A door-to-door pickup dispatching system

Abstract

The application discloses a door-to-door goods pickup scheduling system and relates to the technical field of vehicle scheduling.The door-to-door goods pickup scheduling system comprises a database generation module, a data acquisition module, a condition constraint module, a loading estimation module, a vehicle allocation module, a sudden situation identification module and an abnormality classification module.The abnormality classification module identifies the abnormality of goods pickup and determines whether the abnormality of goods pickup needs emergency treatment.The emergency treatment module acquires the real-time distribution of vehicles and forms an emergency adjustment scheme for the abnormality of goods pickup needing emergency treatment.Through the condition constraint module, the loading estimation module, the vehicle allocation module, the sudden situation identification module and the emergency treatment module, various factors of door-to-door goods pickup are considered, the delay of goods transportation is avoided, and the entire scheduling scheme can be adjusted in time through the classification treatment of sudden situations caused by user or vehicle abnormalities.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle dispatching, in particular to a door-to-door delivery dispatching system. Background Art

[0002] In the field of large-ticket logistics, when picking up goods from shippers, the dispatcher communicates with the shipper about the pick-up time period, and allocates vehicles to pick up goods based on multiple factors such as the loading of the owned vehicles, the allocable time period, the logistics center where the vehicles are diverted, etc., or rents external vehicles to pick up goods.

[0003] Existing scheduling usually uses regional divisions for scheduling, with each delivery person responsible for picking up goods in their own area. However, there are various restrictions on door-to-door pickup, which can lead to uneven business volume distribution and poor cost control, among other situations. In addition, when picking up goods at the door, there will be various unexpected situations, making the original scheduling plan impossible to implement. However, the existing response method is usually to postpone the pickup time to solve the problem, and the rationality of the plan has a lot of room for improvement. Summary of the Invention

[0004] In order to solve the above technical problems, a door-to-door pickup scheduling system is provided. This technical solution solves the problem proposed in the above background technology that the existing scheduling usually uses the area division method for scheduling, and each pickup person is responsible for picking up the goods in his own area. However, there are various restrictive factors in door-to-door pickup, which will lead to uneven business volume distribution and poor cost control and other situations. In addition, when picking up goods at the door, there will be various unexpected situations, which will make the original scheduling plan unable to be implemented. However, the existing response method is usually to postpone the pickup time to solve the problem, and the rationality of the plan has a lot of room for improvement.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0006] A door-to-door pickup dispatching system, comprising:

[0007] A database generation module, wherein the database generation module establishes a cell database within the scheduling area;

[0008] a data acquisition module, wherein the module acquires at least one user who needs to pick up goods on the same day, acquires the user's pick-up time interval, acquires the user's location information, and acquires the specifications and quantity of the goods to be picked up by the user, wherein the pick-up time interval is the time range within which the user can pick up the goods at the user's door, and the specifications of the goods to be picked up are the size and weight of each item to be picked up;

[0009] A loading estimation module estimates the time it takes to transport the goods to be picked up to the vehicle, thereby obtaining a loading time for the goods to be picked up;

[0010] A vehicle distribution module obtains at least one vehicle participating in the pickup on the day, forms at least one vehicle pickup path under the constraints of the pickup cost and the pickup time interval of the user;

[0011] A burst situation recognition module recognizes the pickup process to obtain a pickup abnormal situation;

[0012] An abnormality classification module identifies the pickup abnormal situation and judges whether the pickup abnormal situation needs emergency treatment;

[0013] An emergency treatment module obtains the real-time distribution of the vehicle when the pickup abnormal situation occurs, and forms an emergency adjustment scheme for the pickup abnormal situation needing emergency treatment according to the real-time distribution of the vehicle.

[0014] Preferably, the database generation module establishes a cell database in the dispatching area, including the following steps:

[0015] Obtain at least one cell in the dispatching area, and obtain the location information of the cell;

[0016] Obtain at least one parking location in the cell, and obtain the building coordinates in the cell.

[0017] Preferably, the loading estimation module estimates the time for carrying the goods to be picked up to the vehicle to obtain the loading time of the goods to be picked up, including the following steps:

[0018] According to the location information of the user and the location information of the cell, the cell is identified to obtain the cell where the user is located as the target cell;

[0019] Obtain the building coordinates in the cell where the user is located as the target building coordinates;

[0020] Obtain the parking location closest to the target building in the target cell as the target parking location;

[0021] Based on big data, obtain the average moving speed of the worker carrying the goods;

[0022] Obtain the distance from the target parking location to the target building coordinates as the target distance;

[0023] According to the specifications and quantities of the goods to be picked up by the user, the total occupied space and the total weight of the goods to be picked up are calculated;

[0024] Obtain the weight limit and space limit of the worker's single carrying, and the total occupied space of the goods to be picked up divided by the space limit of the worker's single carrying to obtain the first number, and the total weight of the goods to be picked up divided by the weight limit of the worker's single carrying to obtain the second number;

[0025] The largest of the first and second times is used as the number of times the goods to be picked up are moved;

[0026] Use the time estimation formula to calculate the loading time of the goods to be picked up;

[0027] The time estimation formula is as follows:

[0028]

[0029] Among them, A is the loading time of the goods to be picked up, B is the target distance, C is the average moving speed of the workers carrying the goods, and D is the number of times the goods to be picked up are moved.

[0030] Preferably, forming at least one vehicle pickup route under the constraints of the pickup cost and the user's pickup time interval includes the following steps:

[0031] Get the midpoint of the user's pickup time interval as the feature point;

[0032] Classify users according to their characteristic points to obtain at least one user category, wherein the difference in characteristic points of users in the same user category is less than a preset value, and the difference in characteristic points of users in different user categories is not less than a preset value, wherein the preset value is the average time it takes for a vehicle to reach the next pickup location from one pickup location in historical data;

[0033] Take the average of the feature points of users in the user classification to obtain the classification mean;

[0034] Sort the user categories according to the category mean from small to large to obtain the user category sequence;

[0035] At least one preliminary path group is generated, where the preliminary path group is composed of at least one preliminary path, and the preliminary path is composed of at least one preliminary node, where the preliminary node is a user in a user classification. The arrangement order of the at least one preliminary node in the preliminary path is set according to the order of the user classification in which the preliminary node belongs in the user classification sequence. The preliminary nodes of different preliminary paths in the preliminary path group are different from each other. The number of preliminary nodes of all preliminary paths in a single preliminary path group is consistent with the number of at least one user who needs to pick up goods on that day.

[0036] Calculating the difference between the characteristic points corresponding to the adjacent preparation nodes in the preparation path as the characteristic time difference;

[0037] Obtaining the distance between the cells adjacent to the preparation node in the preparation path as the marking distance, and obtaining the average vehicle moving speed based on historical data, dividing the marking distance by the average vehicle moving speed to obtain the marking time;

[0038] The marking time is added to the loading time of the goods to be picked up at the adjacent preparation nodes that are ranked higher to obtain the verification time;

[0039] When the verification time corresponding to the adjacent preparation nodes does not exceed the characteristic time difference corresponding to the adjacent preparation nodes, the adjacent preparation nodes are marked as qualified nodes, otherwise no processing is performed;

[0040] Obtaining a preliminary path group in which all preliminary nodes in the preliminary path are marked as qualified nodes as a candidate path group;

[0041] When picking up goods, each of the candidate path groups is assigned a vehicle, and the sum of the distances between the cells where the adjacent reserve nodes are located in each of the candidate path groups is calculated to obtain the overall distance;

[0042] The candidate path group with the smallest overall distance is selected, and the backup path is assigned to the vehicle as the vehicle pickup path.

[0043] Preferably, the emergency situation identification module identifies the delivery process and obtains the abnormal delivery situation, including the following steps:

[0044] When the user's goods to be picked up are not picked up at the door within the user's pick-up time interval, an abnormality identification is performed to obtain a pick-up abnormality.

[0045] Preferably, the abnormal classification module identifies the abnormal situation of picking up goods, and determines whether the abnormal situation of picking up goods requires emergency treatment, including the following steps:

[0046] Identify abnormalities in delivery and determine whether they are caused by the vehicle or the user;

[0047] When the abnormal situation of picking up goods is caused by the vehicle, the abnormal repair time of the vehicle is obtained. If the abnormal repair time of the vehicle is greater than the preset time, it is determined that the abnormal situation of picking up goods requires emergency treatment;

[0048] When the abnormal situation of picking up goods is caused by the user, obtain the revised picking up time interval after the user adjusts the picking up time interval;

[0049] Get the users whose vehicles have not yet picked up the goods, and get at least one remaining user;

[0050] When the difference between the midpoint of the revised pickup time interval and the midpoint of the pickup time interval of the remaining users is not less than the preset value, it is determined that the pickup abnormality requires emergency processing.

[0051] Preferably, the emergency processing module obtains the real-time distribution of vehicles when an abnormal situation of picking up goods occurs, including the following steps:

[0052] The real-time position coordinates of at least one vehicle are obtained as the real-time distribution of the vehicles.

[0053] Preferably, forming an emergency adjustment plan for abnormal delivery situations requiring emergency treatment based on the real-time distribution of vehicles includes the following steps:

[0054] When the delivery anomaly is caused by a vehicle, the users corresponding to the delivery time interval whose midpoint and the time difference between the delivery anomaly and the delivery anomaly is less than the vehicle's anomaly repair time are selected as the users to be decomposed;

[0055] Using an exhaustive method, at least one first insertion point is obtained in the vehicle pickup path of vehicles that do not experience pickup anomalies. The number of first insertion points is consistent with the number of users to be decomposed. The first insertion point satisfies that when the user to be decomposed is set at the first insertion point, the vehicle that does not experience pickup anomalies will not be delayed.

[0056] When the delivery anomaly is caused by a user, an exhaustive search method is used to obtain a second insertion point in the delivery path of a vehicle that does not have a delivery anomaly. The second insertion point satisfies that when a user with a delivery anomaly is set at the second insertion point, the vehicle that does not have a delivery anomaly will not be delayed.

[0057] If the first insertion point cannot be obtained, the pickup of the user to be decomposed will be postponed to the next day for scheduling;

[0058] When the second insertion point cannot be obtained, the pickup of users with pickup abnormalities will be postponed to the next day for scheduling.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] By setting up conditional constraint modules, loading estimation modules, vehicle allocation modules, emergency situation identification modules and emergency handling modules, various factors of door-to-door pickup are taken into consideration, and the cost and user's pickup time are considered to ensure that the overall cost is within a reasonable range. At the same time, it is more in line with the user's time to avoid long waiting times for users. In addition, the transportation time of goods is also taken into consideration. When the goods are large or heavy, it will inevitably affect the overall time. This solution can avoid time delays caused by cargo transportation. On the other hand, emergencies caused by user or vehicle abnormalities are classified and handled, so that the entire scheduling plan can be adjusted in time to ensure that in the event of an abnormality, the pickup task is completed as much as possible on the same day. At the same time, it does not have an excessive impact on the time needs of other users. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of the door-to-door delivery dispatching system of the present invention;

[0062] Figure 2 A schematic diagram of a process for the database generation module of the present invention to establish a cell database within a scheduling area;

[0063] Figure 3 A schematic diagram of a process for the loading estimation module of the present invention to estimate the time it takes for the goods to be picked up to be transported to the vehicle, thereby obtaining the loading time of the goods to be picked up;

[0064] Figure 4 A schematic diagram of a process for forming at least one vehicle pickup route under the constraints of the pickup cost and the user's pickup time interval of the present invention;

[0065] Figure 5 This is a flow chart showing how the abnormal classification module of the present invention identifies abnormal delivery situations and determines whether emergency treatment is required for the abnormal delivery situations;

[0066] Figure 6 The present invention is a flow chart of forming an emergency adjustment plan for abnormal delivery situations requiring emergency treatment based on the real-time distribution of vehicles. DETAILED DESCRIPTION

[0067] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0068] Reference Figure 1 As shown, a door-to-door pickup scheduling system includes:

[0069] A database generation module, wherein the database generation module establishes a cell database within the scheduling area;

[0070] a data acquisition module, wherein the module acquires at least one user who needs to pick up goods on the same day, acquires the user's pick-up time interval, acquires the user's location information, and acquires the specifications and quantity of the goods to be picked up by the user, wherein the pick-up time interval is the time range within which the user can pick up the goods at the user's door, and the specifications of the goods to be picked up are the size and weight of each item to be picked up;

[0071] A loading estimation module estimates the time it takes to transport the goods to be picked up to the vehicle, thereby obtaining a loading time for the goods to be picked up;

[0072] A vehicle allocation module, which obtains at least one vehicle participating in the delivery of the goods on the day and forms at least one vehicle delivery route under the constraints of the delivery cost and the delivery time interval of the user;

[0073] An emergency situation identification module, which identifies the delivery process and obtains abnormal delivery situations;

[0074] The abnormality classification module identifies the pickup abnormality situation and determines whether the pickup abnormality situation needs emergency treatment;

[0075] The emergency treatment module obtains the real-time distribution of the vehicle when the pickup abnormality situation occurs, and forms an emergency adjustment scheme for the pickup abnormality situation that needs emergency treatment according to the real-time distribution of the vehicle.

[0076] In the scheme, the loading time of the goods to be picked up is considered. When the goods are heavy or more, the loading time will inevitably increase, which will delay the time to pick up at the next location. Therefore, only according to the distance, the time to the next pickup point may be estimated, which may lead to incorrect estimation of the time, resulting in that the entire scheduling scheme does not match the actual situation. As a result, there will be a lot of user waiting. In addition, under the constraints of the pickup cost and the user's pickup time interval, at least one vehicle pickup path is formed. When picking up according to these vehicle pickup paths, the overall distance will be limited, which will limit the fuel cost, thereby controlling the cost. At the same time, it can also ensure that the time of the door is within the pickup time interval of each user as much as possible. Thus, the user waiting time can be controlled, thereby ensuring the experience of the entire scheme. At the same time, in the actual implementation process of the scheme, there will inevitably be vehicle breakdowns or users having urgent matters temporarily. Therefore, these situations need to be scheduled to solve them as much as possible on the same day and ensure that they do not have a great impact on the pickup of other users. For the scheduling that cannot be completed on the same day, it is re-planned the next day according to the scheduling manner in the scheme.

[0077] Referring to Figure 2 The database generation module establishes a cell database in the scheduling area, which includes the following steps:

[0078] Obtain at least one cell in the scheduling area, and obtain the position information of the cell;

[0079] Obtain at least one parking location in the cell, and obtain the building coordinates in the cell.

[0080] Some roads in the cell will be set up roadblocks, so vehicles cannot drive to some buildings. They can only drive to the parking locations near them, and then carry out the pickup and delivery of goods. Therefore, the parking locations need to be obtained in advance to facilitate subsequent obtaining of the nearest parking location of the building to be picked up. Here, the user's house number does not need to be obtained because the elevators are used to go up and down in the building. Therefore, the floor height does not have a great difference in transportation time.

[0081] Referring to Figure 3As shown, the loading estimation module estimates the time it takes to transport the goods to be picked up to the vehicle, and obtaining the loading time of the goods to be picked up includes the following steps:

[0082] Based on the user's location information and the cell's location information, cell identification is performed to obtain the cell where the user is located as the target cell;

[0083] Get the building coordinates in the user's community as the target building coordinates;

[0084] Obtain the closest available parking spot to the target building in the target community as the target available parking spot;

[0085] Based on big data, obtain the average moving speed of workers carrying goods;

[0086] Get the distance from the target parking location to the target building coordinates as the target distance;

[0087] Calculate the total space and weight of the goods to be picked up based on the specifications and quantity of the goods to be picked up by the user;

[0088] Obtain the weight and space limits for a worker's single lift. Divide the total space occupied by the goods to be picked up by the worker's single lift space limit to obtain the first number. Divide the total weight of the goods to be picked up by the worker's single lift weight limit to obtain the second number.

[0089] The largest of the first and second times is used as the number of times the goods to be picked up are moved;

[0090] Use the time estimation formula to calculate the loading time of the goods to be picked up;

[0091] The time estimation formula is as follows:

[0092]

[0093] Among them, A is the loading time of the goods to be picked up, B is the target distance, C is the average moving speed of the workers carrying the goods, and D is the number of times the goods to be picked up are moved.

[0094] Since the loading time of the goods to be picked up will affect the time to the next point, it is necessary to obtain the loading time of the goods to be picked up. Therefore, scheduling is planned based on this time to ensure that the time requirements of users in the planned route can be met and avoid long waiting times.

[0095] Reference Figure 4 As shown, under the constraints of the pickup cost and the user's pickup time interval, forming at least one vehicle pickup route includes the following steps:

[0096] obtaining a midpoint of the pickup time interval of the user as a feature point;

[0097] classifying the users according to the feature points to obtain at least one user classification, the difference between the feature points of the users in the same user classification being less than a preset value, and the difference between the feature points of the users in different user classifications not being less than the preset value, wherein the preset value is an average time for a vehicle to reach a next pickup location from a pickup location in historical data;

[0098] taking a mean value of the feature points of the users in the user classification to obtain a classification mean value;

[0099] sorting the user classifications according to the classification mean values from small to large to obtain a user classification sequence;

[0100] generating at least one preliminary path group, the preliminary path group being composed of at least one preliminary path, the preliminary path being composed of at least one preliminary node, the preliminary node being a user in the user classification, the arrangement order of the at least one preliminary node in the preliminary path being set according to the order of the user classification in which the preliminary node is located in the user classification sequence, the preliminary nodes in different preliminary paths in the preliminary path group being different from each other, and the number of the preliminary nodes of all the preliminary paths in a single preliminary path group being consistent with the number of at least one user who needs to pick up goods on the day;

[0101] calculating the difference between the feature points corresponding to adjacent preliminary nodes in the preliminary path as a feature time difference;

[0102] obtaining the distance between the cells in which the adjacent preliminary nodes are located in the preliminary path as a marker distance, and obtaining a mean value of the vehicle moving speed based on historical data, the marker distance being divided by the mean value of the vehicle moving speed to obtain a marker time;

[0103] superimposing the marker time and the loading time of the goods to be picked up of the preliminary node with a higher order among the adjacent preliminary nodes to obtain a to-be-verified time;

[0104] when the to-be-verified time corresponding to the adjacent preliminary nodes does not exceed the feature time difference corresponding to the adjacent preliminary nodes, marking the adjacent preliminary nodes as qualified nodes, otherwise, not making any processing;

[0105] obtaining the preliminary path group in which the preliminary nodes in the preliminary path are all marked as qualified nodes as a candidate path group;

[0106] allocating a vehicle to each preliminary path in the candidate path group during the pickup, and calculating the sum of the distances between the cells in which the adjacent preliminary nodes in each preliminary path in the candidate path group are located to obtain a total distance;

[0107] The candidate path group with the smallest overall distance is selected, and the backup path is assigned to the vehicle as the vehicle pickup path.

[0108] This allocation plan takes into account the cost and the user's time requirements. At the same time, when making the allocation, it ensures that the number of pickup points for each pickup person is roughly the same. The cost is mainly fuel, which is determined by the distance. Therefore, the planned path is the shortest path that meets the user's time requirements. When planning, since the possibilities are limited, the exhaustive method is used to exhaustively enumerate each possibility to obtain the required solution.

[0109] The emergency situation identification module identifies the delivery process and obtains abnormal delivery situations, including the following steps:

[0110] When the user's goods to be picked up are not picked up at the door within the user's pick-up time interval, an abnormality identification is performed to obtain a pick-up abnormality.

[0111] Reference Figure 5 As shown, the abnormal classification module identifies the abnormal situation of picking up goods and determines whether the abnormal situation of picking up goods requires emergency treatment, including the following steps:

[0112] Identify abnormalities in delivery and determine whether they are caused by the vehicle or the user;

[0113] When the abnormal situation of picking up goods is caused by the vehicle, the abnormal repair time of the vehicle is obtained. If the abnormal repair time of the vehicle is greater than the preset time, it is determined that the abnormal situation of picking up goods requires emergency treatment;

[0114] When the abnormal situation of picking up goods is caused by the user, obtain the revised picking up time interval after the user adjusts the picking up time interval;

[0115] Get the users whose vehicles have not yet picked up the goods, and get at least one remaining user;

[0116] When the difference between the midpoint of the revised pickup time interval and the midpoint of the pickup time interval of the remaining users is not less than the preset value, it is determined that the pickup abnormality requires emergency processing.

[0117] The causes of emergencies can be divided into two categories: user-caused and vehicle-caused. When a vehicle failure is repaired, the repair time needs to be considered. If the delay is serious, users whose pickup time falls within the repair time need to be diverted so that these users can be picked up by other vehicles. Once the vehicle is repaired, users whose pickup time falls outside the repair time can continue to pick up their goods, thus avoiding users having to wait too long.

[0118] When a user needs to adjust the time, it is determined whether the adjusted time will interfere with the subsequent pickup of the vehicle. That is, in the subsequent pickup process, it is impossible to insert the user who adjusts the pickup time without affecting other users. Therefore, the user needs to be diverted and use another vehicle to pick up the goods.

[0119] The emergency processing module obtains the real-time distribution of vehicles when an abnormal delivery situation occurs, including the following steps:

[0120] The real-time position coordinates of at least one vehicle are obtained as the real-time distribution of the vehicles.

[0121] Reference Figure 6 As shown, based on the real-time distribution of vehicles, an emergency adjustment plan for abnormal delivery situations that require emergency treatment includes the following steps:

[0122] When the delivery anomaly is caused by a vehicle, the users corresponding to the delivery time interval whose midpoint and the time difference between the delivery anomaly and the delivery anomaly is less than the vehicle's anomaly repair time are selected as the users to be decomposed;

[0123] Using an exhaustive method, at least one first insertion point is obtained in the vehicle pickup path of vehicles that do not experience pickup anomalies. The number of first insertion points is consistent with the number of users to be decomposed. The first insertion point satisfies that when the user to be decomposed is set at the first insertion point, the vehicle that does not experience pickup anomalies will not be delayed.

[0124] When the delivery anomaly is caused by a user, an exhaustive search method is used to obtain a second insertion point in the delivery path of a vehicle that does not have a delivery anomaly. The second insertion point satisfies that when a user with a delivery anomaly is set at the second insertion point, the vehicle that does not have a delivery anomaly will not be delayed.

[0125] If the first insertion point cannot be obtained, the pickup of the user to be decomposed will be postponed to the next day for scheduling;

[0126] When the second insertion point cannot be obtained, the pickup of users with pickup abnormalities will be postponed to the next day for scheduling.

[0127] In the pick-up adjustment, not all adjustments can be met. Therefore, for adjustments that cannot be met, they need to be rescheduled and completed the next day.

[0128] Furthermore, the present solution also proposes a storage medium on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned door-to-door pickup scheduling system is run.

[0129] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0130] To sum up, the advantages of the present invention are: by setting a conditional constraint module, a loading estimation module, a vehicle allocation module, an emergency situation identification module and an emergency handling module, various factors of door-to-door pickup are taken into consideration, and the cost and the user's pickup time are considered to ensure that the overall cost is within a reasonable range. At the same time, it is more in line with the user's time to avoid long waiting times for users. In addition, the transportation time of the goods is also taken into consideration. When the goods are more or heavier, it will inevitably affect the overall time. This solution can avoid time delays caused by cargo transportation. On the other hand, emergencies caused by user or vehicle abnormalities are classified and processed, so that the entire scheduling plan can be adjusted in time to ensure that in the event of an abnormality, the pickup task is completed as much as possible on the same day. At the same time, it does not have an excessive impact on the time needs of other users.

[0131] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A door-to-door delivery dispatching system, characterized in that: include: A database generation module, wherein the database generation module establishes a cell database within the scheduling area; a data acquisition module, wherein the module acquires at least one user who needs to pick up goods on the same day, acquires the user's pick-up time interval, acquires the user's location information, and acquires the specifications and quantity of the goods to be picked up by the user, wherein the pick-up time interval is the time range within which the user can pick up the goods at the user's door, and the specifications of the goods to be picked up are the size and weight of each item to be picked up; A loading estimation module estimates the time it takes to transport the goods to be picked up to the vehicle, thereby obtaining a loading time for the goods to be picked up; A vehicle allocation module, which obtains at least one vehicle participating in the delivery of the goods on the day and forms at least one vehicle delivery route under the constraints of the delivery cost and the delivery time interval of the user; An emergency situation identification module, which identifies the delivery process and obtains abnormal delivery situations; An abnormality classification module, which identifies abnormalities in the delivery of goods and determines whether emergency treatment is required for the abnormalities; An emergency processing module, which obtains the real-time distribution of vehicles when an abnormal delivery situation occurs, and forms an emergency adjustment plan for the abnormal delivery situation that requires emergency processing based on the real-time distribution of vehicles; The emergency situation identification module identifies the delivery process and obtains the abnormal delivery situation, which includes the following steps: If the user's goods to be picked up are not picked up at the door within the user's pick-up time interval, an abnormality identification is performed to obtain the abnormality of the pick-up; The abnormal classification module identifies the abnormal situation of picking up goods and determines whether the abnormal situation of picking up goods requires emergency treatment, including the following steps: Identify abnormalities in delivery and determine whether they are caused by the vehicle or the user; When the abnormal situation of picking up goods is caused by the vehicle, the abnormal repair time of the vehicle is obtained. If the abnormal repair time of the vehicle is greater than the preset time, it is determined that the abnormal situation of picking up goods requires emergency treatment; When the abnormal situation of picking up goods is caused by the user, obtain the revised picking up time interval after the user adjusts the picking up time interval; Get the users whose vehicles have not yet picked up the goods, and get at least one remaining user; When the difference between the midpoint of the revised pickup time interval and the midpoint of the pickup time interval of the remaining users is not less than the preset value, it is determined that the pickup is abnormal and requires emergency treatment; The emergency processing module obtains the real-time distribution of vehicles when an abnormal situation occurs in picking up goods, including the following steps: Obtaining the real-time position coordinates of at least one vehicle as the real-time distribution of the vehicles; The emergency adjustment plan for abnormal delivery situations requiring emergency treatment based on the real-time distribution of vehicles includes the following steps: When the delivery anomaly is caused by a vehicle, the users corresponding to the delivery time interval whose midpoint and the time difference between the delivery anomaly and the delivery anomaly is less than the vehicle's anomaly repair time are selected as the users to be decomposed; Using an exhaustive method, at least one first insertion point is obtained in the vehicle pickup path of vehicles that do not experience pickup anomalies. The number of first insertion points is consistent with the number of users to be decomposed. The first insertion point satisfies that when the user to be decomposed is set at the first insertion point, the vehicle that does not experience pickup anomalies will not be delayed. When the delivery anomaly is caused by a user, an exhaustive search method is used to obtain a second insertion point in the delivery path of a vehicle that does not have a delivery anomaly. The second insertion point satisfies that when a user with a delivery anomaly is set at the second insertion point, the vehicle that does not have a delivery anomaly will not be delayed. If the first insertion point cannot be obtained, the pickup of the user to be decomposed will be postponed to the next day for scheduling; When the second insertion point cannot be obtained, the pickup of users with pickup abnormalities will be postponed to the next day for scheduling.

2. A door-to-door delivery dispatching system according to claim 1, characterized in that: The database generation module establishes a cell database in the scheduling area, including the following steps: Obtain at least one cell within the scheduling area and obtain location information of the cell; Get at least one available parking spot in the community and the coordinates of the buildings in the community.

3. A door-to-door delivery dispatching system according to claim 2, characterized in that: The loading estimation module estimates the time it takes to transport the goods to be picked up to the vehicle, and obtaining the loading time of the goods to be picked up includes the following steps: Based on the user's location information and the cell's location information, cell identification is performed to obtain the cell where the user is located as the target cell; Get the building coordinates in the user's community as the target building coordinates; Obtain the closest available parking spot to the target building in the target community as the target available parking spot; Based on big data, obtain the average moving speed of workers carrying goods; Get the distance from the target parking location to the target building coordinates as the target distance; Calculate the total space and weight of the goods to be picked up based on the specifications and quantity of the goods to be picked up by the user; Obtain the weight and space limits for a worker's single lift. Divide the total space occupied by the goods to be picked up by the worker's single lift space limit to obtain the first number. Divide the total weight of the goods to be picked up by the worker's single lift weight limit to obtain the second number. The largest of the first and second times is used as the number of times the goods to be picked up are moved; Use the time estimation formula to calculate the loading time of the goods to be picked up; The time estimation formula is as follows: Among them, A is the loading time of the goods to be picked up, B is the target distance, C is the average moving speed of the workers carrying the goods, and D is the number of times the goods to be picked up are moved.

4. A door-to-door delivery dispatching system according to claim 3, characterized in that: The step of forming at least one vehicle pickup route under the constraints of the pickup cost and the user's pickup time interval comprises the following steps: Get the midpoint of the user's pickup time interval as the feature point; Classify users according to their characteristic points to obtain at least one user category, wherein the difference in characteristic points of users in the same user category is less than a preset value, and the difference in characteristic points of users in different user categories is not less than a preset value, wherein the preset value is the average time it takes for a vehicle to reach the next pickup location from one pickup location in historical data; Take the average of the feature points of users in the user classification to obtain the classification mean; Sort the user categories according to the category mean from small to large to obtain the user category sequence; At least one preliminary path group is generated, where the preliminary path group is composed of at least one preliminary path, and the preliminary path is composed of at least one preliminary node, where the preliminary node is a user in a user classification. The arrangement order of the at least one preliminary node in the preliminary path is set according to the order of the user classification in which the preliminary node belongs in the user classification sequence. The preliminary nodes of different preliminary paths in the preliminary path group are different from each other. The number of preliminary nodes of all preliminary paths in a single preliminary path group is consistent with the number of at least one user who needs to pick up goods on that day. Calculating the difference between the characteristic points corresponding to the adjacent preparation nodes in the preparation path as the characteristic time difference; Obtaining the distance between the cells adjacent to the preparation node in the preparation path as the marking distance, and obtaining the average vehicle moving speed based on historical data, dividing the marking distance by the average vehicle moving speed to obtain the marking time; The marking time is added to the loading time of the goods to be picked up at the adjacent preparation nodes that are ranked higher to obtain the verification time; When the verification time corresponding to the adjacent preparation nodes does not exceed the characteristic time difference corresponding to the adjacent preparation nodes, the adjacent preparation nodes are marked as qualified nodes, otherwise no processing is performed; Obtaining a preliminary path group in which all preliminary nodes in the preliminary path are marked as qualified nodes as a candidate path group; When picking up goods, each of the candidate path groups is assigned a vehicle, and the sum of the distances between the cells where the adjacent reserve nodes are located in each of the candidate path groups is calculated to obtain the overall distance; The candidate path group with the smallest overall distance is selected, and the backup path is assigned to the vehicle as the vehicle pickup path.

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