CN112070442A - Transportation process supervision method and terminal in food safety aspect - Google Patents
Transportation process supervision method and terminal in food safety aspect Download PDFInfo
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- CN112070442A CN112070442A CN202010961206.6A CN202010961206A CN112070442A CN 112070442 A CN112070442 A CN 112070442A CN 202010961206 A CN202010961206 A CN 202010961206A CN 112070442 A CN112070442 A CN 112070442A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
Abstract
The invention relates to a transportation process supervision method and a terminal in the aspect of food safety, which comprise the following steps: s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2; s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score; and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level. Can effectively supervise the driver, the ability of accuse is higher to the risk, when the driver takes place the high risk and drives the operation action, can in time remind its driver, has ensured the security of transportation.
Description
Technical Field
The invention relates to the field of food transportation safety, in particular to a transportation process supervision method and a transportation process supervision terminal in the aspect of food safety.
Background
When meeting certain requirements needing food tracing, according to the traditional method, only a positioner is arranged after loading to check the current position, the speed per hour and the like of a vehicle, and the position information uploaded by the vehicle is received in the transportation process, so that the paths of a starting point, an approach unloading point and a terminal point are drawn, and the food tracing requirements are finished. However, the food tracing method has the following disadvantages:
disadvantage 1: in the transportation process, there is no way to completely and effectively supervise the driver behavior, and whether the driving behavior of the driver is risky cannot be known;
and (2) disadvantage: in the transportation process, the risk handling capacity is low, and when a driver has a high-risk driving operation behavior, the driver cannot be reminded or communicated with the driver quickly;
disadvantage 3: the drivers can not be visually and effectively scored, and the drivers can not be screened according to the food transportation traceability service.
Disclosure of Invention
Technical problem to be solved
In order to solve the above problems in the prior art, the invention provides a transportation process supervision method and a terminal in the aspect of food safety, which can improve the safety of the transportation process.
(II) technical scheme
In order to achieve the purpose, the invention adopts a technical scheme that: a method of supervising a transportation process in terms of food safety, comprising:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
The other technical scheme adopted by the invention is as follows: a transportation supervision terminal for food safety, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
(III) advantageous effects
The invention has the beneficial effects that: the driving behavior of the driver and the driving state of the vehicle can be monitored through the first video information and the second video information, if the risk behavior information exists, the reminding information is sent to the terminal equipment of the cockpit according to the calculated risk level, so that the driver can be effectively supervised, the risk control capability is high, when the driver has a high-risk driving operation behavior, the driver can be timely reminded, and the safety of the transportation process is guaranteed.
Drawings
FIG. 1 is a flow chart of a transportation process supervision method of the food safety aspect of the present invention;
FIG. 2 is a schematic diagram of a transportation supervision terminal according to the food safety aspect of the present invention;
[ description of reference ]
1. A transportation process supervision terminal in the aspect of food safety; 2. a memory; 3. a processor.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a transportation process monitoring method for food safety includes:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
From the above description, the beneficial effects of the present invention are: the driving behavior of the driver and the driving state of the vehicle can be monitored through the first video information and the second video information, if the risk behavior information exists, the reminding information is sent to the terminal equipment of the cockpit according to the calculated risk level, so that the driver can be effectively supervised, the risk control capability is high, when the driver has a high-risk driving operation behavior, the driver can be timely reminded, and the safety of the transportation process is guaranteed.
Further, the S1 is preceded by:
configuring the reminding type of each risk behavior information;
in S2, recording each of the risk behavior information, and calculating a weighted value of each of the risk behavior information through a risk calculation model includes:
recording each piece of risk behavior information, judging whether the risk behavior information needs risk calculation according to the reminding type of the risk behavior information, and if so, calculating the weighted value of each piece of risk behavior information through a risk calculation model.
As can be seen from the above description, when the reminding type of some risky behavior information (e.g., risky behavior such as smoking) is set as not needing to be reminded, only the risky behavior information needs to be recorded, and risk calculation is not needed for the risky behavior information.
Further, the S1 further includes:
and acquiring speed limit data provided by an online map road network and vehicle real-time speed information sent by a vehicle OBD serial port, judging whether the vehicle is overspeed or not, and recording the vehicle real-time speed information as risk behavior information if the vehicle is overspeed.
From the above description, it can be known that whether the vehicle is overspeed or not is judged by comparing the real-time speed information of the monitored vehicle with the speed limit data provided by the online map road network, and the overspeed information is also used as a part of the risk behavior information, so that the comprehensiveness of risk monitoring is improved.
Further, the analyzing the first video information and the second video information to determine whether the risk behavior information exists in S1 includes:
identifying, analyzing and judging whether risk behavior information exists or not by frame for images in the first video information and the second video information;
the recording of each piece of risk behavior information in S2 includes:
and recording each risk behavior information, and storing video data of the frame of image in which the risk behavior information occurs within a preset time period.
As can be seen from the above description, the reliability of the judgment is ensured by analyzing and judging the first and second video information frame by frame, and in addition, the video data in the preset time period of the frame image is stored, so that the video data related to the risk behavior information can be conveniently viewed.
Further, the S3 includes, after:
and after the vehicle finishes the transportation, outputting a risk report of the transportation process according to the risk behavior information, the weighted value of the risk behavior information, the total weighted value score and the risk grade, and grading the driver according to the risk report.
As can be seen from the above description, the drivers can be screened according to the basis by visually and effectively scoring the drivers.
Referring to fig. 2, a transportation process supervision terminal for food safety includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the following steps:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
From the above description, the beneficial effects of the present invention are: the driving behavior of the driver and the driving state of the vehicle can be monitored through the first video information and the second video information, if the risk behavior information exists, the reminding information is sent to the terminal equipment of the cockpit according to the calculated risk level, so that the driver can be effectively supervised, the risk control capability is high, when the driver has a high-risk driving operation behavior, the driver can be timely reminded, and the safety of the transportation process is guaranteed.
Further, the S1 is preceded by:
configuring the reminding type of each risk behavior information;
in S2, recording each of the risk behavior information, and calculating a weighted value of each of the risk behavior information through a risk calculation model includes:
recording each piece of risk behavior information, judging whether the risk behavior information needs risk calculation according to the reminding type of the risk behavior information, and if so, calculating the weighted value of each piece of risk behavior information through a risk calculation model.
As can be seen from the above description, when the reminding type of some risky behavior information (e.g., risky behavior such as smoking) is set as not needing to be reminded, only the risky behavior information needs to be recorded, and risk calculation is not needed for the risky behavior information.
Further, the S1 further includes:
and acquiring speed limit data provided by an online map road network and vehicle real-time speed information sent by a vehicle OBD serial port, judging whether the vehicle is overspeed or not, and recording the vehicle real-time speed information as risk behavior information if the vehicle is overspeed.
From the above description, it can be known that whether the vehicle is overspeed or not is judged by comparing the real-time speed information of the monitored vehicle with the speed limit data provided by the online map road network, and the overspeed information is also used as a part of the risk behavior information, so that the comprehensiveness of risk monitoring is improved.
Further, the analyzing the first video information and the second video information to determine whether the risk behavior information exists in S1 includes:
identifying, analyzing and judging whether risk behavior information exists or not by frame for images in the first video information and the second video information;
the recording of each piece of risk behavior information in S2 includes:
and recording each risk behavior information, and storing video data of the frame of image in which the risk behavior information occurs within a preset time period.
As can be seen from the above description, the reliability of the judgment is ensured by analyzing and judging the first and second video information frame by frame, and in addition, the video data in the preset time period of the frame image is stored, so that the video data related to the risk behavior information can be conveniently viewed.
Further, the S3 includes, after:
and after the vehicle finishes the transportation, outputting a risk report of the transportation process according to the risk behavior information, the weighted value of the risk behavior information, the total weighted value score and the risk grade, and grading the driver according to the risk report.
As can be seen from the above description, the drivers can be screened according to the basis by visually and effectively scoring the drivers.
Example one
Referring to fig. 1, a transportation process monitoring method for food safety includes:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
Specifically, the risk model algorithm logic formula: a risk score of (a event severity of/a + b + c + ·+ n) + b event severity of (b event severity of/a + b + c + ·+ n) +. n event severity of.. n event severity of (n event severity of/a + b + c +... n)), wherein a, b, c.. n is the event weight; wherein the coefficient is based on the 1 st event, starting from 1, the coefficient becomes 1.3 when the second event occurs, and the coefficient is 1.4 when the third event occurs; the number of successes is incremented by 0.1 steps until the 240 th event occurrence factor is 13.45. And if the number of the events is greater than the preset number, directly reporting high risk. (Note: the frequency of event reporting is 1 minute, as a criterion here).
Wherein the step S1 further comprises:
configuring the reminding type of each risk behavior information;
in S2, recording each of the risk behavior information, and calculating a weighted value of each of the risk behavior information through a risk calculation model includes:
recording each piece of risk behavior information, judging whether the risk behavior information needs risk calculation according to the reminding type of the risk behavior information, and if so, calculating the weighted value of each piece of risk behavior information through a risk calculation model. Specifically, the configuration of the alert type includes attributes such as whether risk calculation is required for the risk behavior information, a duration of the risk behavior information (i.e., a duration from a first frame image of occurrence of the risk behavior to a last frame image of end of the risk behavior), a weighting value, whether to alert, and the like, where the S3 includes: and sending reminding information to the terminal equipment of the cockpit according to the risk level and the reminding type.
Wherein the S1 further includes:
and acquiring speed limit data provided by an online map road network and vehicle real-time speed information sent by a vehicle OBD serial port, judging whether the vehicle is overspeed or not, and recording the vehicle real-time speed information as risk behavior information if the vehicle is overspeed.
Wherein, the analyzing the first video information and the second video information to determine whether the risk behavior information exists in S1 includes:
identifying, analyzing and judging whether risk behavior information exists or not by frame for images in the first video information and the second video information; for example, the image in the first video information is analyzed frame by frame, whether risk behavior information, such as a light spot of a cigarette end, a closed-eye behavior of a driver, and the like, exists is judged through key elements in the image, for example, the image in the second video information is analyzed frame by frame, whether the image has risk behavior information, such as lane departure, too short distance to a following vehicle, and the like, is judged, for example, by image recognition through data collected by a camera in the front of the vehicle head, distance judgment is carried out, meanwhile, the relative speed of the two vehicles is calculated, and when the relative distance is shorter than the distance traveled by the relative speed preset time, the vehicle is judged to be too short to the following vehicle.
The recording of each piece of risk behavior information in S2 includes:
and recording each risk behavior information, and storing video data of the frame of image in which the risk behavior information occurs within a preset time period.
Wherein said S3 thereafter comprises:
and after the vehicle finishes the transportation, outputting a risk report of the transportation process according to the risk behavior information, the weighted value of the risk behavior information, the total weighted value score and the risk grade, and grading the driver according to the risk report.
Example two
Referring to fig. 2, a transportation supervision terminal 1 for food safety includes a memory 2, a processor 3, and a computer program stored in the memory 2 and running on the processor 3, wherein the processor 3 implements the steps of the first embodiment when executing the computer program.
In summary, according to the transportation process supervision method and the transportation process supervision terminal in the aspect of food safety provided by the invention, the driving behavior of the driver and the driving state of the vehicle can be monitored through the first video information and the second video information, and if the risk behavior information exists, the reminding information is sent to the terminal equipment in the cockpit according to the calculated risk level, so that the driver can be effectively supervised, the risk handling capacity is high, when the driver has a high-risk driving operation behavior, the driver can be timely reminded, and the safety of the transportation process is ensured.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. A transportation process supervision method in the aspect of food safety is characterized by comprising the following steps:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
2. The method for supervising a transportation process in terms of food safety as claimed in claim 1, wherein said S1 is preceded by:
configuring the reminding type of each risk behavior information;
in S2, recording each of the risk behavior information, and calculating a weighted value of each of the risk behavior information through a risk calculation model includes:
recording each piece of risk behavior information, judging whether the risk behavior information needs risk calculation according to the reminding type of the risk behavior information, and if so, calculating the weighted value of each piece of risk behavior information through a risk calculation model.
3. The method for supervising a transportation process in terms of food safety as claimed in claim 1, wherein said S1 further comprises:
and acquiring speed limit data provided by an online map road network and vehicle real-time speed information sent by a vehicle OBD serial port, judging whether the vehicle is overspeed or not, and recording the vehicle real-time speed information as risk behavior information if the vehicle is overspeed.
4. The method for supervising a transportation process in terms of food safety as claimed in claim 1, wherein said analyzing the first video information and the second video information to determine whether the risk behavior information exists in S1 comprises:
identifying, analyzing and judging whether risk behavior information exists or not by frame for images in the first video information and the second video information;
the recording of each piece of risk behavior information in S2 includes:
and recording each risk behavior information, and storing video data of the frame of image in which the risk behavior information occurs within a preset time period.
5. The method for supervising a transportation process in terms of food safety as claimed in claim 1, wherein said S3 is followed by:
and after the vehicle finishes the transportation, outputting a risk report of the transportation process according to the risk behavior information, the weighted value of the risk behavior information, the total weighted value score and the risk grade, and grading the driver according to the risk report.
6. A transportation supervision terminal for food safety, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:
s1, acquiring first video information of driving behaviors of drivers in a cab and second video information of the front part of the vehicle in real time, analyzing the first video information and the second video information, judging whether risk behavior information exists or not, and if yes, executing S2;
s2, recording each piece of risk behavior information, calculating a weighted value of each piece of risk behavior information through a risk calculation model, calculating a weighted value total score in a preset time period according to the weighted value of each piece of risk behavior information, and grading the risk grade according to the weighted value total score;
and S3, sending reminding information to the terminal equipment of the cockpit according to the risk level.
7. The transportation process supervision terminal in terms of food safety according to claim 6, characterized in that the S1 is preceded by:
configuring the reminding type of each risk behavior information;
in S2, recording each of the risk behavior information, and calculating a weighted value of each of the risk behavior information through a risk calculation model includes:
recording each piece of risk behavior information, judging whether the risk behavior information needs risk calculation according to the reminding type of the risk behavior information, and if so, calculating the weighted value of each piece of risk behavior information through a risk calculation model.
8. The transportation process supervision terminal in terms of food safety according to claim 6, characterized in that the S1 further comprises:
and acquiring speed limit data provided by an online map road network and vehicle real-time speed information sent by a vehicle OBD serial port, judging whether the vehicle is overspeed or not, and recording the vehicle real-time speed information as risk behavior information if the vehicle is overspeed.
9. The transportation process supervision terminal according to claim 6, wherein the analyzing the first video information and the second video information to determine whether the risk behavior information exists in the S1 includes:
identifying, analyzing and judging whether risk behavior information exists or not by frame for images in the first video information and the second video information;
the recording of each piece of risk behavior information in S2 includes:
and recording each risk behavior information, and storing video data of the frame of image in which the risk behavior information occurs within a preset time period.
10. The transportation process supervision terminal in terms of food safety according to claim 6, characterized in that said S3 is followed by:
and after the vehicle finishes the transportation, outputting a risk report of the transportation process according to the risk behavior information, the weighted value of the risk behavior information, the total weighted value score and the risk grade, and grading the driver according to the risk report.
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| CN202010961206.6A CN112070442A (en) | 2020-09-14 | 2020-09-14 | Transportation process supervision method and terminal in food safety aspect |
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| CN202010961206.6A CN112070442A (en) | 2020-09-14 | 2020-09-14 | Transportation process supervision method and terminal in food safety aspect |
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| US10319037B1 (en) * | 2015-09-01 | 2019-06-11 | State Farm Mutual Automobile Insurance Company | Systems and methods for assessing risk based on driver gesture behaviors |
| CN111274881A (en) * | 2020-01-10 | 2020-06-12 | 中国平安财产保险股份有限公司 | Driving safety monitoring method and device, computer equipment and storage medium |
| CN111310562A (en) * | 2020-01-10 | 2020-06-19 | 中国平安财产保险股份有限公司 | Vehicle driving risk management and control method based on artificial intelligence and related equipment thereof |
| CN111353471A (en) * | 2020-03-17 | 2020-06-30 | 北京百度网讯科技有限公司 | Safe driving monitoring method, device, equipment and readable storage medium |
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2020
- 2020-09-14 CN CN202010961206.6A patent/CN112070442A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10319037B1 (en) * | 2015-09-01 | 2019-06-11 | State Farm Mutual Automobile Insurance Company | Systems and methods for assessing risk based on driver gesture behaviors |
| CN111274881A (en) * | 2020-01-10 | 2020-06-12 | 中国平安财产保险股份有限公司 | Driving safety monitoring method and device, computer equipment and storage medium |
| CN111310562A (en) * | 2020-01-10 | 2020-06-19 | 中国平安财产保险股份有限公司 | Vehicle driving risk management and control method based on artificial intelligence and related equipment thereof |
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Application publication date: 20201211 |