A logistics dispatching method based on logistics dispatching algorithm

Abstract

The present invention discloses a logistics allocation method based on a logistics allocation algorithm, comprising the following steps: collecting data, constructing a three-dimensional GIS logistics information macro model, binding a coordinate data set, setting a central resource allocation point, simulating transport vehicles, formulating a main plan, a backup path plan and actual application; the present invention establishes a three-dimensional GIS logistics information macro model, simulates logistics allocation in the model, and the parameters in the model adopt the parameters collected by actual investigation, so that the simulated plan is more in line with reality and more effective, and in a given area, sets a central resource allocation point that can increase the amount of goods, determines the shortest path from each resource allocation point to each regional distribution center, and then adjusts the amount of goods allocated by each resource allocation point according to the total time consumed for delivering goods from each resource allocation point, compares multiple times, selects the most balanced amount, fully considers the amount of goods at the resource allocation point and the optimal path, ensures the balance between the two, and has higher logistics efficiency.

Description

Technical Field

[0001] The present invention relates to the field of logistics deployment design, and in particular to a logistics deployment method based on a logistics deployment algorithm. Background Art

[0002] With the continuous rise of online shopping and e-commerce, the demand for logistics is also increasing. In the transportation of some goods resources, there are often warehouse overflows or slow delivery during peak seasons.

[0003] In the current logistics industry, the constraints on logistics transportation capacity include the path, that is, the effective use of transportation resources, the number of supply points involved in distribution and the amount of resources required for distribution. It is an urgent problem to be solved in the prior art to ensure that the number of points involved in distribution is not too large, and the time, that is, the length of the path, is not too long. Therefore, the present invention proposes a logistics allocation method based on a logistics allocation algorithm to solve the problems existing in the prior art. Summary of the invention

[0004] In view of the above problems, the present invention proposes a logistics allocation method based on a logistics allocation algorithm. The method establishes a three-dimensional GIS logistics information macro model, simulates logistics allocation in the model, fully considers the cargo volume and the optimal path of the resource allocation point, ensures the balance between the two, and improves logistics efficiency.

[0005] In order to solve the above problems, the present invention proposes a logistics allocation method based on a logistics allocation algorithm, comprising the following steps:

[0006] Step 1: Collect data

[0007] Use BeiDou-3 and GPS common frequency points to obtain the global spatiotemporal data of the city's remote sensing images collected by satellite and aviation technology, then install cameras, infrared scanning sensors and laser radars on multiple drones, use multiple drones to scan over the city, and collect GIS data, including transfer distribution stations, regional distribution centers and urban logistics warehouses, and then deploy multiple groups of unmanned vehicles equipped with cameras and laser scanners in the city, use the unmanned vehicles to travel on urban roads to measure paths, take pictures of road conditions, and finally summarize all data in detail;

[0008] Step 2: Construct a 3D GIS logistics information macro model

[0009] The data collected in step 1 is input into the IoT cloud, and the data is analyzed, classified, deduplicated, encrypted and saved. The stereoscopic image is restored and the model is stretched and stereoscopically transformed through 3Dmax software to construct an urban logistics path model. The corresponding GIS spatial model is then output, and the parameter information of the GIS spatial model is vectorized through ArcMAP software. Each vectorized path layer is then loaded into the urban logistics path model, and the data is visualized in 3D. The visual effect is enhanced through symbolization to obtain the coordinate three-dimensional model corresponding to the road, transfer distribution station, regional distribution center and urban logistics warehouse. Finally, the overall model is cut to achieve individualization of each internal element and construct a complete three-dimensional GIS logistics information macro model.

[0010] Step 3: Bind the coordinate dataset

[0011] In the three-dimensional GIS logistics information macro model in step 2, the number of paths from the urban logistics warehouse to each transfer distribution station is first analyzed, and then the number of paths from each transfer distribution station to each regional distribution center is analyzed. In the model, the coordinate data sets between the interconnected urban logistics warehouse and transfer distribution station, and between the transfer distribution station and the regional distribution center are bound;

[0012] Step 4: Set the central resource allocation point

[0013] Assume the transfer distribution station as the resource allocation point, obtain the resource allocation points that can provide resources in the given area, and then calculate the number of resource allocation points and the straight-line distance evaluation time to each regional distribution center in the given area; for all resource allocation points, sort them according to the length of the straight-line distance evaluation time to each regional distribution center; start from the point with the least time to the point with the most time and accumulate the number of resources in sequence upward until the sum of the number of resources reaches the required number of resources, and mark the resource allocation points involved in the accumulation, and obtain the resource allocation point with the widest radiation range and the least time for multiple regional distribution centers in the given area, and set this resource allocation point as the central resource allocation point of the logistics volume inclination object;

[0014] Step 5: Simulate the transport vehicle

[0015] Simulate the transport vehicles in the 3D GIS logistics information macro model and set their transport speed. According to the results in step 3, extract the number of paths between each resource allocation point in the given area and each regional distribution center in step 4. Then use the transport vehicles to transport goods on the path and get the simulation time. Verify the time with the evaluation time in step 4, further determine the central resource allocation point, and at the same time determine the shortest path from each resource allocation point to each regional distribution center.

[0016] Step 6: Develop a main plan

[0017] Set the total amount of goods that need logistics in a given area to N, set the goods allocated to each resource allocation point to N1, N2, N3, etc., according to the results of step 4, tilt the resources to the central resource allocation point obtained in step 4, and increase the amount of goods obtained by this resource allocation point to N+, set the transport volume of the transport vehicle to E, E is the amount of goods required by each regional distribution center, and the shortest time for each resource allocation point to transport goods to each regional distribution center is T1, T2, T3, etc., and at the same time record the number of regional distribution centers radiated by each resource allocation point, then the time for each resource allocation point to transport goods to multiple regional distribution centers in the radiation area is: N1 / E×T1+N1 / E×T2+N1 / E×T3......、N2 / E×T1+N2 / E×T2+N2 / E×T3......, and so on. The time taken by the central resource allocation point to transport the goods to the multiple regional distribution centers in the radiation area is N+ / E×T1+N+ / E×T2+N+ / E×T3......, and the total time consumed in delivering goods at each resource allocation point in the given area is obtained. Then, the values ​​of goods N1, N2, N3...... allocated to each resource allocation point are adjusted, and multiple comparisons are made to select the total amount of goods N to allocate to each resource allocation point. The balanced amount of goods N1, N2, N3.... is selected, and the path selection and resource allocation selection are left as the main plan;

[0018] Step 7: Backup Path Solution

[0019] Input the path conditions collected in step 1 into the 3D GIS logistics information macro model, analyze the road conditions of the shortest path for transportation from each resource allocation point to each regional distribution center, and make a comprehensive assessment, increase the range of time consumed when the road condition is blocked, and then analyze the road conditions of other paths again, and obtain an alternative path plan when the shortest path is blocked, and keep this plan as a backup;

[0020] Step 8: Practical Application

[0021] According to the actual application of the main plan in step six, record the actual resource allocation and actual path time consumption, verify the actual feasibility of the main plan, adjust the resource allocation and backup paths, and obtain the optimal plan. Gradually expand the application scope of the optimal plan for a given area, and carry out actual application in pilot projects targeting urban units.

[0022] A further improvement is that in step 1, the formats, processing methods and expressions of the collected data are different, showing multidimensional, coupled and nonlinear characteristics. When the data is aggregated, the audio data and time attributes are eliminated.

[0023] A further improvement is that in step 2, ContextCapture is used to process the GIS data collected in step 1 and automatically construct a three-dimensional model based on the image, supplemented by spatial coordinate parameter information, so as to output the corresponding GIS spatial model.

[0024] A further improvement is that in step five, a plurality of simulated transport vehicles are set to be of the same type, and their transport speeds are set to conform to the maximum speed on urban roads.

[0025] A further improvement is that in step six, when recording the number of regional distribution centers radiated by each resource allocation point, if there is a duplication of regional distribution centers radiated by each resource allocation point, then the resource allocation point with the shortest path to the regional distribution center is taken as the main one, and the regional distribution center is counted as the only one counted as its radiation number.

[0026] A further improvement is that in step six, if the central resource allocation point has the longest total time consumption, a plan is implemented to increase the number of transport vehicles.

[0027] Further improvement is that in step eight, after actual application with urban units as the target pilot, the three-dimensional GIS logistics information macro model is input into the Internet of Things server, and the logistics allocation situation is queried based on the coordinate data set of the optimal path and resource allocation point.

[0028] The beneficial effects of the present invention are as follows: the present invention establishes a three-dimensional GIS logistics information macro model, simulates logistics allocation in the model, and the parameters therein adopt the parameters collected by actual investigation, so that the simulated scheme is more in line with reality and more effective, and in a given area, by analyzing the resource allocation points with the widest radiation range and the least time of multiple regional distribution centers, a central resource allocation point that can increase the amount of goods is set, and by simulating transport vehicles, the shortest path from each resource allocation point to each regional distribution center is determined, and then the total time consumed for delivering goods at each resource allocation point is adjusted, and the amount of goods allocated to each resource allocation point is adjusted, and the most balanced amount is selected after multiple comparisons, and the path selection and resource allocation selection are reserved as the main scheme. The scheme fully considers the amount of goods at the resource allocation point and the optimal path, ensures the balance between the two, and has higher logistics efficiency. At the same time, an alternative path scheme is selected according to the road conditions and reserved for backup, in case of emergency, to ensure the normal transportation and efficiency of logistics. Finally, the three-dimensional GIS logistics information macro model is input into the Internet of Things server, and the logistics allocation situation is queried based on the optimal path and the coordinate data set of the resource allocation point, which is more convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0030] In order to make the technical means, objectives and effects of the invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0031] like Figure 1 As shown, this embodiment provides a logistics allocation method based on a logistics allocation algorithm, and the specific steps are as follows:

[0032] Step 1: Collect data

[0033] The BeiDou-3 and GPS common frequency points are used to obtain the global spatiotemporal data of the city's remote sensing images collected by satellite and aviation technology. Then, cameras, infrared scanning sensors and laser radars are installed on multiple drones. Multiple drones are used to scan over the city to collect GIS data, including transfer and distribution stations, regional distribution centers and urban logistics warehouses. Multiple groups of unmanned vehicles equipped with cameras and laser scanners are deployed in the city. The unmanned vehicles are used to measure the path and photograph the road conditions on urban roads. Finally, all the data are summarized in detail. The formats, processing methods and expressions of the collected data are different, showing multidimensional, coupled and nonlinear characteristics. When summarizing the data, the audio data and time attributes are eliminated.

[0034] Step 2: Construct a 3D GIS logistics information macro model

[0035] The data collected in step 1 is input into the IoT cloud, and the data is analyzed, classified, deduplicated, encrypted and saved. The stereoscopic image is restored and the model is stretched and stereoscopically transformed through 3Dmax software to construct an urban logistics path model. ContextCapture is then used to process the GIS data collected in step 1 and automatically construct a three-dimensional model based on the image, supplemented by spatial coordinate parameter information, so as to output the corresponding GIS spatial model, and the various parameter information of the GIS spatial model is vectorized through ArcMAP software, and then the vectorized path layers are loaded into the urban logistics path model, the data is visualized in 3D, and the visual effect is enhanced through symbolization, so as to obtain the coordinate three-dimensional models corresponding to the roads, transfer distribution stations, regional distribution centers and urban logistics warehouses, and finally the overall model is cut to achieve individualization of each internal element and build a complete three-dimensional GIS logistics information macro model;

[0036] Step 3: Bind the coordinate dataset

[0037] In the three-dimensional GIS logistics information macro model in step 2, the number of paths from the urban logistics warehouse to each transfer distribution station is first analyzed, and then the number of paths from each transfer distribution station to each regional distribution center is analyzed. In the model, the coordinate data sets between the interconnected urban logistics warehouse and transfer distribution station, and between the transfer distribution station and the regional distribution center are bound;

[0038] Step 4: Set the central resource allocation point

[0039] Assume the transfer distribution station as the resource allocation point, obtain the resource allocation points that can provide resources in the given area, and then calculate the number of resource allocation points and the straight-line distance evaluation time to each regional distribution center in the given area; for all resource allocation points, sort them according to the length of the straight-line distance evaluation time to each regional distribution center; start from the point with the least time to the point with the most time and accumulate the number of resources in sequence upward until the sum of the number of resources reaches the required number of resources, and mark the resource allocation points involved in the accumulation, and obtain the resource allocation point with the widest radiation range and the least time for multiple regional distribution centers in the given area, and set this resource allocation point as the central resource allocation point of the logistics volume inclination object;

[0040] Step 5: Simulate the transport vehicle

[0041] Simulate transport vehicles in the 3D GIS logistics information macro model, set multiple simulated transport vehicles to the same type, and set their transport speed to meet the maximum speed of urban roads. According to the results in step 3, extract the number of paths between each resource allocation point in the given area and each regional distribution center in step 4, and then use the transport vehicles to transport goods on the path to obtain the simulation time, verify the time with the evaluation time in step 4, further determine the central resource allocation point, and at the same time determine the shortest path from each resource allocation point to each regional distribution center;

[0042] Step 6: Develop a main plan

[0043] Set the total amount of goods that need logistics in a given area to N, set the goods allocated to each resource allocation point to N1, N2, N3..., according to the results of step 4, tilt the resources to the central resource allocation point obtained in step 4, and increase the amount of goods obtained by this resource allocation point to N+, set the transportation volume of the transport vehicle to E, E is the amount of goods required by each regional distribution center, and the shortest transportation time from each resource allocation point to each regional distribution center is T1, T2, T3..., and record the number of regional distribution centers radiated by each resource allocation point at the same time. If there is a duplication of regional distribution centers radiated by each resource allocation point, then take the resource allocation point with the shortest path to the regional distribution center as the main one, and count this regional distribution center as the only one as its radiation number, then each resource allocation point will transport goods to multiple areas in the radiation area. The time for the distribution center is: N1 / E×T1+N1 / E×T2+N1 / E×T3......, N2 / E×T1+N2 / E×T2+N2 / E×T3......, and so on. The time for the central resource allocation point to transport the goods to the multiple regional distribution centers in the radiation area is N+ / E×T1+N+ / E×T2+N+ / E×T3......, and the total time consumed for the delivery of goods at each resource allocation point in the given area is obtained. Then, the values ​​of goods N1, N2, N3...... allocated to each resource allocation point are adjusted, and multiple comparisons are made to select the total amount of goods N to allocate to each resource allocation point. The balanced amount of goods N1, N2, N3.... is selected, and the path selection and resource allocation selection are kept as the main plan. If the central resource allocation point has the longest total time consumption, the plan of increasing the number of transport vehicles is implemented;

[0044] Step 7: Backup Path Solution

[0045] Input the path conditions collected in step 1 into the 3D GIS logistics information macro model, analyze the road conditions of the shortest path for transportation from each resource allocation point to each regional distribution center, and make a comprehensive assessment, increase the range of time consumed when the road condition is blocked, and then analyze the road conditions of other paths again, and obtain an alternative path plan when the shortest path is blocked, and keep this plan as a backup;

[0046] Step 8: Practical Application

[0047] According to the actual application of the main plan in step six, record the actual resource allocation situation and the actual path time consumption, verify the actual feasibility of the main plan, adjust the resource allocation amount and the alternative path, and obtain the optimal plan. Gradually expand the application scope of the optimal plan for a given area, and carry out actual application in pilot projects with urban units as the target. At the same time, input the three-dimensional GIS logistics information macro model into the Internet of Things server, and query the logistics allocation situation based on the coordinate data set of the optimal path and resource allocation point.

[0048] The present invention establishes a three-dimensional GIS logistics information macro model, simulates logistics allocation in the model, and the parameters in the model adopt the parameters collected by actual investigation. The simulated scheme is more in line with reality and more effective. In a given area, by analyzing the resource allocation points with the widest radiation range and the least time of multiple regional distribution centers, a central resource allocation point that can increase the amount of goods is set, and by simulating transportation vehicles, the shortest path from each resource allocation point to each regional distribution center is determined. Then, the total time consumed for delivering goods at each resource allocation point is adjusted, and the most balanced amount is selected by multiple comparisons. The path selection and resource allocation selection are reserved as the main scheme. The scheme fully considers the amount of goods at the resource allocation point and the optimal path, ensures the balance between the two, and has higher logistics efficiency. At the same time, an alternative path scheme is selected according to the road conditions and reserved for backup, in case of emergency, to ensure the normal transportation and efficiency of logistics. Finally, the three-dimensional GIS logistics information macro model is input into the Internet of Things server, and the logistics allocation situation is queried based on the optimal path and the coordinate data set of the resource allocation point, which is more convenient.

[0049] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A logistics allocation method based on a logistics allocation algorithm, characterized in that: The following steps are involved: Step 1: Collect data Use the common frequency points of BeiDou-3 and GPS to obtain the global spatiotemporal data of the city's remote sensing images collected by satellite and aviation technology. Then install cameras, infrared scanning sensors and laser radars on multiple drones, use multiple drones to scan over the city, and collect GIS data, including transfer distribution stations, regional distribution centers and urban logistics warehouses. Then deploy multiple groups of unmanned vehicles equipped with cameras and laser scanners in the city, use the unmanned vehicles to measure the path and take pictures of the road conditions on the city roads, and finally summarize all the data in detail. Step 2: Construct a 3D GIS logistics information macro model The data collected in step 1 is input into the IoT cloud, and the data is analyzed, classified, deduplicated, encrypted and saved. The stereoscopic image is restored and the model is stretched and stereoscopically transformed through 3Dmax software to construct an urban logistics path model. The corresponding GIS spatial model is then output, and the parameter information of the GIS spatial model is vectorized through ArcMAP software. Each vectorized path layer is then loaded into the urban logistics path model, and the data is visualized in 3D. The visual effect is enhanced through symbolization to obtain the coordinate three-dimensional model corresponding to the road, transfer distribution station, regional distribution center and urban logistics warehouse. Finally, the overall model is cut to achieve individualization of each internal element and construct a complete three-dimensional GIS logistics information macro model. Step 3: Bind the coordinate dataset In the three-dimensional GIS logistics information macro model in step 2, the number of paths from the urban logistics warehouse to each transfer distribution station is first analyzed, and then the number of paths from each transfer distribution station to each regional distribution center is analyzed. In the model, the coordinate data sets between the interconnected urban logistics warehouse and transfer distribution station, and between the transfer distribution station and the regional distribution center are bound; Step 4: Set the central resource allocation point Assume the transfer distribution station as the resource allocation point, obtain the resource allocation points that can provide resources in the given area, and then calculate the number of resource allocation points and the straight-line distance evaluation time to each regional distribution center in the given area; for all resource allocation points, sort them according to the length of the straight-line distance evaluation time to each regional distribution center; start from the point with the least time to the point with the most time and accumulate the number of resources in sequence upward until the sum of the number of resources reaches the required number of resources, and mark the resource allocation points involved in the accumulation, and obtain the resource allocation point with the widest radiation range and the least time for multiple regional distribution centers in the given area, and set this resource allocation point as the central resource allocation point of the logistics volume inclination object; Step 5: Simulate the transport vehicle Simulate the transport vehicles in the 3D GIS logistics information macro model and set their transport speed. According to the results in step 3, extract the number of paths between each resource allocation point in the given area and each regional distribution center in step 4. Then use the transport vehicles to transport goods on the path and get the simulation time. Verify the time with the evaluation time in step 4, further determine the central resource allocation point, and at the same time determine the shortest path from each resource allocation point to each regional distribution center. Step 6: Develop a main plan Set the total amount of goods that need logistics in a given area to N, set the goods allocated to each resource allocation point to N1, N2, N3, etc., according to the results of step 4, tilt the resources to the central resource allocation point obtained in step 4, and increase the amount of goods obtained by this resource allocation point to N+, set the transport volume of the transport vehicle to E, E is the amount of goods required by each regional distribution center, and the shortest time for each resource allocation point to transport goods to each regional distribution center is T1, T2, T3, etc., and at the same time record the number of regional distribution centers radiated by each resource allocation point, then the time for each resource allocation point to transport goods to multiple regional distribution centers in the radiation area is: N1 / E×T1+N1 / E×T2+N1 / E×T3......、N2 / E×T1+N2 / E×T2+N2 / E×T3......, and so on. The time taken by the central resource allocation point to transport the goods to the multiple regional distribution centers in the radiation area is N+ / E×T1+N+ / E×T2+N+ / E×T3......, and the total time consumed in delivering goods at each resource allocation point in the given area is obtained. Then, the values ​​of goods N1, N2, N3...... allocated to each resource allocation point are adjusted, and multiple comparisons are made to select the total amount of goods N to allocate to each resource allocation point. The balanced amount of goods N1, N2, N3.... is selected, and the path selection and resource allocation selection are left as the main plan; Step 7: Backup Path Solution Input the path conditions collected in step 1 into the 3D GIS logistics information macro model, analyze the road conditions of the shortest path for transportation from each resource allocation point to each regional distribution center, and make a comprehensive assessment, increase the range of time consumed when the road condition is blocked, and then analyze the road conditions of other paths again, and obtain an alternative path plan when the shortest path is blocked, and keep this plan as a backup; Step 8: Practical Application According to the actual application of the main scheme in step six, record the actual resource allocation and the actual path time consumption, verify the actual feasibility of the main scheme, adjust the resource allocation amount and the alternative path, and obtain the optimal scheme. Gradually expand the application scope of the optimal scheme in the given area, and carry out practical application in urban units as the target pilot. In the step one, the format, processing method and expression form of the collected data are different. When the data is aggregated, the audio data and time attributes are removed. In the step two, ContextCapture is used to process the GIS data collected in step one and automatically construct a three-dimensional model based on the image, supplemented by spatial coordinate parameter information, so as to output the corresponding GIS spatial model. In the step five, multiple simulated transport vehicles are set to the same type, and their transport speed is set to meet the maximum speed of urban roads.

2. The logistics allocation method based on the logistics allocation algorithm according to claim 1 is characterized in that: In step six, when recording the number of regional distribution centers radiated by each resource allocation point, if there are duplicate regional distribution centers radiated by each resource allocation point, then the resource allocation point with the shortest path to the regional distribution center is taken as the main one, and the regional distribution center is counted as the only one counted as its radiation number.

3. The logistics allocation method based on the logistics allocation algorithm according to claim 1 is characterized in that: In step six, if the central resource allocation point has the longest total time consumption, a plan to increase transport vehicles is implemented.

4. The logistics allocation method based on the logistics allocation algorithm according to claim 1 is characterized in that: In step eight, after actual application in a pilot project targeting urban units, the three-dimensional GIS logistics information macro model is input into the Internet of Things server, and the logistics deployment situation is queried based on the coordinate data set of the optimal path and resource allocation point.

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