CN113720429A - Vehicle separation method based on axle data in dynamic truck scale - Google Patents
Vehicle separation method based on axle data in dynamic truck scale Download PDFInfo
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- CN113720429A CN113720429A CN202111193582.6A CN202111193582A CN113720429A CN 113720429 A CN113720429 A CN 113720429A CN 202111193582 A CN202111193582 A CN 202111193582A CN 113720429 A CN113720429 A CN 113720429A
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- 238000000926 separation method Methods 0.000 title abstract description 9
- 238000005303 weighing Methods 0.000 claims abstract description 62
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000006467 substitution reaction Methods 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
- G01G19/03—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion
- G01G19/035—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion using electrical weight-sensitive devices
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Abstract
The invention relates to the technical field of weighing, in particular to a vehicle separation method based on axle data in a dynamic truck scale, which comprises the steps of firstly acquiring the axle data through the data acquired by a first bar-shaped weighing sensor and a second bar-shaped weighing sensor; in the dynamic motor scale, a plurality of axle data are fitted into vehicle data, so that the condition that the small-sized vehicle is missed to be detected does not occur by adopting a vehicle separation method based on the axle data to judge the vehicle based on the axle characteristics in the dynamic motor scale. Meanwhile, an annular coil does not need to be installed, the failure rate and the error rate are reduced, and the cost for installing the coil is reduced.
Description
Technical Field
The invention relates to the technical field of weighing, in particular to a vehicle separation method based on axle data in a dynamic truck scale.
Background
The dynamic truck scale usually uses a layout mode of a bar-shaped weighing sensor and a ring coil to complete the acquisition work of dynamic weighing data. Wherein the toroidal coil functions as a separator for a vehicle: when a vehicle enters the detection area, the vehicle runs on the annular coil, the annular coil can generate a high-voltage signal, and when the vehicle leaves the area of the annular coil, the voltage signal is reduced to a zero point, so that the voltage change can judge the entering and leaving of the vehicle, the vehicle to which the weighing data belongs can be further divided, and a series of weighing data can be separated according to the vehicle. The method mainly depends on the voltage signal generated by the loop coil when the vehicle moves on the loop coil. However, the use of toroids to separate vehicles may present a possibility of missed detection, such as when a small vehicle is traveling between two toroids, the toroids may not be able to detect the vehicle due to the limitations of the coverage of the toroids themselves.
Disclosure of Invention
The invention provides a vehicle separation method based on axle data in a dynamic truck scale, which solves the technical problem of possible missed detection when the existing dynamic truck scale uses a loop coil to separate vehicles by calculating and analyzing the collected values of a plurality of bar-type weighing sensors.
In order to achieve the purpose, the invention provides the following technical scheme: a dynamic vehicle scale comprising: the detection area is a section of area arranged along the driving direction of a lane, and a plurality of weighing modules are arranged in the detection area at intervals along the driving direction of the lane; each weighing module is composed of a first strip-shaped weighing sensor and a second strip-shaped weighing sensor, the first strip-shaped weighing sensor and the second strip-shaped weighing sensor are sequentially arranged at intervals along the driving direction of the lane, and the length directions of the first strip-shaped weighing sensor and the second strip-shaped weighing sensor are perpendicular to the driving direction of the lane; the first strip-shaped weighing sensor and the second strip-shaped weighing sensor in each weighing module are respectively and electrically connected with an acquisition interface of the front-end host and are used for feeding back acquired data; and the front-end host is in data communication with the monitoring background through the network communication module.
Preferably, the front-end host is provided with a calculation module and a vehicle type database; the vehicle type database is internally stored with a plurality of pieces of registered vehicle type data; the calculation module calculates the acquired data and generates acquired vehicle type data; the calculation module is also used for comparing the acquired vehicle type data with each registered vehicle type data and acquiring actually measured vehicle type data corresponding to one of the registered vehicle type data; and the communication module sends the actually measured vehicle model data and the acquired data to the monitoring background.
Preferably, a vehicle separation method based on axle data in dynamic truck scale includes the following steps: the front-end host computer obtains axle data through data acquired by the first bar-shaped weighing sensor and the second bar-shaped weighing sensor; and step two, the front-end host machine synthesizes vehicle data through the plurality of axle data.
Preferably, in the first step, the axle data includes maximum rough axle weight data collected by the first strip-type load cell and the second strip-type load cell, respectively, and rough axle speed data is obtained through a peak interval between the two rough axle weight data.
Preferably, in the second step, the axle data time of the first axle of the same vehicle is t1, the corresponding vehicle speed at the moment is v1, the axle data time of the second axle is t2, and the corresponding vehicle speed at the moment is v 2; the motion of the vehicle in the time when the first shaft and the second shaft pass through the weighing module is approximate to uniform acceleration linear motion, and the shaft distance L between the two shafts can be obtained by using the formula L (v1+ v2) × (t2-t 1)/2.
The invention has the beneficial effects that: and the vehicle is judged based on the axle characteristics, so that the condition of missed detection of the small vehicle can be avoided. Meanwhile, an annular coil does not need to be installed, the failure rate and the error rate are reduced, and the cost for installing the coil is reduced. The collected vehicle weighing data is only used for grouping the related axles, and whether the axle is the axle of a normal vehicle is judged according to the axle type relation formed among the axles, so that the related axles form complete vehicle data, and the aim of separating a series of weighing data according to the vehicle is fulfilled. The algorithm fully considers the mutual relation among all the shafts when the vehicle runs, and can completely separate data only by the mutual relation, and the annular coil is not needed to judge the entering and leaving of the vehicle.
Drawings
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the arrangement of the apparatus of the present invention;
FIG. 2 is a data acquisition curve in the weighing module of the present invention;
FIG. 3 is a data acquisition curve for a first strip load cell and a second strip load cell of the present invention;
FIG. 4 is a schematic diagram of a single axle data acquisition of the present invention;
FIG. 5 is a data acquisition curve of the weighing module of the axle set of the present invention
FIG. 6 is a schematic diagram of the axis set data acquisition of the present invention;
FIG. 7 is a flow chart of the separation method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 fig. 1, a dynamic motor scale comprises: the detection area 2 is a section of area arranged along the driving direction of the lane 1, and a plurality of weighing modules 3 are arranged in the detection area 2 at intervals along the driving direction of the lane 1; each weighing module 3 is composed of a first strip-shaped weighing sensor 4 and a second strip-shaped weighing sensor 5, the first strip-shaped weighing sensor 4 and the second strip-shaped weighing sensor 5 are sequentially arranged along the driving direction of the lane 1 at intervals, and the length directions of the first strip-shaped weighing sensor 4 and the second strip-shaped weighing sensor 5 are vertical to the driving direction of the lane 1; the first strip-shaped weighing sensor 4 and the second strip-shaped weighing sensor 5 in each weighing module 3 are respectively and electrically connected with an acquisition interface of the front-end host 6 and used for feeding back acquired data; the front-end host 6 is in data communication with the monitoring background 8 through the network communication module 7. The front-end host 6 is provided with a calculation module and a vehicle model database; the vehicle type database is internally stored with a plurality of pieces of registered vehicle type data; the calculation module calculates the acquired data and generates acquired vehicle type data; the calculation module is also used for comparing the acquired vehicle type data with each registered vehicle type data and acquiring actually measured vehicle type data corresponding to one of the registered vehicle type data; and the communication module sends the actually measured vehicle model data and the collected data to the monitoring background 8.
The weighing module 3 is arranged underground, signal data generated by the first strip-shaped weighing sensor 4 and the second strip-shaped weighing sensor 5 are collected continuously by using a digital signal acquisition card, and the continuous signal data reflect the passing condition of the detection area 2. These successive acquisitions can be plotted in a two-dimensional coordinate system, as shown in fig. 2, 3, and 4 below.
When no vehicle presses the detection area 2, the collected data express a horizontal straight line, and when the axle of the vehicle presses the weighing module 3, signal change is generated, and a protrusion is formed on a data image. And smoothing all signal data by using a mathematical algorithm, and filtering out background data, namely horizontal straight line data, so as to extract protrusion data generated by data change. The data of the protrusions on the images represent various characteristics of the axle when the axle presses the weighing module 3, mainly comprises the time of the axle pressing on and off the sensor, the maximum value of the generated signal change, the time when the signal reaches the maximum value, the area value of the generated whole protrusion and the like, and the data are called axle data.
As an example of practical application, the first strip-shaped load cell 4 and the second strip-shaped load cell 5 are configured as load cells at intervals designed so that when the axle passes through, the data structure collected is as shown in fig. 3, that is, the axle generates two projections, that is, axle signal data, respectively when passing through the first strip-shaped load cell 4 and the second strip-shaped load cell 5 in sequence. And (3) at the maximum moment of each shaft signal data, the time difference of the two moments is the time spent by the axle passing through the front row of sensors and the rear row of sensors, and the average speed of the shaft running between the two rows of sensors can be calculated by combining the distance between the two rows of sensors. Under the condition that a single axle passes through each weighing sensor, the average speed of the axle on each weighing sensor is calculated respectively, linear fitting is carried out on the average speeds, and a change curve of the speed of the axle and the speed value of the axle at each moment can be obtained.
In addition, when a vehicle with a plurality of axles passes through the weighing sensor, the speed change curve of each axle is obtained in the above manner, and the speeds of the axles on the same vehicle at the same time are equal, so that the speed change curve of each axle is further fitted according to time to obtain the speed change curve of the vehicle, and finally the speed value of the multi-axle vehicle at each time is obtained.
In the calculation process of the wheel base of each axle of the vehicle, the front axle and the rear axle pass through the same sensor as an example, and the speed of the vehicle at each moment can be obtained according to the previous algorithm as shown in the following fig. 5 and fig. 6. The axle data time of the first axle is t1, the corresponding vehicle speed at this time is v1, the axle data time of the second axle is t2, and the corresponding vehicle speed at this time is v 2. The motion of the vehicle in the time when the front and rear shafts pass through the sensors is approximate to uniform acceleration linear motion, and the wheel base between the two shafts can be obtained by using the formula L (v1+ v2) × (t2-t 1)/2.
Similarly, when the two shafts pass through the other row of sensors, one wheel base can be calculated according to the method, a plurality of wheel bases can be calculated through a plurality of rows of sensors, and a reasonable average wheel base is finally obtained by processing a plurality of wheel base data (generally, the discrete values of data samples are removed, then the rest data samples are averaged, and the data samples are directly averaged when the number of the data samples is too small). The wheelbase between each two adjacent axles of a vehicle can be calculated as the entire vehicle passes over multiple rows of sensors.
Compared with the coil mode in the prior art, the vehicle separation method based on the axle data does not use a coil, so that the signals of vehicle entering and vehicle leaving are equivalently absent, and the start and the end of one vehicle cannot be determined. And the method adopts intelligent generation and intelligent ending to divide the plurality of axle data into axle group data. When one axle data is generated, firstly judging whether a waiting axle group exists on the lane 1, if not, generating a brand new axle group by starting the axle data, setting the axle group in a waiting state, and waiting for other subsequent axle data; when new axle data are generated subsequently, if a waiting axle group is found, the new axle data can enter the axle group; every time one axle data is newly added to the axle group, the overall speed, the axle weights and the axle distances are recalculated according to the method; the intelligent ending judgment is that whether the shaft group in the waiting state waits for a long enough time or not is monitored in real time, the shaft group generally waits for a certain length of distance backwards, the distance can be configured according to specific conditions, generally 4 meters are obtained, and the value can be set to be smaller in a road section with serious traffic jam and car following. This waiting distance can be converted into a waiting period based on the calculated axle set speed, i.e. counting is started after a new axle enters the axle set, and if no new axle enters the axle set within the set waiting distance, the axle set is terminated, set to a finished state and added to the axle set list for subsequent processing.
In addition, in the process of forming the vehicle by combining the axle groups, once the axle groups are completed and enter the axle group list, the real-time monitoring is started to judge whether a newly completed axle group enters the list or not. When a new shaft group enters the list subsequently, whether the front shaft group and the rear shaft group meet the combination condition or not is detected, if the combination condition is met, the data of the two shaft groups are combined together to form new shaft group data, and after each parameter is recalculated, the new shaft group data are put back into the shaft group list again to continue waiting for the subsequent shaft group; if the combination condition is not met, it indicates that the front and rear axle components belong to two different vehicles, so the front axle group can generate vehicle data alone, and the rear axle group continues to wait in the list.
Similarly, the axle group has a maximum waiting time in the list, and if an axle group waits for a long enough time after entering the list, and no new axle group enters the list subsequently, it means that no axle data is available subsequently, and the axle group is already a complete vehicle, and the calculation of vehicle-related parameters can be started and then output.
And the axle group combination judgment standard is mainly used for judging whether the two axle groups can be combined together according to the axle weight and the axle distance of each axle in the axle groups. The axle type of the vehicle is characterized by referring to the axle type legends of various trucks listed in 'standards for over-limit and overload determination of road freight vehicles' issued by the department of transportation. The arrangement of the axles of the vehicle is regular, and taking a common 6-axle trailer as an example, the arrangement of the axles can be generally divided into two parts, namely a front part 3-axle trailer and a rear part 3-axle trailer, and the arrangement of the front 3-axle trailer has two general conditions: one is that one wheel is arranged at the front and two wheels are arranged at the back, under the condition, the axle distance of 1-2 axles is generally 3-5 m, and the axle distance of 2-3 axles is 1.35 m; the other is that two wheels are arranged behind the front wheel, under the condition, the wheelbase of 1-2 shafts is generally 1.8-2.1 m, and the wheelbase of 2-3 shafts is generally 2-4 m; then 3 axles of trailer part, the wheelbase is all 1.35 m; depending on the length of the trailer, the wheelbase between the 3 rd axle and the 4 th axle in the 6-axle vehicle may be 4m to 10 m.
And according to the characteristics of the axle types of the vehicle and the real-time calculation of the data parameters of each axle, whether two axle group parts can be combined together to belong to the same vehicle can be well judged. For example, the front axle group already contains 3 axles, and the front axle group is judged to be the head of a trailer according to the axle distance, so that a new axle group is generated at the rear, and theoretically, the axle distance between the axles should be about 1.35m, if so, the axle type relationship meets the combination condition, and the axle type relationship can be combined together to form a complete trailer; if not, the axle type relationship of the two-axle group does not satisfy the combination condition, and the former 3-axle group can form the vehicle data output. For another example, in a 3-axle group, the distance between 2 and 3 axles reaches more than 5m, and in this case, the 3 axles form a 3-axle vehicle, so that the vehicle can be directly generated without continuously waiting for subsequent data.
The preferred embodiment:
as shown in fig. 7, in the whole axle group combination judgment logic, in addition to the judgment using the wheelbase axle type, the axle weight information is added for auxiliary judgment. For example, if the axles of the front axle group are heavy and the axles of the rear axle group are obviously lighter, the combination condition is not satisfied by the front axle group and the rear axle group, and obviously the front axle group belongs to a truck and the rear axle group belongs to a passenger car. The specific judgment standard mainly considers the relationship and difference between the wheelbase and the wheelbase type and the axle weight of various vehicle types, and the specific detailed method and the specific detailed standard are not repeated.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A dynamic truck scale is characterized by comprising
The detection area is a section of area arranged along the driving direction of a lane, and a plurality of weighing modules are arranged in the detection area at intervals along the driving direction of the lane;
each weighing module is composed of a first strip-shaped weighing sensor and a second strip-shaped weighing sensor, the first strip-shaped weighing sensor and the second strip-shaped weighing sensor are sequentially arranged at intervals along the driving direction of the lane, and the length directions of the first strip-shaped weighing sensor and the second strip-shaped weighing sensor are perpendicular to the driving direction of the lane;
the first strip-shaped weighing sensor and the second strip-shaped weighing sensor in each weighing module are respectively and electrically connected with an acquisition interface of the front-end host and are used for feeding back acquired data; and the front-end host is in data communication with the monitoring background through the network communication module.
2. The dynamic vehicle scale of claim 1, wherein: the front-end host is provided with a calculation module and a vehicle type database; a plurality of pieces of registered vehicle type data are stored in the vehicle type database; the calculation module calculates the acquired data and generates acquired vehicle type data; the calculation module is also used for comparing the acquired vehicle type data with each registered vehicle type data and acquiring actually measured vehicle type data corresponding to one of the registered vehicle type data; and the communication module sends the actually measured vehicle model data and the acquired data to the monitoring background.
3. A method for separating a vehicle based on axle data in a dynamic truck scale, which is used for the dynamic truck scale of claims 1-2, and comprises the following steps:
the front-end host computer obtains axle data through data acquired by the first bar-shaped weighing sensor and the second bar-shaped weighing sensor;
and step two, the front-end host machine synthesizes vehicle data through the plurality of axle data.
4. The method of claim 3, wherein the method further comprises the steps of: in the first step, the axle data comprises maximum rough axle weight data respectively collected by the first strip-shaped weighing sensor and the second strip-shaped weighing sensor, and rough axle speed data is obtained through the peak value interval of the two rough axle weight data.
5. The method of claim 4, wherein the method further comprises the steps of: in the second step, the axle data time of the first axle of the same vehicle is t1, the corresponding vehicle speed at the moment is v1, the axle data time of the second axle is t2, and the corresponding vehicle speed at the moment is v 2; the motion of the vehicle in the time when the first shaft and the second shaft pass through the weighing module is approximate to uniform acceleration linear motion, and the shaft distance L between the two shafts can be obtained by using the formula L (v1+ v2) × (t2-t 1)/2.
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| CN202111193582.6A CN113720429A (en) | 2021-10-13 | 2021-10-13 | Vehicle separation method based on axle data in dynamic truck scale |
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| CN202111193582.6A CN113720429A (en) | 2021-10-13 | 2021-10-13 | Vehicle separation method based on axle data in dynamic truck scale |
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