CN112837530A - Vehicle driving lap recognition algorithm based on vehicle networking data - Google Patents
Vehicle driving lap recognition algorithm based on vehicle networking data Download PDFInfo
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Abstract
The invention relates to the technical field of engineering vehicle working data statistics, in particular to a vehicle driving pass identification algorithm based on vehicle networking data, which comprises the following steps: firstly, reading data; step two, data cleaning: 2.1, filtering parking/slow moving data; 2.2, filtering abnormal GPS positioning data; 2.3, cleaning result; the third step, the algorithm core: 3.1, setting a 'passing point'; 3.2, detecting the approach of the vehicle to the passing point; 3.3, eliminating missing meter: fourthly, displaying a result: obtaining the result of the driving times of the engineering vehicle; the number of times of the round trip operation of the engineering vehicle can be counted with high real-time performance and high accuracy, and convenience is provided for management and scheduling of a motorcade, so that the process of engineering is guaranteed, and the engineering transportation cost is reduced.
Description
Technical Field
The invention relates to the technical field of engineering vehicle working data statistics, in particular to a vehicle driving lap recognition algorithm based on vehicle networking data.
Background
As is known, a large number of self-unloading engineering vehicles are used in the process of mining transportation, the construction conditions of mines are hard, roads are mostly temporarily opened up and lack of maintenance, and the load capacity of a self-unloading vehicle is large, so that the roads are aged and difficult to operate; meanwhile, due to the influences of on-site dust emission, outdoor severe weather and the like, the situations of traffic jam and slow running of a fleet in mine operation often occur, so that the progress of engineering is influenced, the cost of engineering transportation is increased, the speed information fed back by a GPS system carried by the existing self-unloading engineering vehicle is only used for eliminating the parking and slow running positions at present, and the whole value of the speed information is not exerted.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides the vehicle running time identification algorithm based on the vehicle networking data, which has high real-time performance and high accuracy, counts the times of the round trip operation of the engineering vehicle, provides convenience for the management and the scheduling of a fleet, ensures the progress of engineering and reduces the engineering transportation cost.
(II) technical scheme
In order to achieve the purpose, the invention provides the following technical scheme: a vehicle trip identification algorithm based on internet-of-vehicles data, comprising the steps of:
first step, reading data: and forming a data sequence according to the sequence of the acquisition time:
{A}＝{t，p＝[x，y]，v}；
step two, data cleaning:
2.1, filtering parking/slow moving data: the mine road is complicated and difficult to walk, the condition of queuing and slow walking often occurs, and the dump truck needs to be kept static when loading and unloading materials for a certain time period (t)end-tstart≤Δtlow) Internal continuous v is less than or equal to deltalowOnly 1 value (t) is retained for the data ofstart) Thereby reducing the calculation amount of the subsequent flow;
2.2, filtering abnormal GPS positioning data: because the vehicle-mounted GPS equipment has a certain probability to feed back abnormal positioning information, the coordinates of the vehicle are caused to jump off and return without actual existence, the abnormality has great influence on subsequent algorithms and must be removed. The elimination standard is that the included angle between the pointing vector of the coordinate point and the pointing vector is larger than 90 degrees:
2.3, cleaning result: through the data cleaning, abnormal data are removed, and the data volume is reduced;
the third step, the algorithm core:
3.1, setting a 'passing point': in engineering operations such as mines and the like, the loading and unloading areas of materials are relatively fixed, so that the reciprocating route of the dump truck between the loading area and the unloading area is relatively fixed, and therefore, the judgment of the reciprocating condition of the dump truck on the specified route can be converted into the judgment of the approach of the dump truck to the specified 'passing point' by taking the coordinates of the reasonable position on the route as the 'passing point';
3.2, detecting the approach of the vehicle to the passing point: the judgment standard is that the GPS positioning coordinate of the vehicle falls into a circle which takes the coordinate of the 'pass point' as the center of the circle and takes the appointed length as the radius:
by operation, the sequence { t is obtainedj，k}: the record is used as a direct basis for counting the number of loading and unloading passes of the vehicle, and a time period ([ t)start，tend]) And then the number of times that the dump truck passes through the passing point of the number j is as follows:
Kj＝card({k|tstart≤tj，k≤tend})；
3.3, eliminating missing meter: the dump truck makes a round trip on a relatively fixed path, and the loading and unloading operation performed near the passing point of the number j of each round trip makes the truck make a round trip for 2 times to pass through the passing point, so the result of the round trip statistics is as follows:
Nj＝Kj/2；
fourthly, displaying a result: and obtaining the result of the running times of the engineering vehicle.
Preferably, in 2.3, since the vehicle speed information is no longer needed in the subsequent steps, the { a } is arranged as a new data sequence: { B } - { t, p ═ x, y }.
Preferably, in 3.3, N is simply taken in consideration of complexity of field work, variability of temporary loading/unloading areas and temporary roadsj＝KjIn order to deal with the possible missed meter phenomenon, firstly, when the position of a passing point is selected, the passing point is made to be as close to a loading and unloading point of a material as possible, so that the time difference delta t of two times of approaching the same retracing point in each operation of a vehicle which returns on the original road under the ordinary condition can be small enough; secondly, parameter control is carried out, and only when the time difference delta t of two adjacent times approaching the same foldback point is larger than delta tdisThe case of (2) is regarded as a "two-pass" different job, namely:
preferably, the raw vehicle data is a minimum vehicle speed v that can effectively participate in the algorithmlowIs 5 km/h.
Preferably, the Δ tlow60 seconds, and all vehicle speeds are not higher than v in each time periodlowThe data of (a) will be single sampled.
Preferably, the maximum distance r between the vehicle coordinates and the coordinates of the passing point, which can be judged as approaching, is 80-150 m.
Preferably, the two adjacent approaches to the same passing point are the minimum time interval Δ t of two passes of operationdisWhich is 900 seconds.
(III) advantageous effects
Compared with the prior art, the invention provides a vehicle driving lap recognition algorithm based on vehicle networking data, which has the following beneficial effects:
1. according to the vehicle driving lap recognition algorithm based on the vehicle networking data, under the condition of multi-vehicle transverse statistics, the area range where a plurality of vehicles which are blocked and slowly driven currently are located can be quickly positioned through vehicle speed information, real-time guidance is provided for path selection of other operation vehicles, management and scheduling of a fleet are facilitated, so that the process of a project is guaranteed, and the project transportation cost is reduced;
2. the vehicle driving trip recognition algorithm based on the vehicle networking data can count areas or roads which are frequently blocked, and provides data support for opening up new unloading pits and roads for mine construction.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic illustration of the invention filtering park/creep data;
FIG. 3 is a schematic diagram of the present invention for filtering GPS position anomaly data;
FIG. 4 is a schematic diagram of the present invention setting "waypoints";
FIG. 5 is a flow chart of a decision for detecting an approach of a vehicle to a waypoint in accordance with the present invention;
FIG. 6 is a schematic illustration of a rejection leak in accordance with the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all 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.
Referring to fig. 1, a vehicle driving pass identification algorithm based on internet of vehicles data includes the following steps:
first step, reading data: and forming a data sequence according to the sequence of the acquisition time:
{A}＝{t，p＝[x，y]，v}；
step two, data cleaning:
2.1, filtering parking/slow moving data: the mine road is complicated and difficult to walk, the condition of queuing and slow walking often occurs, and the dump truck needs to be kept static when loading and unloading materials for a certain time period (t)end-tstart≤Δtlow) Internal continuous v is less than or equal to deltalowOnly 1 value (t) is retained for the data ofstart) Thereby reducing the calculation amount of the subsequent flow;
referring to FIG. 2, solid coordinate points indicate v ≦ vlowThe open circles indicate the difference between the earliest and latest acquisition times of the data points within the data points at Δ tlowWithin；
2.2, filtering abnormal GPS positioning data: because the vehicle-mounted GPS equipment has a certain probability to feed back abnormal positioning information, the coordinates of the vehicle are caused to jump off and return without actual existence, the abnormality has great influence on subsequent algorithms and must be removed. The elimination standard is that the included angle between the pointing vector of the coordinate point and the pointing vector is larger than 90 degrees:
as shown in fig. 3: the red circle circles the anomalous location identified as a trip/foldback in this step;
2.3, cleaning result: through the data cleaning, abnormal data are removed, and the data volume is reduced; since the subsequent steps no longer require vehicle speed information, { A } is sorted into a new data series: { B } - { t, p ═ x, y ] };
the third step, the algorithm core:
3.1, setting a 'passing point': in engineering operations such as mines and the like, the loading and unloading areas of materials are relatively fixed, so that the reciprocating route of the dump truck between the loading area and the unloading area is relatively fixed, and therefore, the judgment of the reciprocating condition of the dump truck on the specified route can be converted into the judgment of the approach of the dump truck to the specified 'passing point' by taking the coordinates of the reasonable position on the route as the 'passing point'; through manual setting, data of a 'passing point' is obtained, wherein the data is { E } ═ o ═ x, y } and r }, and a plurality of range circles are preset: with reference to FIG. 4
Given that the range of motion of the vehicle is much greater than r, we do not recognize approximately in the subsequent steps that several range circles do not overlap each other in a plane.
3.2, detecting the approach of the vehicle to the passing point: the judgment standard is that the GPS positioning coordinate of the vehicle falls into a circle which takes the coordinate of the 'pass point' as the center of the circle and takes the appointed length as the radius:
by operation, the sequence { t is obtainedj，k}: the record is used as a direct basis for counting the number of loading and unloading passes of the vehicle, and a time period ([ t)start，tend]) And then the number of times that the dump truck passes through the passing point of the number j is as follows:
Kj＝card({k|tstart≤tj，k≤tend})；
for each passing point EjReferring to fig. 5, the following determination flow chart is performed:
through the operation of the step, the sequence { t is obtainedj，k}: "specifying the time at which the vehicle approaches the jth waypoint the kth time" is a set.
This record is used as a direct basis for counting the number of loading and unloading passes of the vehicle, and a time period ([ t ] is givenstart，tend]) And then the number of times that the dump truck passes through the passing point of the number j is as follows:
Kj＝card({k|tstart≤tj，k≤tend})。
3.3, eliminating missing meter: the dump truck makes a round trip on a relatively fixed path, and the loading and unloading operation performed near the passing point of the number j of each round trip makes the truck make a round trip for 2 times to pass through the passing point, so the result of the round trip statistics is as follows: n is a radical ofj＝Kj2; however, in consideration of the complexity of the field work and the variability of the temporary loading/unloading area and the temporary road, N is simply extractedj＝KjThe/2 is often missed, see FIG. 6: the time information of the data point in the red circle is recorded into tj，k}；
In special cases, namely, the dump truck does not only go back and forth between the points A and B, but also needs to operate at the point C, and does not turn back along the same path;
to cope with this possible missing count phenomenon, our solution is: firstly, when the position of a passing point is selected, the passing point is required to be close to a loading and unloading point of a material as much as possible, so that the time difference delta t of two times of approaching to the same turning point in each operation of a vehicle which is turned back on the original road under the ordinary condition can be small enough; secondly, parameter control is carried out, and only the time difference delta t of two adjacent approaches to the same turning point is larger than delta tdisThe case of (2) is considered as a "two-pass" different job, namely:
fourthly, displaying a result: and obtaining the result of the running times of the engineering vehicle.
The control parameters designed in the algorithm are simply summarized: the following table
In the scheme, under the condition of multi-vehicle transverse statistics, due to the low operation complexity and the strong real-time performance of the algorithm, flexible and efficient statistics and display according to day, hour and any time length can be provided for the real-time operation conditions of all dump trucks, and convenience is provided for management and scheduling of a truck fleet, so that the process of a project is ensured, and the project transportation cost is reduced; the method can be used for counting the areas or roads frequently subjected to traffic jam and providing data support for opening up new unloading pits and roads for mine construction parties, and due to the low operation complexity and the strong real-time property of the algorithm, flexible and efficient counting and displaying according to the day, the hour and any time length can be provided for the real-time operation conditions of all dump trucks, and convenience is provided for fleet management and scheduling; meanwhile, due to the high accuracy and reliability of the algorithm, the earth volume of the next month/day/hour can be predicted besides the statistics of the historical and current workload, so that reference is provided for the arrangement of the start/stop of temporary roads and pit unloading, the monitoring of the project progress and the like.
The invention relates to a vehicle driving lap recognition algorithm based on vehicle networking data
The existing vehicle-mounted GPS equipment is utilized to realize the round trip loading and unloading times of the dump truck in a specified time period;
meanwhile, the working efficiency of the motorcade and the progress of the project are rapidly analyzed and displayed;
the investment of extra RF identification and card swiping base station equipment is saved;
meanwhile, when the loading and unloading point is changed frequently in the mine operation, the system needs the result directly from the data without depending on the characteristics of the base station, and is very excellent in the aspect of improving the industrial efficiency.
It is noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A vehicle travel pass identification algorithm based on vehicle networking data, characterized by comprising the steps of:
first step, reading data: and forming a data sequence according to the sequence of the acquisition time:
{A}＝{t，p＝[x，y]，v}；
step two, data cleaning:
2.1, filtering parking/slow moving data: the condition of queuing and slow running often occurs due to the complex and difficult running of the mine road, and the dump truck needs to keep static when loading and unloading materials for a certain time period (t)end-tstart≤Δtlow) V is less than or equal to vlowOnly 1 value (t) is retained for the data ofstart) Thereby reducing the calculation amount of the subsequent flow;
2.2, filtering abnormal GPS positioning data: because the vehicle-mounted GPS equipment has a certain probability to feed back abnormal positioning information, the coordinates of the vehicle are subjected to tripping and turning back which do not exist actually, the abnormality has a great influence on subsequent algorithms and must be eliminated. The elimination standard is that the included angle between the pointing vector of the coordinate-changed point and the pointing vector is larger than 90 degrees:
2.3, cleaning result: through the data cleaning, abnormal data are eliminated, and the data volume is reduced;
the third step, the algorithm core:
3.1, setting a 'passing point': in engineering operations such as mines and the like, the loading and unloading areas of materials are relatively fixed, so that the reciprocating route of the dump truck between the loading area and the unloading area is relatively fixed, and therefore, the judgment of the reciprocating condition of the dump truck on the specified route can be converted into the judgment of the approach of the dump truck to the specified 'passing point' by taking the coordinates of the reasonable position on the route as the 'passing point';
3.2, detecting the approach of the vehicle to the passing point: the judgment standard is that the GPS positioning coordinate of the vehicle falls into a circle which takes the coordinate of the 'passing point' as the center of the circle and takes the appointed length as the radius:
by operation, the sequence { t is obtainedj，k}: the record is used as a direct basis for counting the number of loading and unloading passes of the vehicle, and a time period ([ t)start，tend]) And then the number of times that the dump truck passes through the passing point of the number j is as follows:
Kj＝card({k|tstart≤tj，k≤tend})；
3.3, eliminating missing meter: the dump truck makes a round trip on a relatively fixed path, and the loading and unloading operation performed near the passing point of the number j of each round trip makes the truck make a round trip for 2 times to pass through the passing point, so the result of the round trip statistics is as follows:
Nj＝Kj/2；
fourthly, displaying a result: and obtaining the result of the running times of the engineering vehicle.
2. The vehicle driving trip recognition algorithm based on vehicle networking data according to claim 1, wherein in the 2.3, since the vehicle speed information is no longer needed in the subsequent steps, { A } is arranged into a new data sequence: { B } - { t, p ═ x, y }.
3. The vehicle driving trip recognition algorithm based on vehicle networking data as claimed in claim 1, wherein in 3.3, simply take N in consideration of complexity of field work, variability of temporary loading and unloading area and temporary roadj＝KjIn order to deal with the possible missed-meter phenomenon with the approximate rate, firstly, when the position of a passing point is selected, the passing point is made to be as close to a loading and unloading point of a material as possible, so that the time difference delta t of two times of approaching the same retracing point in each operation of a vehicle which returns on the original road under the ordinary condition can be small enough; secondly, parameter control is carried out, and only when the time difference delta t of two adjacent times approaching the same foldback point is larger than delta tdisThe case of (2) is considered as a "two-pass" different job, namely:
4. the vehicle driving trip recognition algorithm based on vehicle networking data according to claim 1, wherein the raw vehicle data can effectively participate in the minimum vehicle speed v of the algorithmlowIs 5 km/h.
5. Vehicle driving trip recognition algorithm based on vehicle networking data according to claim 1, characterized in that Δ tlow60 seconds, and all vehicle speeds are not higher than v in each time periodlowThe data of (a) will be single sampled.
6. The vehicle driving trip recognition algorithm based on vehicle networking data according to claim 1, wherein the maximum distance r that can be judged to be approaching between the vehicle coordinates and the coordinates of the passing points is 80-150 m.
7. The vehicle driving trip identification algorithm based on vehicle networking data as claimed in claim 1, wherein the adjacent two approaches to the same passing point is the minimum time interval Δ t of two tripsdisWhich is 900 seconds.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117292541A (en) * | 2023-08-14 | 2023-12-26 | 陕西天行健车联网信息技术有限公司 | Transportation time statistical analysis method, transportation time statistical analysis device, transportation time statistical analysis equipment and transportation time statistical analysis medium |
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