A method and device for selecting a delivery warehouse for cargo transportation equipment

Abstract

The present invention discloses a method and device for selecting a delivery warehouse for cargo transportation equipment, and relates to the field of computer technology. A specific implementation of the method includes: according to the total amount of cargo of the cargo transportation equipment, calculating the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses; according to the total amount of cargo to be processed, the amount of backlog cargo and the processing efficiency of the delivery warehouse, calculating the processing time of the delivery warehouse to determine the coefficient of variation of the processing time; comparing the coefficient of variation with a preset variation threshold, and taking the delivery warehouse corresponding to the coefficient of variation that is less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment. The method determines the coefficient of variation of the processing time by calculating the processing time of the delivery warehouse, compares the coefficient of variation with the preset variation threshold, and selects the delivery warehouse for the cargo transportation equipment according to the comparison result, thereby ensuring that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a method and device for selecting a delivery warehouse for cargo transportation equipment. Background Art

[0002] After the purchaser purchases the relevant goods from the supplier, the supplier transports the goods to the purchaser's delivery warehouse through cargo transportation equipment, such as a loading truck, and the purchaser reassembles the goods and then sends them to the target storage warehouse. When the supplier sends the goods to the target area through multiple cargo transportation equipment, and there are multiple delivery warehouses in the target area, it is necessary to determine the delivery warehouse corresponding to each cargo transportation equipment. In the prior art, before the supplier's cargo transportation equipment departs, a delivery warehouse is assigned to each cargo transportation equipment based on experience; after the cargo transportation equipment arrives at the target area, the goods are directly handed over according to the previously assigned delivery warehouse.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art:

[0004] (1) Selecting delivery warehouses based on experience is greatly influenced by human subjective judgment, which can easily lead to excessive backlogs of vehicles at some delivery warehouses and vacant space at others, resulting in low overall transportation efficiency.

[0005] (2) The determination method of the prior art does not take into account the target storage warehouse of the goods. When the purchaser sends the reassembled goods to the target storage warehouse via a shipping truck, the shipping truck has a high non-full load rate, which increases the purchaser's internal transportation costs. Summary of the invention

[0006] In view of this, an embodiment of the present invention provides a method and device for selecting a delivery warehouse for a cargo transportation device, which calculates the processing time of the delivery warehouse to determine the coefficient of variation of the processing time, compares the coefficient of variation with a preset variation threshold, and selects a delivery warehouse for the cargo transportation device according to the comparison result. This method ensures that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse.

[0007] To achieve the above objective, according to one aspect of an embodiment of the present invention, a method for selecting a delivery warehouse for a cargo transportation device is provided.

[0008] A method for selecting a delivery warehouse for cargo transportation equipment in an embodiment of the present invention includes: calculating the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses based on the total amount of cargo of the cargo transportation equipment; calculating the processing time of the delivery warehouse based on the total amount of cargo to be processed, the amount of backlog cargo and the processing efficiency of the delivery warehouse to determine the coefficient of variation of the processing time; comparing the coefficient of variation with a preset variation threshold, and using the delivery warehouse corresponding to the coefficient of variation that is less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment.

[0009] Optionally, before the step of calculating the processing time of the delivery warehouse, it also includes: calculating the sum of the total amount of cargo of the cargo transport equipment that has not been processed by the delivery warehouse to obtain the initial backlog of cargo in the delivery warehouse; calculating the remaining amount of cargo of the cargo transport equipment being processed by the delivery warehouse, and summing the initial backlog of cargo and the remaining amount of cargo to obtain the backlog of cargo.

[0010] Optionally, before the step of calculating the processing time of the delivery warehouse, it also includes: obtaining the number of operating service stations of the delivery warehouse, and multiplying the number of operating service stations by the number of goods processed per unit time by the service station to obtain the processing efficiency.

[0011] Optionally, determining the coefficient of variation of the processing time includes: calculating an average value and a standard deviation of the processing time from the cargo transportation equipment to the corresponding delivery warehouse, and dividing the standard deviation by the average value to obtain the coefficient of variation.

[0012] Optionally, the delivery warehouse to which the cargo transportation equipment belongs is a delivery warehouse whose total cost does not exceed the standard, and the method further includes: calculating the total cost of the cargo transportation equipment to the corresponding delivery warehouse and the average total cost, and calculating the total cost deviation coefficient based on the total cost and the average total cost; comparing the total cost deviation coefficient with a preset deviation coefficient threshold to determine the delivery warehouse for the cargo transportation equipment whose total cost does not exceed the standard.

[0013] Optionally, when there are multiple delivery warehouses corresponding to the coefficient of variation that is less than or equal to a preset variation threshold, the method further includes: calculating the cargo priority and the cargo priority average of the cargo transportation equipment according to the out-of-stock quantity of the storage warehouse and the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment, so as to determine the cargo priority deviation coefficient; calculating the matching value and the matching average between the cargo transportation equipment and the corresponding delivery warehouse according to the rated full load cargo weight, the total amount of cargo to be sent and the allocated cargo quantity, so as to determine the matching value deviation coefficient; adding the cargo priority deviation coefficient, the matching value deviation coefficient and the total cost deviation coefficient corresponding to the same cargo transportation equipment and the same delivery warehouse to obtain the total deviation coefficient; adding the total deviation coefficients of multiple cargo transportation equipment in the corresponding delivery warehouse to obtain the total deviation value, and taking the delivery warehouse corresponding to the maximum total deviation value as the delivery warehouse finally selected by multiple cargo transportation equipment.

[0014] Optionally, the calculation of the cargo priority of the cargo transportation equipment includes: setting an initial value for the cargo priority of the cargo in the cargo transportation equipment; searching for the same cargo number, the same storage bin number corresponding to the out-of-stock quantity list and the cargo and target storage bin mapping list of the cargo transportation equipment, and the out-of-stock quantity and the allocated cargo quantity; wherein the out-of-stock quantity list includes the cargo number, the storage bin number and the corresponding out-of-stock quantity, and the cargo and target storage bin mapping list of the cargo transportation equipment includes the cargo number, the target storage bin number and the corresponding allocated cargo quantity; if the out-of-stock quantity is less than or equal to 0, the cargo priority corresponding to the cargo number remains unchanged; if the out-of-stock quantity is greater than 0, the cargo priority corresponding to the cargo number is increased according to the out-of-stock quantity and the allocated cargo quantity.

[0015] Optionally, the calculation of the matching value between the cargo transportation equipment and the corresponding delivery warehouse includes: calculating the truck full load cargo quantity interval of the target storage warehouse based on the rated full load cargo weight and the average weight of the cargo in the target storage warehouse; wherein the truck full load cargo quantity interval includes a truck full load cargo quantity interval lower limit and a truck full load cargo quantity interval upper limit; comparing the total amount of cargo to be sent in the cargo list to be sent to the target storage warehouse with the truck full load cargo quantity interval lower limit and the truck full load cargo quantity interval upper limit; and based on the comparison result, updating the preset initial matching value to obtain the matching value.

[0016] Optionally, the method also includes: according to the cargo number of the cargo transportation equipment, obtaining the total amount of goods to be stored in the storage warehouse of the corresponding goods to be stored, so as to generate a list of quantities to be stored; wherein the list of quantities to be stored includes the cargo number, the storage warehouse number and the corresponding total amount of goods to be stored; according to the cargo information of the cargo transportation equipment, generating a list of cargo number quantities; wherein the list of cargo number quantities includes the cargo number and the corresponding amount of goods; calculating the number of cargo numbers in the list of quantities to be stored that is the same as the current cargo number in the list of cargo number quantities; calculating the target storage warehouse and the number of allocated goods corresponding to the cargo of the cargo transportation equipment according to the number of cargo numbers, the total amount of goods to be stored and the amount of goods.

[0017] Optionally, the method also includes: determining a recommended route from the cargo transportation equipment to the corresponding delivery warehouse, and performing overlapping calculations on multiple recommended routes to extract the bifurcation point of the recommended route according to preset extraction rules; calculating the distance from the location point of the cargo transportation equipment to the bifurcation point, so as to obtain the expected time from the location point to the nearest bifurcation point based on the distance and the average driving speed of the cargo transportation equipment; determining whether the expected time is less than or equal to a preset time threshold, and if the expected time is less than or equal to the time threshold, executing the processing logic for selecting a delivery warehouse for the cargo transportation equipment.

[0018] Optionally, the extraction rule is: all routes from the location point of the cargo transportation equipment to the bifurcation point can be covered by the recommended route; and all routes from the bifurcation point to any delivery warehouse will not be covered by the recommended route.

[0019] To achieve the above objective, according to another aspect of an embodiment of the present invention, a device for selecting a delivery warehouse for a cargo transportation device is provided.

[0020] An apparatus for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention comprises: a calculation module for calculating the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses based on the total amount of cargo of the cargo transportation equipment; a determination module for calculating the processing time of the delivery warehouse based on the total amount of cargo to be processed, the amount of backlog cargo and the processing efficiency of the delivery warehouse, so as to determine the coefficient of variation of the processing time; a selection module for comparing the coefficient of variation with a preset variation threshold, and using the delivery warehouse corresponding to the coefficient of variation that is less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment.

[0021] Optionally, the device also includes: a backlog cargo quantity determination module, which is used to calculate the sum of the total amount of cargo of the cargo transport equipment that has not yet been processed by the delivery warehouse to obtain the initial backlog cargo quantity of the delivery warehouse; and calculate the remaining quantity of cargo of the cargo transport equipment being processed by the delivery warehouse, sum the initial backlog cargo quantity and the remaining cargo quantity to obtain the backlog cargo quantity.

[0022] Optionally, the device further comprises: a processing efficiency determination module, configured to obtain the number of operating service stations in the delivery warehouse, and to obtain the processing efficiency by multiplying the number of operating service stations by the number of goods processed per unit time by the service stations.

[0023] Optionally, the selection module is further used to: calculate the average value and standard deviation of the processing time from the cargo transportation equipment to the corresponding delivery warehouse, and divide the standard deviation by the average value to obtain the coefficient of variation.

[0024] Optionally, the delivery warehouse to which the cargo transportation equipment belongs is a delivery warehouse whose total cost does not exceed the standard, and the device also includes: a delivery warehouse determination module, used to calculate the total cost and the average total cost of the cargo transportation equipment to the corresponding delivery warehouse, and calculate the total cost deviation coefficient based on the total cost and the average total cost; and compare the total cost deviation coefficient with a preset deviation coefficient threshold to determine the delivery warehouse for the cargo transportation equipment whose total cost does not exceed the standard.

[0025] Optionally, when there are multiple delivery warehouses corresponding to the coefficient of variation that is less than or equal to a preset variation threshold, the device further includes: a final selection module, for calculating the cargo priority and cargo priority average of the cargo transportation equipment according to the out-of-stock quantity of the storage warehouse and the target storage warehouse and allocated cargo quantity corresponding to the cargo of the cargo transportation equipment, so as to determine the cargo priority deviation coefficient; calculating the matching value and matching average between the cargo transportation equipment and the corresponding delivery warehouse according to the rated full load cargo weight, the total amount of cargo to be sent and the allocated cargo quantity, so as to determine the matching value deviation coefficient; adding the cargo priority deviation coefficient, the matching value deviation coefficient and the total cost deviation coefficient corresponding to the same cargo transportation equipment and the same delivery warehouse to obtain a total deviation coefficient; and adding the total deviation coefficients of multiple cargo transportation equipment in the corresponding delivery warehouse to obtain a total deviation value, and taking the delivery warehouse corresponding to the maximum total deviation value as the delivery warehouse finally selected by multiple cargo transportation equipment.

[0026] Optionally, the final selection module is also used to: set an initial value for the cargo priority of the cargo in the cargo transportation equipment; search for the same cargo number, the same storage bin number corresponding to the out-of-stock quantity list and the cargo and target storage bin mapping list of the cargo transportation equipment, and the out-of-stock quantity and the allocated cargo quantity; wherein the out-of-stock quantity list includes the cargo number, the storage bin number and the corresponding out-of-stock quantity, and the cargo and target storage bin mapping list of the cargo transportation equipment includes the cargo number, the target storage bin number and the corresponding allocated cargo quantity; and if the out-of-stock quantity is less than or equal to 0, the cargo priority corresponding to the cargo number remains unchanged; if the out-of-stock quantity is greater than 0, the cargo priority corresponding to the cargo number is increased according to the out-of-stock quantity and the allocated cargo quantity.

[0027] Optionally, the final selection module is further used to: calculate the truck full load cargo quantity interval of the target storage warehouse based on the rated full load cargo weight and the average weight of the cargo in the target storage warehouse; wherein the truck full load cargo quantity interval includes the truck full load cargo quantity interval lower limit and the truck full load cargo quantity interval upper limit; compare the total amount of goods to be sent in the cargo list to be sent to the target storage warehouse with the truck full load cargo quantity interval lower limit and the truck full load cargo quantity interval upper limit; and based on the comparison result, update the preset initial matching value to obtain the matching value.

[0028] Optionally, the device also includes: a target storage warehouse determination module, which is used to obtain the total amount of to-be-stored goods of the corresponding goods in the storage warehouse according to the cargo number of the cargo transportation equipment, so as to generate a to-be-stored quantity list; wherein the to-be-stored quantity list includes the cargo number, the storage warehouse number and the corresponding total amount of to-be-stored goods; generate a cargo number quantity list according to the cargo information of the cargo transportation equipment; wherein the cargo number quantity list includes the cargo number and the corresponding cargo quantity; calculate the number of cargo numbers in the to-be-stored quantity list that is the same as the current cargo number in the cargo number quantity list; and calculate the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment according to the cargo number quantity, the total amount of to-be-stored goods and the cargo quantity.

[0029] Optionally, the device also includes: an execution time determination module, used to determine the recommended route from the cargo transportation equipment to the corresponding delivery warehouse, and perform overlapping calculations on multiple recommended routes to extract the bifurcation points of the recommended routes according to preset extraction rules; calculate the distance from the location point of the cargo transportation equipment to the bifurcation point, so as to obtain the expected time from the location point to the nearest bifurcation point based on the distance and the average driving speed of the cargo transportation equipment; and determine whether the expected time is less than or equal to a preset time threshold. If the expected time is less than or equal to the time threshold, execute the processing logic of selecting a delivery warehouse for the cargo transportation equipment.

[0030] Optionally, the extraction rule is: all routes from the location point of the cargo transportation equipment to the bifurcation point can be covered by the recommended route; and all routes from the bifurcation point to any delivery warehouse will not be covered by the recommended route.

[0031] To achieve the above objective, according to another aspect of an embodiment of the present invention, an electronic device is provided.

[0032] An electronic device according to an embodiment of the present invention comprises: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention.

[0033] To achieve the above objective, according to another aspect of the embodiments of the present invention, a computer readable medium is provided.

[0034] A computer-readable medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention.

[0035] An embodiment of the above invention has the following advantages or beneficial effects: by calculating the processing time of the delivery warehouse, the coefficient of variation of the processing time is determined, the coefficient of variation is compared with the preset variation threshold, and the delivery warehouse is selected for the cargo transportation equipment according to the comparison result, so as to ensure that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse and improving the overall work efficiency; when selecting a delivery warehouse for the cargo transportation equipment, the total cost of the cargo transportation equipment to the delivery warehouse is considered, which reduces the logistics cost and improves the economic benefit; when selecting a delivery warehouse for the cargo transportation equipment, the cargo priority of the cargo transportation equipment, the matching value of the cargo transportation equipment and the delivery warehouse, and the total cost of the cargo transportation equipment to the delivery warehouse are considered, so as to improve the priority of the cargo transportation equipment containing out-of-stock goods, improve the logistics efficiency, increase the full load rate of the shipping truck, and reduce the transportation cost of the goods from the delivery warehouse to the target storage warehouse; by determining the bifurcation point of multiple recommended routes from the cargo transportation equipment to the delivery warehouse, a delivery warehouse is selected for the cargo transportation equipment as late as possible before the cargo transportation equipment reaches the bifurcation point, which does not affect the normal driving of the cargo transportation equipment and can use the latest data to select a delivery warehouse for the cargo transportation equipment.

[0036] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0038] Figure 1 is a schematic diagram of main steps of a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention;

[0039] Figure 2 is a schematic diagram of the main flow of a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention;

[0040] Figure 3 It is a schematic diagram of the main process of constructing location attribute information and obtaining delivery warehouse data according to an embodiment of the present invention;

[0041] Figure 4 A schematic diagram of a circular buffer zone according to an embodiment of the present invention;

[0042] Figure 5 It is a schematic diagram of the main process of determining various data of cargo transportation equipment to a corresponding delivery warehouse according to an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of a main process of calculating the expected time from a location point to the nearest bifurcation point according to an embodiment of the present invention;

[0044] Figure 7A schematic diagram of the principle of extracting bifurcation points according to an embodiment of the present invention;

[0045] Figure 8 is a schematic diagram of a main process of calculating a target storage bin corresponding to cargo of cargo transport equipment according to an embodiment of the present invention;

[0046] Fig. 9 A schematic diagram of a main process of calculating the cargo priority of each cargo in cargo transportation equipment according to an embodiment of the present invention;

[0047] Fig.10 A schematic diagram of the main process of calculating the matching value between cargo transportation equipment and the corresponding delivery warehouse according to an embodiment of the present invention;

[0048] Fig.11 is a schematic diagram of main modules of an apparatus for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention;

[0049] Fig.12 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0050] Fig.13 It is a schematic diagram of the structure of a computer device suitable for implementing the electronic device of the embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0052] Figure 1 FIG. 1 is a schematic diagram of the main steps of a method for selecting a delivery warehouse for a cargo transport device according to an embodiment of the present invention. Figure 1 As shown, the method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention mainly includes the following steps:

[0053] Step S101: Calculate the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses based on the total amount of cargo of the cargo transportation equipment. The cargo transportation equipment is a device used by the supplier to transport the goods purchased by the purchaser, such as a truck. In the embodiment, the supplier's truck is referred to as an incoming truck. The destination of each cargo transportation equipment and the quantity of various goods are known, so all delivery warehouses at the destination can be obtained, and the quantity of all goods in the cargo transportation equipment is summed to obtain the total amount of cargo of the cargo transportation equipment. When the cargo transportation equipment belongs to different delivery warehouses, the sum of the total amount of cargo of all cargo transportation equipment belonging to the same delivery warehouse is calculated, and the sum is the total amount of cargo to be processed in the delivery warehouse. For example, at the current moment, cargo transportation equipment 1 belongs to delivery warehouse 1, and cargo transportation equipment 2 belongs to delivery warehouse 2. Then the total amount of cargo to be processed in delivery warehouse 1 is the total amount of cargo of cargo transportation equipment 1, and the total amount of cargo to be processed in delivery warehouse 2 is the total amount of cargo transportation equipment 2; at the next moment, cargo transportation equipment 1 and cargo transportation equipment 2 both belong to delivery warehouse 1, then the total amount of cargo to be processed in delivery warehouse 1 is the sum of the total amount of cargo transportation equipment 1 and cargo transportation equipment 2, and the total amount of cargo to be processed in delivery warehouse 2 is 0.

[0054] Step S102: Calculate the processing time of the delivery warehouse according to the total amount of goods to be processed, the amount of backlogged goods and the processing efficiency of the delivery warehouse to determine the coefficient of variation of the processing time. Calculate the amount of backlogged goods and the processing efficiency of the delivery warehouse in advance, sum the total amount of goods to be processed and the amount of backlogged goods in the delivery warehouse, and divide them by the processing efficiency of the delivery warehouse to obtain the processing time of the delivery warehouse. Then, calculate the average value and standard deviation of the processing time of cargo transportation equipment with the same destination to the corresponding delivery warehouse according to the coefficient of variation formula, and divide the standard deviation by the average value to obtain the coefficient of variation.

[0055] Step S103: Compare the coefficient of variation with a preset variation threshold, and use the delivery warehouse corresponding to the coefficient of variation that is less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment. The smaller the coefficient of variation, the closer the processing time of each delivery warehouse. When the coefficient of variation is 0, it means that the processing time of each delivery warehouse is the same, that is, the load of each delivery warehouse is exactly the same. The variation threshold is determined by experience, and the delivery warehouses corresponding to the coefficient of variation that is less than or equal to the variation threshold process the cargo of the corresponding cargo transportation equipment, which can ensure that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse.

[0056] Figure 2 FIG. 2 is a schematic diagram of the main flow of a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention. Figure 2As shown in the figure, the method for selecting a delivery warehouse for a goods transportation device according to an embodiment of the present invention mainly includes the following steps:

[0057] Step S201: Receive the parameter information of the goods transportation device, construct location attribute information according to the parameter information, and obtain the corresponding delivery warehouse data. In the embodiment, the goods transportation device is an incoming truck, and the parameter information includes the goods transportation device identifier, such as the license plate number licensePlateID, the GPS locator number gpsLocator, the destination, such as the destination city destCity, the fuel consumption SFC, and the goods information cargoDetails. The goods information includes the goods number SkuID and the corresponding quantity quantity. The unit of fuel consumption is liters per 100 kilometers (L / 100km). SKU (Stock Keeping Unit, the minimum inventory management unit) is defined as the smallest available unit in inventory management. For example, in textiles, one SKU usually represents specifications, colors, and styles, while in chain retail stores, sometimes a single product is called a SKU. For example, IPHONE6S, white, 32G is one SKU, and IPHONE6S, white, 64G is another SKU. The specific parameter information is as follows:

[0058] {"licensePlateID":"Beijing P88888",

[0059] "gpsLocator":"IULEJ12323",

[0060] "destCity":"Beijing",

[0061] "SFC":40,

[0062] "cargoDetails":

[0063] {"SkuID":"1111","quantity":110},

[0064] {"SkuID":"2222","quantity":30},

[0065] {"SkuID":"3333","quantity":50},

[0066] {"SkuID":"4444","quantity":65}]

[0067] }

[0068] The location attribute information is used to store frequently used or frequently updated fields for easy reading. The delivery warehouse data includes the number, address and quantity of goods handled per unit time of each delivery warehouse in the destination city. For specific location attribute information and the process of obtaining delivery warehouse data, please refer to the following Figure 3 Description.

[0069] In a preferred embodiment, licensePlateID, gpsLocator, destCity, SFC, equipment state (truckState) and cargoDetail are stored in a cargo transport equipment information table (truckInfo table), with licensePlateID as the primary key in the truckInfo table. truckState=1 means that the cargo transport equipment has not selected a delivery warehouse, truckState=2 means that a delivery warehouse has been selected and has not been processed, truckState=3 means that the delivery warehouse is processing, and truckState=4 means that the delivery warehouse has been processed.

[0070] Step S202: Construct the delivery warehouse attribute information according to the delivery warehouse data, and set a buffer for storing the travel speed of the cargo transportation equipment. In an embodiment, the delivery warehouse attribute information includes the number and address of the delivery warehouse and the number of cargoes processed per unit time by the service station. The delivery warehouse data is updated in a timely manner to keep the delivery warehouse attribute information in a relatively new state. The buffer can be a circular buffer or a list to cache the travel speed, wherein a circular buffer, also known as a circular queue, a cyclic buffer, or a ring buffer, is a data structure used to represent a fixed-size, head-to-tail connected buffer, suitable for caching data streams.

[0071] The following is an example of a circular buffer caching driving speed: a circular buffer is set in the location attribute information, and the circular buffer is used to store the driving speed of the cargo transportation equipment. In the embodiment, the circular buffer is initially empty, and the preset length is 7 (the length is an empirical value). If the circular buffer is full of data and new data needs to be written, the oldest data will be automatically overwritten. Figure 4 FIG. 1 is a schematic diagram of a circular buffer zone according to an embodiment of the present invention. Figure 4 It can be seen that the length of the circular buffer in the embodiment is 7. Figure 4 The line with an arrow in the middle indicates that the address space is connected head to tail to form a circular address space.

[0072] Step S203: Determine the recommended route from the cargo transport equipment to the corresponding delivery warehouse, the time from the cargo transport equipment to the corresponding delivery warehouse, the shortest time from the cargo transport equipment to any delivery warehouse, and the fuel consumption from the cargo transport equipment to the corresponding delivery warehouse. The corresponding delivery warehouse of the cargo transport equipment is at least one or all delivery warehouses under the destination city of the parameter information of the cargo transport equipment. This step needs to be performed when the truckState of the cargo transport equipment is 1, that is, the cargo transport equipment has not selected a delivery warehouse. The specific implementation process of this step is described in the subsequent description. Figure 5 Description.

[0073] Step S204: Extract the bifurcation points of multiple recommended routes of the cargo transportation equipment, calculate the distance from the location point of the cargo transportation equipment to the bifurcation point, and obtain the expected time from the location point to the nearest bifurcation point. The location point is the current location of the cargo transportation equipment displayed on the map, and the location point moves with the movement of the cargo transportation equipment. This step needs to be performed when the truckState of the cargo transportation equipment is 1, that is, the cargo transportation equipment has not selected a delivery warehouse. The specific implementation process of this step is described in the subsequent section about Figure 6 Description.

[0074] Step S205: Determine whether the expected time is less than or equal to a preset time threshold, if the expected time is less than or equal to the time threshold, execute step S206; if the expected time is greater than the time threshold, end this process. From step S206, enter the processing logic of selecting a delivery warehouse for the cargo transportation equipment.

[0075] Step S206: Check whether the destination of the cargo transportation equipment exists in the pre-established cargo transportation equipment mapping table of the delivery warehouse to be selected. If so, write the cargo transportation equipment identifier into the cargo transportation equipment mapping table of the delivery warehouse to be selected; if not, write the cargo transportation equipment identifier and the destination into the cargo transportation equipment mapping table of the delivery warehouse to be selected. The cargo transportation equipment mapping table of the delivery warehouse to be selected needs to be established in advance, and in the cargo transportation equipment mapping table of the delivery warehouse to be selected, the Key is the destination of the cargo transportation equipment, and the Value is the cargo transportation equipment of the delivery warehouse to be selected and whose destination is the same as the Key. The newly established cargo transportation equipment mapping table of the delivery warehouse to be selected is used to save the cargo transportation equipment that needs to select the delivery warehouse calculated in this step. In the embodiment, the Key is the destination city of the cargo transportation equipment, and the Value is the license plate number of the cargo transportation equipment of the delivery warehouse to be selected. Table 1 is a cargo transportation equipment mapping table of the delivery warehouse to be selected in one embodiment.

[0076] Table 1 Cargo transportation equipment mapping table for selected delivery warehouse

[0077] Destination City The license plate number of the cargo transport equipment to be delivered to the selected warehouse Beijing Beijing P8888, Hebei A12365 Shijiazhuang Hebei C11111, Hebei D22222

[0078] In actual engineering applications, the delivery warehouse should be selected as late as possible for the supplier's cargo transportation equipment, but it should not affect the normal driving of the cargo transportation equipment. Through steps S201-S206, the bifurcation point of the recommended route and the cargo transportation equipment to be selected for the delivery warehouse are found. The above requirements can be met by completing the selection of the delivery warehouse before the cargo transportation equipment to be selected for the delivery warehouse arrives at the bifurcation point. Starting from step S207, it is introduced how to select the delivery warehouse for the cargo transportation equipment to be selected for the delivery warehouse.

[0079] Step S207: Determine whether the cargo transportation equipment mapping table of the delivery warehouse to be selected is empty. If not, execute step S208; if it is empty, end this process. In a preferred embodiment, if the cargo transportation equipment mapping table of the delivery warehouse to be selected is not empty, the cargo transportation equipment identifiers in the cargo transportation equipment mapping table of the delivery warehouse to be selected are sorted according to the shortest time from the cargo transportation equipment to any delivery warehouse in the delivery warehouse list to obtain a new cargo transportation equipment mapping table of the delivery warehouse to be selected. The sorting is in ascending order of the shortest time. Re-sorting the cargo transportation equipment mapping table of the delivery warehouse to be selected can ensure that the subsequent calculation of the target storage warehouse of the cargo transportation equipment is more accurate.

[0080] Step S208: According to the cargo number of the cargo transportation equipment, the total amount of the corresponding cargo to be stored in the storage warehouse is obtained to calculate the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment. In the embodiment, according to the cargo number of the cargo transportation equipment, the total amount of the corresponding cargo to be stored in all storage warehouses of the destination city is obtained to generate a list of quantities to be stored; according to the cargo information of the cargo transportation equipment, a list of cargo number quantities is generated; the number of cargo numbers in the list of quantities to be stored that is the same as the current cargo number in the list of cargo number quantities is calculated; different processing is performed according to the cargo number quantity to obtain the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment. For the specific implementation process of this step, see the subsequent description on Figure 8 Description.

[0081] After this step is completed, that is, after the target storage warehouse is determined for the cargo of the cargo transportation equipment, the equipment state truckState of the cargo transportation equipment is changed to 2, and the delivery warehouse is selected according to steps S209 to S217.

[0082] Step S209: Calculate the cargo priority of the cargo transport equipment according to the out-of-stock quantity of the storage warehouse and the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transport equipment. The out-of-stock quantity of the storage warehouse is obtained by subtracting the quantity of the cargo in the unproduced order from the inventory quantity of the cargo. The specific implementation process of this step is described in the subsequent Fig. 9 Description.

[0083] Step S210: Calculate the matching value between the cargo transportation equipment and the corresponding delivery warehouse according to the rated full-load cargo weight, the total amount of cargo to be sent, and the allocated cargo quantity. Obtain the rated full-load cargo weight of the shipping truck (the vehicle that transports cargo from the delivery warehouse to the target storage warehouse) to calculate the truck full-load cargo quantity interval for each target storage warehouse; then calculate the matching value according to the truck full-load cargo quantity interval, the total amount of cargo to be sent, and the target storage warehouse and allocated cargo quantity corresponding to the cargo of the cargo transportation equipment. This step can make the shipping truck fully loaded as much as possible when transporting the cargo quantity from the delivery warehouse to the target storage warehouse. For the specific implementation process, see the subsequent description on Fig.10 Description.

[0084] Step S211: Calculate the estimated processing time of the cargo transportation equipment in the corresponding delivery warehouse according to the cargo information in the parameter information of the cargo transportation equipment. The calculation formula of the estimated processing time of the cargo transportation equipment in the corresponding delivery warehouse is:

[0085] truckDuration = totalQuantity / quantity of goods handled per unit time by the service desk Formula 1

[0086] In the formula, truckDuration is the estimated processing time, and totalQuantity is the total quantity of goods.

[0087] According to the above formula, the estimated processing time of each cargo transport equipment in the cargo transport equipment mapping table of the delivery warehouse to be selected is calculated for each delivery warehouse in the delivery warehouse list. In a preferred embodiment, the estimated processing time mapping table truckDurationMap of the cargo transport equipment in the corresponding delivery warehouse is constructed with the delivery warehouse number as Key and the estimated processing time as Value. The estimated processing time mapping table truckDurationMap is saved in the location attribute information of the cargo transport equipment.

[0088] Step S212: Obtain the operating cost per unit time of the cargo transportation equipment to calculate the total cost of the cargo transportation equipment to the corresponding delivery warehouse. When calculating the total cost of the cargo transportation equipment to the corresponding delivery warehouse, it is necessary to obtain the current fuel price and the operating cost per unit time of the cargo transportation equipment, and the operating cost includes labor cost, loss cost and opportunity cost. In the embodiment, the fuel price is the average fuel price, the unit time is per hour, and the calculation formula for the total cost of the cargo transportation equipment to the corresponding delivery warehouse is:

[0089] truckTotalCost=oilPrice×fuelCons+costing×(truckDuration+truckRunTime) Formula 2

[0090] Where truckTotalCost is the total cost, oilPrice is the fuel price, fuelCons is the fuel consumption of the cargo transport equipment to the corresponding delivery warehouse, costing is the hourly operating cost of the cargo transport equipment, and truckRunTime is the time it takes for the cargo transport equipment to reach the corresponding delivery warehouse.

[0091] According to the above formula, the total cost of each cargo transportation equipment in the cargo transportation equipment mapping table of the delivery warehouse to be selected to each delivery warehouse in the delivery warehouse list is calculated. In a preferred embodiment, the total cost mapping table truckTotalCostMap of the cargo transportation equipment to the corresponding delivery warehouse is constructed with the delivery warehouse number as the key and the total cost as the value. The total cost mapping table truckTotalCostMap is saved in the location attribute information of the cargo transportation equipment.

[0092] Step S213: Calculate the number of backlogged goods in multiple delivery warehouses and the processing efficiency of the delivery warehouses. When calculating the number of backlogged goods in a delivery warehouse, first add the total amount of goods to be sent in the list of goods to be sent to the target storage warehouse of the delivery warehouse (that is, the total amount of goods of the cargo transportation equipment whose equipment status is 2 and the selected delivery warehouse is the delivery warehouse) to obtain the initial backlog of goods in the delivery warehouse; then obtain the cargo transportation equipment whose equipment status is 3 and the selected delivery warehouse is the delivery warehouse to form a list of cargo transportation equipment being processed; then calculate the remaining number of goods of the cargo transportation equipment in the list of cargo transportation equipment being processed according to the estimated processing time, the start processing time in the location attribute information, and the number of goods processed per unit time by the service station. Among them, the calculation formula for the remaining number of goods is:

[0093] The remaining quantity of goods = the quantity of goods processed by the service desk per unit time × (estimated processing time - (current time - start processing time)) Formula 3

[0094] The remaining quantity is added to the quantity of goods being processed, and the quantity of goods in the delivery warehouse is obtained according to the initial quantity of goods in the delivery warehouse and the quantity of goods in the delivery warehouse. The initial value of the quantity of goods in the delivery warehouse is 0. The formula for calculating the quantity of goods in the delivery warehouse is:

[0095] Backlog quantity = initial backlog quantity + number of goods being processed Formula 4

[0096] The quantity of backlogged goods in the delivery warehouse corresponding to each cargo transport equipment in the list of cargo transport equipment being processed is calculated according to the above formula. In a preferred embodiment, a list of backlogged goods in the delivery warehouse is constructed according to the delivery warehouse number and the corresponding backlogged goods quantity, and the list of backlogged goods in the delivery warehouse is cached. An index element in the list of backlogged goods in the delivery warehouse represents the value corresponding to the same index delivery warehouse in the delivery warehouse list.

[0097] When calculating the processing efficiency of each delivery warehouse, it is necessary to obtain the mapping table of the number of running service stations of each delivery warehouse based on the delivery warehouse list. Among them, in the mapping table of the number of running service stations of the delivery warehouse, the Key is the delivery warehouse number, and the Value is the number of running service stations. The delivery warehouse will change the number of running service stations at any time according to the current situation, so the number of running service stations of the delivery warehouse must be obtained in real time. The calculation formula for the processing efficiency of the delivery warehouse (that is, the number of goods processed by the delivery warehouse in a unit of time) is:

[0098] Processing efficiency = number of service stations in operation × number of goods processed per unit time by the service station Formula 5

[0099] The processing efficiency of each delivery warehouse in the delivery warehouse list is calculated according to the above formula. In a preferred embodiment, a delivery warehouse processing efficiency list is constructed according to the delivery warehouse number and the corresponding processing efficiency, and the delivery warehouse processing efficiency list is cached. An index element in the delivery warehouse processing efficiency list represents the value corresponding to the delivery warehouse with the same index in the delivery warehouse list.

[0100] Step S214: Generate a cargo priority matrix, a cargo total matrix, a matching value matrix, a total cost matrix, a cargo backlog matrix and a processing efficiency matrix respectively according to the cargo priority, the cargo total volume of the cargo transport equipment, the matching value, the total cost, the cargo backlog quantity and the processing efficiency. The specific implementation process of the above six matrices is as follows:

[0101] Generate a cargo priority matrix: The cargo priority matrix P is an m×1 matrix, where m is the number of cargo transport equipment of the delivery warehouse to be selected, and each element of the cargo priority matrix P is a cargo priority goodsPriorValues. The order of the rows of the cargo priority matrix P from top to bottom is the same as the order of the cargo transport equipment list of the delivery warehouse to be selected.

[0102] Generate a cargo total quantity matrix: the cargo total quantity matrix T is an m×1 matrix, where m is the number of cargo transport equipment of the delivery warehouse to be selected, and each element of the cargo total quantity matrix T is the cargo totalQuantity. The order of the rows of the cargo total quantity matrix T from top to bottom is the same as the order of the cargo transport equipment list of the delivery warehouse to be selected.

[0103] Generate a matching value matrix: The matching value matrix M is an m×n matrix, where m is the number of cargo transportation equipment of the delivery warehouse to be selected, n is the number of delivery warehouses in the delivery warehouse list, and each element of the cargo total quantity matrix T is a matching value matchValue. The matching value matrix M starts from the upper left corner of the matrix, and the order of the rows from top to bottom is the same as the order of the cargo transportation equipment list of the delivery warehouse to be selected, and the order of the columns from left to right is the same as the order of the delivery warehouse list. Table 2 is a matching value matrix M of an embodiment, where M[1, 3] represents that the matching value of the incoming truck with license plate number Beijing P88888 and the delivery warehouse numbered 202 is 5.

[0104] Table 2 Matching value matrix

[0105] License plate number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Beijing P88888 60 0 5 10 JI A12365 70 10 10 4 Hebei C11111 40 20 10 6 Hebei D22222 30 50 20 60

[0106] Generate the total cost matrix of cargo transportation equipment to the corresponding delivery warehouse: the total cost matrix CL is an m×n matrix, m is the number of cargo transportation equipment to be selected for the delivery warehouse, n is the number of delivery warehouses in the delivery warehouse list, and each element of the total cost matrix CL is the total cost truckTotalCost. The total cost matrix CL starts from the upper left corner of the matrix, and the order of rows from top to bottom is the same as the order of the cargo transportation equipment list to be selected for the delivery warehouse, and the order of columns from left to right is the same as the order of the delivery warehouse list. Table 3 is the total cost matrix CL of an embodiment, CL[1,3] represents the total cost of 500 for the incoming truck with license plate number Beijing P88888 to the delivery warehouse numbered 202.

[0107] Table 3 Total cost matrix

[0108]

[0109]

[0110] Generate a delivery warehouse backlog quantity matrix: the delivery warehouse backlog quantity matrix O is a 1×n matrix, n is the number of delivery warehouses in the delivery warehouse list, and each element of the delivery warehouse backlog quantity matrix O is the number of delivery warehouse backlog goods. The columns of the delivery warehouse backlog quantity matrix O are ordered from left to right in the same order as the delivery warehouse list. Table 4 is a delivery warehouse backlog quantity matrix O of an embodiment.

[0111] Table 4 Delivery warehouse backlog quantity matrix

[0112] Delivery warehouse number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Number of goods in backlog at delivery warehouse 30 60 75 88

[0113] Generate a delivery warehouse processing efficiency matrix: The delivery warehouse processing efficiency matrix H is a 1×n matrix, where n is the number of delivery warehouses in the delivery warehouse list, and each element of the delivery warehouse processing efficiency matrix H is the delivery warehouse processing efficiency. The columns of the delivery warehouse processing efficiency matrix H are ordered from left to right in the same order as the delivery warehouse list. Table 5 is a delivery warehouse processing efficiency matrix H of an embodiment.

[0114] Table 5 Delivery warehouse processing efficiency matrix

[0115] Delivery warehouse number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Delivery warehouse processing efficiency 100 110 90 150

[0116] Step S215: Generate a priority deviation coefficient matrix according to the priority matrix, generate a matching value deviation coefficient matrix according to the matching value matrix, and generate a total cost deviation coefficient matrix and a total cost excess matrix according to the total cost matrix. The above matrices are specifically implemented as follows:

[0117] Priority deviation coefficient matrix: Calculate the deviation coefficient of each element in the cargo priority matrix to generate a cargo priority deviation coefficient matrix. The cargo priority deviation coefficient matrix Q is an m×n matrix, where m is the number of cargo transportation equipment for the delivery warehouse to be selected, and n is the number of delivery warehouses in the delivery warehouse list. Each element in the cargo priority deviation coefficient matrix Q is a cargo priority deviation coefficient. The cargo priority deviation coefficient matrix Q starts from the upper left corner of the matrix, and the order of the rows from top to bottom is the same as the order of the cargo transportation equipment list for the delivery warehouse to be selected, and the order of the columns from left to right is the same as the order of the delivery warehouse list. Among them, the calculation formula of the cargo priority deviation coefficient is:

[0118] δ [i] =(P[i]-PriorMean) / PriorMean Formula 6

[0119] In the formula, δ [i]is the cargo priority deviation coefficient of the i-th row of the cargo priority deviation coefficient matrix Q, P[i] is the cargo priority of the i-th row of the cargo priority matrix P, and PriorMean is the average value (i.e., the cargo priority average value) of all elements in the cargo priority matrix P. Table 6 is a cargo priority deviation coefficient matrix Q of an embodiment, and all elements in each row of the cargo priority deviation coefficient matrix Q are cargo priority deviation coefficients of the corresponding cargo transportation equipment.

[0120] Table 6 Cargo priority deviation coefficient matrix

[0121] License plate number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Beijing P88888 0.2 0.2 0.2 0.2 JI A12365 0.1 0.1 0.1 0.1 Hebei C11111 0.25 0.25 0.25 0.25 Hebei D22222 -0.3 -0.3 -0.3 -0.3

[0122] Matching value deviation coefficient matrix: Calculate the deviation coefficient of each element in the matching value matrix to generate a matching value deviation coefficient matrix. The matching value deviation coefficient matrix MP is an m×n matrix, where m is the number of cargo transportation equipment for the delivery warehouse to be selected, and n is the number of delivery warehouses in the delivery warehouse list. Each element in the matching value deviation coefficient matrix MP is a matching value deviation coefficient. The matching value deviation coefficient matrix MP starts from the upper left corner of the matrix, and the order of the rows from top to bottom is the same as the order of the list of cargo transportation equipment for the delivery warehouse to be selected, and the order of the columns from left to right is the same as the order of the delivery warehouse list. Among them, the calculation formula of the matching value deviation coefficient is:

[0123] δ [i,j] =(M[i,j]-b) / b Formula 7

[0124] In the formula, δ [i,j] is the matching value deviation coefficient of the i-th row and j-th column of the matching value deviation coefficient matrix MP, M[i,j] is the matching value of the i-th row and j-th column of the matching value matrix M, and b is the average value of all elements in the matching value matrix M (ie, the matching average value).

[0125] Total cost deviation coefficient matrix: Calculate the deviation coefficient of each element in the total cost matrix to generate the total cost deviation coefficient matrix. The total cost deviation coefficient matrix CP is an m×n matrix, where m is the number of cargo transportation equipment for the delivery warehouse to be selected, and n is the number of delivery warehouses in the delivery warehouse list. Each element in the total cost deviation coefficient matrix CP is a total cost deviation coefficient. The total cost deviation coefficient matrix CP starts from the upper left corner of the matrix, and the order of the rows from top to bottom is the same as the order of the list of cargo transportation equipment for the delivery warehouse to be selected, and the order of the columns from left to right is the same as the order of the delivery warehouse list. Among them, the calculation formula of the total cost deviation coefficient is:

[0126] δ [i,j] =(CL[i,j]-a i ) / a i Formula 8

[0127] In the formula, δ[i,j] is the total cost deviation coefficient of the total cost deviation coefficient matrix CP in the i-th row and j-th column, CL[i,j] is the total cost of the total cost matrix CL in the i-th row and j-th column, a i is the average value of all elements in the ith row of the total cost matrix CL (ie, the total cost average value). Table 7 is a total cost deviation coefficient matrix CP of an embodiment.

[0128] Table 7 Total cost deviation coefficient matrix

[0129] License plate number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Beijing P88888 0.5 -0.5 0.5 -0.2 JI A12365 0.4 0.1 -0.2 0.26 Hebei C11111 -0.1 0.1 -0.1 0.3 Hebei D22222 -0.1 0.4 -0.1 0.1

[0130] Total cost excess matrix: Compare each element in the total cost deviation coefficient matrix with the preset deviation coefficient threshold to generate the total cost excess matrix of the cargo transportation equipment to the corresponding delivery warehouse according to the comparison result. The total cost excess matrix C is an m×n matrix, m is the number of cargo transportation equipment of the delivery warehouse to be selected, n is the number of delivery warehouses in the delivery warehouse list, and each element in the total cost excess matrix C is 1 or 0, 1 represents the total cost excess, and 0 represents the total cost not exceeded. The total cost excess matrix C starts from the upper left corner of the matrix, and the order of the rows from top to bottom is the same as the order of the cargo transportation equipment list of the delivery warehouse to be selected, and the order of the columns from left to right is the same as the order of the delivery warehouse list. In the embodiment, if the total cost deviation coefficient in the total cost deviation coefficient matrix CP is greater than the deviation coefficient threshold, the corresponding position of the total cost excess matrix C is set to 1; if the total cost deviation coefficient is less than or equal to the deviation coefficient threshold, the corresponding position of the total cost excess matrix C is set to 0. Table 8 is the total cost excess matrix C of an embodiment.

[0131] Table 8 Total cost excess matrix

[0132] License plate number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Beijing P88888 1 0 1 0 JI A12365 1 0 0 1 Hebei C11111 0 0 0 1 Hebei D22222 0 1 0 0

[0133] Step S216: According to the preset constraints and the cargo total quantity matrix, an initial delivery warehouse matrix that satisfies the constraints is obtained. A new delivery warehouse matrix can be created before this step is executed. The delivery warehouse matrix A is an m×n matrix, where m is the number of cargo transportation equipment of the delivery warehouse to be selected, and n is the number of delivery warehouses in the delivery warehouse list. The element in the i-th row and j-th column of the delivery warehouse matrix A is recorded as A[i,j]. Table 9 is a delivery warehouse matrix A of an embodiment.

[0134] Table 9 Delivery warehouse matrix

[0135] License plate number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Beijing P88888 A[1,1] A[1,2] A[1,3] A[1,4] JI A12365 A[2,1] A[2,2] A[2,3] A[2,4] Hebei C11111 A[3,1] A[3,2] A[3,3] A[3,4] Hebei D22222 A[4,1] A[4,2] A[4,3] A[4,4]

[0136] The constraint condition includes four sub-conditions. The delivery warehouse matrix must at least meet the first sub-condition, the second sub-condition and the fourth sub-condition. The third sub-condition is for further optimization.

[0137] The first sub-condition is: each row in the delivery warehouse matrix has only one element greater than 0, and the other elements are equal to 0. This sub-condition limits each cargo transport device to only select one delivery warehouse.

[0138] The second sub-condition is: the value of each element greater than 0 in the delivery warehouse matrix is ​​the total quantity of goods in the same row in the goods quantity matrix. This sub-condition stipulates that the value of the element greater than 0 in A[i,] is equal to the value of T[i].

[0139] The third sub-condition is: the row and column number of the element greater than 0 in the delivery warehouse matrix corresponds to the value of the element with the same row and column number in the total cost excess matrix is ​​0. This sub-condition is used to limit the selected delivery warehouse to the delivery warehouse where the total cost of the cargo transportation equipment is not exceeded. Assuming that the element greater than 0 in A[i,] is A[i,k], the prerequisite that needs to be met is: C[i,k]=0 in the total cost excess matrix C

[0140] The fourth sub-condition is that the coefficient of variation of each element in the delivery warehouse final processing time matrix is ​​less than or equal to the preset variation threshold. The coefficient of variation can be used to compare the discreteness of populations with significantly different means, and can eliminate the influence of different units and / or means on the comparison of the degree of variation of two or more data. The following is an explanation of how to obtain the coefficient of variation of each element in the delivery warehouse final processing time matrix OT:

[0141] First, add the elements of each column in the delivery warehouse matrix A to obtain the quantity matrix AN of goods to be processed. For example, the quantity matrix AN of goods to be processed is |100 60 300 260|.

[0142] Afterwards, the to-be-processed goods quantity matrix AN is added to the delivery warehouse goods backlog quantity matrix O to obtain the delivery warehouse latest goods backlog quantity matrix ON. For example, the delivery warehouse latest goods backlog quantity matrix ON = AN + O = | 100 60300 260 | + | 30 60 75 88 | = | 130 120 375 348 |

[0143] Then, divide the elements of each column in the latest goods backlog quantity matrix ON of the delivery warehouse by the processing efficiency of the corresponding row in the processing efficiency matrix H to obtain the final processing time matrix OT of the delivery warehouse. For example, the final processing time matrix OT of the delivery warehouse = ON / H = |130 120 375 348 | / |100 110 90 150 |= |1.3 1.09 4.17 2.32 |

[0144] Finally, the coefficient of variation CV of each element in the final processing time matrix OT of the delivery warehouse is calculated. The calculation formula of the coefficient of variation CV is:

[0145] CV = standard deviation / mean value Formula 9

[0146] Where, standard deviation is the standard deviation of all elements in OT, and mean is the mean of all elements in OT.

[0147] At least one initial delivery warehouse matrix is ​​obtained according to the above constraints. If only one initial delivery warehouse matrix is ​​obtained, the initial delivery warehouse matrix is ​​used as the final delivery warehouse matrix and the process ends; if multiple initial delivery warehouse matrices are obtained, step S217 is executed.

[0148] Step S217: Determine a delivery warehouse matrix that satisfies a preset objective function from the multiple initial delivery warehouse matrices. The specific implementation of this step is:

[0149] First, the goods priority deviation coefficient matrix Q, the matching value deviation coefficient matrix MP and the total cost deviation coefficient matrix CP are added to obtain the total deviation coefficient matrix R. The total deviation coefficient matrix R = MP + Q - CP. When selecting a delivery warehouse, the higher the goods priority and matching value, the more likely it is to choose the delivery warehouse, and the lower the total cost, the more likely it is to choose the delivery warehouse.

[0150] Afterwards, the values ​​of the elements in the total deviation coefficient matrix R, which have the same row and column numbers as the elements greater than 0 in the delivery warehouse matrix A, are obtained, and the obtained values ​​are used as the total deviation values ​​of the cargo transportation equipment.

[0151] Finally, the delivery warehouse matrix of each cargo transportation equipment is determined by taking the maximum total deviation value of all cargo transportation equipment as the objective function. Table 10 shows the determined delivery warehouse matrix of each cargo transportation equipment.

[0152] Table 10 Delivery warehouse matrix for each cargo transport equipment

[0153] License plate number Delivery Warehouse 101 Delivery warehouse 102 Delivery warehouse 202 Delivery warehouse 305 Beijing P88888 0 100 0 0 JI A12365 0 0 500 0 Hebei C11111 0 300 0 0 Hebei D22222 100 0 0 0

[0154] If multiple delivery warehouse matrices are still obtained after processing in step S217, a delivery warehouse matrix can be randomly selected as the final delivery warehouse matrix, and the cargo of each cargo transportation equipment is delivered to the corresponding delivery warehouse according to the delivery warehouse matrix.

[0155] In a preferred embodiment, according to the determined delivery warehouse matrix of each cargo transportation equipment, the location point corresponding to the license plate number is found on the map, and the determined delivery warehouse number is filled in the location attribute information of the corresponding cargo transportation equipment.

[0156] In a preferred embodiment, after step S217 is completed, the list of goods to be sent to the target storage warehouse needs to be updated. The specific updating process is as follows:

[0157] (1) If the current target storage warehouse in the target storage warehouse quantity list truckStoreImportList exists in the list of goods to be sent to the target storage warehouse, then the total amount of goods to be sent to the corresponding target storage warehouse in the list of goods to be sent to the target storage warehouse, totalCount, is added to the corresponding allocated quantity of goods count in the target storage warehouse quantity list truckStoreImportList. Among them, the list of goods to be sent to the target storage warehouse needs to be established in advance and saved in the Redis database, which is used to save the quantity of goods for cargo transportation equipment with a device status of 2 in each delivery warehouse, including the target storage warehouse number TStoreID and the totalCount quantity of goods to be sent to the target storage warehouse. Redis is a high-performance Key-Value storage system that supports the storage of Value types including String, List, Set, and Zset.

[0158] (2) If the current target storage warehouse in the target storage warehouse quantity list truckStoreImportList does not exist in the list of goods to be sent to the target storage warehouse, then the corresponding target storage warehouse and the total amount of goods to be sent totalCount are inserted into the list of goods to be sent to the target storage warehouse, and the total amount of goods to be sent totalCount is equal to the allocated goods quantity count. For example, as described above, the list of goods to be sent to the target storage warehouse in truckStoreImportList is:

[0159] [{"TStoreID":10,"totalCount":40},

[0160] {"TStoreID":62,"totalCount":50},

[0161] {"TStoreID":3,"totalCount":30}]

[0162] After the update, the list of goods to be sent to the target storage warehouse in the delivery warehouse is:

[0163] [{"TStoreID":10,"totalCount":60},

[0164] {"TStoreID":62,"totalCount":62},

[0165] {"TStoreID":3,"totalCount":45},

[0166] {"TStoreID":2,"totalCount":5}]

[0167] For the situation where multiple incoming trucks from suppliers have goods in the same target storage warehouse, but these incoming trucks select different delivery warehouses, resulting in the freight volume of the outgoing trucks corresponding to each delivery warehouse to the target storage warehouse being far less than the rated load, updating the list of goods to be delivered to the target storage warehouse can reduce the internal transportation cost of the goods. In the above manner, it is determined whether each target storage warehouse in the target storage warehouse quantity list truckStoreImportList exists in the list of goods to be delivered to the target storage warehouse, and the totalCount of goods is updated based on the judgment result. In a preferred embodiment, the delivery warehouse number is used as the key and the list of goods to be delivered to the target storage warehouse is used as the value, and the updated list of goods to be delivered to the target storage warehouse is stored in the Redis database.

[0168] In the above embodiment, when selecting a delivery warehouse for the cargo transport equipment, in order to quickly circulate the cargo and avoid the cargo piling up in the delivery warehouse, the cargo needs to be handed over to the delivery truck as soon as possible after entering the delivery warehouse, so there is no need to consider the equipment status 3 (delivery warehouse is processing) and 4 (processed). If the above two situations are considered, the cargo in the delivery warehouse may have to wait for the cargo on the way for a long time, resulting in low processing efficiency of the delivery warehouse.

[0169] In another preferred embodiment, when the delivery warehouse starts to process the cargo of a cargo transport device, the processing interface is called. When the delivery warehouse completes processing the cargo of a cargo transport device, the processing completion interface is called. The functions of the processing interface and the processing completion interface are described below.

[0170] Processing interface: When the delivery warehouse starts to process the cargo of a cargo transport device, the processing interface is called to pass in the license plate number licensePlateID of the cargo transport device;

[0171] The equipment state truckState of the cargo transport equipment is changed to 3, indicating that the delivery warehouse is processing, and the current time is used as the start processing time and saved in the location attribute information.

[0172] Read the target storage warehouse cargo quantity list truckStoreImportList and the cargo list to be sent to the target storage warehouse. For example, the target storage warehouse cargo quantity list truckStoreImportList is:

[0173] [{"TStoreID":2,"count":12},

[0174] {"TStoreID":3,"count":15}]

[0175] Subtract the allocated quantity of the corresponding target storage warehouse in the target storage warehouse quantity list from the total quantity of the goods to be delivered to the current target storage warehouse in the target storage warehouse quantity list. In the above manner, update the totalCount of the goods in the target storage warehouse quantity list corresponding to each target storage warehouse number in the target storage warehouse quantity list truckStoreImportList. For example, the list of goods to be delivered to the target storage warehouse is:

[0176] [{"TStoreID":2,"totalCount":40},

[0177] {"TStoreID":3,"totalCount":30}]

[0178] The updated list of goods to be sent to the target storage warehouse is:

[0179] [{"TStoreID":2,"totalCount":28},

[0180] {"TStoreID":3,"totalCount":15}]

[0181] Processing completion interface: When the delivery warehouse has finished processing the cargo of a cargo transport device, the processing completion interface is called to pass in the license plate number licensePlateID of the cargo transport device;

[0182] The equipment state truckState of the cargo transport equipment is changed to 4, indicating that the delivery warehouse has processed it, and the location point with the equipment state truckState of 4 in the location attribute information on the map is deleted. The cargo of the cargo transport equipment corresponding to the location point with truckState of 4 has been processed, and these cargo transport equipment no longer need to occupy any time or financial cost for the delivery warehouse to purchase goods.

[0183] The basic idea of ​​dynamic programming theory is: decompose the problem to be solved into several sub-problems (stages), solve the sub-stages in sequence, and the solution of the previous sub-problem provides useful information for solving the next sub-problem. When solving any sub-problem, list various possible local solutions, retain those local solutions that may reach the optimal through decision-making, and discard other local solutions. Solve each sub-problem in turn, and the last sub-problem is the solution to the initial problem. Dynamic programming theory is applicable to overlapping sub-problems, that is, the sub-problems are not independent, and a sub-problem may be used multiple times in the next stage of decision-making. The present invention uses the above-mentioned dynamic programming theory to decompose the problem to be solved into several sub-problems, and a sub-problem may be used multiple times in the next stage of decision-making. The sub-problems are solved step by step, and finally a delivery warehouse is selected for cargo transportation equipment based on operations research theory.

[0184] Figure 3 FIG. 1 is a schematic diagram of the main process of constructing location attribute information and obtaining delivery warehouse data according to an embodiment of the present invention. Figure 3 As shown, the embodiment of the present invention constructs location attribute information according to the parameter information and obtains corresponding delivery warehouse data, including:

[0185] Step S301: According to the GPS locator number of the parameter information, the location information of the cargo transport equipment is obtained, and the location information is displayed on the map in the form of a location point, and then the location attribute information is added to the location point. In the embodiment, the location information is longitude and latitude information, and the location attribute information includes the license plate number licensePlateID, the GPS locator number gpsLocator, the destination city destCity, the cargo transport equipment state truckState, the total amount of cargo totalQuantity, the location information and the time when the location information is obtained, and truckState=1 is set. Add up all the cargo quantities quantity in the cargo information cargoDetails to obtain the total amount of cargo totalQuantity of the cargo transport equipment.

[0186] In a preferred embodiment, at every preset time interval (ie, the time interval between two adjacent acquisitions of location information), location information is acquired according to the GPS locator number gpsLocator of the cargo transportation equipment to update the location point of the cargo transportation equipment.

[0187] Step S302: According to the destination city of the parameter information, the delivery warehouse data is obtained. The delivery warehouse data includes the number, address and quantity of goods processed per unit time of each delivery warehouse in the destination city. In the embodiment, each delivery warehouse is traversed to determine whether each delivery warehouse is displayed on the map in the form of POI (Point of Interest). If the delivery warehouse is not displayed on the map in the form of POI, the position of the delivery warehouse on the map is determined according to the longitude and latitude of the delivery warehouse, and the corresponding position on the map is displayed in the form of POI. Bubble icons are generally used to represent POIs on electronic maps, such as scenic spots, government agencies, companies, shopping malls, restaurants, etc., which are all POIs. The quantity of goods processed per unit time by the service station can be the average quantity of goods processed by the service station per hour.

[0188] Figure 5 FIG. 1 is a schematic diagram of the main process of determining various data of cargo transportation equipment to the corresponding delivery warehouse according to an embodiment of the present invention. Figure 5 As shown, the specific implementation process of step S203 in the embodiment of the present invention includes:

[0189] Step S501: Calculate the travel distance according to the last location information and the current location information in the location attribute information, and obtain the travel time by subtracting the last location information acquisition time in the location attribute information from the current location information acquisition time. This step requires first reading the last location information and the last location information acquisition time from the location attribute information, and then calculating the travel distance and the travel time. In an embodiment, the current location information and the current location information acquisition time are updated to the location attribute information of the cargo transport equipment.

[0190] Step S502: Calculate the driving speed according to the driving distance and the driving time, and write the driving speed after the most recent data in the circular buffer. In the embodiment, when writing data to the circular buffer for the first time, the writing position is random; if the circular buffer is full and new data needs to be written, the oldest data is overwritten. The calculation formula of the driving speed is:

[0191] Driving speed = driving distance / driving time Formula 10

[0192] Step S503: Obtain all driving speeds in the circular buffer zone in the location information attribute, calculate the average of all driving speeds, and obtain an average driving speed. The average driving speed is more consistent with the actual driving conditions of the cargo transport equipment in the recent period of time than the current actual driving speed and the average driving speed of the route that has been traveled.

[0193] Step S504: According to the destination city in the location attribute information, the delivery warehouse of the destination city is obtained to form a delivery warehouse list. This step requires reading the destination city of the cargo transportation equipment from the location attribute information. In the embodiment, the delivery warehouses are sorted in ascending order according to their numbers to form a delivery warehouse list.

[0194] Step S505: Create a recommended route mapping table from the cargo transportation equipment to the corresponding delivery warehouse, a time mapping table from the cargo transportation equipment to the corresponding delivery warehouse, and a fuel consumption mapping table from the cargo transportation equipment to the corresponding delivery warehouse.

[0195] Step S506: Taking the location point of the cargo transportation equipment as the starting point and each delivery warehouse in the delivery warehouse list as the end point, respectively obtain at least one recommended route from the starting point to the end point and the length of the recommended route, and write the recommended route into the recommended route mapping table. In the embodiment, the number of the delivery warehouse is used as the key (Key), and the recommended route from the cargo transportation equipment to each delivery warehouse is the value (Value), which is written into the recommended route mapping table. For example, the recommended routes from cargo transportation equipment 1 to delivery warehouse 101 are 1A, 1B and 1C, and the recommended routes to delivery warehouse 102 are 2A and 2B, then the recommended route mapping table is {101: [1A, 1B, 1C], 102: [2A, 2B]}.

[0196] Step S507: Calculate the shortest time from the cargo transport equipment to each delivery warehouse in the delivery warehouse list, and write the shortest time into the time mapping table. In the embodiment, the number of the delivery warehouse is the key, and the shortest time from the cargo transport equipment to each delivery warehouse is the value, which is written into the time mapping table. The calculation formula for the shortest time from the cargo transport equipment to each delivery warehouse is:

[0197] The shortest time from cargo transport equipment to delivery warehouse = length of the shortest recommended route / average driving speed Formula 11

[0198] Wherein, the average driving speed is calculated in step S503.

[0199] Step S508: The minimum value of the shortest time from the cargo transportation equipment to all the delivery warehouses in the delivery warehouse list is used as the shortest time from the cargo transportation equipment to any delivery warehouse in the delivery warehouse list.

[0200] Step S509: Calculate the minimum fuel consumption of the cargo transport equipment to each delivery warehouse in the delivery warehouse list, and write the minimum fuel consumption into the fuel consumption mapping table. In the embodiment, the number of the delivery warehouse is the key, and the minimum fuel consumption of the cargo transport equipment to each delivery warehouse is the value, which is written into the fuel consumption mapping table. The calculation formula of the minimum fuel consumption of the cargo transport equipment to each delivery warehouse is:

[0201] Minimum fuel consumption from cargo transport equipment to delivery warehouse = length of shortest recommended route / 100 / fuel consumption formula 12

[0202] Step S510: The recommended route mapping table, the shortest time for the cargo transportation equipment to reach any delivery warehouse, the time mapping table and the fuel consumption mapping table are saved in the location attribute information of the cargo transportation equipment.

[0203] Figure 6FIG. 1 is a schematic diagram of the main process of calculating the expected time from a location point to the nearest bifurcation point according to an embodiment of the present invention. Figure 6 As shown, the specific implementation process of step S204 in the embodiment of the present invention includes:

[0204] Step S601: According to the recommended route mapping table of the cargo transportation equipment, multiple recommended routes are overlapped and calculated on the map to extract bifurcation points. The bifurcation points must meet the following conditions: the routes from the location point of the cargo transportation equipment to the bifurcation points can be covered by all the recommended routes in the recommended route mapping table; and the routes from the bifurcation points to any delivery warehouse are not covered by all the recommended routes in the recommended route mapping table.

[0205] Figure 7 FIG. 1 is a schematic diagram showing the principle of extracting bifurcation points according to an embodiment of the present invention. Figure 7 It can be seen that the recommended routes from the cargo transport equipment 1 to the delivery warehouse 101 are 1A, 1B and 1C, and the routes to the delivery warehouse 102 are 2A and 2B. The bifurcation point A and the bifurcation point B can be obtained by calculation.

[0206] Step S602: Calculate the distance from the location point of the cargo transportation equipment to each bifurcation point to calculate the expected time from the location point to the nearest bifurcation point. The calculation formula of the expected time is:

[0207] Expected time = distance from the location of the cargo transport equipment to the nearest fork point / average driving speed Formula 13

[0208] Figure 8 1 is a schematic diagram of the main process of calculating the target storage bin corresponding to the cargo of the cargo transport equipment according to an embodiment of the present invention. Figure 8 As shown, the specific implementation process of step S208 in the embodiment of the present invention includes:

[0209] Step S801: Add the quantity of goods with the same cargo number in the cargo information of the cargo transportation equipment of the delivery warehouse to be selected to obtain a list of unallocated goods. The unallocated cargo list includes the cargo number SkuID and the total amount of goods corresponding to the cargo number. The list is used to store goods that are in transit for purchase and have not yet determined the storage warehouse. Purchasing in transit means that the goods have been sent out by the supplier's shipping truck and are on the way to the delivery warehouse. In the embodiment, it is necessary to obtain from the location attribute information the cargo transportation equipment with the same Key as the mapping table of the cargo transportation equipment of the delivery warehouse to be selected and truckState=1 as the cargo transportation equipment of the delivery warehouse to be selected. Then obtain the cargo information of these cargo transportation equipment from the truckInfo table. The specific form of the unallocated cargo list is as follows:

[0210] [{"SkuID":"1111","amount":10},

[0211] {"SkuID":"2222","amount":40},

[0212] {"SkuID":"3333","amount":30}]

[0213] Step S802: According to the cargo number of the unallocated cargo list, the total amount of cargo to be stored in the corresponding cargo in the storage warehouse is obtained to generate a quantity list to be stored. In the embodiment, the quantity list to be stored is represented by storeSkuINQTY, including the cargo number SkuID, the storage warehouse number storeID and the corresponding total amount of cargo to be stored amount*, and the specific form is as follows:

[0214] [{"SkuID":"1111","storeID":62,"amount*":10},

[0215] {"SkuID":"1111","storeID":10,"amount*":6},

[0216] {"SkuID":"2222","storeID":10,"amount*":40},

[0217] {"SkuID":"3333","storeID":3,"amount*":20},

[0218] {"SkuID":"3333","storeID":62,"amount*":10}]

[0219] Step S803: Remove the total amount of goods to be stored in the list of quantities to be stored, and obtain a storage warehouse list. The specific form is as follows:

[0220] [{"SkuID":"1111","storeID":62},

[0221] {"SkuID":"1111","storeID":10},

[0222] {"SkuID":"2222","storeID":10},

[0223] {"SkuID":"3333","storeID":3},

[0224] {"SkuID":"3333","storeID":62}]

[0225] Step S804: Obtain the inventory quantity of each item number in the storage bin list in the storage bin to obtain the out-of-stock quantity list of the storage bin. If the out-of-stock quantity value is a negative number, it means that the existing quantity of the item meets the current production. The inventory quantity is the quantity of items available for trading. The out-of-stock quantity list includes the item number SkuID, the storage bin number storeID and the out-of-stock quantity stockout, and the specific format is as follows:

[0226] [{"SkuID":"1111","storeID":62,"stockout":20},

[0227] {"SkuID":"1111","storeID":10,"stockout":-10},

[0228] {"SkuID":"2222","storeID":10,"stockout":0},

[0229] {"SkuID":"3333","storeID":3,"stockout":2},

[0230] {"SkuID":"3333","storeID":62,"stockout":12}]

[0231] Step S805: Generate a list of cargo numbers and quantities based on the cargo information in the parameter information of the cargo transport equipment. Since the cargo of the cargo transport equipment is purchased in transit and has not been assigned a storage warehouse, the quantity corresponding to each cargo number in the list of cargo numbers and quantities must be less than or equal to the amount corresponding to the same cargo number in the list of quantities to be stored. The specific form of the list of cargo numbers and quantities is as follows:

[0232] [{"SkuID":"1111","quantity":3},

[0233] {"SkuID":"2222","quantity":20},

[0234] {"SkuID":"3333","quantity":25}]

[0235] Step S806: Create a mapping list between cargo and target storage warehouse of the cargo transport equipment, and calculate the number of cargo numbers in the to-be-warehouse quantity list that are the same as the current cargo number in the cargo number quantity list. Extract cargo numbers from the to-be-warehouse quantity list, and calculate the number of cargo numbers that are the same as the current cargo number in the cargo number quantity list. In an embodiment, the mapping list between cargo and target storage warehouse of the cargo transport equipment can be represented by truckSkuStoreList, including cargo number SkuID, target storage warehouse number TStoreID, and allocated cargo number count.

[0236] Step S807: Perform different processing according to the number of the goods to obtain a mapping list of the goods and target storage warehouses of the goods transport equipment. Steps S807 to S809 need to be processed for each goods transport equipment in the mapping list of the goods transport equipment to be selected for the delivery warehouse. The following four cases are described:

[0237] (1) If there is only one cargo number in the to-be-stored quantity list that is the same as the current cargo number in the cargo number quantity list, then the target storage bin corresponding to the cargo number is set to the corresponding storage bin in the to-be-stored quantity list, and the allocated cargo quantity is set to the cargo quantity in the cargo number quantity list. For example, the field in the cargo number quantity list is {"SkuID":"2222","quantity":20}, and the field in the to-be-stored quantity list storeSkuINQTY that matches the cargo number in the cargo number quantity list is: {"SkuID":"2222","storeID":10,"amount*":40}, then a field of the cargo and target storage bin mapping list truckSkuStoreList of the cargo transport equipment is obtained: {"SkuID":"2222","TStoreID":10,"count":20}.

[0238] (2) If there are multiple cargo numbers in the to-be-warehouse quantity list that are the same as the current cargo number in the cargo number quantity list, and there is only one cargo number whose total amount of to-be-warehouse cargo in the to-be-warehouse quantity list is greater than or equal to the cargo quantity in the cargo number quantity list, then the target storage bin corresponding to the cargo number is set to the storage bin corresponding to the cargo number whose total amount of to-be-warehouse cargo is greater than or equal to the cargo quantity, and the allocated cargo quantity is set to the cargo quantity in the cargo number quantity list. For example, the field {"SkuID":"1111","quantity":7} in the cargo number quantity list, and the field in the to-be-stored quantity list storeSkuINQTY that matches the cargo number in the cargo number quantity list and satisfies that the total amount of to-be-stored cargo corresponding to the cargo number is greater than or equal to the quantity of cargo in the cargo number quantity list is: {"SkuID":"1111","storeID":62,"amount*":10}, then a field of the cargo and target storage warehouse mapping list truckSkuStoreList of the cargo transport equipment is obtained: {"SkuID":"1111","TStoreID":62,"count":7}.

[0239] (3) If there are multiple cargo numbers in the to-be-warehouse quantity list that are the same as the current cargo number in the cargo number quantity list, and there are multiple cargo numbers whose total amount of to-be-warehouse cargo in the to-be-warehouse quantity list is greater than or equal to the cargo quantity in the cargo number quantity list, then the storage bins corresponding to the multiple cargo numbers are sorted from largest to smallest according to the number of occurrences, and then the target storage bin corresponding to the cargo number is set as the storage bin with the highest sorting, and the allocated cargo quantity is set as the cargo quantity in the cargo number quantity list. In the embodiment, if multiple storage bins have the same number of occurrences, the storage bin with a smaller number can be put in front. The reason is that, in general, the storage bin with a smaller number has a longer history of establishment and a larger area. For example, the field {"SkuID":"3333","quantity":5} in the cargo number quantity list, in the to-be-stocked quantity list storeSkuINQTY, the fields that match the cargo number in the cargo number quantity list and satisfy that the total amount of to-be-stocked cargo corresponding to the cargo number is greater than or equal to the quantity of cargo in the cargo number quantity list are: {"SkuID":"3333","storeID":3,"amount*":20} and {"SkuID":"3333","storeID":62,"amount*":10}, the number of occurrences of storeID=62 is greater than the number of occurrences of storeID=3, then a field of truckSkuStoreList is obtained: {"Sku ID":"3333","TStoreID":62,"count":5}.

[0240] (4) If there are multiple cargo numbers in the to-be-warehouse quantity list that are the same as the current cargo number in the cargo number quantity list, and the total amount of the to-be-warehouse cargo in the to-be-warehouse quantity list is less than the cargo quantity in the cargo number quantity list, the cargo of the cargo transport equipment is transported to different target storage warehouses. The specific implementation is:

[0241] First, the storage bins corresponding to the cargo numbers are sorted from largest to smallest according to the number of occurrences to obtain a storage bin sorting list. In an embodiment, if the number of occurrences of multiple storage bins is the same, the storage bin with a smaller number can be placed in front. The storage bin sorting list can be represented by store Rank.

[0242] After that, according to the sorting order, the storage bins in the storage bin sorting list are taken as one of the target storage bins corresponding to the cargo number, and the allocated cargo quantity is set as the total amount of cargo to be stored in the to-be-stored quantity list. The storage bin sorting list storeRank is looped, and the storage bin number storeID is taken out from the storage bin sorting list storeRank in turn as one of the target storage bins corresponding to the cargo number.

[0243] Finally, the total amount of goods to be stored is subtracted from the quantity of goods corresponding to the goods number, and the calculation of the next element in the storage bin sorting list is continued until the quantity of goods is 0. In this case, since the goods corresponding to the same goods number will be sent to multiple target storage bins, multiple fields will be obtained. For example, the field of the cargo number quantity list is {"SkuID":"4444","quantity":20}, and in the to-be-stocked quantity list storeSkuINQTY, there are three fields that match the cargo number in the cargo number quantity list, namely: {"SkuID":"4444","storeID":2,"amount*":10}, {"SkuID":"4444","storeID":3,"amount*":15} and {"SkuID":"4444","storeID":4,"amount*":13}. The storage warehouse sorting list storeRank obtained by sorting by the number of occurrences of storage warehouses is: [3,2,4]. The calculated truckSkuStoreList includes two fields, namely: {"SkuID":"4444","TStoreID":3,"count":15} and {"SkuID":"4444","TStoreID":2,"count":5}.

[0244] Step S808: Update the total amount of goods to be stored in the to-be-stored quantity list according to the mapping list of goods and target storage warehouses of the cargo transportation equipment. The specific implementation is: obtain the cargo number SkuID and storage warehouse number StoreID in the to-be-stored quantity list storeSkuINQTY that are the same as the cargo number SkuID in truckSkuStoreList and the target storage warehouse number TStoreID, and subtract the total amount of goods to be stored amount* corresponding to the cargo number SkuID in the to-be-stored quantity list storeSkuINQTY from the allocated cargo number count in the truckSkuStoreList, that is, amount*=amount*-count. The updated to-be-stored quantity list is:

[0245] [{"SkuID":"1111","storeID":10,"amount*":6},

[0246] {"SkuID":"2222","storeID":10,"amount*":40},

[0247] {"SkuID":"3333","storeID":3,"amount*":20}]

[0248] Step S809: Save the mapping list of the goods of the obtained goods transportation equipment to the target storage warehouse into the database. In the embodiment, save the mapping list truckSkuStoreList of the goods of the goods transportation equipment to the Redis database. The form of truckSkuStoreList obtained through the above process is as follows:

[0249] [{"SkuID":"2222","TStoreID":10,"count":20},

[0250] {"SkuID":"1111","TStoreID":62,"count":7},

[0251] {"SkuID":"3333","TStoreID":62,"count":5}

[0252] {"SkuID":"4444","TStoreID":3,"count":15},

[0253] {"SkuID":"4444","TStoreID":2,"count":5}]

[0254] Store the obtained truckSkuStoreList into the Redis database in the Key-Value format. Among them, the Key can be 'A_' + the license plate number of the goods transportation equipment, such as A_Beijing P88888, and the Value is truckSkuStoreList.

[0255] In order to facilitate the subsequent update of the list of goods to be sent to the target storage warehouse, in a preferred embodiment, the allocated quantity of goods in the same target storage warehouse in truckSkuStoreList can be added up to obtain the target storage warehouse quantity list. In the embodiment, the target storage warehouse quantity list can be expressed as truckStoreImportList, and the specific form is:

[0256] [{"TStoreID":10,"count":20},

[0257] {"TStoreID":62,"count":12},

[0258] {"TStoreID":3,"count":15},

[0259] {"TStoreID":2,"count":5}]

[0260] Store the obtained target storage warehouse quantity list truckStoreImportList in the Redis database in the format of Key-Value. Among them, the Key is 'B_' + the license plate number of the goods transportation equipment, such as B_Beijing P88888, and the Value is truckStoreImportList.

[0261] The present invention tries to send the same goods in the goods transportation equipment to the same target storage warehouse as much as possible. In modern operations, many situations are pallet operations. If the same goods are sent to the same target storage warehouse, the delivery warehouse can directly reach the destination target storage warehouse without changing the original state of the goods on the pallet, giving full play to the effect of pallet transportation. If the target storage warehouses of the goods on the pallet are different, the delivery warehouse needs to split the goods on the pallet and recombine them into a new pallet structure, and the new pallet is transported to the target storage warehouse. Such operations take a lot of time and result in low efficiency of the goods flowing in the delivery warehouse. Among them, a pallet refers to a platform device used for containerization, stacking, handling, and transportation, and for placing goods and products as unit loads.

[0262] In addition, for the situation where there are multiple suppliers for a certain good and the good is distributed to multiple target storage warehouses, the present invention can determine a highly efficient solution according to the current situation by calculating the target storage warehouses of each good in the goods transportation equipment in this step.

[0263] Fig. 9 It is a schematic diagram of the main process for calculating the goods priority of each good in the goods transportation equipment according to an embodiment of the present invention. As Fig. 9 shown, the specific implementation of step S209 includes:

[0264] Step S901: Set the initial value of the goods priority of each good in the goods transportation equipment. In the embodiment, the goods priority can be represented by goodsPriorValues, and the initial value is 0.

[0265] Step S902: Search for the out-of-stock quantity and the allocated cargo quantity corresponding to the same cargo number and the same storage bin number in the out-of-stock quantity list and the cargo and target storage bin mapping list of the cargo transportation equipment. This step requires first reading the out-of-stock quantity list from the cache, reading the cargo and target storage bin mapping list of the cargo transportation equipment from the Redis database, and then searching. Assuming that the SkuID in the cargo and target storage bin mapping list truckSkuStoreList of the cargo transportation equipment is called SkuID_T, and TStoreID is called TStoreID_T, this step searches for the corresponding out-of-stock quantity stockout in the out-of-stock quantity list and the corresponding allocated cargo quantity count in truckSkuStoreList when SkuID=SkuID_T and StoreID=TStoreID_T.

[0266] Step S903: Compare the out-of-stock quantity with 0. If the out-of-stock quantity is less than or equal to 0, the priority of the goods corresponding to the goods number remains unchanged; if the out-of-stock quantity is greater than 0, the priority of the goods corresponding to the goods number is increased. That is, if stockout≤0, the goodsPriorValues ​​corresponding to the goods number is still 0. The formula for increasing the priority of goods is:

[0267] goodsPriorValues=goodsPriorValues+count / stockout Formula 14

[0268] According to the above method, the cargo priority of each cargo in truckSkuStoreList is calculated, and the calculated cargo priority goodsPriorValues ​​is saved in the location information attribute of the cargo transportation equipment.

[0269] Fig.10 Schematic diagram of the main process of calculating the matching value between the cargo transport equipment and the corresponding delivery warehouse according to an embodiment of the present invention. Fig.10 As shown, the specific implementation of step S210 includes:

[0270] Step S1001: Obtain the lower limit and upper limit of the rated full-load cargo weight of the shipping truck. The rated full-load cargo weight refers to the maximum cargo mass allowed to be loaded by the manufacturer when the vehicle is running; the lower limit of the interval refers to the full-load cargo weight of the truck type with the smallest load weight among the shipping trucks; the upper limit of the interval refers to the full-load cargo weight of the truck type with the largest load weight among the shipping trucks. In an embodiment, the lower limit of the rated full-load cargo weight can be represented by truckLoadLowerLimit, and the upper limit of the rated full-load cargo weight can be represented by truckLoadUpperLimit. For example, the obtained truckLoadLowerLimit is 1000, and the truckLoadUpperLimit is 10000, in kilograms (kg).

[0271] Step S1002: Obtain the average weight of the goods in each target storage bin in the target storage bin quantity list. In the embodiment, the average weight TStoreID AverWeight of the goods in each target storage bin in the target storage bin quantity list truckStoreImportList is represented by the average weight list of goods, and the unit of the average weight of goods is kg, and the specific form is:

[0272] [{"TStoreID":10,"averWeight":20},

[0273] {"TStoreID":62,"averWeight":10},

[0274] {"TStoreID":3,"averWeight":40},

[0275] {"TStoreID":2,"averWeight":25}]

[0276] Step S1003: Calculate the truck full load quantity interval for each target storage warehouse. The truck full load quantity interval includes the truck full load quantity interval lower limit and the truck full load quantity interval upper limit. The truck full load quantity interval lower limit represents the estimated minimum quantity of goods when the minimum load type shipping truck is fully loaded for transportation, and the calculation formula is:

[0277] goodsQtyLowerLimit=truckLoadLowerLimit / TStoreID AverWeight Formula 15

[0278] Where goodsQtyLowerLimit is the lower limit of the range of the quantity of goods fully loaded on the truck.

[0279] The upper limit of the range of the number of goods fully loaded on the truck represents the maximum load type of the shipping truck.

[0280] The maximum estimated quantity of cargo when transporting a full load is calculated as follows:

[0281] goodsQtyUpperLimit=truckLoadUpperLimit / TStoreID AverWeight Formula 16

[0282] Where goodsQtyUpperLimit is the upper limit of the range of the quantity of goods fully loaded on the truck.

[0283] According to Formula 15 and Formula 16, a list of truck full load quantity intervals including the target storage warehouse, the lower limit of the truck full load quantity interval and the upper limit of the truck full load quantity interval is obtained. The specific form is:

[0284] [{"TStoreID":10,"goodsQtyLowerLimit":50,"goodsQtyUpperLimit":500},

[0285] {"TStoreID":62,"goodsQtyLowerLimit":100,"goodsQtyUpperLimit":1000},

[0286] {"TStoreID":3,"goodsQtyLowerLimit":25,"goodsQtyUpperLimit":250},

[0287] {"TStoreID":2,"goodsQtyLowerLimit":25,"goodsQtyUpperLimit":250}]

[0288] Step S1004: Create a matching value mapping table between the cargo transport equipment and the corresponding delivery warehouse, wherein the Key in the matching value mapping table is the delivery warehouse number, and the Value is the matching value between the cargo transport equipment and the delivery warehouse. In the embodiment, the matching value is represented by matchValue and initialized to 0.

[0289] Step S1005: Determine whether the current target storage warehouse in the target storage warehouse quantity list exists in the list of goods to be delivered to the target storage warehouse. If so, execute step S1006; if not, execute step S1007. The list of goods to be delivered to the target storage warehouse is stored in the Redis database, and the delivery warehouse number is used as the key to obtain it from the Redis database. For example, the list of goods to be delivered to the target storage warehouse is:

[0290] Delivery Warehouse Number 101

[0291] [{"TStoreID":10,"totalCount":60},

[0292] {"TStoreID":62,"totalCount":40},

[0293] {"TStoreID":2,"totalCount":56},

[0294] {"TStoreID":3,"totalCount":270}]

[0295] Delivery Warehouse Number 102

[0296] [{"TStoreID":10,"totalCount":10},

[0297] {"TStoreID":3,"totalCount":240}]

[0298] Step S1006: Compare the total quantity of goods to be sent in the list of goods to be sent to the target storage warehouse with the range of the full-load quantity of goods in the target storage warehouse, and perform different processing according to the comparison result to determine the matching value. That is, compare the total quantity of goods to be sent totalCount in the list of goods to be sent to the target storage warehouse with the lower limit goodsQtyLowerLimit and the upper limit goodsQtyUpperLimit of the range of the full-load quantity of goods. The processing procedures for different comparison results are as follows:

[0299] (1) If the total quantity of goods to be sent is greater than 0 and less than the lower limit of the range of the full-load quantity of goods, and the sum of the total quantity of goods to be sent and the allocated quantity of goods is greater than the lower limit of the range of the full-load quantity of goods and less than or equal to the upper limit of the range of the full-load quantity of goods, then update the matching value in the matching value mapping table to the sum of the initial matching value and the allocated quantity of goods. That is,

[0300] If 0 < totalCount < goodsQtyLowerLimit and

[0301] If goodsQtyLowerLimit ≤ totalCount + count ≤ goodsQtyUpperLimit, then update the matching value: matching value = matching value + count. In this case, if the total quantity of goods to be sent plus the quantity of allocated goods transported by the goods transportation equipment to the target storage warehouse can make the obtained quantity of goods reach the lower limit of the full-load quantity range of the freight truck.

[0302] (2) If the total quantity of goods to be sent is greater than 0 and less than the lower limit of the full-load quantity range of the freight truck, and the sum of the total quantity of goods to be sent and the quantity of allocated goods is less than or equal to the lower limit of the full-load quantity range of the freight truck, then update the matching value in the matching value mapping table to the sum of the initial matching value and the quantity of allocated goods. That is,

[0303] If 0 < totalCount < goodsQtyLowerLimit and

[0304] totalCount + count ≤ goodsQtyLowerLimit, then update the matching value: matching value = matching value + count. In this case, although the sum of the total quantity of goods to be sent and the quantity of allocated goods cannot make the obtained quantity of goods reach the lower limit of the full-load quantity range of the freight truck, it can try to reach the lower limit of the full-load quantity range of the freight truck as much as possible.

[0305] (3) If the total quantity of goods to be sent is greater than 0 and less than the lower limit of the full-load quantity range of the freight truck, and the sum of the total quantity of goods to be sent and the quantity of allocated goods is greater than the upper limit of the full-load quantity range of the freight truck, then the matching value in the matching value mapping table remains unchanged. That is,

[0306] If 0 < totalCount < goodsQtyLowerLimit and

[0307] totalCount + count > goodsQtyUpperLimit, then do not update the matching value. In this case, if the total quantity of goods to be sent without adding the quantity of allocated goods is less than the lower limit of the full-load quantity range of one outbound freight truck, and adding the quantity of allocated goods requires multiple outbound freight trucks, there is no cost savings.

[0308] (4) If the total quantity of goods to be sent is greater than or equal to the lower limit of the full-load quantity range of the freight truck and less than or equal to the upper limit of the full-load quantity range of the freight truck, and the sum of the total quantity of goods to be sent and the quantity of allocated goods is less than or equal to the lower limit of the full-load quantity range of the freight truck, then the matching value in the matching value mapping table remains unchanged. That is,

[0309] If goodsQtyLowerLimit≤totalCount≤goodsQtyUpperLimit and totalCount+count≤goodsQtyUpperLimit, the matching value is not updated. In this case, both the total amount of goods to be shipped and the total amount of goods to be shipped plus the allocated goods are within the range of the truck's full load quantity, so there is no need to update the matching value.

[0310] (5) If the total amount of goods to be sent is greater than or equal to the lower limit of the interval of the full load of goods on the truck and less than or equal to the upper limit of the interval of the full load of goods on the truck, and the sum of the total amount of goods to be sent and the allocated amount of goods is greater than the upper limit of the interval of the full load of goods on the truck, then the matching value in the matching value mapping table is updated to the difference between the current matching value and the allocated amount of goods. That is,

[0311] If goodsQtyLowerLimit≤totalCount≤goodsQtyUpperLimit and totalCount+count>goodsQtyUpperLimit, then update the matching value: matching value=matching value-count. In this case, the total amount of goods to be shipped belongs to the full load quantity range of a shipping truck, but two vehicles are required to transport the goods with the allocated quantity.

[0312] (6) If the total amount of goods to be sent is greater than the upper limit of the range of the full load of goods on the truck, the matching value in the matching value mapping table remains unchanged. That is,

[0313] If totalCount>goodsQtyUpperLimit, the matching value is not updated. In this case, regardless of whether the cargo transport equipment selects the delivery warehouse, the delivery warehouse can reasonably select multiple delivery trucks of different load types to fully load the delivery trucks. In actual engineering applications, the types of trucks range from light trucks to heavy trucks, and their load capacities vary greatly, so there is no need to worry about the range of the full load quantity of the delivery truck.

[0314] Step S1007: taking the next target storage bin in the target storage bin quantity list as the current target storage bin, and executing step S1005.

[0315] According to the above method, the matching value between each cargo transport equipment in truckSkuStoreList and each delivery warehouse in the delivery warehouse list is calculated. In a preferred embodiment, the matching value between the cargo transport equipment and the corresponding delivery warehouse calculated above is updated to the matching value mapping table matchValueMap. The matching value mapping table matchValueMap is saved to the location information attribute of the cargo transport equipment.

[0316] It can be seen from the method for selecting a delivery warehouse for a cargo transportation device according to the embodiment of the present invention that the coefficient of variation of the processing time is determined by calculating the processing time of the delivery warehouse, the coefficient of variation is compared with the preset variation threshold, and the delivery warehouse is selected for the cargo transportation device according to the comparison result, so as to ensure that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse and improving the overall work efficiency; when selecting a delivery warehouse for the cargo transportation device, the total cost of the cargo transportation device to the delivery warehouse is considered, which reduces the logistics cost and improves the economic benefit; when selecting a delivery warehouse for the cargo transportation device, the cargo priority of the cargo transportation device, the matching value between the cargo transportation device and the delivery warehouse, and the total cost of the cargo transportation device to the delivery warehouse are considered, so as to improve the priority of the cargo transportation device containing out-of-stock goods, improve the logistics efficiency, increase the full load rate of the shipping truck, and reduce the transportation cost of the goods from the delivery warehouse to the target storage warehouse; by determining the bifurcation point of multiple recommended routes from the cargo transportation device to the delivery warehouse, a delivery warehouse is selected for the cargo transportation device as late as possible before the cargo transportation device arrives at the bifurcation point, which does not affect the normal driving of the cargo transportation device and can use the latest data to select a delivery warehouse for the cargo transportation device.

[0317] Fig.11 Schematic diagram of main modules of an apparatus for selecting a delivery warehouse for cargo transport equipment according to an embodiment of the present invention. Fig.11 As shown, the device 1100 for selecting a delivery warehouse for cargo transportation equipment according to the embodiment of the present invention mainly includes:

[0318] The calculation module 1101 is used to calculate the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses based on the total amount of cargo of the cargo transportation equipment. The cargo transportation equipment is a device used by the supplier to transport the goods purchased by the purchaser, such as a truck. In the embodiment, the supplier's truck is referred to as an incoming truck. The destination of each cargo transportation equipment and the quantity of cargo of various goods are known, so all delivery warehouses at the destination can be obtained, and the total amount of cargo of all goods can be summed up to obtain the total amount of cargo. When the cargo transportation equipment belongs to different delivery warehouses, the sum of the total amount of cargo of all cargo transportation equipment belonging to the same delivery warehouse is calculated, and the sum is the total amount of cargo to be processed in the delivery warehouse.

[0319] The determination module 1102 is used to calculate the processing time of the delivery warehouse according to the total amount of goods to be processed, the amount of backlogged goods and the processing efficiency of the delivery warehouse, so as to determine the coefficient of variation of the processing time. The amount of backlogged goods and the processing efficiency of the delivery warehouse are calculated in advance, and the total amount of goods to be processed and the amount of backlogged goods of the delivery warehouse are summed and divided by the processing efficiency of the delivery warehouse to obtain the processing time of the delivery warehouse. Then, according to the coefficient of variation formula, the average value and standard deviation of the processing time of the cargo transportation equipment with the same destination to the corresponding delivery warehouse are calculated, and the coefficient of variation is obtained by dividing the standard deviation by the average value.

[0320] The selection module 1103 is used to compare the coefficient of variation with a preset variation threshold, and select the delivery warehouse corresponding to the coefficient of variation less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment. The smaller the coefficient of variation, the closer the processing time of each delivery warehouse. When the coefficient of variation is 0, it means that the processing time of each delivery warehouse is the same, that is, the load of each delivery warehouse is exactly the same. The variation threshold is determined by experience, and the delivery warehouses corresponding to the coefficient of variation less than or equal to the variation threshold process the cargo of the corresponding cargo transportation equipment, which can ensure that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse.

[0321] In addition, the device 1100 for selecting a delivery warehouse for cargo transportation equipment according to the embodiment of the present invention may also include: a backlog cargo quantity determination module, a processing efficiency determination module, a delivery warehouse determination module, a final selection module, a target storage warehouse determination module, and an execution time determination module ( Fig.11 The functions of each module are as described above.

[0322] From the above description, it can be seen that by calculating the processing time of the delivery warehouse, the coefficient of variation of the processing time is determined, the coefficient of variation is compared with the preset variation threshold, and the delivery warehouse is selected for the cargo transportation equipment according to the comparison result, which ensures that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse and improving the overall work efficiency; when selecting a delivery warehouse for the cargo transportation equipment, the total cost of the cargo transportation equipment to the delivery warehouse is considered, which reduces the logistics cost and improves the economic benefit; when selecting a delivery warehouse for the cargo transportation equipment, the cargo priority of the cargo transportation equipment, the matching value between the cargo transportation equipment and the delivery warehouse, and the total cost of the cargo transportation equipment to the delivery warehouse are considered, so as to improve the priority of the cargo transportation equipment containing out-of-stock goods, improve the logistics efficiency, increase the full load rate of the shipping truck, and reduce the transportation cost of the goods from the delivery warehouse to the target storage warehouse; by determining the bifurcation point of multiple recommended routes from the cargo transportation equipment to the delivery warehouse, a delivery warehouse is selected for the cargo transportation equipment as late as possible before the cargo transportation equipment reaches the bifurcation point, which does not affect the normal driving of the cargo transportation equipment and can use the latest data to select a delivery warehouse for the cargo transportation equipment.

[0323] Fig.12 An exemplary system architecture 1200 is shown to which the method for selecting a delivery warehouse for cargo transport equipment or the apparatus for selecting a delivery warehouse for cargo transport equipment according to the embodiments of the present invention can be applied.

[0324] like Fig.12 As shown, the system architecture 1200 may include terminal devices 1201, 1202, 1203, a network 1204, and a server 1205. The network 1204 is used to provide a medium for communication links between the terminal devices 1201, 1202, 1203 and the server 1205. The network 1204 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0325] Users can use terminal devices 1201, 1202, 1203 to interact with server 1205 through network 1204 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 1201, 1202, 1203, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0326] The terminal devices 1201 , 1202 , and 1203 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers, etc.

[0327] The server 1205 may be a server that provides various services, such as an administrator inputting constraints and objective functions into a background management server using terminal devices 1201, 1202, and 1203 (for example only). The background management server may analyze and process the received parameter information of the cargo transportation equipment, and feed back the processing results (such as a delivery warehouse matrix) to the terminal device.

[0328] It should be noted that the method for selecting a delivery warehouse for cargo transportation equipment provided in the embodiment of the present application is generally executed by the server 1205 , and accordingly, the device for selecting a delivery warehouse for cargo transportation equipment is generally set in the server 1205 .

[0329] It should be understood that Fig.12 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0330] According to an embodiment of the present invention, the present invention also provides an electronic device and a computer-readable medium.

[0331] The electronic device of the present invention includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention.

[0332] The computer-readable medium of the present invention stores a computer program thereon, and when the program is executed by a processor, a method for selecting a delivery warehouse for cargo transportation equipment according to an embodiment of the present invention is implemented.

[0333] Reference below Fig.13 , which shows a schematic diagram of the structure of a computer system 1300 suitable for implementing an electronic device of an embodiment of the present invention. Fig.13 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0334] like Fig.13 As shown, the computer system 1300 includes a central processing unit (CPU) 1301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage part 1308 into a random access memory (RAM) 1303. In the RAM 1303, various programs and data required for the operation of the computer system 1300 are also stored. The CPU 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0335] The following components are connected to the I / O interface 1305: an input section 1306 including a keyboard, a mouse, etc.; an output section 1307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1308 including a hard disk, etc.; and a communication section 1309 including a network interface card such as a LAN card, a modem, etc. The communication section 1309 performs communication processing via a network such as the Internet. A drive 1310 is also connected to the I / O interface 1305 as needed. A removable medium 1313, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1310 as needed, so that a computer program read therefrom is installed into the storage section 1308 as needed.

[0336] In particular, according to the embodiments disclosed in the present invention, the process described in the main step diagram above can be implemented as a computer software program. For example, the embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the main step diagram. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1309, and / or installed from the removable medium 1311. When the computer program is executed by the central processing unit (CPU) 1301, the above-mentioned functions defined in the system of the present invention are executed.

[0337] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0338] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0339] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, they may be described as: a processor includes a calculation module, a determination module and a selection module. The names of these modules do not constitute limitations on the modules themselves in certain cases. For example, the calculation module may also be described as "a module for calculating the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses based on the total amount of cargo of the cargo transportation equipment".

[0340] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by one of the devices, the device includes: according to the total amount of cargo of the cargo transportation equipment, calculating the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses; according to the total amount of cargo to be processed, the amount of backlog cargo and the processing efficiency of the delivery warehouse, calculating the processing time of the delivery warehouse to determine the coefficient of variation of the processing time; comparing the coefficient of variation with a preset variation threshold, and taking the delivery warehouse corresponding to the coefficient of variation less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment.

[0341] From the above description, it can be seen that the coefficient of variation of the processing time is determined by calculating the processing time of the delivery warehouse, and the coefficient of variation is compared with the preset variation threshold. The delivery warehouse is selected for the cargo transportation equipment according to the comparison result, which ensures that the processing time of each delivery warehouse is close, thereby balancing the load of each delivery warehouse and improving the overall work efficiency. When selecting a delivery warehouse for the cargo transportation equipment, the total cost from the cargo transportation equipment to the delivery warehouse is taken into consideration, which reduces logistics costs and improves economic benefits.

[0342] The above product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0343] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for selecting a delivery warehouse for a cargo transport device, characterized in that: include: Calculating the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses according to the total amount of cargo of the cargo transportation equipment; Calculate the processing time of the delivery warehouse according to the total amount of goods to be processed, the amount of backlog goods and the processing efficiency of the delivery warehouse to determine the coefficient of variation of the processing time; Compare the coefficient of variation with a preset variation threshold, and use the delivery warehouse corresponding to the coefficient of variation that is less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment; The determining of the coefficient of variation of the processing time comprises: Calculating the average value and standard deviation of the processing time from the cargo transport equipment to the corresponding delivery warehouse, and dividing the standard deviation by the average value to obtain the coefficient of variation; The method further includes: determining a recommended route from the cargo transport equipment to the corresponding delivery warehouse, performing overlapping calculations on a plurality of the recommended routes to extract bifurcation points of the recommended routes according to a preset extraction rule; Calculating the distance from the location point of the cargo transport equipment to the bifurcation point, so as to obtain the expected time from the location point to the nearest bifurcation point according to the distance and the average travel speed of the cargo transport equipment; It is determined whether the expected time is less than or equal to a preset time threshold. If the expected time is less than or equal to the time threshold, a processing logic of selecting a delivery warehouse for the cargo transportation equipment is executed.

2. The method according to claim 1, characterized in that Before the step of calculating the processing time of the delivery warehouse, the method further includes: Calculate the total amount of cargo that has not been processed by the cargo transport equipment of the delivery warehouse to obtain the initial backlog of cargo of the delivery warehouse; The remaining cargo quantity of the cargo transport equipment being processed by the delivery warehouse is calculated, and the initial backlog cargo quantity and the remaining cargo quantity are summed to obtain the backlog cargo quantity.

3. The method according to claim 1, characterized in that Before the step of calculating the processing time of the delivery warehouse, the method further includes: The number of operating service stations in the delivery warehouse is obtained, and the processing efficiency is obtained by multiplying the number of operating service stations by the number of goods processed per unit time by the service stations.

4. The method according to claim 1, characterized in that: The delivery warehouse to which the cargo transportation equipment belongs is a delivery warehouse whose total cost does not exceed the standard, and the method further includes: Calculate the total cost of the cargo transportation equipment to the corresponding delivery warehouse and the average total cost, and calculate the total cost deviation coefficient according to the total cost and the average total cost; The total cost deviation coefficient is compared with a preset deviation coefficient threshold value to determine a delivery warehouse for the cargo transportation equipment whose total cost does not exceed the standard.

5. The method according to any one of claims 1 to 4, characterized in that: When there are multiple delivery warehouses corresponding to the coefficient of variation that is less than or equal to the preset variation threshold, the method further includes: Calculate the cargo priority and the cargo priority average of the cargo transportation equipment according to the out-of-stock quantity of the storage warehouse and the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment to determine the cargo priority deviation coefficient; Calculate the matching value and matching average value between the cargo transport equipment and the corresponding delivery warehouse according to the rated full load cargo weight, the total amount of cargo to be sent and the allocated cargo quantity to determine the matching value deviation coefficient; Adding the cargo priority deviation coefficient, the matching value deviation coefficient and the total cost deviation coefficient corresponding to the same cargo transportation equipment and the same delivery warehouse to obtain a total deviation coefficient; The total deviation coefficients of the plurality of cargo transport equipment in the corresponding delivery warehouses are added together to obtain a total deviation value, and the delivery warehouse corresponding to the maximum total deviation value is used as the delivery warehouse finally selected by the plurality of cargo transport equipment.

6. The method according to claim 5, characterized in that The calculating the cargo priority of the cargo transport equipment comprises: setting an initial value for a cargo priority of cargo in the cargo transport device; Searching for the same cargo number, the same storage bin number and the allocated cargo quantity corresponding to the same cargo number in the out-of-stock quantity list and the cargo and target storage bin mapping list of the cargo transport equipment; wherein the out-of-stock quantity list includes the cargo number, the storage bin number and the corresponding out-of-stock quantity, and the cargo and target storage bin mapping list of the cargo transport equipment includes the cargo number, the target storage bin number and the corresponding allocated cargo quantity; If the out-of-stock quantity is less than or equal to 0, the priority level of the goods corresponding to the goods number remains unchanged; if the out-of-stock quantity is greater than 0, the priority level of the goods corresponding to the goods number is increased according to the out-of-stock quantity and the allocated goods quantity.

7. The method according to claim 5, characterized in that The calculating the matching value between the cargo transport equipment and the corresponding delivery warehouse includes: Calculate the truck full load cargo quantity interval of the target storage bin according to the rated full load cargo weight and the average cargo weight of the target storage bin; wherein the truck full load cargo quantity interval includes the truck full load cargo quantity interval lower limit and the truck full load cargo quantity interval upper limit; Compare the total amount of goods to be sent in the list of goods to be sent to the target storage warehouse with the lower limit of the range of the full load of goods on the truck and the upper limit of the range of the full load of goods on the truck; According to the comparison result, the preset initial matching value is updated to obtain a matching value.

8. The method according to claim 5, characterized in that The method further comprises: According to the cargo number of the cargo transport equipment, the total amount of the corresponding cargo to be stored in the storage bin is obtained to generate a list of quantities to be stored; wherein the list of quantities to be stored includes the cargo number, the storage bin number and the corresponding total amount of the cargo to be stored; Generate a cargo number and quantity list according to the cargo information of the cargo transport equipment; wherein the cargo number and quantity list includes the cargo number and the corresponding cargo quantity; Calculate the number of goods with the same number as the current goods in the list of goods number quantities in the list of goods number quantities in the list of goods number quantities; The target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment are calculated according to the cargo number quantity, the total amount of cargo to be stored and the cargo quantity.

9. The method according to claim 1, characterized in that: The extraction rule is: all routes from the location point of the cargo transportation equipment to the bifurcation point can be covered by the recommended route; and all routes from the bifurcation point to any delivery warehouse will not be covered by the recommended route.

10. A device for selecting a delivery warehouse for cargo transport equipment, characterized in that: include: A calculation module, configured to calculate the total amount of cargo to be processed in the delivery warehouse when the cargo transportation equipment belongs to different delivery warehouses according to the total amount of cargo of the cargo transportation equipment; A determination module, configured to calculate the processing time of the delivery warehouse according to the total amount of goods to be processed, the amount of backlog goods and the processing efficiency of the delivery warehouse, so as to determine the coefficient of variation of the processing time; A selection module, for comparing the coefficient of variation with a preset variation threshold, and selecting a delivery warehouse corresponding to a coefficient of variation less than or equal to the variation threshold as the delivery warehouse selected by the cargo transportation equipment; The selection module is further used for: Calculating the average value and standard deviation of the processing time from the cargo transport equipment to the corresponding delivery warehouse, and dividing the standard deviation by the average value to obtain the coefficient of variation; An execution time determination module is used to determine a recommended route from the cargo transport equipment to the corresponding delivery warehouse, and to perform overlapping calculations on a plurality of the recommended routes to extract bifurcation points of the recommended routes according to a preset extraction rule; Calculating the distance from the location point of the cargo transport equipment to the bifurcation point, so as to obtain the expected time from the location point to the nearest bifurcation point according to the distance and the average travel speed of the cargo transport equipment; It is determined whether the expected time is less than or equal to a preset time threshold. If the expected time is less than or equal to the time threshold, a processing logic of selecting a delivery warehouse for the cargo transportation equipment is executed.

11. The device according to claim 10, characterized in that The device also includes: a backlog goods quantity determination module for Calculating the total amount of cargo that has not been processed by the cargo transport equipment of the delivery warehouse to obtain the initial backlog of cargo of the delivery warehouse; and The remaining cargo quantity of the cargo transport equipment being processed by the delivery warehouse is calculated, and the initial backlog cargo quantity and the remaining cargo quantity are summed to obtain the backlog cargo quantity.

12. The device according to claim 10, characterized in that The device also includes: a processing efficiency determination module for The number of operating service stations in the delivery warehouse is obtained, and the processing efficiency is obtained by multiplying the number of operating service stations by the number of goods processed per unit time by the service stations.

13. The device according to claim 10, characterized in that The delivery warehouse to which the cargo transportation equipment belongs is a delivery warehouse whose total cost does not exceed the standard. The device also includes: a delivery warehouse determination module for Calculating the total cost of the cargo transport equipment to the corresponding delivery warehouse and the average total cost, and calculating the total cost deviation coefficient according to the total cost and the average total cost; and The total cost deviation coefficient is compared with a preset deviation coefficient threshold value to determine a delivery warehouse for the cargo transportation equipment whose total cost does not exceed the standard.

14. The device according to any one of claims 10 to 13, characterized in that When there are multiple delivery warehouses corresponding to the coefficient of variation that is less than or equal to the preset variation threshold, the device further includes: a final selection module for Calculate the cargo priority and the cargo priority average of the cargo transportation equipment according to the out-of-stock quantity of the storage warehouse and the target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment to determine the cargo priority deviation coefficient; Calculate the matching value and matching average value between the cargo transport equipment and the corresponding delivery warehouse according to the rated full load cargo weight, the total amount of cargo to be sent and the allocated cargo quantity to determine the matching value deviation coefficient; Adding the cargo priority deviation coefficient, the matching value deviation coefficient and the total cost deviation coefficient corresponding to the same cargo transport equipment and the same delivery warehouse to obtain a total deviation coefficient; and The total deviation coefficients of the plurality of cargo transport equipment in the corresponding delivery warehouses are added together to obtain a total deviation value, and the delivery warehouse corresponding to the maximum total deviation value is used as the delivery warehouse finally selected by the plurality of cargo transport equipment.

15. The device according to claim 14, characterized in that The final selection module is further used for: setting an initial value for a cargo priority of cargo in the cargo transport device; Searching for the same cargo number, the same storage bin number and the allocated cargo quantity corresponding to the same cargo number in the out-of-stock quantity list and the cargo and target storage bin mapping list of the cargo transport equipment; wherein the out-of-stock quantity list includes the cargo number, the storage bin number and the corresponding out-of-stock quantity, and the cargo and target storage bin mapping list of the cargo transport equipment includes the cargo number, the target storage bin number and the corresponding allocated cargo quantity; and If the out-of-stock quantity is less than or equal to 0, the priority level of the goods corresponding to the goods number remains unchanged; if the out-of-stock quantity is greater than 0, the priority level of the goods corresponding to the goods number is increased according to the out-of-stock quantity and the allocated goods quantity.

16. The device according to claim 14, characterized in that The final selection module is also used for: Calculate the truck full load cargo quantity interval of the target storage bin according to the rated full load cargo weight and the average cargo weight of the target storage bin; wherein the truck full load cargo quantity interval includes the truck full load cargo quantity interval lower limit and the truck full load cargo quantity interval upper limit; Comparing the total amount of goods to be sent in the list of goods to be sent to the target storage warehouse with the lower limit of the range of the full load of goods on the truck and the upper limit of the range of the full load of goods on the truck; and According to the comparison result, the preset initial matching value is updated to obtain a matching value.

17. The device according to claim 14, characterized in that The device also includes: a target storage bin determination module for According to the cargo number of the cargo transport equipment, the total amount of the corresponding cargo to be stored in the storage bin is obtained to generate a list of quantities to be stored; wherein the list of quantities to be stored includes the cargo number, the storage bin number and the corresponding total amount of the cargo to be stored; Generate a cargo number and quantity list according to the cargo information of the cargo transport equipment; wherein the cargo number and quantity list includes the cargo number and the corresponding cargo quantity; Calculate the number of goods numbers in the to-be-stocked quantity list that are identical to the current goods number in the goods number quantity list; and The target storage warehouse and the allocated cargo quantity corresponding to the cargo of the cargo transportation equipment are calculated according to the cargo number quantity, the total amount of cargo to be stored and the cargo quantity.

18. The device according to claim 10, characterized in that The extraction rule is: all routes from the location point of the cargo transportation equipment to the bifurcation point can be covered by the recommended route; and all routes from the bifurcation point to any delivery warehouse will not be covered by the recommended route.

19. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 9.

20. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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