CN114049059A - Goods delivery intelligent management system for logistics - Google Patents
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Abstract
The invention discloses an intelligent management system for logistics cargo delivery, and relates to the technical field of logistics delivery; the system comprises a cargo matching unit, an evaluation unit, a line planning unit and a driver matching unit; the goods matching unit is used for matching the goods which meet the transportation conditions on the optimal path; the evaluation unit is used for evaluating the priority of the goods meeting the transportation condition; the route planning unit is used for generating an optimal route for the goods subjected to priority evaluation; the driver matching unit is used for arranging drivers to transport cargos which accord with the transportation conditions on the optimal path, the optimal path and the optimal drivers can be selected to transport the cargos through the cargo matching unit and the driver matching unit, the cargo transportation speed is the fastest, the safety of the drivers is guaranteed, and fatigue driving is avoided.
Description
Technical Field
The invention relates to the technical field of logistics distribution, in particular to an intelligent management system for logistics cargo distribution.
Background
Since the 21 st century, the logistics industry has been a rapid development as an emerging industry, and is considered as a third profit source after the profit sources of two enterprises, namely resource consumption reduction and labor productivity improvement. The logistics distribution vehicle path problem arouses the high attention of disciplinary experts such as operational research, management, computer application, graph theory and the like. Specifically, according to customer requirements, on the premise of meeting time limit, vehicle load capacity limit, mileage limit and the like, a reasonable route for the delivery vehicle to travel is designed, so that multiple targets such as shortest time, shortest distance to travel, lowest cost, high vehicle utilization rate and the like are achieved in the delivery process, and finally the customer requirements are met.
However, most of the technologies adopted for solving the logistics distribution vehicle path problem are heuristic methods, repeated iteration is needed for finding the optimal solution, or multiple solutions are needed for comparative optimization, meanwhile, in the prior art, whether drivers capable of driving in generation exist on the optimal route is often ignored for finding the optimal route, when the drivers meet emergency situations, the drivers can find the appropriate driving in generation in time for driving, and loss caused in the transportation process is avoided.
Disclosure of Invention
The invention aims to provide an intelligent management system for cargo delivery for logistics, which is used for solving the problems of how to ensure that different cargos arrive at the optimal time and avoid fatigue driving of a driver in the transportation process in the conventional logistics technology.
The purpose of the invention can be realized by the following technical scheme:
the intelligent management system for logistics cargo distribution comprises a cargo matching unit, an evaluation unit, a line planning unit and a driver matching unit;
the goods matching unit is used for matching the goods which meet the transportation conditions on the optimal path;
the evaluation unit is used for evaluating the priority of the goods meeting the transportation condition;
the route planning unit is used for generating an optimal route for the goods subjected to priority evaluation;
the driver matching unit is used for arranging drivers to transport the cargos which accord with the transportation conditions on the optimal path.
Further, the driver matching unit is used for arranging the driver to transport the goods meeting the transport condition on the optimal path, and comprises the following steps: acquiring driver face video image information and a driving duration value, and acquiring a driver fatigue driving value through a fatigue driving model; selecting a driver with a fatigue driving value smaller than the optimal path fatigue driving threshold value, and sending a driving request to the driver; and selecting the driver which firstly receives the driving request for transportation.
Further, the fatigue driving model specifically includes:
whether fatigue driving exists or not is judged through face recognition, and the method comprises the following steps: when the driver has the situations of nodding or/and frowning and yawning, the fatigue driving score of the driver is increased by A score; when the driver shakes the head left and right, the fatigue driving score of the driver is increased by B; when the driver falls asleep or/and eyes are not opened at all, the fatigue driving score of the driver is increased by the score C. Acquiring a historical condition value of a traffic accident of the optimal path through an Internet of things platform; and adding the historical condition value of the traffic accident of the optimal path into the fatigue driving part of the driver to obtain the fatigue driving value of the driver.
Further, the optimal path fatigue driving threshold value is specifically obtained by acquiring a current driving duration value of a driver, a historical condition value of a traffic accident on the optimal path and a historical condition value of a traffic accident on a preset driving route; adding the driving time length value, the historical condition value of the traffic accident on the optimal path and the historical condition value of the traffic accident on the preset driving route to obtain a predicted driving threshold value; multiplying the predicted driving threshold value by the optimal path driver value to obtain an optimal path fatigue driving threshold value; and the optimal path driver value is the sum of the vehicles running on the optimal path multiplied by a preset proportional adjustment coefficient.
Further, the historical value of the traffic accident occurring on the optimal path is specifically as follows: acquiring initial intersection information, cut-off intersection information and path intersection information of the optimal path; the system comprises a starting intersection, an ending intersection and an approach intersection, wherein the starting intersection information, the ending intersection information and the approach intersection information comprise the number of traffic accidents of road sections between adjacent intersections and the occurrence time of the road sections; acquiring the number of days with the largest interval with the current time in the occurrence time as the reference number of days; and dividing the number of the car accidents by the reference number of days to obtain the historical condition value of the car accidents on the optimal path.
Further, the preset historical situation value of the traffic accident on the driving route is specifically as follows: acquiring alternative starting intersection information, alternative ending intersection information and alternative path intersection information of alternative paths; the alternative starting intersection information, the alternative cut-off intersection information and the alternative path intersection information comprise alternative car accident times of alternative road sections between adjacent alternative intersections and alternative occurrence time of the alternative road sections;
acquiring the number of days with the largest interval with the current time in the alternative occurrence time as the alternative reference number of days; and dividing the alternative car accident frequency by the alternative reference days to obtain the historical condition value of the car accident on the preset driving route.
Further, the evaluation unit is configured to perform priority evaluation on the cargo meeting the transportation condition, specifically: acquiring the total quantity Dz and daily consumption Dr of the goods at the client port of the buyer, and calculating the number Rs of the goods used at the client port of the buyer;
acquiring the delivery position coordinates of a customer port of a buyer, marking the delivery position coordinates as cut-off intersection information or alternative cut-off intersection information, and marking the distance as Jl; acquiring the average transportation speed of the goods, and marking the average transportation speed of the goods as Si; calculating the transport day number R of the goods by using a formula R ═ Jl/Si; obtaining the goods consumption Dx of the buyer during the goods transportation days by using the formula Dx-RxDr; acquiring the goods storage quantity of a client port of a buyer, marking the goods storage quantity as Cl, comparing the goods storage quantity Cl with the goods consumption Dx during the transportation days of the goods, and marking the client port of the buyer as normal delivery if Cl is more than Dx + T2; when Cl is less than or equal to Dx + T2, the buyer client port selects an urgent delivery terminal and performs preferential delivery; wherein T2 represents extra consumables, which include inferior spoiled goods, artificially spoiled goods and external factor spoiled goods.
Further, the cut-off intersection information and the alternative cut-off intersection information are the same intersection.
Further, the cargo matching unit for matching the cargo meeting the transportation condition on the optimal path includes: acquiring road conditions corresponding to the optimal path, wherein the road conditions comprise asphalt road, cement road and dirt road; and selecting the road condition to be matched with a preset transportation table, and outputting the goods successfully matched as the goods which can be transported, wherein the preset transportation table is preset.
Further, the generating of the optimal path for the cargo subjected to the priority evaluation by the route planning unit specifically includes: and acquiring delivery position coordinates and delivery addresses of customer ports of buyers, and acquiring uniform speed routes through map software, wherein the route with the shortest distance is an optimal route, and the rest routes are alternative routes.
Compared with the prior art, the invention has the beneficial effects that:
(1) the optimal route and the optimal driver can be selected to transport the goods through the goods matching unit and the driver matching unit, so that the fast speed of transporting the goods is ensured, the safety of the driver is ensured, and the fatigue driving is avoided;
(2) acquiring data of a client port of a buyer, comparing the material storage quantity Cl with the material consumption Dx during the material transportation days, and marking the client port of the buyer as a normal delivery buyer if Cl is more than Dx + T2; when Cl is less than or equal to Dx + T2, the buyer client port selects an urgent delivery terminal and performs preferential delivery; wherein, T2 represents extra consumption material, and extra consumption material includes that the inferior quality damages the material, artifical damage material and external factors damage the material for can carry out the route planning through consulting transit time and transport priority during the logistics transportation route planning, avoid the condition of overtime transport to take place.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects according to the present invention will be made with reference to the accompanying drawings and preferred embodiments.
Referring to fig. 1, the intelligent management system for cargo distribution for logistics includes a cargo matching unit, an evaluation unit, a route planning unit, and a driver matching unit;
the goods matching unit is used for matching the goods which meet the transportation conditions on the optimal path; the evaluation unit is used for evaluating the priority of the goods meeting the transportation condition; the route planning unit is used for generating an optimal route for the goods subjected to priority evaluation; the driver matching unit is used for arranging drivers to transport the cargos which accord with the transportation conditions on the optimal path, and the transportation safety of the cargos is ensured.
The driver matching unit is used for arranging drivers to transport the cargos which accord with the transportation conditions on the optimal path, and comprises the following steps: acquiring driver face video image information and a driving duration value, and acquiring a driver fatigue driving value through a fatigue driving model; selecting a driver with a fatigue driving value smaller than the optimal path fatigue driving threshold value, and sending a driving request to the driver; and selecting the driver which firstly receives the driving request for transportation, and ensuring the fairness of the selected driver.
The fatigue driving model specifically comprises the following steps: whether fatigue driving exists or not is judged through face recognition, and the method comprises the following steps: when the driver has the situations of nodding or/and frowning and yawning, the fatigue driving score of the driver is increased by A score; when the driver shakes the head left and right, the fatigue driving score of the driver is increased by B; when the driver falls asleep or/and eyes are not opened at all, the fatigue driving score of the driver is increased by the score C. Acquiring a historical condition value of a traffic accident of the optimal path through an Internet of things platform; and adding the historical condition value of the traffic accident of the optimal path into the fatigue driving part of the driver to obtain a fatigue driving value of the driver, and performing subsequent selection work on the basis of the fatigue driving value.
The optimal path fatigue driving threshold value is specifically obtained by acquiring a current driving time value of a driver, a historical condition value of a traffic accident on the optimal path and a historical condition value of a traffic accident on a preset driving route; adding the driving time length value, the historical condition value of the traffic accident on the optimal path and the historical condition value of the traffic accident on the preset driving route to obtain a predicted driving threshold value; multiplying the predicted driving threshold value by the optimal path driver value to obtain an optimal path fatigue driving threshold value; and the optimal path driver value is the sum of the vehicles running on the optimal path multiplied by a preset proportional adjustment coefficient.
The historical situation value of the traffic accident of the optimal path is specifically as follows: acquiring initial intersection information, cut-off intersection information and path intersection information of the optimal path; the system comprises a starting intersection, an ending intersection and an approach intersection, wherein the starting intersection information, the ending intersection information and the approach intersection information comprise the number of traffic accidents of road sections between adjacent intersections and the occurrence time of the road sections; acquiring the number of days with the largest interval with the current time in the occurrence time as the reference number of days; and dividing the number of the car accidents by the reference number of days to obtain the historical condition value of the car accidents on the optimal path.
The preset historical condition value of the traffic accident on the driving route is specifically as follows: acquiring alternative starting intersection information, alternative ending intersection information and alternative path intersection information of alternative paths; the alternative starting intersection information, the alternative cut-off intersection information and the alternative path intersection information comprise alternative car accident times of alternative road sections between adjacent alternative intersections and alternative occurrence time of the alternative road sections; acquiring the number of days with the largest interval with the current time in the alternative occurrence time as the alternative reference number of days; and dividing the alternative car accident frequency by the alternative reference days to obtain the historical condition value of the car accident on the preset driving route.
In the specific implementation of the invention, the navigation APP can select a Baidu map, a Gaode map, an Tencent map, a dog search map and the like, and the specific implementation of the invention is not limited at all;
when the present invention is implemented, the data connection between the modules may include a wired communication component or a wireless communication component; the wired communication component can be a transmission line and a USB interface; the wireless communication component may include a Bluetooth module, a wifi module, a 3G/4G/5G module, etc.
The evaluation unit is used for evaluating the priority of the goods meeting the transportation condition, and specifically comprises the following steps: acquiring the total quantity Dz and daily consumption Dr of the goods at the client port of the buyer, and calculating the number Rs of the goods used at the client port of the buyer;
acquiring the delivery position coordinates of a customer port of a buyer, marking the delivery position coordinates as cut-off intersection information or alternative cut-off intersection information, and marking the distance as Jl; acquiring the average transportation speed of the goods, and marking the average transportation speed of the goods as Si; calculating the transport day number R of the goods by using a formula R ═ Jl/Si; obtaining the goods consumption Dx of the buyer during the goods transportation days by using the formula Dx-RxDr; acquiring the goods storage quantity of a client port of a buyer, marking the goods storage quantity as Cl, comparing the goods storage quantity Cl with the goods consumption Dx during the transportation days of the goods, and marking the client port of the buyer as normal delivery if Cl is more than Dx + T2; when Cl is less than or equal to Dx + T2, the buyer client port selects an urgent delivery terminal and performs preferential delivery; wherein, T2 represents extra consumption goods, the extra consumption goods include inferior damaged goods, artificially damaged goods and damaged goods caused by external factors, and the cut-off intersection information and the alternative cut-off intersection information are the same intersection.
The cargo matching unit is used for matching the cargo meeting the transportation condition on the optimal path and comprises the following steps: acquiring road conditions corresponding to the optimal path, wherein the road conditions comprise asphalt road, cement road and dirt road; and selecting the road condition to be matched with a preset transportation table, and outputting the goods successfully matched as the goods which can be transported, wherein the preset transportation table is preset.
The route planning unit is configured to generate an optimal route for the cargo subjected to priority evaluation, specifically: and acquiring delivery position coordinates and delivery addresses of customer ports of buyers, and acquiring uniform speed routes through map software, wherein the route with the shortest distance is an optimal route, and the rest routes are alternative routes.
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. The intelligent management system for logistics cargo distribution is characterized by comprising a cargo matching unit, an evaluation unit, a line planning unit and a driver matching unit;
the goods matching unit is used for matching the goods which meet the transportation conditions on the optimal path;
the evaluation unit is used for evaluating the priority of the goods meeting the transportation condition;
the route planning unit is used for generating an optimal route for the goods subjected to priority evaluation;
the driver matching unit is used for arranging drivers to transport the cargos which accord with the transportation conditions on the optimal path.
2. The intelligent management system for cargo distribution for logistics according to claim 1, wherein the driver matching unit is configured to arrange the driver to transport the cargo meeting the transportation condition on the optimal path, and comprises:
acquiring driver face video image information and a driving duration value, and acquiring a driver fatigue driving value through a fatigue driving model;
selecting a driver with a fatigue driving value smaller than the optimal path fatigue driving threshold value, and sending a driving request to the driver;
and selecting the driver which firstly receives the driving request for transportation.
3. The intelligent management system for logistics cargo delivery according to claim 2, wherein the fatigue driving model is specifically:
whether fatigue driving exists or not is judged through face recognition, and the method comprises the following steps:
when the driver has the situations of nodding or/and frowning and yawning, the fatigue driving score of the driver is increased by A score;
when the driver shakes the head left and right, the fatigue driving score of the driver is increased by B;
when the driver falls asleep or/and eyes are not opened at all, the fatigue driving score of the driver is increased by C score;
acquiring a historical condition value of a traffic accident of the optimal path through an Internet of things platform;
and adding the historical condition value of the traffic accident of the optimal path into the fatigue driving part of the driver to obtain the fatigue driving value of the driver.
4. The intelligent management system for logistics cargo delivery according to claim 2, wherein the optimal path fatigue driving threshold is specifically:
acquiring a current driving time value of a driver, a historical condition value of a traffic accident on the optimal path and a historical condition value of a traffic accident on a preset driving route;
adding the driving time length value, the historical condition value of the traffic accident on the optimal path and the historical condition value of the traffic accident on the preset driving route to obtain a predicted driving threshold value;
multiplying the predicted driving threshold value by the optimal path driver value to obtain an optimal path fatigue driving threshold value;
and the optimal path driver value is the sum of the vehicles running on the optimal path multiplied by a preset proportional adjustment coefficient.
5. The intelligent management system for cargo delivery for logistics according to claim 4, wherein the historical value of the car accident occurring on the optimal path is specifically:
acquiring initial intersection information, cut-off intersection information and path intersection information of the optimal path; the system comprises a starting intersection, an ending intersection and an approach intersection, wherein the starting intersection information, the ending intersection information and the approach intersection information comprise the number of traffic accidents of road sections between adjacent intersections and the occurrence time of the road sections;
acquiring the number of days with the largest interval with the current time in the occurrence time as the reference number of days;
and dividing the number of the car accidents by the reference number of days to obtain the historical condition value of the car accidents on the optimal path.
6. The intelligent management system for cargo delivery for logistics according to claim 4, wherein the historical situation values of the car accident occurring on the preset driving route are specifically:
acquiring alternative starting intersection information, alternative ending intersection information and alternative path intersection information of alternative paths; the alternative starting intersection information, the alternative cut-off intersection information and the alternative path intersection information comprise alternative car accident times of alternative road sections between adjacent alternative intersections and alternative occurrence time of the alternative road sections;
acquiring the number of days with the largest interval with the current time in the alternative occurrence time as the alternative reference number of days;
and dividing the alternative car accident frequency by the alternative reference days to obtain the historical condition value of the car accident on the preset driving route.
7. The intelligent management system for logistics cargo delivery according to claim 1, wherein the evaluation unit is configured to perform priority evaluation on the cargo meeting the transportation condition, specifically:
acquiring the total quantity Dz and daily consumption Dr of the goods at the client port of the buyer, and calculating the number Rs of the goods used at the client port of the buyer;
acquiring the delivery position coordinates of a customer port of a buyer, marking the delivery position coordinates as cut-off intersection information or alternative cut-off intersection information, and marking the distance as Jl;
acquiring the average transportation speed of the goods, and marking the average transportation speed of the goods as Si;
calculating the transport day number R of the goods by using a formula R ═ Jl/Si;
obtaining the goods consumption Dx of the buyer during the goods transportation days by using the formula Dx-RxDr;
acquiring the goods storage quantity of a client port of a buyer, marking the goods storage quantity as Cl, comparing the goods storage quantity Cl with the goods consumption Dx during the transportation days of the goods, and marking the client port of the buyer as normal delivery if Cl is more than Dx + T2;
when Cl is less than or equal to Dx + T2, the buyer client port selects an urgent delivery terminal and performs preferential delivery;
wherein T2 represents extra consumables, which include inferior spoiled goods, artificially spoiled goods and external factor spoiled goods.
8. The intelligent management system for logistics cargo delivery according to claim 7, wherein the intersection information and the alternative intersection information are the same intersection.
9. The intelligent management system for cargo distribution for logistics according to claim 1, wherein the cargo matching unit for matching the cargo meeting the transportation condition on the optimal path comprises:
acquiring road conditions corresponding to the optimal path, wherein the road conditions comprise asphalt road, cement road and dirt road;
and selecting the road condition to be matched with a preset transportation table, and outputting the goods successfully matched as the goods which can be transported, wherein the preset transportation table is preset.
10. The intelligent management system for logistics cargo delivery according to claim 1, wherein the route planning unit is configured to generate an optimal path for the cargo subjected to priority evaluation, specifically:
and acquiring delivery position coordinates and delivery addresses of customer ports of buyers, and acquiring uniform speed routes through map software, wherein the route with the shortest distance is an optimal route, and the rest routes are alternative routes.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117079220A (en) * | 2023-10-13 | 2023-11-17 | 凌雄技术(深圳)有限公司 | Supply chain intelligent supervision system and method based on Internet of things |
| CN117332979A (en) * | 2023-07-20 | 2024-01-02 | 河南建设产业投资有限公司 | Agricultural product supply chain information management system |
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2021
- 2021-09-30 CN CN202111160919.3A patent/CN114049059A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117332979A (en) * | 2023-07-20 | 2024-01-02 | 河南建设产业投资有限公司 | Agricultural product supply chain information management system |
| CN117332979B (en) * | 2023-07-20 | 2024-05-10 | 河南建设产业投资有限公司 | Agricultural product supply chain information management system |
| CN117079220A (en) * | 2023-10-13 | 2023-11-17 | 凌雄技术(深圳)有限公司 | Supply chain intelligent supervision system and method based on Internet of things |
| CN117079220B (en) * | 2023-10-13 | 2023-12-26 | 凌雄技术(深圳)有限公司 | Supply chain intelligent supervision system and method based on Internet of things |
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