CNN model according to a vector Yi formed by the mass of a standard weight detected by an inclination angle sensor and a matrix Xij formed by a corresponding sensing data group; finally, obtaining the load-carrying mass of the automobile through a vehicle load model; the effect of high-precision detection and early warning for avoiding the overload of the automobile is achieved; compared with the traditional detection method, the detection method has higher accuracy and better detection comprehensiveness.
Description
Technical Field
The invention belongs to the technical field of load detection, and relates to a freight car weighing system and a weighing method thereof.
Background
With the continuous development of highway transportation systems and automobile industry technologies in China, a large amount of automobile overload phenomena also ensues. The overload transportation brings serious influence to bridges and highways, greatly shortens the service life of the highways, and causes certain harm to traffic safety, such as:
first, traffic accidents are easily induced. When the vehicle runs in overload for a long time, mechanical parts such as a brake system, a suspension system and the like are easy to generate fatigue damage and lose efficacy. After overload, the inertia force is increased, so that the brake is difficult to brake in a short time in emergency, and traffic accidents are easily induced.
Second, the traffic flow is reduced. After the vehicle is overloaded, when the vehicle runs on a highway, the vehicle must run at a speed greatly lower than the normal speed in order to ensure safety, so that the subsequent vehicle overstock can be caused, and the flow rate of a traffic system is reduced.
Third, the traffic system infrastructure is destroyed. The highway pavement materials prematurely develop fatigue failure under alternating pressures that exceed the design load, reducing the service life of the highway. Particularly, in a bridge system, under a large alternating load, abnormal fatigue fracture of the bridge in the service life period can happen, and even serious accidents such as bending, shearing and crushing of the bridge are directly exceeded. The bridge breakage accidents which occur many times in recent years in China are mostly caused by overloaded vehicles.
Fourthly, the highway maintenance cost is greatly increased. According to incomplete statistics, the highway departments in China generate maintenance cost of billions of yuan of extra road maintenance due to overload transportation every year. Therefore, the method has important practical significance for vigorously managing the vehicle overload and developing corresponding overload detection equipment.
Currently, two methods, static weighing and dynamic weighing, are mainly used for an overload detection system. Static weighing is that an overload detection station is arranged on a highway, and an overload vehicle is detected by using a static weighing device or a low-speed weighing device. Because the vehicle needs to be stationary on a large-scale weighbridge, the detection time of the overloaded vehicle is long, the traffic jam phenomenon of a highway at an overload detection station is easily caused, and the efficiency is low; on the other hand, many existing highways have no reserved areas for building overload detection stations in the construction period and have no redundant empty spaces for placing overload cargos, so that a dynamic weighing system is developed. Dynamic weighing is the weighing of a vehicle without stopping the vehicle. The dynamic weighing system has the advantage that the vehicle does not need to be stopped during weighing, but the weighing is less accurate than static weighing.
The main problem of the current mainstream vehicle-mounted weighing technology is that although the error of weighing data can be about 10% -20%, the error of the error touch increases with the time (reaches 70% -80%) under the condition of long-time use. This has greatly influenced the application and the popularization of on-vehicle dynamic weighing technique. One of the key factors affecting these two problems is the absence of a load model with strong generalization ability. The accuracy and stability of the load model with strong generalization capability have a crucial influence on data consideration, because the calibration of the sensor is performed under a static state, when the data of the automobile sensor are greatly different from the data of the automobile sensor in a moving state and a static state, if the generalization capability of the load model is not strong, the weighing data is inevitably large in error and unstable.
Reference documents:
[1] square wave, etc., based on the cantilever beam modal frequency mass weighing method of two-dimensional frequency domain optical vibration coherence tomography, vibration and impact, Vol 39, No.6,2020;
[2] popelin, what is analyzed by van chassis is the car body frame structure? Truck home, 2019.10.28;
http://www.360che.com/news/191028/119698.html；
[3] what is the chassis of the vehicle? Which parts it includes? Is we see the bottom of the car as the chassis? Fox search website, 2018.02.21;
https://www.sohu.com/a/223405432_560095；
[4] von bei, experimental study of low speed truck vibration, university of jiangsu, master academic thesis, 2007.04;
[5] the invention discloses a real-time vehicle-mounted weighing method, and patent application numbers 202010003153.7 and 2020.01.02 are disclosed;
[6] the invention discloses a dynamic intelligent monitoring load alarm device applied to a truck, which is authorized in patent application numbers 201720744295 and 2017.06.23;
[7] a high-precision vehicle-mounted region weighing method is 202010003157.5 Hande network science and technology Limited, Shenzhen.
Disclosure of Invention
In order to achieve the purpose, the invention adopts the following technical scheme:
a truck weighing system comprising: the vehicle-mounted terminal is electrically connected with the inclination angle sensor, and is in wireless data connection with the Internet of things platform.
As a further scheme of the invention: further comprising: a temperature sensor.
A weighing method for a freight car comprises the following steps:
s1: acquiring data to obtain a calibration data set;
s2: training a deep neural network model through a calibration data set; obtaining a vehicle load model;
s3: and deducing and outputting the vehicle cargo mass according to the vehicle load model.
As a further scheme of the invention: the calibration data set in S1 specifically includes: and a matrix Xij formed by a vector Yi formed by the mass of a plurality of standard weights and the corresponding sensing data group.
As a further scheme of the invention: the calibration data acquisition method specifically comprises the following steps:
starting from the empty state of the vehicle, after each piece of goods with the standard weight mass is loaded, the position of the goods is moved randomly once every certain time period within the period, and therefore each piece of standard weight mass can obtain a set of loading sensing data (A x B is N), wherein A is duration, B is interval time, and N is the quantity of the loading sensing data;
after loading, unloading, wherein after loading a piece of goods with standard weight mass, the position of the goods is moved randomly once every certain time period within a certain time period, so that each standard weight mass can obtain a group of unloading sensing data (A '. B'. N '), wherein A' is duration, B 'is interval time, and N' is the quantity of the unloading sensing data, and the step is the reverse process of the steps, so that the quantity of the loading sensing data is the same as the quantity of the unloading sensing data;
each standard weight mass can generate a group of loading sensing data and a group of unloading sensing data;
when a standard weight is loaded in the loading process, each inclination angle sensor on the vehicle can generate a data record;
thus, a standard weight mass will produce N sets of on-load and N sets of off-load sensory data (N corresponding to the number of vehicle-mounted tilt sensors) that make up the sensory data set.
As a further scheme of the invention: in said S2: the specific method for training the deep neural network model comprises the following steps: the calibration data set is sent to the Internet of things platform through the vehicle-mounted terminal;
the Internet of things platform selects a CNN model, inputs a calibration data set for training, but the last layer does not use an activation function, but directly takes an output vector for regression, and adopts a mean square error function as an optimized objective function to finish the training of a vehicle load model.
As a further scheme of the invention: in said S3: the specific method for deducing the mass of the goods output by the vehicle according to the load model of the vehicle comprises the following steps: and calling a comparison load model which is the same as the vehicle model by the platform of the Internet of things, and performing inference calculation by taking the current real-time sensor data record as the input of the load model to obtain the quality of the goods loaded on the vehicle.
As a further scheme of the invention: the vehicle-mounted terminal is internally provided with a GPS module, and the running state of the vehicle is detected through the GPS module.
As a further scheme of the invention: the following steps are also included after S3:
s4: installing an identification camera in a cargo carriage of a vehicle;
the vehicle-mounted terminal controls the recognition camera, after a period of time of carrying work of the running vehicle, the recognition camera recognizes the carriage, and when the carriage is in a no-load static state, the vehicle-mounted terminal controls the tilt angle sensor to carry out 0 value calibration.
The invention has the beneficial effects that: aiming at the defects of low precision and poor stability of the conventional vehicle-mounted load detection, a vehicle load model is obtained by utilizing a CNN model according to a vector Yi formed by the mass of a standard weight detected by an inclination angle sensor and a matrix Xij formed by a corresponding sensing data group; finally, obtaining the load-carrying mass of the automobile through a vehicle load model; the effect of high-precision detection and early warning for avoiding the overload of the automobile is achieved;
compared with the traditional detection method, the detection method has higher accuracy and better detection comprehensiveness.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described, and it should be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments, and it should be understood that the present application is not limited to the example embodiments disclosed and described herein. 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 application.
In an embodiment of the present invention, a truck weighing system includes: the system comprises a plurality of inclination sensors arranged on a spring steel plate of a vehicle to be detected, a vehicle-mounted terminal arranged in the vehicle to be detected and an Internet of things platform, wherein a plurality of comparison load models corresponding to each vehicle type are preset in the Internet of things platform, the vehicle-mounted terminal is in electric signal connection with the inclination sensors, and the vehicle-mounted terminal is in wireless data connection with the Internet of things platform;
the inclination angle sensors are generally installed at two ends of a spring steel plate of a vehicle, and the deformation degree of the position is high; in actual processing work, the spring steel plate is defined according to different vehicle types and the classification of the spring steel plate of the vehicle;
after the tilt sensor is installed, a system initialization calibration is performed to determine the cargo 0 value of the vehicle and set the threshold value for overload determination.
The initial calibration mode is to install the system in the no-load state. After the installation is finished, data are immediately collected to obtain the current mass value reported by the weighing system; then, calibrating and reporting the quality value to the Internet of things platform; the Internet of things platform fills the quality value into the no-load quality offset value of the truck; after formal operation, the vehicle overload judgment threshold correspondingly subtracts the offset value.
Further, the method also comprises the following steps: the temperature sensor, the precision of inclination sensor also can be influenced to temperature in inclination sensor's practical application, for this reason detects the external world so that the expend with heat and contract with cold effect of compensation spring steel sheet through temperature sensor, and in the work of use, can select the inclination sensor who has the temperature to detect the function according to actual presetting and cost, need not to purchase temperature sensor in addition.
Further, the number of the tilt sensors is determined according to the vehicle type, wherein the number of the tilt sensors of the 2-axle truck is 8, the number of the tilt sensors of the 3-axle truck is 12, and the like.
A weighing method for a freight car comprises the following steps:
s1: acquiring data to obtain a calibration data set;
s2: training a deep neural network model through a calibration data set; obtaining a vehicle load model;
s3: and deducing and outputting the vehicle cargo mass according to the vehicle load model.
Further, the calibration data set in S1 specifically includes: and a matrix Xij formed by a vector Yi formed by the mass of a plurality of standard weights and the corresponding sensing data group.
Further, the method for acquiring calibration data specifically includes:
starting from the empty state of the vehicle, after each piece of goods with the standard weight mass is loaded, the goods are continuously moved for a period of time, in the period, the goods are randomly moved once every certain time period, and the situations of side approaching, centering and the like are covered as far as possible, so that each standard weight mass can obtain a group of loading sensing data (A is the duration, B is the interval time, and N is the quantity of the loading sensing data);
after loading is finished, unloading is carried out, the unloading is that the reverse deduction calculation of the vehicle-mounted terminal is not manual unloading, after each piece of goods with the standard weight mass is loaded, the unloading lasts for a period of time, the position of the goods is randomly moved once every certain period of time in the period of time, and the goods are covered to the side, the center and other situations as far as possible, so that each piece of unloading sensing data (A '. B'. equals to N ') can be obtained by each piece of standard weight mass, wherein A' is duration, B 'is interval time, and N' is the quantity of the unloading sensing data, and the step is the reverse process of the step, so that the quantity of the loading sensing data is the same as the quantity of the unloading sensing data;
each standard weight mass can generate a group of loading sensing data and a group of unloading sensing data;
when a standard weight is loaded in the loading process, each inclination angle sensor on the vehicle can generate a data record;
therefore, the mass of one standard weight can generate N groups of loaded sensing data and N groups of unloaded sensing data which form a sensing data group (N corresponds to the number of the vehicle-mounted inclination sensors);
in addition, the vehicle can load and unload cargos only when stopped, so that the cargo carrying quality during driving can be regarded as unchanged; the calibration data is collected only when the vehicle is stopped;
practical application example:
in the embodiment, the total mass limit value of the 2-axle truck is 18 tons as a reference; the mass of the standard weight is 100 KG;
when the vehicle is loaded in an unloaded state, when the mass of one standard weight is increased (namely 100KG) continuously for 1 minute, the position of the cargo is randomly moved once every 3 seconds in the period, and various situations such as side leaning, centering and the like are covered as far as possible, so that when the mass of one standard weight is loaded, at least 120 pieces of data are generated by one inclination angle sensor;
the total mass limit of the 2-axle truck is 18 tons, so that 120x180 is 21600 pieces of data;
then, the vehicle-mounted terminal carries out unloading reverse deduction under the full-load state, and 120x 180-21600 pieces of data are generated in the same way;
it should be noted that the derived duration and the derived interval are not fixed, and the more the derived duration is longer and the more the derived interval is shorter in the application, the more the generated data amount is more accurate.
Further, in S2: the specific method for training the deep neural network model comprises the following steps: the calibration data set is sent to the Internet of things platform through the vehicle-mounted terminal;
the Internet of things platform selects a CNN model (such as LeNet, ResNet-18 and the like), inputs a calibration data set for training, but the last layer does not use an activation function, directly takes an output vector for regression, and adopts a mean square error function as an optimized target function to finish the training of a vehicle load model;
in the scheme, the relation between the physical observation quantity and the load-bearing cargo mass is established in the vehicle load model, and the vehicle load model is not subjected to dynamic stress (gravity, lateral pressure and wheel driving force are not considered).
It should be noted that it is directed to the load-bearing quality, and does not include the quality of the vehicle body and the driver. During overload monitoring calculation, the vehicle body mass, the driver mass and the load bearing cargo mass deduced by the vehicle load model are added to obtain the total overall mass, and then comparison is carried out.
Further, in S3: the specific method for deducing the mass of the goods output by the vehicle according to the load model of the vehicle comprises the following steps: and calling a comparison load model which is the same as the vehicle model by the platform of the Internet of things, and performing inference calculation by taking the current real-time sensor data record as the input of the load model to obtain the quality of the goods loaded on the vehicle.
Furthermore, a GPS module is arranged in the vehicle-mounted terminal, and the vehicle running state is detected through the GPS module.
Further, after S3, the method further includes the following steps:
s4: installing an identification camera in a cargo carriage of a vehicle;
the vehicle-mounted terminal controls the recognition camera,
along with the time lapse, the spring steel plate can be deformed to different degrees after being stressed for a long time, so that after a period of time of carrying work of vehicle running, the recognition camera recognizes the carriage, when the vehicle is in a no-load static state, the state is stable, the calibration data is more accurate, the vehicle-mounted terminal controls the tilt angle sensor to perform 0 value calibration, and the detection precision of the tilt angle sensor is higher by the method;
meanwhile, the freight bill data of the motorcade can be accessed through the vehicle-mounted terminal, so that the sensing data in the corresponding time period can be automatically found through the vehicle-mounted terminal, and the corresponding bearing quality value can be automatically calibrated.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A truck weighing system, comprising: the vehicle-mounted terminal is electrically connected with the inclination angle sensor, and is in wireless data connection with the Internet of things platform.
2. The truck weighing system of claim 1, further comprising: a temperature sensor.
3. A truck weighing method, a truck weighing system according to any one of claims 1 to 3, comprising the steps of:
s1: acquiring data to obtain a calibration data set;
s2: training a deep neural network model through a calibration data set; obtaining a vehicle load model;
s3: and deducing and outputting the vehicle cargo mass according to the vehicle load model.
4. The method for weighing a cargo vehicle according to claim 3, wherein the calibration data set in S1 specifically comprises: and a matrix consisting of a vector Yi formed by the mass of a plurality of standard weights and a corresponding sensing data group.
5. The method for weighing the freight car according to claim 4, wherein the method for acquiring the calibration data specifically comprises:
starting from the empty state of the vehicle, after each piece of goods with the standard weight mass is loaded, the position of the goods is moved randomly once every certain time period within the period, and therefore each piece of standard weight mass can obtain a set of loading sensing data (A x B is N), wherein A is duration, B is interval time, and N is the quantity of the loading sensing data;
after loading, unloading, wherein after loading a piece of goods with standard weight mass, the position of the goods is moved randomly once every certain time period within a certain time period, so that each standard weight mass can obtain a group of unloading sensing data (A '. B'. N '), wherein A' is duration, B 'is interval time, and N' is the quantity of the unloading sensing data, and the step is the reverse process of the steps, so that the quantity of the loading sensing data is the same as the quantity of the unloading sensing data;
each standard weight mass can generate a group of loading sensing data and a group of unloading sensing data;
when a standard weight is loaded in the loading process, each inclination angle sensor on the vehicle can generate a data record;
thus, a standard weight mass will produce N sets of on-load and N sets of off-load sensory data (N corresponding to the number of vehicle-mounted tilt sensors) that make up the sensory data set.
6. The method for weighing a cargo car according to claim 3, wherein in the step S2: the specific method for training the deep neural network model comprises the following steps: the calibration data set is sent to the Internet of things platform through the vehicle-mounted terminal;
the Internet of things platform selects a CNN model, inputs a calibration data set for training, but the last layer does not use an activation function, but directly takes an output vector for regression, and adopts a mean square error function as an optimized objective function to finish the training of a vehicle load model.
7. The method for weighing a cargo car according to claim 3, wherein in the step S3: the specific method for deducing the mass of the goods output by the vehicle according to the load model of the vehicle comprises the following steps: and calling a comparison load model which is the same as the vehicle model by the platform of the Internet of things, and performing inference calculation by taking the current real-time sensor data record as the input of the load model to obtain the quality of the goods loaded on the vehicle.
8. The weighing method for the freight cars as claimed in claim 3, wherein a GPS module is provided in the vehicle-mounted terminal, and the driving state of the cars is detected by the GPS module.
9. A method for weighing a freight car as claimed in claim 3, further comprising the following steps after S3:
s4: installing an identification camera in a cargo carriage of a vehicle;
the vehicle-mounted terminal controls the recognition camera, after a period of time of carrying work of the running vehicle, the recognition camera recognizes the carriage, and when the carriage is in a no-load static state, the vehicle-mounted terminal controls the tilt angle sensor to carry out 0 value calibration.
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| CN117788871A (en) * | 2023-12-26 | 2024-03-29 | 海南言发高科技有限公司 | Vehicle-mounted weighing management method and platform based on artificial intelligence |
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