A logistics supply chain management system

Abstract

The present application discloses a logistics supply chain management system, which relates to the technical field of logistics management. The system comprises: a path simulation selection subsystem, which is used to receive order information, perform path simulation and select a final transportation path; the path simulation module is used to obtain order information, and obtain the location information of nodes that can process the order information according to the order information, and simulate the distribution path according to the location information of the nodes; the local optimal module is used to receive the preliminary distribution path information in the path simulation module, and form the local optimal path time; the global optimal module predicts the transportation time required for each preliminary distribution path, compares it with the local optimal path according to the comparison rules, and selects the final distribution path; the local optimal module and the global optimal module are combined to comprehensively consider all paths in the global range, so as to achieve the effect of global consideration of the simulated path and reasonable selection of the path.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics supply chain management system. Background Art

[0002] Logistics has penetrated into many aspects of life. Faced with more and more personalized demands, the form of logistics is constantly changing, and warehousing has begun to be pre-positioned according to regional preferences in order to reduce logistics costs as much as possible. However, in the actual operation process, for items in pre-warehousing, users expect the delivery time to be as fast as possible, but unlimited shortening of delivery time will increase logistics costs.

[0003] The Chinese invention patent with application number 202010852919.9 provides a supply chain management method and device, the method comprising: obtaining first order information, the first order information including item information and quantity information; obtaining location information of a storage node capable of processing the first order information; simulating a delivery path according to the location information of the storage node to generate first delivery path information, the number of first delivery path information being equal to the number of storage nodes; obtaining congestion level information for each first delivery path information; selecting first delivery path information that is lower than the set congestion level information, recorded as second delivery path information; calculating cost information corresponding to each second delivery path information and selecting the second delivery path information with the smallest cost information value.

[0004] The existing patented technology generates the optimal path that balances time and cost by simulating the path. However, the existing patented technology only considers the optimal node path in the process of node selection, and does not consider the global optimal path. If the first node is immediately abandoned when it is congested, but the time used is still the least when the subsequent nodes are unobstructed, this path is ignored, and the global optimization of the node is ignored, resulting in unreasonable path selection and prolonged delivery time. Summary of the invention

[0005] The present application provides a logistics supply chain management system to solve the problem in the prior art of ignoring the global optimization of nodes, resulting in unreasonable path selection and prolonged delivery time, and achieves the technical effect of global consideration of simulated paths, reasonable path selection, and reduced delivery time.

[0006] The present application provides a logistics supply chain management system, the system comprising:

[0007] A path simulation selection subsystem is used to receive order information, perform path simulation and select the final transportation path; the path simulation selection subsystem includes a path simulation module, a local optimal module and a global optimal module;

[0008] The path time subsystem is used to record the final delivery path information, total predicted time, total actual time and predicted time and actual time information of each node selected each time, and establish a path time list for storage; the path time subsystem calculates the time information of each node by the arrival time and the departure time of the node;

[0009] The path simulation module is used to obtain order information, and obtain the location information of the node that can process the order information according to the order information, simulate the delivery path according to the location information of the node, and generate the preliminary delivery path. The number of the preliminary delivery paths is equal to the number of nodes.

[0010] The local optimal module is used to receive the preliminary distribution path information in the path simulation module, obtain the congestion level information of each preliminary distribution path; select the preliminary distribution path with a congestion level lower than the set information and record it as the predetermined distribution path; calculate the cost value corresponding to each predetermined distribution path, select the predetermined distribution path with the smallest cost value as the local optimal path; and predict the time required for transportation of the local optimal path to form the local optimal path time;

[0011] The global optimal module predicts the transportation time required for each initially selected delivery path, compares it with the local optimal path according to the comparison rules, and selects the final delivery path.

[0012] Furthermore, the comparison rule is to compare the time of all the preliminary delivery paths with the time of the local optimal path. If the local optimal path takes the least time, the local optimal path is used as the final delivery path; if there is a path in the preliminary delivery paths that takes less time than the local optimal path, it is recorded as the target delivery path, and then the cost value corresponding to the target delivery path is calculated and compared with the cost value of the local optimal path. If the cost value of the target delivery path is lower, it is used as the final delivery path; if the cost value of the target delivery path is higher than the cost value of the local optimal path, it is necessary to select the final delivery path based on the comprehensive evaluation index.

[0013] Furthermore, the system also includes a node collaboration subsystem, which is used to update the current node status and the next node status in real time, and choose whether to re-perform path simulation based on the next node status; if the next node status shows congestion, the information is transmitted to the path simulation selection subsystem to re-perform secondary path simulation and select a new path; if the next node status is normal, no operation is required.

[0014] Furthermore, the secondary path simulation uses the path simulation selection subsystem to re-simulate the distribution path with the current node as the starting point to generate a new preliminary distribution path; the local optimal module and the global optimal module receive the new preliminary distribution path, and perform path analysis and selection based on the new node status data to generate a new final distribution path; the path time subsystem will record the time and reason of the secondary path simulation and the change information of the nodes before and after the secondary path simulation.

[0015] Furthermore, the system also includes a node reservation subsystem for performing priority evaluation on nodes and selecting nodes with high priority as reserved nodes; the priority evaluation is performed based on an analysis of the historical usage of the nodes in the path time list, including the processing capacity of the nodes, average processing time, congestion conditions and usage frequency.

[0016] Furthermore, the processing capacity is inversely proportional to the congestion situation, and the less congestion, the higher the processing capacity; the processing time of each node is recorded, and the average processing time is calculated based on the number of orders processed within the preset time and the total processing time; the usage frequency is based on the ratio of the number of orders passing through the node within the preset time to the total number of orders.

[0017] Furthermore, when receiving an order, the reserved node will reserve idle time for transporting urgent orders; the range of the idle time is set based on the shortest time for a single order passing through the reserved node and the relative shortest time for multiple orders; the shortest time for a single order is the shortest time taken for the transportation process for an order passing through the reserved node; the relative shortest time for multiple orders is the average time taken for the transportation process for all orders passing through the reserved node.

[0018] Furthermore, the rush order is to obtain the limit time and predicted time of the order, and the predicted time is compared with the limit time. If the ratio is not less than 70%, it is defined as a rush order; if the ratio is less than 70%, the order is subjected to reverse path simulation, and the reverse path simulation is to reversely deduce the final delivery path based on the latest deadline time to obtain the maximum processing time of each node, and obtain the actual time information of each node in the path time list in real time during the transportation process, and compare the actual time with the maximum processing time in real time. If the actual time of a node exceeds the maximum processing time, it is marked as a timeout node, and the number of allowed timeout nodes is set. If the actual number of timed-out nodes is greater than the allowed number of timeout nodes, it is defined as a rush order; when transmitting, the rush order gives priority to reserved nodes for transmission, and uses the idle time of reserved nodes for priority processing.

[0019] Furthermore, the node reservation subsystem also includes an idle period adaptation module, which is used to dynamically adjust the idle time and optimize the relative shortest time of multiple orders; the dynamic adjustment of idle time is to monitor the idle time usage of the reserved node in real time and make dynamic adjustments; the dynamic adjustment is to make a comprehensive analysis based on the idle time usage rate and the relative shortest time of multiple orders of the reserved node, generate the idle time usage rate based on the actual idle time used and the preset idle time, set the idle time usage rate threshold range, if the actual idle time usage rate is lower than the lower limit of the idle time usage rate threshold range, it means that the idle time resources of the reserved node are in excess; if the actual idle time usage rate is within the idle time usage rate threshold range, it means that the idle time resources of the reserved node are sufficient; if the actual idle time usage rate is higher than the upper limit of the idle time usage rate threshold, it means that the idle time resources of the reserved node are insufficient.

[0020] Furthermore, the idle period adaptation module obtains the historical order processing time data of each reserved node and the relative shortest time of multiple orders, analyzes the distribution of the relative shortest time of multiple orders and sorts them, uses the quantile method to screen out the reserved nodes with the longest relative shortest time of multiple orders, and defines the reserved node with the longest relative shortest time of multiple orders as the bearer reserved node, obtains the shortest time of all single orders in the reserved node with the long relative shortest time of multiple orders, uses the quantile method to analyze, obtains the single order with the long time and records the time range, and re-sets the idle time for the reserved node and the bearer reserved node according to the time range and the relative shortest time of multiple orders.

[0021] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0022] By combining the local optimal module with the global optimal module, comprehensively considering all paths within the global scope, and using a global perspective to comprehensively consider time and cost, we can achieve global consideration of simulated paths, reasonable selection of paths, and reduce delivery time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the overall architecture of a logistics supply chain management system in an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of a path simulation selection subsystem of a logistics supply chain management system according to an embodiment of the present invention;

[0025] Figure 3 Schematic diagram of a path time subsystem of a logistics supply chain management system in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0028] In order to solve the problem that the existing technology ignores the global optimization of nodes, resulting in unreasonable path selection and prolonged delivery time, this application adopts local optimization and global coordination mechanism to achieve global consideration of simulated paths, reasonable path selection, and reduce delivery time.

[0029] Embodiment 1: Figure 1 As shown, a logistics supply chain management system, the system includes:

[0030] like Figure 2 As shown, the path simulation selection subsystem is used to receive order information, perform path simulation and select the final transportation path; the path simulation selection subsystem includes a path simulation module, a local optimal module and a global optimal module;

[0031] The path simulation module is used to obtain order information, and obtain the location information of the node that can process the order information according to the order information, simulate the delivery path according to the location information of the node, and generate the preliminary delivery path, and the number of the preliminary delivery paths is equal to the number of nodes;

[0032] Specifically, the path simulation module obtains the item information and quantity information in the order information, analyzes it to obtain the location information of the nodes that can process the order information; simulates the delivery path according to the node location information, generates preliminary delivery paths, and the number of preliminary delivery paths is equal to the number of nodes.

[0033] The local optimal module is used to receive the preliminary distribution path information in the path simulation module, obtain the congestion level information of each preliminary distribution path; select the preliminary distribution path with a congestion level lower than the set information and record it as the predetermined distribution path; calculate the cost value corresponding to each predetermined distribution path, select the predetermined distribution path with the smallest cost value as the local optimal path; and predict the time required for transportation of the local optimal path to form the local optimal path time;

[0034] Specifically, the method for the local optimal module to select the local optimal path can refer to the method in the prior art patent with publication number CN111898959B, and the specific method is as follows: obtain the total number of nodes on each preliminary distribution path; obtain the busyness information of each node; divide the nodes into available nodes and unavailable nodes according to the busyness information; and select the preliminary distribution path with unavailable nodes less than the number threshold as the predetermined distribution path; when the first node on the preliminary distribution path is an unavailable node, abandon this preliminary distribution path; when the first nodes on all the preliminary distribution paths are unavailable nodes, refer to the second node on the preliminary distribution path; when the second node on the preliminary distribution path is an unavailable node, abandon this preliminary distribution path; when the second node on all the preliminary distribution paths is an unavailable node, refer to the third node on the preliminary distribution path; and so on.

[0035] The global optimal module predicts the transportation time required for each initially selected delivery path, compares it with the local optimal path according to the comparison rules, and selects the final delivery path;

[0036] Specifically, unlike the local optimal module, the global optimal module makes predictions based on all the preliminary delivery routes. The predicted time includes all the time generated when the nodes are congested. When the local optimal module is selecting, if the first node is an unavailable node, it means congestion has occurred, and the path will be abandoned. In the global optimal module, the path is not abandoned, but the congested time is taken into account, and the time of the subsequent nodes is calculated at the same time to form the final time for comparison with the time taken by the local optimal path. If the first node is congested, but all the subsequent nodes are unobstructed, the overall time is not long. Setting the global optimal module can avoid missing the optimal path.

[0037] The comparison rule is to compare the time of all the preliminary delivery paths with the time of the local optimal path respectively. If the local optimal path takes the least time, the local optimal path is used as the final delivery path; if there is a path in the preliminary delivery path that takes less time than the local optimal path, it is recorded as the target delivery path, and then the cost value corresponding to the target delivery path is calculated and compared with the cost value of the local optimal path. If the cost value of the target delivery path is lower, it is used as the final delivery path; if the cost value of the target delivery path is higher than the cost value of the local optimal path, it is necessary to select the final delivery path according to the comprehensive evaluation index;

[0038] The comprehensive evaluation index is calculated by comprehensively considering the time difference and the cost difference. The time difference is the time of the target delivery path minus the time of the local optimal path; the cost difference is the cost value of the target delivery path minus the cost value of the local optimal path; then the standard time difference and the standard cost difference are calculated, and the time difference and the cost difference are compared with the time of the local optimal path and the cost value of the local optimal path to obtain a ratio, and the time and cost are set with weights respectively. The comprehensive evaluation index is calculated by the weighted sum method; the comprehensive evaluation index reflects the comprehensive performance of the target delivery path relative to the local optimal path in terms of time and cost. If the comprehensive evaluation index is less than zero, the target delivery path is selected as the final path; otherwise, the local optimal path is selected as the final delivery path. It can optimize the delivery time as much as possible and improve customer satisfaction while ensuring cost-effectiveness.

[0039] Continue reading Figure 1 , the path time subsystem is used to record the final delivery path information, total predicted time, total actual time and predicted time and actual time information of each node selected each time, and establish a path time list for storage; the path time subsystem calculates the time information of each node through the arrival time and departure time of the node.

[0040] Specifically, Figure 3 As shown in the figure, the path time subsystem uses the path event list to store the final distribution path information, predicted time, actual time and time information of each node, and sets up real-time monitoring of the time information of each node. The time information of each node is calculated based on the time when the logistics arrives at the node and the time when it leaves the node. The final distribution path information includes the information of each node passed, and the predicted time and actual time of each node are followed by each node information. The total predicted time and total actual time of the final distribution path, as well as the predicted time and actual time information of each node are recorded. Through real-time recording, the effect of timely feedback optimization can be achieved, and continuous optimization can be performed to make the time prediction more accurate.

[0041] In this embodiment, a path simulation module, a local optimal module, and a global optimal module are set to comprehensively select and determine the final delivery path. The path simulation module is used to select the preliminary delivery path according to the order information. The local optimal module and the global optimal module are used to analyze, screen and compare the preliminary delivery paths respectively. The local optimal paths obtained by the global optimal module and the local optimal module are compared one by one to select the delivery path with the best combination of time and cost. The comparison rules are set to comprehensively analyze the differences in time and cost to obtain comprehensive evaluation indicators. The local optimal path is selected by the local optimal module, and the time and cost data of all paths are comprehensively considered by the global optimal module, and compared with the local optimal path to select the final delivery path.

[0042] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0043] This application combines the local optimal module and the global optimal module, comprehensively considers all paths in the global scope, uses a global perspective to comprehensively consider time and cost, optimizes resource allocation, and improves the quality of path selection, achieving global consideration of simulated paths, reasonable selection of paths, and reducing delivery time.

[0044] Embodiment 2: In embodiment 1, the local optimal module and the global optimal module are used to comprehensively consider and select the final delivery path. However, during the delivery process, the situation of the node may change at any time, so the above embodiment is further limited.

[0045] Continue reading Figure 1 , the system also includes a node collaboration subsystem, which is used to update the current node status and the next node status in real time, and choose whether to re-simulate the path according to the next node status; if the next node status shows congestion, the information is transmitted to the path simulation selection subsystem to re-simulate the secondary path and select a new path; if the next node status is normal, no operation is required;

[0046] The secondary path simulation is to use the path simulation selection subsystem to re-simulate the delivery path with the current node as the starting point to generate a new preliminary delivery path; the local optimal module and the global optimal module receive the new preliminary delivery path, and perform path analysis and selection based on the new node status data to generate a new final delivery path; the path time subsystem will record the time and reason of the secondary path simulation and the change information of the nodes before and after the secondary path simulation;

[0047] Specifically, the node collaboration subsystem is used to monitor and update the current node status and the next node status in real time. When the node collaboration subsystem detects a change in the node status, it displays a congested or unavailable status and sends the updated node status information to the path simulation module. The path simulation module re-simulates the distribution path according to the new node status information and generates a new preliminary distribution path. The node collaboration subsystem can monitor the status of each node in real time through sensors, GPS positioning and other technical means, including congestion, cargo handling capacity, availability, etc. A data sharing mechanism is established between the node collaboration subsystem and the path simulation selection subsystem to ensure that both parties can obtain the latest node status and path simulation results in real time.

[0048] In the above embodiment, a path simulation selection subsystem is used to select a final distribution path that balances local and global optimality and fully saves time and cost. However, in the transportation process, due to the uncertainty of time, the state of the node is constantly changing. If the final distribution path always maintains the original path for transportation when the node state changes, it will cause errors in the overall predicted time, which is very likely to cause the goods to be overdue. In this embodiment, a node collaboration subsystem is used to monitor the status information of the current node and the next node in real time, and adjust at any time according to the status information of the next node to achieve the effect of dynamic distribution path, and horizontal collaboration is carried out between different nodes, which strengthens the data interaction and dynamic update mechanism between systems, improves the intelligence and flexibility of logistics supply chain management, and improves logistics efficiency. Especially in an emergency state, the distribution path can be flexibly adjusted according to the actual situation, avoiding distribution delays caused by node congestion or failure, which greatly guarantees the transportation time of the goods. Traditional path planning is often static, while in this embodiment, dynamic optimization of the path is realized, and the distribution path can be continuously adjusted according to real-time data, which improves the distribution efficiency.

[0049] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0050] This application adopts a node collaboration subsystem to monitor the node status in real time, which can respond to changes quickly and ensure that the distribution path is always kept in the optimal state; it realizes the effect of dynamic distribution path; it conducts horizontal collaboration between different nodes, strengthens the data interaction and dynamic update mechanism between systems, improves the intelligence and flexibility of logistics supply chain management, and achieves the effect of reducing delivery time and reducing costs.

[0051] Embodiment 3: In the above embodiment, the effect of dynamically adjusting the distribution path is achieved by real-time monitoring and updating the current node status and the next node status information. This embodiment makes further limitations on the above embodiment.

[0052] Continue reading Figure 1 , the system also includes a node reservation subsystem, which is used to evaluate the priority of nodes and select nodes with high priority as reserved nodes; the priority evaluation is analyzed based on the historical usage of nodes in the path time list, including the processing capacity, average processing time, congestion and usage frequency of the nodes;

[0053] The processing capacity is inversely proportional to the congestion situation. The less congestion, the higher the processing capacity. The processing time of each node is recorded, and the average processing time is calculated based on the number of orders processed within a preset time and the total processing time. The usage frequency is the ratio of the number of orders passing through the node within a preset time to the total number of orders.

[0054] Specifically, the priority evaluation needs to obtain the recorded information in the path time list of the path time subsystem for analysis. The usage of a certain node can be obtained through the final distribution path information. According to the preset time, preferably, the analysis can be performed based on 24 hours as the preset time. The usage frequency of the node is determined according to the ratio of the order volume passing through the node and the total order volume within 24 hours. For example, within 24 hours, a total of 1,000 orders are transported, of which 100 orders are transferred from node A, then the usage frequency of node A is 10%. By calculating all the nodes in the path time list, the usage frequency of all nodes can be obtained; the congestion situation corresponding to the node is obtained according to the path time list. In the above embodiment, the status information of the node is monitored and updated in real time, and the congestion situation of the node is obtained according to the status information of the node. The more congestion, the lower the processing capacity; conversely, the less congestion, the higher the processing capacity; the processing time of each node is recorded, and the average processing time is calculated according to the order volume processed within the preset time and the total processing time. The preset time can be adjusted according to the actual situation. Preferably, it should be consistent with the time for calculating the usage frequency, so that the evaluation of the node can be more accurate.

[0055] When receiving an order, the reserved node will reserve idle time for transporting urgent orders; the range of the idle time is set based on the shortest time for a single order passing through the reserved node and the shortest relative time for multiple orders; the shortest time for a single order is the shortest time used for the transportation process of an order passing through the reserved node; the shortest relative time for multiple orders is the average time used for the transportation process of all orders passing through the reserved node;

[0056] Specifically, the shortest time for a single order is the shortest time used in the transportation process of the order passing through the reserved node. The transportation process mentioned here refers to the total time taken for the order from the start of shipment to delivery. The final delivery paths and the total actual time taken for all the orders passing through the reserved nodes in the path time list are obtained. The shortest time in the total actual time is taken as the shortest time for a single order; the average of all the total actual times is the relative shortest time for multiple orders. The idle time is a period of time that the reserved node deliberately sets aside in addition to processing regular orders to deal with possible urgent orders. This period of time is set to ensure that when urgent orders appear, the reserved node can respond quickly without affecting the normal processing of other orders.

[0057] The rush order is to obtain the limit time and predicted time of the order, and use the predicted time to make a ratio between the limit time. If the ratio is not less than 70%, it is defined as a rush order; if the ratio is less than 70%, the order is subjected to reverse path simulation, and the reverse path simulation is to reversely deduce the final delivery path based on the latest deadline to obtain the maximum processing time of each node, and obtain the actual time information of each node in the path time list in real time during the transportation process, and compare the actual time with the maximum processing time in real time. If the actual time of a node exceeds the maximum processing time, it is marked as a timeout node, and the number of allowed timeout nodes is set. If the actual number of timed-out nodes is greater than the allowed number of timeout nodes, it is defined as a rush order; when transmitting, the rush order gives priority to reserved nodes for transmission, and uses the idle time of reserved nodes for priority processing.

[0058] Specifically, the limit time is obtained based on the latest arrival time, that is, the maximum time that can be transported and processed on the road and at the node. The limit time is compared with the predicted time generated by the forward path simulation. If the ratio is above 70%, it is defined as an urgent order. For example, the limit time is 48 hours, which means that the transportation time cannot exceed 48 hours to reach the user's designated location on time. Once it exceeds, it will cause a timeout. However, the predicted time at this time is 36 hours. The ratio of the two is 75%. If it exceeds 70%, then this order is very likely to be delayed if it is transported along the normal route, and it is defined as an urgent order.

[0059] Specifically, the reverse path simulation takes the destination as the starting point, and reversely deduces the maximum slack time for node processing and transportation based on the latest deadline time, removes the journey time of the transportation process, and the time in between is the processing time of the node, and obtains two simulated paths, one with the largest number of nodes and the other with the smallest number of nodes. The maximum processing time of the node is calculated based on the number of nodes respectively, and the actual processing time of the node is compared with the maximum processing time in real time. If the actual processing time of a node exceeds the maximum processing time, it is marked as a timeout node, and the number of allowed timeout nodes is set. The number of allowed timeout nodes is determined based on the total number of nodes passed by the final distribution path. Preferably, 20% of the total number of nodes is taken as the number of allowed timeout nodes. The actual time and maximum processing time of the node are monitored in real time. If a timeout occurs, it is marked immediately. Preferably, a limit is also set for the timeout time. If the timeout time at a certain node exceeds 50% of the maximum processing time, a secondary marking is performed. Every time the maximum processing time is exceeded, a timeout is marked again. For example, if there is a delivery route with 10 nodes and the node timeout percentage is set to 20%, the number of allowed timeout nodes is 2, which means that in the actual delivery process, at most 2 nodes are allowed to have actual time exceeding the maximum processing time. During the transportation process, the actual time information of each node in the path time list is obtained in real time and compared with the maximum processing time. Suppose that in the real-time comparison process, it is found that the actual time of 3 nodes exceeds the maximum processing time, and the timeout does not exceed 50% of the maximum processing time, which exceeds the allowed number of timeout nodes 2, so the order is defined as an urgent order; in another case, if the actual time of one node exceeds the maximum processing time, and the timeout has exceeded 50% of the maximum processing time, a second mark is made, which is equivalent to two nodes timing out. If it still times out and exceeds 50% of the maximum processing time, it is marked again. At this time, the number of timeout nodes is 3, which is greater than the allowed number of timeout nodes, and it is defined as an urgent order for processing.

[0060] The use of reserved nodes can effectively handle urgent orders to the greatest extent and prevent timeouts. At the same time, using reserved nodes for transmission can balance time and cost. Under normal circumstances, for urgent orders, in order to prevent timeouts, the delivery time will be shortened indefinitely, resulting in high costs. In this embodiment, nodes in reasonable locations are selected as reserved nodes through node priority evaluation, which can minimize costs and avoid timeouts when facing urgent orders.

[0061] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0062] This application analyzes the usage of nodes, divides priorities and sets reserved nodes, and uses reserved nodes to process urgent orders, which can balance time and cost to the greatest extent and prevent timeouts, achieving a more flexible and efficient effect.

[0063] Embodiment 4: In the above embodiment, the reserved node is obtained through priority evaluation, and the idle time is set by the time range of the shortest time of a single order and the relatively shortest time of multiple orders, but the range of the idle time cannot be adjusted dynamically.

[0064] Continue reading Figure 1 The node reservation subsystem also includes an idle period adaptation module, which is used to dynamically adjust the idle time and optimize the relative shortest time of multiple orders; the dynamic adjustment of idle time is to monitor the idle time usage of the reserved node in real time and make dynamic adjustments; the dynamic adjustment is to make a comprehensive analysis based on the idle time usage rate and the relative shortest time of multiple orders of the reserved node, generate the idle time usage rate based on the actual idle time and the preset idle time, set the idle time usage rate threshold range, if the actual idle time usage rate is lower than the lower limit of the idle time usage rate threshold range, it means that the idle time resources of the reserved node are in excess; if the actual idle time usage rate is within the idle time usage rate threshold range, it means that the idle time resources of the reserved node are sufficient; if the actual idle time usage rate is higher than the upper limit of the idle time usage rate threshold, it means that the idle time resources of the reserved node are insufficient.

[0065] Specifically, there is an interactive influence between the idle time and the shortest time relative to multiple orders. By adjusting the idle time, the idle time utilization rate threshold range is set to between (93%-97%) to ensure that the utilization rate of each reserved node can reach more than 95%. The utilization rate is the ratio of the actual idle time to the preset idle time. A utilization rate of 95% means that 95% of the preset idle time is used. The set utilization rate threshold range actually includes a reasonable error range. The reasonable error range is set to 2%, thereby generating a threshold range.

[0066] The idle period adaptation module obtains the historical order processing time data of each reserved node and the relative shortest time of multiple orders, analyzes the distribution of the relative shortest time of multiple orders and sorts them, uses the quantile method to screen out the reserved nodes with the longest relative shortest time of multiple orders, and defines the reserved node with the longest relative shortest time of multiple orders as the bearer reserved node, obtains the shortest time of all single orders in the reserved node with the long relative shortest time of multiple orders, uses the quantile method to analyze, obtains single orders with long time and records the time range, and re-sets the idle time for the reserved node and the bearer reserved node according to the time range and the relative shortest time of multiple orders.

[0067] Among them, the idle period adaptation module obtains the historical order processing time data of each reserved node and the relative shortest time of multiple orders, sorts the relative shortest time of multiple orders, uses the quantile method to screen out the reserved nodes with longer relative shortest time of multiple orders, and defines the reserved node with the longest relative shortest time of multiple orders as the bearer reserved node. The reserved node with the longest time is selected from all reserved nodes as the bearer reserved node. The purpose is to divide the reserved nodes according to the length of time. The processing time of orders with longer time and the required amount of reserved node resources are relatively long, and the relative shortest time of multiple orders is comprehensively determined based on the time of a single order. If the relative shortest time of multiple orders is the longest, it means that the time of a single order may be longer; obtain the shortest time of all single orders in the reserved node with a longer relative shortest time of multiple orders, use the quantile method to analyze, obtain a single order with a longer time and record the time range, screen out a single order with a longer time, and obtain the time range, and screen other subsequent orders according to the time range. Orders within the time range are all orders with a longer time, and such orders are mainly allocated to the bearer reserved node.

[0068] The effect to be achieved above is to screen and divide the orders according to their length, allocate single orders with longer time to the bearer reserved nodes, and set the idle time for the bearer reserved nodes according to the longer single order time and the relative shortest time of multiple orders; for other orders, the order time is also evenly distributed according to the sorting generation, and the relative shortest time of multiple orders of the remaining other reserved nodes is at a relatively average value, thereby improving the relative shortest time of multiple orders of most reserved nodes; the allocated amount of idle time for each reserved node is allocated according to the idle time utilization rate calculated above, and the allocation is adjusted synchronously with the changes in idle time.

[0069] For example, the original idle time utilization rate of reserved node A was 98%, indicating that the idle time resources were insufficient. After dynamic adjustment and optimization of the shortest time relative to multiple orders, reserved node A was set as a bearer reserved node. Combined with the original idle time utilization rate, the idle time of bearer reserved node A should increase to a certain extent. If there are many orders for a longer time at present, the idle time regained by bearer reserved node A is 150% of the original idle time; the original idle time utilization rate of reserved node B was also 98%, indicating that the idle time resources were insufficient. After dynamic adjustment and optimization of the shortest time relative to multiple orders, reserved node B was still set as a reserved node. Since it is a reserved node, the order time received is within a shorter time range. Combined with the original idle time utilization rate, the idle time of reserved node B can remain unchanged; based on real-time monitoring of all reserved nodes, adjustments can be made based on the idle time utilization rate.

[0070] Specifically, a multi-order relative shortest time is generated for all orders of a certain reserved node, the multi-order relative shortest time of all reserved nodes is obtained, and the reserved nodes with large values ​​in the multi-order relative shortest time are screened out using the quantile method. The quantile method first sorts all multi-order relative shortest times from small to large, and then finds the data point at the 75% position of the data set. Specifically, if the total amount of data is n, then the position of the quantile is roughly at n*0.75. If the calculated position is not an integer, it is usually rounded up to determine the specific value of the quantile. For example, there are multiple reserved nodes, and the relative shortest time of multiple orders of all reserved nodes are (56, 48, 43, 42, 41, 45, 70, 23, 36, 35, 53, 49, 45), all in hours. There are 13 nodes in total, and they are sorted from small to large as (23, 35, 36, 41, 42, 43, 45, 45, 48, 49, 53, 56, 70). 75% of 13 is 9.75, which is rounded up to 10. The quantile is the number ranked 10th, that is, 49, which means that 75% of the order time is 49 hours or less, while the remaining 25% is longer. Therefore, we select the shortest relative time of multiple orders exceeding 49 hours as the longer time, that is, 53, 56, and 70. We select these three reserved nodes, and obtain the shortest time of all single orders of these three reserved nodes for analysis. We use the same method to sort the shortest time of single orders in these three reserved nodes, select the orders with longer time, and transfer all the orders with longer time to the reserved node with the longest time among the three reserved nodes, that is, the reserved node with 70 hours. We define this node as the load-bearing reserved node to process orders with longer time.

[0071] In order to use reserved nodes more reasonably, the relative shortest time of multiple orders is adjusted to make the time difference of each order of the reserved node as consistent as possible, and the orders with longer overall time are uniformly assigned to the same reserved node, with longer idle time, and different reserved nodes are used. At the same time, the relative shortest time of multiple orders is shortened as much as possible to further improve logistics efficiency. The relative shortest time of multiple orders is closely related to the time of all orders as a whole. If the relative shortest time of multiple orders is longer, it will lead to a waste of reserved node resources. If the relative shortest time of multiple orders is shorter, it will lead to insufficient reserved node resources and reduce processing efficiency. Through collaboration between nodes, the idle time of each reserved node is within a reasonable range.

[0072] In the above embodiment, if there are multiple orders with a long estimated time, but other orders are short, these long orders will affect the shortest time relative to multiple orders, resulting in longer idle time of the reserved node and waste of resources. In this embodiment, the time of all orders is comprehensively considered so that the shortest time relative to multiple orders of each reserved node reaches a reasonable level. Preferably, a minimum idle time is set for the carrying reserved node to ensure flexibility when receiving long orders.

[0073] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0074] The present application sets up an idle period adaptation module to dynamically adjust the idle time of the reserved nodes and optimize the relative shortest time of multiple orders. Based on a comprehensive analysis of the idle time utilization rate and the relative shortest time of multiple orders of the reserved nodes, the orders are reasonably allocated to different reserved nodes, thereby improving the overall processing efficiency and achieving the effect of effective utilization of reserved node resources.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A logistics supply chain management system, characterized in that: The system comprises: A path simulation selection subsystem is used to receive order information, perform path simulation and select the final transportation path; the path simulation selection subsystem includes a path simulation module, a local optimal module and a global optimal module; The path time subsystem is used to record the final delivery path information, total predicted time, total actual time and predicted time and actual time information of each node selected each time, and establish a path time list for storage; the path time subsystem calculates the time information of each node by the arrival time and the departure time of the node; The path simulation module is used to obtain order information, and obtain the location information of the node that can process the order information according to the order information, simulate the delivery path according to the location information of the node, and generate the preliminary delivery path. The number of the preliminary delivery paths is equal to the number of nodes. The local optimal module is used to receive the preliminary distribution path information in the path simulation module, obtain the congestion level information of each preliminary distribution path; select the preliminary distribution path with a congestion level lower than the set information and record it as the predetermined distribution path; calculate the cost value corresponding to each predetermined distribution path, select the predetermined distribution path with the smallest cost value as the local optimal path; and predict the time required for transportation of the local optimal path to form the local optimal path time; The global optimal module predicts the transportation time required for each initially selected delivery path, compares it with the local optimal path according to the comparison rules, and selects the final delivery path; The comparison rule is to compare the time of all the preliminary delivery paths with the time of the local optimal path. If the local optimal path takes the least time, the local optimal path will be used as the final delivery path; if there is a path in the preliminary delivery paths that takes less time than the local optimal path, it will be recorded as the target delivery path, and then the cost value corresponding to the target delivery path will be calculated and compared with the cost value of the local optimal path. If the cost value of the target delivery path is lower, it will be used as the final delivery path; if the cost value of the target delivery path is higher than the cost value of the local optimal path, it is necessary to select the final delivery path based on the comprehensive evaluation index.

2. A logistics supply chain management system as claimed in claim 1, characterized in that: The system also includes a node collaboration subsystem, which is used to update the current node status and the next node status in real time, and choose whether to re-simulate the path according to the next node status; if the next node status shows congestion, the information is transmitted to the path simulation selection subsystem to re-simulate the secondary path and select a new path; if the next node status is normal, no operation is required.

3. A logistics supply chain management system as claimed in claim 2, characterized in that: The secondary path simulation uses the path simulation selection subsystem to re-simulate the delivery path with the current node as the starting point to generate a new preliminary delivery path; the local optimal module and the global optimal module receive the new preliminary delivery path, and perform path analysis and selection based on the new node status data to generate a new final delivery path; the path time subsystem will record the time and reason of the secondary path simulation and the change information of the nodes before and after the secondary path simulation.

4. A logistics supply chain management system as claimed in claim 1, characterized in that: The system also includes a node reservation subsystem for evaluating the priority of nodes and selecting nodes with high priority as reserved nodes; the priority evaluation is based on the analysis of the historical usage of the nodes in the path time list, including the processing capacity, average processing time, congestion and usage frequency of the nodes.

5. A logistics supply chain management system as claimed in claim 4, characterized in that: The processing capacity is inversely proportional to the congestion situation. The less congestion, the higher the processing capacity. The processing time of each node is recorded, and the average processing time is calculated based on the number of orders processed within the preset time and the total processing time. The usage frequency is based on the ratio of the order volume passing through the node to the total order volume within a preset time.

6. A logistics supply chain management system as claimed in claim 4, characterized in that: When receiving an order, the reserved node will reserve idle time for transporting urgent orders; the range of the idle time is set based on the shortest time for a single order passing through the reserved node and the shortest relative time for multiple orders; the shortest time for a single order is the shortest time used for the transportation process of an order passing through the reserved node; The multi-order relative shortest time is the average time used in the transportation process of all orders passing through the reserved node.

7. A logistics supply chain management system as claimed in claim 6, characterized in that: The rush order is to obtain the limit time and predicted time of the order, and use the predicted time to make a ratio between the limit time. If the ratio is not less than 70%, it is defined as a rush order; if the ratio is less than 70%, the order is subjected to reverse path simulation, and the reverse path simulation is to reversely deduce the final delivery path based on the latest deadline to obtain the maximum processing time of each node, and obtain the actual time information of each node in the path time list in real time during the transportation process, and compare the actual time with the maximum processing time in real time. If the actual time of a node exceeds the maximum processing time, it is marked as a timeout node, and the number of allowed timeout nodes is set. If the actual number of timed-out nodes is greater than the allowed number of timeout nodes, it is defined as a rush order; when transmitting the rush order, reserved nodes are given priority for transmission, and the idle time of the reserved nodes is used for priority processing.

8. A logistics supply chain management system as claimed in claim 4, characterized in that: The node reservation subsystem also includes an idle period adaptation module, which is used to dynamically adjust the idle time and optimize the relative shortest time of multiple orders; the dynamic adjustment of idle time is to monitor the idle time usage of the reserved node in real time and make dynamic adjustments; The dynamic adjustment is to conduct a comprehensive analysis based on the idle time utilization rate and the shortest time relative to multiple orders of the reserved node, generate the idle time utilization rate based on the actual idle time and the preset idle time, set the idle time utilization rate threshold range, and if the actual idle time utilization rate is lower than the lower limit of the idle time utilization rate threshold range, it means that the idle time resources of the reserved node are in excess; If the actual idle time usage rate is within the idle time usage rate threshold range, it means that the idle time resources of the reserved node are sufficient; If the actual idle time usage is higher than the idle time usage upper threshold, it means that the idle time resources of the reserved node are insufficient.

9. A logistics supply chain management system as claimed in claim 8, characterized in that: The idle period adaptation module obtains the historical order processing time data of each reserved node and the relative shortest time of multiple orders, analyzes the distribution of the relative shortest time of multiple orders and sorts them, uses the quantile method to screen out the reserved nodes with the longest relative shortest time of multiple orders, and defines the reserved node with the longest relative shortest time of multiple orders as the bearer reserved node, obtains the shortest time of all single orders in the reserved node with the long relative shortest time of multiple orders, uses the quantile method to analyze, obtains single orders with long time and records the time range, and re-sets the idle time for the reserved node and the bearer reserved node according to the time range and the relative shortest time of multiple orders.

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