Information determination method, device and equipment and computer readable storage medium

Abstract

The embodiment of the invention discloses an information determination method. The method comprises the following steps: determining a first order quantity and a second order quantity of orders in each sorting area of a target area; wherein the first order quantity represents the order quantity of orders interacted with the outside of the target area, and the second order quantity represents the order quantity of orders interacted with the inside of the target area; based on the plurality of first single quantities and the plurality of second single quantities, determining a first total single quantity of the target area; based on the historical daily output efficiency of the work object corresponding to each sorting area, determining the total daily output efficiency of each sorting area; wherein the daily output efficiency refers to the daily working efficiency of the working object; based on the first total single quantity and the total daily output efficiency, the daily number of the working objects corresponding to each sorting sub-area is determined; wherein the sub-sorting area is an area corresponding to each operation type of the order. The embodiment of the invention further discloses an information determination device and equipment and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the field of logistics, and in particular to an information determination method, device, equipment, and computer-readable storage medium. Background Art

[0002] Logistics management includes several links, including collection, sorting, and delivery. Sorting is the process of combining physical goods and diverting them according to destination. With the development of technology, the sorting process is now semi-automated. Although the manpower required is much less than that of terminal delivery staff, the labor cost of sorting still accounts for a large proportion of the total sorting cost and directly affects the overall logistics timeliness. Therefore, it is necessary to reasonably determine the actual personnel needs of the sorting site.

[0003] At present, the existing technology generally adopts the predicted cargo volume of the sorting site divided by the per capita work efficiency of the sorting site to obtain the total number of people in the sorting site. Then the site manager assigns these staff members to each operation process in the sorting site according to experience, that is, the number of people working in each operation process in the sorting site is allocated based on experience; however, in the implementation process, the inventor found that there are at least the following problems in the existing technology: the total number of people in the sorting site is allocated to different operation processes based on experience, resulting in a difference between the number of people actually required for each operation process and the number allocated based on experience, that is, there is a shortage of personnel assigned to a certain operation process, resulting in cargo detention, or there is an excess of personnel assigned to a certain operation process, resulting in waste of personnel, thereby resulting in a low accuracy rate of the number of people assigned to different operation processes. Summary of the Invention

[0004] To solve the above technical problems, the embodiments of the present application hope to provide an information determination method, apparatus, device and computer-readable storage medium, which can solve the problem of low accuracy in the number of people assigned to different work processes in the related art.

[0005] The technical solution of this application is achieved as follows:

[0006] An information determination method, the method comprising:

[0007] Determine a first order quantity and a second order quantity of orders for each sorting area of ​​the target area; wherein the first order quantity represents the order quantity of orders interacting with areas outside the target area, and the second order quantity represents the order quantity of orders interacting with areas within the target area;

[0008] determining a first total order volume of the target area based on a plurality of the first order volumes and a plurality of the second order volumes;

[0009] Determining the total daily output efficiency of each sorting area based on the historical daily output efficiency of the work objects corresponding to each sorting area; wherein the daily output efficiency refers to the daily work efficiency of the work objects;

[0010] Based on the first total order quantity and the total daily output efficiency, a daily quantity of work objects corresponding to each sub-sorting area is determined; wherein the sub-sorting area is an area corresponding to each operation type of the order.

[0011] In the above solution, determining the first order quantity and the second order quantity of each sorting area in the target area includes:

[0012] For each target moment in the first time period, determining a first sub-order quantity for each target moment based on the initial order quantity for each target moment, the revised order quantity for each target moment, the adjustment factor, and the second total order quantity for the first time period;

[0013] For each second time, determining a second sub-order quantity for each second time based on the target order quantity prediction model and the historical order quantity corresponding to each historical time; wherein the second time is a time after the first time; and the historical time and the second time have a corresponding relationship;

[0014] determining the first order quantity based on a plurality of the first sub-order quantities and the second sub-order quantities;

[0015] The second order quantity is determined based on the first order quantity, the circulation order quantity circulating between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area.

[0016] In the above solution, determining the first sub-order quantity at each target moment based on the initial order quantity at each target moment, the revised order quantity at each target moment, the adjustment factor, and the second total order quantity at the first time includes:

[0017] For each target moment, determining a first to-be-processed order quantity based on the initial order quantity, the first adjustment factor, the revised order quantity, and a complementary adjustment factor; wherein the adjustment factor includes the first adjustment factor; and the complementary adjustment factor is complementary to the first adjustment factor;

[0018] Determining a second to-be-processed order quantity based on the second total order quantity and the second adjustment factor; wherein the adjustment factor includes the second adjustment factor;

[0019] Based on the first to-be-processed order quantity and the second to-be-processed order quantity, each first sub-order quantity is determined.

[0020] In the above solution, determining the second sub-order quantity at each second time based on the target order quantity prediction model and the historical order quantity corresponding to each historical time includes:

[0021] For each historical time, determining the sub-historical order quantity of each cargo source in the sorting area within the target area;

[0022] Determining the current order quantity of each of the goods sources based on the target order quantity prediction model and each of the sub-historical order quantities;

[0023] Based on a plurality of the current order quantities, the second sub-order quantity is determined.

[0024] In the above solution, determining the current order quantity of each source of goods based on the target order quantity prediction model and each sub-historical order quantity includes:

[0025] Determine a first sub-current order quantity of the first cargo source, a second sub-current order quantity of the second cargo source, and a third sub-current order quantity of the third cargo source based on the target order quantity prediction model and a first sub-historical order quantity of the first cargo source, a second sub-historical order quantity of the second cargo source, and a third sub-historical order quantity of the third cargo source;

[0026] Determine a third total order quantity for the second cargo source and the third cargo source based on a first historical predicted order quantity corresponding to the historical time, a second historical predicted order quantity corresponding to the target historical time, a historical actual order quantity corresponding to the target historical time, a third adjustment factor, the second sub-current order quantity, and the third sub-current order quantity;

[0027] Based on the third total order quantity and proportion parameter, the second sub-current order quantity and the third sub-current order quantity are determined.

[0028] In the above solution, determining the second order quantity based on the first order quantity, the flow order quantity between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area includes:

[0029] Determining a first ratio based on a first circulating order volume and a first sorting order volume; wherein the first circulating order volume represents the number of orders interacting with the same province within the target area; the circulating order volume includes the first circulating order volume, and the sorting order volume includes the first sorting order volume;

[0030] Determining a second ratio based on a second circulating order volume and a second sorting order volume; wherein the first circulating order volume represents the volume of orders interacting with different provinces within the target area; the circulating order volume includes the second circulating order volume, and the sorting order volume includes the second sorting order volume;

[0031] The second order quantity is determined based on the first ratio, the second ratio, and the first order quantity.

[0032] In the above solution, determining the daily number of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency includes:

[0033] Determining a total daily order quantity for each sorting area based on the first total order quantity;

[0034] Calculating the total daily order quantity and the total daily output efficiency to obtain the total daily quantity of work objects corresponding to each sorting area;

[0035] For each of the sorting areas, the daily quantity corresponding to each sub-sorting area is determined based on a historical daily quantity ratio of work objects corresponding to each sub-sorting area and the daily total quantity.

[0036] In the above solution, determining the daily number of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency includes:

[0037] Determining a target order quantity for each sub-sorting area based on the first total order quantity and the order quantity ratio of each sub-sorting area;

[0038] Determining a target output efficiency for each sub-sorting area based on the total daily output efficiency and the output efficiency ratio of each sub-sorting area;

[0039] Based on the target order quantity and the target output efficiency, a daily quantity of work objects corresponding to each sub-sorting area is determined.

[0040] An information determination device, comprising:

[0041] a processing unit, configured to determine a first order quantity and a second order quantity of orders for each sorting area of ​​a target area; wherein the first order quantity represents the order quantity of orders interacting with areas outside the target area, and the second order quantity represents the order quantity of orders interacting with areas within the target area;

[0042] a merging unit, configured to determine a first total order quantity of the target area based on a plurality of the first order quantities and a plurality of the second order quantities;

[0043] an acquiring unit, configured to determine a total daily output efficiency of each sorting area based on a historical daily output efficiency of the work objects corresponding to each sorting area; wherein the daily output efficiency refers to the daily work efficiency of the work objects;

[0044] a determining unit, configured to determine a daily quantity of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency; wherein the sub-sorting area is an area corresponding to each operation type of the order.

[0045] An information determination device, the device comprising: a processor, a memory, and a communication bus;

[0046] The communication bus is used to realize the communication connection between the processor and the memory;

[0047] The processor is configured to execute the information determination program stored in the memory to implement the steps of the above-mentioned information determination method.

[0048] A computer-readable storage medium stores one or more programs, wherein the one or more programs can be executed by one or more processors to implement the steps of the above-mentioned information determination method.

[0049] Because the first and second order quantities of orders for each sorting area in the target area are determined, and the first order quantity represents the order quantity of orders interacting with outside the target area, and the second order quantity represents the order quantity of orders interacting with within the target area, then the first total order quantity of the target area is determined based on multiple first order quantities and multiple second order quantities. Then, based on the historical daily output efficiency of the work objects corresponding to each sorting area, the total daily output efficiency of each sorting area is determined, and the daily output efficiency refers to the daily work efficiency of the work objects. Then, based on the first total order quantity and the total daily output efficiency, the daily number of work objects corresponding to each sub-sorting area is determined, and the sub-sorting area is the area corresponding to each operation type of the order. In this way, each sorting area can be first divided according to the operation type of the order, and then the daily number of work objects corresponding to each sub-sorting area can be directly calculated based on the first total order quantity and the total daily output efficiency. Instead of requiring the site manager to allocate the total number of people in the sorting site to different operation processes based on experience after determining the total number of people in the sorting site, as in the related art, the accuracy of the number of people allocated to different operation processes is improved, thereby improving the efficiency of different operation processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of an information determination method provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of a neutron sorting area in an information determination method provided in an embodiment of the present application;

[0052] Figure 3 A flowchart of another information determination method provided in an embodiment of the present application;

[0053] Figure 4 A schematic diagram of the cargo volume in the sub-sorting area in a method for determining information provided in an embodiment of the present application;

[0054] Figure 5 A schematic diagram of the structure of an information determination device provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of the structure of an information determination device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application.

[0057] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0058] It should be noted that traditional logistics management generally only covers the entire sorting site and does not care about the operations within the sorting site. In addition, for Logistics A, since Logistics A has both self-operated businesses and personal collection businesses, it leads to complex express delivery operations, numerous process positions, and unreasonable job arrangements due to staff scheduling based on experience. After investigating the operations of Logistics A's sorting / transfer operation team, there is room for optimization in the configuration of each workstation on the site.

[0059] Based on this, the embodiment of the present application provides an information determination method, which can be applied to an information determination device, referring to Figure 1 As shown, the method includes the following steps:

[0060] Step 101: Determine the first order quantity and the second order quantity of the order of each sorting area in the target area.

[0061] The first order quantity represents the order quantity of orders interacting with outside the target area, and the second order quantity represents the order quantity of orders interacting with inside the target area.

[0062] In an embodiment of the present application, the target area may refer to any area divided by the national geographical area; the sorting area may refer to the sorting center (i.e., the sorting site) in the target area, and there may be multiple sorting areas in a target area; the first order quantity and the second order quantity are both predicted order quantities; the first order quantity may be determined first based on the predicted order quantity at each target moment in the first time and the predicted order quantity at each moment in the second time, and then the second order quantity may be determined based on the first order quantity.

[0063] In a feasible implementation, the target area may refer to any one of the East China region, the South China region, the North China region, the Central China region, the Southwest region, the Northwest region and the Northeast region.

[0064] Step 102: Determine a first total order quantity in the target area based on multiple first order quantities and multiple second order quantities.

[0065] In an embodiment of the present application, the first total order quantity may refer to the total amount of orders in all sorting areas in the target area, and the first total order quantity is also a predicted quantity; multiple first order quantities and multiple second order quantities may be summed to obtain the first total order quantity of the target area.

[0066] Step 103: Determine the total daily output efficiency of each sorting area based on the historical daily output efficiency of the work objects corresponding to each sorting area.

[0067] Among them, daily output efficiency refers to the daily work efficiency of the work object.

[0068] In the embodiment of the present application, the historical daily output efficiency and the total daily output efficiency have a corresponding relationship; for each sorting area, the historical daily output efficiency of the work objects in each sorting area can be obtained first, and then the total daily output efficiency can be predicted based on the obtained historical daily output efficiency.

[0069] In a feasible implementation method, the historical daily output efficiency and the total daily output efficiency have a corresponding relationship, specifically: for each sorting area, when the total daily output efficiency on November 11, 2023 is determined, the daily output efficiency on November 11, 2022 should be used for prediction; when the total daily output efficiency on December 12, 2023 is determined, the daily output efficiency on December 12, 2022 should be used for prediction; the work object can refer to the staff.

[0070] Step 104: Determine the daily quantity of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency.

[0071] The sub-sorting area is the area corresponding to each operation type of the order.

[0072] In the embodiment of the present application, different operation processes of an order may refer to actual operation processes for an order; the sorting area may be divided according to different operation types of an order to obtain multiple sub-sorting areas, and each sorting area may be divided into multiple sub-sorting areas, where the sub-sorting area is the smallest management dimension of the sorting area; the order quantity of each sub-sorting area may be first determined based on the first total order quantity, and the daily output efficiency of each sub-sorting area may be determined based on the total daily output efficiency; then, the order quantity of each sub-sorting area and the daily output efficiency of each sub-sorting area may be calculated to obtain the daily number of work objects corresponding to each sub-sorting area. In this way, refined management of each sorting site may be achieved.

[0073] In a feasible implementation, the sorting area is divided into multiple sub-sorting areas according to different operation types (i.e., operation processes) of the order. Figure 2 As shown, multiple sub-sorting areas may include immediate loading area, immediate unloading area, initial sorting area, landing sorting area and small piece sorting area; different operation processes may include warehouse distribution, pure distribution (i.e. collection) and port entry.

[0074] The information determination method provided in the embodiment of the present application can first divide each sorting area according to the operation type of the order, and then directly calculate the daily number of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency. This is instead of requiring the site manager to allocate the total number of people in the sorting site to different operation processes based on experience after determining the total number of people in the sorting site, as in the related art. This improves the accuracy of the number of people allocated to different operation processes, thereby improving the efficiency of different operation processes.

[0075] Based on the above embodiments, the present application provides an information determination method, referring to Figure 3 As shown, the method may include the following steps:

[0076] Step 201: The information determination device determines the first sub-order quantity at each target moment in the first time based on the initial order quantity at each target moment, the revised order quantity at each target moment, the adjustment factor and the second total order quantity at the first time.

[0077] In an embodiment of the present application, the first time may refer to the time from the current hourly moment to 24:00 on the same day; the target time may refer to the hourly moment obtained with the current hourly moment as the starting point and a fixed time as an interval in the first time; the initial order quantity of the target time may refer to the order quantity obtained by predicting the order quantity of each target time based on the order quantity of each historical moment; the corrected order quantity of the target time may refer to the order quantity obtained after correcting the initial order quantity; the second total order quantity may refer to the sum of the initial order quantities of multiple target moments in the first time; for each target moment in the first time, the initial order quantity of the target moment, the corrected order quantity of the target moment and the adjustment factor may be calculated first, and the adjustment factor and the second total order quantity may be calculated, and then the first sub-order quantity of each target moment may be determined based on the results of the two calculations, so that it is convenient for the site operation management personnel to adjust the work situation of the personnel in real time.

[0078] In a feasible implementation, when the current hour (i.e. T) is 10:00 and the fixed time is 2 hours, the target time may refer to 12:00 (i.e. T+2), 14:00 (i.e. T+4), 16:00 (i.e. T+6), 18:00 (i.e. T+8), 20:00 (i.e. T+10), 22:00 (i.e. T+12) and 24:00 (i.e. T+14).

[0079] It should be noted that for the time that has occurred (that is, the time before the current hour), the actual statistical order quantity can be used, and the predicted order quantity in the future hourly dimension can be calculated once every hour. The work objects in the sorting area can generally accurately monitor the goods (i.e., goods corresponding to the order) that have arrived at the fence of the sorting site (i.e., the sorting area). However, in the case of large promotions, there will be more waiting situations, that is, there is a certain difference between the operation time for the goods to arrive at the sorting site and the time of arrival at the sorting site. This requires the correction of the time-sharing cargo volume in the recent time, especially T+2 hours; the related art cannot well predict the order volume for the future T+2 hours (the sorting processing time under normal circumstances), and confusion will occur for personnel arrangements for longer periods of time; however, in the embodiment of the present application, the initial order volume at each target time of different routes such as ferries, trunk and branch lines, and returns can be corrected. Specifically, the initial order volume actually arriving at the sorting center at each target time can be predicted and corrected based on influencing factors such as route length, historical punctuality, and road condition information; in addition, the corrected order volume at each target time can be adjusted based on the value calculated by dividing the cumulative order volume before the target time of the first time by the historical second total order volume, converting continuous data into real-time arrival volume in the hourly dimension.

[0080] It should be noted that step 201 can be implemented in the following ways:

[0081] Step 201A1: The information determination device determines, for each target moment, a first order quantity to be processed based on the initial order quantity, the first adjustment factor, the corrected order quantity, and the complementary adjustment factor.

[0082] The adjustment factor includes a first adjustment factor; and the complementary adjustment factor is complementary to the first adjustment factor.

[0083] In the embodiment of the present application, the first adjustment factor can be determined based on historical experimental data, and the first adjustment factor can be w t To express it, the complementary adjustment factor can be expressed as 1-w tTo express it; the initial order quantity at each target moment and the first adjustment factor can be multiplied to obtain a first result, and the corrected order quantity at each target moment and the complementary adjustment factor can be multiplied to obtain a second result, and then the first result and the second result are summed to obtain the first order quantity to be processed.

[0084] Step 201A2: The information determination device determines the second to-be-processed order quantity based on the second total order quantity and the second adjustment factor.

[0085] The adjustment factor includes a second adjustment factor.

[0086] In the embodiment of the present application, the second adjustment factor is determined by the cumulative order quantity before the target time of the first time and the second total order quantity in history. Specifically, the second adjustment factor can be obtained by dividing the cumulative order quantity before the target time and the second total order quantity in history, and the second adjustment factor can be calculated using p t The second total order quantity and the second adjustment factor can be multiplied to obtain the second to-be-processed order quantity. It should be noted that the historical second total order quantity may refer to the total order quantity within the historical time corresponding to the first time.

[0087] Step 201A3: The information determination device determines each first sub-order quantity based on the first order quantity to be processed and the second order quantity to be processed.

[0088] In an embodiment of the present application, for each target moment, the first to-be-processed order quantity and the second to-be-processed order quantity at each target moment can be compared, and the smaller order quantity of the first to-be-processed order quantity and the second to-be-processed order quantity at each target moment can be determined as the first sub-order quantity. Specifically, if at a certain target moment, the order quantity of the first to-be-processed order quantity is smaller, the first to-be-processed order quantity will be determined as the first sub-order quantity corresponding to the target moment; if at a certain target moment, the order quantity of the second to-be-processed order quantity is smaller, the second to-be-processed order quantity will be determined as the first sub-order quantity corresponding to the target moment; in this way, determining the smaller order quantity from the first to-be-processed order quantity and the second to-be-processed order quantity as the first sub-order quantity can meet the production capacity limit of the sorting area.

[0089] Step 202: The information determination device determines, for each second time, a second sub-order quantity at each second time based on the target order quantity prediction model and the historical order quantity corresponding to each historical time.

[0090] Among them, the second time is the time after the first time; the historical time and the second time have a corresponding relationship.

[0091] In an embodiment of the present application, the target order quantity prediction model may refer to a traditional time series model or a machine learning model that can perform predictions; the historical order quantity corresponding to each historical time may be input into multiple order quantity prediction models to obtain multiple predicted order quantities to be screened for each second time, and then a cross-validation strategy is used to determine the prediction result of the optimal prediction model (i.e., the target order quantity prediction model) from the multiple predicted order quantities to be screened as the second sub-order quantity for each time. In a feasible implementation method, the second time may refer to T+1 to T+30 days, and when the first time is December 1, 2023, the second time may be December 2 to December 31, 2023, and the historical time may be December 2 to December 31, 2022.

[0092] It should be noted that the above-mentioned times (i.e., the first time, the second time, and the historical time, etc.) refer to the lunar calendar dates during traditional holidays such as the New Year's Festival, and refer to the solar calendar dates for other dates.

[0093] It should be noted that step 202 can be implemented in the following ways:

[0094] Step 202B1: The information determination device determines the sub-historical order quantity of each cargo source in the sorting area within the target area for each historical time.

[0095] In an embodiment of the present application, the sorting area within the target area may include multiple sources of goods, such as the first source of goods, the second source of goods, and the third source of goods; the sub-historical order quantity may refer to the order quantity of each source of goods within the historical time; in a feasible implementation method, the first source of goods may be warehouse distribution, the second source of goods may be pure distribution, and the third source of goods may be incoming port.

[0096] Step 202B2: The information determination device determines the current order quantity of each source of goods based on the target order quantity prediction model and each sub-historical order quantity.

[0097] In an embodiment of the present application, the current order quantity may refer to the predicted order quantity obtained by using a target order quantity prediction model to predict the historical order quantity of historical time; for each source of goods, the sub-historical order quantity of the source of goods is input into the target order quantity prediction model to obtain the current order quantity of the source of goods.

[0098] It should be noted that step 202B2 can be implemented in the following ways:

[0099] Step 202b1: The information determination device determines the first sub-current order quantity of the first source of goods, the second sub-current order quantity of the second source of goods, and the third sub-current order quantity of the third source of goods based on the target order quantity prediction model and the first sub-historical order quantity of the first source of goods, the second sub-historical order quantity of the second source of goods, and the third sub-historical order quantity of the third source of goods.

[0100] Table 1

[0101] First sorting out of warehouse Single warehouse 2022 / 5 / 1 2022 / 5 / 2 2022 / 5 / 3 Chifeng Sorting Center Chifeng Local Warehouse No. 1 2764 2542 3029 Dalian bulk cargo sorting center Dalian FDC Warehouse A1 8985 8533 9105 Dalian bulk cargo sorting center Dalian FDC Warehouse B1 18460 17717 20289 Dalian FDC warehouse virtual receiving warehouse Dalian FDC Warehouse B2 0 0 0 Dalian bulk cargo sorting center Dalian Fresh Food Warehouse No. 1 804 764 863 … … … … …

[0102] Table 2

[0103]

[0104] In an embodiment of the present application, the first sub-historical order quantity of the first source of goods can be input into the target order quantity prediction model to obtain the first sub-current equivalent of the first source of goods, and the second sub-historical order quantity of the second source of goods can be input into the target order quantity prediction model to obtain the second sub-current equivalent of the second source of goods, and the third sub-historical order quantity of the third source of goods can be input into the target order quantity prediction model to obtain the third sub-current equivalent of the third source of goods.

[0105] In a feasible implementation, when the target area is the Northeast, the order quantity of each single warehouse is shown in Table 1 above; and after obtaining the order quantity of each single warehouse, the correspondence between the warehouse and the sorting center can be used to match the warehouse and the sorting center to obtain the first sub-current order quantity of each sorting center in the Northeast region (that is, the warehouse distribution order quantity of all sorting centers in the Northeast region), as shown in Table 2 above.

[0106] Step 202b2: The information determination device determines the third total order quantity of the second source of goods and the third source of goods based on the first historical predicted order quantity corresponding to the historical time, the second historical predicted order quantity corresponding to the target historical time, the historical actual order quantity corresponding to the historical time, the third adjustment factor, the second sub-current order quantity, and the third sub-current order quantity.

[0107] Among them, a second complementary adjustment factor related to the third adjustment factor can be determined based on the third adjustment factor, and when the third adjustment factor is represented by w, the second complementary adjustment factor can be represented by 1-w; the first historical predicted order quantity can refer to the predicted order quantity within the historical time, the historical actual order quantity refers to the actual order quantity arrived within the historical time, and the second historical predicted order quantity can refer to the predicted order quantity multiple days before the historical time.

[0108] In the embodiment of the present application, the second sub-current order quantity and the third sub-current order quantity may be first summed to obtain the current total order quantity. Then, the first historical predicted order quantity, the second historical predicted order quantity corresponding to the target historical time, the historical actual order quantity corresponding to the historical time, the third adjustment factor, the second complementary adjustment factor, and the third current total order quantity may be calculated to obtain the third total order quantity. Specifically, the calculation may be as follows: In this way, the combination of adjusting the proportion parameter of increasing the target historical time and the result of model prediction improves the accuracy of the prediction; in a feasible implementation, the target historical time can be nearly 7 days of historical time.

[0109] Step 202b3: The information determination device determines the second sub-current order quantity and the third sub-current order quantity based on the third total order quantity and the proportion parameter.

[0110] In this embodiment of the present application, the proportion parameter can be derived based on historical data and refers to the ratio between the order volume of the second source and the order volume of the third source. The second sub-current order volume can refer to the order volume corresponding to the second source, and the third sub-current order volume can refer to the order volume corresponding to the third source. Because the proportion of the order volume of the second source and the order volume of the third source in the sorting area within the target zone is relatively stable, the third total order volume can be directly decomposed based on the proportion parameter to obtain the second sub-current order volume and the third sub-current order volume.

[0111] In a feasible implementation, when the target area is the Northeast, the second sub-current order quantity obtained by disassembly is shown in Table 3 below, and the third sub-current order quantity obtained by disassembly is shown in Table 4 below.

[0112] It should be noted that not all sorting centers can accept cargo from other areas (i.e., inbound orders). Therefore, it is relatively simpler to decompose the inbound order quantity (i.e., the third sub-current order quantity) based on the third total order quantity and proportion parameters than to decompose the pure distribution order quantity (i.e., the second sub-current order quantity). Therefore, the inbound order quantity can be decomposed based on the third total order quantity and proportion parameters first.

[0113] Table 3

[0114] Total number of orders collected 213709 226987 246411 Collection and first sorting 2022 / 5 / 1 2022 / 5 / 2 2022 / 5 / 3 Shenyang Hunnan Sorting Center 90356 95970 104183 Shenyang Shenbei Cargo Collection and Sorting Center 855 908 986 Shenyang Wensu Cargo Collection and Sorting Center 5108 5425 5889 Dalian bulk cargo sorting center 15857 16842 18284 Dalian Ganjingzi Sorting Center 23316 24764 26883 Jinzhou Sorting Center 5044 5357 5815 Panjin Sorting Center 3312 3518 3819 Chifeng Sorting Center 5236 5561 6037 Tongliao Sorting Center 3526 3745 4066 Harbin Sorting Center 24534 26058 28288 Qiqihar Sorting Center 2201 2338 2538 Jiamusi Sorting Center 1539 1634 1774 Mudanjiang Sorting Center 1111 1180 1281 Changchun Sorting Center 39515 41970 45561 Tonghua sorting center 3163 3359 3647 Changchun Cargo Collection and Sorting Center 23166 24605 26711

[0115] Table 4

[0116] Inbound orders 289150 303480 346652 First sorting at the port 2022 / 5 / 1 2022 / 5 / 2 2022 / 5 / 3 Shenyang Hunnan Sorting Center 214549 225182 257216 Shenyang Shenbei Cargo Collection and Sorting Center 0 0 0 Shenyang Wensu Cargo Collection and Sorting Center 9368 9833 11232 Dalian bulk cargo sorting center 6188 6494 7418 Dalian Ganjingzi Sorting Center 0 0 0 Jinzhou Sorting Center 4077 4279 4888 Panjin Sorting Center 0 0 0 Chifeng Sorting Center 2226 2337 2669 Tongliao Sorting Center 0 0 0 Harbin Sorting Center 35594 37358 42673 Qiqihar Sorting Center 0 0 0 Jiamusi Sorting Center 0 0 0 Mudanjiang Sorting Center 0 0 0 Changchun Sorting Center 28539 29953 34215 Tonghua sorting center 0 0 0 Changchun Cargo Collection and Sorting Center 0 0 0

[0117] Step 202B3: The information determination device determines the second sub-order quantity based on multiple current order quantities.

[0118] In an embodiment of the present application, multiple current order quantities (i.e., the first sub-current order quantity, the second sub-current order quantity, and the third sub-current order quantity) can be summed to obtain the second sub-order quantity.

[0119] Step 203: The information determination device determines the first order quantity based on multiple first sub-order quantities and second sub-order quantities.

[0120] In the embodiment of the present application, a plurality of first sub-unit quantities and second sub-unit quantities may be summed to obtain a first unit quantity.

[0121] Step 204: The information determination device determines a second order quantity based on the first order quantity, the flow order quantity flowing between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area.

[0122] In an embodiment of the present application, the flow order quantity between different sorting areas within the target area and the sorting order quantity of the target sorting area within the target area can be calculated to obtain a calculation result, and then the first order quantity and the calculation result are calculated to obtain a second order quantity.

[0123] It should be noted that step 204 can be implemented in the following ways:

[0124] Step 204C1: The information determination device determines a first ratio based on the first flow order quantity and the first sorting order quantity.

[0125] Among them, the first circulation order quantity represents the order quantity of orders interacting with the same province in the target area; the circulation order quantity includes the first circulation order quantity, and the sorting order quantity includes the first sorting order quantity.

[0126] In this embodiment of the present application, the first transfer order volume may refer to the sum of multiple target transfer order volumes from the target sorting area in the same province to other sorting areas within the target area. The first sorting order volume may refer to the total order volume of the target sorting area in the same province. The first ratio (i.e., the intra-provincial branch line ratio) represents the intra-provincial branch line ratio of each sorting area. The first ratio is obtained by dividing the first transfer order volume and the first sorting order volume. In a feasible implementation, the target sorting area may refer to a first-level sorting area, and the multiple target transfer order volumes may refer to the transfer order volumes of the top three in terms of cargo volume.

[0127] Step 204C2: The information determination device determines a second ratio based on the second flow order quantity and the second sorting order quantity.

[0128] Among them, the first circulation order quantity represents the order quantity of orders interacting with different provinces in the target area; the circulation order quantity includes the second circulation order quantity, and the sorting order quantity includes the second sorting order quantity.

[0129] In an embodiment of the present application, the second flow order quantity may refer to the sum of multiple target flow order quantities from target sorting areas in different provinces to other sorting areas within the target area, and the second sorting order quantity may refer to the total order quantity of target sorting areas in different provinces; the second ratio (i.e., the out-of-province branch line ratio) represents the out-of-province branch line ratio of each sorting area; the second flow order quantity and the second sorting order quantity are divided to obtain the second ratio.

[0130] Step 204C3: The information determination device determines the second order quantity based on the first ratio, the second ratio and the first order quantity.

[0131] In an embodiment of the present application, the first ratio and the first unit quantity can be multiplied to obtain a unit quantity, and the second ratio and the first unit quantity can be multiplied to obtain a unit quantity, and then the two calculated unit quantities are summed to obtain the second unit quantity.

[0132] Table 5

[0133]

[0134] In a feasible implementation, when the target area is the Northeast, the branch line ratio (ie, the first ratio and the second ratio) of each sorting center in the Northeast is determined as shown in Table 5 above.

[0135] Step 205: The information determination device determines a first total order quantity of the target area based on the multiple first order quantities and the multiple second order quantities.

[0136] It should be noted that the main operating unit of the sorting area is the number of packages. Therefore, it is necessary to multiply the first total order quantity by the package and order quantity conversion ratio to finally obtain the total number of packages in the target area.

[0137] Step 206: The information determination device determines the total daily output efficiency of each sorting area based on the historical daily output efficiency of the work objects corresponding to each sorting area.

[0138] Among them, daily output efficiency refers to the daily work efficiency of the work object.

[0139] It should be noted that after step 206, steps 207 to 209 may be performed, or steps 210 to 212 may be performed:

[0140] Step 207: The information determination device determines the total daily order quantity of each sorting area based on the first total order quantity.

[0141] In the embodiment of the present application, the first total order quantity can be decomposed to obtain the total daily order quantity of each sorting area.

[0142] Step 208: The information determination device calculates the total daily order quantity and the total daily output efficiency to obtain the total daily quantity of work objects corresponding to each sorting area.

[0143] In the embodiment of the present application, for each sorting area, the total daily order quantity of each sorting area and the total daily output efficiency of each sorting area can be divided to obtain the total daily number of work objects corresponding to each sorting area.

[0144] Step 209: The information determination device determines the daily quantity corresponding to each sub-sorting area based on the historical daily quantity ratio and the total daily quantity of work objects corresponding to each sub-sorting area for each sorting area.

[0145] In the embodiment of the present application, the daily total quantity can be decomposed using the historical daily quantity ratio of the work objects corresponding to each sub-sorting area to obtain the daily quantity corresponding to each sub-sorting area.

[0146] Step 210: The information determination device determines a target order quantity for each sub-sorting area based on the first total order quantity and the order quantity ratio of each sub-sorting area.

[0147] In the embodiment of the present application, the first total order quantity can be decomposed using the order quantity ratio of each sub-sorting area to obtain the target order quantity of each sub-sorting area.

[0148] In a feasible implementation, taking the sorting and loading (such as the unloading area) as an example, the entire cargo volume will definitely enter the sorting and loading link, but the cargo volume in the unloading area will change over time. The full-day cargo volume of the sorting and loading link displayed in the hourly dimension is as follows: Figure 4 As shown, and Figure 4 The data in the table are shown in the following table 6:

[0149] Table 6

[0150]

[0151] It should be noted that the operation of each grid (i.e., sub-sorting area) is subject to capacity constraints, and capacity varies across sorting sites and grids. The actual operational adjustment is the actual volume of cargo entering the grid's operations based on the above forecasting method and capacity constraints, i.e., the adjusted hourly forecast (i.e., the corrected order volume at each target time). Capacity adjustments are based on "peak shaving" within the time period. Volume currently exceeding hourly capacity is held back until capacity is met.

[0152] Step 211: The information determination device determines the target output efficiency of each sub-sorting area based on the total daily output efficiency and the output efficiency ratio of each sub-sorting area.

[0153] In the embodiment of the present application, the total daily output efficiency can be decomposed using the output efficiency ratio of each sub-sorting area to obtain the target output efficiency of each sub-sorting area.

[0154] Step 212: The information determination device determines the daily quantity of work objects corresponding to each sub-sorting area based on the target order quantity and target output efficiency.

[0155] In the embodiment of the present application, a division operation may be performed on the target order quantity and the target output efficiency to obtain the daily number of work objects corresponding to each sub-sorting area.

[0156] It should be noted that, for the description of the same steps and contents in this embodiment as those in other embodiments, reference can be made to the description in other embodiments and will not be repeated here.

[0157] The information determination method provided in the embodiment of the present application can first divide each sorting area according to the operation type of the order, and then directly calculate the daily number of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency. This is instead of requiring the site manager to allocate the total number of people in the sorting site to different operation processes based on experience after determining the total number of people in the sorting site, as in the related art. This improves the accuracy of the number of people allocated to different operation processes, thereby improving the efficiency of different operation processes.

[0158] Based on the above embodiments, the present invention provides an information determination device, which can be applied to Figure 1 and Figure 3 In the information determination method provided in the corresponding embodiment, refer to Figure 5 As shown, the information determination device 3 may include: a processing unit 31, a merging unit 32, an acquisition unit 33 and a determination unit 34, wherein:

[0159] The processing unit 31 is configured to determine a first order quantity and a second order quantity of orders for each sorting area in the target area; wherein the first order quantity represents the order quantity of orders interacting with the outside of the target area, and the second order quantity represents the order quantity of orders interacting with the inside of the target area;

[0160] a merging unit 32, configured to determine a first total order quantity in a target area based on the plurality of first order quantities and the plurality of second order quantities;

[0161] An acquisition unit 33 is configured to determine the total daily output efficiency of each sorting area based on the historical daily output efficiency of the work objects corresponding to each sorting area; wherein the daily output efficiency refers to the daily work efficiency of the work objects;

[0162] The determining unit 34 is configured to determine a daily quantity of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency; wherein the sub-sorting area is an area corresponding to each operation type of the order.

[0163] In other embodiments of the present application, the processing unit 31 is further configured to perform the following steps:

[0164] For each target moment in the first time period, determine a first sub-order quantity for each target moment based on the initial order quantity for each target moment, the revised order quantity for each target moment, the adjustment factor, and the second total order quantity for the first time period;

[0165] For each second time, based on the target order quantity prediction model and the historical order quantity corresponding to each historical time, determine a second sub-order quantity for each second time; wherein the second time is a time after the first time; and the historical time and the second time have a corresponding relationship;

[0166] Determining a first order quantity based on the plurality of first sub-order quantities and the second sub-order quantities;

[0167] The second order quantity is determined based on the first order quantity, the circulation order quantity circulating between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area.

[0168] In other embodiments of the present application, the processing unit 31 is further configured to perform the following steps:

[0169] For each target time, determining a first to-be-processed order quantity based on the initial order quantity, the first adjustment factor, the revised order quantity, and the complementary adjustment factor; wherein the adjustment factor includes the first adjustment factor; and the complementary adjustment factor is complementary to the first adjustment factor;

[0170] Determining a second to-be-processed order quantity based on the second total order quantity and the second adjustment factor; wherein the adjustment factor includes the second adjustment factor;

[0171] Based on the first to-be-processed order quantity and the second to-be-processed order quantity, each first sub-order quantity is determined.

[0172] In other embodiments of the present application, the processing unit 31 is further configured to perform the following steps:

[0173] For each historical time, determine the sub-historical order quantity of each cargo source in the sorting area within the target area;

[0174] Determine the current order quantity for each source of goods based on the target order quantity forecast model and each sub-historical order quantity;

[0175] Based on multiple current order quantities, a second sub-order quantity is determined.

[0176] In other embodiments of the present application, the processing unit 31 is further configured to perform the following steps:

[0177] Determine a first sub-current order quantity of the first cargo source, a second sub-current order quantity of the second cargo source, and a third sub-current order quantity of the third cargo source based on the target order quantity prediction model and the first sub-historical order quantity of the first cargo source, the second sub-historical order quantity of the second cargo source, and the third sub-historical order quantity of the third cargo source;

[0178] Determine a third total order quantity for the second and third goods sources based on a first historical forecast order quantity corresponding to the historical time, a second historical forecast order quantity corresponding to the target historical time, the historical actual order quantity corresponding to the target historical time, a third adjustment factor, the second sub-current order quantity, and the third sub-current order quantity.

[0179] Based on the third total order quantity and proportion parameter, the second sub-current order quantity and the third sub-current order quantity are determined.

[0180] In other embodiments of the present application, the processing unit 31 is further configured to perform the following steps:

[0181] Determining a first ratio based on a first circulating order quantity and a first sorting order quantity; wherein the first circulating order quantity represents the quantity of orders interacting with the same province in the target area; the circulating order quantity includes the first circulating order quantity, and the sorting order quantity includes the first sorting order quantity;

[0182] Determining a second ratio based on the second circulating order quantity and the second sorting order quantity; wherein the first circulating order quantity represents the quantity of orders interacting with different provinces in the target area; the circulating order quantity includes the second circulating order quantity, and the sorting order quantity includes the second sorting order quantity;

[0183] Based on the first ratio, the second ratio, and the first order quantity, a second order quantity is determined.

[0184] In other embodiments of the present application, the determining unit 34 is further configured to perform the following steps:

[0185] Determine the total daily order quantity for each sorting area based on the first total order quantity;

[0186] Calculate the total daily order quantity and total daily output efficiency to obtain the total daily number of work objects corresponding to each sorting area;

[0187] For each sorting area, the daily quantity corresponding to each sub-sorting area is determined based on the historical daily quantity ratio and the total daily quantity of work objects corresponding to each sub-sorting area.

[0188] In other embodiments of the present application, the determining unit 34 is further configured to perform the following steps:

[0189] Determining a target order quantity for each sub-sorting area based on the first total order quantity and the order quantity ratio of each sub-sorting area;

[0190] Determine the target output efficiency of each sub-sorting area based on the total daily output efficiency and the output efficiency ratio of each sub-sorting area;

[0191] Based on the target order quantity and target output efficiency, determine the daily number of work objects corresponding to each sub-sorting area.

[0192] It should be noted that the specific implementation process of the steps performed by each module in the embodiment of the present application can be referred to Figure 1 and Figure 3 The implementation process of the information determination method provided in the corresponding embodiment will not be repeated here.

[0193] The information determination device provided in the embodiments of the present application can first divide each sorting area according to the operation type of the order, and then directly calculate the daily number of work objects corresponding to each sub-sorting area based on the first total order volume and the total daily output efficiency. This is instead of requiring the site manager to allocate the total number of people in the sorting site to different operation processes based on experience after determining the total number of people in the sorting site, as in the related art. This improves the accuracy of the number of people allocated to different operation processes, thereby improving the efficiency of different operation processes.

[0194] Based on the above embodiments, the embodiments of the present application provide an information determination device, which can be applied to Figure 1 and Figure 3 In the information determination method provided in the corresponding embodiment, refer to Figure 6 As shown, the information determination device 4 may include: a processor 41, a memory 42 and a communication bus 43, wherein:

[0195] The communication bus 43 is used to realize the communication connection between the processor 41 and the memory 42;

[0196] The processor 41 is configured to execute the information determination program in the memory 42 to implement the following steps:

[0197] Determine a first order quantity and a second order quantity for each sorting area of ​​the target area; wherein the first order quantity represents the order quantity of orders interacting with areas outside the target area, and the second order quantity represents the order quantity of orders interacting with areas within the target area;

[0198] Determining a first total order volume in the target area based on the plurality of first order volumes and the plurality of second order volumes;

[0199] Determine the total daily output efficiency of each sorting area based on the historical daily output efficiency of the work objects corresponding to each sorting area; where daily output efficiency refers to the daily work efficiency of the work objects;

[0200] Based on the first total order quantity and the total daily output efficiency, a daily quantity of work objects corresponding to each sub-sorting area is determined; wherein the sub-sorting area is an area corresponding to each operation type of the order.

[0201] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the first order quantity and the second order quantity of the order for each sorting area in the target area, so as to implement the following steps:

[0202] For each target moment in the first time period, determine a first sub-order quantity for each target moment based on the initial order quantity for each target moment, the revised order quantity for each target moment, the adjustment factor, and the second total order quantity for the first time period;

[0203] For each second time, based on the target order quantity prediction model and the historical order quantity corresponding to each historical time, determine a second sub-order quantity for each second time; wherein the second time is a time after the first time; and the historical time and the second time have a corresponding relationship;

[0204] Determining a first order quantity based on the plurality of first sub-order quantities and the second sub-order quantities;

[0205] The second order quantity is determined based on the first order quantity, the circulation order quantity circulating between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area.

[0206] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the first sub-order quantity at each target moment based on the initial order quantity at each target moment, the revised order quantity at each target moment, the adjustment factor, and the second total order quantity at the first time, so as to implement the following steps:

[0207] For each target time, determining a first to-be-processed order quantity based on the initial order quantity, the first adjustment factor, the revised order quantity, and the complementary adjustment factor; wherein the adjustment factor includes the first adjustment factor; and the complementary adjustment factor is complementary to the first adjustment factor;

[0208] Determining a second to-be-processed order quantity based on the second total order quantity and the second adjustment factor; wherein the adjustment factor includes the second adjustment factor;

[0209] Based on the first to-be-processed order quantity and the second to-be-processed order quantity, each first sub-order quantity is determined.

[0210] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the second sub-order quantity at each second time based on the target order quantity prediction model and the historical order quantity corresponding to each historical time, so as to implement the following steps:

[0211] For each historical time, determine the sub-historical order quantity of each cargo source in the sorting area within the target area;

[0212] Determine the current order quantity for each source of goods based on the target order quantity forecast model and each sub-historical order quantity;

[0213] Based on multiple current order quantities, a second sub-order quantity is determined.

[0214] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the current order quantity of each source of goods based on the target order quantity prediction model and each sub-historical order quantity, so as to implement the following steps:

[0215] Determine a first sub-current order quantity of the first cargo source, a second sub-current order quantity of the second cargo source, and a third sub-current order quantity of the third cargo source based on the target order quantity prediction model and the first sub-historical order quantity of the first cargo source, the second sub-historical order quantity of the second cargo source, and the third sub-historical order quantity of the third cargo source;

[0216] Determine a third total order quantity for the second and third goods sources based on a first historical forecast order quantity corresponding to the historical time, a second historical forecast order quantity corresponding to the target historical time, the historical actual order quantity corresponding to the target historical time, a third adjustment factor, the second sub-current order quantity, and the third sub-current order quantity.

[0217] Based on the third total order quantity and proportion parameter, the second sub-current order quantity and the third sub-current order quantity are determined.

[0218] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the second order quantity based on the first order quantity, the flow order quantity flowing between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area, so as to implement the following steps:

[0219] Determining a first ratio based on a first circulating order quantity and a first sorting order quantity; wherein the first circulating order quantity represents the quantity of orders interacting with the same province in the target area; the circulating order quantity includes the first circulating order quantity, and the sorting order quantity includes the first sorting order quantity;

[0220] Determining a second ratio based on the second circulating order quantity and the second sorting order quantity; wherein the first circulating order quantity represents the quantity of orders interacting with different provinces in the target area; the circulating order quantity includes the second circulating order quantity, and the sorting order quantity includes the second sorting order quantity;

[0221] Based on the first ratio, the second ratio, and the first order quantity, a second order quantity is determined.

[0222] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the daily quantity of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency, so as to implement the following steps:

[0223] Determine the total daily order quantity for each sorting area based on the first total order quantity;

[0224] Calculate the total daily order quantity and total daily output efficiency to obtain the total daily number of work objects corresponding to each sorting area;

[0225] For each sorting area, the daily quantity corresponding to each sub-sorting area is determined based on the historical daily quantity ratio and the total daily quantity of work objects corresponding to each sub-sorting area.

[0226] In other embodiments of the present application, the processor 41 is configured to execute the information determination program in the memory 42 to determine the daily quantity of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency, so as to implement the following steps:

[0227] Determining a target order quantity for each sub-sorting area based on the first total order quantity and the order quantity ratio of each sub-sorting area;

[0228] Determine the target output efficiency of each sub-sorting area based on the total daily output efficiency and the output efficiency ratio of each sub-sorting area;

[0229] Based on the target order quantity and target output efficiency, determine the daily number of work objects corresponding to each sub-sorting area.

[0230] It should be noted that the specific description of the steps performed by the processor can be referred to Figure 1 and Figure 3 The implementation process of the information determination method provided in the corresponding embodiment will not be repeated here.

[0231] The information determination device provided in the embodiment of the present application can first divide each sorting area according to the operation type of the order, and then directly calculate the daily number of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency. This is instead of requiring the site manager to allocate the total number of people in the sorting site to different operation processes based on experience after determining the total number of people in the sorting site as in the related art. This improves the accuracy of the number of people allocated to different operation processes, thereby improving the efficiency of different operation processes.

[0232] Based on the above embodiments, the present invention provides a computer-readable storage medium, which stores one or more programs, which can be executed by one or more processors to implement Figure 1and Figure 3 The corresponding embodiments provide steps in the information determination method.

[0233] It should be noted that the above-mentioned computer-readable storage medium can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface storage, an optical disc, or a compact disc read-only memory (CD-ROM); it can also be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0234] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0235] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0236] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0237] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0238] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0239] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0240] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for determining information, characterized in that: The method comprises: Determine a first order quantity and a second order quantity of orders for each sorting area of ​​the target area; wherein the first order quantity represents the order quantity of orders interacting with areas outside the target area, and the second order quantity represents the order quantity of orders interacting with areas within the target area; determining a first total order volume of the target area based on a plurality of the first order volumes and a plurality of the second order volumes; Determining the total daily output efficiency of each sorting area based on the historical daily output efficiency of the work objects corresponding to each sorting area; wherein the daily output efficiency refers to the daily work efficiency of the work objects; Based on the first total order quantity and the total daily output efficiency, a daily quantity of work objects corresponding to each sub-sorting area is determined; wherein the sub-sorting area is an area corresponding to each operation type of the order.

2. The method according to claim 1, characterized in that The determining of the first order quantity and the second order quantity of the orders in each sorting area of ​​the target zone includes: For each target moment in the first time period, determining a first sub-order quantity for each target moment based on the initial order quantity for each target moment, the revised order quantity for each target moment, the adjustment factor, and the second total order quantity for the first time period; For each second time, determining a second sub-order quantity for each second time based on the target order quantity prediction model and the historical order quantity corresponding to each historical time; wherein the second time is a time after the first time; and the historical time and the second time have a corresponding relationship; determining the first order quantity based on a plurality of the first sub-order quantities and the second sub-order quantities; The second order quantity is determined based on the first order quantity, the circulation order quantity circulating between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area.

3. The method according to claim 2, characterized in that The determining the first sub-order quantity at each moment based on the initial order quantity at each target moment, the revised order quantity at each target moment, the adjustment factor, and the second total order quantity at the first time includes: For each target moment, determining a first to-be-processed order quantity based on the initial order quantity, the first adjustment factor, the revised order quantity, and a complementary adjustment factor; wherein the adjustment factor includes the first adjustment factor; and the complementary adjustment factor is complementary to the first adjustment factor; Determining a second to-be-processed order quantity based on the second total order quantity and the second adjustment factor; wherein the adjustment factor includes the second adjustment factor; Based on the first to-be-processed order quantity and the second to-be-processed order quantity, each first sub-order quantity is determined.

4. The method according to claim 2, characterized in that The determining the second sub-order quantity at each second time based on the target order quantity prediction model and the historical order quantity corresponding to each historical time includes: For each historical time, determining the sub-historical order quantity of each cargo source in the sorting area within the target area; Determining the current order quantity of each of the goods sources based on the target order quantity prediction model and each of the sub-historical order quantities; Based on a plurality of the current order quantities, the second sub-order quantity is determined.

5. The method according to claim 4, characterized in that The determining, based on the target order quantity prediction model and each of the sub-historical order quantities, of the current order quantity of each of the goods sources includes: Determine a first sub-current order quantity of the first cargo source, a second sub-current order quantity of the second cargo source, and a third sub-current order quantity of the third cargo source based on the target order quantity prediction model and a first sub-historical order quantity of the first cargo source, a second sub-historical order quantity of the second cargo source, and a third sub-historical order quantity of the third cargo source; Determine a third total order quantity for the second cargo source and the third cargo source based on a first historical predicted order quantity corresponding to the historical time, a second historical predicted order quantity corresponding to the target historical time, a historical actual order quantity corresponding to the target historical time, a third adjustment factor, the second sub-current order quantity, and the third sub-current order quantity; Based on the third total order quantity and proportion parameter, the second sub-current order quantity and the third sub-current order quantity are determined.

6. The method according to claim 2, characterized in that The determining the second order quantity based on the first order quantity, the flow order quantity flowing between different sorting areas within the target area, and the sorting order quantity of the target sorting area within the target area includes: Determining a first ratio based on a first circulating order volume and a first sorting order volume; wherein the first circulating order volume represents the number of orders interacting with the same province within the target area; the circulating order volume includes the first circulating order volume, and the sorting order volume includes the first sorting order volume; Determining a second ratio based on a second circulating order volume and a second sorting order volume; wherein the first circulating order volume represents the volume of orders interacting with different provinces within the target area; the circulating order volume includes the second circulating order volume, and the sorting order volume includes the second sorting order volume; The second order quantity is determined based on the first ratio, the second ratio, and the first order quantity.

7. The method according to claim 1, characterized in that The determining, based on the first total order quantity and the total daily output efficiency, the daily quantity of work objects corresponding to each sub-sorting area includes: Determining a total daily order quantity for each sorting area based on the first total order quantity; Calculating the total daily order quantity and the total daily output efficiency to obtain the total daily quantity of work objects corresponding to each sorting area; For each of the sorting areas, the daily quantity corresponding to each sub-sorting area is determined based on a historical daily quantity ratio of work objects corresponding to each sub-sorting area and the daily total quantity.

8. The method according to claim 1, characterized in that The determining, based on the first total order quantity and the total daily output efficiency, the daily quantity of work objects corresponding to each sub-sorting area includes: Determining a target order quantity for each sub-sorting area based on the first total order quantity and the order quantity ratio of each sub-sorting area; Determining a target output efficiency for each sub-sorting area based on the total daily output efficiency and the output efficiency ratio of each sub-sorting area; Based on the target order quantity and the target output efficiency, a daily quantity of work objects corresponding to each sub-sorting area is determined.

9. An information determination device, characterized in that: The device comprises: a processing unit, configured to determine a first order quantity and a second order quantity of orders for each sorting area of ​​a target area; wherein the first order quantity represents the order quantity of orders interacting with areas outside the target area, and the second order quantity represents the order quantity of orders interacting with areas within the target area; a merging unit, configured to determine a first total order quantity of the target area based on a plurality of the first order quantities and a plurality of the second order quantities; an acquiring unit, configured to determine a total daily output efficiency of each sorting area based on a historical daily output efficiency of the work objects corresponding to each sorting area; wherein the daily output efficiency refers to the daily work efficiency of the work objects; a determining unit, configured to determine a daily quantity of work objects corresponding to each sub-sorting area based on the first total order quantity and the total daily output efficiency; wherein the sub-sorting area is an area corresponding to each operation type of the order.

10. An information determination device, characterized in that The device includes: a processor, a memory and a communication bus; The communication bus is used to realize the communication connection between the processor and the memory; The processor is configured to execute the information determination program stored in the memory to implement the steps of the information determination method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the information determination method according to any one of claims 1 to 8.

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