CN117292541A - Transportation time statistical analysis method, transportation time statistical analysis device, transportation time statistical analysis equipment and transportation time statistical analysis medium - Google Patents
Transportation time statistical analysis method, transportation time statistical analysis device, transportation time statistical analysis equipment and transportation time statistical analysis medium Download PDFInfo
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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
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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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
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
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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
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
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Abstract
The invention discloses a transportation time statistical analysis method, a transportation time statistical analysis device, transportation time statistical analysis equipment and transportation time statistical analysis media. According to the scheme, acquired data of a vehicle-mounted terminal of a target vehicle at all times are firstly acquired, longitude and latitude matched with a plurality of continuous times are determined to be all parking points of the target vehicle according to the matching degree of the longitude and latitude in the acquired data at all times, then a first parking point is used as a reference point according to a timestamp, whether the distance between the current parking point and the reference point is smaller than the distance between the previous parking point and the reference point or not is calculated through traversal, if so, the reference point to the previous parking point is determined to be one transportation trip, the reference point is updated, a plurality of transportation trips are obtained through multi-pass calculation, and statistical analysis is carried out on all the transportation trips according to the acquired data contained in all the transportation trips. By determining the stop points of the collected data in the running process of the target vehicle and analyzing the distance relation among the stop points, each transport pass is counted and analyzed one by one, and the recognition efficiency and accuracy of the transport passes are improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a transportation time statistical analysis method, a transportation time statistical analysis device, transportation time statistical analysis equipment and transportation time statistical analysis media.
Background
The trip analysis of the heavy truck has been paid attention in the industry, and the characteristics of the vehicle such as the distance between the transportation, the operation efficiency, the distribution area and the like can be further analyzed through the transportation trip data. Meanwhile, the situation that the transportation process is incomplete due to natural day cutting can be avoided, and particularly, the automatic calculation of the passes is important in the industry of paying for part of the time data establishment work orders, such as urban construction dregs transportation.
The existing time calculation method is to input a starting point and an ending point at a service platform, count the time by matching the vehicle positioning data with a service input address, and is suitable for a scene that a vehicle operation route is fixed or a customer operation route is clearly known, and has great limitation on service application of traffic time statistics of commercial vehicles.
Under the inapplicable scene, for example, a scene that the vehicle operation route is not fixed or the operation route is not clear, the current trip information is mostly derived from statistics of offline manpower, the labor cost is high, the problem of trip missing counting exists, and the efficiency and the accuracy of the trip statistics are low.
Disclosure of Invention
Based on the above, it is necessary to provide a transportation trip statistical analysis method, device, equipment and medium for solving the above technical problems.
The technical scheme adopted in the specification is as follows:
the specification provides a transportation trip statistical analysis method, comprising:
acquiring acquisition data of a target vehicle-mounted terminal at each moment; wherein, the collected data at least comprises a time stamp and longitude and latitude;
according to the matching degree of the longitude and latitude in the acquired data at each moment, determining the longitude and latitude matched with a plurality of continuous moments as each stop point of the target vehicle;
arranging the stop points according to the sequence of the time stamps, and traversing and calculating the distance between each stop point and the reference point from front to back by taking the current first stop point as the reference point;
if the distance between the current stop point and the reference point is smaller than the distance between the previous stop point and the reference point, determining the reference point to the previous stop point as a transportation pass, taking the current stop point as a new reference point, and traversing from front to back to calculate the distance between the new reference point and other stop points after the new reference point;
and obtaining the transportation pass of the target vehicle through multi-pass traversal calculation, and carrying out statistical analysis on each transportation pass according to the acquired data contained in each transportation pass.
Optionally, determining, according to the matching degree of the longitude and latitude in the collected data at each time, the longitude and latitude matched with the continuous multiple times as each stop point of the target vehicle specifically includes:
the hash values corresponding to the longitudes and the latitudes in the collected data at each moment are arranged according to the time sequence, and when the hash values corresponding to the longitudes and the latitudes in the collected data at the continuous moment are the same, the starting moment and the ending moment of the moment range with the same hash values are determined;
when the difference between the starting time and the ending time is larger than the preset standard stopping time, determining the longitude and latitude of the acquired data corresponding to the time range with the same hash value as the stopping point of the target vehicle.
Optionally, the collected data further comprises a vehicle pulse speed, an engine speed and a GPS vehicle speed;
according to the collected data contained in each transportation pass, carrying out statistical analysis on each transportation pass, wherein the method specifically comprises the following steps:
calculating the engine speed characteristics of the stop points according to the engine speed, the GPS speed and the longitude and latitude in the collected data of the stop points of the transport passes aiming at each transport pass;
determining a stop point with the acquisition interval larger than a preset interval in the acquired data of the moment range corresponding to the stop point and the GPS vehicle speed equal to zero and the vehicle pulse speed equal to zero as a suspected unloading point;
determining the engine speed interval duty ratio of the suspected unloading point according to the engine speed characteristics of the suspected unloading point, so as to calculate the similarity between the engine speed interval duty ratio of the suspected unloading point and the predetermined standard unloading point engine speed interval duty ratio;
judging whether the similarity of the suspected unloading point with the maximum similarity in the starting stop point and the ending stop point is larger than a preset threshold value or not; if yes, determining the suspected unloading point with the maximum similarity as the unloading point; if not, determining that no unloading point exists in the suspected unloading points.
Optionally, the collected data further includes engine condition data;
according to the collected data contained in each transportation pass, carrying out statistical analysis on each transportation pass, wherein the method specifically comprises the following steps:
calculating the load of the target vehicle according to at least part of collected data in the transportation pass and the vehicle speed ratio attribute information of the target vehicle by using a vehicle mechanics equilibrium equation aiming at each transportation pass;
judging whether the target vehicle is in a load state according to the load of the target vehicle and the dead weight of the target vehicle;
if so, determining the starting stop point of the transportation pass as a loading point and determining the ending stop point of the transportation pass as a unloading point.
Optionally, the collected data further comprises a GPS vehicle speed; the method further comprises the steps of:
determining goods yard information according to the map POI, and constructing a goods yard area;
acquiring historical acquisition data of a target vehicle model in a cargo area region, analyzing the vehicle speed change in the historical acquisition data through big data, and identifying a deceleration strip region in the cargo area region;
performing cluster analysis on GPS speed passing through a deceleration strip in the historical acquisition data through a neural network model, and respectively determining speed characteristics in an idle state and a loading state;
determining whether the GPS speed of the target vehicle at the loading point passes through the deceleration strip meets the speed characteristic under the no-load state or not so as to determine whether the initial stop point of the transportation pass is the loading point or not;
and determining whether the GPS vehicle speed of the target vehicle when the unloading point passes through the deceleration strip meets the vehicle speed characteristic under the loading state so as to determine whether the ending stop point of the transportation pass is the unloading point.
Optionally, the statistical analysis is performed on each transport pass according to the collected data contained in each transport pass, and specifically includes:
for each transportation pass, determining road attributes corresponding to the longitude and latitude from the map according to the longitude and latitude in each acquired data contained in the transportation pass so as to determine road grades corresponding to each acquired data;
and (3) carrying out statistical analysis on the ratio of the collected data corresponding to different road grades to the total quantity of the collected data contained in the transportation pass.
Optionally, the collected data further comprises a current total mileage and a current total oil consumption;
analyzing each transportation pass according to the collected data contained in each transportation pass, and specifically comprising the following steps:
for each transport trip, according to the current total mileage in each acquired data contained in the start stop point and the end stop point corresponding to the transport trip, the total mileage of the transport trip is counted and analyzed;
and according to the current total oil consumption in each acquired data contained in the starting stop point and the ending stop point corresponding to the transportation time, the total oil consumption of the transportation time is counted and analyzed.
The present specification provides a transportation trip statistical analysis device, comprising:
the acquisition module is used for acquiring the acquired data of the vehicle-mounted terminal of the target vehicle at all moments; wherein, the collected data at least comprises a time stamp and longitude and latitude;
the matching module is used for determining the longitude and latitude matched with a plurality of continuous moments as each stop point of the target vehicle according to the matching degree of the longitude and latitude in the acquired data at each moment;
the calculation module is used for arranging the stop points according to the sequence of the time stamps, taking the current first stop point as a reference point, and traversing from front to back to calculate the distance between each stop point and the reference point;
the determining module is used for determining that the reference point is a transportation pass from the previous reference point if the distance between the current reference point and the reference point is smaller than the distance between the previous reference point and the reference point, taking the current reference point as a new reference point, and calculating the distance between the new reference point and other reference points after the new reference point from front to back;
the statistical module is used for obtaining the transportation pass of the target vehicle through multi-pass calculation, and carrying out statistical analysis on each transportation pass according to the acquired data contained in each transportation pass.
The present specification provides a computer readable storage medium storing a computer program which when executed by a processor implements the transportation pass statistical analysis method described above.
The present specification provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the transportation pass statistical analysis method described above when executing the program.
The above-mentioned at least one technical scheme that this specification adopted can reach following beneficial effect:
according to the method, acquired data of each moment of a vehicle-mounted terminal of a target vehicle are firstly acquired, then, according to the matching degree of longitudes and latitudes in the acquired data of each moment, the longitudes and latitudes matched at a plurality of continuous moments are determined to be each stop point of the target vehicle, each stop point is arranged according to the sequence of time stamps, the current first stop point is used as a reference point, the distance between each stop point and the reference point is calculated through traversing from front to back, if the distance between the current stop point and the reference point is smaller than the distance between the previous stop point and the reference point, the reference point is determined to be one transportation pass, the current stop point is used as a new reference point, the distances between the other stop points and the new reference point after the new reference point are calculated through traversing from front to back, the plurality of transportation passes of the target vehicle are obtained through traversing calculation, and statistical analysis is carried out on each transportation pass according to the acquired data contained in each transportation pass.
According to the invention, the stop points for collecting data in the running process of the target vehicle are determined first, the distance relation between the stop points is analyzed, and as the transportation of the vehicle is usually in the form of turning back running, whether the vehicle turns back or not can be judged through the distance relation between the reference point and the stop points, so that the transportation passes are analyzed one by one, and the recognition efficiency and accuracy of the transportation passes of the vehicle are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic flow chart of a statistical analysis method for transportation passes provided in the present specification;
FIG. 2 is a schematic illustration of a dock identification provided herein;
FIG. 3 is a schematic illustration of a vehicle stop provided herein;
FIG. 4 is a schematic flow chart of another statistical analysis method for transportation passes provided in the present specification;
FIG. 5 is a schematic diagram of a transportation trip statistical analysis device provided in the present specification;
fig. 6 is a schematic diagram of a computer device for implementing the transportation trip statistical analysis method provided in the present specification.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present specification more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the disclosure, are intended to be within the scope of the present application based on the embodiments described herein.
The current time information is mostly derived from statistics of offline manpower, time consumption, labor consumption and the like are caused, the existing time calculation methods also have the problem of time missing, and the problems that the existing time missing is caused, the loading and unloading points are not recognized and the like are not solved effectively.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a statistical analysis method for transportation passes in the present specification, specifically including the following steps:
s101: acquiring acquisition data of a target vehicle-mounted terminal at each moment; the acquired data at least comprises a time stamp and longitude and latitude.
In practical application, when the server of the service platform performs transportation time statistical analysis, the collected data of the vehicle-mounted terminal of the target vehicle at all times can be acquired first.
The specific type of data included in the collected data can be determined according to needs, and the specification does not limit the specific type of data. For example, each piece of acquired data may include: data number, timestamp, location identification, latitude, longitude, total mileage, total fuel consumption, etc. In general, in the running process of the target vehicle, vehicle-mounted terminal data such as longitude and latitude and/or CAN data CAN be collected and recorded in real time according to a preset time interval, and the vehicle-mounted terminal data is prepared for statistical analysis by a subsequent server. The data collected by the target vehicle is stored in any manner, and the present specification is not limited thereto, and may be stored in an Hbase database, for example.
After the server obtains the collected data, in one or more embodiments of the present disclosure, the longitude and latitude in the collected data may be converted into a character string by a hash method, and then the change of the position of the target vehicle at the time of collection may be determined by comparing the hash values. The specific hash method is not limited in this specification, and for example, a Geohash8 method may be adopted.
The server mentioned in the present specification may be a server provided on a service platform, or a device such as a desktop, a notebook, or the like capable of executing the aspects of the present specification. For convenience of explanation, only the server is used as the execution subject.
S102: and determining the longitude and latitude matched with a plurality of continuous moments as each stop point of the target vehicle according to the matching degree of the longitude and latitude in the acquired data at each moment.
After the longitude and latitude in the acquired data are acquired, the server can further determine the longitude and latitude matched with a plurality of continuous moments as each stop point of the target vehicle according to the matching degree of the longitude and latitude in the acquired data at each moment, namely, the target vehicle is at similar longitude and latitude at the plurality of continuous moments, so that the situation that the target vehicle is likely to stop is indicated.
Specifically, the server may arrange hash values corresponding to the collected data at each time according to a time sequence, when hash values corresponding to the collected data at consecutive times are the same, determine a start time and an end time of a time range in which the hash values are the same, and when a difference between the start time and the end time is greater than a preset standard parking duration, the server may determine longitude and latitude of the collected data corresponding to the time range in which the hash values are the same as a parking point of the target vehicle.
The preset standard parking time length can be set according to service requirements, and the specification does not limit the preset standard parking time length. Of course, the server may also determine the parking time period, i.e. the difference between the end time and the start time, from the time range.
Fig. 2 is a schematic view of parking point recognition provided in the present specification, where each white point in fig. 2 represents a corresponding acquisition location of the acquired data of the target vehicle at each moment, and it can be seen that the whole right side in fig. 2 is sparse, which indicates that the target vehicle is in the process of transportation, and the three rectangular frames on the left side are denser, which indicates that the target vehicle has a parking phenomenon at the corresponding acquisition locations of the three rectangular frames, that is, the parking points exist in the three rectangular frames.
S103: and arranging the stopping points according to the sequence of the time stamps, taking the current first stopping point as a reference point, and traversing from front to back to calculate the distance between each stopping point and the reference point.
S104: if the distance between the current stop point and the reference point is smaller than the distance between the previous stop point and the reference point, determining the reference point to the previous stop point as a transportation pass, taking the current stop point as a new reference point, and traversing from front to back to calculate the distance between the new reference point and other stop points after the new reference point.
After determining each stop point in step S102, the server may first arrange each stop point according to the sequence of time stamps of the collected data corresponding to each stop point, and then perform traversal calculation to determine transport passes one by one.
Taking fig. 3 as an example for explanation, fig. 3 is a schematic view of a vehicle stop point provided in the present specification. As can be seen from fig. 3, the travel path of the target vehicle is in the order of A, B, C, D, E, wherein each letter indicates the stop determined in step S102.
The server may then first traverse the stop points (B/C/D/E) at the time after the point a as the reference point, calculate the AB, AC distance, if AB > AC, AB is an effective pass, store the transport pass, and terminate the traversal with a as the reference point, otherwise continue the traversal, calculate the distance of AC, AD, and so on until the end. And if the effective pass is found, taking the point B as a new reference point in sequence, if the effective pass is found, such as the effective pass BD, taking the next stopping point E of the effective pass ending point D as a new reference point, and then traversing the calculation to find out the remaining effective passes until the end.
When the server takes each point as a datum point, the current traversal is terminated by finding out the effective time, the datum point is switched to be the next stop point of the ending point of the effective time, and all the effective time is stored until the traversal is ended. If the effective pass is found, the switching reference point is the next stop point of the effective pass end point, and if the effective pass is not found, the switching reference point is the next stop point of the time sequence.
Each transport pass may be stored as: the method comprises the steps of starting a stop point, starting time of the starting stop point, ending stop point, starting time of the ending stop point and ending time of the ending stop point.
S105: and obtaining a plurality of transportation passes of the target vehicle through multi-pass traversal calculation, and carrying out statistical analysis on each transportation pass according to the acquired data contained in each transportation pass.
After the multiple transportation passes of the target vehicle are obtained through the multi-pass calculation, the server can carry out statistical analysis on each transportation pass according to the acquired data contained in each transportation pass.
Specifically, the server may count the transport passes corresponding to the acquired data of the vehicle-mounted terminal of the target vehicle at each moment, the start stop point and the end stop point corresponding to each transport pass, the stop time of each stop point, and the like, and may apply the statistical analysis data of the transport passes in other services later.
Based on the statistical analysis method of the transportation passes shown in fig. 1, acquiring acquisition data of a vehicle-mounted terminal of a target vehicle at each moment, determining longitude and latitude matched at a plurality of continuous moments as each stopping point of the target vehicle according to the matching degree of the longitude and latitude in the acquisition data at each moment, arranging the stopping points according to the sequence of time stamps, taking the current first stopping point as a reference point, calculating the distance between each stopping point and the reference point from front to back in a traversing manner, determining the distance from the reference point to the last stopping point as one transportation pass if the distance between the current stopping point and the reference point is smaller than the distance between the last stopping point and the reference point, taking the current stopping point as a new reference point, calculating the distance between other stopping points and the new reference point after the new reference point from front to back in a traversing manner, obtaining a plurality of transportation passes of the target vehicle through multi-pass calculation, and carrying out statistical analysis on the transportation passes according to the acquisition data contained in each transportation pass.
According to the invention, the stop points for collecting data in the running process of the target vehicle are determined first, the distance relation between the stop points is analyzed, and as the transportation of the vehicle is usually in the form of turning back running, whether the vehicle turns back or not can be judged through the distance relation between the reference point and the stop points, so that the transportation passes are analyzed one by one, and the recognition efficiency and accuracy of the transportation passes of the vehicle are improved.
When the transportation time statistical analysis method provided in the present specification is applied, the method may not be executed according to the sequence of steps shown in fig. 1, and the execution sequence of the specific steps may be determined according to needs, which is not limited in the present specification.
In addition, in one or more embodiments of the present disclosure, in step S105, when the collected data of each moment of the vehicle-mounted terminal of the target vehicle further includes the vehicle pulse speed, the engine speed, and the GPS vehicle speed, the server may further calculate, for each transportation trip, the engine speed characteristic of the starting stop point according to the engine speed, the GPS vehicle speed, and the longitude and latitude in the collected data of the stopping point of the transportation trip.
And then, determining the stop point with the acquisition interval larger than the preset interval in the acquired data of the time range corresponding to the stop point and the GPS vehicle speed equal to zero and the vehicle pulse speed equal to zero as a suspected discharge point, and determining the engine speed interval duty ratio of the suspected discharge point according to the engine speed characteristics of the suspected discharge point so as to calculate the similarity between the engine speed interval duty ratio of the suspected discharge point and the predetermined standard discharge point engine speed interval duty ratio.
And finally, judging whether the similarity of the suspected unloading point with the maximum similarity in the starting parking point and the ending parking point is larger than a preset threshold value, if so, determining that the suspected unloading point with the maximum similarity is the unloading point, and if not, determining that no unloading point exists in the suspected unloading points.
The judgment of whether or not each amount is zero can be performed with a certain degree of accuracy according to the actual need. In general, the rotational speed of the engine varies at all times, and the vehicle corresponds to different rotational speed variation characteristics in different motion states. For example, the engine speed may be divided into three speed sections of low, medium and high, so that the time that the engine may be in the medium speed section is longer and the time in the other two sections is shorter when the vehicle is running normally on the ordinary road. If the vehicle is ascending, the engine may be in a high-speed section for a longer period of time and in the other two sections for a shorter period of time. That is, different situations may correspond to different section distribution situations.
The server can acquire data in advance according to the model history of the target vehicle, and determine the corresponding engine speed interval duty ratio at the standard unloading point when the target vehicle is in the unloading state. And then, according to the engine speed characteristics of the suspected unloading point, determining the engine speed interval duty ratio of the suspected unloading point, and performing characteristic comparison to judge whether the suspected unloading point is the unloading point.
In addition, in one or more embodiments of the present disclosure, in step S105, when the collected data of the on-board terminal of the target vehicle at each moment further includes engine operating condition data, the server may further calculate, for each transportation trip, the load of the target vehicle according to the vehicle mechanical balance equation and the engine operating condition data and the vehicle speed ratio attribute information of the target vehicle in at least part of the collected data included in the transportation trip.
And judging whether the target vehicle is in a load state according to the load of the target vehicle and the dead weight of the target vehicle, if so, determining the starting stop point of the transportation pass as a loading point and determining the ending stop point of the transportation pass as a unloading point.
For example, if the calculated load M > =the dead weight M0 x1 of the target vehicle is satisfied, the load state is recorded, if the calculated load M0 x1> M0 x2 is satisfied, the empty state is recorded, and otherwise, the invalid data is recorded. Wherein, x1 and x2 can be determined according to practical situations.
Further, in one or more embodiments of the present disclosure, in step S105, when the collected data of the vehicle-mounted terminal of the target vehicle at each moment further includes a GPS vehicle speed, based on the above method, the server may further determine the cargo area information according to the map POI, construct the cargo area, obtain the historical collected data of the vehicle model of the target vehicle in the cargo area, analyze the vehicle speed change in the historical collected data through the big data, and identify the deceleration strip area in the cargo area.
And then, performing cluster analysis on the GPS speed passing through the deceleration strip in the historical acquisition data through a neural network model, and respectively determining the speed characteristics under the no-load state and the loading state.
And finally, determining whether the GPS speed of the target vehicle when the loading point passes through the deceleration strip meets the speed characteristic under the no-load state or not so as to determine whether the starting stopping point of the transportation pass is the loading point or not, and determining whether the GPS speed of the target vehicle when the unloading point passes through the deceleration strip meets the speed characteristic under the loading state or not so as to determine whether the ending stopping point of the transportation pass is the unloading point or not.
The loading point and/or unloading point information obtained by the final analysis can be marked in the stop points in the statistical analysis data of each transportation pass of the target vehicle so as to be used by other businesses.
In addition, in one or more embodiments of the present disclosure, in step S105, the server may further determine, for each transportation pass, a road attribute corresponding to the longitude and latitude from the map according to the longitude and latitude in each collected data included in the transportation pass, so as to determine a road class corresponding to each collected data, and statistically analyze a ratio of the collected data corresponding to different road classes to the total amount of collected data included in the transportation pass. The duty ratio weight information of each road can also be recorded in the statistical analysis data of each transportation trip of the target vehicle so as to be convenient for other businesses to apply.
In addition, in one or more embodiments of the present disclosure, in step S105, when the collected data of each moment of the vehicle-mounted terminal of the target vehicle further includes a current total mileage and a current total oil consumption, the server may further statistically analyze, for each transport trip, a total mileage of the transport trip according to a current total mileage in each collected data included in the start stop point and the end stop point corresponding to the transport trip, and statistically analyze a total oil consumption of the transport trip according to a current total oil consumption in each collected data included in the start stop point and the end stop point corresponding to the transport trip. And the mileage information and the oil consumption information are counted into the statistical analysis data of each transportation trip of the target vehicle so as to be convenient for other businesses to apply.
Then, after the transportation pass of the target vehicle is statistically analyzed by the scheme, statistical analysis information can be output:
data number |single trip mileage|single trip fuel consumption|start time|start stop point|end stop point|start point city county|end point city county|high speed weight|national road weight|province road weight|county road weight|village weight. Wherein the discharge point may be marked in the start stop or the loading point in the end stop.
Fig. 4 is a schematic diagram of a statistical analysis method for transportation trip provided in the present specification, in combination with the foregoing description, in one or more embodiments of the present specification, a server may acquire collected data of a target vehicle at each moment in real time and determine road attributes corresponding to the collected data according to a map, then identify stop points based on matching degree of a geohash8 value obtained by a geohash8 method, then calculate each stop point by traversal to determine transportation trip one by one according to a distance relationship, and then obtain analysis of a standard discharge point rotation speed interval distribution by relying on historical collected data, and identify loading points/unloading points according to engine rotation speed characteristics of the target vehicle. Further, load state judgment can be added to identify loading/unloading points. Furthermore, road network data can be integrated, and the speed characteristics of the vehicle passing through the freight yard deceleration strip can be analyzed based on the historical acquisition data to identify loading points/unloading points.
The transport trip statistical analysis method provided above for one or more embodiments of the present specification further provides a corresponding transport trip statistical analysis device based on the same concept, as shown in fig. 5.
Fig. 5 is a schematic diagram of a transportation trip statistical analysis device provided in the present specification, including:
the acquisition module 501 is used for acquiring acquisition data of the vehicle-mounted terminal of the target vehicle at each moment; wherein, the collected data at least comprises a time stamp and longitude and latitude;
the matching module 502 is configured to determine, according to the matching degree of the longitude and latitude in the collected data at each time, the longitude and latitude that are matched at a plurality of continuous times as each stop point of the target vehicle;
a calculating module 503, configured to arrange the stop points according to the sequence of the time stamps, and calculate the distance between each stop point and the reference point by traversing from front to back with the current first stop point as the reference point;
a determining module 504, configured to determine that the reference point is a transportation pass from the previous reference point if the distance between the current reference point and the reference point is smaller than the distance between the previous reference point and the reference point, and calculate the distance between the new reference point and other reference points after traversing from front to back by taking the current reference point as the new reference point;
the statistics module 505 is configured to obtain transportation passes of the target vehicle through multiple traversal calculations, and perform statistical analysis on each transportation pass according to collected data included in each transportation pass.
Optionally, the matching module 502 arranges hash values corresponding to the longitude and latitude in the collected data at each moment according to a time sequence, when the hash values corresponding to the longitude and latitude in the collected data at successive moments are the same, determines a start moment and an end moment of a moment range in which the hash values are the same, and when a difference between the start moment and the end moment is greater than a preset standard parking duration, determines the longitude and latitude of the collected data corresponding to the moment range in which the hash values are the same as a parking point of the target vehicle.
Optionally, the collected data further comprises a vehicle pulse speed, an engine speed, and a GPS vehicle speed.
The statistics module 505 calculates, for each transport trip, an engine rotation speed characteristic of a stop point according to an engine rotation speed, a GPS vehicle speed and a longitude and latitude in collected data of the stop point of the transport trip, determines that a collection interval in the collected data of a time range corresponding to the stop point is greater than a preset interval, and the stop point with the GPS vehicle speed being zero and the vehicle pulse speed being equal to zero is a suspected discharge point, determines an engine rotation speed interval ratio of the suspected discharge point according to the engine rotation speed characteristic of the suspected discharge point, calculates a similarity between the engine rotation speed interval ratio of the suspected discharge point and an engine rotation speed interval ratio of a predetermined standard discharge point, determines whether the similarity between a start stop point and a suspected discharge point with the largest similarity is greater than a preset threshold, if yes, determines that the suspected discharge point with the largest similarity is the discharge point, if no discharge point is determined in the suspected discharge point.
Optionally, the collected data further comprises engine operating condition data.
The statistics module 505 calculates, for each transportation trip, a target vehicle load according to at least part of collected data contained in the transportation trip and vehicle speed ratio attribute information of the target vehicle by using a vehicle mechanical balance equation, determines whether the target vehicle is in a load state according to the target vehicle load and the target vehicle dead weight, if yes, determines a starting stop point of the transportation trip as a loading point, and determines an ending stop point of the transportation trip as a unloading point.
Optionally, the collected data further includes GPS vehicle speed.
The apparatus further comprises: the analysis module 506 is configured to determine cargo yard information according to the map POI, construct a cargo yard area, obtain historical collection data of a target vehicle model in the cargo yard area, analyze a vehicle speed change in the historical collection data through big data, identify a deceleration strip area in the cargo yard area, perform cluster analysis on a GPS vehicle speed passing through the deceleration strip in the historical collection data through a neural network model, respectively determine a vehicle speed characteristic in an empty state and a loading state, determine whether the GPS vehicle speed of the target vehicle passing through the deceleration strip at a loading point meets the vehicle speed characteristic in the empty state, so as to determine whether a starting stop point of a transportation trip is a loading point, determine whether the GPS vehicle speed of the target vehicle passing through the deceleration strip meets the vehicle speed characteristic in the loading state, and determine whether an ending stop point of the transportation trip is a unloading point.
Optionally, the statistics module 505 determines, for each transportation trip, a road attribute corresponding to the longitude and latitude from the map according to the longitude and latitude in each collected data included in the transportation trip, so as to determine a road class corresponding to each collected data, and performs a statistical analysis on a ratio of the collected data corresponding to different road classes to the total amount of the collected data included in the transportation trip.
Optionally, the collected data further comprises a current total mileage and a current total oil consumption.
The statistics module 505 performs statistics analysis on total mileage of the transportation trip according to current total mileage in each collected data contained in the start stop point and the end stop point corresponding to the transportation trip, and performs statistics analysis on total fuel consumption of the transportation trip according to current total fuel consumption in each collected data contained in the start stop point and the end stop point corresponding to the transportation trip.
For specific limitations of the transportation pass statistical analysis device, reference may be made to the above limitations of the transportation pass statistical analysis method, and no further description is given here. The individual modules in the transport pass statistical analysis device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
The present specification also provides a computer readable storage medium storing a computer program operable to perform the transportation pass statistical analysis method provided in fig. 1 above.
The present specification also provides a schematic structural diagram of the computer device shown in fig. 6, where, as shown in fig. 6, the computer device includes a processor, an internal bus, a network interface, a memory, and a nonvolatile memory, and may include hardware required by other services. The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs to implement the transportation trip statistical analysis method provided in fig. 1.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static RandomAccess Memory, SRAM) or dynamic random access memory (Dynamic RandomAccess Memory, DRAM), and the like.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
Claims (10)
1. A transportation pass statistical analysis method, comprising:
acquiring acquisition data of a target vehicle-mounted terminal at each moment; wherein, the collected data at least comprises a time stamp and longitude and latitude;
according to the matching degree of the longitude and latitude in the acquired data at each moment, determining the longitude and latitude matched with a plurality of continuous moments as each stop point of the target vehicle;
arranging the stop points according to the sequence of the time stamps, and traversing and calculating the distance between each stop point and the reference point from front to back by taking the current first stop point as the reference point;
if the distance between the current stop point and the reference point is smaller than the distance between the previous stop point and the reference point, determining the reference point to the previous stop point as a transportation pass, taking the current stop point as a new reference point, and traversing from front to back to calculate the distance between the new reference point and other stop points after the new reference point;
and obtaining the transportation pass of the target vehicle through multi-pass traversal calculation, and carrying out statistical analysis on each transportation pass according to the acquired data contained in each transportation pass.
2. The transportation trip statistical analysis method according to claim 1, wherein determining longitude and latitude matched at a plurality of consecutive moments as each stop point of the target vehicle according to the matching degree of longitude and latitude in the collected data at each moment specifically comprises:
the hash values corresponding to the longitudes and the latitudes in the collected data at each moment are arranged according to the time sequence, and when the hash values corresponding to the longitudes and the latitudes in the collected data at the continuous moment are the same, the starting moment and the ending moment of the moment range with the same hash values are determined;
when the difference between the starting time and the ending time is larger than the preset standard stopping time, determining the longitude and latitude of the acquired data corresponding to the time range with the same hash value as the stopping point of the target vehicle.
3. The transportation pass statistical analysis method of claim 2, wherein the collected data further comprises vehicle pulse speed, engine speed, GPS vehicle speed;
according to the collected data contained in each transportation pass, carrying out statistical analysis on each transportation pass, wherein the method specifically comprises the following steps:
calculating the engine speed characteristics of the stop points according to the engine speed, the GPS speed and the longitude and latitude in the collected data of the stop points of the transport passes aiming at each transport pass;
determining a stop point with the acquisition interval larger than a preset interval in the acquired data of the moment range corresponding to the stop point and the GPS vehicle speed equal to zero and the vehicle pulse speed equal to zero as a suspected unloading point;
determining the engine speed interval duty ratio of the suspected unloading point according to the engine speed characteristics of the suspected unloading point, so as to calculate the similarity between the engine speed interval duty ratio of the suspected unloading point and the predetermined standard unloading point engine speed interval duty ratio;
judging whether the similarity of the suspected unloading point with the maximum similarity in the starting stop point and the ending stop point is larger than a preset threshold value or not; if yes, determining the suspected unloading point with the maximum similarity as the unloading point; if not, determining that no unloading point exists in the suspected unloading points.
4. The transportation pass statistical analysis method of claim 1, wherein the collected data further comprises engine operating condition data;
according to the collected data contained in each transportation pass, carrying out statistical analysis on each transportation pass, wherein the method specifically comprises the following steps:
calculating the load of the target vehicle according to at least part of collected data in the transportation pass and the vehicle speed ratio attribute information of the target vehicle by using a vehicle mechanics equilibrium equation aiming at each transportation pass;
judging whether the target vehicle is in a load state according to the load of the target vehicle and the dead weight of the target vehicle;
if so, determining the starting stop point of the transportation pass as a loading point and determining the ending stop point of the transportation pass as a unloading point.
5. The transportation pass statistical analysis method of claim 4, wherein the collected data further comprises a GPS vehicle speed; the method further comprises the steps of:
determining goods yard information according to the map POI, and constructing a goods yard area;
acquiring historical acquisition data of a target vehicle model in a cargo area region, analyzing the vehicle speed change in the historical acquisition data through big data, and identifying a deceleration strip region in the cargo area region;
performing cluster analysis on GPS speed passing through a deceleration strip in the historical acquisition data through a neural network model, and respectively determining speed characteristics in an idle state and a loading state;
determining whether the GPS speed of the target vehicle at the loading point passes through the deceleration strip meets the speed characteristic under the no-load state or not so as to determine whether the initial stop point of the transportation pass is the loading point or not;
and determining whether the GPS vehicle speed of the target vehicle when the unloading point passes through the deceleration strip meets the vehicle speed characteristic under the loading state so as to determine whether the ending stop point of the transportation pass is the unloading point.
6. The transportation pass statistical analysis method according to claim 1, wherein the statistical analysis is performed on each transportation pass according to the collected data contained in each transportation pass, specifically comprising:
for each transportation pass, determining road attributes corresponding to the longitude and latitude from the map according to the longitude and latitude in each acquired data contained in the transportation pass so as to determine road grades corresponding to each acquired data;
and (3) carrying out statistical analysis on the ratio of the collected data corresponding to different road grades to the total quantity of the collected data contained in the transportation pass.
7. The transportation trip statistical analysis method of claim 1, wherein the collected data further comprises a current total mileage, a current total fuel consumption;
analyzing each transportation pass according to the collected data contained in each transportation pass, and specifically comprising the following steps:
for each transport trip, according to the current total mileage in each acquired data contained in the start stop point and the end stop point corresponding to the transport trip, the total mileage of the transport trip is counted and analyzed;
and according to the current total oil consumption in each acquired data contained in the starting stop point and the ending stop point corresponding to the transportation time, the total oil consumption of the transportation time is counted and analyzed.
8. A transportation trip statistical analysis device, comprising:
the acquisition module is used for acquiring the acquired data of the vehicle-mounted terminal of the target vehicle at all moments; wherein, the collected data at least comprises a time stamp and longitude and latitude;
the matching module is used for determining the longitude and latitude matched with a plurality of continuous moments as each stop point of the target vehicle according to the matching degree of the longitude and latitude in the acquired data at each moment;
the calculation module is used for arranging the stop points according to the sequence of the time stamps, taking the current first stop point as a reference point, and traversing from front to back to calculate the distance between each stop point and the reference point;
the determining module is used for determining that the reference point is a transportation pass from the previous reference point if the distance between the current reference point and the reference point is smaller than the distance between the previous reference point and the reference point, taking the current reference point as a new reference point, and calculating the distance between the new reference point and other reference points after the new reference point from front to back;
the statistical module is used for obtaining the transportation pass of the target vehicle through multi-pass calculation, and carrying out statistical analysis on each transportation pass according to the acquired data contained in each transportation pass.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the transportation pass statistical analysis method of any one of the preceding claims 1 to 7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the transportation pass statistical analysis method of any one of the preceding claims 1 to 7 when the program is executed.
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