CN117332979A - Agricultural product supply chain information management system - Google Patents
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Abstract
The invention discloses an agricultural product supply chain information management system which comprises a data acquisition unit, a data analysis unit, a data processing unit, a data matching unit, a data display unit and an abnormality early warning unit. According to the invention, the new transportation orders are combined with the historical transportation orders of all drivers to obtain the matching analysis coefficient of each driver to receive the new transportation orders, the data processing unit is used for obtaining the transportation time interval corresponding to each driver, and each driver is matched with each new transportation order through the data matching unit, so that the problems of long-term transportation and long-distance new transportation orders are solved, and the problem of large transportation safety hidden danger exists, meanwhile, the safe transportation of agricultural products is effectively ensured, so that the good circulation of a supply chain is ensured, meanwhile, the situation that part of the new transportation orders cannot be rapidly delivered is avoided, and the logistics transportation efficiency in the agricultural product supply chain is affected.
Description
Technical Field
The invention relates to the technical field of information management, in particular to an agricultural product supply chain information management system.
Background
The material flow, the information flow and the fund flow exist in each link of the supply chain; the logistics is the most obvious and visual flow in the supply chain, but under the background of the rapid development of the prior informatization, the use condition of one device in the whole circulation link is possible to be tracked, so that the trend of agricultural product supply can be tracked, and the agricultural product supply source can be reversely traced.
In the management of the logistics information in the agricultural product supply chain, particularly in the process of transporting the agricultural product, the transportation amount of the agricultural product in a certain time is large, so that a transportation driver needs to transport the agricultural product for a plurality of long distances, fatigue driving of the driver is caused by unreasonable task allocation in the transportation process, and further, the transportation is likely to fail due to potential safety hazards, so that the problems of larger economic loss, personnel injury and the like are caused, the management of the logistics information in the supply chain cannot necessarily lack to track and monitor the data of the agricultural product in the transportation process, and then the reasonable task allocation is carried out, so that the larger loss can be avoided.
Accordingly, the present invention proposes an agricultural product supply chain information management system to manage logistics information in an agricultural product supply chain.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an agricultural product supply chain information management system, which solves the problems in the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme: an agricultural product supply chain information management system, comprising:
the data acquisition unit is used for acquiring a plurality of new transportation orders of agricultural products to be transported and a plurality of historical transportation orders of each driver, and then sending the new transportation orders and the historical transportation orders to the data analysis unit; the new transportation orders comprise estimated starting time and estimated ending time of the orders, and the estimated starting time of the plurality of new transportation orders is the same; the historical transportation orders comprise the starting time and the ending time of each order and the driver state information during corresponding transportation, wherein the driver state information acquires a monitoring video of a driver through a monitoring probe arranged on a transportation vehicle, and then the monitoring video is analyzed to obtain information of normal mental state and abnormal state of the driver;
the data analysis unit is used for combining the new transportation orders with the historical transportation orders of each driver to obtain the transportation duration of each historical transportation order and the interval duration between two adjacent historical transportation orders, combining the transportation duration and the interval duration with the number of the historical transportation orders with abnormal states, calculating the matching analysis coefficient of each driver for receiving the new transportation orders, and then sending the matching analysis coefficient to the data processing unit;
the data processing unit is used for importing the matching analysis coefficients of each driver into a preset processing model, then the processing model matches the corresponding analysis coefficient interval according to the matching analysis coefficients of each driver, the corresponding transportation time interval is obtained, and then the transportation time interval corresponding to each driver is sent to the data matching unit; the processing model comprises a plurality of groups of preset analysis coefficient intervals, and the analysis intervals are correspondingly provided with a group of transportation time intervals;
the data matching unit is used for counting the estimated transportation time of the corresponding new transportation order according to the estimated starting time and the estimated ending time of the plurality of new transportation orders, then respectively matching the transportation time intervals of the drivers obtained by the data processing unit with the estimated transportation time of the new transportation orders, and then generating a corresponding order matching table according to the matching result.
Preferably, the driver status information is obtained as follows:
AS1, dismantling a monitoring video into a plurality of video frames according to the time sequence, wherein the interval time among the video frames is the same, and then importing the video frames into a pre-trained state analysis model;
AS2, the state analysis model obtains the facial feature vector of the facial outline of the driver through a face recognition technology, wherein the face recognition technology is realized in any one recognition mode of a geometric feature-based recognition method, an algebraic feature-based recognition method and a connection mechanism-based recognition method;
AS3, then, the state analysis model performs similarity analysis on the feature vectors of the five sense organs in each video frame and the feature vector samples trained in advance to obtain feature similarity of the feature vectors of the five sense organs and the feature vector samples; the feature vector samples are obtained by training and analyzing the feature vectors of the five sense organs under a plurality of pre-extracted abnormal states;
AS4, then comparing the feature similarity with a preset similarity threshold by a state analysis model:
if the feature similarity is greater than or equal to a similarity threshold, extracting a corresponding video frame, marking the video frame as an abnormal frame, and then entering the next step;
if the feature similarity is greater than or equal to a similarity threshold, deleting the corresponding video frame;
AS5, sequencing all video frames according to the time sequence, and generating a frame table;
then extracting the number of deleted video frames between each group of adjacent abnormal frames according to the marks of the abnormal frames in a frame table, and marking the deleted video frames as SCi, wherein i=1, 2, … …, n and n represent the number of the adjacent abnormal frames, and i represents the number of the adjacent abnormal frames;
AS6, then comparing the SCi with a preset contrast threshold SCy 1;
if SCi is less than SCy1, judging that the state of the driver at the corresponding moment is abnormal, wherein the state abnormality indicates that the driver has dangerous driving behaviors.
Preferably, in step AS6, if SCi is greater than or equal to SC0, a corresponding i is obtained, then i-1 is made, SCi-1 and SCi are added to obtain a secondary analysis value SC0, and then SC0 is compared with a preset comparison threshold SCy:
when SC0 is less than SCy2, the state of the driver at the corresponding moment is judged to be abnormal, otherwise, the state of the driver at the corresponding moment is judged to be normal.
Preferably, the specific analysis mode of the data analysis unit is as follows:
SS1, selecting a driver, and acquiring m historical transportation orders of the driver in a period close to the current period;
SS2, counting the transportation time length of each historical transportation order according to the starting time and the ending time of m historical transportation orders, and marking the transportation time length as Y1, Y2 and … … Ym respectively;
SS3, acquiring interval duration between two adjacent orders in the n historical transportation orders according to the starting time and the ending time of the m historical transportation orders, and marking the interval duration as G1, G2, … … and Gm-1 respectively; meanwhile, according to the ending time of one historical transport order closest to the current period, calculating the interval duration between the two orders by combining with the estimated starting time of the new transport order, and marking the interval duration as Gm;
SS4, acquiring historical transportation orders containing state abnormality from driver state information of m historical transportation orders, counting the number of the historical transportation orders containing state abnormality, and marking the number as Z;
SS5, then pass throughAnd obtaining a matching analysis coefficient P of the driver for receiving the corresponding order, wherein beta 1, beta 2 and beta 3 are all preset proportional coefficients.
Preferably, the data processing unit acquires the driver transportation time interval as follows:
selecting a driver, and firstly, importing a matching analysis coefficient of the driver into a preset processing model;
then the processing model acquires an analysis coefficient interval containing the matched analysis coefficient according to the matched analysis coefficient;
and finally, taking the corresponding transportation time interval in the acquired analysis coefficient interval as the transportation time interval of the driver.
Preferably, if none of the plurality of groups of preset analysis coefficient intervals includes a corresponding matching analysis coefficient, the personal state of the driver is indicated to be inconsistent with the requirement of safe driving.
Preferably, the matching manner of the data matching unit is as follows:
selecting a driver and a new transportation order;
if the estimated transportation time of the new transportation order is less than or equal to the maximum value in the transportation time interval of the driver, the driver can receive the new transportation order;
if the estimated time of the new transportation order is greater than the maximum value of the driver's transportation time interval, it indicates that the driver cannot receive the new transportation order.
Preferably, when each driver is matched with each new transport order, the data matching unit sorts the estimated transport time of each new transport order according to the order from big to small to obtain a priority allocation table, and then matches each new transport order with the transport time interval of each driver one by one according to the order from front to back in the priority allocation table.
Preferably, the system further comprises a data display unit for displaying the order matching table.
Preferably, the system further comprises an abnormality early warning unit which is used for acquiring the driver state information acquired by the data acquisition unit and triggering an audible and visual prompt alarm according to the state abnormality information in the driver state information.
Advantageous effects
The invention provides an agricultural product supply chain information management system. Compared with the prior art, the method has the following beneficial effects:
according to the invention, the new transportation orders are combined with the historical transportation orders of all drivers, the matching analysis coefficient of each driver for receiving the new transportation orders is analyzed, then the transportation time interval corresponding to each driver is acquired through the data processing unit, and then each driver is respectively matched with each new transportation order through the data matching unit, so that the problems of long-term transportation and not good resting of the drivers master and long-distance new transportation orders are solved, and the problem of large transportation potential safety hazards is solved, and meanwhile, the safe transportation of agricultural products is effectively ensured, so that good circulation of a supply chain is ensured;
according to the method, the estimated transportation time of each new transportation order is ordered according to the order from big to small, and then the estimated transportation time is matched with the transportation time interval of each driver according to the priority allocation table, so that the problem that after the driver with a big transportation time interval is matched with the new transportation order with a short estimated transportation time, the driver with a small subsequent transportation time interval cannot be matched with the new transportation order with a long estimated transportation time is effectively avoided, and further, the problem that part of new transportation orders cannot be delivered quickly, so that the logistics transportation efficiency in an agricultural product supply chain is influenced is further avoided;
the invention analyzes the information of normal state and abnormal state of the driver through the data acquisition unit, further monitors the real-time state of the driver of the transport vehicle, can timely know the driving state of the vehicle in the agricultural product transport process, and then timely gives an alarm according to the abnormal state problem in the monitoring of the abnormal early warning unit so as to remind a driver to drive the vehicle to a safe rest area for rest, thereby avoiding traffic accidents caused by fatigue driving of the driver and avoiding cargo loss generated after the traffic accidents caused by fatigue driving of an agricultural product supply chain.
Drawings
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a flow chart of the judgment of the driver status information according to the present invention.
Detailed Description
The following description of the embodiments of the present invention 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 invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As an embodiment of the invention
Referring to fig. 2, the present invention provides a technical solution: an agricultural product supply chain information management system, comprising:
the data acquisition unit is used for acquiring a plurality of new transportation orders of agricultural products to be transported and a plurality of historical transportation orders of each driver; subsequently sending the new and the historical shipping orders to a data analysis unit;
the new transportation orders comprise estimated starting time and estimated ending time of the orders, and the estimated starting time of the plurality of new transportation orders is the same; the historical shipping orders include the start time, end time of each order, and driver status information during the corresponding shipment; the driver state information is used for acquiring a monitoring video of a driver through a monitoring probe arranged on the transport vehicle, and then analyzing the monitoring video to obtain information of normal state and abnormal state of the driver;
the driver state information is obtained as follows:
AS1, dismantling a monitoring video into a plurality of video frames according to the time sequence, wherein the interval time among the video frames is the same, and then importing the video frames into a pre-trained state analysis model;
AS2, the state analysis model obtains the facial feature vector of the facial outline of the driver through a face recognition technology, wherein the face recognition technology is realized in any one recognition mode of a geometric feature-based recognition method, an algebraic feature-based recognition method and a connection mechanism-based recognition method;
AS3, then, the state analysis model performs similarity analysis on the feature vectors of the five sense organs in each video frame and the feature vector samples trained in advance to obtain feature similarity of the feature vectors of the five sense organs and the feature vector samples; the feature vector samples are obtained by training and analyzing the feature vectors of the five sense organs under a plurality of pre-extracted abnormal states;
AS4, then comparing the feature similarity with a preset similarity threshold by a state analysis model:
if the feature similarity is greater than or equal to a similarity threshold, extracting a corresponding video frame, marking the video frame as an abnormal frame, and then entering the next step;
if the feature similarity is greater than or equal to a similarity threshold, deleting the corresponding video frame;
AS5, sequencing all video frames according to the time sequence, and generating a frame table;
then extracting the number of deleted video frames between each group of adjacent abnormal frames according to the marks of the abnormal frames in a frame table, and marking the deleted video frames as SCi, wherein i=1, 2, … …, n and n represent the number of the adjacent abnormal frames, and i represents the number of the adjacent abnormal frames;
AS6, then comparing the SCi with a preset contrast threshold SCy 1;
if SCi is less than SCy1, judging that the state of the driver at the corresponding moment is abnormal, wherein the state abnormality indicates that dangerous driving behaviors exist in the driver; wherein in a historical shipping order, there is a SCi < SCy1, indicating that the driver is in abnormal condition in the historical shipping order;
if SCi is greater than or equal to SC0, obtaining corresponding i, then letting i-1, adding SCi-1 and SCi to obtain a secondary analysis value SC0, and comparing SC0 with a preset comparison threshold SCy 2:
when SC0 is less than SCy2, judging that the state of the driver at the corresponding moment is abnormal, wherein in a historical transportation order, SC0 is less than SCy2, namely, the state of the driver in the historical transportation order is abnormal; otherwise, judging that the state of the driver at the corresponding moment is normal, wherein the normal state indicates that the driver belongs to a safe driving vehicle;
the data analysis unit is used for analyzing according to the information acquired by the data acquisition unit, obtaining the matching analysis coefficients of the new transportation orders received by each driver, and then sending the matching analysis coefficients to the data processing unit;
taking one driver as an example for analysis:
SS1, acquiring m historical transportation orders close to a current period;
SS2, counting the transportation time length of each historical transportation order according to the starting time and the ending time of m historical transportation orders, and marking the transportation time length as Y1, Y2 and … … Ym respectively;
SS3, acquiring interval duration between two adjacent orders in the n historical transportation orders according to the starting time and the ending time of the m historical transportation orders, and marking the interval duration as G1, G2, … … and Gm-1 respectively; meanwhile, according to the ending time of one historical transport order closest to the current period, calculating the interval duration between the two orders by combining with the estimated starting time of the new transport order, and marking the interval duration as Gm;
SS4, acquiring historical transportation orders containing state abnormality from driver state information of m historical transportation orders, counting the number of the historical transportation orders containing state abnormality, and marking the number as Z;
SS5, then pass throughObtaining a matching analysis coefficient P of the driver for receiving the corresponding order, wherein beta 1, beta 2 and beta 3 are preset proportional coefficients;
the data processing unit is used for importing the matching analysis coefficients of each driver into a preset processing model, then the processing model matches the corresponding analysis coefficient interval according to the matching analysis coefficients of each driver, the corresponding transportation time interval is obtained, and then the transportation time interval corresponding to each driver is sent to the data matching unit;
the processing model comprises a plurality of groups of preset analysis coefficient intervals, and the analysis intervals are correspondingly provided with a group of transportation time intervals;
the driver obtains the corresponding transportation time interval as follows:
taking a driver as an example, and importing a preset processing model by using a matching analysis coefficient of the driver;
the processing model acquires an analysis coefficient interval containing the matching analysis coefficient according to the matching analysis coefficient;
if the multiple groups of preset analysis coefficient intervals do not contain corresponding matching analysis coefficients, the personal state of the driver is not in accordance with the requirement of safe driving, so that logistics transportation cannot be carried out;
then taking the corresponding transportation time interval in the acquired analysis coefficient interval as the transportation time interval of the driver;
the data matching unit is used for counting the estimated transportation time of the corresponding new transportation order according to the estimated starting time and the estimated ending time of the plurality of new transportation orders, then respectively matching the transportation time intervals of each driver obtained by the data processing unit with the estimated transportation time of each new transportation order, then generating a corresponding order matching table according to the matching result, and then sending the order matching table to the data display unit;
the matching mode is as follows:
take one driver and one new transport order as an example;
if the estimated transportation time of the new transportation order is less than or equal to the maximum value in the transportation time interval of the driver, the driver can receive the new transportation order;
if the estimated transportation time of the new transportation order is greater than the maximum value in the transportation time interval of the driver, the driver cannot receive the new transportation order;
the data display unit is used for displaying an order matching table, so that each driver can observe a new transportation order which needs to be executed by himself;
according to the embodiment, the new transportation orders are combined with the historical transportation orders of all drivers, the matching analysis coefficient of each driver for receiving the new transportation orders is analyzed, then the transportation time interval corresponding to each driver is obtained through the data processing unit, then each driver is matched with each new transportation order through the data matching unit, the problems that long-term transportation is carried out and a driver master with good rest is not obtained are solved, long-distance new transportation orders are received again, and therefore large transportation potential safety hazards exist, meanwhile safe transportation of agricultural products is effectively guaranteed, and good circulation of a supply chain is guaranteed.
As embodiment II of the present invention
The embodiment expands the data matching unit in the first embodiment based on the first embodiment, and the specific expansion content is as follows:
when each driver is matched with each new transport order, firstly sequencing the estimated transport time of each new transport order according to the sequence from big to small to obtain a priority allocation table, then in the priority allocation table, matching each new transport order with the transport time interval of each driver one by one according to the sequence from front to back, wherein after the driver with a large transport time interval is matched with the new transport order with a short estimated transport time, the driver with a small subsequent transport time interval cannot be matched with the new transport order with a long estimated transport time, and further partial new transport orders cannot be rapidly delivered, so that the logistics transport efficiency in an agricultural product supply chain is affected;
embodiment III as the present invention
The embodiment further comprises an abnormality early-warning unit on the basis of the first embodiment, and the abnormality early-warning unit is in communication connection with the data acquisition unit;
the abnormal early warning unit is used for acquiring the driver state information acquired by the data acquisition unit, and then triggering an audible and visual prompt alarm in real time according to the state abnormal information in the driver state information, wherein the audible and visual prompt alarm technology is the prior art, so that the description is omitted. According to the embodiment, the information of normal state and abnormal state of the driver is obtained through analysis of the data acquisition unit, and then the real-time state monitoring is carried out on the driver of the transport vehicle, so that the driving state of the vehicle in the agricultural product transport process can be timely known, the problem of the abnormal state can be timely alarmed according to the monitoring, the driver is reminded to drive the vehicle to a safe rest area for rest, traffic accidents caused by fatigue driving of the driver are avoided, and meanwhile, the cargo loss caused by the traffic accidents caused by fatigue driving of an agricultural product supply chain is avoided.
Fourth embodiment of the invention
Referring to fig. 1, this embodiment is implemented by fusing the first embodiment, the second embodiment and the third embodiment.
And all that is not described in detail in this specification is well known to those skilled in the art.
The foregoing describes one embodiment of the present invention in detail, but the disclosure is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (10)
1. An agricultural product supply chain information management system, comprising:
the data acquisition unit is used for acquiring a plurality of new transportation orders of agricultural products to be transported and a plurality of historical transportation orders of each driver, and then sending the new transportation orders and the historical transportation orders to the data analysis unit; the new transportation orders comprise estimated starting time and estimated ending time of the orders, and the estimated starting time of the plurality of new transportation orders is the same; the historical transportation orders comprise the starting time and the ending time of each order and the driver state information during corresponding transportation, wherein the driver state information acquires a monitoring video of a driver through a monitoring probe arranged on a transportation vehicle, and then the monitoring video is analyzed to obtain information of normal mental state and abnormal state of the driver;
the data analysis unit is used for combining the new transportation orders with the historical transportation orders of each driver to obtain the transportation duration of each historical transportation order and the interval duration between two adjacent historical transportation orders, combining the transportation duration and the interval duration with the number of the historical transportation orders with abnormal states, calculating the matching analysis coefficient of each driver for receiving the new transportation orders, and then sending the matching analysis coefficient to the data processing unit;
the data processing unit is used for importing the matching analysis coefficients of each driver into a preset processing model, then the processing model matches the corresponding analysis coefficient interval according to the matching analysis coefficients of each driver, the corresponding transportation time interval is obtained, and then the transportation time interval corresponding to each driver is sent to the data matching unit; the processing model comprises a plurality of groups of preset analysis coefficient intervals, and the analysis intervals are correspondingly provided with a group of transportation time intervals;
the data matching unit is used for counting the estimated transportation time of the corresponding new transportation order according to the estimated starting time and the estimated ending time of the plurality of new transportation orders, then respectively matching the transportation time intervals of the drivers obtained by the data processing unit with the estimated transportation time of the new transportation orders, and then generating a corresponding order matching table according to the matching result.
2. The agricultural product supply chain information management system according to claim 1, wherein the driver status information is obtained as follows:
AS1, dismantling a monitoring video into a plurality of video frames according to the time sequence, wherein the interval time among the video frames is the same, and then importing the video frames into a pre-trained state analysis model;
AS2, the state analysis model obtains the facial feature vector of the facial outline of the driver through a face recognition technology, wherein the face recognition technology is realized in any one recognition mode of a geometric feature-based recognition method, an algebraic feature-based recognition method and a connection mechanism-based recognition method;
AS3, then, the state analysis model performs similarity analysis on the feature vectors of the five sense organs in each video frame and the feature vector samples trained in advance to obtain feature similarity of the feature vectors of the five sense organs and the feature vector samples; the feature vector samples are obtained by training and analyzing the feature vectors of the five sense organs under a plurality of pre-extracted abnormal states;
AS4, then comparing the feature similarity with a preset similarity threshold by a state analysis model:
if the feature similarity is greater than or equal to a similarity threshold, extracting a corresponding video frame, marking the video frame as an abnormal frame, and then entering the next step;
if the feature similarity is greater than or equal to a similarity threshold, deleting the corresponding video frame;
AS5, sequencing all video frames according to the time sequence, and generating a frame table;
then extracting the number of deleted video frames between each group of adjacent abnormal frames according to the marks of the abnormal frames in a frame table, and marking the deleted video frames as SCi, wherein i=1, 2, … …, n and n represent the number of the adjacent abnormal frames, and i represents the number of the adjacent abnormal frames;
AS6, then comparing the SCi with a preset contrast threshold SCy 1;
if SCi is less than SCy1, judging that the state of the driver at the corresponding moment is abnormal, wherein the state abnormality indicates that the driver has dangerous driving behaviors.
3. The agricultural product supply chain information management system according to claim 2, wherein in step AS6, if SCi is equal to or greater than SC0, a corresponding i is obtained, i-1 is then made, SCi-1 and SCi are added to obtain a secondary analysis value SC0, and then SC0 is compared with a preset comparison threshold SCy 2:
when SC0 is less than SCy2, the state of the driver at the corresponding moment is judged to be abnormal, otherwise, the state of the driver at the corresponding moment is judged to be normal.
4. The agricultural product supply chain information management system according to claim 1, wherein the specific analysis mode of the data analysis unit is as follows:
SS1, selecting a driver, and acquiring m historical transportation orders of the driver in a period close to the current period;
SS2, counting the transportation time length of each historical transportation order according to the starting time and the ending time of m historical transportation orders, and marking the transportation time length as Y1, Y2 and … … Ym respectively;
SS3, acquiring interval duration between two adjacent orders in the n historical transportation orders according to the starting time and the ending time of the m historical transportation orders, and marking the interval duration as G1, G2, … … and Gm-1 respectively; meanwhile, according to the ending time of one historical transport order closest to the current period, calculating the interval duration between the two orders by combining with the estimated starting time of the new transport order, and marking the interval duration as Gm;
SS4, acquiring historical transportation orders containing state abnormality from driver state information of m historical transportation orders, counting the number of the historical transportation orders containing state abnormality, and marking the number as Z;
SS5, then pass throughAnd obtaining a matching analysis coefficient P of the driver for receiving the corresponding order, wherein beta 1, beta 2 and beta 3 are all preset proportional coefficients.
5. The agricultural product supply chain information management system of claim 1, wherein the data processing unit acquires the driver transportation time interval in the following manner:
selecting a driver, and firstly, importing a matching analysis coefficient of the driver into a preset processing model;
then the processing model acquires an analysis coefficient interval containing the matched analysis coefficient according to the matched analysis coefficient;
and finally, taking the corresponding transportation time interval in the acquired analysis coefficient interval as the transportation time interval of the driver.
6. The agricultural product supply chain information management system according to claim 5, wherein if none of the plurality of sets of predetermined analysis coefficient intervals includes a corresponding matching analysis coefficient, the personal status of the driver is indicated to be not in compliance with the safety driving requirement.
7. The agricultural product supply chain information management system of claim 1, wherein the data matching unit is matched in the following manner:
selecting a driver and a new transportation order;
if the estimated transportation time of the new transportation order is less than or equal to the maximum value in the transportation time interval of the driver, the driver can receive the new transportation order;
if the estimated time of the new transportation order is greater than the maximum value of the driver's transportation time interval, it indicates that the driver cannot receive the new transportation order.
8. The agricultural product supply chain information management system according to claim 7, wherein the data matching unit, when matching each driver with each new transportation order, first ranks the estimated transportation times of each new transportation order in order from the large to the small to obtain a priority allocation table, and then matches each new transportation order with the transportation time interval of each driver one by one in order from the front to the rear in the priority allocation table.
9. The agricultural product supply chain information management system of claim 1, wherein: the system also comprises a data display unit for displaying the order matching table.
10. The agricultural product supply chain information management system of claim 1, wherein: the system also comprises an abnormality early warning unit which is used for acquiring the driver state information acquired by the data acquisition unit and triggering an audible and visual prompt alarm according to the state abnormality information in the driver state information.
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| US20170154394A1 (en) * | 2014-07-24 | 2017-06-01 | Pulsar Informatics, Inc. | Fatigue Optimized Routing & Tracking |
| KR20200076584A (en) * | 2019-11-20 | 2020-06-29 | 브이씨엔씨(주) | Method and Server for compulsive allocation of car considering driver's fatigue |
| CN111738570A (en) * | 2020-06-05 | 2020-10-02 | 苏州华易通物流有限公司 | Logistics management system based on internet |
| CN114049059A (en) * | 2021-09-30 | 2022-02-15 | 马鞍山钢晨钢铁物流园有限公司 | Goods delivery intelligent management system for logistics |
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|---|---|---|---|---|
| US20170154394A1 (en) * | 2014-07-24 | 2017-06-01 | Pulsar Informatics, Inc. | Fatigue Optimized Routing & Tracking |
| KR20200076584A (en) * | 2019-11-20 | 2020-06-29 | 브이씨엔씨(주) | Method and Server for compulsive allocation of car considering driver's fatigue |
| CN111738570A (en) * | 2020-06-05 | 2020-10-02 | 苏州华易通物流有限公司 | Logistics management system based on internet |
| CN114049059A (en) * | 2021-09-30 | 2022-02-15 | 马鞍山钢晨钢铁物流园有限公司 | Goods delivery intelligent management system for logistics |
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