Logistics distribution optimization method and system based on big data

Abstract

The invention is suitable for the technical field of logistics distribution optimization, and particularly relates to a logistics distribution optimization method and system based on big data, and the method comprises the steps: delimiting a logistics distribution region, drawing a planar distribution map of logistics distribution facilities, reading position coordinates, and determining a position coordinate of the logistics distribution region, a position coordinate of the logistics distribution facility, a position coordinate of the logistics distribution facility, and a position coordinate of the logistics distribution facility; and by taking the position coordinate of the transfer point as a starting point and the position coordinate of the express cabinet as an ending point, generating a logistics distribution task, and integrating a cargo list. By collecting real-time monitoring data, it is ensured that the goods can be transferred to the corresponding cabinet number, confusion is avoided, the goods pickup efficiency is greatly improved, the target user is utilized to process the transfer task, the scene limitation of an unmanned express delivery vehicle can be overcome, the logistics distribution flexibility and the user satisfaction can be improved, and the logistics delivery efficiency is improved. And meanwhile, logistics distribution is promoted to develop towards the intelligent and diversified directions, and the logistics distribution efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics distribution optimization, and in particular to a logistics distribution optimization method and system based on big data. Background Art

[0002] With the popularization of unmanned delivery vehicles, logistics distribution efficiency has been further improved. Unmanned delivery vehicles are equipped with a variety of sensors and positioning devices, and are connected to big data platforms in real time. They can respond to various emergencies and meet the scheduling needs of different regions, making the delivery process more flexible and efficient.

[0003] However, the current unmanned express delivery vehicles can only transport goods from the transfer center to the distribution station. The last mile from the distribution station to the user's hands still needs to be completed by delivery personnel, which greatly limits the use scenarios of unmanned express delivery vehicles. Therefore, "how to deliver goods directly to the express cabinet" is the technical problem that the present invention needs to solve. Summary of the invention

[0004] The purpose of the present invention is to provide a logistics distribution optimization method and system based on big data to solve the problem of "how to deliver goods directly to express cabinets" raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A logistics distribution optimization method based on big data, the method comprising: Delineate the logistics distribution area, draw a plan distribution map of the logistics distribution facilities, wherein the logistics distribution facilities include: express cabinets, post stations and transfer points, and read the location coordinates, take the location coordinates of the transfer point as the starting point, and the location coordinates of the express cabinet as the end point, generate a logistics distribution task, and integrate the cargo list, wherein the cargo list includes: number items, cabinet number items and attribute items, send the logistics distribution task to the unmanned express vehicle, obtain the real-time location of the unmanned express vehicle, and calculate the arrival time based on the distance between the real-time location and the end point; Build a cargo transfer platform, read the personal information uploaded by the registrant, locate the residence of the registrant, define the registrant whose residence is within the preset range of the destination as the intended user, integrate the arrival time into the preset template, generate a transfer task, and push it to the intended user through a pop-up window, open a selection button in the pop-up window, and determine the target user according to the selection result; Acquire the real-time monitoring data at the destination, authenticate the identity of the target user, read the cargo number based on the identification device pre-integrated in the unmanned express delivery vehicle, obtain the door opening authority of the corresponding cabinet number based on the attribute data, and issue a door opening instruction to the express cabinet, and determine whether the transfer task is completed based on the real-time monitoring data. If so, continue to activate the logistics distribution task.

[0006] Furthermore, the steps of demarcating the logistics distribution area and drawing a plan distribution map of the logistics distribution facilities include: In the plan distribution map, several commonly used driving routes are marked; The corresponding relationship between the commonly used driving routes and logistics delivery tasks is configured.

[0007] Furthermore, the step of generating a logistics delivery task with the location coordinates of the transfer point as the starting point and the location coordinates of the express cabinet as the end point includes: Inserting waypoints into the logistics distribution task, and dividing the logistics distribution task into a plurality of subtasks; A target item is selected from the cargo list, and a mapping between the target item and the subtask is established.

[0008] Furthermore, the step of sending the logistics delivery task to the unmanned courier vehicle includes: Define backup facilities and calculate available capacity of logistics distribution facilities; When the available capacity is less than the storage requirement of the logistics distribution task, the destination is adjusted to a backup facility.

[0009] Furthermore, the step of integrating the arrival time into a preset template, generating a transfer task, and pushing it to the target user through a pop-up window, and opening a selection button in the pop-up window includes: Obtaining the selection result of the intended user and recording the time when the selection result was generated; The intended user with the earliest generation time is defined as the target user, and the goods list is pushed to the target user.

[0010] Furthermore, the steps of obtaining the real-time monitoring data at the destination, authenticating the target user, and reading the cargo number based on the identification device pre-integrated in the unmanned express delivery vehicle include: Extracting the target user's portrait features from the real-time monitoring data; The target user is authenticated through the personal data and portrait features.

[0011] Furthermore, the method further comprises: The post is inserted into the logistics distribution task, and when the real-time position coincides with the position coordinates of the post, a pickup message is generated, and the pickup message is pushed to a preset terminal; The activation authority is opened to the preset terminal.

[0012] Furthermore, the system comprises: A calculation module is used to delineate a logistics distribution area, draw a plan distribution map of logistics distribution facilities, wherein the logistics distribution facilities include: express cabinets, post stations and transfer points, and read out the location coordinates, take the location coordinates of the transfer point as the starting point and the location coordinates of the express cabinet as the end point, generate a logistics distribution task, and integrate a cargo list, wherein the cargo list includes: number items, cabinet number items and attribute items, send the logistics distribution task to the unmanned express vehicle, obtain the real-time location of the unmanned express vehicle, and calculate the arrival time based on the distance between the real-time location and the end point; A determination module is used to build a cargo transfer platform, read the personal information uploaded by the registrant, locate the residence of the registrant, define the registrant whose residence is within the preset range of the destination as the intended user, integrate the arrival time into the preset template, generate a transfer task, and push it to the intended user through a pop-up window, open a selection button in the pop-up window, and determine the target user according to the selection result; The judgment module is used to obtain the real-time monitoring data at the end point, authenticate the target user, read the cargo number based on the identification device pre-integrated in the unmanned express delivery vehicle, obtain the opening authority of the cabinet door corresponding to the cabinet number based on the attribute data, and issue a door opening instruction to the express cabinet, and judge whether the transfer task is completed based on the real-time monitoring data. If so, continue to activate the logistics distribution task.

[0013] Furthermore, the calculation module includes: A marking unit, used for marking a number of commonly used driving routes in the plan distribution map; A configuration unit, used to configure the corresponding relationship between the commonly used driving routes and the logistics distribution tasks; A splitting unit, used to insert waypoints into the logistics distribution task and split the logistics distribution task into a plurality of subtasks; An establishing unit, used for selecting a target item from the cargo list and establishing a mapping between the target item and a subtask; Definition unit, used to define backup facilities and calculate the available capacity of logistics distribution facilities; An adjustment unit is used to adjust the destination to a backup facility when the available capacity is less than the storage requirement of the logistics distribution task.

[0014] Furthermore, the determination module includes: A recording unit, used to obtain the selection result of the intended user and record the generation time of the selection result; The push unit is used to define the intended user with the earliest generation time as a target user, and push the goods list to the target user.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By calculating the arrival time, intended users can plan their trips reasonably, reduce the uncertainty of waiting, and shorten the processing time of transfer tasks. By generating transfer tasks, the utilization rate of express cabinets is greatly improved, which makes up for the limitations of unmanned express vehicles and shortens the delivery time. By collecting real-time monitoring data, it ensures that the goods can be transferred to the corresponding cabinet number to avoid confusion, which greatly improves the efficiency of picking up goods. By utilizing target users to handle transfer tasks, it can not only overcome the scenario limitations of unmanned express vehicles, but also improve the flexibility of logistics distribution and user satisfaction. At the same time, it promotes the development of logistics distribution towards intelligence and diversification, greatly improving the efficiency of logistics distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a logistics distribution optimization method based on big data provided by an embodiment of the present invention; Figure 2 A first sub-process flowchart of a logistics distribution optimization method based on big data provided by an embodiment of the present invention; Figure 3 A second sub-process flowchart of the logistics distribution optimization method based on big data provided by an embodiment of the present invention; Figure 4 A third sub-process flowchart of the logistics distribution optimization method based on big data provided by an embodiment of the present invention; Figure 5 A block diagram of the composition of a logistics distribution optimization system based on big data provided by an embodiment of the present invention; Figure 6 A block diagram of the composition of a computing module in a logistics distribution optimization system based on big data provided by an embodiment of the present invention; Figure 7 A block diagram of the composition of a determination module in a logistics distribution optimization system based on big data provided by an embodiment of the present invention; Figure 8 A block diagram of the composition of the judgment module in the logistics distribution optimization system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In Example 1, Figure 1 The implementation process of the logistics distribution optimization method based on big data provided by the embodiment of the present invention is shown and described in detail below: S100: Delineate the logistics distribution area, draw a plan distribution map of the logistics distribution facilities, wherein the logistics distribution facilities include: express cabinets, post stations and transfer points, and read the location coordinates, take the location coordinates of the transfer point as the starting point, and the location coordinates of the express cabinet as the end point, generate a logistics distribution task, and integrate the cargo list, wherein the cargo list includes: number items, cabinet number items and attribute items, send the logistics distribution task to the unmanned express vehicle, obtain the real-time location of the unmanned express vehicle, and calculate the arrival time based on the distance between the real-time location and the end point.

[0019] Before logistics distribution, the distribution area is delineated and a plan distribution map of the logistics distribution facilities is drawn; the locations of express cabinets, stations and transfer points are determined in the plan distribution map, and the location of each logistics distribution facility is marked to determine the location coordinates; based on actual logistics distribution needs, the transfer points and express cabinets corresponding to the goods to be delivered are determined, and the transfer points are used as the starting point of the unmanned express vehicle and the express cabinets as the end point to generate logistics distribution tasks; it should be noted that there can be multiple end points here.

[0020] Find the goods that need to be delivered, and determine the serial number of each item, the cabinet number it needs to be placed in, and the weight, size and other data of the goods. Use this data to generate a cargo list, where each destination corresponds to at least one cargo list; send the logistics delivery task to the unmanned courier, and during the delivery process, track the location of the unmanned courier in real time, and use the distance between the real-time location and the destination (express cabinet) to calculate the estimated arrival time.

[0021] S200: Build a cargo transfer platform, read the personal information uploaded by the registrant, locate the residence of the registrant, define the registrant whose residence is within the preset range of the destination as the intended user, integrate the arrival time into the preset template, generate the transfer task, and push it to the intended user through a pop-up window, open a selection button in the pop-up window, and determine the target user according to the selection result.

[0022] Build a cargo transfer platform and receive personal data uploaded by registrants, where the personal data should include name, contact information, and residential location; define the request range with the end point as the center and the preset distance as the radius, and define registrants whose residence is within the request range as intended users, i.e. those who intend to transfer goods from unmanned courier vehicles to courier lockers; if the number of intended users is lower than the threshold, the request range should be expanded.

[0023] The destination, arrival time, cargo list, etc. are written into the preset template to generate a transfer task, and the transfer task is simultaneously published to all potential users on the cargo transfer platform; the pop-up window content includes the destination, arrival time, and a selection button for the potential user to confirm, and the selection button should include two options: "Yes" and "No"; the potential user can accept or reject the task by clicking the selection button, and the potential user who chooses to accept the task is defined as the target user.

[0024] In the above process, in order to ensure the privacy of the cargo list, the right to view the cargo list is issued to the intended user only after the intended user confirms to accept the task (see S202).

[0025] S300: Acquire the real-time monitoring data at the destination, authenticate the target user, read the shipment number based on the identification device pre-integrated in the unmanned express delivery vehicle, obtain the door opening authority of the corresponding cabinet number based on the attribute data, and issue a door opening instruction to the express cabinet, and determine whether the transfer task is completed based on the real-time monitoring data. If so, continue to activate the logistics distribution task.

[0026] When the target user arrives at the destination and starts to transfer the goods, the video surveillance equipment integrated in the express cabinet is used to collect real-time monitoring data, extract portrait features, perform face recognition on the target user, and verify the target user by comparing the facial data of the personal profile; when the verification is passed, the door of the unmanned express vehicle is opened for the target user to pick up the goods. After picking up the goods, the identification device is used to read the shipment number, query the cargo list, traverse the doors of the express cabinets corresponding to the goods, and open the corresponding doors; by analyzing the real-time monitoring data, it is determined whether the target user has placed the goods into the corresponding cabinet door. If so, and the cabinet door is closed, the transfer task is completed and the next logistics distribution task is activated.

[0027] In Example 2, Figure 2 The implementation process of the logistics distribution optimization method based on big data provided by an embodiment of the present invention is shown. The steps of demarcating the logistics distribution area and drawing the plan distribution map of the logistics distribution facilities are described in detail as follows: S101: Marking a number of commonly used driving routes in the plan distribution map.

[0028] Since the locations of transfer points and express lockers are relatively fixed, several commonly used driving routes can be determined in advance; by planning driving routes in advance, the amount of data processing for logistics distribution tasks can be reduced, thereby improving distribution efficiency.

[0029] S102: Configuring a correspondence between the commonly used driving routes and logistics delivery tasks.

[0030] According to the location coordinates of the destination, the corresponding common driving route is determined, thereby establishing a corresponding relationship between the common driving route and the logistics distribution task.

[0031] In Example 3, Figure 2 The implementation process of the logistics distribution optimization method based on big data provided by an embodiment of the present invention is shown. The steps of generating a logistics distribution task with the location coordinates of the transfer point as the starting point and the location coordinates of the express cabinet as the end point are described in detail as follows: S103: inserting waypoints into the logistics distribution task, and dividing the logistics distribution task into a plurality of subtasks.

[0032] Insert several waypoints into the logistics distribution task, where the unmanned delivery vehicle can allow preset users to pick up the goods in a self-service manner. The waypoints here must be located in the driving route of the unmanned delivery vehicle, and the preset users are the corresponding users on the goods list; in addition, the goods picked up by the preset users should be confirmed through video surveillance equipment and identification equipment.

[0033] S104: Select a target item from the cargo list and establish a mapping between the target item and the subtask.

[0034] In the cargo list, the cargo that the preset user needs to pick up is identified as the target item, where each subtask corresponds to a target item.

[0035] In Example 4, Figure 2 The implementation process of the logistics distribution optimization method based on big data provided by an embodiment of the present invention is shown. The steps of sending the logistics distribution task to the unmanned express delivery vehicle are described in detail as follows: S105: Define backup facilities and calculate available capacity of logistics distribution facilities.

[0036] If the express locker space at the destination is full or cannot meet the storage needs of the goods in the logistics distribution task, the available capacity of the logistics distribution facility is calculated.

[0037] S106: When the available capacity is less than the storage requirement of the logistics distribution task, the destination is adjusted to a backup facility.

[0038] If the available capacity of the express cabinet at the destination is less than the storage requirement of the logistics distribution task, the express cabinet closest to the destination is found and defined as a backup facility. If the backup facility can meet the storage requirement, the destination is adjusted to the backup facility.

[0039] In Example 5, Figure 3 The implementation process of the logistics distribution optimization method based on big data provided by an embodiment of the present invention is shown. The following is a detailed description of the steps of integrating the arrival time into a preset template, generating a transfer task, and pushing it to the target user through a pop-up window, and opening a selection button in the pop-up window, as follows: S201: Obtain the selection result of the intended user and record the generation time of the selection result.

[0040] According to the selection results of the intended user in the pop-up window, the intended user who will first accept the transfer task is determined, this intended user is determined as the target user, and the cargo list is pushed to the target user.

[0041] S202: defining the intended user with the earliest generation time as a target user, and pushing the goods list to the target user.

[0042] In Example 6, Figure 4 The implementation process of the logistics distribution optimization method based on big data provided by an embodiment of the present invention is shown. The steps of obtaining the real-time monitoring data at the destination, authenticating the target user, and reading the shipment number based on the identification device pre-integrated in the unmanned express delivery vehicle are described in detail as follows: S301: Extracting portrait features of a target user from the real-time monitoring data.

[0043] S302: Authentication of the target user is performed based on the personal information and portrait features.

[0044] In Example 7, different from Example 1, in this embodiment of the present invention, the method further includes: The post is inserted into the logistics distribution task, and when the real-time position coincides with the position coordinates of the post, a pickup message is generated, and the pickup message is pushed to a preset terminal; The activation authority is opened to the preset terminal.

[0045] In addition to express lockers, the destination can also be a post station. When the real-time location of the unmanned express vehicle coincides with the location coordinates of the post station, a pickup message is sent to the user terminal corresponding to the post station to remind the cargo management personnel in the post station to transfer the cargo in the unmanned express vehicle to the post station. The cargo management personnel can be the post station manager or the target user, and the activation authority can be opened to the user terminal. After the cargo transfer is completed, the unmanned express vehicle is started through the activation authority to continue the logistics distribution task.

[0046] Figure 5 The block diagram of the composition structure of the logistics distribution optimization system based on big data provided by an embodiment of the present invention is shown. The logistics distribution optimization system based on big data 1 includes: The calculation module 11 is used to define the logistics distribution area, draw a plan distribution map of the logistics distribution facilities, wherein the logistics distribution facilities include: express cabinets, post stations and transfer points, and read the location coordinates, take the location coordinates of the transfer point as the starting point and the location coordinates of the express cabinet as the end point, generate a logistics distribution task, and integrate the cargo list, wherein the cargo list includes: number items, cabinet number items and attribute items, send the logistics distribution task to the unmanned express vehicle, obtain the real-time location of the unmanned express vehicle, and calculate the arrival time based on the distance between the real-time location and the end point; The determination module 12 is used to build a cargo transfer platform, read the personal information uploaded by the registrant, locate the residence of the registrant, define the registrant whose residence is within the preset range of the destination as the intended user, integrate the arrival time into the preset template, generate a transfer task, and push it to the intended user through a pop-up window, open a selection button in the pop-up window, and determine the target user according to the selection result; The judgment module 13 is used to obtain the real-time monitoring data at the end point, authenticate the identity of the target user, read the cargo number based on the identification device pre-integrated in the unmanned express delivery vehicle, obtain the opening authority of the cabinet door corresponding to the cabinet number based on the attribute data, and issue a door opening instruction to the express cabinet, and judge whether the transfer task is completed based on the real-time monitoring data. If so, continue to activate the logistics distribution task.

[0047] Figure 6 The block diagram of the composition structure of the logistics distribution optimization system based on big data provided by an embodiment of the present invention is shown, and the calculation module 11 includes: A marking unit 111, used to mark a number of common driving routes in the plan distribution map; A configuration unit 112, configured to configure a correspondence between the commonly used driving routes and logistics delivery tasks; A splitting unit 113, used to insert waypoints into the logistics distribution task and split the logistics distribution task into a plurality of subtasks; An establishing unit 114, configured to select a target item from the cargo list and establish a mapping between the target item and a subtask; A definition unit 115, used to define backup facilities and calculate the available capacity of logistics distribution facilities; The adjustment unit 116 is configured to adjust the end point to a backup facility when the available capacity is less than a preset threshold.

[0048] Figure 7 The block diagram of the composition structure of the logistics distribution optimization system based on big data provided by an embodiment of the present invention is shown, and the determination module 12 includes: The recording unit 121 is used to obtain the selection result of the intended user and record the generation time of the selection result; The push unit 122 is used to define the intended user with the earliest generation time as a target user, and push the goods list to the target user.

[0049] Figure 8 The block diagram of the composition structure of the logistics distribution optimization system based on big data provided by an embodiment of the present invention is shown, and the judgment module 13 includes: The extraction unit 131 is used to extract the portrait features of the target user from the real-time monitoring data.

[0050] The verification unit 132 is used to authenticate the target user through the personal information and portrait features.

[0051] The calculation module 11 is used to complete step S100, the determination module 12 is used to complete step S200, and the judgment module 13 is used to complete step S300; The marking unit 111 is used to complete step S101, the configuration unit 112 is used to complete step S102, the segmentation unit 113 is used to complete step S103, the establishment unit 114 is used to complete step S104, the definition unit 115 is used to complete step S105, and the adjustment unit 116 is used to complete step S106; The recording unit 121 is used to complete step S201, and the pushing unit 122 is used to complete step S202; The extracting unit 131 is used to complete step S301, and the verifying unit 132 is used to complete step S302.

[0052] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A logistics distribution optimization method based on big data, characterized in that: The method comprises: Delineate the logistics distribution area, draw a plan distribution map of the logistics distribution facilities, wherein the logistics distribution facilities include: express cabinets, post stations and transfer points, and read the location coordinates, take the location coordinates of the transfer point as the starting point, and the location coordinates of the express cabinet as the end point, generate a logistics distribution task, and integrate the cargo list, wherein the cargo list includes: number items, cabinet number items and attribute items, send the logistics distribution task to the unmanned express vehicle, obtain the real-time location of the unmanned express vehicle, and calculate the arrival time based on the distance between the real-time location and the end point; Build a cargo transfer platform, read the personal information uploaded by the registrant, locate the registrant's residence, define the registrant whose residence is within the preset range of the destination as the intended user, integrate the arrival time into the preset template, generate a transfer task, and push it to the intended user through a pop-up window, open a selection button in the pop-up window, and determine the target user based on the selection result; The real-time monitoring data at the destination is obtained, the identity of the target user is authenticated, the cargo number is read based on the identification device pre-integrated in the unmanned express delivery vehicle, the door opening authority of the cabinet number is obtained based on the attribute items, and a door opening instruction is issued to the express cabinet, and based on the real-time monitoring data, it is determined whether the transfer task is completed, and if so, the logistics distribution task continues to be activated.

2. The logistics distribution optimization method based on big data according to claim 1 is characterized in that: The steps of demarcating the logistics distribution area and drawing a plan distribution map of the logistics distribution facilities include: In the plan distribution map, several commonly used driving routes are marked; The corresponding relationship between the commonly used driving routes and logistics delivery tasks is configured.

3. The logistics distribution optimization method based on big data according to claim 2 is characterized in that: The step of generating a logistics distribution task with the location coordinates of the transfer point as the starting point and the location coordinates of the express cabinet as the end point includes: Inserting waypoints into the logistics distribution task, and dividing the logistics distribution task into a plurality of subtasks; A target item is selected from the cargo list, and a mapping between the target item and the subtask is established.

4. The logistics distribution optimization method based on big data according to claim 1 is characterized in that: The step of sending the logistics delivery task to the unmanned courier vehicle comprises: Define backup facilities and calculate available capacity of logistics distribution facilities; When the available capacity is less than the storage requirement of the logistics distribution task, the destination is adjusted to a backup facility.

5. The logistics distribution optimization method based on big data according to claim 1 is characterized in that: The step of integrating the arrival time into a preset template, generating a transfer task, and pushing it to the target user through a pop-up window, and opening a selection button in the pop-up window includes: Obtaining the selection result of the intended user and recording the time when the selection result was generated; The intended user with the earliest generation time is defined as the target user, and the goods list is pushed to the target user.

6. The logistics distribution optimization method based on big data according to claim 1 is characterized in that: The steps of obtaining the real-time monitoring data at the destination, authenticating the target user, and reading the cargo number based on the identification device pre-integrated in the unmanned express delivery vehicle include: Extracting the target user's portrait features from the real-time monitoring data; The target user is authenticated through the personal data and portrait features.

7. The logistics distribution optimization method based on big data according to claim 1 is characterized in that: The method further comprises: The post is inserted into the logistics distribution task, and when the real-time position coincides with the position coordinates of the post, a pickup message is generated, and the pickup message is pushed to a preset terminal; The activation authority is opened to the preset terminal.

8. A logistics distribution optimization system based on big data, characterized in that: The system comprises: A calculation module is used to delineate a logistics distribution area, draw a plan distribution map of logistics distribution facilities, wherein the logistics distribution facilities include: express cabinets, post stations and transfer points, and read out the location coordinates, take the location coordinates of the transfer point as the starting point and the location coordinates of the express cabinet as the end point, generate a logistics distribution task, and integrate a cargo list, wherein the cargo list includes: number items, cabinet number items and attribute items, send the logistics distribution task to the unmanned express vehicle, obtain the real-time location of the unmanned express vehicle, and calculate the arrival time based on the distance between the real-time location and the end point; The determination module is used to build a cargo transfer platform, read the personal information uploaded by the registrant, locate the residence of the registrant, define the registrant whose residence is within the preset range of the destination as the intended user, integrate the arrival time into the preset template, generate a transfer task, and push it to the intended user through a pop-up window, open a selection button in the pop-up window, and determine the target user according to the selection result; The judgment module is used to obtain the real-time monitoring data at the end point, authenticate the target user, read the cargo number based on the identification device pre-integrated in the unmanned express delivery vehicle, obtain the opening authority of the cabinet door corresponding to the cabinet number based on the attribute items, and issue a door opening instruction to the express cabinet, and judge whether the transfer task is completed based on the real-time monitoring data. If so, continue to activate the logistics distribution task.

9. The logistics distribution optimization system based on big data according to claim 8 is characterized in that: The calculation module comprises: A marking unit, used for marking a number of commonly used driving routes in the plan distribution map; A configuration unit, used to configure the corresponding relationship between the commonly used driving routes and the logistics distribution tasks; A splitting unit, used to insert waypoints into the logistics distribution task and split the logistics distribution task into a plurality of subtasks; An establishing unit, used for selecting a target item from the cargo list and establishing a mapping between the target item and a subtask; Definition unit, used to define backup facilities and calculate the available capacity of logistics distribution facilities; An adjustment unit is used to adjust the destination to a backup facility when the available capacity is less than the storage requirement of the logistics distribution task.

10. The logistics distribution optimization system based on big data according to claim 8 is characterized in that: The determination module comprises: A recording unit, used to obtain the selection result of the intended user and record the generation time of the selection result; The push unit is used to define the intended user with the earliest generation time as a target user, and push the goods list to the target user.

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