CN116720800A - Intelligent logistics scheduling system - Google Patents
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Abstract
The application discloses an intelligent logistics scheduling system, which relates to the technical field of logistics scheduling and comprises a data acquisition module, a logistics server, a data processing module, a fatigue index analysis module, a scheduling recommendation module and a display terminal; the technical problems that when a dispatching decision is made on a driver, the dispatching management cannot be carried out on the driver according to the fatigue degree of the driver, the working pressure of the driver is increased, and the working quality and the production efficiency of the driver are reduced are solved: through the analysis to driver eye image data, can acquire the fatigue index at present to dispatch the driver, avoid driver fatigue to drive and cause the potential safety hazard, improved logistics dispatch's efficiency and security, also can reduce driver's operating pressure simultaneously, improve driver's work quality and work efficiency.
Description
Technical Field
The application relates to the technical field of logistics scheduling, in particular to an intelligent logistics scheduling system.
Background
The logistics means the whole process of planning, implementing and managing raw materials, semi-finished products, finished products or related information from the production place of the commodity to the consumption place of the commodity by means of transportation, storage, distribution and the like in order to meet the demands of customers at the lowest cost. The existing logistics management method comprises loading, unloading, carrying, packaging, storing and transporting links, and reasonable dispatching management is needed to be carried out on drivers in the logistics transportation links.
The patent publication No. CN107067201A discloses an intelligent logistics scheduling system, which comprises a scheduling center, a line arrangement module, an order information module, a line planning module, a vehicle management module, a delivery vehicle scheduling module, a monitoring module and a vehicle-mounted terminal arranged on a vehicle; the dispatching center is respectively connected with the line arrangement module, the route planning module and the monitoring module; the line arrangement module is connected with the order information module; the vehicle management module is respectively connected with the route planning module and the delivery vehicle dispatching module, and the monitoring module is connected with the vehicle-mounted terminal.
However, in the process of route planning of the delivery vehicle, the fatigue degree of the driver is not considered, when the dispatching decision is made on the driver, the dispatching management cannot be performed on the delivery vehicle according to the fatigue degree of the driver, the working pressure of the driver is increased, the working quality and the working efficiency of the driver are reduced, the fatigue driving phenomenon of the driver is easily caused, and the traffic safety hidden trouble is caused.
Disclosure of Invention
The application aims to provide an intelligent logistics dispatching system, which solves the technical problems that when a dispatching decision is made for a driver, the dispatching management cannot be carried out on the driver according to the fatigue degree of the driver, the working pressure of the driver is increased, and the working quality and the production efficiency of the driver are reduced.
The aim of the application can be achieved by the following technical scheme:
the intelligent logistics scheduling system comprises a data acquisition module, a logistics server, a data processing module, a fatigue index analysis module, a scheduling recommendation module and a display terminal;
the system comprises a data acquisition module, a logistics server and a data processing module, wherein the data acquisition module is used for acquiring eye image data, eye movement data, driving habit data, current day driving data and task driving data of each driver, transmitting the eye image data, the eye movement data and the task driving data of the driver to the logistics server for storage, the acquired eye image data, the eye movement data and the task driving data of the driver are all data in a logistics task which is completed by the driver last time, the eye image data of the driver comprises closing time of left eyes and right eyes and eye blinking times of eyes, the eye movement data comprises eyeball fixation point number and fixation total duration, and the driving habit data comprises driving time, driving distance, average speed and braking times; the driving data of the same day comprise the driving time of the driver of the same day, the driving distance of the same day and the daily completion logistics task amount;
the data processing module is used for acquiring eye image data of a logistics task which is completed by each driver in the logistics server last time, acquiring the current eye closure degree and blink interval time of each driver through analysis and calculation of the eye closure time and blink times in the eye image data of each driver, analyzing the current corresponding eye closure degree and blink interval time of each driver through the fatigue index analysis module, further acquiring the current corresponding fatigue index of each driver, finally analyzing and processing the current corresponding fatigue index and driving time of each driver through the scheduling analysis module, further acquiring the current corresponding scheduling recommended value of each driver, and simultaneously transmitting the current corresponding scheduling recommended value to the logistics server for storage;
the dispatch recommendation module is used for comparing the dispatch recommendation values corresponding to the drivers at present, selecting the driver corresponding to the minimum dispatch value as the recommended dispatch driver, and transmitting the recommended dispatch driver and the driver information corresponding to the recommended dispatch driver to the display terminal;
and the display terminal is used for displaying the recommended dispatch drivers and the corresponding driver information.
As a further scheme of the application: the specific steps for acquiring the current eye closure degree and blink interval time of each driver are as follows:
s1: selecting one driver as a target driver;
s2: marking the task driving duration of the logistic task which is completed last time before the current time point of the distance acquisition data of the target driver as T1, and marking the closing time of the left eye and the right eye of the target driver in the logistic task which is completed last time as T2 and T3 respectively;
s21: the closing degree of the left eye and the right eye of the target driver in the last completed logistics task is calculated by using a formula, wherein the calculation formula of the closing degree of the left eye is as follows: BL= (T2/T1) ×100%; the calculation formula of the right eye closure degree is as follows: br= (T3/T1) ×100%;
s22: calculating to obtain the current eye closure degree YB1 of the target driver through YB1= [ (BL+BR)/2 x beta 1] × (1+beta 2), wherein beta 1 is a sitting posture correction constant and beta 2 is a driving environment correction constant;
s3: marking the blink frequency of a target driver in the last completed logistics task as C1;
s31: the general formula tp1 = T1/C1, calculating to obtain the blink interval time tp1 of the target driver in the last completed logistics task, wherein the blink interval time tp1 refers to the average time interval between two blinks of the target driver;
s4: repeating the steps S1-S3, the eye closure degree YB i and the blink interval time tp i corresponding to each driver currently can be calculated and obtained, wherein i refers to the number of each driver, i=1, 2, … and b.
As a further scheme of the application: the specific way to obtain the fatigue index corresponding to each driver currently is as follows:
c1: obtaining daily driving time length, daily driving distance and daily completion logistics task amount of a target driver on the same day, and marking the daily driving time length, the daily driving distance and the daily completion logistics task amount as DT1, DL1 and DS1 respectively, wherein the same day refers to a time period from zero point to twenty-four points of the same day when data are obtained;
c2: performing dequantization processing on the daily driving time length, the daily driving distance, the daily completion logistics task amount, the eye closure YB1 and the blink interval time tp1 of the target driver, and taking the numerical values;
by the formulaCalculating to obtain a current fatigue index PB1 corresponding to a target driver;
here, b1, b2, b3, b4 and b5 are preset proportionality coefficients, λ is a road condition influence constant θ1, and an eye data correction coefficient;
and C3: and (3) repeating the steps C1-C2, so that the fatigue index corresponding to each driver at present can be calculated and obtained, and the fatigue indexes are marked as PB1, PB2, … and PB i in sequence.
As a further scheme of the application: the specific mode for obtaining the scheduling recommended value corresponding to each driver currently is as follows:
d1: marking task driving time periods corresponding to logistic tasks which are completed by each driver last time as T1, T2, … and T i;
d2: dequantizing the driving time length and the current fatigue index of each driver and taking the values of the dequantized driving time length and the current fatigue index, and calculating to obtain a current corresponding scheduling value XB i of each driver through a formula XBI=θ2× (alpha 1× T i +alpha 2×PB i); here, α1 and α2 are both preset coefficients, and θ2 is a correction constant.
As a further scheme of the application: the mode of judging and selecting the recommended dispatching drivers can be as follows:
a1: selecting one driver as a target driver, and acquiring the number of eyeball fixation points and the corresponding total fixation duration of the target driver in front of a road in a last completed logistics task;
a2: marking the total gaze duration as ZT2;
a3: calculating to obtain the current gaze index Zs1 of the target driver through zs1= (T2/T1) ×100%;
a4: repeating the steps A1-A3, calculating to obtain the corresponding current gaze index of each driver, and marking the current gaze index as Zs1, zs2, … and Zs i respectively;
a5: and sequencing the current gaze indexes corresponding to the drivers according to the sequence from large to small, selecting the driver corresponding to the maximum current gaze index Zs as the recommended dispatch driver, and transmitting the recommended dispatch driver and the driver information corresponding to the recommended dispatch driver to the display terminal.
As a further scheme of the application: after the step D2 is finished, the driving habit data of each driver and the scheduling value corresponding to each driver are comprehensively analyzed and calculated, so that the current corresponding recommended value of each driver is obtained, then the driver recommending and scheduling is judged according to the corresponding recommended value of each driver, and the specific mode for judging the driver recommending and scheduling according to the corresponding recommended value of each driver is as follows:
e1: selecting a driver as a target driver, and marking task driving time length, task driving distance, task driving average speed and task driving brake times corresponding to a logistics task which is completed by the target driver last time as T1, D, V and A respectively;
e2: performing dequantization processing on the task driving duration, the task driving distance, the task driving average speed and the task driving brake frequency, and taking the numerical values, and calculating to obtain a habit index P1 corresponding to a target driver through a formula P1=w1×T1+w2×D+w3×V+w4×A, wherein w1, w2, w3 and w4 are preset weight coefficients respectively;
e3: the habit indexes of the target driver and the corresponding scheduling values are dequantized and the values are taken, and then the recommendation values corresponding to the target driver at present are calculated and obtained through the formula p1+xb1=g1;
e4: repeating the steps E1-E3 to calculate and obtain the current corresponding recommended value of each driver, and marking the current corresponding recommended value as G1, G2, … and G i respectively;
e5: and sequencing the current corresponding recommended values of all the drivers according to the sequence from small to large, selecting the driver corresponding to the smallest recommended value as the recommended dispatching driver, and simultaneously transmitting the recommended dispatching driver and the corresponding driver information to the display terminal.
As a further scheme of the application: the eye image data of the driver is collected through equipment such as a camera, an infrared sensor and the like which are arranged in the vehicle; the eye movement data are detected by installing an eye movement tracking device on the seat of the driver, all the devices are controlled and recorded by a controller, and the controller is arranged in the logistics server.
The application has the beneficial effects that:
according to the application, the current fatigue index of the driver can be obtained through analysis of the eye image data of the driver, so that the driver is dispatched, potential safety hazards caused by fatigue driving of the driver are avoided, the efficiency and safety of logistics dispatching are improved, meanwhile, the working pressure of the driver can be reduced, the working quality and working efficiency of the driver are improved, the phenomenon of potential traffic hazards caused by fatigue driving of the driver is avoided, and the safety and efficiency of logistics dispatching are further improved.
Drawings
The application is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of a framework structure of an intelligent logistics scheduling system of the present application;
FIG. 2 is a schematic diagram of a method structure of the intelligent logistics scheduling system of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1
Referring to fig. 1-2, the application discloses an intelligent logistics scheduling system, which comprises a data acquisition module, a logistics server, a data processing module, a fatigue index analysis module, a scheduling recommendation module and a display terminal;
the system comprises a data acquisition module, a physical distribution server and a data processing module, wherein the data acquisition module is used for acquiring eye image data, eye movement data, driving habit data, current day driving data and task driving data of each driver, transmitting the eye image data, the eye movement data and the task driving data of each driver into the physical distribution server for storage, the acquired eye image data, the eye movement data and the task driving data of the driver are all data in a physical distribution task which is completed last time before the current time point of the data acquisition of the driver, the eye image data of the driver comprises closing time of left eyes and right eyes and the blink times of eyes, the eye movement data comprises eyeball fixation point number and fixation total duration, and the driving habit information comprises task driving average speed and task driving brake times; the driving data of the same day comprise the driving time of the driver of the same day, the driving distance of the same day and the daily completion logistics task amount; the task driving data is the task driving duration and the task driving distance corresponding to the logistic task which is completed by the driver last time;
the eye image data of the driver comprises closing time of the left eye and the right eye and blink times of the eyes; the eye movement data comprise the number of eye gaze points and the total gazing time, the driving habit information comprises the average task driving speed and the task driving braking frequency, and the task driving data correspond to the task driving time and the task driving distance of the logistic task which is completed by the driver last time; the driving data of the same day comprises the driving time of the driver of the same day, the driving distance of the same day and the logistics task amount of the same day, wherein the driving time of the same day is the time period from zero point to twenty-four points of the same day when the driving data of the same day are obtained;
the eye image data of the driver is collected through equipment such as a camera, an infrared sensor and the like which are arranged in the vehicle; the eye movement data are detected by installing eye movement tracking equipment on a driver seat, the equipment can monitor the eye movement of the driver through infrared rays or cameras and other technologies, and eyeballs of the driver are tracked through infrared sensors and cameras, so that eyeball tracking data of the driver in the driving process of the driver are obtained; the eye movement tracking equipment continuously collects eye movement data of a driver and has eye movement data, all the equipment are controlled and recorded by a controller, and the controller is arranged in the logistics server;
the data processing module is used for acquiring eye image data of a logistic task which is completed by each driver in the logistic server last time, acquiring the current eye closure degree and blink interval time of each driver through analysis and calculation of the eye closure time and blink times in the eye image data of each driver, and simultaneously transmitting the current eye closure degree and blink interval time to the logistic server for storage, wherein the specific steps for acquiring the current eye closure degree and blink interval time of each driver are as follows:
s1: selecting one driver as a target driver;
s2: marking task driving time of a target driver as T1, marking eyes of the target driver as left and right parts respectively, and marking closing time of the left and right eyes of the target driver as T2 and T3 respectively in the last completed logistics task;
s21: the closing degree of the left eye and the right eye of the target driver in the last completed logistics task is calculated by using a formula, wherein the calculation formula of the closing degree of the left eye is as follows: BL= (T2/T1) ×100%; the calculation formula of the right eye closure degree is as follows: br= (T3/T1) ×100%;
s22: calculating to obtain the current eye closure degree YB1 of the target driver through YB1= [ (BL+BR)/2×β1] ×β2, wherein β1 is a sitting posture correction constant and β2 is a driving environment correction constant;
s3: marking the blink frequency of a target driver in the last completed logistics task as C1;
s31: typically, the formula tp1=t1/C1, and calculating to obtain the blink interval time tp1 of the target driver in the last completed logistics task;
wherein, the blink interval time tp1 refers to the average time interval between blinks of the target driver; in general, the higher the eye closure and blink count, the more serious the driver's fatigue;
s4: repeating the steps S1-S3, and calculating to obtain the eye closure degree YB i and blink interval time tp i corresponding to each driver currently, wherein i refers to the number of each driver, i=1, 2, … and b;
the fatigue index analysis module is used for acquiring the eye closure degree and blink interval time corresponding to each driver currently in the logistics server, analyzing the eye closure degree and blink interval time to acquire the fatigue index corresponding to each driver currently, and simultaneously sending the fatigue index to the logistics server for storage, wherein the specific mode for acquiring the fatigue index corresponding to each driver currently is as follows:
c1: obtaining daily driving time length, daily driving distance and daily completion logistics task amount of a target driver on the same day, and marking the daily driving time length, the daily driving distance and the daily completion logistics task amount as DT1, DL1 and DS1 respectively;
the day refers to the time period from zero point to twenty-four points of the day when the data is acquired;
c2: performing dequantization processing on the daily driving time length, the daily driving distance, the daily completion logistics task amount, the eye closure YB1 and the blink interval time tp1 of the target driver, and taking the numerical values;
by the formulaCalculating to obtain a current fatigue index PB1 corresponding to a target driver;
here, b1, b2, b3, b4 and b5 are preset proportionality coefficients, λ is a road condition influence constant θ1, and an eye data correction coefficient;
and C3: repeating the steps C1-C2, calculating and obtaining the fatigue index corresponding to each driver currently, and marking the fatigue index as PB1, PB2, … and PB i in sequence;
the scheduling analysis module is used for acquiring the fatigue index corresponding to each driver currently from the logistics server, simultaneously acquiring the driving time length corresponding to the logistics task completed by each driver last time, analyzing and processing the fatigue index and the driving time length corresponding to each driver currently, further acquiring the scheduling recommended value corresponding to each driver currently, and simultaneously transmitting the scheduling recommended value to the logistics server for storage, wherein the specific mode for acquiring the scheduling recommended value corresponding to each driver currently is as follows:
d1: marking task driving time periods corresponding to logistic tasks which are completed by each driver last time as T1, T2, … and T i;
d2: dequantizing the driving time length and the current fatigue index of each driver and taking the values of the dequantized driving time length and the current fatigue index, and calculating to obtain a current corresponding scheduling value XB i of each driver through a formula XBI=θ2× (alpha 1× T i +alpha 2×PB i); here, α1 and α2 are both preset coefficients, and θ2 is a correction constant;
the scheduling recommendation module is used for acquiring the scheduling recommendation value corresponding to each driver in the logistics server at present, comparing the scheduling recommendation value corresponding to each driver at present, selecting the scheduling driver according to the comparison result, and transmitting the scheduling driver and the driver information corresponding to the scheduling driver to the display terminal, wherein the specific mode for selecting the scheduling driver is as follows:
sequencing the current corresponding scheduling values XB i of all drivers according to the sequence from small to large, selecting the driver corresponding to the smallest scheduling value XB i as the recommended scheduling driver, and simultaneously transmitting the recommended scheduling driver and the corresponding driver information to the display terminal;
the display terminal is used for displaying recommended dispatch drivers and corresponding driver information thereof, so that relevant personnel can conveniently check and dispatch correspondingly;
example two
As an embodiment two of the present application, when the present application is implemented in practice, compared with the embodiment one, the difference in this embodiment is that the basis for selecting the recommended dispatch drivers in this embodiment is the current gaze index corresponding to each driver, and the specific manner for obtaining the current gaze index corresponding to each driver is as follows:
a1: selecting one driver as a target driver, and acquiring the number of eyeball fixation points and the corresponding total fixation duration of the target driver in front of a road in a last completed logistics task;
a2: marking the total gaze duration as ZT2;
a3: calculating to obtain the current gaze index Zs1 of the target driver through zs1= (T2/T1) ×100%;
a4: repeating the steps A1-A3, calculating to obtain the corresponding current gaze index of each driver, and marking the current gaze index as Zs1, zs2, … and Zs i respectively;
gaze index: the time that eyeballs stay in the position of the area right in front of the road accounts for the total driving time of a target driver, and the higher the gaze index is, the longer the period that the eyeballs of the driver gaze at the position of the area right in front of the road is, and the higher the attention degree of the driver in the driving process is;
a5: sequencing the current gaze indexes corresponding to all drivers in a sequence from large to small, selecting the driver corresponding to the maximum current gaze index Zs as a recommended dispatching driver, and transmitting the recommended dispatching driver and the driver information corresponding to the recommended dispatching driver to a display terminal;
number of gaze points: the number of gazing times that eyeballs stay in the position of the area right in front of the road is larger, and the more the number of gazing points is, the more the attention of a driver is focused;
total duration of fixation: the neutralization of the time length of each fixation point of the eyeball which stays right in front of the short road, and the longer the total fixation time length is, the higher the attention of the driver to the right before the road is;
example III
As an embodiment three of the present application, when the present application is specifically implemented, compared with the first embodiment and the second embodiment, the technical solution of the present embodiment differs from the first embodiment and the second embodiment only in that in the present embodiment, through calculation and analysis of driving habit data of each driver, a corresponding habit index of each driver is obtained, then a current corresponding scheduling value of each driver is obtained, the current corresponding scheduling value and habit index of each driver are calculated, further a corresponding recommended value of each driver is obtained, and the recommended scheduling driver is determined according to the recommended value, where the specific manner of determining the recommended scheduling driver is as follows:
e1: selecting a driver as a target driver, and marking task driving time length, task driving distance, task driving average speed and task driving brake times corresponding to a logistics task which is completed by the target driver last time as T1, D, V and A respectively;
e2: after dequantizing the driving time, the driving distance, the average speed, the sudden acceleration and the sudden braking times and taking the numerical values, calculating and obtaining a habit index P1 corresponding to the target driver through a formula P1=w1×T1+w2×D+w3×V+w4×A, wherein w1, w2, w3 and w4 are respectively preset weight coefficients, and related personnel can correspondingly adjust the numerical values according to actual conditions;
e3: after the habit indexes of the target driver and the corresponding scheduling values are dequantized and the values are taken, calculating to obtain the current corresponding recommended values of the target driver through a formula w5+w6 xXB1=G1, wherein w5 and w6 are preset weight coefficients;
e4: repeating the steps E1-E3 to calculate and obtain the current corresponding recommended value of each driver, and marking the current corresponding recommended value as G1, G2, … and G i respectively;
e5: sequencing the current corresponding recommended values of all drivers according to the sequence from small to large, selecting the driver corresponding to the smallest recommended value as the recommended dispatching driver, and simultaneously transmitting the recommended dispatching driver and the corresponding driver information to the display terminal;
example IV
As an embodiment four of the present application, in the implementation of the present application, the solutions of the above embodiment one, embodiment two and embodiment three are combined and implemented as compared with the embodiment one, embodiment two and embodiment three;
the working principle of the application is as follows: the eye closing time and the eye blinking times of the left eye and the right eye in the eye image data of the logistic task which are completed by each driver last time are obtained and analyzed to obtain the eye closing degree and the eye blinking interval time which are corresponding to each driver at present, the eye closing degree and the eye blinking interval time which are corresponding to each driver at present are analyzed to obtain the fatigue index which is corresponding to each driver at present, finally, the fatigue index and the driving duration which are corresponding to each driver at present are analyzed and processed to obtain the dispatching recommended value which is corresponding to each driver at present, the driver corresponding to the smallest dispatching recommended value is selected as the dispatching recommended driver by comparing the dispatching recommended value which is corresponding to each driver at present, and meanwhile, the information of the recommended dispatching driver and the corresponding driver is transmitted to the display terminal, so that the relevant dispatching driver is beneficial to dispatching the driver according to the current state of each driver, the potential safety hazard caused by fatigue driving of the driver is avoided, and meanwhile, the working pressure of the driver is reduced when the driver is subjected to a dispatching decision is improved.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. Intelligent logistics scheduling system, its characterized in that includes:
the system comprises a data acquisition module, a logistics server and a task driving module, wherein the data acquisition module is used for acquiring eye image data, eye movement data, driving habit data, current day driving data and task driving data of each driver, transmitting the eye image data, the eye movement data and the task driving data of each driver into the logistics server for storage, the acquired eye image data, the eye movement data and the task driving data of the driver are all data in a logistics task which is completed last time before the current time point of the data acquisition of the driver, the eye image data of the driver comprises closing time of left eyes and right eyes and the blink times of eyes, the eye movement data comprises eyeball fixation point number and fixation total duration, and the driving habit information comprises task driving average speed and task driving brake times; the driving data of the same day comprise the driving time of the driver of the same day, the driving distance of the same day and the daily completion logistics task amount; the task driving data is the task driving duration and the task driving distance corresponding to the logistic task which is completed by the driver last time;
the data processing module is used for acquiring eye image data of a logistic task which is completed by each driver in the logistic server last time, acquiring the current eye closure degree and blink interval time of each driver through analysis and calculation of the eye closure time and blink times in the eye image data of each driver, and simultaneously transmitting the current eye closure degree and blink interval time to the logistic server for storage;
the fatigue index analysis module is used for acquiring the eye closure degree and the blink interval time corresponding to each driver currently from the logistics server, analyzing the eye closure degree and the blink interval time to acquire the fatigue index corresponding to each driver currently, and simultaneously sending the fatigue index to the logistics server for storage;
the scheduling analysis module is used for acquiring the fatigue index corresponding to each driver currently from the logistics server, acquiring the driving time length corresponding to the logistics task completed by each driver last time, analyzing and processing the fatigue index and the driving time length corresponding to each driver currently, further acquiring the scheduling recommended value corresponding to each driver currently, and transmitting the scheduling recommended value to the logistics server for storage;
the dispatch recommendation module is used for comparing the dispatch recommendation values corresponding to the drivers at present, selecting the driver corresponding to the minimum dispatch value as the recommended dispatch driver, and transmitting the recommended dispatch driver and the driver information corresponding to the recommended dispatch driver to the display terminal.
2. The intelligent logistics dispatching system of claim 1, wherein the specific steps of obtaining the current eye closure and blink interval time of each driver are:
s1: selecting one driver as a target driver;
s2: marking the task driving duration of the logistic task which is completed last time before the current time point of the distance acquisition data of the target driver as T1, and marking the closing time of the left eye and the right eye of the target driver in the logistic task which is completed last time as T2 and T3 respectively;
s21: the closing degree of the left eye and the right eye of the target driver in the last completed logistics task is calculated by using a formula, wherein the calculation formula of the closing degree of the left eye is as follows: BL= (T2/T1) ×100%; the calculation formula of the right eye closure degree is as follows: br= (T3/T1) ×100%;
s22: calculating to obtain the current eye closure degree YB1 of the target driver through YB1= [ (BL+BR)/2×β1] ×β2, wherein β1 is a sitting posture correction constant and β2 is a driving environment correction constant;
s3: marking the blink frequency of a target driver in the last completed logistics task as C1;
s31: the general formula tp1 = T1/C1, calculating to obtain the blink interval time tp1 of the target driver in the last completed logistics task, wherein the blink interval time tp1 refers to the average time interval between two blinks of the target driver;
s4: repeating steps S1-S3, the eye closure YBi and blink interval time tp i corresponding to each driver currently can be calculated, wherein i refers to the number of each driver, i=1, 2, …, b.
3. The intelligent logistics scheduling system of claim 2, wherein the specific manner of obtaining the current corresponding fatigue index for each driver is:
c1: obtaining daily driving time length, daily driving distance and daily completion logistics task amount of a target driver on the same day, and marking the daily driving time length, the daily driving distance and the daily completion logistics task amount as DT1, DL1 and DS1 respectively;
c2: performing dequantization processing on the daily driving time length, the daily driving distance, the daily completion logistics task amount, the eye closure YB1 and the blink interval time tp1 of the target driver, and taking the numerical values;
by the formulaCalculating to obtain a current fatigue index PB1 corresponding to a target driver;
here, b1, b2, b3, b4 and b5 are preset proportionality coefficients, λ is a road condition influence constant θ1, and an eye data correction coefficient;
and C3: and (3) repeating the steps C1-C2, calculating and obtaining the fatigue index corresponding to each driver currently, and marking the fatigue index as PB1, PB2, … and PBi in sequence.
4. The intelligent logistics dispatching system of claim 2, wherein the specific manner of obtaining the current corresponding dispatching recommendation value for each driver is:
d1: marking task driving time periods corresponding to logistic tasks which are completed by each driver last time as T1, T2, … and Ti;
d2: dequantizing the driving time length and the current fatigue index of each driver and taking the values of the dequantized driving time length and the current fatigue index, and calculating to obtain a current corresponding scheduling value XBI of each driver through a formula XBI=θ2× (alpha 1×Ti+alpha 2×PBi); here, α1 and α2 are both preset coefficients, and θ2 is a correction constant.
5. The intelligent logistics dispatching system of claim 2, wherein the means for selecting the recommended dispatch driver further comprises:
a1: selecting one driver as a target driver, and acquiring the number of eyeball fixation points and the corresponding total fixation duration of the target driver in front of a road in a last completed logistics task;
a2: marking the total gaze duration as ZT2;
a3: calculating to obtain the current gaze index Zs1 of the target driver through zs1= (T2/T1) ×100%;
a4: repeating the steps A1-A3, calculating to obtain the corresponding current gaze index of each driver, and marking the current gaze index as Zs1, zs2, … and Zsi respectively;
a5: and sequencing the current gaze indexes corresponding to the drivers according to the sequence from large to small, selecting the driver corresponding to the maximum current gaze index Zsi as the recommended dispatching driver, and transmitting the recommended dispatching driver and the driver information corresponding to the recommended dispatching driver to the display terminal.
6. The intelligent logistics dispatching system of claim 4, wherein after the step D2 is performed, the driving habit data of each driver and the dispatching value corresponding to each driver are comprehensively analyzed and calculated, so as to obtain the current corresponding recommended value of each driver, and then the driver who recommends dispatching is judged according to the corresponding recommended value of each driver, and the specific way of judging the driver who recommends dispatching according to the corresponding recommended value of each driver is as follows:
e1: selecting a driver as a target driver, and marking task driving time length, task driving distance, task driving average speed and task driving brake times corresponding to a logistics task which is completed by the target driver last time as T1, D, V and A respectively;
e2: performing dequantization processing on the task driving duration, the task driving distance, the task driving average speed and the task driving brake frequency, and taking the numerical values, and calculating to obtain a habit index P1 corresponding to a target driver through a formula P1=w1×T1+w2×D+w3×V+w4×A, wherein w1, w2, w3 and w4 are preset weight coefficients respectively;
e3: after the habit indexes of the target driver and the corresponding scheduling values are dequantized and the values are taken, calculating to obtain the current corresponding recommended values of the target driver through a formula w5+w6 xXB1=G1, wherein w5 and w6 are preset weight coefficients;
e4: repeating the steps E1-E3 to calculate and obtain the current corresponding recommended value of each driver, and marking the current corresponding recommended value as G1, G2, … and Gi respectively;
e5: and sequencing the current corresponding recommended values of all the drivers according to the sequence from small to large, selecting the driver corresponding to the smallest recommended value as the recommended dispatching driver, and simultaneously transmitting the recommended dispatching driver and the corresponding driver information to the display terminal.
7. The intelligent logistics dispatching system of claim 1, wherein the eye image data of the driver is collected by a camera, an infrared sensor and the like installed in the vehicle; the eye movement data are detected by installing an eye movement tracking device on the seat of the driver, all the devices are controlled and recorded by a controller, and the controller is arranged in the logistics server.
8. The intelligent logistics dispatching system of claim 1, wherein the display terminal is configured to display the recommended dispatch driver and corresponding driver information.
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