Express receiving and sending method, computer equipment, readable storage medium and program product

Abstract

The invention relates to an express receiving and sending method, computer equipment, a readable storage medium and a program product, and relates to the technical field of logistics. The method comprises the following steps: in response to a receiving and dispatching instruction for at least one to-be-received and dispatched express, respectively obtaining a receiving and dispatching address of each to-be-received and dispatched express; based on each receiving and dispatching address, carrying out region clustering to obtain at least one target class, and respectively obtaining a receiving and dispatching region corresponding to each target class; taking a receiving and sending area as a target area, screening out at least one target express belonging to the target area, and performing vectorization processing on a receiving and sending address of each target express to obtain a receiving and sending address vector of each target express; classifying each target express according to the receiving and sending address vector to obtain a receiving and sending point to which each target express belongs; and on the basis of the receiving and dispatching priority of the receiving and dispatching point to which each target express belongs, generating a receiving and dispatching task of each target express. By adopting the method, the express delivery efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of logistics technology, and in particular to a method for express delivery, computer equipment, readable storage medium, and program product. Background Technology

[0002] Currently, most express delivery companies use manual sorting for their parcel collection and delivery services. This means that couriers sort the parcels that need to be collected and delivered, and then collect and deliver them based on the sorting results.

[0003] However, the manual sorting of packages often relies on human subjective judgment, which can easily lead to sorting errors, thereby increasing labor costs and delivery time, and ultimately reducing the efficiency of package collection and delivery. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency of express delivery in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for express delivery collection and delivery, comprising: in response to a collection and delivery instruction for at least one express delivery to be collected and delivered, obtaining the collection and delivery address of each express delivery to be collected and delivered; performing regional clustering based on each collection and delivery address to obtain at least one target class, and obtaining the collection and delivery area corresponding to each target class; taking a collection and delivery area as a target area, and selecting at least one target express delivery belonging to the target area from among the express delivery to be collected and delivered; performing vectorization processing on the collection and delivery address of each target express delivery to obtain the collection and delivery address vector of each target express delivery; classifying each target express delivery according to the collection and delivery address vector to obtain the collection and delivery point to which each target express delivery belongs; and generating a collection and delivery task for each target express delivery based on the collection and delivery priority of the collection and delivery point to which each target express delivery belongs.

[0006] In one embodiment, based on each delivery and collection address, regional clustering is performed to obtain at least one target class, including: parsing each delivery and collection address to obtain the latitude and longitude information of each delivery and collection address; obtaining map points of interest, performing point of interest matching based on the latitude and longitude information to obtain points of interest that match the latitude and longitude information; and clustering each point of interest to obtain at least one target class.

[0007] In one embodiment, clustering each point of interest to obtain at least one target class includes: acquiring the interest point information for each point of interest; the interest point information includes the interest point name and interest point coordinates; selecting at least one target interest point from each point of interest; for each target interest point, determining the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points; performing similarity analysis on the interest point name of the target interest point and the interest point names of other interest points to obtain the name similarity between the interest point name of the target interest point and the interest point names of other interest points; selecting clustered interest points from other interest points whose coordinate distance and name similarity both satisfy the clustering conditions; and using the target interest point as the cluster center to cluster the clustered interest points to obtain at least one target class.

[0008] In one embodiment, the pickup and delivery address of each target express delivery is vectorized to obtain the pickup and delivery address vector of each target express delivery, including: performing word segmentation on the pickup and delivery address of each target express delivery to obtain at least one target address word segment; performing vectorization on each target address word segment to obtain the word vector of each target address word segment; and summing up the word vectors to obtain the pickup and delivery address vector of the target express delivery.

[0009] In one embodiment, the method further includes: for each candidate pickup and delivery point in the target area, obtaining the pickup and delivery point address vector of each candidate pickup and delivery point; classifying each target express delivery according to the pickup and delivery address vector to obtain the pickup and delivery point to which each target express delivery belongs, including: determining the address vector distance between the pickup and delivery address vector of each target express delivery and the address vector of each pickup and delivery point; and taking the candidate pickup and delivery point as the pickup and delivery point to which the target express delivery belongs if the address vector distance does not exceed a distance threshold.

[0010] In one embodiment, the method further includes: determining the target vector distance between each candidate collection and delivery point based on the collection and delivery point address vector of each candidate collection and delivery point; and sorting each candidate collection and delivery point by priority based on the target vector distance to obtain the collection and delivery priority of each candidate collection and delivery point.

[0011] In one embodiment, based on the collection and delivery priority of the collection and delivery point to which each target express delivery belongs, a collection and delivery task for each target express delivery is generated, including: obtaining the express delivery information of each target express delivery; and generating the collection and delivery task for the target express delivery based on the collection and delivery priority of the collection and delivery point to which the target express delivery belongs and the express delivery information of the target express delivery.

[0012] Secondly, this application also provides a courier pickup and delivery device, comprising: a pickup and delivery address acquisition module, configured to acquire the pickup and delivery address of each courier in response to a pickup and delivery instruction for at least one courier to be picked up and delivered; a region clustering module, configured to perform region clustering based on each pickup and delivery address to obtain at least one target class, and acquire the pickup and delivery area corresponding to each target class; a courier screening module, configured to select a pickup and delivery area as a target area, and screen at least one target courier belonging to the target area from among the courier to be picked up and delivered; a vectorization processing module, configured to perform vectorization processing on the pickup and delivery address of each target courier to obtain the pickup and delivery address vector of each target courier; a classification module, configured to classify each target courier according to the pickup and delivery address vector to obtain the pickup and delivery point to which each target courier belongs; and a pickup and delivery task generation module, configured to generate pickup and delivery tasks for each target courier based on the pickup and delivery priority of the pickup and delivery point to which each target courier belongs.

[0013] Thirdly, this application also provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: in response to a pickup / delivery instruction for at least one package to be picked up and delivered, obtaining the pickup / delivery address of each package to be picked up and delivered; based on each pickup / delivery address, performing region clustering to obtain at least one target class, and obtaining the pickup / delivery area corresponding to each target class; taking a pickup / delivery area as a target area, and selecting at least one target package belonging to the target area from among the packages to be picked up and delivered; vectorizing the pickup / delivery address of each target package to obtain the pickup / delivery address vector of each target package; classifying each target package according to the pickup / delivery address vector to obtain the pickup / delivery point to which each target package belongs; and generating a pickup / delivery task for each target package based on the pickup / delivery priority of the pickup / delivery point to which each target package belongs.

[0014] Fourthly, this application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps: in response to a pickup / delivery instruction for at least one package to be picked up and delivered, obtain the pickup / delivery address of each package to be picked up and delivered; based on each pickup / delivery address, perform region clustering to obtain at least one target class, and obtain the pickup / delivery area corresponding to each target class; take a pickup / delivery area as a target area, and select at least one target package belonging to the target area from among the packages to be picked up and delivered; vectorize the pickup / delivery address of each target package to obtain the pickup / delivery address vector of each target package; classify each target package according to the pickup / delivery address vector to obtain the pickup / delivery point to which each target package belongs; and generate a pickup / delivery task for each target package based on the pickup / delivery priority of the pickup / delivery point to which each target package belongs.

[0015] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps: in response to a pickup / delivery instruction for at least one pending pickup / delivery package, obtaining the pickup / delivery address for each pending pickup / delivery package; based on each pickup / delivery address, performing region clustering to obtain at least one target class, and obtaining the pickup / delivery area corresponding to each target class; taking one pickup / delivery area as a target area, and selecting at least one target package belonging to the target area from among the pending pickup / delivery packages; vectorizing the pickup / delivery address of each target package to obtain a pickup / delivery address vector for each target package; classifying each target package according to the pickup / delivery address vector to obtain the pickup / delivery point to which each target package belongs; and generating a pickup / delivery task for each target package based on the pickup / delivery priority of the pickup / delivery point to which each target package belongs.

[0016] The aforementioned express delivery collection and delivery method, apparatus, computer equipment, computer-readable storage medium, and computer program product, in response to a collection and delivery instruction for at least one express delivery to be collected or delivered, firstly, obtain the collection and delivery address of each express delivery to be collected or delivered, and then perform regional clustering based on each collection and delivery address to obtain at least one target class, and obtain the collection and delivery area corresponding to each target class. Collection and delivery area clustering facilitates subsequent targeted further classification of express deliveries in each delivery area, improving the efficiency of express delivery classification, thereby improving collection and delivery efficiency. Next, a collection and delivery area is selected as the target area, and each target express delivery belonging to the target area is selected from the express delivery to be collected or delivered. The collection and delivery address of each target express delivery is vectorized to obtain the collection and delivery address vector of each target express delivery. It can be understood that vectorization can improve the standardization of express delivery addresses. Thus, based on the collection and delivery address vector, each target express delivery can be efficiently and accurately classified, thereby obtaining the collection and delivery point to which each target express delivery belongs. Finally, based on the collection and delivery priority of the collection and delivery point and the express delivery information, a collection and delivery task for the target express delivery is generated. The entire solution narrows down the collection and delivery area through regional clustering and standardizes express delivery addresses using vectorization. This allows for the quick and accurate determination of the collection and delivery point for express deliveries within the same area, eliminating the need for manual sorting and improving the efficiency and accuracy of express delivery. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1This is a diagram illustrating the application environment of a courier pickup and delivery method in one embodiment.

[0019] Figure 2 This is a flowchart illustrating a courier pickup and delivery method in one embodiment;

[0020] Figure 3 This is a schematic diagram of the interest point matching process in one embodiment;

[0021] Figure 4 This is a flowchart illustrating the region clustering process in one embodiment;

[0022] Figure 5 This is a schematic diagram of the vectorization process in one embodiment;

[0023] Figure 6 This is a flowchart illustrating the process of categorizing express deliveries in one embodiment;

[0024] Figure 7 This is a flowchart illustrating the process of determining the priority of collection and delivery points in one embodiment;

[0025] Figure 8 This is a flowchart illustrating a specific embodiment of a courier pickup and delivery method.

[0026] Figure 9 This is a structural block diagram of a courier delivery device in one embodiment;

[0027] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0029] The express delivery method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104. A data storage system can store the data that server 104 needs to process. This data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Specifically, server 104 responds to the pickup / delivery instructions sent by terminal 102 for at least one pending package, obtaining the pickup / delivery address for each package. Based on these addresses, it performs regional clustering to obtain at least one target class, and obtains the corresponding pickup / delivery area for each target class. Taking one pickup / delivery area as the target area, it selects at least one target package belonging to that target area from among the pending packages. It then vectorizes the pickup / delivery address of each target package to obtain its own pickup / delivery address vector. Based on the pickup / delivery address vector, it categorizes each target package to obtain its respective pickup / delivery point. Finally, based on the pickup / delivery priority of each target package's pickup / delivery point, it generates a pickup / delivery task for each target package. Terminal 102 can be, but is not limited to, various smartphones, tablets, and handheld terminal devices such as barcode scanners. Barcode scanners, also known as logistics PDAs (personal digital assistants) or logistics handheld terminals, integrate operating systems, scanning engines, and other functions, serving as a carrier for data storage related to the collection and delivery of packages. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0030] In one exemplary embodiment, such as Figure 2 As shown, a method for express delivery collection and delivery is provided, which can be applied to... Figure 1 Taking server 104 as an example, the following steps are included:

[0031] Step S202: In response to a pickup / delivery instruction for at least one package to be picked up or delivered, obtain the pickup / delivery address for each package to be picked up or delivered.

[0032] In this context, "pending pickup and delivery" refers to packages awaiting pickup or delivery. "Pickup" means the courier will collect the package, while "delivery" means the courier will deliver it. "Pickup and delivery instructions" can be either pickup or delivery instructions for the packages awaiting pickup and delivery. "Pickup and delivery address" can be either the pickup address or the delivery address for the packages awaiting pickup and delivery.

[0033] For example, when a server receives a pickup instruction from a terminal regarding at least one package to be received, it can first obtain the pickup address for each package. Similarly, when a server receives a delivery instruction from a terminal regarding at least one package to be delivered, it can first obtain the delivery address for each package. Obtaining the pickup or delivery address facilitates subsequent clustering of pickup and delivery areas.

[0034] In practical applications, the pickup and delivery addresses can be obtained through the terminal. For example, the terminal can pre-scan the barcode of each package to be picked up or delivered to obtain the pickup and delivery information, including the pickup and delivery address. When initiating a pickup and delivery instruction to the server, the terminal can simultaneously send the pickup and delivery address to the server. Alternatively, the server can pre-store the pickup and delivery information of each package in a database. Upon receiving a pickup and delivery instruction from the terminal, the server can directly query the database to obtain the pickup and delivery address for each package. In addition, the server can also obtain the pickup and delivery address of each package from the logistics management platform. The specific method of obtaining the pickup and delivery address can be determined according to the actual situation, and this embodiment does not impose any restrictions. It should be noted that, in addition to the pickup and delivery address, the pickup and delivery information may also include, but is not limited to, the sender's name, sender's contact information, recipient's name, recipient's contact information, delivery address, recipient's address, package size, and package type.

[0035] Step S204: Based on each collection and delivery address, perform regional clustering to obtain at least one target class, and obtain the collection and delivery area corresponding to each target class.

[0036] In this context, "region clustering" refers to the process of grouping all delivery and pickup addresses into multiple classes based on their similarity to each other, as in this embodiment. Delivery and pickup addresses within the same class are similar to each other, while those in other classes are different. A target class can be understood as a set of similar delivery and pickup addresses. A delivery and pickup area can refer to the area where express delivery and pickup are carried out, such as a residential community, a shopping mall, or an office building. It is understood that each target class can correspond to a delivery and pickup area.

[0037] In one example, the similarity between delivery and pickup addresses can be represented by the similarity between the points of interest (POIs) corresponding to the delivery and pickup addresses, and the similarity between POIs can be reflected by the coordinates and names of the POIs.

[0038] For example, after obtaining the pick-up and delivery addresses of each package, the server can perform regional clustering to improve efficiency. This involves grouping similar pick-up and delivery addresses into a set, or target class, based on their similarity, thus obtaining the corresponding pick-up and delivery area for each target class. This allows for targeted analysis of the specific pick-up and delivery points for each package within its designated area, thereby improving overall package pickup and delivery efficiency.

[0039] In one example, the specific method for determining the delivery area corresponding to each target class can be based on the common address information among the delivery addresses within each target class. For instance, suppose target class A includes delivery address 1 (Building 1 of Community A), delivery address 2 (Building 2 of Community A), and delivery address 3 (Building 3 of Community A). It can be seen that the common address information for delivery addresses 1, 2, and 3 is Community A; therefore, the delivery area corresponding to target class A can be set as Community A. Of course, in practical applications, the delivery area can be expanded to streets, districts, etc., depending on the actual situation. This embodiment does not limit the specific scope of the delivery area.

[0040] Step S206: Select a collection and delivery area as the target area, and select at least one target package belonging to the target area from all packages to be collected and delivered.

[0041] The target area can be any one of multiple collection and delivery areas. A target courier can refer to a courier whose collection and delivery address belongs to the target area, or a courier whose collection and delivery address belongs to the target category corresponding to that target area.

[0042] For example, after obtaining the collection and delivery areas corresponding to each target class, the server can arbitrarily select one collection and delivery area as the target area. Of course, in practical applications, the server can also arbitrarily select multiple collection and delivery areas as target areas. Then, based on the collection and delivery addresses included in the target class corresponding to the target area, the server can filter out the express packages corresponding to each collection and delivery address from the express packages to be collected and delivered, which are the target express packages belonging to the target area.

[0043] Step S208: Vectorize the pickup and delivery address of each target express delivery to obtain the pickup and delivery address vector of each target express delivery.

[0044] The delivery address vector refers to the numerical vector of the delivery address. Essentially, the delivery address was originally text information; after vectorization, its numerical value is obtained. Vectorization is a technique that transforms non-numerical data into numerical data to standardize the data, enabling mathematical models to learn and process it.

[0045] For example, after the server filters out each target express delivery belonging to the target area, it can perform vectorization processing on the delivery address of each target express delivery, that is, convert the express delivery address of the target express delivery into a numerical vector, thereby obtaining the delivery address vector of each target express delivery.

[0046] Step S210: Based on the delivery address vector, classify each target express delivery to obtain the delivery point to which each target express delivery belongs.

[0047] Here, "collection and delivery point" refers to the specific location of the target package, including but not limited to buildings and floors. For example, if the collection and delivery area is a certain residential community, the collection and delivery point could be a specific building within that community, or more specifically, a specific floor within that building. "Categorization" is the process of assigning each target package within the same collection and delivery area to its specific collection and delivery location within that area.

[0048] For example, after obtaining the collection and delivery address vectors of each target express delivery in the same collection and delivery area, the server can further calculate the vector distance between each target express delivery and each collection and delivery point based on each collection and delivery address vector, thereby classifying each target express delivery to its respective collection and delivery point based on the vector distance.

[0049] Step S212: Based on the collection and delivery priority of the collection and delivery point to which each target express belongs, generate a collection and delivery task for each target express.

[0050] Among them, the collection and delivery priority refers to the priority of executing the collection and delivery tasks of the target express at the collection and delivery point. It can be understood that the higher the collection and delivery priority of the collection and delivery point, the more likely the collection and delivery tasks of the target express at that collection and delivery point can be executed first. The lower the collection and delivery priority of the collection and delivery point, the more likely the collection and delivery tasks of the target express at that collection and delivery point can be executed later.

[0051] In one example, the collection and dispatch priority can be determined based on the address vector distance between each collection and dispatch point.

[0052] For example, after the server determines the collection and delivery point to which each target express delivery belongs, it can generate the collection and delivery task for the target express delivery at that collection and delivery point based on the collection and delivery priority of that collection and delivery point, so that the collection and delivery tasks of the target express delivery at each collection and delivery point can be executed sequentially according to the collection and delivery priority.

[0053] In this embodiment, in response to a pickup and delivery instruction initiated by a terminal for at least one pending package, the server first obtains the pickup and delivery address of each package. Based on these addresses, it performs regional clustering to obtain at least one target class, and then obtains the corresponding pickup and delivery area for each target class. This regional clustering facilitates further targeted classification of packages within each delivery area, improving the efficiency of package classification and thus increasing pickup and delivery efficiency. Next, a pickup and delivery area is selected as the target area, and the packages belonging to this target area are selected from the pending packages. The pickup and delivery address of each target package is then vectorized to obtain its own pickup and delivery address vector. Vectorization improves the standardization of package addresses. Based on the pickup and delivery address vectors, each target package can be efficiently and accurately classified, thus determining its respective pickup and delivery point. Finally, based on the pickup and delivery priority of the pickup and delivery points and the package information, a pickup and delivery task for the target package is generated. The entire solution narrows down the collection and delivery area through regional clustering and standardizes express delivery addresses using vectorization. This allows for the quick and accurate determination of the collection and delivery point for express deliveries within the same area, eliminating the need for manual sorting and improving the efficiency and accuracy of express delivery.

[0054] In one exemplary embodiment, such as Figure 3 As shown, based on each delivery and pickup address, regional clustering is performed to obtain at least one target class, including:

[0055] Step S302: parse each receiving and dispatching address to obtain the latitude and longitude information of each address.

[0056] Here, latitude and longitude information refers to the coordinates composed of longitude and latitude. It is understandable that matching points of interest based solely on delivery and pickup addresses is not accurate enough, as there may be instances where delivery and pickup addresses are described the same but do not belong to the same location. Therefore, this embodiment chooses to match points of interest based on latitude and longitude information to ensure the accuracy of point of interest matching, thereby improving the accuracy of express delivery.

[0057] For example, after obtaining the pick-up and delivery address of each package to be picked up and delivered, the server can parse each pick-up and delivery address to obtain the specific latitude and longitude coordinates of each pick-up and delivery address. For example, the pick-up and delivery address can be input into a map tool such as Google Maps, Baidu Maps or other map services for parsing to obtain the latitude and longitude coordinates output by the map tool.

[0058] In one example, besides using map tools to obtain latitude and longitude coordinates, the latitude and longitude of the delivery address can also be obtained using a mobile phone's location function. Alternatively, a professional GPS (Global Positioning System) device can be used to obtain the latitude and longitude coordinates of the delivery address. Furthermore, the latitude and longitude coordinates corresponding to the delivery address can be found in map data, such as paper or electronic maps. The specific methods for obtaining the latitude and longitude coordinates of the delivery address are not limited here.

[0059] Step S304: Obtain map points of interest (POIs), perform POI matching based on latitude and longitude information, and obtain POIs that match the latitude and longitude information.

[0060] Points of interest (POIs) are objects that can be a building, a shop, a mailbox, a bus stop, etc., in a geographic information system.

[0061] For example, after obtaining the latitude and longitude coordinates corresponding to the delivery address, the server obtains map points of interest, compares the coordinates of each point of interest with the latitude and longitude coordinates, and if the comparison is consistent, it is considered that the coordinates of the point of interest match the latitude and longitude information of the delivery address.

[0062] Step S306: Cluster the points of interest to obtain at least one target class.

[0063] Understandably, since the accuracy of delivery and pickup addresses is not high, directly using delivery and pickup addresses for regional clustering would affect the accuracy of clustering. Therefore, this implementation chooses to use points of interest that match delivery and pickup addresses for regional clustering to ensure the accuracy of regional clustering. In addition, points of interest are also rich in information and can effectively reflect the structural and functional characteristics of delivery and pickup areas, thereby further improving the efficiency of express delivery and pickup.

[0064] For example, after the server obtains the points of interest that match each target address, it can cluster the points of interest. That is, it can randomly select a cluster center from each point of interest, analyze the similarity between other points of interest and the cluster center, and if the similarity meets the clustering conditions, then the other points of interest and the cluster center are classified into one class, which is a target class.

[0065] In one example, the K-means algorithm can be used for region clustering. The K-means algorithm has relatively low computational complexity and is highly efficient when processing large datasets, such as a large number of package addresses, effectively reducing computation time and resource consumption. Of course, other clustering algorithms can also be used for region clustering in practical applications; the specific region clustering method is not limited here.

[0066] In this embodiment, by obtaining the latitude and longitude coordinates of each delivery address, points of interest matching the delivery address are obtained based on the latitude and longitude coordinates. Then, similar points of interest are clustered to obtain multiple delivery areas, which improves the accuracy of area clustering and the accuracy of subsequent express delivery.

[0067] In one exemplary embodiment, such as Figure 4 As shown, clustering of each point of interest yields at least one target class, including:

[0068] Step S402: Obtain the interest point information for each interest point.

[0069] Points of interest (POIs) information refers to the basic information of a POI, including but not limited to its name and coordinates. The POI name is the name given to the POI, such as a shopping mall, school, restaurant, or office building. The POI coordinates are its latitude and longitude coordinates.

[0070] For example, after obtaining the points of interest that match the delivery address, the server can further obtain basic information about the points of interest, such as the name of the points of interest and the latitude and longitude coordinates of the points of interest. Of course, in practical applications, other information such as the category of the points of interest and the attributes of the points of interest can also be obtained, which can be used as auxiliary information for subsequent clustering processes to further improve the accuracy of regional clustering.

[0071] Step S404: Select at least one target interest point among all interest points; for each target interest point, determine the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points.

[0072] The target point of interest can be any one of the points of interest, and can be used as a cluster center for subsequent clustering. Coordinate distance refers to the distance between the latitude and longitude coordinates of the target point of interest and the latitude and longitude coordinates of other points of interest.

[0073] For example, for each point of interest in the same delivery area, the server can first randomly select any number of target points of interest as cluster centers, and then calculate the distance between other points of interest and the target points of interest, that is, the distance between the latitude and longitude coordinates of other points of interest and the latitude and longitude coordinates of the target points of interest, so as to perform regional clustering based on coordinate distance.

[0074] Step S406: Perform similarity analysis on the name of the target point of interest and the name of the other points of interest respectively to obtain the name similarity between the name of the target point of interest and the name of the other points of interest respectively.

[0075] Name similarity can refer to the degree of similarity between the name of the target point of interest and the names of other points of interest.

[0076] For example, the server can perform name similarity analysis between the target point of interest and other points of interest, that is, analyze whether the name of the target point of interest is similar to the names of other points of interest, so as to perform region clustering based on name similarity.

[0077] Step S408: Among other points of interest, select clustered points of interest that satisfy both the coordinate distance and name similarity conditions.

[0078] Step S410: Using the target interest points as cluster centers, cluster the interest points to obtain at least one target class.

[0079] Clustering conditions refer to the prerequisites for being grouped with a cluster center. These include coordinate distance conditions (e.g., the coordinate distance must be within a preset distance threshold) and name similarity conditions (e.g., the name similarity must reach a preset similarity threshold). It is understood that, to further improve the accuracy of region clustering, this embodiment requires not only coordinate distance between points of interest but also name similarity between them. Only when both clustering conditions are met can other points of interest be grouped with the target point of interest. A cluster center is the center of a cluster. In this embodiment, any number of target points of interest can be randomly selected as cluster centers. Of course, in practical applications, a specific point of interest can be designated as the cluster center based on the actual situation.

[0080] For example, after calculating the coordinate distance and name similarity between the target point of interest and other points of interest, the server can further determine whether the coordinate distance is within a preset distance threshold and whether the name similarity reaches a preset similarity threshold. If the coordinate distance is within the preset distance threshold and the name similarity reaches the preset similarity threshold, the target point of interest can be used as the cluster center, and other points of interest can be grouped into the same category as the target point of interest. This process is repeated until all points of interest have been clustered, resulting in each target category.

[0081] In this embodiment, region clustering is performed based on the coordinate distance between points of interest and the name similarity between points of interest to obtain each target class, thereby improving the accuracy of region clustering and thus improving the accuracy and efficiency of express delivery.

[0082] In one exemplary embodiment, such as Figure 5 As shown, the pickup and delivery addresses of each target express delivery are vectorized to obtain the pickup and delivery address vector for each target express delivery, including:

[0083] Step S502: Perform word segmentation on the delivery address of each target express delivery to obtain at least one target address word segmentation.

[0084] Among them, target address segmentation can refer to dividing the delivery address into multiple words.

[0085] For example, when the server performs vectorization processing on the delivery address, it can first perform word segmentation processing on the delivery address of the target express delivery, thereby obtaining multiple independent words, namely target address word segments.

[0086] In one example, word segmentation methods may include, but are not limited to, mechanical word segmentation, which is based on a pre-built dictionary and achieves word segmentation by scanning the delivery address text and matching words in the dictionary; feature-based word library method, which builds a word library containing various segmentation features and then uses these features to segment the delivery address text; statistical method, which achieves word segmentation by building a language model, training the model with a large amount of labeled corpus, and determining the word segmentation result by statistically analyzing the frequency and probability between words; and deep learning method, which uses a neural network model for word segmentation, and learns the dependency relationship between words and their context by training the model, thereby achieving word segmentation.

[0087] Step S504: Vectorize each word segment at each target address to obtain the word vector for each word segment at each target address.

[0088] Here, word vectors can refer to the numerical vectors of each word segmented at each target address.

[0089] For example, after the server divides the target address into words, it can perform vectorization processing on each target address word to obtain the numerical vector corresponding to each target address word.

[0090] In one example, vectorization methods include, but are not limited to, word embedding and one-hot encoding.

[0091] Step S506: Summarize the word vectors to obtain the delivery address vector of the target express delivery.

[0092] For example, after the server obtains the word vectors of each target address segment, it can summarize the word vectors to obtain the delivery address vector of the target express delivery.

[0093] In this embodiment, the delivery address is divided into multiple address words through word segmentation. Each address word is then vectorized, and the resulting word vectors are aggregated to obtain the delivery address vector. This improves the efficiency of vectorization and thus improves the efficiency of subsequent express delivery.

[0094] In one embodiment, for each candidate pickup / delivery point within the target area, the server can also obtain the pickup / delivery point address vector for each candidate pickup / delivery point. Here, the candidate pickup / delivery point may refer to each pickup / delivery point pre-stored by the server, and the pickup / delivery point address vector may be a numerical vector of the pickup / delivery point addresses of each candidate pickup / delivery point. Therefore, in an exemplary embodiment, as... Figure 6 As shown, based on the delivery and pickup address vectors, each target package is categorized to obtain the delivery and pickup points belonging to each target package, including:

[0095] Step S602: Determine the address vector distance between the pickup and delivery address vector of each target express delivery and the address vector of each pickup and delivery point.

[0096] Step S604: If the address vector distance does not exceed the distance threshold, the candidate pickup and delivery point is taken as the pickup and delivery point to which the target express delivery belongs.

[0097] Address vector distance refers to the distance between the target express delivery's pickup and delivery address vector and the pickup and delivery point address vector. Distance threshold can be a pre-set threshold for address vector distance.

[0098] For example, after the server pre-obtains the address vectors of each candidate pickup and delivery point, it can calculate the distance between the pickup and delivery address vector of each target package and the address vector of each pickup and delivery point. If the address vector distance does not exceed a distance threshold, it means that the pickup and delivery address of the target package is close enough to the candidate pickup and delivery point, and the target package can be considered to belong to that candidate pickup and delivery point. If the address vector distance exceeds the distance threshold, it means that the pickup and delivery address of the target package is far away from the candidate pickup and delivery point, and the target package can be considered not to belong to that candidate pickup and delivery point, and other candidate pickup and delivery points need to be found.

[0099] In one example, if the address vector distance does not exceed a distance threshold, candidate pickup and delivery points can be used as the pickup and delivery points to which the target package belongs. If the address vector distance exceeds the distance threshold, the KNNS (K-Nearest Neighbors) algorithm can be used to determine the package point to which the target package belongs. That is, the KNNS algorithm is applied to find the K nearest neighbor packages of the target package. If most of the K nearest neighbor packages belong to a certain package point, then the target package is considered to belong to that package point as well. Here, the nearest neighbor package can be understood as the package that is closest to the target package, that is, the address vector distance between the nearest neighbor package and the target package is the shortest.

[0100] In this embodiment, the address vector distance between the collection and delivery point and the target express delivery address is calculated to determine whether the target express delivery belongs to the collection and delivery point, thereby improving the accuracy of collection and delivery point determination and thus improving the accuracy and efficiency of express delivery.

[0101] In one exemplary embodiment, such as Figure 7 As shown, the process of determining delivery priorities also includes:

[0102] Step S702: Determine the target vector distance between each candidate collection and delivery point based on the collection and delivery point address vector of each candidate collection and delivery point.

[0103] The target vector distance can refer to the vector distance between the address vectors of any two candidate collection and delivery points. In one example, any two candidate collection and delivery points can be two adjacent candidate collection and delivery points. Adjacency can be determined by the names of the collection and delivery points, such as Building 1 and Building 2.

[0104] For example, when determining the collection and delivery priority of a collection and delivery point, the server can further calculate the address vector distance between any two candidate collection and delivery points after obtaining the address vector of each candidate collection and delivery point. It can be understood that each candidate collection and delivery point includes the collection and delivery point to which the target express delivery belongs. When determining the collection and delivery point to which the target express delivery belongs, the server can obtain the collection and delivery priority of that collection and delivery point.

[0105] Step S704: Based on the target vector distance, sort the priority of each candidate collection and dispatch point to obtain the collection and dispatch priority of each candidate collection and dispatch point.

[0106] For example, based on the address vector distance between candidate collection and delivery points, the server sorts each candidate collection and delivery point by priority, thereby obtaining the collection and delivery priority of each candidate collection and delivery point.

[0107] For example, suppose there are three buildings: Building 1, Building 2, and Building 3. Building 1 is adjacent to Building 2, Building 2 is adjacent to Building 3, and Building 1 is not adjacent to Building 3. The server can calculate the address vector distance A between Building 1 and Building 2, and the address vector distance B between Building 2 and Building 3. If address vector distance A is greater than address vector distance B, the delivery priority can be Building 3, Building 2, Building 1, or Building 2, Building 3, Building 1. The delivery priority for Building 2 and Building 3 can be determined based on the distance between the delivery personnel's current location or the location of the courier station and Building 2, prioritizing the delivery to the building with the shorter distance. If address vector distance A is less than address vector distance B, the delivery priority can be Building 1, Building 2, Building 3, or Building 2, Building 1, Building 3. Of course, in practical applications, it is not limited to adjacent buildings. For example, if Building 4 and Building 1 are not adjacent, but their address vector distance is the shortest, then Building 4 can be given a higher priority for collection and delivery. Furthermore, based on the distance between Building 4 and Building 1 and the current location of the collection and delivery personnel or the location of the express station, it can be determined whether to collect and deliver packages from Building 1 or Building 4 first.

[0108] In one embodiment, in addition to determining the execution order of collection and delivery tasks, the server can also classify express packages according to the collection and delivery priorities of different collection and delivery points, that is, first classify express packages from delivery points with high collection and delivery priorities.

[0109] In this embodiment, the priority of each collection and dispatch point is sorted according to the address vector distance between the collection and dispatch points, which improves the accuracy of collection and dispatch priority and thus improves the efficiency of subsequent collection and dispatch tasks based on collection and dispatch priority.

[0110] In an exemplary embodiment, based on the collection and delivery priority of the collection and delivery point to which each target express delivery belongs, a collection and delivery task for each target express delivery is generated, including: obtaining the express delivery information of each target express delivery; and generating the collection and delivery task for the target express delivery based on the collection and delivery priority of the collection and delivery point to which the target express delivery belongs and the express delivery information of the target express delivery.

[0111] The express delivery information may include, but is not limited to, the target express delivery's pick-up and delivery address, tracking number, express delivery type, and express delivery size. The pick-up and delivery task refers to the task of picking up or delivering the target express delivery, including but not limited to the pick-up and delivery area, pick-up and delivery point, pick-up and delivery priority, tracking number, and pick-up and delivery personnel.

[0112] For example, the server can obtain information such as the pickup and delivery address, tracking number, package type, and package size for each target package, and then associate this package information with the pickup and delivery priority of the pickup and delivery point to which the target package belongs, thereby generating a pickup and delivery task for the target package. The server then sends the pickup and delivery tasks for each target package to a handheld terminal device, which can then display each pickup and delivery task to the pickup and delivery personnel, allowing the personnel to execute the pickup and delivery tasks for each target package at each pickup and delivery point in sequence according to the pickup and delivery priority. The handheld terminal device can also receive pickup and delivery task execution information reported by the pickup and delivery personnel after completing each pickup and delivery task, including but not limited to the signatory, signing time, signing location, and signing method.

[0113] In this embodiment, based on the collection and delivery priority of the collection and delivery points and the express delivery information of the target express, a collection and delivery task for the target express is generated, so that the collection and delivery personnel can execute each collection and delivery task in sequence according to the collection and delivery priority, thereby improving the efficiency of express delivery.

[0114] In one specific embodiment, such as Figure 8 As shown, the express delivery method also includes the following steps:

[0115] S1: In response to a pickup / delivery instruction for at least one pending pickup / delivery package, the server obtains the pickup / delivery address for each pending pickup / delivery package.

[0116] S2: Analyze each delivery / receiver address to obtain its latitude and longitude coordinates. Based on the latitude and longitude coordinates and pre-acquired map points of interest (POIs), perform POI matching to obtain the matched POIs for each delivery / receiver address, and obtain the POI name and coordinates for each POI. Based on the K-means algorithm, select at least one target POI from each POI. For each target POI, determine the coordinate distance between the target POI's coordinates and the coordinates of other POIs, and perform similarity analysis on the target POI's POI name and the names of other POIs to obtain the name similarity between the target POI's POI name and the names of other POIs. From the other POIs, select clustered POIs that satisfy both coordinate distance and name similarity conditions. Use the target POI as the cluster center to cluster the clustered POIs, obtaining at least one target class, and obtain the delivery / receiver area corresponding to each target class.

[0117] S3: Select a pickup and delivery area as the target area. Among all the packages to be picked up and delivered, select at least one target package belonging to the target area. Perform word segmentation on the pickup and delivery address of each target package to obtain at least one target address word. Using word embedding, vectorize each target address word to obtain its own word vector. Summarize all word vectors to obtain the pickup and delivery address vector of the target package.

[0118] S4: For each candidate pickup / delivery point within the target area, obtain the pickup / delivery point address vector for each candidate pickup / delivery point. Based on the address vector of each candidate pickup / delivery point, determine the target vector distance between each candidate pickup / delivery point. Based on the target vector distance, prioritize each candidate pickup / delivery point to obtain its own pickup / delivery priority.

[0119] S5: Based on the pickup and delivery address vectors and pickup / delivery priorities, prioritize the pickup and delivery points with high priorities for package classification. If the address vector distance between the target package and a candidate pickup / delivery point does not exceed a distance threshold, the candidate pickup / delivery point can be considered the pickup / delivery point to which the target package belongs. If the address vector distance exceeds the distance threshold, the KNNS algorithm can be used to determine the package to which the target package belongs. Specifically, the KNNS algorithm is applied to find the K nearest neighbor packages of the target package; if the majority of the K nearest neighbor packages belong to a certain package to which the target package belongs, then the target package is considered to belong to that package to which the target package also belongs.

[0120] S6: Obtain the individual package information for each target package. Based on the collection and delivery priority of the collection and delivery point to which the target package belongs and the package information, generate a collection and delivery task for the target package. Send this collection and delivery task to the barcode scanner, which displays the collection and delivery task for the target packages, allowing collection and delivery personnel to execute the collection and delivery tasks for the target packages at each collection and delivery point in sequence according to the collection and delivery priority. The barcode scanner can also receive the collection and delivery task execution information reported by the collection and delivery personnel after completing each collection and delivery task, including the signatory, signing time, signing location, and signing method.

[0121] In this embodiment, in response to a pickup / delivery instruction for at least one parcel to be picked up and delivered, the pickup and delivery addresses of each parcel are first obtained. Based on these addresses, region clustering is performed to obtain at least one target class, and the corresponding pickup and delivery area for each target class is then obtained. Pickup and delivery area clustering facilitates further targeted classification of parcels within each delivery area, improving parcel classification efficiency and thus pickup and delivery efficiency. Next, a pickup and delivery area is selected as the target area, and the parcels belonging to this target area are selected from the remaining parcels. The pickup and delivery addresses of each target parcel are then vectorized to obtain their respective pickup and delivery address vectors. Vectorization improves the standardization of parcel addresses. Thus, based on the pickup and delivery address vectors, each target parcel can be efficiently and accurately classified, resulting in the pickup and delivery point for each parcel. Finally, based on the pickup and delivery priority of the pickup and delivery points and the parcel information, a pickup and delivery task for the target parcel is generated. The entire solution narrows down the collection and delivery area through regional clustering and standardizes express delivery addresses using vectorization. This allows for the quick and accurate determination of the collection and delivery point for express deliveries within the same area, eliminating the need for manual sorting and improving the efficiency and accuracy of express delivery.

[0122] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0123] Based on the same inventive concept, this application also provides a courier delivery device for implementing the courier delivery method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more courier delivery device embodiments provided below can be found in the limitations of the courier delivery method described above, and will not be repeated here.

[0124] In one exemplary embodiment, such as Figure 9 As shown, a courier pickup and delivery device is provided, comprising: a pickup and delivery address acquisition module 902, used to acquire the pickup and delivery address of each courier to be picked up and delivered in response to a pickup and delivery instruction for at least one courier to be picked up and delivered; a region clustering module 904, used to perform region clustering based on each pickup and delivery address to obtain at least one target class, and to acquire the pickup and delivery area corresponding to each target class; a courier filtering module 906, used to select a pickup and delivery area as a target area, and to filter at least one target courier belonging to the target area from among the courier to be picked up and delivered; a vectorization processing module 908, used to perform vectorization processing on the pickup and delivery address of each target courier to obtain the pickup and delivery address vector of each target courier; a classification module 910, used to classify each target courier according to the pickup and delivery address vector to obtain the pickup and delivery point to which each target courier belongs; and a pickup and delivery task generation module 912, used to generate pickup and delivery tasks for each target courier based on the pickup and delivery priority of the pickup and delivery point to which each target courier belongs.

[0125] In one embodiment, the region clustering module 904 further includes: a parsing unit, used to parse each delivery address to obtain the latitude and longitude information of each delivery address; an interest point matching unit, used to obtain map interest points, and perform interest point matching based on the latitude and longitude information to obtain interest points that match the latitude and longitude information; and a clustering unit, used to cluster each interest point to obtain at least one target class.

[0126] In one embodiment, the clustering unit is further configured to: acquire interest point information for each interest point; the interest point information includes the interest point name and interest point coordinates; select at least one target interest point among the interest points; for each target interest point, determine the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points; perform similarity analysis on the interest point name of the target interest point and the interest point names of other interest points to obtain the name similarity between the interest point name of the target interest point and the interest point names of other interest points; among the other interest points, select clustered interest points whose coordinate distance and name similarity both satisfy the clustering conditions; and use the target interest point as the cluster center to cluster the clustered interest points to obtain at least one target class.

[0127] In one embodiment, the vectorization processing module 908 is further configured to: perform word segmentation processing on the delivery address of each target express delivery to obtain at least one target address word segment; perform vectorization processing on each target address word segment to obtain the word vector of each target address word segment; and summarize the word vectors to obtain the delivery address vector of the target express delivery.

[0128] In one embodiment, the device is further configured to: obtain the respective collection and delivery point address vector of each candidate collection and delivery point in the target area; the classification module 910 is further configured to: determine the address vector distance between the collection and delivery address vector of each target express and the address vector of each collection and delivery point; and, if the address vector distance does not exceed the distance threshold, classify the candidate collection and delivery point as the collection and delivery point to which the target express belongs.

[0129] In one embodiment, the apparatus is further configured to: determine the target vector distance between each candidate collection and delivery point based on the respective collection and delivery point address vector of each candidate collection and delivery point; and sort each candidate collection and delivery point by priority based on the target vector distance to obtain the collection and delivery priority of each candidate collection and delivery point.

[0130] In one embodiment, the pickup and delivery task generation module 912 is further configured to: associate the pickup and delivery priority of the pickup and delivery point with the express delivery identifier of the target express delivery to obtain the pickup and delivery task of the target express delivery; send the pickup and delivery task to the handheld terminal device and display the pickup and delivery task of the target express delivery through the handheld terminal device.

[0131] Each module in the aforementioned express delivery device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0132] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores express delivery data. The I / O interfaces allow the processor to exchange information with external devices. The communication interface allows communication with external terminals via a network connection. When executed by the processor, the computer program implements an express delivery method.

[0133] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0134] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps: in response to a pickup / delivery instruction for at least one package to be picked up and delivered, obtaining the pickup / delivery address of each package to be picked up and delivered; based on each pickup / delivery address, performing region clustering to obtain at least one target class, and obtaining the pickup / delivery area corresponding to each target class; taking a pickup / delivery area as a target area, selecting at least one target package belonging to the target area from among the packages to be picked up and delivered, and vectorizing the pickup / delivery address of each target package to obtain a pickup / delivery address vector for each target package; classifying each target package according to the pickup / delivery address vector to obtain the pickup / delivery point to which each target package belongs; and generating a pickup / delivery task for each target package based on the pickup / delivery priority of the pickup / delivery point to which each target package belongs.

[0135] In one embodiment, when the processor executes the computer program, it further performs the following steps: parsing each receiving and dispatching address to obtain the latitude and longitude information of each receiving and dispatching address; acquiring map points of interest, performing point of interest matching based on the latitude and longitude information to obtain points of interest that match the latitude and longitude information; and clustering each point of interest to obtain at least one target class.

[0136] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring interest point information for each interest point; the interest point information includes the interest point name and interest point coordinates; selecting at least one target interest point among the interest points; for each target interest point, determining the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points; performing similarity analysis on the interest point name of the target interest point and the interest point names of other interest points to obtain the name similarity between the interest point name of the target interest point and the interest point names of other interest points; selecting clustered interest points among other interest points whose coordinate distance and name similarity both satisfy the clustering conditions; using the target interest point as the cluster center, clustering the clustered interest points to obtain at least one target class.

[0137] In one embodiment, when the processor executes the computer program, it further performs the following steps: performing word segmentation on the delivery address of each target express delivery to obtain at least one target address word; performing vectorization on each target address word to obtain a word vector for each target address word; and summing up the word vectors to obtain a delivery address vector for the target express delivery.

[0138] In one embodiment, when the processor executes the computer program, it further implements the following steps: for each candidate pickup and delivery point in the target area, obtain the pickup and delivery point address vector of each candidate pickup and delivery point; classify each target express delivery according to the pickup and delivery address vector to obtain the pickup and delivery point to which each target express delivery belongs, including: determining the address vector distance between the pickup and delivery address vector of each target express delivery and the address vector of each pickup and delivery point; if the address vector distance does not exceed the distance threshold, take the candidate pickup and delivery point as the pickup and delivery point to which the target express delivery belongs.

[0139] In one embodiment, when the processor executes the computer program, it further performs the following steps: determining the target vector distance between each candidate collection and delivery point based on the respective collection and delivery point address vector of each candidate collection and delivery point; and prioritizing each candidate collection and delivery point based on the target vector distance to obtain the collection and delivery priority of each candidate collection and delivery point.

[0140] In one embodiment, when the processor executes the computer program, it further performs the following steps: acquiring the express delivery information of each target express delivery; and generating a delivery task for the target express delivery based on the delivery priority of the delivery point to which the target express delivery belongs and the express delivery information of the target express delivery.

[0141] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program performs the following steps: in response to a pickup / delivery instruction for at least one package to be picked up and delivered, obtaining the pickup / delivery address of each package to be picked up and delivered; based on each pickup / delivery address, performing region clustering to obtain at least one target class, and obtaining the pickup / delivery area corresponding to each target class; taking a pickup / delivery area as a target area, selecting at least one target package belonging to the target area from among the packages to be picked up and delivered, and vectorizing the pickup / delivery address of each target package to obtain the pickup / delivery address vector of each target package; classifying each target package according to the pickup / delivery address vector to obtain the pickup / delivery point to which each target package belongs; and generating a pickup / delivery task for each target package based on the pickup / delivery priority of the pickup / delivery point to which each target package belongs.

[0142] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: parsing each receiving and dispatching address to obtain the latitude and longitude information of each receiving and dispatching address; acquiring map points of interest, performing point of interest matching based on the latitude and longitude information to obtain points of interest that match the latitude and longitude information; and clustering each point of interest to obtain at least one target class.

[0143] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring interest point information for each interest point; the interest point information includes the interest point name and interest point coordinates; selecting at least one target interest point among the interest points; for each target interest point, determining the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points; performing similarity analysis on the interest point name of the target interest point and the interest point names of other interest points to obtain the name similarity between the interest point name of the target interest point and the interest point names of other interest points; selecting clustered interest points among other interest points whose coordinate distance and name similarity both satisfy the clustering conditions; using the target interest point as the cluster center, clustering the clustered interest points to obtain at least one target class.

[0144] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing word segmentation on the delivery address of each target express delivery to obtain at least one target address word segment; performing vectorization on each target address word segment to obtain the word vector of each target address word segment; and summing up the word vectors to obtain the delivery address vector of the target express delivery.

[0145] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: for each candidate pickup and delivery point in the target area, obtain the pickup and delivery point address vector of each candidate pickup and delivery point; classify each target express delivery according to the pickup and delivery address vector to obtain the pickup and delivery point to which each target express delivery belongs, including: determining the address vector distance between the pickup and delivery address vector of each target express delivery and the address vector of each pickup and delivery point; if the address vector distance does not exceed the distance threshold, take the candidate pickup and delivery point as the pickup and delivery point to which the target express delivery belongs.

[0146] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the target vector distance between each candidate collection and delivery point based on the respective collection and delivery point address vector of each candidate collection and delivery point; and prioritizing each candidate collection and delivery point based on the target vector distance to obtain the collection and delivery priority of each candidate collection and delivery point.

[0147] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring the express delivery information of each target express delivery; and generating a delivery task for the target express delivery based on the delivery priority of the delivery point to which the target express delivery belongs and the express delivery information of the target express delivery.

[0148] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps: in response to a pickup / delivery instruction for at least one pending pickup / delivery package, obtaining the pickup / delivery address for each pending pickup / delivery package; based on each pickup / delivery address, performing region clustering to obtain at least one target class, and obtaining the pickup / delivery area corresponding to each target class; taking one pickup / delivery area as a target area, selecting at least one target package belonging to the target area from among the pending pickup / delivery packages, and vectorizing the pickup / delivery address of each target package to obtain a pickup / delivery address vector for each target package; classifying each target package according to the pickup / delivery address vector to obtain the pickup / delivery point to which each target package belongs; and generating a pickup / delivery task for each target package based on the pickup / delivery priority of the pickup / delivery point to which each target package belongs.

[0149] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: parsing each receiving and dispatching address to obtain the latitude and longitude information of each receiving and dispatching address; acquiring map points of interest, performing point of interest matching based on the latitude and longitude information to obtain points of interest that match the latitude and longitude information; and clustering each point of interest to obtain at least one target class.

[0150] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring interest point information for each interest point; the interest point information includes the interest point name and interest point coordinates; selecting at least one target interest point among the interest points; for each target interest point, determining the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points; performing similarity analysis on the interest point name of the target interest point and the interest point names of other interest points to obtain the name similarity between the interest point name of the target interest point and the interest point names of other interest points; selecting clustered interest points among other interest points whose coordinate distance and name similarity both satisfy the clustering conditions; using the target interest point as the cluster center, clustering the clustered interest points to obtain at least one target class.

[0151] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: performing word segmentation on the delivery address of each target express delivery to obtain at least one target address word segment; performing vectorization on each target address word segment to obtain the word vector of each target address word segment; and summing up the word vectors to obtain the delivery address vector of the target express delivery.

[0152] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: for each candidate pickup and delivery point in the target area, obtain the pickup and delivery point address vector of each candidate pickup and delivery point; classify each target express delivery according to the pickup and delivery address vector to obtain the pickup and delivery point to which each target express delivery belongs, including: determining the address vector distance between the pickup and delivery address vector of each target express delivery and the address vector of each pickup and delivery point; if the address vector distance does not exceed the distance threshold, take the candidate pickup and delivery point as the pickup and delivery point to which the target express delivery belongs.

[0153] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: determining the target vector distance between each candidate collection and delivery point based on the respective collection and delivery point address vector of each candidate collection and delivery point; and prioritizing each candidate collection and delivery point based on the target vector distance to obtain the collection and delivery priority of each candidate collection and delivery point.

[0154] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: acquiring the express delivery information of each target express delivery; and generating a delivery task for the target express delivery based on the delivery priority of the delivery point to which the target express delivery belongs and the express delivery information of the target express delivery.

[0155] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0156] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0157] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above 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 application.

[0158] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for express delivery collection and delivery, characterized in that, The method includes: In response to a pickup or delivery instruction for at least one pending pickup or delivery package, the pickup or delivery address for each of the pending pickup or delivery packages is obtained. Based on each of the collection and delivery addresses, perform regional clustering to obtain at least one target class, and obtain the collection and delivery area corresponding to each target class respectively. Take one of the collection and delivery areas as the target area, and select at least one target express delivery belonging to the target area from all the express deliveries to be collected and delivered. The pickup and delivery addresses of each target express delivery are vectorized to obtain the pickup and delivery address vectors of each target express delivery. Based on the pickup and delivery address vector, each target express delivery is classified to obtain the pickup and delivery point to which each target express delivery belongs. Based on the collection and delivery priority of the collection and delivery point to which each target express delivery belongs, a collection and delivery task is generated for each target express delivery.

2. The method according to claim 1, characterized in that, The step of performing regional clustering based on each of the aforementioned delivery and pickup addresses to obtain at least one target class includes: Each of the aforementioned delivery and collection addresses is parsed to obtain the latitude and longitude information of each address. Obtain map points of interest, and perform point of interest matching based on the latitude and longitude information to obtain points of interest that match the latitude and longitude information; Cluster the points of interest to obtain at least one target class.

3. The method according to claim 2, characterized in that, The clustering of each of the interest points to obtain at least one target class includes: Each of the aforementioned points of interest (POIs) is individually acquired; the POI information includes the POI name and POI coordinates. Select at least one target interest point from among the aforementioned interest points; for each target interest point, determine the coordinate distance between the interest point coordinates of the target interest point and the interest point coordinates of other interest points; A similarity analysis is performed on the name of the target point of interest and the name of the other points of interest to obtain the name similarity between the name of the target point of interest and the name of the other points of interest. Among the other points of interest, clustered points of interest that satisfy both the coordinate distance and the name similarity conditions are selected; Using the target interest points as cluster centers, cluster the interest points to obtain at least one target class.

4. The method according to claim 1, characterized in that, The step of vectorizing the pickup and delivery addresses of each target express delivery to obtain the pickup and delivery address vector for each target express delivery includes: Each target express delivery address is segmented into words to obtain at least one target address word; Each target address is segmented into words and then vectorized to obtain the word vector for each target address segment. The word vectors are summarized to obtain the delivery address vector of the target express delivery.

5. The method according to claim 1, characterized in that, The method further includes: For each candidate collection and dispatch point within the target area, obtain the collection and dispatch point address vector for each candidate collection and dispatch point. The step of classifying each target express delivery according to the delivery address vector to obtain the delivery point to which each target express delivery belongs includes: Determine the address vector distance between the pickup and delivery address vector of each target express delivery and the address vector of each pickup and delivery point; If the address vector distance does not exceed the distance threshold, the candidate pickup and delivery point will be used as the pickup and delivery point to which the target express delivery belongs.

6. The method according to claim 5, characterized in that, The method further includes: The target vector distance between each candidate collection and delivery point is determined based on the collection and delivery point address vector of each candidate collection and delivery point. Based on the target vector distance, each candidate collection and dispatch point is prioritized to obtain the collection and dispatch priority of each candidate collection and dispatch point.

7. The method according to claim 1, characterized in that, The step of generating separate collection and delivery tasks for each target express delivery based on the collection and delivery priority of the collection and delivery point to which each target express delivery belongs includes: Obtain the individual express delivery information for each of the target express deliveries; Based on the collection and delivery priority of the collection and delivery point to which the target express belongs and the express information of the target express, a collection and delivery task for the target express is generated.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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