Logistics transportation intelligent scheduling method and system, computer equipment and medium

Abstract

The invention discloses a logistics transportation intelligent scheduling method and system, computer equipment and a medium. Receiving an order sending request, obtaining goods collection position information, and taking a goods collection position as a reference origin (x0, y0); searching information of vehicles meeting transportation requirements, positioning coordinate points of the positions of the vehicles, and taking the coordinate points with x -x < = z and y -y < = z as alternative points; planning a driving route between the reference origin and each alternative point, and respectively calculating a driving distance; according to the driving distance and the real-time road condition, respectively calculating the driving time from the reference original point to each alternative point; respectively calculating the execution capability E of each alternative point according to each driving distance and each driving time; and a vehicle corresponding to the alternative point corresponding to the Emin is selected to go to a goods collection position to wait for goods collection. According to the method, factors such as the physical distance, the driving distance and the driving time are integrated, analyzed and searched for the vehicle with the optimal execution capacity, interference of the overall calculation result is balanced through weight distribution, and the efficiency and precision of vehicle scheduling are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics cargo collection and scheduling, and in particular to a logistics transportation intelligent scheduling method, system, computer equipment and medium. Background Art

[0002] The operation of logistics not only determines the overall operating costs of logistics companies, but also directly affects the stability and balance of the operation of the entire logistics system; and logistics transportation scheduling is one of the core activities of logistics.

[0003] The efficiency of logistics and transportation scheduling directly affects logistics costs and vehicle transportation cycles. Due to the diversity and complexity of scheduling, traditional manual scheduling methods are difficult to fully balance the dynamic demands of distance, time, and energy costs. If there is a problem in any link, it will bring about a series of adverse effects. Summary of the Invention

[0004] In order to solve the technical problems existing in the background technology, the present invention proposes a logistics transportation intelligent scheduling method, system, computer equipment and medium.

[0005] The present invention proposes an intelligent logistics transportation scheduling method, comprising:

[0006] S1. Receive a dispatch request, obtain the cargo collection location information, and use the cargo collection location as the reference origin (x0, y0);

[0007] S2, search for vehicle information that meets the transportation requirements, locate the coordinate points of each vehicle position, and convert |x i -x0|≤z and|y i -y0|≤z coordinate point is selected as the candidate point; i is the horizontal coordinate value of the vehicle position that meets the transportation requirements, y i is the ordinate value of the vehicle position that meets the transportation requirements, and z is the preset value;

[0008] S3, plan the driving route between the reference origin and each alternative point, and calculate the driving distance S1, S2...S respectively n ;

[0009] S4. Calculate the travel time t1, t2, ..., t from the reference origin to each alternative point based on the travel distance and real-time road conditions. n ;

[0010] S5. Calculate the execution capability E of each candidate point based on each driving distance and each driving time. j =alog2S j +bt j ; Where a and b are preset weight values, j∈n;

[0011] S6. Select E min The corresponding candidate point is used as the target point, and the vehicle corresponding to the target point is selected to go to the collection location to wait for collection; the E min =min(E1,E2,…,E n ).

[0012] Optionally, in step S2, when the number of candidate points is greater than a preset value D max When the number of candidate points is less than the preset value D, adjust z to z', z'=cz; min When , adjust z to z″, z″=dz; where 0<c<1, d>1.

[0013] Optionally, in step S3, when planning the driving distance between the reference origin and the candidate point k, if the difference between the driving distances of the h planned routes is less than a preset value S min When the h planned routes are retained, the travel distance is recorded as S k1 、S k2 …S kh ; where k∈n, h≥2.

[0014] Optionally, in step S4, when calculating the travel time between the reference origin and the candidate point k, the travel time t is calculated based on the h planned routes and the real-time road conditions. k1 , t k2 …t kh ; where k∈n, h≥2.

[0015] Optionally, in step S5, the execution capability E of the candidate point k k =min(E k1 ,E k2 …E kh ), E k1 =alog2S k1 +bt k1 , E k2 =alog2S k2 +bt k2 , E kh =alog2S kh +bt kh ; where k∈n, h≥2.

[0016] The present invention also proposes an intelligent logistics and transportation scheduling system, comprising:

[0017] The reference location analysis unit receives the dispatch request, obtains the cargo collection location information, and uses the cargo collection location as the reference origin (x0, y0);

[0018] The vehicle position analysis unit searches for vehicle information that meets the transportation requirements, locates the coordinate points of each vehicle position, and i -x0|≤z and|y i -y0|≤z coordinate point is selected as the candidate point; i is the horizontal coordinate value of the vehicle position that meets the transportation requirements, y i is the ordinate value of the vehicle position that meets the transportation requirements, and z is the preset value;

[0019] The driving distance analysis unit plans the driving route between the reference origin and each alternative point, and calculates the driving distance S1, S2...S respectively. n ;

[0020] The driving time analysis unit calculates the driving time t1, t2…t from the reference origin to each alternative point based on the driving distance and real-time road conditions. n ;

[0021] The execution capability analysis unit calculates the execution capability E of each candidate point according to each driving distance and each driving time. j =alog2S j +bt j ; Where a and b are preset weight values, j∈n;

[0022] Target vehicle selection unit, select E min The corresponding candidate point is used as the target point, and the vehicle corresponding to the target point is selected to go to the collection location to wait for collection; the E min =min(E1,E2,…,E n ).

[0023] Optionally, when the vehicle position analysis unit selects candidate points, when the number of candidate points is greater than a preset value D max When the number of candidate points is less than the preset value D, adjust z to z', z'=cz; min When , adjust z to z″, z″=dz; where 0<c<1, d>1.

[0024] Optionally, when the driving distance analysis unit plans the driving distance between the reference origin and the candidate point k, if the difference between the driving distances of the h planned routes is less than a preset value S min When the h planned routes are retained, the travel distance is recorded as S k1 、S k2 …S kh ;

[0025] When the driving time analysis unit calculates the driving time between the reference origin and the candidate point k, the driving time t is calculated based on the h planned routes and the real-time road conditions. k1 , tk2 …t kh ;

[0026] The execution capability analysis unit calculates the execution capability E of the candidate point k k =min(E k1 ,E k2 …E kh ), E k1 =alog2S k1 +bt k1 , E k2 =alog2S k2 +bt k2 , E kh =alog2S kh +bt kh ;

[0027] Among them, k∈n, h≥2.

[0028] The present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the intelligent logistics transportation scheduling method when executing the computer program.

[0029] The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the intelligent logistics transportation scheduling method is implemented.

[0030] It can be seen from the above solutions that the present application provides at least the following beneficial effects compared with the prior art:

[0031] Taking into account multiple factors such as location information, driving routes, driving time, and real-time road conditions, an intelligent algorithm is used to gradually analyze and find vehicles with the best execution capabilities, comprehensively balance the needs between distance, time, and energy costs, and improve the efficiency and accuracy of vehicle collection and dispatch. Specifically: This application first includes each vehicle close to the collection location as an alternative, and then plans the driving route of each vehicle to the collection location separately, and then combines the real-time road conditions to determine the driving time of each vehicle. Finally, weights are assigned to the two factors of driving distance and driving time to ensure the relative importance of the above two factors to the calculation results of the execution capability of each alternative vehicle, avoid the low accuracy and low reliability when using a single factor, and improve the rationality and scientificity of dispatch allocation. Furthermore, when there are too many or too few alternative points, this application promptly adjusts the size of the preset value to avoid too many or too few alternative points affecting the calculation accuracy of the algorithm, thereby improving the accuracy of the calculation results to ensure the accuracy of vehicle dispatch. When planning the driving distance between a single alternative point and a reference origin, this application lists multiple planned routes with similar distances as candidate routes, and then calculates the driving time based on the real-time traffic conditions of each planned route, thereby increasing the breadth of route selection and avoiding the loss caused by having to abandon the alternative point when traffic problems occur on a single planned route. This application comprehensively analyzes factors that affect vehicle scheduling efficiency, such as physical distance, driving route, driving distance, and driving time, and uses weight distribution to balance the impact of various influencing factors on the overall calculation results, reducing the interference of single factors on the overall results, and fully improving the efficiency and accuracy of vehicle scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flowchart of an intelligent logistics transportation scheduling method;

[0033] Figure 2 This is a module block diagram of an intelligent logistics transportation scheduling system. DETAILED DESCRIPTION

[0034] like Figure 1 As shown, Figure 1 This is a flow chart of an intelligent logistics transportation scheduling method proposed by the present invention.

[0035] Reference Figure 1 The intelligent logistics transportation scheduling method proposed by the present invention includes:

[0036] S1. Receive a dispatch request, obtain the cargo collection location information, and use the cargo collection location as the reference origin (x0, y0);

[0037] This step can use the world coordinate system to determine the coordinate value of the reference origin; the dispatch request in this step includes at least the location information of the collection point, the weight and volume of the transported goods, the state of the transported goods (solid, liquid, etc.), the distance between the starting and ending points of the transportation, and other information.

[0038] S2, search for vehicle information that meets the transportation requirements, locate the coordinate points of each vehicle position, and convert |x i -x0|≤z and|y i -y0|≤z coordinate point is selected as the candidate point; i is the horizontal coordinate value of the vehicle position that meets the transportation requirements, y i is the ordinate value of the vehicle position that meets the transportation requirements, and z is the preset value;

[0039] This step determines the type of truck required for this transport task based on information such as cargo weight and volume, cargo state (solid, liquid, etc.), and distance between the origin and destination points, ensuring targeted and accurate vehicle selection. The abscissa and ordinate values are used to calculate the straight-line distance between each vehicle's position and the collection location to select suitable vehicles near the collection location. This first step narrows the area of potential vehicles to be selected.

[0040] S3, plan the driving route between the reference origin and each alternative point, and calculate the driving distance S1, S2...S respectively n ;

[0041] S4. Calculate the travel time t1, t2, ..., t from the reference origin to each alternative point based on the travel distance and real-time road conditions. n ;

[0042] S5. Calculate the execution capability E of each candidate point based on each driving distance and each driving time. j =alog2S j +bt j ; Where a and b are preset weight values, j∈n;

[0043] S6. Select E min The corresponding candidate point is used as the target point, and the vehicle corresponding to the target point is selected to go to the collection location to wait for collection; the E min =min(E1,E2,…,E n ).

[0044] Based on the above method, this application first includes each vehicle close to the cargo collection location as an alternative, and then plans the driving route of each vehicle to the cargo collection location separately, and then determines the driving time of each vehicle in combination with the real-time road conditions. Finally, weights are assigned to the two factors of driving distance and driving time to ensure the relative importance of the above two factors to the calculation results of the execution capability of each alternative vehicle, avoid the low accuracy and low reliability when using a single factor, and improve the rationality and scientificity of scheduling allocation. By comprehensively analyzing the factors that affect the efficiency of vehicle scheduling, such as physical distance, driving route, driving distance, driving time, etc., and using weight allocation to balance the impact of each influencing factor on the overall calculation result, the interference of a single factor on the overall result is reduced, and the efficiency and accuracy of vehicle scheduling are fully improved.

[0045] In a further embodiment, in step S2, when the number of candidate points is greater than a preset value D max When the number of candidate points is less than the preset value D, adjust z to z', z'=cz; min , adjust z to z″, z″=dz; where 0<c<1, d>1. Thus, when there are too many or too few candidate points, the preset values can be adjusted appropriately to keep the number of candidate points within a reasonable range. This avoids computational redundancy caused by a large number of candidate points, while also preventing a small number of candidate points from reducing the accuracy of the results due to a small sample size. This improves the accuracy of the calculation results and ensures the precision of vehicle scheduling. If the number of candidate points is still not ideal after one adjustment, further adaptive adjustments can be made to c or d until the number of candidate points is within a reasonable range.

[0046] In a further embodiment, in step S3, when planning the driving distance between the reference origin and the candidate point k, if the difference between the driving distances of the h planned routes is less than the preset value S min When the h planned routes are retained, the travel distance is recorded as S k1 、S k2 …S kh In step S4, when calculating the travel time between the reference origin and the alternative point k, the travel time t is calculated based on the h planned routes and the real-time road conditions. k1 , t k2 …t kh In step S5, the execution capability E of the candidate point k k =min(E k1 ,E k2 …E kh ), E k1 =alog2S k1 +bt k1 , E k2 =alog2S k2 +bt k2, E kh =alog2S kh +bt kh Where k∈n and h≥2. When planning the driving distance between a single candidate point k and a reference origin, this embodiment lists multiple planned routes with very close actual distances as candidate routes. The travel time is then calculated based on the real-time traffic conditions of each planned route. This increases the breadth of route options between a single candidate point k and the reference origin, avoiding the loss caused by immediately abandoning the candidate point k when traffic congestion occurs on that route when only a single route is planned between the two points. Comprehensive planning further improves the accuracy and efficiency of vehicle scheduling.

[0047] Reference Figure 2 The present invention also proposes an intelligent logistics and transportation scheduling system, comprising:

[0048] The reference location analysis unit receives the dispatch request, obtains the cargo collection location information, and uses the cargo collection location as the reference origin (x0, y0);

[0049] The above operation can use the world coordinate system to determine the coordinate value of the reference origin; the dispatch request includes at least the location information of the collection point, the weight and volume of the transported goods, the state of the transported goods (solid, liquid, etc.), the distance between the starting and ending points of the transportation, and other information.

[0050] The vehicle position analysis unit searches for vehicle information that meets the transportation requirements, locates the coordinate points of each vehicle position, and i -x0|≤z and|y i -y0|≤z coordinate point is selected as the candidate point; i is the horizontal coordinate value of the vehicle position that meets the transportation requirements, y i is the ordinate value of the vehicle position that meets the transportation requirements, and z is the preset value;

[0051] The vehicle location analysis unit determines the truck type required for the transport task based on information such as the weight and volume of the cargo, the cargo state (solid, liquid, etc.), and the distance between the origin and destination points, ensuring targeted and accurate vehicle selection. Using the horizontal and vertical coordinates, the unit calculates the straight-line distance between each vehicle's location and the collection point to select suitable vehicles near the collection point, thus narrowing the area of potential vehicles.

[0052] The driving distance analysis unit plans the driving route between the reference origin and each alternative point, and calculates the driving distance S1, S2...S respectively. n ;

[0053] The driving time analysis unit calculates the driving time t1, t2…t from the reference origin to each alternative point based on the driving distance and real-time road conditions. n;

[0054] The execution capability analysis unit calculates the execution capability E of each candidate point according to each driving distance and each driving time. j =alog2S j +bt j ; Where a and b are preset weight values, j∈n;

[0055] Target vehicle selection unit, select E min The corresponding candidate point is used as the target point, and the vehicle corresponding to the target point is selected to go to the collection location to wait for collection; the E min =min(E1,E2,…,E n ).

[0056] This system first selects vehicles near the collection location as candidate vehicles based on the horizontal and vertical coordinate values of the world coordinate system. It then plans the routes for each vehicle to the collection location, and determines the travel time of each vehicle based on real-time road conditions. Finally, it assigns weights to the two factors of travel distance and travel time to ensure the relative importance of these two factors to the calculation results of the execution capability of each candidate vehicle, avoiding the low accuracy and low reliability of using a single factor, and improving the rationality and scientific nature of scheduling allocation. By comprehensively analyzing factors that affect vehicle scheduling efficiency, such as physical distance, travel route, travel distance, and travel time, and using weight allocation to balance the impact of each influencing factor on the overall calculation results, it reduces the interference of a single factor on the overall result and fully improves the efficiency and accuracy of vehicle scheduling.

[0057] In a further embodiment, when the vehicle position analysis unit selects candidate points, when the number of candidate points is greater than a preset value D max When the number of candidate points is less than the preset value D, adjust z to z', z'=cz; min , adjust z to z″, z″=dz; where 0<c<1, d>1. Thus, when there are too many or too few candidate points, the preset values can be adjusted appropriately to keep the number of candidate points within a reasonable range. This avoids computational redundancy caused by a large number of candidate points, while also preventing a small number of candidate points from reducing the accuracy of the results due to a small sample size. This improves the accuracy of the calculation results and ensures the precision of vehicle scheduling. If the number of candidate points is still not ideal after one adjustment, further adaptive adjustments can be made to c or d until the number of candidate points is within a reasonable range.

[0058] In a further embodiment, when the driving distance analysis unit plans the driving distance between the reference origin and the candidate point k, if the difference between the driving distances of the h planned routes is less than the preset value S min When the h planned routes are retained, the travel distance is recorded as Sk1 、S k2 …S kh When the driving time analysis unit calculates the driving time between the reference origin and the alternative point k, the driving time t is calculated based on the h planned routes and the real-time road conditions. k1 , t k2 …t kh The execution capability analysis unit calculates the execution capability E of the candidate point k k =min(E k1 ,E k2 …E kh ), E k1 =alog2S k1 +bt k1 , E k2 =alog2S k2 +bt k2 , E kh =alog2S kh +bt kh Where k∈n and h≥2. When planning the driving distance between a single candidate point k and a reference origin, this embodiment lists multiple planned routes with very close actual distances as candidate routes. The travel time is then calculated based on the real-time traffic conditions of each planned route. This increases the breadth of route options between a single candidate point k and the reference origin, avoiding the loss caused by immediately abandoning the candidate point k when traffic congestion occurs on that route when only a single route is planned between the two points. Comprehensive planning further improves the accuracy and efficiency of vehicle scheduling.

[0059] The present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the intelligent logistics transportation scheduling method when executing the computer program.

[0060] The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the intelligent logistics transportation scheduling method is implemented.

[0061] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for intelligent logistics and transportation scheduling, characterized in that: include: S1. Receive a dispatch request, obtain the cargo collection location information, and use the cargo collection location as the reference origin (x0, y0); S2, search for vehicle information that meets the transportation requirements, locate the coordinate points of each vehicle position, and convert |x i -x0|≤z and|y i -y0|≤z coordinate point is selected as the candidate point; i is the horizontal coordinate value of the vehicle position that meets the transportation requirements, y i is the ordinate value of the vehicle position that meets the transportation requirements, and z is the preset value; S3, plan the driving route between the reference origin and each alternative point, and calculate the driving distance S1, S2...S respectively n ; S4. Calculate the travel time t1, t2, ..., t from the reference origin to each alternative point based on the travel distance and real-time road conditions. n ; S5. Calculate the execution capability E of each candidate point based on each driving distance and each driving time. j =alog2S j +bt j ; Where a and b are preset weight values, j∈n; S6. Select E min The corresponding candidate point is used as the target point, and the vehicle corresponding to the target point is selected to go to the collection location to wait for collection; the E min =min(E1,E2,…,E n ).

2. The intelligent logistics transportation scheduling method according to claim 1 is characterized in that: In step S2, when the number of candidate points is greater than the preset value D max When the number of candidate points is less than the preset value D, adjust z to z', z'=cz; min When , adjust z to z″, z″=dz; where 0<c<1, d>1.

3. The intelligent logistics transportation scheduling method according to claim 1 is characterized in that: In step S3, when planning the driving distance between the reference origin and the candidate point k, if the difference between the driving distances of the h planned routes is less than the preset value S min When the h planned routes are retained, the travel distance is recorded as S k1 、S k2 …S kh ; where k∈n, h≥2.

4. The intelligent logistics transportation scheduling method according to claim 3 is characterized in that: In step S4, when calculating the travel time between the reference origin and the candidate point k, the travel time t is calculated based on the h planned routes and the real-time road conditions. k1 , t k2 …t kh ; where k∈n, h≥2.

5. The intelligent logistics transportation scheduling method according to claim 4 is characterized in that: In step S5, the execution capability E of the candidate point k k =min(E k1 ,E k2 …E kh ), E k1 =alog2S k1 +bt k1 , E k2 =alog2S k2 +bt k2 , E kh =alog2S kh +bt kh ; where k∈n, h≥2.

6. An intelligent logistics and transportation dispatching system, characterized in that: include: The reference location analysis unit receives the dispatch request, obtains the cargo collection location information, and uses the cargo collection location as the reference origin (x0, y0); The vehicle position analysis unit searches for vehicle information that meets the transportation requirements, locates the coordinate points of each vehicle position, and i -x0|≤z and|y i -y0|≤z coordinate point is selected as the candidate point; i is the horizontal coordinate value of the vehicle position that meets the transportation requirements, y i is the ordinate value of the vehicle position that meets the transportation requirements, and z is the preset value; The driving distance analysis unit plans the driving route between the reference origin and each alternative point, and calculates the driving distance S1, S2...S respectively. n ; The driving time analysis unit calculates the driving time t1, t2…t from the reference origin to each alternative point based on the driving distance and real-time road conditions. n ; The execution capability analysis unit calculates the execution capability E of each candidate point according to each driving distance and each driving time. j =alog2S j +bt j ; Where a and b are preset weight values, j∈n; Target vehicle selection unit, select E min The corresponding candidate point is used as the target point, and the vehicle corresponding to the target point is selected to go to the collection location to wait for collection; the E min =min(E1,E2,…,E n ).

7. The intelligent logistics and transportation dispatching system according to claim 6 is characterized in that: When the vehicle position analysis unit selects candidate points, if the number of candidate points is greater than the preset value D max When the number of candidate points is less than the preset value D, adjust z to z', z'=cz; min When , adjust z to z″, z″=dz; where 0<c<1, d>1.

8. The intelligent logistics and transportation dispatching system according to claim 6 is characterized in that: When the driving distance analysis unit plans the driving distance between the reference origin and the candidate point k, if the difference between the driving distances of the h planned routes is less than the preset value S min When the h planned routes are retained, the travel distance is recorded as S k1 、S k2 …S kh ; When the driving time analysis unit calculates the driving time between the reference origin and the candidate point k, the driving time t is calculated based on the h planned routes and the real-time road conditions. k1 , t k2 …t kh ; The execution capability analysis unit calculates the execution capability E of the candidate point k k =min(E k1 ,E k2 …E kh ), E k1 =alog2S k1 +bt k1 , E k2 =alog2S k2 +bt k2 , E kh =alog2S kh +bt kh ; Among them, k∈n, h≥2.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the intelligent logistics transportation scheduling method described in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent logistics transportation scheduling method described in any one of claims 1 to 5 is implemented.

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