CN111242416A - Grain quality safety assessment method and system in automobile transportation process - Google Patents

Grain quality safety assessment method and system in automobile transportation process Download PDF

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CN111242416A
CN111242416A CN201911386020.6A CN201911386020A CN111242416A CN 111242416 A CN111242416 A CN 111242416A CN 201911386020 A CN201911386020 A CN 201911386020A CN 111242416 A CN111242416 A CN 111242416A
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vehicle
risk coefficient
abscissa
time
ordinate
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CN111242416B (en
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彭远
刘恬
刘申
张东辉
杨松松
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Aisino Corp
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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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06395—Quality analysis or management
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining

Abstract

The invention discloses a grain quality safety assessment method and a grain quality safety assessment system in an automobile transportation process, wherein the method comprises the following steps: collecting data of preset types in the automobile transportation process; calculating to obtain a risk coefficient of the automobile transportation time according to the shipment time and the arrival time; calculating according to the loading quality and the unloading quality to obtain a loss risk coefficient; calculating to obtain a position risk coefficient according to the driving track data; calculating according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight to obtain a vehicle transportation risk coefficient, and generating a grain quality safety evaluation result according to the vehicle transportation risk coefficient; according to the method and the system, the grain quality risk possibly occurring in the automobile transportation process in three different dimensions of carrying time, grain loss and vehicle positioning is evaluated, so that a quantitative evaluation result aiming at the risk potential danger possibly existing in the automobile transportation process of the grain is obtained, the method and the system are favorable for guiding management work and replanning a logistics line flow.

Description

Grain quality safety assessment method and system in automobile transportation process
Technical Field
The invention relates to the field of grain quality control, in particular to a grain quality safety assessment method and system in an automobile transportation process.
Background
During bulk grain transportation, the grain quality can be influenced by repeated transportation, repeated loading and unloading, transportation mode change, remote external environment change and the like of automobiles, trains and ships, information in the transportation process is difficult to obtain, and the grain quality change risk is difficult to evaluate. Automobile transportation is the most common transportation means for grain transportation, and because of the movement uncertainty and transportation specificity of automobile transportation, the grain quality safety in the automobile transportation process cannot be well evaluated at present.
Disclosure of Invention
In order to solve the problem of difficulty in evaluating the grain quality safety in the automobile transportation process in the background technology, the invention provides a grain quality safety evaluation method and a grain quality safety evaluation system in the automobile transportation process; according to the method and the system, the grain quality risk possibly occurring in the automobile transportation process in three different dimensions of carrying time, grain loss and vehicle positioning is evaluated, and different risk weight proportions can be set according to the actual environment, so that the grain quality safety evaluation result in the automobile transportation process is obtained; the grain quality safety assessment method in the automobile transportation process comprises the following steps:
collecting data of preset types in the automobile transportation process; the data comprises the shipment time and the arrival time of each vehicle for transporting the batch of grains, the loading quality and the unloading quality of each vehicle and the running track data of each vehicle;
calculating to obtain a risk coefficient of the automobile transportation time according to the shipment time and the arrival time;
calculating according to the loading quality and the unloading quality to obtain a loss risk coefficient;
calculating to obtain a position risk coefficient according to the driving track data;
and calculating according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight to obtain a vehicle transportation risk coefficient, and generating a grain quality safety evaluation result according to the vehicle transportation risk coefficient.
Further, calculating and obtaining a risk coefficient of the transportation time according to the shipment time and the arrival time, wherein the risk coefficient of the transportation time comprises the following steps:
calculating and obtaining the in-transit time of each vehicle according to the shipment time and the arrival time of each vehicle;
calculating the variance of the time-in-transit queues formed by the time-in-transit of each vehicle;
the time risk coefficient rlogtimeThe calculation formula of (2) is as follows:
Figure BDA0002343656540000021
wherein, tmaxIs the maximum time-in-transit in the time-in-transit queue;
Figure BDA0002343656540000022
is the variance of the time-in-transit queue.
Further, the obtaining of the loss risk coefficient by calculating according to the loading quality and the unloading quality includes:
calculating to obtain a loss value of each vehicle according to the loading quality and the unloading quality of each vehicle;
calculating the average value of all vehicle loss values, and counting the number of vehicles lower than the average value;
the calculation formula of the loss risk coefficient is as follows:
Figure BDA0002343656540000023
wherein r iswasteIs the loss risk factor.
Further, the calculating and obtaining a position risk coefficient according to the travel track data includes:
presetting a standard driving route, and extracting a standard abscissa series and a standard ordinate series on a map according to a preset time interval and a preset vehicle speed;
generating an abscissa array and an ordinate array corresponding to each vehicle according to the driving track data of each vehicle; the method comprises the steps of collecting driving track data of each vehicle, wherein the driving track data comprises an abscissa and an ordinate which are extracted according to a preset time interval in the driving process of each vehicle on a map;
calculating an abscissa correlation coefficient of each vehicle abscissa series and a standard abscissa series, and calculating an ordinate correlation coefficient of each vehicle ordinate series and a standard ordinate series;
calculating according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle through a preset rule to obtain a risk coefficient corresponding to the vehicle;
and taking the average value of the risk coefficients of all vehicles as the position risk coefficient.
Further, the formula for calculating the abscissa correlation coefficient of each vehicle abscissa series and the standard abscissa series and the formula for calculating the ordinate correlation coefficient of each vehicle ordinate series and the standard ordinate series are respectively as follows:
Figure BDA0002343656540000031
Figure BDA0002343656540000032
wherein, ikhorizontalIs the abscissa correlation coefficient, ik, of the kth vehicleverticalThe correlation coefficient of the ordinate of the kth vehicle is taken as the correlation coefficient of the ordinate of the kth vehicle; ak is the abscissa number series of the kth vehicle, and Bk is the ordinate number series of the kth vehicle; x is a standard abscissa series, and Y is a standard ordinate series.
Further, the method for obtaining the risk coefficient corresponding to the same vehicle through calculation according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle by a preset rule comprises the following steps:
obtaining the abscissa risk coefficient of each vehicle according to the reciprocal of the absolute value of the abscissa correlation coefficient of each vehicle;
obtaining the longitudinal coordinate risk coefficient of each vehicle according to the reciprocal of the absolute value of the longitudinal coordinate correlation coefficient of each vehicle;
and obtaining the risk coefficient corresponding to each vehicle according to the average value of the abscissa risk coefficient and the ordinate risk coefficient of each vehicle.
The grain quality safety assessment system in the automobile transportation process comprises:
the data acquisition unit is used for acquiring data of preset types in the automobile transportation process; the data comprises the shipment time and the arrival time of each vehicle for transporting the batch of grains, the loading quality and the unloading quality of each vehicle and the running track data of each vehicle;
the time risk coefficient calculation unit is used for calculating and obtaining a car transportation time risk coefficient according to the shipment time and the arrival time;
the loss risk coefficient calculation unit is used for calculating and obtaining a loss risk coefficient according to the loading quality and the unloading quality;
the position risk coefficient calculation unit is used for calculating and obtaining a position risk coefficient according to the traveling track data;
and the quality safety evaluation unit is used for calculating and obtaining a car transportation risk coefficient according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight, and generating a grain quality safety evaluation result according to the car transportation risk coefficient.
Further, the time risk coefficient calculation unit is used for calculating and obtaining the in-transit time of each vehicle according to the shipment time and the arrival time of each vehicle;
the time risk coefficient calculation unit is used for calculating the variance of a time-in-transit queue formed by the time-in-transit of each vehicle;
the time risk coefficient rlogtimeThe calculation formula of (2) is as follows:
Figure BDA0002343656540000041
wherein, tmaxIs the maximum time-in-transit in the time-in-transit queue;
Figure BDA0002343656540000042
is the variance of the time-in-transit queue.
Further, the loss risk coefficient calculation unit is configured to calculate a loss value of each vehicle according to the loading quality and the unloading quality of each vehicle;
the loss risk coefficient calculation unit is used for calculating the average value of all vehicle loss values and counting the number of vehicles lower than the average value;
the calculation formula of the loss risk coefficient is as follows:
Figure BDA0002343656540000043
wherein r iswasteIs the loss risk factor.
Further, the position risk coefficient calculation unit is used for presetting a standard driving route, and extracting a standard abscissa series and a standard ordinate series on the map according to a preset time interval and a preset vehicle speed;
the position risk coefficient calculation unit is used for generating an abscissa array and an ordinate array corresponding to each vehicle according to the running track data of each vehicle; the method comprises the steps of collecting driving track data of each vehicle, wherein the driving track data comprises an abscissa and an ordinate which are extracted according to a preset time interval in the driving process of each vehicle on a map;
the position risk coefficient calculation unit is used for calculating an abscissa correlation coefficient of each vehicle abscissa array and a standard abscissa array, and calculating an ordinate correlation coefficient of each vehicle ordinate array and a standard ordinate array;
the position risk coefficient calculation unit is used for calculating a risk coefficient corresponding to the same vehicle according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle through a preset rule;
the position risk coefficient calculation unit is used for taking the average value of the risk coefficients of all the vehicles as the position risk coefficient.
Further, the position risk coefficient calculation unit is configured to calculate an abscissa correlation coefficient of each vehicle abscissa series and the standard abscissa series, and calculate an ordinate correlation coefficient of each vehicle ordinate series and the standard ordinate series according to the following formula:
Figure BDA0002343656540000051
Figure BDA0002343656540000052
wherein, ikhorizontalIs the abscissa correlation coefficient, ik, of the kth vehicleverticalThe correlation coefficient of the ordinate of the kth vehicle is taken as the correlation coefficient of the ordinate of the kth vehicle; ak is the abscissa number series of the kth vehicle, and Bk is the ordinate number series of the kth vehicle; x is a standard abscissa series, and Y is a standard ordinate series.
Further, the position risk coefficient calculation unit is used for obtaining the abscissa risk coefficient of each vehicle according to the reciprocal of the absolute value of the abscissa correlation coefficient of each vehicle;
the position risk coefficient calculation unit is used for obtaining the longitudinal coordinate risk coefficient of each vehicle according to the reciprocal of the absolute value of the longitudinal coordinate correlation coefficient of each vehicle;
and the position risk coefficient calculation unit is used for obtaining the risk coefficient corresponding to each vehicle according to the average value of the abscissa risk coefficient and the ordinate risk coefficient of each vehicle.
The invention has the beneficial effects that: the technical scheme of the invention provides a method and a system for evaluating grain quality safety in the automobile transportation process; according to the method and the system, the grain quality risk possibly occurring in the automobile transportation process in three different dimensions of carrying time, grain loss and vehicle positioning is evaluated, and different risk weight proportions can be set according to the actual environment, so that the grain quality safety evaluation result in the automobile transportation process is obtained; the method and the system quantitatively evaluate the potential risk hazards of the grains in the automobile transportation process. According to the risk index conditions of different dimensions, a manager can guide management work, or re-examine/change suppliers, or re-perform sanitation inspection on grains, or re-plan logistics lines and processes.
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A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:
FIG. 1 is a flow chart of a method for evaluating grain quality safety during automobile transportation according to an embodiment of the present invention;
fig. 2 is a structural diagram of a grain quality safety evaluation system in an automobile transportation process according to an embodiment of the invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
FIG. 1 is a flow chart of a method for evaluating grain quality safety during automobile transportation according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step 110, collecting data of preset types in the automobile transportation process; the data comprises the shipment time and the arrival time of each vehicle for transporting the batch of grains, the loading quality and the unloading quality of each vehicle and the running track data of each vehicle;
the loss caused by scattering in the transportation process is a normal condition, so that a certain company or a driver can privately withhold a small amount of goods for profit in the transportation process, and meanwhile, whether the pollution is caused to grains or not cannot be judged, and the grain quality safety is influenced. The method is directly embodied in the dispatching time and the arrival time of a certain vehicle, the loading quality and the unloading quality, and the geographic position information of the vehicle in the transportation process.
The method comprises the steps of evaluating possible grain quality risks in the automobile transportation process in three different dimensions of carrying time, grain loss and vehicle positioning, and collecting the data in the automobile transportation process according to the content to be evaluated.
Step 120, calculating and obtaining a risk coefficient of the transportation time according to the shipment time and the arrival time;
calculating and obtaining a vapour transport time risk coefficient according to the shipment time and the arrival time, wherein the vapour transport time risk coefficient comprises the following steps:
calculating and obtaining the in-transit time of each vehicle according to the shipment time and the arrival time of each vehicle;
calculating the variance of the time-in-transit queues formed by the time-in-transit of each vehicle;
the time risk coefficient rlogtimeThe calculation formula of (2) is as follows:
Figure BDA0002343656540000071
wherein, tmaxIs the maximum time-in-transit in the time-in-transit queue;
Figure BDA0002343656540000072
is the variance of the time-in-transit queue.
Step 130, calculating to obtain a loss risk coefficient according to the loading quality and the unloading quality;
the calculating according to the loading quality and the unloading quality to obtain the loss risk coefficient comprises the following steps:
calculating to obtain a loss value of each vehicle according to the loading quality and the unloading quality of each vehicle;
calculating the average value of all vehicle loss values, and counting the number of vehicles lower than the average value;
the calculation formula of the loss risk coefficient is as follows:
Figure BDA0002343656540000073
wherein r iswasteIs the loss risk factor.
Step 140, calculating to obtain a position risk coefficient according to the traveling track data;
the calculating and obtaining the position risk coefficient according to the traveling track data comprises the following steps:
presetting a standard driving route, and extracting a standard abscissa series and a standard ordinate series on a map according to a preset time interval and a preset vehicle speed;
generating an abscissa array and an ordinate array corresponding to each vehicle according to the driving track data of each vehicle; the method comprises the steps of collecting driving track data of each vehicle, wherein the driving track data comprises an abscissa and an ordinate which are extracted according to a preset time interval in the driving process of each vehicle on a map;
calculating an abscissa correlation coefficient of each vehicle abscissa series and a standard abscissa series, and calculating an ordinate correlation coefficient of each vehicle ordinate series and a standard ordinate series;
further, the formula for calculating the abscissa correlation coefficient of each vehicle abscissa series and the standard abscissa series and the formula for calculating the ordinate correlation coefficient of each vehicle ordinate series and the standard ordinate series are respectively as follows:
Figure BDA0002343656540000081
Figure BDA0002343656540000082
wherein, ikhorizontalIs the abscissa correlation coefficient, ik, of the kth vehiclevertical is a correlation coefficient of the ordinate of the kth vehicle; ak is the abscissa number series of the kth vehicle, and Bk is the ordinate number series of the kth vehicle; x is a standard abscissa series, and Y is a standard ordinate series.
Calculating according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle through a preset rule to obtain a risk coefficient corresponding to the vehicle;
specifically, the preset rule is as follows: obtaining the abscissa risk coefficient of each vehicle according to the reciprocal of the absolute value of the abscissa correlation coefficient of each vehicle; obtaining the longitudinal coordinate risk coefficient of each vehicle according to the reciprocal of the absolute value of the longitudinal coordinate correlation coefficient of each vehicle; and obtaining the risk coefficient corresponding to each vehicle according to the average value of the abscissa risk coefficient and the ordinate risk coefficient of each vehicle.
And taking the average value of the risk coefficients of all vehicles as the position risk coefficient.
And 150, calculating according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight to obtain a car transportation risk coefficient, and generating a grain quality safety evaluation result according to the car transportation risk coefficient.
Each risk coefficient described in this embodiment is a value within an interval from 0 to 1, and the risk is smaller when the risk coefficient is closer to 1; otherwise the risk is greater.
If the automobile transportation risk coefficient is lower than a certain preset threshold value, the grain quality safety storage risk is higher in the automobile transportation process; the time risk coefficient, the loss risk coefficient and the position risk coefficient fluctuation in other batches of grain transportation of automobiles are transversely compared, the abnormal transportation is obviously lower than the average risk coefficient, the abnormal transportation possibly is a risk part, and a safety risk tracing result is generated according to the risk part.
Fig. 2 is a structural diagram of a grain quality safety evaluation system in an automobile transportation process according to an embodiment of the invention. As shown in fig. 2, the system includes:
the data acquisition unit 210 is used for acquiring data of preset types in the automobile transportation process; the data comprises the shipment time and the arrival time of each vehicle for transporting the batch of grains, the loading quality and the unloading quality of each vehicle and the running track data of each vehicle;
a time risk coefficient calculation unit 220, wherein the time risk coefficient calculation unit 220 is used for calculating and obtaining a car transportation time risk coefficient according to the shipment time and the arrival time;
further, the time risk coefficient calculating unit 220 is configured to calculate and obtain the time in transit of each vehicle according to the shipment time and the arrival time of each vehicle;
the time-risk factor calculation unit 220 is used to calculate the variance of the time-in-transit queue formed by the time-in-transit of each vehicle;
the time risk coefficient tlogtimeThe calculation formula of (2) is as follows:
Figure BDA0002343656540000091
wherein, tmaxIs the maximum time-in-transit in the time-in-transit queue;
Figure BDA0002343656540000092
is the variance of the time-in-transit queue.
A loss risk coefficient calculation unit 230, where the loss risk coefficient calculation unit 230 is configured to calculate and obtain a loss risk coefficient according to the loading quality and the unloading quality;
further, the loss risk coefficient calculating unit 230 is configured to calculate a loss value of each vehicle according to the loading quality and the unloading quality of each vehicle;
the loss risk coefficient calculation unit 230 is configured to calculate an average value of all vehicle loss values, and count the number of vehicles lower than the average value;
the calculation formula of the loss risk coefficient is as follows:
Figure BDA0002343656540000093
wherein r iswasteIs the loss risk factor.
A position risk coefficient calculation unit 240, wherein the position risk coefficient calculation unit 240 is configured to calculate and obtain a position risk coefficient according to the travel track data;
further, the position risk coefficient calculation unit 240 is configured to preset a standard driving route, and extract a standard abscissa series and a standard ordinate series on the map according to a preset time interval and a preset vehicle speed;
the position risk coefficient calculation unit 240 is configured to generate an abscissa series and an ordinate series corresponding to each vehicle according to the travel track data of each vehicle; the method comprises the steps of collecting driving track data of each vehicle, wherein the driving track data comprises an abscissa and an ordinate which are extracted according to a preset time interval in the driving process of each vehicle on a map;
the position risk coefficient calculation unit 240 is configured to calculate an abscissa correlation coefficient of each vehicle abscissa series and the standard abscissa series, and calculate an ordinate correlation coefficient of each vehicle ordinate series and the standard ordinate series;
the position risk coefficient calculation unit 240 is configured to calculate, according to a preset rule, an abscissa correlation coefficient and an ordinate correlation coefficient of the same vehicle, and obtain a risk coefficient corresponding to the vehicle;
the location risk factor calculation unit 240 is configured to use an average value of the risk factors of all the vehicles as the location risk factor.
Further, the position risk coefficient calculating unit 240 is configured to calculate an abscissa correlation coefficient of each vehicle abscissa series and the standard abscissa series, and calculate an ordinate correlation coefficient of each vehicle ordinate series and the standard ordinate series by the following formula:
Figure BDA0002343656540000101
Figure BDA0002343656540000102
wherein, ikhorizontalIs the abscissa correlation coefficient, ik, of the kth vehicleverticalThe correlation coefficient of the ordinate of the kth vehicle is taken as the correlation coefficient of the ordinate of the kth vehicle; ak is the abscissa number series of the kth vehicle, and Bk is the ordinate number series of the kth vehicle; x is a standard abscissa series, and Y is a standard ordinate series.
Further, the position risk coefficient calculation unit 240 is configured to obtain an abscissa risk coefficient of each vehicle according to an inverse of an absolute value of the abscissa correlation coefficient of the vehicle;
the position risk coefficient calculation unit 240 is configured to obtain a vertical coordinate risk coefficient of each vehicle according to an inverse of an absolute value of the vertical coordinate correlation coefficient of the vehicle;
the position risk coefficient calculation unit 240 is configured to obtain a risk coefficient corresponding to each vehicle according to an average value of the abscissa risk coefficient and the ordinate risk coefficient of each vehicle.
And the quality safety evaluation unit 250 is used for calculating and obtaining a transportation risk coefficient according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight, and generating a grain quality safety evaluation result according to the transportation risk coefficient.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the disclosure may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Reference to step numbers in this specification is only for distinguishing between steps and is not intended to limit the temporal or logical relationship between steps, which includes all possible scenarios unless the context clearly dictates otherwise.
Moreover, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the disclosure and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination.
Various component embodiments of the disclosure may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. The present disclosure may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present disclosure may be stored on a computer-readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the disclosure, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The disclosure may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware.
The foregoing is directed to embodiments of the present disclosure, and it is noted that numerous improvements, modifications, and variations may be made by those skilled in the art without departing from the spirit of the disclosure, and that such improvements, modifications, and variations are considered to be within the scope of the present disclosure.

Claims (12)

1. A grain quality safety assessment method in the automobile transportation process is characterized by comprising the following steps:
collecting data of preset types in the automobile transportation process; the data comprises the shipment time and the arrival time of each vehicle for transporting the batch of grains, the loading quality and the unloading quality of each vehicle and the running track data of each vehicle;
calculating to obtain a risk coefficient of the automobile transportation time according to the shipment time and the arrival time;
calculating according to the loading quality and the unloading quality to obtain a loss risk coefficient;
calculating to obtain a position risk coefficient according to the driving track data;
and calculating according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight to obtain a vehicle transportation risk coefficient, and generating a grain quality safety evaluation result according to the vehicle transportation risk coefficient.
2. The method of claim 1, wherein obtaining a transit time risk factor from the transit time and arrival time calculations comprises:
calculating and obtaining the in-transit time of each vehicle according to the shipment time and the arrival time of each vehicle;
calculating the variance of the time-in-transit queues formed by the time-in-transit of each vehicle;
the time risk coefficient rlogtimeThe calculation formula of (2) is as follows:
Figure FDA0002343656530000011
wherein, tmaxIs the maximum time-in-transit in the time-in-transit queue;
Figure FDA0002343656530000012
is the variance of the time-in-transit queue.
3. The method of claim 1, wherein said calculating a loss risk factor from said loading mass and said unloading mass comprises:
calculating to obtain a loss value of each vehicle according to the loading quality and the unloading quality of each vehicle;
calculating the average value of all vehicle loss values, and counting the number of vehicles lower than the average value;
the calculation formula of the loss risk coefficient is as follows:
Figure FDA0002343656530000021
wherein r iswasteIs the loss risk factor.
4. The method of claim 1, wherein the calculating a position risk factor from the travel track data comprises:
presetting a standard driving route, and extracting a standard abscissa series and a standard ordinate series on a map according to a preset time interval and a preset vehicle speed;
generating an abscissa array and an ordinate array corresponding to each vehicle according to the driving track data of each vehicle; the method comprises the steps of collecting driving track data of each vehicle, wherein the driving track data comprises an abscissa and an ordinate which are extracted according to a preset time interval in the driving process of each vehicle on a map;
calculating an abscissa correlation coefficient of each vehicle abscissa series and a standard abscissa series, and calculating an ordinate correlation coefficient of each vehicle ordinate series and a standard ordinate series;
calculating according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle through a preset rule to obtain a risk coefficient corresponding to the vehicle;
and taking the average value of the risk coefficients of all vehicles as the position risk coefficient.
5. The method of claim 4, wherein: the formula for calculating the abscissa correlation coefficient of each vehicle abscissa series and the standard abscissa series and the formula for calculating the ordinate correlation coefficient of each vehicle ordinate series and the standard ordinate series are respectively as follows:
Figure FDA0002343656530000022
Figure FDA0002343656530000023
wherein, ikhorizontalIs the abscissa correlation coefficient, ik, of the kth vehicleverticalThe correlation coefficient of the ordinate of the kth vehicle is taken as the correlation coefficient of the ordinate of the kth vehicle; ak is the abscissa number series of the kth vehicle, and Bk is the ordinate number series of the kth vehicle; x is a standard abscissa series, and Y is a standard ordinate series.
6. The method according to claim 4, wherein the calculating according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle and the preset rule to obtain the risk coefficient corresponding to the vehicle comprises:
obtaining the abscissa risk coefficient of each vehicle according to the reciprocal of the absolute value of the abscissa correlation coefficient of each vehicle;
obtaining the longitudinal coordinate risk coefficient of each vehicle according to the reciprocal of the absolute value of the longitudinal coordinate correlation coefficient of each vehicle;
and obtaining the risk coefficient corresponding to each vehicle according to the average value of the abscissa risk coefficient and the ordinate risk coefficient of each vehicle.
7. A grain quality safety assessment system during automobile transportation, the system comprising:
the data acquisition unit is used for acquiring data of preset types in the automobile transportation process; the data comprises the shipment time and the arrival time of each vehicle for transporting the batch of grains, the loading quality and the unloading quality of each vehicle and the running track data of each vehicle;
the time risk coefficient calculation unit is used for calculating and obtaining a car transportation time risk coefficient according to the shipment time and the arrival time;
the loss risk coefficient calculation unit is used for calculating and obtaining a loss risk coefficient according to the loading quality and the unloading quality;
the position risk coefficient calculation unit is used for calculating and obtaining a position risk coefficient according to the traveling track data;
and the quality safety evaluation unit is used for calculating and obtaining a car transportation risk coefficient according to the time risk coefficient, the loss risk coefficient, the position risk coefficient and a preset weight, and generating a grain quality safety evaluation result according to the car transportation risk coefficient.
8. The system of claim 7, wherein:
the time risk coefficient calculation unit is used for calculating and obtaining the in-transit time of each vehicle according to the shipment time and the arrival time of each vehicle;
the time risk coefficient calculation unit is used for calculating the variance of a time-in-transit queue formed by the time-in-transit of each vehicle;
the time risk coefficient rlogtimeThe calculation formula of (2) is as follows:
Figure FDA0002343656530000041
wherein, tmaxIs the maximum time-in-transit in the time-in-transit queue;
Figure FDA0002343656530000042
is the variance of the time-in-transit queue.
9. The system of claim 7, wherein:
the loss risk coefficient calculation unit is used for calculating and obtaining a loss value of each vehicle according to the loading quality and the unloading quality of each vehicle;
the loss risk coefficient calculation unit is used for calculating the average value of all vehicle loss values and counting the number of vehicles lower than the average value;
the calculation formula of the loss risk coefficient is as follows:
Figure FDA0002343656530000043
wherein r iswasteIs the loss risk factor.
10. The system of claim 7, wherein:
the position risk coefficient calculation unit is used for presetting a standard driving route and extracting a standard abscissa series and a standard ordinate series on a map according to a preset time interval and a preset vehicle speed;
the position risk coefficient calculation unit is used for generating an abscissa array and an ordinate array corresponding to each vehicle according to the running track data of each vehicle; the method comprises the steps of collecting driving track data of each vehicle, wherein the driving track data comprises an abscissa and an ordinate which are extracted according to a preset time interval in the driving process of each vehicle on a map;
the position risk coefficient calculation unit is used for calculating an abscissa correlation coefficient of each vehicle abscissa array and a standard abscissa array, and calculating an ordinate correlation coefficient of each vehicle ordinate array and a standard ordinate array;
the position risk coefficient calculation unit is used for calculating a risk coefficient corresponding to the same vehicle according to the abscissa correlation coefficient and the ordinate correlation coefficient of the same vehicle through a preset rule;
the position risk coefficient calculation unit is used for taking the average value of the risk coefficients of all the vehicles as the position risk coefficient.
11. The system of claim 10, wherein: the position risk coefficient calculation unit is used for calculating an abscissa correlation coefficient of each vehicle abscissa series and a standard abscissa series, and calculating a formula of an ordinate correlation coefficient of each vehicle ordinate series and a standard ordinate series, wherein the formula is respectively as follows:
Figure FDA0002343656530000051
Figure FDA0002343656530000052
wherein, ikhorizontalIs the abscissa correlation coefficient, ik, of the kth vehicleverticalThe correlation coefficient of the ordinate of the kth vehicle is taken as the correlation coefficient of the ordinate of the kth vehicle; ak is the abscissa number series of the kth vehicle, and Bk is the ordinate number series of the kth vehicle; x is a standard abscissa series, and Y is a standard ordinate series.
12. The system of claim 10,
the position risk coefficient calculation unit is used for obtaining the abscissa risk coefficient of each vehicle according to the reciprocal of the absolute value of the abscissa correlation coefficient of each vehicle;
the position risk coefficient calculation unit is used for obtaining the longitudinal coordinate risk coefficient of each vehicle according to the reciprocal of the absolute value of the longitudinal coordinate correlation coefficient of each vehicle;
and the position risk coefficient calculation unit is used for obtaining the risk coefficient corresponding to each vehicle according to the average value of the abscissa risk coefficient and the ordinate risk coefficient of each vehicle.
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