CN115235598A - Weighing method of quartz weighing sensor - Google Patents
Weighing method of quartz weighing sensor Download PDFInfo
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- CN115235598A CN115235598A CN202211001820.3A CN202211001820A CN115235598A CN 115235598 A CN115235598 A CN 115235598A CN 202211001820 A CN202211001820 A CN 202211001820A CN 115235598 A CN115235598 A CN 115235598A
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- 238000005303 weighing Methods 0.000 title claims abstract description 137
- 238000000034 method Methods 0.000 title claims abstract description 29
- 239000010453 quartz Substances 0.000 title claims abstract description 29
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 title claims abstract description 29
- 230000006870 function Effects 0.000 claims description 21
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 230000002787 reinforcement Effects 0.000 claims description 3
- 230000001052 transient effect Effects 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 206010048669 Terminal state Diseases 0.000 claims description 2
- 230000002159 abnormal effect Effects 0.000 abstract description 7
- 230000009191 jumping Effects 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 3
- 238000009434 installation Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G3/00—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances
- G01G3/12—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances wherein the weighing element is in the form of a solid body stressed by pressure or tension during weighing
- G01G3/13—Weighing apparatus characterised by the use of elastically-deformable members, e.g. spring balances wherein the weighing element is in the form of a solid body stressed by pressure or tension during weighing having piezoelectric or piezoresistive properties
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- Physics & Mathematics (AREA)
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- Force Measurement Appropriate To Specific Purposes (AREA)
Abstract
The invention belongs to the technical field of weighing sensors, and particularly relates to a weighing method of a quartz weighing sensor. The method comprises the following steps of firstly, installing the mounting strips on the road along the length direction of the road in a rectangular array mode, dividing the mounting strips into two groups, then installing the mounting strips at equal intervals, and secondly, marking all weighing sensors on the mounting strips. According to the weighing method of the quartz weighing sensor, the mounting bars are arranged and distributed at equal intervals after being divided into two groups, 3-6 rows of weighing sensors 1 can be arranged, the distance between every two adjacent rows of weighing sensors is 40-80 cm, a wire groove can be conveniently arranged at a large distance, the length of the weighing sensor for analyzing abnormal vehicle running is 1.5-2 m, and the weighing sensors pressed by the abnormal vehicle running can be continuously arranged or arranged in a row jumping manner.
Description
Technical Field
The invention relates to the technical field of weighing sensors, in particular to a weighing method of a quartz weighing sensor.
Background
The width of a standard lane is about 3.75m, in order to achieve the vehicle weighing effect, two quartz weighing sensors (1.75m + 2m) need to be transversely laid, a plurality of rows are laid along the longitudinal direction, usually, more than one lane is provided, and when a double lane is provided, the vehicle has the condition of cross-lane driving, so that tires pass through the butt joint end of the quartz weighing sensors, weighing data is influenced, and the problem needs to be solved.
In addition, in the existing weighbridge weighing method, most of the states of the weighed vehicles on the weighbridge are corrected or judged by means of manual or visual cameras, so that not only is the manual labor wasted, but also certain errors exist in manual judgment, and the standards are difficult to master particularly when the states are rimmed.
Disclosure of Invention
The invention provides a weighing method of a quartz weighing sensor based on the technical problem that an existing weighing sensor weighing method is prone to errors.
The weighing method of the quartz weighing sensor comprises the steps that the weighing sensor is installed on a mounting strip of the weighing sensor in a rectangular array mode, and a road used for installing the mounting strip or a road through which a vehicle passes is directly installed.
Step one, mounting the mounting strips on the road along a rectangular array in the length direction of the road, wherein the mounting strips are divided into two groups and then are mounted at equal intervals.
And step two, marking all the weighing sensors on the mounting strip, synchronizing the marked information into the wagon balance weighing system and displaying the information on a display.
And step three, after the vehicle wheels run to the road, feeding back the pressure value displayed by the weighing sensor pressed below the wheels into the wagon balance system to calculate the position information of the wheels.
And step four, judging whether the position of the vehicle on the road accords with a normal vehicle position value or not according to the position information of the wheels.
Preferably, pressure switches for sensing the entering direction of the vehicle and controlling the start and stop of the whole wagon balance are arranged at the entering and exiting ends of the road.
Preferably, in the step one, one set of the mounting strips is arranged along the width direction of the road and then extends to the two side edges of the road, and the other set of the mounting strips is distributed and mounted on the road along the entering track of the vehicle wheels and is distributed in a mirror symmetry manner by taking the center line of the road in the length direction as a symmetry line.
Through above-mentioned technical scheme, when can weighing to the vehicle that normally traveles, can weigh the vehicle through weighing sensor because of crossing lane and driving, reach the effect of weighing the vehicle of unusual distribution state.
Preferably, the weighing sensors adjacent to each other in the front-back direction and the left-right direction are distributed in a rectangular array, and the distance between the weighing sensors is smaller than the width of the wheel.
Through above-mentioned technical scheme, the width that the interval between the weighing sensor is less than the wheel can guarantee that the wheel can press a weighing sensor at least, has effectively guaranteed the accuracy of weighing data.
Preferably, in the second step, the marked weighing sensors are placed in a reinforcement learning model to form an intelligent learning system.
Preferably, the intelligent learning system comprises the steps of:
p1, constructing an environment;
p2, reward strategy;
p3, digital model.
Through the technical scheme, the weighing system can be used as a learning model for the weighbridge weighing system, and the intelligent machine learning model is given to the weighbridge weighing system, so that the weighing system for rapidly calculating the weighbridge through digital simulation is realized, and the intelligent effect is achieved.
Preferably, the pressure switch is extruded by the wheels, then the wagon balance system is started, the advancing direction of the wheels is judged to be the target direction, and an environment coordinate system is constructed;
setting the income/cost obtained by the vehicle moving from the current pressure switch zone to another approach target zone as a reward, setting the action of moving to the adjacent weighing sensor zone as an instant reward, and setting the sum of the instant rewards of the actions approaching the specified target zone to be a positive reward, otherwise, a negative reward; through the strategy of reward, states and action sequences adopted by the vehicle weighing from an initial state to a final state are specified, the sequence is called a track, and the sum of instantaneous rewards along one track is called reward;
when the sum of the awards is equal, the first path is taken as the one with the least number of turns or turns.
Preferably, the current driving state s of the wheels of the wagon balance system is determined by the time t of each weighing sensor t E, S, and then selecting an action a of the position and the place of the target weighing sensor by the system t E.g. A, actual displacement s produced by wheel t+1 E.g., S, and receive a momentary prize
r t ＝r(s t ，a t ，s t+1 )∈R
S and A are respectively a state space and an action space, and accordingly r (S) t ，a t ，s / ) Referred to as the transient reward function;
initial position s 1 Determined from the probability distribution of the initial position of the wheel in the area of the load cell in which it is currently located, the state space S is now divided into discrete and continuous:
if the state space S is discrete, i.e. the initial probability distribution is determined by the probability mass function P (S) and satisfies
If the state space S is continuous, i.e. the initial probability distribution is determined by the probability density function p (S) and satisfies:
∫ s∈S p(s)ds＝1
since the probability mass function can be expressed using the dirac function δ(s) and the probability density function:
preferably, when the motion state of the wheel driving is a continuous state space: the dynamics of the environment within the target area set in the weighbridge system are represented from state s to state s when action a is employed / By a given conditional probability density p(s) / S, a) is expressed by a transition probability distribution:
the decision of the vehicle is determined by a strategy pi, which can be taken as a function of the state:
the action a may be discrete or continuous, and in a weighbridge system, the direction of wheel travel is uncertain, so a random strategy is employed in which the action taken by each state is determined probabilistically, which is considered to be the conditional probability density of the state s taking the action a:
preferably, in the fourth step, whether the weighing position of the vehicle exceeds a preset value is judged according to information fed back by a weighing sensor pressed by the wheel;
the information fed back by the weighing sensor comprises an included angle A between the vehicle and the road and a distance L between the vehicle center point and the road center line.
Through the technical scheme, whether the vehicle is separated from a normal weighing state on the road or not can be judged by considering the numerical values of the set included angle A and the distance L.
The beneficial effects of the invention are as follows:
1. the mounting bars are arranged to be divided into two groups and then arranged at equal intervals, 3-6 rows of weighing sensors 1 can be arranged, the distance between two adjacent rows of weighing sensors is 40-80 cm, the wire grooves are convenient to arrange at large distance, the length of the weighing sensors for analyzing abnormal vehicle running is 1.5-2 m, and the weighing sensors pressed by abnormal vehicle running can be continuously arranged or arranged in a row jumping manner.
2. Through setting up intelligent learning system, can do the study model to the weighbridge weighing system, give the model of weighbridge weighing system intelligent machine study, realize the weighing system of digital simulation fast calculation weighbridge, reach intelligent effect.
Drawings
FIG. 1 is a schematic diagram of a weighing method of a quartz weighing sensor according to the present invention;
FIG. 2 is a top view of a weighing cell structure of a weighing method of a quartz weighing cell according to the present invention;
FIG. 3 is a top view of a wheel hold down load cell of a weighing method for a quartz load cell in accordance with the present invention;
FIG. 4 is a diagram of a right-turning state of a wheel in a weighing method of a quartz weighing sensor according to the invention;
FIG. 5 is a left-turning state diagram of a wheel of a weighing method of a quartz weighing sensor according to the present invention;
FIG. 6 is a rectangular array distribution diagram of a weighing cell structure of a weighing method of a quartz weighing cell according to the present invention;
FIG. 7 is a diagram of an intelligent learning system for a weighing method of a quartz load cell according to the present invention;
fig. 8 is a top view of the wheel and road positions of a weighing method of a quartz weighing cell according to the present invention.
In the figure: 1. a weighing sensor; 2. mounting a bar; 3. a road; 4. a pressure switch; 5. and (7) wheels.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-8, a weighing method of a quartz weighing sensor comprises mounting strips 2 with weighing sensors 1 mounted in a rectangular array and a road 3 for mounting the mounting strips 2 or directly mounted on a road through which a vehicle passes, and the mounting strips are directly mounted on a road surface, so that the road 3 is not required to be independently built, the vehicle can still be weighed under the condition that the vehicle is not stopped, and dynamic weighing can be realized as long as the weighing sensors 1 can adopt quartz weighing sensors.
Further, pressure switches 4 for sensing the vehicle entering direction and controlling the start and stop of the whole wagon balance are arranged at the inlet end and the outlet end of the road 3.
Step one, mounting the mounting strips 2 on the road 3 along the rectangular array in the length direction of the road 3, and mounting the mounting strips 2 after being divided into two groups at equal intervals.
Further, in the first step, one set of the mounting strips 2 is arranged along the width direction of the road 3 and then extends to the two side edges of the road 3, and the other set of the mounting strips 2 is distributed and mounted on the road 3 along the entering track of the vehicle wheels 5 and is distributed in a mirror symmetry manner by taking the center line of the road 3 in the length direction as a symmetry line.
The vehicle that can go normally is weighed the time, can weigh the vehicle through weighing sensor 1 because of crossing lane and go, reaches the effect of weighing to the vehicle of abnormal distribution state.
Further, the weighing sensors 1 adjacent to each other in the front-back direction and the left-right direction are distributed in a rectangular array, and the distance between the weighing sensors 1 is smaller than the width of the wheel 5.
The distance between the weighing sensors 1 is smaller than the width of the wheel 5, so that the wheel can be pressed onto at least one weighing sensor 1, and the accuracy of weighing data is effectively guaranteed.
And step two, marking all the weighing sensors 1 on the mounting strip 2, synchronizing the marked information into the wagon balance weighing system and displaying the information on a display.
In order to further count or learn or simulate the position information of the vehicle on the road 3, in step two, the marked load cell 1 is inserted into the reinforcement learning model to form an intelligent learning system.
Further, the intelligent learning system comprises the following steps:
p1, environment construction:
further, the pressure switch 4 is extruded by the wheels 5, then a wagon balance system is started, and the advancing direction of the wheels 5 is judged as a target direction according to the pressure switch, so that an environment coordinate system is constructed;
as shown in fig. 7, P2, reward policy:
setting the profit/cost obtained by the vehicle moving from the current pressure switch 4 zone to another approach target zone as a reward, and setting the action of moving to the adjacent load cell 1 zone as an instantaneous reward, the sum of the instantaneous rewards of the actions approaching the specified target zone constituting a positive reward, and vice versa as a negative reward; by rewarding this strategy, the sequence of states and actions taken by a vehicle weighing from an initial state to a terminal state is specified, such sequence being referred to as a trajectory, and the sum of instantaneous rewards along a trajectory being referred to as a reward.
When the sum of the awards is equal, the first path is taken as the one with the least number of turns or turns.
P3, a digital model:
the current driving state s of the wagon wheels 5 of the wagon balance system is determined by the time t of each weighing cell 1 t E.g. S, and then the system selects an action a of the position and the place of the target weighing sensor 1 t E.g. A, actual displacement s produced by wheel 5 t+1 Belongs to S and receives an instant prize
r t ＝r(s t ，a t ，s t+1 )∈R
S and A are respectively a state space and an action space, respectively, r (S) t ，a t ，s / ) Referred to as the transient reward function.
Initial position s 1 Determined from the probability distribution of the initial position of the wheel 5 in the region of the load cell 1 in which it is currently located, the state space S is divided into discrete and continuous:
if the state space S is discrete, i.e. the initial probability distribution is determined by the probability mass function P (S) and satisfies
If the state space S is continuous, i.e. the initial probability distribution is determined by the probability density function p (S) and satisfies:
∫ s∈S p(s)ds＝1
since the probability mass function can be expressed using the dirac function δ(s) and the probability density function:
further, when the operating state of the wheel 5 running is the continuous state space: the dynamics of the environment within the target area set in the weighbridge system are represented from state s to state s when action a is employed / By a given conditional probability density p(s) / | s, a) is expressed by the transition probability distribution:
the decision of the vehicle is determined by a strategy pi, which can be taken as a function of the state:
the action a may be either discrete or continuous, and in a weighbridge system the direction of travel of the wheel 5 is uncertain, so a random strategy is employed in which the action taken by each state is determined probabilistically, which is considered to be the conditional probability density of the state s taking the action a:
the weighing system can be used as a learning model for the weighbridge weighing system, and the weighbridge weighing system is provided with an intelligent machine learning model, so that the weighing system for rapidly calculating the weighbridge through digital simulation is realized, and an intelligent effect is achieved.
And step three, after the vehicle wheels 5 run to the road 3, feeding back the pressure value displayed by the weighing sensor 1 pressed below the wheels 5 into the wagon balance system to calculate the position information of the wheels 5.
And step four, judging whether the position of the vehicle on the road 3 accords with a normal vehicle position value or not according to the position information of the wheels 5.
Further, in the fourth step, whether the weighing position of the vehicle exceeds a preset value is judged according to the information fed back by the weighing sensor 1 pressed by the wheel 5;
the information fed back by the weighing sensor 1 comprises an included angle A between the vehicle and the road 3 and a distance L between the center point of the vehicle and the center line of the road 3.
Whether the vehicle is out of a normal weighing condition on the road 3 can be determined by considering the values of the set included angle a and the distance L.
Through setting up the installation strip 2 and divide into the equidistant interval distribution installation in two sets of backs, 3 to 6 rows can be established to multirow weighing sensor 1, and the distance between two adjacent rows of weighing sensor 1 is between 40 to 80cm, and the great distance is more convenient for set up the wire casing, and the length of vehicle abnormal running analysis weighing sensor 1 is 1.5m to 2m, and the weighing sensor 1 that the vehicle abnormal running pushed down can set up in succession or jump row setting.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. A weighing method of a quartz weighing sensor is characterized by comprising the following steps: the device comprises a mounting bar (2) with weighing sensors (1) arranged in a rectangular array and a road (3) for mounting the mounting bar (2);
step one, mounting the mounting strips (2) on the road (3) along a rectangular array in the length direction of the road (3), wherein the mounting strips (2) are divided into two groups and then are mounted at equal intervals;
marking all the weighing sensors (1) on the mounting bar (2), synchronizing the marked information into the wagon balance weighing system and displaying the information on a display;
step three, after the vehicle wheels (5) are driven to the road (3), feeding back the pressure value displayed by the weighing sensor (1) pressed below the wheels (5) into the wagon balance system to calculate the position information of the wheels (5);
and step four, judging whether the position of the vehicle on the road (3) accords with a normal vehicle position value or not according to the position information of the wheels (5).
2. A method of weighing a quartz load cell (1) as claimed in claim 1, characterized in that: and pressure switches (4) used for sensing the entering direction of the vehicle and controlling the start and stop of the whole wagon balance are arranged at the inlet end and the outlet end of the road (3).
3. A method of weighing a quartz load cell (1) as claimed in claim 1, characterized in that: in the first step, one group of the mounting strips (2) extends to the edges of the two sides of the road (3) after being arranged in the width direction of the road (3), and the other group of the mounting strips (2) is distributed and mounted on the road (3) along the entering track of the vehicle wheels (5) and distributed in a mirror symmetry mode by taking the center line of the road (3) in the length direction as a symmetry line.
4. A method of weighing a quartz load cell (1) as claimed in claim 3, characterized in that: the front and back direction and the left and right direction are adjacent it distributes to be rectangular array between weighing sensor (1), the interval between weighing sensor (1) is less than the width of wheel (5).
5. A method of weighing a quartz load cell (1) according to claim 2, characterized in that: in the second step, the marked weighing sensor (1) is placed into a reinforcement learning model to form an intelligent learning system.
6. A weighing method of a quartz load cell (1) according to claim 5, characterized in that: the intelligent learning system comprises the following steps:
p1, constructing an environment;
p2, reward strategy;
p3, a digital model.
7. A weighing method of a quartz load cell (1) according to claim 6, characterized in that: the pressure switch (4) is extruded by the wheels (5) and then a wagon balance system is started, and the advancing direction of the wheels (5) is judged to be the target direction, so that an environment coordinate system is constructed;
setting the income/expense obtained when the vehicle moves from the current pressure switch (4) area to another area close to the target area as a reward, setting the action of moving to the area adjacent to the weighing sensor (1) as an instant reward, wherein the sum of the instant rewards of the actions close to the specified target area forms a positive reward, and the negative reward is formed otherwise; through the strategy of reward, states and action sequences adopted by vehicle weighing from an initial state to a terminal state are specified, such sequences are called tracks, and the sum of instantaneous rewards along one track is called reward;
when the sum of the awards is equal, the first path is taken as the one with the least number of turns or turns.
8. Weighing method of a quartz weighing cell (1) according to claim 7, characterised in that: the current running state s of the wagon wheels (5) of the wagon balance system is determined by the time t of each weighing sensor (1) t E to S, and then the system selects an action a of the position where the target weighing sensor (1) is positioned t E.g. A, the actual displacement s generated by the wheel (5) t+1 Belongs to S and receives an instant prize
r t ＝r(s t ，a t ，s t+1 )∈R
S and A are respectively a state space and an action space, and accordingly r (S) t ，a t ，s / ) Referred to as the transient reward function;
initial position s 1 Is determined from the probability distribution of the initial position of the wheel (5) in the region of the weighing cell (1) in which it is currently located, the state space S then being divided into discrete and continuous:
if the state space S is discrete, the initial probability distribution is determined by the probability mass function P (S) and satisfies
If the state space S is continuous, i.e. the initial probability distribution is determined by the probability density function p (S) and satisfies:
∫ s∈S p(s)ds＝1
since the probability mass function can be expressed using the dirac function δ(s) and the probability density function:
9. weighing method of a quartz weighing cell (1) according to claim 8, characterised in that: when the motion state of the wheel (5) is a continuous state space: the dynamics of the environment within the target area set in the weighbridge system are represented from state s to state s when action a is employed / By a given conditional probability density p(s) / S, a) is expressed by a transition probability distribution:
the decision of the vehicle is determined by a strategy pi, which can be taken as a function of the state:
the action a can be either discrete or continuous, in a wagon balance system, the direction of travel of the wheel (5) is uncertain, so a random strategy is adopted in which the action taken by each state is determined by probability, which is considered as the conditional probability density of the state s taking the action a:
10. a method of weighing a quartz load cell (1) as claimed in claim 1, characterized in that: in the fourth step, whether the weighing position of the vehicle exceeds a preset value or not is judged according to information fed back by the weighing sensor (1) pressed by the wheel (5);
the information fed back by the weighing sensor (1) comprises an included angle A between a vehicle and the road (3) and a distance L between a center point of the vehicle and the center line of the road (3).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211001820.3A CN115235598A (en) | 2022-08-20 | 2022-08-20 | Weighing method of quartz weighing sensor |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211001820.3A CN115235598A (en) | 2022-08-20 | 2022-08-20 | Weighing method of quartz weighing sensor |
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| CN115235598A true CN115235598A (en) | 2022-10-25 |
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| CN202211001820.3A Pending CN115235598A (en) | 2022-08-20 | 2022-08-20 | Weighing method of quartz weighing sensor |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100985734B1 (en) * | 2009-06-24 | 2010-10-06 | (주)뉴컨스텍 | System for measuring weight of a traveling vehicle |
| JP2011089835A (en) * | 2009-10-21 | 2011-05-06 | Yamato Scale Co Ltd | Weight measuring system for wheel or axle |
| CN108318117A (en) * | 2018-02-09 | 2018-07-24 | 四川奇石缘科技股份有限公司 | A kind of vehicle dynamic weighing compensation sensor array, System and method for |
| CN209280117U (en) * | 2018-12-28 | 2019-08-20 | 北京万集科技股份有限公司 | A kind of dynamic weighing system |
| CN209280116U (en) * | 2018-12-28 | 2019-08-20 | 北京万集科技股份有限公司 | A kind of dynamic weighing system comprising underground concealed formula point array sensor |
| JP2021174101A (en) * | 2020-04-21 | 2021-11-01 | 株式会社豊田中央研究所 | Object detection system |
| CN114399745A (en) * | 2022-01-05 | 2022-04-26 | 中交第二公路工程局有限公司 | Automatic positioning system of dynamic compaction machine |
| CN216954782U (en) * | 2022-02-16 | 2022-07-12 | 广州聚杰智能称重实业有限公司 | Strip sensor installation layout device |
-
2022
- 2022-08-20 CN CN202211001820.3A patent/CN115235598A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100985734B1 (en) * | 2009-06-24 | 2010-10-06 | (주)뉴컨스텍 | System for measuring weight of a traveling vehicle |
| JP2011089835A (en) * | 2009-10-21 | 2011-05-06 | Yamato Scale Co Ltd | Weight measuring system for wheel or axle |
| CN108318117A (en) * | 2018-02-09 | 2018-07-24 | 四川奇石缘科技股份有限公司 | A kind of vehicle dynamic weighing compensation sensor array, System and method for |
| CN209280117U (en) * | 2018-12-28 | 2019-08-20 | 北京万集科技股份有限公司 | A kind of dynamic weighing system |
| CN209280116U (en) * | 2018-12-28 | 2019-08-20 | 北京万集科技股份有限公司 | A kind of dynamic weighing system comprising underground concealed formula point array sensor |
| JP2021174101A (en) * | 2020-04-21 | 2021-11-01 | 株式会社豊田中央研究所 | Object detection system |
| CN114399745A (en) * | 2022-01-05 | 2022-04-26 | 中交第二公路工程局有限公司 | Automatic positioning system of dynamic compaction machine |
| CN216954782U (en) * | 2022-02-16 | 2022-07-12 | 广州聚杰智能称重实业有限公司 | Strip sensor installation layout device |
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