Logistics cargo distribution method, device, equipment and storage medium

Abstract

The present invention relates to the field of logistics, and discloses a method, device, equipment and storage medium for allocating logistics goods, which are used to improve the allocation efficiency of logistics goods, thereby improving the collection efficiency of logistics goods. The method for allocating logistics goods includes: obtaining the address information of the logistics goods and the location data of multiple salesmen, wherein the logistics goods are mailed goods; performing calculations based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple collection distance information, wherein different salesmen correspond to different collection distance information; performing calculations based on the multiple collection distance information and a preset weighted algorithm to generate multiple prediction data, wherein different collection distance information corresponds to different prediction data; determining a target salesman corresponding to the logistics goods from multiple salesmen based on the multiple prediction data, and transmitting the distribution instruction of the logistics goods to the terminal corresponding to the target salesman.

Description

Technical Field

[0001] The present invention relates to the field of logistics technology, and in particular to a distribution method, device, equipment and storage medium for logistics goods. Background Art

[0002] With the rapid development of information and communication technology and the popularization of various applications based on the Internet / mobile Internet, e-commerce with the purchase and sale of goods as its core has gradually become a new highlight of economic growth. Third-party logistics is the infrastructure that provides logistics services for e-commerce activities. Its basic process consists of three stages: warehousing system, transportation backbone network, and "last mile" distribution. In the cargo collection service at the end of logistics, couriers start from outlets and deliver express parcels to customers along a pre-set path, while receiving express parcels from customers and finally returning all received parcels to outlets. However, this way of receiving express parcels causes couriers to frequently travel between customers and outlets, resulting in low efficiency in collecting logistics goods. Summary of the invention

[0003] The present invention provides a method, device, equipment and storage medium for allocating logistics goods, which are used to improve the efficiency of allocating logistics goods, thereby improving the efficiency of collecting logistics goods.

[0004] A first aspect of the present invention provides a method for allocating logistics goods, comprising: acquiring address information of the logistics goods and location data of multiple salesmen, wherein the logistics goods are mailed goods; performing calculations based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pickup distance information, wherein different salesmen correspond to different pickup distance information; performing calculations based on the multiple pickup distance information and a preset weighted algorithm to generate multiple prediction data, wherein different pickup distance information corresponds to different prediction data; determining a target salesman corresponding to the logistics goods from among multiple salesmen based on the multiple prediction data, and transmitting the distribution instructions of the logistics goods to the terminal corresponding to the target salesman.

[0005] Optionally, in a first implementation method of the first aspect of the present invention, the calculation is performed based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pickup distance information, and different salesmen correspond to different pickup distance information, including: calling a preset address retrieval algorithm to convert the address information of the logistics goods to generate the location data of the logistics goods; calculating the Euclidean distance based on the location data of the logistics goods and the location data of each salesman to generate the pickup distance information corresponding to each salesman, and obtaining multiple pickup distance information.

[0006] Optionally, in a second implementation of the first aspect of the present invention, the Euclidean distance is calculated based on the location data of the logistics goods and the location data of each salesperson to generate the pickup distance information corresponding to each salesperson, and multiple pickup distance information are obtained, including: normalizing the location data of the logistics goods to generate normalized cargo location data; normalizing the location data of the multiple salespersons respectively to generate multiple normalized salesperson location data; calculating the Euclidean distance based on a preset Euclidean distance formula and the normalized cargo location data and each normalized salesperson location data to generate the pickup distance information corresponding to each normalized salesperson location data, and multiple pickup distance information are obtained.

[0007] Optionally, in a third implementation method of the first aspect of the present invention, the calculation is performed based on the multiple pick-up distance information and a preset weighting algorithm to generate multiple prediction data, and different pick-up distance information corresponds to different prediction data, including: filtering the pick-up distance information greater than the distance range threshold in the multiple pick-up distance information to obtain multiple filtered pick-up distance information; reading the salesperson direction data from each normalized salesperson location data to obtain multiple salesperson direction data; combining with the preset weighting algorithm, calculating based on each filtered pick-up distance information and the corresponding salesperson direction data to generate a weight value corresponding to each filtered pick-up distance information to obtain multiple weight values; combining with the preset probability formula to calculate each weight value to generate prediction data corresponding to each weight value to obtain multiple prediction data, and different pick-up distance information corresponds to different prediction data.

[0008] Optionally, in a fourth implementation method of the first aspect of the present invention, determining a target salesperson corresponding to the logistics goods from multiple salespersons based on the multiple prediction data, and transmitting the allocation instruction of the logistics goods to a terminal corresponding to the target salesperson includes: comparing the multiple prediction data to generate the maximum prediction data; determining the salesperson corresponding to the maximum prediction data as the target salesperson; generating an allocation instruction based on the address information of the logistics goods, and transmitting the allocation instruction to the terminal corresponding to the target salesperson.

[0009] The second aspect of the present invention provides a distribution device for logistics goods, including: an acquisition module, used to acquire the address information of the logistics goods and the location data of multiple salesmen, wherein the logistics goods are mailed goods; a first calculation module, used to perform calculations based on the address information of the logistics goods and the location data of the multiple salesmen, and generate multiple collection distance information, and different salesmen correspond to different collection distance information; a second calculation module, used to perform calculations based on the multiple collection distance information and a preset weighted algorithm, and generate multiple prediction data, and different collection distance information corresponds to different prediction data; a transmission module, used to determine a target salesman corresponding to the logistics goods from multiple salesmen based on the multiple prediction data, and transmit the distribution instructions of the logistics goods to the terminal corresponding to the target salesman.

[0010] Optionally, in a first implementation method of the second aspect of the present invention, the first calculation module includes: a conversion unit, used to call a preset address retrieval algorithm to convert the address information of the logistics goods, and generate location data of the logistics goods; a calculation unit, used to calculate the Euclidean distance based on the location data of the logistics goods and the location data of each salesperson, generate the pickup distance information corresponding to each salesperson, and obtain multiple pickup distance information.

[0011] Optionally, in a second implementation method of the second aspect of the present invention, the calculation unit can also be specifically used to: normalize the location data of the logistics goods to generate normalized goods location data; normalize the location data of the multiple salesmen respectively to generate multiple normalized salesmen location data; calculate the Euclidean distance based on a preset Euclidean distance formula and the normalized goods location data and each normalized salesman location data, generate the pickup distance information corresponding to each normalized salesman location data, and obtain multiple pickup distance information.

[0012] Optionally, in a third implementation of the second aspect of the present invention, the second calculation module can also be specifically used to: filter the pick-up distance information that is greater than the distance range threshold among the multiple pick-up distance information to obtain multiple filtered pick-up distance information; read the salesperson direction data from each normalized salesperson location data to obtain multiple salesperson direction data; combine with a preset weighting algorithm, perform calculations based on each filtered pick-up distance information and the corresponding salesperson direction data, generate a weight value corresponding to each filtered pick-up distance information, and obtain multiple weight values; combine with a preset probability formula to calculate each weight value, generate prediction data corresponding to each weight value, and obtain multiple prediction data, and different pick-up distance information corresponds to different prediction data.

[0013] Optionally, in a fourth implementation method of the second aspect of the present invention, the transmission module can also be specifically used to: compare the multiple prediction data to generate maximum prediction data; determine the salesperson corresponding to the maximum prediction data as the target salesperson; generate an allocation instruction based on the address information of the logistics goods, and transmit the allocation instruction to the terminal corresponding to the target salesperson.

[0014] The third aspect of the present invention provides a logistics cargo distribution device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the logistics cargo distribution device executes the above-mentioned logistics cargo distribution method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for allocating logistics goods.

[0016] In the technical solution provided by the present invention, the address information of the logistics goods and the location data of multiple salesmen are obtained, and the logistics goods are mailed goods; calculations are performed based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pickup route information, and different salesmen correspond to different pickup route information; calculations are performed based on the multiple pickup route information and a preset weighted algorithm to generate multiple prediction data, and different pickup route information corresponds to different prediction data; based on the multiple prediction data, the target salesman corresponding to the logistics goods is determined from the multiple salesmen, and the distribution instructions of the logistics goods are transmitted to the terminal corresponding to the target salesman. In the embodiment of the present invention, after obtaining the address information of the logistics goods and the location data of multiple salesmen, the prediction data is calculated in combination with the weighted algorithm, and finally the target salesman responsible for the logistics goods is determined according to the prediction data, thereby improving the distribution efficiency of the logistics goods, thereby improving the collection efficiency of the logistics goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of an embodiment of a method for allocating logistics goods in an embodiment of the present invention;

[0018] Figure 2 It is a schematic diagram of another embodiment of the method for allocating logistics goods in an embodiment of the present invention;

[0019] Figure 3 It is a schematic diagram of an embodiment of a distribution device for logistics goods in an embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of another embodiment of the distribution device for logistics goods in an embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of an embodiment of a distribution device for logistics goods in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The embodiments of the present invention provide a method, device, equipment and storage medium for allocating logistics goods, which are used to improve the efficiency of allocating logistics goods, thereby improving the efficiency of collecting logistics goods.

[0023] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the method for allocating logistics goods in the embodiment of the present invention includes:

[0025] 101. Obtaining the address information of logistics goods and the location data of multiple salesmen, where the logistics goods are consignment goods;

[0026] The server obtains the address information of the shipment, that is, the address information of the logistics shipment, and the server also obtains the location data of multiple salesmen.

[0027] When the shipping customer places an order, the task of obtaining the address information is triggered. For example, when Ms. Wang needs to ship goods, she needs to use an application or applet to place an order. After filling in the shipping information, the server obtains the address information of the shipping goods, that is, the address information of the logistics goods. For example, the shipping address is No. XXX, Shaanxi South Road, Jing'an District, Shanghai, and the receiving address is No. XXX, Workers' Stadium North Road, Chaoyang District, Beijing. At the same time, the server also obtains the location data of multiple salesmen. It should be noted that the server only obtains the location data of the same area as the address information of the logistics goods. For example, if the address information of the logistics goods belongs to Jing'an District, Shanghai, then the server only obtains the location data of the salesman responsible for Jing'an District, Shanghai, and the location data of the salesman is real-time location data.

[0028] It is understandable that the execution subject of the present invention may be a distribution device for logistics goods, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0029] 102. Calculate based on the address information of the logistics goods and the location data of multiple salesmen to generate multiple pickup route information, and different salesmen correspond to different pickup route information;

[0030] The server calculates the pickup distance information according to the address information of the logistics goods and the location data of multiple salesmen, obtains the pickup distance information corresponding to the location data of each salesman, and obtains multiple pickup distance information.

[0031] Although most salesmen have pre-set the routes for picking up couriers based on historical delivery data, some express delivery services occur in real time, and there are many salesmen working in the same area, so reasonable business allocation is required. Therefore, after obtaining the address information of the logistics goods and the location data of multiple salesmen, the server must first perform a distance calculation on the location data of each salesman and the address information of the logistics goods, so as to generate the pickup route information corresponding to each salesman, thereby obtaining multiple pickup route information.

[0032] 103. Calculate based on multiple pieces of pickup distance information and a preset weighted algorithm to generate multiple prediction data, where different pieces of pickup distance information correspond to different prediction data;

[0033] The server performs calculations based on multiple pieces of pickup distance information and a preset weighted algorithm to generate multiple prediction data. Different pieces of pickup distance information correspond to different prediction data.

[0034] After generating multiple pieces of pickup distance information, the server also performs a second calculation in combination with a preset weighted algorithm. This calculation can be understood as a predicted probability calculation, thereby generating prediction data corresponding to each piece of pickup distance information. The preset weighted algorithm is expanded on the Euclidean distance formula. The above-mentioned piece of pickup distance information is the result of the Euclidean distance. Therefore, the server calls the preset weighted algorithm to calculate each piece of pickup distance information, and generates prediction data corresponding to each piece of pickup distance information, that is, prediction data corresponding to each salesperson, thereby obtaining multiple prediction data.

[0035] 104. Determine a target salesperson corresponding to the logistics goods from among multiple salespersons based on the multiple forecast data, and transmit a distribution instruction of the logistics goods to a terminal corresponding to the target salesperson.

[0036] The server determines a target salesperson corresponding to the logistics goods from among multiple salespersons based on multiple prediction data, and transmits an allocation instruction of the logistics goods to a terminal corresponding to the target salesperson.

[0037] In this embodiment, the server can determine a target salesperson from multiple salespersons based on the prediction data corresponding to each salesperson. The target salesperson is the salesperson responsible for picking up the logistics goods. After determining the target salesperson, the server generates an allocation instruction, which is generated based on the address information of the logistics goods. Finally, the server transmits the allocation instruction to the terminal corresponding to the target salesperson, and then the server can pick up the logistics goods based on the allocation instruction.

[0038] In an embodiment of the present invention, after obtaining the address information of the logistics goods and the location data of multiple salesmen, the prediction data is calculated in combination with a weighted algorithm, and finally the target salesman responsible for the logistics goods is determined based on the prediction data, thereby improving the distribution efficiency of the logistics goods and thus improving the collection efficiency of the logistics goods.

[0039] See also Figure 2 Another embodiment of the method for distributing logistics goods in the embodiment of the present invention includes:

[0040] 201. Obtaining address information of logistics goods and location data of multiple salesmen, where the logistics goods are consignment goods;

[0041] The server obtains the address information of the shipment, that is, the address information of the logistics shipment, and the server also obtains the location data of multiple salesmen.

[0042] When the shipping customer places an order, the task of obtaining the address information is triggered. For example, when Ms. Wang needs to ship goods, she needs to use an application or applet to place an order. After filling in the shipping information, the server obtains the address information of the shipping goods, that is, the address information of the logistics goods. For example, the shipping address is No. XXX, Shaanxi South Road, Jing'an District, Shanghai, and the receiving address is No. XXX, Workers' Stadium North Road, Chaoyang District, Beijing. At the same time, the server also obtains the location data of multiple salesmen. It should be noted that the server only obtains the location data of the same area as the address information of the logistics goods. For example, if the address information of the logistics goods belongs to Jing'an District, Shanghai, then the server only obtains the location data of the salesman responsible for Jing'an District, Shanghai, and the location data of the salesman is real-time location data.

[0043] It is understandable that the execution subject of the present invention may be a distribution device for logistics goods, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0044] 202. Calling a preset address search algorithm to convert the address information of the logistics goods to generate location data of the logistics goods;

[0045] The server calls a preset address retrieval algorithm to convert the address information of the logistics goods into the location data of the logistics goods.

[0046] The server calls a preset address retrieval algorithm to convert the address information of the logistics goods. The conversion can be a conversion of the data format or a conversion of the data standard. In this embodiment, the server converts the address information of the street name and the community name into longitude and latitude information. For example, the address information of No. XXX, Shaanxi South Road, Jing'an District, Shanghai is converted into 31°23′01″ north latitude and 121°23′01″ east longitude.

[0047] 203. Calculate the Euclidean distance based on the location data of the logistics goods and the location data of each salesperson, generate the pickup distance information corresponding to each salesperson, and obtain multiple pickup distance information;

[0048] The server calculates the Euclidean distance based on the location data of the logistics goods and the location data of each salesperson, generates the pickup route information corresponding to each salesperson, and obtains multiple pickup route information.

[0049] Specifically, the server normalizes the location data of the logistics goods to generate normalized goods location data; the server normalizes the location data of multiple salesmen respectively to generate multiple normalized salesmen location data; the server calculates the Euclidean distance based on a preset Euclidean distance formula and the normalized goods location data and each normalized salesman location data, generates the pickup distance information corresponding to each normalized salesman location data, and obtains multiple pickup distance information.

[0050] In the process of calculating the distance, in order to ensure a high accuracy of the calculated distance, the server first normalizes the location data of multiple salesmen and the location data of logistics goods before calculating the distance, and obtains multiple normalized salesmen location data and normalized goods location data, thereby reducing the accuracy loss in the calculation and improving the accuracy of the calculation. After generating multiple normalized salesmen location data and normalized goods location data, the server also calculates each normalized salesmen location data and normalized goods location data based on the preset Euclidean distance formula, and generates the pickup distance information corresponding to each normalized salesmen location data. The preset Euclidean distance formula is:

[0051]

[0052] Among them, D is the pickup distance information, X is the normalized cargo location data, and Y is the normalized salesperson location data. According to this calculation method, multiple pickup distance information is obtained.

[0053] 204. Calculate based on the multiple pieces of pickup distance information and a preset weighted algorithm to generate multiple prediction data, where different pieces of pickup distance information correspond to different prediction data;

[0054] The server performs calculations based on multiple pieces of pickup distance information and a preset weighted algorithm to generate multiple prediction data. Different pieces of pickup distance information correspond to different prediction data.

[0055] After generating multiple pieces of pickup distance information, the server also performs a second calculation in combination with a preset weighted algorithm. This calculation can be understood as a predicted probability calculation, thereby generating prediction data corresponding to each piece of pickup distance information. The preset weighted algorithm is expanded on the Euclidean distance formula. The above-mentioned piece of pickup distance information is the result of the Euclidean distance. Therefore, the server calls the preset weighted algorithm to calculate each piece of pickup distance information, and generates prediction data corresponding to each piece of pickup distance information, that is, prediction data corresponding to each salesperson, thereby obtaining multiple prediction data.

[0056] Specifically, the server filters out the pickup route information that is greater than the distance range threshold from multiple pickup route information to obtain multiple filtered pickup route information; the server reads the salesperson direction data from each normalized salesperson location data to obtain multiple salesperson direction data; the server combines a preset weighted algorithm to perform calculations based on each filtered pickup route information and the corresponding salesperson direction data to generate a weight value corresponding to each filtered pickup route information to obtain multiple weight values; the server combines a preset probability formula to calculate each weight value to generate prediction data corresponding to each weight value to obtain multiple prediction data, and different pickup route information corresponds to different prediction data.

[0057] The server first filters multiple pickup distance information, and filters out the pickup distance information that is not within the distance range threshold, thereby obtaining multiple filtered pickup distance information. The server then reads the salesperson direction data from the normalized salesperson location data to obtain multiple salesperson direction data. The direction data exists in the form of label probability, for example, the probability of being in the same direction as the location data of the logistics goods is 90%. Then the server calculates each filtered pickup distance information and each salesperson direction data in combination with a preset weighting algorithm, and generates a weight value corresponding to each filtered pickup distance information. The weighting algorithm is:

[0058]

[0059] Among them, W i is the weight value, D is the pickup route information, and const is the location data of the logistics goods. The server calculates each weight value in combination with the preset probability formula to obtain the prediction data corresponding to each weight value, where the preset probability formula is:

[0060]

[0061] Where k represents the number of weight values, D is the pickup distance information, and W i is the weight value. According to this calculation method, multiple prediction data are obtained.

[0062] 205. Determine a target salesperson corresponding to the logistics goods from among multiple salespersons based on the multiple prediction data, and transmit the distribution instruction of the logistics goods to the terminal corresponding to the target salesperson.

[0063] The server determines a target salesperson corresponding to the logistics goods from among multiple salespersons based on multiple prediction data, and transmits an allocation instruction of the logistics goods to a terminal corresponding to the target salesperson.

[0064] In this embodiment, the server can determine a target salesperson from multiple salespersons based on the prediction data corresponding to each salesperson. The target salesperson is the salesperson responsible for picking up the logistics goods. After determining the target salesperson, the server generates an allocation instruction, which is generated based on the address information of the logistics goods. Finally, the server transmits the allocation instruction to the terminal corresponding to the target salesperson, and then the server can pick up the logistics goods based on the allocation instruction.

[0065] Specifically, the server compares multiple prediction data and generates the maximum prediction data; the server determines the salesperson corresponding to the maximum prediction data as the target salesperson; the server generates an allocation instruction based on the address information of the logistics goods, and transmits the allocation instruction to the terminal corresponding to the target salesperson.

[0066] The server compares multiple prediction data to obtain the maximum prediction data. The salesperson corresponding to the maximum data is the target salesperson. Then the server generates a distribution instruction based on the address information of the logistics goods. For example, based on the address information of No. XXX, Shaanxi South Road, Jing'an District, Shanghai, a distribution instruction of "Go to No. XXX, Shaanxi South Road, Jing'an District, Shanghai to collect goods" is generated, or based on the location data of "31°23′01" north latitude, 121°23′01" east longitude", a distribution instruction of "Go to 31°23′01" north latitude, 121°23′01" east longitude to collect goods" is generated. The server transmits the distribution instruction to the terminal corresponding to the target salesperson, and the target salesperson goes to the corresponding address to collect goods according to the distribution instruction.

[0067] In an embodiment of the present invention, after obtaining the address information of the logistics goods and the location data of multiple salesmen, the prediction data is calculated in combination with a weighted algorithm, and finally the target salesman responsible for the logistics goods is determined based on the prediction data, thereby improving the distribution efficiency of the logistics goods and thus improving the collection efficiency of the logistics goods.

[0068] The above describes the distribution method of logistics goods in the embodiment of the present invention. The following describes the distribution device of logistics goods in the embodiment of the present invention. Figure 3 In one embodiment of the present invention, a distribution device for logistics goods includes:

[0069] The acquisition module 301 is used to acquire the address information of the logistics goods and the location data of multiple salesmen, wherein the logistics goods are mailed goods;

[0070] The first calculation module 302 is used to calculate based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pieces of pickup distance information, and different salesmen correspond to different pieces of pickup distance information;

[0071] The second calculation module 303 is used to calculate based on the multiple pieces of pickup distance information and a preset weighting algorithm to generate multiple prediction data, where different pieces of pickup distance information correspond to different prediction data;

[0072] The transmission module 304 is used to determine a target salesperson corresponding to the logistics goods from among multiple salespersons based on the multiple prediction data, and transmit the distribution instruction of the logistics goods to the terminal corresponding to the target salesperson.

[0073] In an embodiment of the present invention, after obtaining the address information of the logistics goods and the location data of multiple salesmen, the prediction data is calculated in combination with a weighted algorithm, and finally the target salesman responsible for the logistics goods is determined based on the prediction data, thereby improving the distribution efficiency of the logistics goods and thus improving the collection efficiency of the logistics goods.

[0074] See also Figure 4 Another embodiment of the distribution device for logistics goods in the embodiment of the present invention includes:

[0075] The acquisition module 301 is used to acquire the address information of the logistics goods and the location data of multiple salesmen, wherein the logistics goods are mailed goods;

[0076] The first calculation module 302 is used to calculate based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pieces of pickup distance information, and different salesmen correspond to different pieces of pickup distance information;

[0077] The second calculation module 303 is used to calculate based on the multiple pieces of pickup distance information and a preset weighting algorithm to generate multiple prediction data, where different pieces of pickup distance information correspond to different prediction data;

[0078] The transmission module 304 is used to determine the corresponding logistics goods among multiple salesmen based on the multiple prediction data.

[0079] Optionally, the first calculation module 302 includes:

[0080] The conversion unit 3021 is used to call a preset address search algorithm to convert the address information of the logistics goods to generate location data of the logistics goods;

[0081] The calculation unit 3022 is used to calculate the Euclidean distance based on the location data of the logistics goods and the location data of each salesperson, generate the pickup distance information corresponding to each salesperson, and obtain multiple pickup distance information.

[0082] Optionally, the calculation unit 3022 may also be specifically configured to:

[0083] Normalizing the location data of the logistics goods to generate normalized goods location data;

[0084] Normalizing the location data of the multiple salesmen respectively to generate multiple normalized salesmen location data;

[0085] Based on the preset Euclidean distance formula and the normalized cargo location data and each normalized salesperson location data, the Euclidean distance is calculated to generate the pickup distance information corresponding to each normalized salesperson location data, and obtain multiple pickup distance information.

[0086] Optionally, the second calculation module 303 may also be specifically configured to:

[0087] Filtering the pickup distance information that is greater than the distance range threshold among the plurality of pickup distance information to obtain a plurality of filtered pickup distance information;

[0088] Reading salesperson direction data from each normalized salesperson position data to obtain multiple salesperson direction data;

[0089] Combined with a preset weighting algorithm, calculation is performed based on each filtered pickup route information and the corresponding salesperson direction data to generate a weight value corresponding to each filtered pickup route information, thereby obtaining multiple weight values;

[0090] Each weight value is calculated in combination with a preset probability formula to generate prediction data corresponding to each weight value, and multiple prediction data are obtained. Different pick-up route information corresponds to different prediction data.

[0091] Optionally, the transmission module 304 may also be specifically configured to:

[0092] Comparing the plurality of prediction data to generate maximum prediction data; determining the salesperson corresponding to the maximum prediction data as the target salesperson;

[0093] A distribution instruction is generated based on the address information of the logistics goods, and the distribution instruction is transmitted to a terminal corresponding to the target salesperson.

[0094] In an embodiment of the present invention, after obtaining the address information of the logistics goods and the location data of multiple salesmen, the prediction data is calculated in combination with a weighted algorithm, and finally the target salesman responsible for the logistics goods is determined based on the prediction data, thereby improving the distribution efficiency of the logistics goods and thus improving the collection efficiency of the logistics goods.

[0095] above Figure 3 and Figure 4 The distribution device for logistics goods in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The distribution device for logistics goods in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0096] Figure 51 is a schematic diagram of the structure of a distribution device for logistics goods provided by an embodiment of the present invention. The distribution device 500 for logistics goods may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 510 (for example, one or more processors) and a memory 520, and one or more storage media 530 (for example, one or more mass storage devices) storing application programs 533 or data 532. Among them, the memory 520 and the storage medium 530 can be short-term storage or permanent storage. The program stored in the storage medium 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the distribution device 500 for logistics goods. Furthermore, the processor 510 can be configured to communicate with the storage medium 530 to execute a series of instruction operations in the storage medium 530 on the distribution device 500 for logistics goods.

[0097] The distribution device 500 for logistics goods may also include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input and output interfaces 560, and / or one or more operating systems 531, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 5 The structure of the logistics cargo distribution equipment shown does not constitute a limitation on the logistics cargo distribution equipment, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0098] The present invention also provides a logistics cargo distribution device, the computer device includes a memory and a processor, the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the logistics cargo distribution method in the above-mentioned embodiments.

[0099] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for distributing logistics goods.

[0100] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0101] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0102] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for distributing logistics goods, characterized in that: The method for distributing the logistics goods includes: Acquire address information of logistics goods and location data of multiple salesmen, wherein the logistics goods are mailed goods; Calculate based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pieces of pickup route information, and different salesmen correspond to different pieces of pickup route information; Calculating based on the multiple pieces of pickup distance information and a preset weighting algorithm to generate multiple prediction data, where different pieces of pickup distance information correspond to different prediction data; Determine a target salesperson corresponding to the logistics goods from among multiple salespersons based on the multiple forecast data, and transmit the distribution instruction of the logistics goods to a terminal corresponding to the target salesperson; The calculation is performed based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pieces of pickup distance information, and different salesmen correspond to different pieces of pickup distance information, including: calling a preset address retrieval algorithm to convert the address information of the logistics goods to generate the location data of the logistics goods; calculating the Euclidean distance based on the location data of the logistics goods and the location data of each salesman to generate the pickup distance information corresponding to each salesman, and obtaining multiple pieces of pickup distance information; The calculation based on the multiple pickup distance information and the preset weighting algorithm to generate multiple prediction data, different pickup distance information corresponds to different prediction data, including: filtering the pickup distance information greater than the distance range threshold in the multiple pickup distance information to obtain multiple filtered pickup distance information; reading the salesperson direction data from each normalized salesperson position data to obtain multiple salesperson direction data; combining with the preset weighting algorithm, calculating based on each filtered pickup distance information and the corresponding salesperson direction data to generate a weight value corresponding to each filtered pickup distance information to obtain multiple weight values; combining with the preset probability formula to calculate each weight value to generate prediction data corresponding to each weight value to obtain multiple prediction data, different pickup distance information corresponds to different prediction data; The weighted algorithm is: Among them, W i is the weight value, D is the pickup route information, and const is the location data of the logistics goods; The probability formula is: Where k represents the number of weight values, D is the pickup distance information, and W i is the weight value; The method of determining a target salesperson corresponding to the logistics goods from among multiple salespersons based on the multiple prediction data, and transmitting the allocation instruction of the logistics goods to the terminal corresponding to the target salesperson includes: comparing the multiple prediction data to generate the maximum prediction data; determining the salesperson corresponding to the maximum prediction data as the target salesperson; generating an allocation instruction based on the address information of the logistics goods, and transmitting the allocation instruction to the terminal corresponding to the target salesperson.

2. The method for distributing logistics goods according to claim 1, characterized in that: The calculation of the Euclidean distance based on the location data of the logistics goods and the location data of each salesperson is performed to generate the pickup distance information corresponding to each salesperson, and the obtained multiple pickup distance information includes: Normalizing the location data of the logistics goods to generate normalized goods location data; Normalizing the location data of the multiple salesmen respectively to generate multiple normalized salesmen location data; Based on the preset Euclidean distance formula and the normalized cargo location data and each normalized salesperson location data, the Euclidean distance is calculated to generate the pickup distance information corresponding to each normalized salesperson location data, and obtain multiple pickup distance information.

3. A distribution device for logistics goods, characterized in that: The distribution device for the logistics goods comprises: An acquisition module, used to acquire address information of logistics goods and location data of multiple salesmen, wherein the logistics goods are mailed goods; A first calculation module is used to calculate based on the address information of the logistics goods and the location data of the multiple salesmen to generate multiple pieces of pickup distance information, and different salesmen correspond to different pieces of pickup distance information; A second calculation module is used to calculate based on the plurality of pieces of pickup distance information and a preset weighting algorithm to generate a plurality of prediction data, wherein different pieces of pickup distance information correspond to different prediction data; A transmission module, used to determine a target salesperson corresponding to the logistics goods from among a plurality of salespersons based on the plurality of forecast data, and transmit the distribution instruction of the logistics goods to a terminal corresponding to the target salesperson; The first calculation module includes: a conversion unit, which is used to call a preset address search algorithm to convert the address information of the logistics goods to generate the location data of the logistics goods; a calculation unit, which is used to calculate the Euclidean distance based on the location data of the logistics goods and the location data of each salesperson, generate the pickup distance information corresponding to each salesperson, and obtain multiple pickup distance information; The second calculation module is specifically used to: filter the pickup distance information greater than the distance range threshold among the multiple pickup distance information to obtain multiple filtered pickup distance information; read the salesperson direction data from each normalized salesperson position data to obtain multiple salesperson direction data; combine with a preset weighting algorithm, calculate based on each filtered pickup distance information and the corresponding salesperson direction data, generate a weight value corresponding to each filtered pickup distance information, and obtain multiple weight values; combine with a preset probability formula to calculate each weight value, generate prediction data corresponding to each weight value, and obtain multiple prediction data, different pickup distance information corresponds to different prediction data; The weighted algorithm is: Among them, W i is the weight value, D is the pickup route information, and const is the location data of the logistics goods; The probability formula is: Where k represents the number of weight values, D is the pickup distance information, and W i is the weight value; The transmission module is specifically used to: compare the multiple prediction data to generate the maximum prediction data; determine the salesperson corresponding to the maximum prediction data as the target salesperson; generate a distribution instruction based on the address information of the logistics goods, and transmit the distribution instruction to the terminal corresponding to the target salesperson.

4. A distribution device for logistics goods, characterized in that: The logistics goods distribution device includes: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the logistics cargo distribution device to execute the logistics cargo distribution method as described in any one of claims 1-2.

5. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the method for distributing logistics goods as described in any one of claims 1-2 is implemented.

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