CN116645828A - Logistics vehicle track deviation alarm method and system - Google Patents
Logistics vehicle track deviation alarm method and system Download PDFInfo
- Publication number
- CN116645828A CN116645828A CN202310611711.1A CN202310611711A CN116645828A CN 116645828 A CN116645828 A CN 116645828A CN 202310611711 A CN202310611711 A CN 202310611711A CN 116645828 A CN116645828 A CN 116645828A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- deviation
- road section
- determining
- threshold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 239000000725 suspension Substances 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000013135 deep learning Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- 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
- G08G1/133—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 within the vehicle ; Indicators inside the vehicles or at stops
-
- 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
-
- 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
-
- 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
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Traffic Control Systems (AREA)
Abstract
The application discloses a logistics vehicle track deviation alarming method and system, which relate to the technical field of logistics vehicle management.
Description
Technical Field
The application relates to the technical field of logistics management, in particular to a logistics vehicle track deviation alarm method and system.
Background
The logistics means the whole process of planning, implementing and managing raw materials, semi-finished products, finished products and related information from the place of production of the commodity to the place of consumption of the commodity by means of transportation, storage, distribution and the like in order to meet the needs of users at the lowest cost. The logistics consists of links such as transportation, distribution, storage, packaging, carrying, loading and unloading, circulation processing and related logistics information of the commodities.
In the technical field of logistics vehicle monitoring management, how to monitor the running track of a vehicle is a technical problem to be solved. The application patent with the application number of CN202110186714.6 in the prior art discloses a vehicle track monitoring method and device, wherein the method comprises the following steps: obtaining a planned path of a vehicle, dividing the planned path into a plurality of path segments according to feature points on the planned path, and obtaining positioning points of the vehicle at the current moment, wherein the feature points comprise: one or a combination of an intersection, a road inflection point and a preset road length; and determining whether the vehicle deviates from the planned path according to the deviation value of the vehicle positioning relative to the path segment.
The prior art has the following problems: firstly, the allowable deviation value is fixed, the alarm accuracy is low, because not all the roads corresponding to the planned route are five lanes or four lanes, that is, the allowable deviation value of the vehicle is the same for not all the planned routes, and because the vehicle conditions of different roads are different, the allowable deviation value of the vehicle should be increased in complex vehicle conditions or poor vehicle conditions, and the allowable deviation value of the vehicle should be reduced in good vehicle conditions; secondly, the alarm mode is single, can not satisfy multiple commodity circulation transportation scene, leads to the alarm effect poor.
Disclosure of Invention
Aiming at the defects in the prior art, the application provides a logistics vehicle track deviation alarm method and system.
In one aspect, a method for warning of deviation of a logistics vehicle track includes:
acquiring vehicle data, wherein the vehicle data comprises vehicle positions and vehicle track data, and the vehicle track data comprises real-time vehicle speeds and suspension height change rates;
determining the road section type of a road section where a vehicle is located according to the vehicle position, determining the road condition grade of the road section where the vehicle is located according to the vehicle driving data, and determining the target deviation threshold of the vehicle according to the road section type and the road condition grade;
and judging whether the vehicle track deviates according to the target deviation threshold value, counting the deviation times, and alarming according to the deviation times.
Preferably, the acquiring of the vehicle data includes, before:
the method comprises the steps of obtaining a planned path of a vehicle, and dividing the planned path into a plurality of arc road sections and straight road sections according to feature points on the planned path, wherein the feature points comprise: crossing, road inflection point, and preset road length.
Preferably, determining the road condition grade of the road section where the vehicle is located according to the vehicle driving data includes:
if the real-time speed of the vehicle is greater than the first speed threshold and less than the second speed threshold, and the change rate of the suspension height is less than the first change rate threshold, determining that the vehicle is in the first-level road condition;
if the real-time speed of the vehicle is greater than the third speed threshold and smaller than the first speed threshold, and the change rate of the suspension height is greater than the first change rate threshold and smaller than the second change rate threshold, determining that the vehicle is in the second road condition;
if the real-time speed of the vehicle is greater than a fourth speed threshold and less than a third speed threshold, and the change rate of the suspension height is greater than a second change rate threshold, determining that the vehicle is in a three-level road condition, wherein the fourth speed threshold is less than the third speed threshold, the third speed threshold is less than the first speed threshold, the first speed threshold is less than the second speed threshold, and the first change rate threshold is less than the second change rate threshold.
Preferably, determining the target departure threshold of the vehicle according to the road segment type and the road condition level includes:
determining a standard deviation threshold of the vehicle according to the road section type;
determining a deviation penalty value of the vehicle according to the road condition grade, wherein the higher the road condition grade is, the higher the deviation penalty value is;
and determining a target deviation threshold of the vehicle according to the standard deviation threshold and the deviation penalty parameter.
Preferably, determining the standard deviation threshold of the vehicle according to the road segment type comprises:
determining the road section type of a road section where the vehicle is located according to the vehicle position, wherein the road section type comprises a straight road section and an arc road section;
if the road section type where the vehicle is located is a straight road section, acquiring a standard deviation threshold value of the straight road section according to the width and the length of the straight road section;
and if the type of the road section where the vehicle is located is an arc road section, acquiring a standard deviation threshold value of the arc road section according to the radian and the radius of the arc road section.
Preferably, the formula for determining the target deviation threshold of the vehicle according to the standard deviation threshold and the deviation penalty parameter is:
D＝D 0 +ad
wherein D is a target deviation threshold value, D 0 And d is a deviation penalty value, s is a road condition grade, and a is a constant.
Preferably, determining whether the vehicle track is offset according to the target deviation threshold value includes:
acquiring an actual offset value of the vehicle according to an offset item calculation method corresponding to an offset item, wherein the offset item comprises at least one of a distance and an angle;
if the actual offset value is greater than the target offset threshold, determining that the vehicle track has been offset;
and if the actual offset value is smaller than or equal to the target offset threshold value, determining that the vehicle track is not offset.
Preferably, counting the offset times, and alarming according to the offset times comprises:
determining an alarm level according to the offset times, and generating alarm information according to the alarm level;
acquiring state information of terminal equipment bound with the vehicle, wherein the state information comprises online and offline, and determining an alarm mode according to the state information;
and sending the alarm information to the terminal equipment according to the alarm mode.
In another aspect, a logistic vehicle track deviation warning system includes:
the system comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring vehicle data, the vehicle data comprise vehicle positions and vehicle track data, and the vehicle track data comprise real-time vehicle speeds and suspension height change rates;
the deviation threshold determining module is used for determining the road section type of the road section where the vehicle is located according to the vehicle position, determining the road condition grade of the road section where the vehicle is located according to the vehicle running data, and determining the target deviation threshold of the vehicle according to the road section type and the road condition grade;
and the deviation alarm module judges whether the vehicle track deviates according to the target deviation threshold value, counts the deviation times and alarms according to the deviation times.
The beneficial effects of the application are as follows: the application provides a logistics vehicle track deviation alarming method and system, which can acquire vehicle data, determine road section type and road condition data according to the vehicle data, dynamically adjust the deviation threshold value of the vehicle according to the road section type and the road condition grade, further improve the track deviation alarming precision, and can carry out online multi-stage alarming and offline multi-stage alarming according to the track deviation times and the state of a device terminal bound with the vehicle, so that the alarming effect is better.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. Like elements or portions are generally identified by like reference numerals throughout the several figures. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a flow chart of a method for alarming deviation of a logistics vehicle track, which is provided by an embodiment of the application;
fig. 2 is a schematic structural diagram of a logistic vehicle track deviation alarm system according to an embodiment of the present application.
Detailed Description
Embodiments of the technical scheme of the present application will be described in detail below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and thus are merely examples, and are not intended to limit the scope of the present application.
It is noted that unless otherwise indicated, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this application belongs.
As shown in fig. 1, fig. 1 is a method for alarming deviation of a logistics vehicle track, provided by an embodiment of the present application, including:
step 1, acquiring vehicle data, wherein the vehicle data comprises vehicle positions and vehicle track data, and the vehicle track data comprises real-time vehicle speeds and suspension height change rates;
the vehicle data is acquired by the vehicle-mounted terminal.
In an embodiment of the present application, before acquiring vehicle data, the method includes: the method comprises the steps of obtaining a planned path of a vehicle, and dividing the planned path into a plurality of arc road sections and straight road sections according to feature points on the planned path, wherein the feature points comprise: crossing, road inflection point, and preset road length.
Specifically, the planning path of the vehicle is determined according to a logistics transportation task, the logistics transportation task at least comprises a logistics transportation starting point and a logistics transportation ending point, and in the actual operation process, if special conditions such as road construction and the like are sent, the planning path of the vehicle can be updated.
For example, the planned path may be divided into a number of straight line segments of a fixed length and a number of arc path segments according to characteristic points on the planned path, such as a road inflection point, a road intersection point, a road curvature change point, a road ramp start point, a ramp end point, a curve start point, a curve end point, and the like.
Because the widths and the lengths of the straight line sections are different, and the radii and the radians of the arc line sections are different, the allowable offset amounts of the straight line sections are different, and therefore the embodiment of the application divides the line sections, is convenient to determine the standard offset amount of the straight line sections, and is beneficial to improving the accuracy of offset alarm.
Step 2, determining the road section type of the road section where the vehicle is located according to the vehicle position, determining the road condition grade of the road section where the vehicle is located according to the vehicle driving data, and determining the target deviation threshold of the vehicle according to the road section type and the road condition grade;
in the embodiment of the application, determining the road condition grade of the road section where the vehicle is located according to the vehicle running data comprises the following steps: if the real-time speed of the vehicle is greater than the first speed threshold and less than the second speed threshold, and the change rate of the suspension height is less than the first change rate threshold, determining that the vehicle is in the first-level road condition; if the real-time speed of the vehicle is greater than the third speed threshold and smaller than the first speed threshold, and the change rate of the suspension height is greater than the first change rate threshold and smaller than the second change rate threshold, determining that the vehicle is in the second road condition; if the real-time speed of the vehicle is greater than a fourth speed threshold and less than a third speed threshold, and the change rate of the suspension height is greater than a second change rate threshold, determining that the vehicle is in a three-level road condition, wherein the fourth speed threshold is less than the third speed threshold, the third speed threshold is less than the first speed threshold, the first speed threshold is less than the second speed threshold, and the first change rate threshold is less than the second change rate threshold.
The first speed threshold, the second speed threshold, the third speed threshold and the fourth speed threshold may be set according to practical situations, and the embodiment of the present application is not limited thereto, and may be, for example, the first speed threshold is 80, the second speed threshold is 120, the third speed threshold is 60 and the fourth speed threshold is 40. The first change rate threshold value and the second change rate threshold value may be set according to actual situations, which is not limited in the embodiment of the present application.
In an embodiment of the present application, determining the target departure threshold of the vehicle according to the road section type and the road condition level includes: determining a standard deviation threshold of the vehicle according to the road section type; determining a deviation penalty value of the vehicle according to the road condition grade, wherein the higher the road condition grade is, the higher the deviation penalty value is; and determining a target deviation threshold of the vehicle according to the standard deviation threshold and the deviation penalty parameter.
In an embodiment of the present application, determining a standard deviation threshold of a vehicle according to the road segment type includes: determining the road section type of a road section where the vehicle is located according to the vehicle position, wherein the road section type comprises a straight road section and an arc road section; if the road section type where the vehicle is located is a straight road section, acquiring a standard deviation threshold value of the straight road section according to the width and the length of the straight road section; and if the type of the road section where the vehicle is located is an arc road section, acquiring a standard deviation threshold value of the arc road section according to the radian and the radius of the arc road section.
In the embodiment of the application, a formula for determining the target deviation threshold of the vehicle according to the standard deviation threshold and the deviation penalty parameter is as follows:
D＝D 0 +ad
wherein D is a target deviation threshold value, D 0 And d is a deviation penalty value, s is a road condition grade, a is a constant, a is related to the road section type, and a is less than 0.
For ease of calculation, in some embodiments, the deviation penalty value may be set to a fixed value that varies according to the road condition level, with higher road condition levels having higher deviation penalty values; in order to improve the calculation accuracy of the target deviation threshold value, the deviation penalty value may be set to a dynamic value that varies according to the road condition level, the higher the road condition level is, the smaller the rate of change of the deviation penalty value is, since the operability of the driver on the vehicle is limited.
The traditional road condition analysis method is generally realized through deep learning. Such a method generally takes information such as traffic flow on a road as input, so that the machine model predicts the road condition of the road. The methods such as NeuralCD, DKT and the like based on the deep learning method can perform modeling analysis on road conditions to a certain extent, but the data size is large, and the training time is long; in this regard, the embodiment of the application provides a method for determining road conditions by combining the speed and the suspension height change rate, and the method has the advantages of small calculated amount and accurate analysis result.
The higher the road condition is, the higher the operation feasibility of a driver is, and correspondingly, the lower the deviation degree of the vehicle is, therefore, the application provides a deviation penalty value to optimize a deviation threshold value, and further improves the accuracy of vehicle track deviation alarm.
And step 3, judging whether the vehicle track deviates according to the target deviation threshold value, counting the deviation times, and alarming according to the deviation times.
In an embodiment of the present application, determining whether the vehicle track is offset according to the target deviation threshold includes: acquiring an actual offset value of the vehicle according to an offset item calculation method corresponding to the offset item, and if the actual offset value is larger than the target offset threshold value, determining that the vehicle track is offset; and if the actual offset value is smaller than or equal to the target offset threshold value, determining that the vehicle track is not offset.
Specifically, the offset term may be one of an angle and a distance, and in the embodiment of the present application, without limitation, if the offset term is the distance, for a straight line section, a perpendicular distance between a vehicle position and a line between a start point and an end point of the straight line section is calculated, the perpendicular distance is taken as an actual offset value, for an arc section, an absolute value of a difference between a distance between a vehicle position and an arc center of the arc section and a distance between the arc center of the arc section is calculated, and the absolute value is taken as an actual offset value.
In the embodiment of the application, counting the offset times and alarming according to the offset times comprises the following steps: determining an alarm level according to the offset times, and generating alarm information according to the alarm level; acquiring state information of terminal equipment bound with the vehicle, wherein the state information comprises online and offline, and determining an alarm mode according to the state information; and sending the alarm information to the terminal equipment according to the alarm mode.
With the increase of the offset times, the alarm level can be increased, and alarm information of different levels can be generated according to the alarm level, so that the alarm effect is improved.
Specifically, the vehicle may bind one or more terminal devices, where the terminal devices may be one or more of a mobile phone, a computer, a tablet, and a vehicle-mounted terminal, which is not limited in the embodiment of the present application. When the terminal equipment is online, the alarm information can be sent to the terminal equipment in a voice broadcasting and short message mode, and when the terminal equipment is offline, the alarm information can be sent to the terminal equipment in a short message mode.
In summary, the embodiment of the application provides a logistics vehicle track deviation alarming method, which can acquire vehicle data, determine road section type and road condition data according to the vehicle data, dynamically adjust a deviation threshold value of a vehicle according to the road section type and the road condition grade, further improve track deviation alarming precision, and can carry out online multi-stage alarming and offline multi-stage alarming according to track deviation times and the state of a device terminal bound with the vehicle, so that the alarming effect is better.
Example 2
As shown in fig. 2, the system for alarming deviation of a logistics vehicle track provided by the embodiment of the application includes: the system comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring vehicle data, the vehicle data comprise vehicle positions and vehicle track data, and the vehicle track data comprise real-time vehicle speeds and suspension height change rates; the deviation threshold determining module is used for determining the road section type of the road section where the vehicle is located according to the vehicle position, determining the road condition grade of the road section where the vehicle is located according to the vehicle running data, and determining the target deviation threshold of the vehicle according to the road section type and the road condition grade; and the deviation alarm module judges whether the vehicle track deviates according to the target deviation threshold value, counts the deviation times and alarms according to the deviation times.
Of course, the system can also monitor and alarm other data of the vehicle, including but not limited to, deviation, stay, off-line, etc.
It should be understood that, for the same conception of the present application, the more specific working principle of each module in the embodiment of the present application may refer to the above embodiment, and details are not repeated in the embodiment of the present application.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application, and are intended to be included within the scope of the appended claims and description.
Claims (9)
1. A logistic vehicle track deviation warning method, characterized by comprising the following steps:
acquiring vehicle data, wherein the vehicle data comprises vehicle positions and vehicle track data, and the vehicle track data comprises real-time vehicle speeds and suspension height change rates;
determining the road section type of a road section where a vehicle is located according to the vehicle position, determining the road condition grade of the road section where the vehicle is located according to the vehicle driving data, and determining the target deviation threshold of the vehicle according to the road section type and the road condition grade;
and judging whether the vehicle track deviates according to the target deviation threshold value, counting the deviation times, and alarming according to the deviation times.
2. The logistic vehicle track deviation warning method according to claim 1, characterized in that before acquiring the vehicle data, comprising:
the method comprises the steps of obtaining a planned path of a vehicle, and dividing the planned path into a plurality of arc road sections and straight road sections according to feature points on the planned path, wherein the feature points comprise: crossing, road inflection point, and preset road length.
3. The logistical vehicle track deviation warning method according to claim 1, wherein determining the road condition level of the road section on which the vehicle is located according to the vehicle running data comprises:
if the real-time speed of the vehicle is greater than the first speed threshold and less than the second speed threshold, and the change rate of the suspension height is less than the first change rate threshold, determining that the vehicle is in the first-level road condition;
if the real-time speed of the vehicle is greater than the third speed threshold and smaller than the first speed threshold, and the change rate of the suspension height is greater than the first change rate threshold and smaller than the second change rate threshold, determining that the vehicle is in the second road condition;
if the real-time speed of the vehicle is greater than a fourth speed threshold and less than a third speed threshold, and the change rate of the suspension height is greater than a second change rate threshold, determining that the vehicle is in a three-level road condition, wherein the fourth speed threshold is less than the third speed threshold, the third speed threshold is less than the first speed threshold, the first speed threshold is less than the second speed threshold, and the first change rate threshold is less than the second change rate threshold.
4. The logistic vehicle track deviation warning method according to claim 2, wherein determining the target deviation threshold value of the vehicle according to the road section type and the road condition level comprises:
determining a standard deviation threshold of the vehicle according to the road section type;
determining a deviation penalty value of the vehicle according to the road condition grade, wherein the higher the road condition grade is, the higher the deviation penalty value is;
and calculating a target deviation threshold of the vehicle according to the standard deviation threshold and the deviation penalty value.
5. The logistic vehicle track deviation warning method according to claim 4, wherein determining the standard deviation threshold value of the vehicle according to the road section type comprises:
determining the road section type of a road section where the vehicle is located according to the vehicle position, wherein the road section type comprises a straight road section and an arc road section;
if the road section type where the vehicle is located is a straight road section, acquiring a standard deviation threshold value of the straight road section according to the width and the length of the straight road section;
and if the type of the road section where the vehicle is located is an arc road section, acquiring a standard deviation threshold value of the arc road section according to the radian and the radius of the arc road section.
6. The logistic vehicle track deviation warning method according to claim 5, wherein the formula for calculating the target deviation threshold of the vehicle from the standard deviation threshold and the deviation penalty value is:
D＝D 0 +ad
wherein D is a target deviation threshold value, D 0 For the standard deviation threshold, d is the deviation penalty value and a is a constant.
7. The logistic vehicle track deviation warning method according to claim 5, wherein judging whether the vehicle track is deviated or not according to the target deviation threshold value comprises:
acquiring an actual offset value of the vehicle according to an offset item calculation method corresponding to an offset item, wherein the offset item comprises at least one of a distance and an angle;
if the actual offset value is greater than the target offset threshold, determining that the vehicle track has been offset;
and if the actual offset value is smaller than or equal to the target offset threshold value, determining that the vehicle track is not offset.
8. The method for providing a deviation alert for a vehicle track according to claim 5, wherein counting the number of deviations, and wherein alerting based on the number of deviations comprises:
determining an alarm level according to the offset times, and generating alarm information according to the alarm level;
acquiring state information of terminal equipment bound with the vehicle, wherein the state information comprises online and offline, and determining an alarm mode according to the state information;
and sending the alarm information to the terminal equipment according to the alarm mode.
9. A logistic vehicle track deviation warning system, comprising:
the system comprises a data acquisition module, a control module and a control module, wherein the data acquisition module is used for acquiring vehicle data, the vehicle data comprise vehicle positions and vehicle track data, and the vehicle track data comprise real-time vehicle speeds and suspension height change rates;
the threshold value determining module is used for determining the road section type of the road section where the vehicle is located according to the vehicle position, determining the road condition grade of the road section where the vehicle is located according to the vehicle running data, and determining the target deviation threshold value of the vehicle according to the road section type and the road condition grade;
and the deviation alarm module judges whether the vehicle track deviates according to the target deviation threshold value, counts the deviation times and alarms according to the deviation times.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310611711.1A CN116645828B (en) | 2023-05-26 | 2023-05-26 | Logistics vehicle track deviation alarm method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310611711.1A CN116645828B (en) | 2023-05-26 | 2023-05-26 | Logistics vehicle track deviation alarm method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116645828A true CN116645828A (en) | 2023-08-25 |
| CN116645828B CN116645828B (en) | 2024-04-16 |
Family
ID=87622459
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310611711.1A Active CN116645828B (en) | 2023-05-26 | 2023-05-26 | Logistics vehicle track deviation alarm method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116645828B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018072362A1 (en) * | 2016-10-20 | 2018-04-26 | 深圳市元征科技股份有限公司 | Real-time vehicle trajectory prediction method and prediction system |
| CN109841078A (en) * | 2017-11-27 | 2019-06-04 | 腾讯科技（深圳）有限公司 | Navigation data processing method and its device, storage medium |
| CN115092159A (en) * | 2022-08-12 | 2022-09-23 | 智小途(上海)数字科技有限公司 | Lane line autonomous intelligent mapping system and method |
| CN115375234A (en) * | 2022-08-24 | 2022-11-22 | 安徽仓擎机器人有限公司 | GNSS-based transportation vehicle operation track planning method |
-
2023
- 2023-05-26 CN CN202310611711.1A patent/CN116645828B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018072362A1 (en) * | 2016-10-20 | 2018-04-26 | 深圳市元征科技股份有限公司 | Real-time vehicle trajectory prediction method and prediction system |
| CN109841078A (en) * | 2017-11-27 | 2019-06-04 | 腾讯科技（深圳）有限公司 | Navigation data processing method and its device, storage medium |
| CN115092159A (en) * | 2022-08-12 | 2022-09-23 | 智小途(上海)数字科技有限公司 | Lane line autonomous intelligent mapping system and method |
| CN115375234A (en) * | 2022-08-24 | 2022-11-22 | 安徽仓擎机器人有限公司 | GNSS-based transportation vehicle operation track planning method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116645828B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11187547B2 (en) | Tire conditioning optimization for a collection of mining vehicles | |
| EP3959576A1 (en) | Methods and systems for trajectory forecasting with recurrent neural networks using inertial behavioral rollout | |
| CN110843789A (en) | Vehicle lane change intention prediction method based on time sequence convolution network | |
| WO2012105889A1 (en) | Method and management unit pertaining to vehicle trains | |
| CN104680719A (en) | System and method for monitoring fatigue driving time indexes | |
| CN112249008B (en) | Unmanned automobile early warning method aiming at complex dynamic environment | |
| CN113219933B (en) | Strip mine unmanned truck dispatching system and method based on digital twin prediction | |
| CN112644518B (en) | Vehicle track prediction method, device, equipment and storage medium | |
| CN104205186A (en) | Device for determining sensitivity to prediction of unexpected situations | |
| US20220227391A1 (en) | Systems and methods for scenario dependent trajectory scoring | |
| CN110136469A (en) | The compensation method of the running data of bus and device | |
| CN112382109A (en) | Emergency rescue vehicle cooperative control method, system and medium in intelligent networking state | |
| CN114512002B (en) | Road surface abnormity detection method and device | |
| CN116645828B (en) | Logistics vehicle track deviation alarm method and system | |
| CN115164922A (en) | Path planning method, system, equipment and storage medium | |
| CN112633812B (en) | Track segmentation method, device, equipment and storage medium for freight vehicle | |
| CN116686028A (en) | Driving assistance method and related equipment | |
| CN117405111A (en) | Informationized checking method for inspection intelligent machine | |
| CN114872735B (en) | Neural network algorithm-based decision-making method and device for automatically-driven logistics vehicles | |
| CN115817470A (en) | Method and apparatus for predicting lane-changing intention of straight lane of vehicle, and computer storage medium | |
| US20230211777A1 (en) | Assistance system with leader determination module for automated vehicle in a merging trajectory | |
| JP2023005015A (en) | Traffic condition forecasting device and traffic condition forecasting method | |
| CN115416656A (en) | Automatic driving lane changing method, device and medium based on multi-target track planning | |
| US20220198923A1 (en) | Method, apparatus, and computer program product for determining a split lane traffic pattern | |
| CN113837691A (en) | Vehicle yaw detection method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |