Logistics resource dynamic allocation method based on big data

Abstract

The invention belongs to the field of logistics resource dynamic allocation, relates to a data analysis technology, and is used for solving the problem that an existing logistics resource allocation method cannot split whole-process logistics resource allocation into logistics resource allocation among a plurality of nodes, in particular to a logistics resource dynamic allocation method based on big data. Comprising the following steps: integrating information, building a logistics management platform, selecting a head node, selecting an intermediate node, distributing a transportation tool, and finally evaluating the logistics service quality. According to the invention, the whole flow of logistics distribution can be divided into a plurality of stages from one node to another node, so that the logistics resource distribution of the whole flow can be divided into the logistics resource distribution among a plurality of nodes, the optimal distribution scheme among the nodes is realized, the optimal distribution scheme of the whole flow is realized, and the distribution efficiency of the whole flow is improved. The efficiency of logistics distribution is improved, dynamic and accurate distribution of logistics resources is realized, and the resources are utilized to the maximum extent.

Description

Technical Field

[0001] The present invention belongs to the field of dynamic allocation of logistics resources, relates to data analysis technology, and specifically is a dynamic allocation method of logistics resources based on big data. Background Art

[0002] With the booming development of global trade and the rapid rise of e-commerce, the logistics industry is facing unprecedented opportunities and challenges. In the process of logistics operations, the effective allocation of logistics resources has become a key factor affecting corporate competitiveness and operational efficiency.

[0003] Traditional logistics resource allocation methods often rely on empirical rules and are difficult to adapt to the rapid changes in cargo flow and direction in modern logistics business, as well as customers' increasingly stringent requirements for delivery timeliness and service quality. For example, during the peak period of logistics distribution, transport vehicles in some areas may be overloaded or empty due to unreasonable task allocation. At the same time, the use of storage space is uneven, with some warehouses full and others idle, resulting in high logistics costs and low service efficiency.

[0004] Existing logistics resource allocation methods are often unable to divide the entire logistics distribution process into multiple stages from one node to another, and are further unable to split the entire process of logistics resource allocation into logistics resource allocation between multiple nodes, resulting in the inability to find the optimal allocation plan and poor service quality.

[0005] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0006] The purpose of the present invention is to provide a logistics resource dynamic allocation method based on big data, which is used to solve the problem that the existing logistics resource allocation method cannot divide the whole process of logistics distribution into multiple stages from one node to another node, and further cannot split the logistics resource allocation of the whole process into logistics resource allocation between multiple nodes; The technical problem to be solved by the present invention is how to provide a dynamic logistics resource allocation method based on big data that can divide the entire process of logistics distribution into multiple stages from one node to another, and then split the logistics resource allocation of the entire process into logistics resource allocation between multiple nodes.

[0007] The purpose of the present invention can be achieved by the following technical solutions: A method for dynamic allocation of logistics resources based on big data, comprising the following steps: Step 1: Integrate information: Build a logistics management platform, which includes an order system, inventory warehouse system, and distribution center system; divide the entire process of logistics distribution into several stages from the inventory warehouse of the goods to the customer's delivery address through multiple distribution centers. The inventory warehouse and distribution center are marked as nodes of logistics distribution, among which the inventory warehouse is recorded as the head node and the distribution center is recorded as the intermediate node; Step 2: Select the head node: Get the inventory warehouse of the order that needs logistics distribution, and generate the warehouse distance sequence and warehouse use sequence according to the inventory warehouse, select the alternative warehouse from the warehouse distance sequence, and then determine the head node for logistics distribution of the goods based on the sequence number of the alternative warehouse in the warehouse use sequence; Step 3: Selecting the intermediate node: After selecting the head node, select the first intermediate node with the head node as a reference, select the second intermediate node with the first intermediate node as a reference, select the third intermediate node with the second intermediate node as a reference, and so on until all the intermediate nodes are determined; Step 4: Assigning transportation tools: Each time a delivery task needs to be arranged after selecting and completing an intermediate node, obtain the delivery time difference of different transportation tools in the intermediate node, and mark the transportation tool with the smallest delivery time difference as the transportation tool for delivery; Step 5: Logistics service quality assessment: Complete the distribution of goods by allocating logistics resources and calculate the logistics service coefficient FW. Use the logistics service coefficient FW to judge whether the logistics service quality meets the requirements.

[0008] Furthermore, in step one, the order system receives order information pushed by various platforms in real time, and the order information includes: the weight, volume, order time, delivery address and inventory warehouse where the goods are stored; the inventory warehouse records the quantity HL of various goods stored in real time; the distribution center system obtains the distribution center information owned by the logistics company in real time, and the distribution center information includes the remaining storage space SK and the number of vehicles owned CY.

[0009] Furthermore, in step 2, the specific generation process of the warehouse distance sequence is as follows: the actual address of the inventory warehouse is recorded as the departure address, the departure address and the delivery address are input through the online map to obtain the total transportation length QC, and the inventory warehouses are arranged in order from small to large according to the total transportation length QC to obtain the warehouse distance sequence; the specific generation process of the warehouse usage sequence is as follows: the cargo volume HL of the goods stored in the inventory warehouse is obtained, the last time the goods were transported out of the inventory warehouse and marked as the last time, the order time of the current goods is marked as the current time, and the difference between the current time and the last time is calculated to obtain the delivery interval FJ; the cargo volume HL and the delivery interval FJ are numerically calculated to obtain the usage coefficient SY of the goods in the inventory warehouse, and the inventory warehouses are arranged in order from large to small according to the usage coefficient SY to obtain the warehouse usage sequence.

[0010] Furthermore, in step three, the selection process of the first intermediate node includes: after the head node is selected, the distribution center within a radius of R with the inventory warehouse corresponding to the head node as the center is obtained and marked as an alternative node, the address of the alternative node and the delivery address are input through the online map to obtain the full length BX of the alternative node, the full length BX of the alternative node is compared with the full length QC of the transportation, if the full length BX of the alternative node is greater than the full length QC of the transportation, it is eliminated, otherwise it is retained, and the remaining alternative nodes after elimination are marked as reserved nodes; the remaining storage space SK and the number of vehicles CY of the distribution center corresponding to the reserved node are obtained, the remaining storage space SK and the number of vehicles CY are numerically calculated to obtain the distribution coefficient PS of the reserved node, and the reserved node with the largest distribution coefficient PS is selected as the first intermediate node after the head node.

[0011] Furthermore, the second intermediate node is selected with reference to the first intermediate node, and the third intermediate node is selected with reference to the second intermediate node... until there is no alternative node whose total length BX is less than the distance from the current intermediate node to the delivery address.

[0012] Furthermore, in step four, the specific process of allocating transportation tools includes: each time a delivery task needs to be arranged after the intermediate node is selected, the transportation distance of the intermediate node and the full-load transportation speed of the transportation tool are obtained, the transportation distance is the actual distance from the intermediate node to the next intermediate node or the delivery address, and the full-load transportation speed is the average speed of the transportation tool when it is fully loaded; the transportation distance is calculated by ratioing the full-load transportation speeds of different transportation tools to obtain the delivery time; an expected delivery time threshold is set, the delivery time is subtracted from the expected delivery time threshold, and the absolute value is taken to calculate the delivery time difference, and the transportation tool with the smallest delivery time difference is marked as the transportation tool for delivery.

[0013] Furthermore, in step five, the calculation process of the logistics service coefficient FW includes: completing the distribution of goods by allocating logistics resources, and obtaining the total number of completed orders ZS and customer complaint information, the complaint information includes the number of shipping errors FC, the number of delayed deliveries WJ, and the number of damaged goods SH; the logistics service coefficient FW is obtained by combining the number of shipping errors FC, the number of delayed deliveries WJ, the number of damaged goods SH, and the total number of orders ZS.

[0014] Furthermore, in step five, the specific process of judging whether the logistics service quality meets the requirements includes: comparing the logistics service coefficient FW with the preset logistics service threshold FWmax: if the logistics service coefficient FW is less than the logistics service threshold FWmax, then it is judged that the logistics distribution quality based on the dynamic allocation of logistics resources meets the requirements and no processing is required; if the logistics service coefficient FW is greater than or equal to the logistics service threshold FWmax, then it is judged that the logistics distribution quality based on the dynamic allocation of logistics resources does not meet the requirements; a node observation signal is generated and the signal is sent to the mobile phone terminal of the manager, and corresponding measures are taken for optimization.

[0015] The present invention has the following beneficial effects: 1. By integrating the information of orders, inventory warehouses and distribution centers, the optimization planning of the whole process is realized, which can provide data support for the rapid and accurate selection of the optimal logistics path and resource allocation plan; at the same time, the reasonable selection of head nodes, intermediate nodes and transportation tools can avoid the waste of resources and unnecessary transportation costs, improve the utilization rate of vehicles and storage space, and thus reduce the overall logistics costs; 2. By comprehensively considering multiple factors such as cargo volume, delivery interval, remaining space in the distribution center, and number of vehicles, the dynamic and accurate allocation of logistics resources is achieved, maximizing the utilization of resources; 3. Through logistics service quality assessment, problems in logistics distribution can be discovered in time, and corresponding measures can be taken to optimize them; at the same time, based on the analysis and calculation of big data, scientific data support is provided for logistics management, making decisions more accurate and reasonable, and being able to flexibly respond to changes in orders and dynamic adjustments in the logistics environment, quickly respond to market demand, and improve the company's ability to respond in the complex and ever-changing logistics market; 4. By dividing the entire process of logistics distribution into multiple stages from one node to another, the logistics resource allocation of the entire process can be split into the logistics resource allocation between multiple nodes, and the optimal allocation plan between nodes can be achieved, thus achieving the optimal allocation plan for the entire process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 This is an architecture diagram of the logistics management platform of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1-2 As shown, a method for dynamic allocation of logistics resources based on big data includes the following steps: Step 1: Integrate information: Build a logistics management platform, which includes an order system, an inventory warehouse system, and a distribution center system; the order system receives order information pushed by various platforms in real time, including: the weight, volume, order time, delivery address, and inventory warehouse where the goods are stored; the inventory warehouse records the quantity HL of various goods stored in real time; the distribution center system obtains the distribution center information owned by the logistics company in real time, including the remaining storage space SK and the number of vehicles owned CY; the entire logistics distribution process is divided into several stages from the inventory warehouse of the goods through multiple distribution centers to the customer's delivery address, Inventory warehouses and distribution centers are marked as nodes for logistics distribution, where inventory warehouses are marked as head nodes and distribution centers are marked as intermediate nodes. The entire process of selecting appropriate logistics resources for logistics distribution can be regarded as the process of selecting head nodes, several intermediate nodes, and allocating transportation tools. By integrating the information of orders, inventory warehouses, and distribution centers, the optimization planning of the entire process is realized, which can provide data support for the rapid and accurate selection of the optimal logistics path and resource allocation plan. At the same time, the rational selection of head nodes, intermediate nodes, and transportation tools avoids waste of resources and unnecessary transportation costs, improves the utilization rate of vehicles and storage space, and thus reduces the overall logistics costs. Step 2: Select the head node: obtain the inventory warehouse of the order that needs logistics distribution, and generate the warehouse distance sequence and warehouse use sequence according to the inventory warehouse; the specific generation process of the warehouse distance sequence is: record the actual address of the inventory warehouse as the departure address, enter the departure address and the delivery address through the online map to obtain the total transportation length QC, and arrange the inventory warehouses in the order of the total transportation length QC from small to large to obtain the warehouse distance sequence; the specific generation process of the warehouse use sequence is: obtain the cargo volume HL of the cargo stored in the inventory warehouse, record the last time the cargo was transported out of the inventory warehouse and mark it as the last time, and mark the order time of the current cargo as the current time. The previous time is calculated by taking the difference between the current time and the previous time to get the delivery interval FJ; the usage coefficient SY of the goods in the inventory warehouse is obtained by the formula SY=k1×HL+k2×FJ, where k1 and k2 are both proportional coefficients. The larger the value of the usage coefficient SY, the less goods are shipped from the inventory warehouse; the inventory warehouses are arranged in descending order according to the usage coefficient SY to get the warehouse usage sequence; when selecting the head node, first select the first L1 delivery warehouses from the warehouse distance sequence as candidate warehouses, and then compare the serial numbers of the L1 candidate warehouses in the warehouse usage sequence, and the candidate warehouse with the smallest serial number is marked as the head node for logistics distribution of the goods; Step 3: Select the intermediate node: After selecting the head node, obtain the distribution center within the radius R with the inventory warehouse corresponding to the head node as the center and mark it as an alternative node. Enter the address of the alternative node and the delivery address through the online map to obtain the full length BX of the alternative node. Compare the full length BX of the alternative node with the full length QC of the transportation. If the full length BX of the alternative node is greater than the full length QC of the transportation, it will be eliminated. Otherwise, it will be retained and the remaining alternative nodes after elimination will be marked as reserved nodes. Obtain the remaining storage space SK and the number of vehicles owned CY of the distribution center corresponding to the reserved node, and use the formula PS=t1×SK+t2 ×CY obtains the distribution coefficient PS of the reserved node, t1 and t2 are both proportional coefficients, and selects the node with the largest distribution coefficient PS as the first intermediate node after the head node; and so on, selects the second intermediate node with the first intermediate node as a reference, and selects the third intermediate node with the second intermediate node as a reference... until there is no candidate node whose total length BX is less than the distance from the current intermediate node to the delivery address; by comprehensively considering multiple factors such as cargo volume, delivery interval, remaining space in the distribution center and number of vehicles owned, the dynamic and accurate allocation of logistics resources is realized, so that resources are maximized; Step 4: Allocate transportation tools: Each time you need to arrange a delivery task after selecting and completing an intermediate node, obtain the transportation distance of the intermediate node and the full-load transportation speed of the transportation tool. The transportation distance is the actual distance from the intermediate node to the next intermediate node or the delivery address, and the full-load transportation speed is the average speed of the transportation tool when it is fully loaded. Calculate the delivery time by comparing the transportation distance with the full-load transportation speed of different transportation tools. Set the expected delivery time threshold, subtract the delivery time from the expected delivery time threshold, and calculate the delivery time difference by taking the absolute value, and mark the transportation tool with the smallest delivery time difference as the delivery tool. Step 5: Logistics service quality evaluation: complete the delivery of goods by allocating logistics resources, and obtain the total number of completed orders ZS and customer complaint information, including the number of shipment errors FC, the number of untimely deliveries WJ, and the number of damaged goods SH; through the formula The logistics service coefficient FW is obtained, and α1, α2 and α3 are all proportional coefficients; the logistics service coefficient FW is compared with the preset logistics service threshold FWmax: if the logistics service coefficient FW is less than the logistics service threshold FWmax, it is judged that the logistics distribution quality based on the dynamic allocation of logistics resources meets the requirements and no processing is required; if the logistics service coefficient FW is greater than or equal to the logistics service threshold FWmax, it is judged that the logistics distribution quality based on the dynamic allocation of logistics resources does not meet the requirements; a node observation signal is generated and sent to the mobile phone terminal of the manager, and corresponding measures are taken for optimization; through the logistics service quality evaluation, problems in logistics distribution can be discovered in time, and corresponding measures can be taken for optimization; at the same time, based on the analysis and calculation of big data, scientific data support is provided for logistics management, making decisions more accurate and reasonable, and being able to flexibly respond to changes in orders and dynamic adjustments in the logistics environment, quickly respond to market demand, and improve the resilience of enterprises in the complex and changeable logistics market.

[0020] A method for dynamic allocation of logistics resources based on big data. When working, a logistics management platform integrating order system, inventory warehouse system and distribution center system is first built to integrate the key information of the whole process of logistics distribution. When selecting the head node, the total length of transportation and the warehouse utilization coefficient are comprehensively considered. By generating the warehouse distance sequence and the warehouse utilization sequence, the warehouse with closer distance is selected first, and then the final head node is determined according to the utilization coefficient. When selecting the intermediate node, the distribution center within a certain radius is determined as the candidate node based on the head node; the distribution coefficient is calculated according to the distance from the candidate node to the delivery address and the remaining storage space and vehicle number of the distribution center, and the node with the largest distribution coefficient is selected as the intermediate node in turn until no better candidate node can be found; when allocating transportation tools, the delivery time is calculated according to the transportation distance and the full-load transportation speed of different transportation tools, and the transportation tool with the smallest delivery time difference is selected; finally, through the logistics service quality evaluation, the node observation signal is generated to notify the management personnel to take optimization measures.

[0021] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0022] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula SY=k1×HL+k2×FJ; technicians in this field collect multiple groups of sample data and set corresponding usage coefficients for each group of sample data; substitute the set usage coefficients and the collected sample data into the formula, any two formulas constitute a set of two-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 3.03 and 2.61 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding usage coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the usage coefficient is proportional to the value of the delivery interval.

[0023] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0024] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for dynamic allocation of logistics resources based on big data, characterized in that: The following steps are involved: Step 1: Integrate information: Build a logistics management platform, which includes order system, inventory warehouse system and distribution center system; The whole process of logistics distribution is divided into several stages from the inventory warehouse of goods to the customer's delivery address through multiple distribution centers. The inventory warehouse and distribution center are marked as nodes of logistics distribution, among which the inventory warehouse is recorded as the head node and the distribution center is recorded as the intermediate node; Step 2: Select the head node: Get the inventory warehouse of the order that needs logistics distribution, and generate the warehouse distance sequence and warehouse use sequence according to the inventory warehouse, select the alternative warehouse from the warehouse distance sequence, and then determine the head node for logistics distribution of the goods based on the sequence number of the alternative warehouse in the warehouse use sequence; Step 3: Selecting the intermediate node: After selecting the head node, select the first intermediate node with the head node as a reference, select the second intermediate node with the first intermediate node as a reference, select the third intermediate node with the second intermediate node as a reference, and so on until all the intermediate nodes are determined; Step 4: Assigning transportation tools: Each time a delivery task needs to be arranged after selecting and completing an intermediate node, obtain the delivery time difference of different transportation tools in the intermediate node, and mark the transportation tool with the smallest delivery time difference as the transportation tool for delivery; Step 5: Logistics service quality assessment: Complete the distribution of goods by allocating logistics resources and calculate the logistics service coefficient FW. Use the logistics service coefficient FW to judge whether the logistics service quality meets the requirements.

2. According to the big data-based dynamic allocation method for logistics resources according to claim 1, it is characterized in that: In step one, the order system receives order information pushed by various platforms in real time. The order information includes: the weight, volume, order time, delivery address and inventory warehouse where the goods are stored; the inventory warehouse records the quantity HL of various goods stored in real time; the distribution center system obtains the distribution center information owned by the logistics company in real time. The distribution center information includes the remaining storage space SK and the number of vehicles owned CY.

3. The method for dynamic allocation of logistics resources based on big data according to claim 2 is characterized in that: In step 2, the specific generation process of the warehouse distance sequence is as follows: the actual address of the inventory warehouse is recorded as the departure address, the departure address and the delivery address are input through the online map to obtain the total transport length QC, and the inventory warehouses are arranged in order from small to large according to the total transport length QC to obtain the warehouse distance sequence.

4. The method for dynamic allocation of logistics resources based on big data according to claim 3 is characterized in that: In step 2, the specific generation process of the warehouse usage sequence is as follows: obtain the cargo quantity HL of the goods stored in the inventory warehouse, record the last time the goods were transported out of the inventory warehouse and mark it as the last time, mark the order time of the current goods as the current time, and calculate the difference between the current time and the last time to obtain the delivery interval FJ; perform numerical calculation on the cargo quantity HL and the delivery interval FJ to obtain the usage coefficient SY of the goods in the inventory warehouse, and arrange the inventory warehouses in descending order of the usage coefficient SY to obtain the warehouse usage sequence.

5. The method for dynamic allocation of logistics resources based on big data according to claim 4 is characterized in that: In step three, the selection process of the first intermediate node includes: after the head node is selected, the distribution center within the radius R with the inventory warehouse corresponding to the head node as the center is obtained and marked as an alternative node, the address of the alternative node and the delivery address are input through the online map to obtain the total length BX of the alternative node, and the total length BX of the alternative node is compared with the total length QC of the transportation. If the total length BX of the alternative node is greater than the total length QC of the transportation, it is eliminated, otherwise it is retained, and the remaining alternative nodes after elimination are marked as reserved nodes; the remaining storage space SK and the number of vehicles CY of the distribution center corresponding to the reserved node are obtained, the remaining storage space SK and the number of vehicles CY are numerically calculated to obtain the distribution coefficient PS of the reserved node, and the reserved node with the largest distribution coefficient PS is selected as the first intermediate node after the head node.

6. A method for dynamic allocation of logistics resources based on big data according to claim 5, characterized in that: The second intermediate node is selected with reference to the first intermediate node, and the third intermediate node is selected with reference to the second intermediate node... until there is no alternative node whose total length BX is less than the distance from the current intermediate node to the delivery address.

7. A method for dynamic allocation of logistics resources based on big data according to claim 6, characterized in that: In step 4, the specific process of allocating transportation tools includes: each time a delivery task needs to be arranged after the intermediate node is selected, the transportation distance of the intermediate node and the full-load transportation speed of the transportation tool are obtained, the transportation distance is the actual distance from the intermediate node to the next intermediate node or the delivery address, and the full-load transportation speed is the average speed when the transportation tool is fully loaded; the transportation distance is calculated by ratioing the full-load transportation speed of different transportation tools to obtain the delivery time; the expected delivery time threshold is set, the delivery time is subtracted from the expected delivery time threshold, and the absolute value is calculated to obtain the delivery time difference, and the transportation tool with the smallest delivery time difference is marked as the transportation tool for delivery.

8. The method for dynamic allocation of logistics resources based on big data according to claim 7 is characterized in that: In step five, the calculation process of the logistics service coefficient FW includes: completing the distribution of goods by allocating logistics resources, and obtaining the total number of completed orders ZS and customer complaint information, the complaint information includes the number of shipping errors FC, the number of delayed deliveries WJ, and the number of damaged goods SH; the logistics service coefficient FW is obtained by combining the number of shipping errors FC, the number of delayed deliveries WJ, the number of damaged goods SH, and the total number of orders ZS.

9. The method for dynamic allocation of logistics resources based on big data according to claim 8 is characterized in that: In step five, the specific process of judging whether the logistics service quality meets the requirements includes: comparing the logistics service coefficient FW with the preset logistics service threshold FWmax: if the logistics service coefficient FW is less than the logistics service threshold FWmax, it is judged that the logistics distribution quality based on the dynamic allocation of logistics resources meets the requirements and no processing is required.

10. A method for dynamic allocation of logistics resources based on big data according to claim 9, characterized in that: In step five, if the logistics service coefficient FW is greater than or equal to the logistics service threshold FWmax, it is judged that the logistics distribution quality based on the dynamic allocation of logistics resources does not meet the requirements; a node observation signal is generated and sent to the mobile phone terminal of the manager, and corresponding measures are taken for optimization.

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