CN113223294A - Expressway automobile balance period checking method based on social vehicle big data - Google Patents

Expressway automobile balance period checking method based on social vehicle big data Download PDF

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CN113223294A
CN113223294A CN202110496691.9A CN202110496691A CN113223294A CN 113223294 A CN113223294 A CN 113223294A CN 202110496691 A CN202110496691 A CN 202110496691A CN 113223294 A CN113223294 A CN 113223294A
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CN113223294B (en
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张洪军
周学建
赵晓东
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China Jiliang University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
    • G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
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Abstract

The invention discloses a social vehicle big data-based highway truck scale period checking method, which comprises the steps of networking a truck scale in a to-be-checked area as a network node and uploading related data information; setting two standard automobile scales; enabling a plurality of vehicles to pass through the two standard automobile scales in sequence, carrying out statistical analysis on the metering data, and establishing a relation model between the oil consumption and the total weight of the vehicles; correcting the relation model to obtain a corrected model; obtaining deviation data of two successive truck scales in the network according to the correction model by taking the standard truck scale as a base point; averaging a plurality of deviation data of the same pair of automobile scales to serve as calibration data of the wheel check; setting a checking radius and a limit deviation, and checking the truck scale in the radius range area by taking the calibration data as a checking base point; and taking the checked truck scale as a new round of checked standard truck scale, and repeating the steps to check the truck scale in the area to be checked by turns. The method is accurate and high in efficiency.

Description

Expressway automobile balance period checking method based on social vehicle big data
Technical Field
The invention relates to the technical field of weighing instrument measurement, in particular to a method for checking the weighing period of an expressway automobile based on social vehicle big data.
Background
At present, a dynamic road truck scale (hereinafter referred to as a truck scale) is widely applied to road toll and overrun overload detection. The truck scale has the advantages of severe working environment and high use frequency, and needs to be periodically verified to ensure that the measured data is accurate and reliable.
The truck scale has the advantages of large vehicle flow, high vehicle speed, severe environment, high field working strength of detection personnel and high danger on the detection field. With the development of computers and automation technology, the records of the calibration site of the truck scale are gradually collected by the computer instead of manual records, so that the workload of personnel is reduced, and the conditions of manual writing omission and error calculation errors are avoided. However, the large-tonnage weighing-detecting vehicle has higher road running and field detection cost, and the lane needs to be temporarily closed due to the field detection, so that the road traffic is influenced. In addition, under the influence of installation conditions, local climate, self quality and the like, the long-term stability conditions of the truck scale installed on the road are different, some performances can ensure long-term stability, and some metering errors of the truck scale in the verification period exceed the index requirements.
In order to gain more insight into the performance of the truck scale metrology during the certification cycle, a during-equipment check may be performed.
For example, a truck scale with known weight is arranged to run on the road and pass through the truck scale to be checked, whether the accuracy of the measured data of the truck scale exceeds the standard or not is known, and verification and calibration are required if part of the truck scale is found to be abnormal in weighing.
In addition, in the daily operation of roads, when some specific vehicles (such as a service car, a military vehicle, a road administration rescue vehicle, etc. registered with basic vehicle information such as axle type, vehicle service quality, etc.) are checked for traffic flow records, some abnormal weighing of the truck scale is found, and verification and calibration are required.
In the above two period checking methods, there are the following disadvantages:
(1) the weighing-scale inspection vehicle is adopted for on-road inspection, so that the metering cost is increased;
(2) the auxiliary detection is carried out by utilizing specific vehicle data, only partial problems (large negative deviation) can be found, and the information amount is very limited.
Disclosure of Invention
The invention aims to provide a social vehicle big data-based highway automobile balance period checking method, which can solve one or more of the technical problems.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a highway vehicle balance period checking method based on social vehicle big data comprises the steps of
(1) The method comprises the following steps that motor scales in an area to be checked are networked as network nodes, and each network node records and uploads vehicle information of passing vehicles and corresponding vehicle weight;
(2) setting two standard automobile scales, wherein one standard automobile scale is positioned at the inlet of a certain toll station, and the second standard automobile scale is positioned at the outlet of the other toll station; enabling a plurality of vehicles to pass through the two standard automobile scales in sequence, carrying out statistical analysis on the metering data, and establishing a relation model between the oil consumption and the total weight of the vehicles;
(3) correcting the relation model of the oil consumption and the total weight of the vehicle in the step (2) to obtain a corrected model;
(4) obtaining deviation data of two successive truck scales in the network according to the correction model by taking the standard truck scale as a base point;
(5) averaging a plurality of deviation data of the same pair of automobile scales to serve as calibration data of the wheel check;
(6) setting a checking radius and a limit deviation, and checking the automobile balance in the radius range area by taking the calibration data in the step (5) as a checking base point;
(7) and (4) taking the truck scale checked in the step (6) as a standard truck scale checked in a new round, and repeating the step (4) -the step (6) to check the truck scale in the area to be checked by rounds.
Further: and (4) eliminating problem data and suspicious data by adopting a 3 sigma method in the step (3) to obtain a correction model of the oil consumption of the unit mileage.
Further: and (4) sequentially using two truck scales which are passed by the same vehicle and are respectively positioned at the entrance and the exit of the toll station.
Further: and (4) setting a running time threshold of the vehicle for the checking radius in the step (6).
Further: the increase and decrease of the vehicle weight measured by each truck scale is only related to the fuel consumption.
Further: and (5) performing network calibration on all the motor balances in the checking radius through the calibration data in the step (5).
The invention has the technical effects that:
the weighing data of the motor scales in the road network are networked, a large number of social vehicles link the motor scales in the road network, the data are fully utilized to be deeply mined, and the motor scales in use are checked during the performance measuring period without increasing extra cost or with very low cost.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention.
In the drawings:
FIG. 1 is a schematic diagram of the relationship between the highway network and the truck scale;
FIG. 2 shows the results of the first round of calculation;
FIG. 3 shows the results of the second round of calculation, which is associated with the entry truck scale data associated with exit truck scale number 6;
FIG. 4 shows the results of the second round of calculation, which is associated with the entry truck scale data associated with exit truck scale number 8;
fig. 5 third round of calculation results.
Detailed Description
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as unduly limiting the invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A highway vehicle balance period checking method based on social vehicle big data comprises the steps of
(1) The method comprises the following steps that motor scales in an area to be checked are networked as network nodes, and each network node records and uploads vehicle information of passing vehicles and corresponding vehicle weight;
(2) setting two standard automobile scales, wherein one standard automobile scale is positioned at the inlet of a certain toll station, and the second standard automobile scale is positioned at the outlet of the other toll station; enabling a plurality of vehicles to pass through the two standard automobile scales in sequence, carrying out statistical analysis on the metering data, and establishing a relation model between the oil consumption and the total weight of the vehicles;
(3) correcting the relation model of the oil consumption and the total weight of the vehicle in the step (2) to obtain a corrected model;
(4) obtaining deviation data of two successive truck scales in the network according to the correction model by taking the standard truck scale as a base point;
(5) averaging a plurality of deviation data of the same pair of automobile scales to serve as calibration data of the wheel check;
(6) setting a checking radius and a limit deviation, and checking the automobile balance in the radius range area by taking the calibration data in the step (5) as a checking base point;
(7) and (4) taking the truck scale checked in the step (6) as a standard truck scale checked in a new round, and repeating the step (4) -the step (6) to check the truck scale in the area to be checked by rounds.
Further: and (4) eliminating problem data and suspicious data by adopting a 3 sigma method in the step (3) to obtain a correction model of the oil consumption of the unit mileage.
Further: and (4) sequentially using two truck scales which are passed by the same vehicle and are respectively positioned at the entrance and the exit of the toll station.
Further: and (4) setting a running time threshold of the vehicle for the checking radius in the step (6).
Further: the increase and decrease of the vehicle weight measured by each truck scale is only related to the fuel consumption.
Further: and (5) performing network calibration on all the motor balances in the checking radius through the calibration data in the step (5).
In the invention, the relationship between two different truck scales within a checking radius (distance threshold) and a time threshold is established by setting a standard truck scale and counting the data of the whole network truck scale; the relation of the vehicle weight is only related to the change of the oil consumption, and the change of the oil consumption is corrected through effective data, so that the relation accuracy is improved; the standard truck scale is used as a base point and continuously radiates to the truck scale of the whole network, so that the deviation data of the truck scale of any two stations of the whole network is obtained, and whether the weighing value of any station meets the specification or not can be checked through setting the limit deviation.
In the checking process, the weighing data of each truck scale is uploaded to the network, so that the related or corresponding truck scales can be calibrated through the calibration data in the step (5), and the calibration is carried out while checking, so that the checking efficiency is improved.
The following detailed explanation is given with specific examples:
firstly, using motor weighers of each toll station or detection station in a highway network in a certain area as network nodes, wherein at least two motor weighers are standard motor weighers, and the weighing result of the standard motor weighers is credible and accurate after verification or calibration; one of the two standard automobile scales is positioned at the entrance of a certain toll station, and the second standard automobile scale is positioned at the exit of the other toll station with moderate distance; the distance between the two standard motor balances is within a distance threshold and a time threshold;
secondly, when a certain social vehicle (truck) passes through two truck scales in the network in sequence, the two truck scales respectively weigh the weight of the vehicle, and meanwhile, the license plate recognition system recognizes vehicle information;
counting multiple metering data of two motor balances on the same vehicle and the same road section so as to obtain deviation data of the two motor balances and calculating the average of the obtained deviation data of a plurality of vehicles;
then the truck scale of the whole network is numbered, and the numbering principle is to distinguish whether the truck scale is installed at the entrance or the exit of a toll station.
In this embodiment, the entrance truck scale is numbered as singular and the exit truck scale is even.
And the number of the standard truck scale positioned at the inlet is designated as No. 1, and the number of the standard truck scale positioned at the outlet is designated as No. 2.
For some cases before analysis: because the change of the vehicle weight is only related to the oil consumption, if the distance between the two truck scales is too far, the oil is added in the middle of the two truck scales, the uncertainty of the data is increased, and therefore, a distance threshold value d between the two truck scales can be setcr(checking radius), which distance threshold ensures that the change in vehicle weight is influenced by fuel consumption; data less than the distance threshold may be recalled for calculation, and a check of each wheel for vehicle balance is developed with the threshold radius.
In addition, even if the distance meets the requirement, if the consumed time exceeds the normal time, the possibility of accidents such as traffic jam and the like in the middle is increased, fuel is additionally increased, and the dataTo eliminate this, a threshold value V for the driving speed of the truck is setcrTime-consuming threshold Δ tij,cr＝dij/Vcr。
Different paths can be arranged between two toll stations in the highway network, but the shortest path is selected by a general driver, so that the shortest driving distance between any two toll stations is required in calculation. It is difficult to directly give the shortest distance between any two toll stations, but it is possible to give the distance between the toll station (truck scale) and the adjacent toll station (truck scale) of each toll station (truck scale), and then the shortest distance d between any two entrance and exit truck scales can be obtained by adopting ant colony algorithm and the likeij。
Thirdly, for convenient analysis in the process of establishing the model, the following marks are introduced:
m- -number of motor balances in the network;
n-the number of trucks;
dij-the shortest distance between two truck scales;
Figure BDA0003054628930000051
the ith truck passes through the section i-j for the s time, and the weighing data of the number i truck scale;
Figure BDA0003054628930000052
the s-th time that the truck k passes through the i-j road section, weighing data of the j-th truck scale;
Figure BDA0003054628930000053
the difference between the weighing data of the two truck scales the s th time truck k passes the i-j section,
Figure BDA0003054628930000054
Figure BDA0003054628930000055
time of truck k passing through section i-j for the s-th time;
Figure BDA0003054628930000056
-time of truck k passing through truck number j on section i-j for the s-th time;
Figure BDA0003054628930000057
the elapsed time for truck k to make the s-th pass through the i-j road segment,
Figure BDA0003054628930000058
since the truck fuel consumption is assumed to be a function of the total weight of the vehicle in the present invention, and the unit gas mileage is recorded as (W), the difference between any two truck scale weighing data is:
Figure BDA0003054628930000059
fourthly, calling valid data of standard motor scales of all entrances
Figure BDA00030546289300000510
Valid data for all export standard truck scales
Figure BDA00030546289300000511
Then calculate out
Figure BDA00030546289300000512
And then, according to the effective measurement data, removing problem data and suspicious data by adopting a 3 sigma method, and calculating to obtain a fuel consumption per unit (W) calculation function e.
Fifthly, taking the number 1 truck scale as a datum point and taking a distance threshold value dcrSearching all exit truck scales with double numbers for the radius, and calculating the theory of each searched truck scale according to the formula (1)
Figure BDA00030546289300000513
The value (the deviation data between two successive motor balances) is calculated by averaging all theoretical data in the step
Figure BDA00030546289300000514
(a plurality of cars pass through the average value of the deviation data of two identical truck scales in sequence), the average value is the calibration data, and in the formula: k ∈ (called truck number), N is the total accumulated number.
Setting a limit deviation [ delta W ] for each truck scale to be checkedi]And when the difference between the actual measured value and the reference theoretical data exceeds the value, the motor truck scale is determined as the problem motor truck scale.
As shown in fig. 1-5, a checking process with a standard truck scale as the center and threshold values (distance threshold, time threshold, speed threshold) as the radius is shown, after the first round of checking, the next round of checking is continued with the truck scale without problems in the first round as the center, and the checked truck scales are skipped until all the truck scales in the area are checked.
After the above-described inspection centering on the standard truck scale (entry standard truck scale denoted by 1) is completed, all entry truck scales (except truck scale 1) not exceeding dcr from a certain exit truck scale (j) are searched. As shown in fig. 3 and 4, the truck scale associated with the truck scale No. 6 includes truck scales No. 3, 7, and 11, and the truck scale associated with the truck scale No. 8 includes 5 truck scales No. 3, 5, 9, and 11. All effective weighing data are called and calculated
Figure BDA0003054628930000061
(i ∈ set of selected entry truck scale numbers) and calculate the average
Figure BDA0003054628930000062
The average value is the calibration data of the current round of inspection, wherein: k belongs to the number of the called truck, and N is the total accumulated times; after calculation processing is carried out on each exit truck scale of the wheel, a plurality of automobile scales related to the i-th entry truck scale are obtained
Figure BDA0003054628930000063
(j is all possible values involved in all calculations of this round), take all
Figure BDA0003054628930000064
The arithmetic mean value of the (i) th entrance automobile scale is used as deviation data to check and calibrate the (i) th entrance automobile scale. When the deviation data exceeds the limit deviation, the truck scale is marked as a problem truck scale and is not used for subsequent calculation.
Data retrieval and analysis is performed for each entry truck scale involved in the previous round of calculation. For example, for an entry truck scale i, search distance d is not more than d from the truck scalecrAnd all the export motor balances are removed from the export motor balances which are checked in the front wheels. Referring to the 3 rd and 4 th steps, the checking and the calibration of the wheel exit truck scale are completed. In fig. 5, the current round of calculation is performed, and 7 motor balances such as 4, 10, 12 and the like are checked, wherein the deviation of 12 is over limit and marked as a problem motor balance.
And the above-mentioned multiple rounds of calculation are alternatively repeated, so that the check of the automobile scales in all the networks can be completed.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1.基于社会车辆大数据的高速公路汽车衡期间核查方法，其特征在于：包括1. The method for checking the period of expressway truck scale based on social vehicle big data is characterized in that: comprising: (1)将待核查区域内的汽车衡作为网络节点联网，每一网络节点记录并上传经过车辆的车辆信息以及对应的车辆重量；(1) Network the truck scale in the area to be checked as a network node, and each network node records and uploads the vehicle information of the passing vehicle and the corresponding vehicle weight; (2)设定两台标准汽车衡，其中一台位于某个收费站入口，第二台位于另一收费站出口；让多辆车先后经过这两台标准汽车衡，对计量数据进行统计分析，建立油耗与车辆总重关系模型；(2) Set up two standard truck scales, one of which is located at the entrance of a toll station and the second one is located at the exit of another toll station; let multiple vehicles pass through the two standard truck scales successively, and perform statistical analysis on the measurement data , to establish a relationship model between fuel consumption and total vehicle weight; (3)对步骤(2)中的油耗与车辆总重关系模型进行修正得到修正模型；(3) Correcting the relationship model between fuel consumption and total vehicle weight in step (2) to obtain a corrected model; (4)以标准汽车衡为基点、根据修正模型获得网络中先后两台汽车衡的偏差数据；(4) Taking the standard truck scale as the base point and obtaining the deviation data of two truck scales in the network according to the correction model; (5)对同一对汽车衡的若干偏差数据求取平均数作为该轮核查的校准数据；(5) Obtain the average of several deviation data of the same pair of truck scales as the calibration data for this round of verification; (6)设定核查半径、极限偏差、以步骤(5)的校准数据为核查基点、对半径范围区域内的汽车衡进行核查；(6) Set the check radius and limit deviation, take the calibration data of step (5) as the check base point, and check the truck scale within the radius range; (7)将步骤(6)核查之后的汽车衡作为新一轮核查的标准汽车衡、重复步骤(4)—步骤(6)对待核查区域内的汽车衡进行逐轮核查。(7) Take the truck scale verified in step (6) as the standard truck scale for a new round of verification, and repeat steps (4) to (6) to conduct round-by-round verification of truck scales in the area to be verified. 2.根据权利要求1所述的基于社会车辆大数据的高速公路汽车衡期间核查方法，其特征在于：对步骤(3)中采用3σ方法剔除问题数据和可疑数据，获得单位里程油耗的修正模型。2. the method for checking the period of highway truck scale based on social vehicle big data according to claim 1, is characterized in that: adopt 3σ method to eliminate problem data and suspicious data in step (3), obtain the correction model of unit mileage fuel consumption . 3.根据权利要求1所述的基于社会车辆大数据的高速公路汽车衡期间核查方法，其特征在于：在步骤(4)中的先后两台汽车衡为同一辆车经过的、分别位于收费站的进口处的汽车衡和出口处的汽车衡。3. the method for checking the period of highway truck scale based on social vehicle big data according to claim 1, it is characterized in that: two truck scales in step (4) are that the same vehicle passes through, are respectively located in the toll station The truck scale at the entrance and the truck scale at the exit. 4.根据权利要求1所述的基于社会车辆大数据的高速公路汽车衡期间核查方法，其特征在于：对步骤(6)中的核查半径设定车辆的行使时间阈值。4 . The method for checking the duration of highway truck scales based on social vehicle big data according to claim 1 , characterized in that: a travel time threshold of the vehicle is set for the checking radius in step (6). 5.根据权利要求1所述的基于社会车辆大数据的高速公路汽车衡期间核查方法，其特征在于：各汽车衡所测量的车辆重量的增减只与油耗相关。5 . The method for checking the period of highway truck scales based on social vehicle big data according to claim 1 , wherein the increase or decrease of vehicle weight measured by each truck scale is only related to fuel consumption. 6 . 6.根据权利要求1所述的基于社会车辆大数据的高速公路汽车衡期间核查方法，其特征在于：通过步骤(5)中的校准数据可以对核查半径内的所有汽车衡进行网络校准。6 . The method for checking highway truck scales based on social vehicle big data according to claim 1 , wherein the calibration data in step (5) can perform network calibration on all truck scales within the verification radius. 7 .
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