Hybrid scenario train configuration method, device, computer equipment and storage medium

Abstract

The present application relates to a method, device, computer equipment and storage medium for configuring train schedules in a mixed scenario. The method includes: obtaining schedule data, ticket routing data, ticket information data and special customer list data; determining a schedule base table according to the schedule data; obtaining first ticket preprocessing data according to the ticket routing data, ticket information data and special customer list data; determining special customer direct train schedules and special customer direct load capacity according to target data with customer labels as special customers in the first ticket preprocessing data; determining hub point direct invoices according to the special customer direct load capacity and the second ticket preprocessing data obtained from the first ticket preprocessing data; determining tickets for each schedule according to hub point direct invoices and the schedule base table; and determining the corresponding hub point direct train schedules according to the schedule labels based on the tickets for each schedule. The use of this method can improve the efficiency and accuracy of train schedule configuration in mixed scenarios.

Description

Technical Field

[0001] The present application relates to the field of logistics and transportation technology, and in particular to a method, device, computer equipment and storage medium for configuring train schedules in mixed scenarios. Background Art

[0002] In the logistics and transportation industry, with the continuous growth of business volume, customers' timeliness requirements for product transportation are becoming more and more diversified, and the number of pieces with different timeliness requirements is also increasing. In order to ensure brand effect and save costs, it is necessary to carry out differentiated transportation for products with different scenarios and different timeliness requirements. The identification of direct shipment is the basis for achieving differentiated transportation, but the identification of direct shipment currently has the following difficulties. First, there are generally customers with specific transportation needs. Such customers have characteristics such as large quantity, considerable value and random address distribution, which can be collectively referred to as special customers. Special customers are usually cross-mixed with the operating areas of the logistics company's own hubs. If all the tickets of special customers are transferred to the hub for shipment, there will be problems such as high shipping pressure at the hub, many transfer links, high costs and small win-win space for both parties. If all the tickets of special customers are directly shipped at the special customers, it is not refined enough and will cause waste of resources. Therefore, in this mixed scenario of special customers and hubs, it is necessary to distinguish and calculate the direct shipment of special customers and the direct shipment of hubs. Secondly, for direct shipments from hubs, since hubs have different departure schedules, the volume of each schedule is different, and the proportion of fast and slow time-sensitive tickets is also different. Therefore, in the mixed scenario of special customers and hubs, how to reasonably allocate special customer direct departures and hub direct departures is a problem worthy of attention.

[0003] At present, simple estimates of direct trains for special customers and direct trains from hubs are usually made manually based on the statistical values ​​of tickets, which requires a lot of manpower and material resources and a long estimation time. In addition, due to the limitations of manual experience, the estimation results have high errors, resulting in low efficiency and accuracy in train allocation. Summary of the invention

[0004] Based on this, it is necessary to provide a train configuration method, device, computer equipment and storage medium for hybrid scenarios that can improve the efficiency and accuracy of train configuration in hybrid scenarios in response to the above technical problems.

[0005] A mixed scenario train configuration method, the method comprising:

[0006] Obtain shift data, ticket routing data, ticket information data and special customer list data;

[0007] Determine the shift time and shift label corresponding to each shift according to the shift data, and obtain a corresponding shift base table according to the shift time and the shift label;

[0008] Obtaining first ticket preprocessing data according to the ticket routing data, the ticket information data and the special customer list data;

[0009] Performing variable integration on the first ticket preprocessing data to obtain second ticket preprocessing data;

[0010] Extracting target data with a customer tag of a special customer from the first ticket preprocessing data, and determining a direct train number and a direct load capacity for the special customer according to the target data;

[0011] Determining the hub point direct delivery of documents according to the direct delivery load of the special customer and the second document pre-processing data;

[0012] Determine the tickets for each flight according to the direct invoices from the hub point and the flight base table;

[0013] According to the tickets of each flight, the direct train number from the hub point within the corresponding flight is determined according to the direct train number determination method corresponding to the corresponding flight label.

[0014] Direct departures from hubs within the schedule.

[0015] In one embodiment, determining the shift time and shift label corresponding to each shift according to the shift data includes:

[0016] According to the initial start time and initial cut-off time of each shift in the shift data, the initial cut-off time is processed to obtain the corresponding shift end time;

[0017] The end time of the previous shift is used as the start time of the current shift;

[0018] Get the corresponding shift time according to the start time and end time of each shift;

[0019] grouping the shifts according to their shift start times;

[0020] The shifts are sorted according to the shift start time within each shift group, and the shift label of each shift is determined according to the sorting.

[0021] In one embodiment, obtaining the first ticket pre-processing data according to the ticket routing data, the ticket information data and the special customer list data includes:

[0022] Determine the warehouse entry time of each ticket according to the ticket routing data;

[0023] Extracting a target variable corresponding to each ticket from the ticket information data;

[0024] Determine the customer label corresponding to each ticket according to the special customer list data;

[0025] The first ticket preprocessing data is obtained according to the warehouse entry time, target variable and customer label corresponding to each of the tickets.

[0026] In one embodiment, the step of performing variable integration on the first ticket pre-processing data to obtain the second ticket pre-processing data includes:

[0027] Slicing the tickets in the first ticket preprocessing data according to the warehouse entry time to obtain a plurality of slice units;

[0028] Summarize and analyze the weight of each ticket in each slicing unit to obtain the total weight of the corresponding ticket;

[0029] The total weight of the tickets of each slice unit is accumulated and calculated in time sequence to obtain the accumulated ticket weight corresponding to each slice unit;

[0030] The second ticket preprocessing data is obtained according to the first ticket preprocessing data, the total ticket weight corresponding to each slicing unit and the accumulated ticket weight.

[0031] In one embodiment, the step of determining the hub point direct delivery of documents according to the direct delivery load of the special customer and the second document pre-processing data includes:

[0032] Determine, from the second ticket preprocessing data, a target slice unit whose cumulative ticket weight is greater than or equal to the direct shipping weight for the special customer;

[0033] Extracting the entry time corresponding to the earliest ticket entry time from the target slice unit;

[0034] The tickets in the second ticket pre-processing data whose warehousing time is greater than the extracted warehousing time are determined as hub point direct invoices.

[0035] In one embodiment, the step of determining the tickets for each shift based on the hub point direct invoice and the shift base table includes:

[0036] Determine the outlets to which each hub point direct invoice belongs;

[0037] According to the shift times of each shift in each outlet and the warehousing time of the invoices issued directly at each hub point, the invoices of each shift are determined from the invoices issued directly at the hub point.

[0038] In one embodiment, the step of determining the direct train number from the hub point within the corresponding shift according to the ticket items of each shift and the direct train number determination method corresponding to the corresponding shift label comprises:

[0039] Traversing the shifts in the shift base table;

[0040] When the flight label corresponding to the currently traversed flight is a non-sweep-up flight, filter out tickets with product type labels of non-special products from the currently traversed flight, and determine the number of direct departures from the hub point within the currently traversed flight and the accumulated stockpiling quantity based on the filtered tickets;

[0041] When the flight label corresponding to the currently traversed flight is a sweeping flight, the direct departure train number from the hub point in the currently traversed flight is determined according to each ticket in the currently traversed flight and the accumulated stock volume.

[0042] A mixed scenario train number configuration device, the device comprising:

[0043] The acquisition module is used to obtain shift data, ticket routing data, ticket information data and special customer list data;

[0044] A shift base table generation module is used to determine the shift time and shift label corresponding to each shift according to the shift data, and obtain a corresponding shift base table according to the shift time and the shift label;

[0045] A first data processing module, used for obtaining first ticket pre-processing data according to the ticket routing data, the ticket information data and the special customer list data;

[0046] A second data processing module, used for performing variable integration on the first ticket pre-processing data to obtain second ticket pre-processing data;

[0047] A first shift configuration module is used to extract target data of special customers with customer tags from the first ticket pre-processing data, and determine the direct dispatch train number and direct dispatch load of the special customers according to the target data;

[0048] A hub point ticket determination module, used to determine the hub point direct delivery document according to the special customer direct delivery load and the second ticket pre-processing data;

[0049] A flight ticket determination module, used to determine the tickets for each flight according to the hub point direct invoice and the flight base table;

[0050] The second shift configuration module is used to determine the direct departure trains from the hub within the corresponding shift according to the tickets of each shift and the direct departure train determination method corresponding to the corresponding shift label.

[0051] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0052] A computer-readable storage medium stores a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0053] The train configuration method, device, computer equipment and storage medium of the above-mentioned mixed scenario process the acquired shift data to obtain the shift time and shift label corresponding to each shift, automatically generate a shift base table according to the shift time and shift label of each shift, obtain the first ticket preprocessing data based on the acquired ticket routing data, ticket information data and special customer list data, extract the target data with the customer label as the special customer from the first ticket preprocessing data, automatically determine the special customer direct departure train and the special customer direct departure load according to the target data, determine the hub point direct departure ticket according to the second preprocessed ticket data obtained by further processing the first ticket preprocessing data, and the special customer direct departure load, determine the hub point direct departure ticket according to the hub point direct departure ticket and the pre-generated shift base table, and determine the hub point direct departure train in each shift in turn according to the ticket of each shift and the direct departure train determination method corresponding to the corresponding shift label. In this way, direct trains for special customers are automatically determined based on the shift data, ticket routing data, ticket information data and special customer list data, and the direct trains from the hub point within each shift are automatically determined based on the determined direct trains for special customers. Compared with manual estimation, this can save a lot of manpower and material resources and estimation time and reduce estimation errors, thereby improving the efficiency and accuracy of train configuration in mixed scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a flow chart of a method for configuring train schedules in a mixed scenario in one embodiment;

[0055] Figure 2 A schematic diagram of a flow chart of a method for configuring train schedules in a mixed scenario in another embodiment;

[0056] Figure 3 It is a structural block diagram of a train number configuration device for a mixed scenario in one embodiment;

[0057] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] In one embodiment, Figure 1As shown, a hybrid scenario train configuration method is provided. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0060] Step 102, obtaining shift data, ticket routing data, ticket information data and special customer list data.

[0061] Among them, the shift data refers to the data corresponding to each shift at the hub point, which is used to characterize the scheduling of the hub point. The shift data may specifically include the shift sequence number, initial start time, initial cut-off time, initial departure time and effective date corresponding to each shift in each outlet in the hub point. The hub point corresponds to the special customer location. The hub point is a general term for outlets. The outlet is also a logistics outlet, which is the smallest branch unit established by a logistics company. Each outlet has a corresponding outlet identification. The shift sequence number refers to the ranking number of the shift in each shift in the corresponding outlet. The shift sequence number is understood as a shift identification, which is used to uniquely identify the shift. Specifically, it can be used to uniquely identify a shift in the corresponding outlet, and it can also be used to uniquely identify a shift in the entire hub point.

[0062] For example, suppose there are two outlets a and b, and each outlet is configured with 3 shifts. If the shift numbers of the 3 shifts in outlet a are 1, 2 and 3 respectively, correspondingly, the shift numbers of the 3 shifts in outlet b are also 1, 2 and 3 respectively. Since the shift numbers in different outlets overlap, the shift number of each shift is used to uniquely identify the shift in the corresponding outlet. If the shift numbers of the 3 shifts in outlet a are a1, a2 and a3 respectively, correspondingly, the shift numbers of the 3 shifts in outlet b are b1, b2 and b3 respectively. Since the shift numbers in different outlets are different, the shift number of each shift is used to uniquely identify the shift in the entire hub. It is worth noting that the above-mentioned configuration method of the shift number is only used as an example and is not used for specific limitation.

[0063] The initial start time, initial cut-off time, and initial departure time corresponding to a shift refer to the shift start time, shift cut-off time, and shift departure time initially configured for the shift in the shift data, respectively. The initial start time, initial cut-off time, and initial departure time corresponding to each shift in the shift data are stored in the HHmm (HH represents hours, mm represents minutes) format, which means that the initial start time, initial cut-off time, and initial departure time only include hours and minutes, such as 9:10, 10:50, and 11:20, respectively. The effective date corresponding to a shift refers to the date on which the shift takes effect, which may specifically refer to a date in the week system, such as effective from Monday to Friday, or effective on Saturday and Sunday.

[0064] The ticket routing data refers to the data generated during the routing process of the ticket, which is used to characterize the operation path information of the ticket, and may specifically include the waybill number corresponding to the ticket, the operation identifier and the operation time corresponding to each operation identifier. The operation identifier is used to uniquely identify the ticket operation. Ticket operations include the receipt operation, warehousing operation and dispatch operation of the ticket, and accordingly, the operation time in the ticket routing data includes the receipt time, warehousing time and dispatch time. Ticket information data refers to the data corresponding to the ticket information, and may specifically include the sender identifier, sender address, recipient identifier, recipient address, product type label, ticket weight, and may also include the customer card number corresponding to the ticket. It can be understood that special customers are usually equipped with corresponding customer card numbers. The product type label is used to identify the product type, and the product type may specifically include special products and non-special products. Special products refer to products with large quantities but the lowest priority in direct shipment. The timeliness requirements of such products are usually low. Therefore, in order to maximize the use of transportation resources, such products are usually filled in different shifts, stockpiled and covered. The ticket weight refers to the weight of the ticket. The special customer list data refers to the data including the special customer list, which may specifically include the customer ID and customer card number corresponding to the special customer.

[0065] Specifically, the server obtains the pre-configured and stored shift data, ticket routing data, ticket information data and special customer list data from the database. In one embodiment, the server is associated with the logistics business system. When there is an update of the ticket routing data and ticket information data in the logistics business system, the updated ticket routing data and ticket information data are updated to the server accordingly. When the business personnel configure the shift data and special customer list data through the logistics business system, the configured shift data and special customer list data are stored to the server accordingly.

[0066] Step 104, determining the shift time and shift label corresponding to each shift according to the shift data, and obtaining the corresponding shift base table according to the shift time and shift label.

[0067] Among them, the shift time is the time corresponding to the shift, which may include the shift start time and the shift end time, and may also include the shift departure time. The shift label refers to the label of the shift, which may include the tail shift and the non-tail shift. The shift base table includes the shift time and shift label of each shift, and may also include the shift sequence number of each shift.

[0068] Specifically, the server performs time processing on the shift data to obtain the shift time corresponding to each shift, and adds a corresponding shift label to each shift according to the shift time of each shift, and then obtains the corresponding shift base table according to the shift time and shift label corresponding to each shift.

[0069] In one embodiment, the server divides the shift data according to the outlets to obtain the shift sub-data corresponding to each outlet, obtains the shift time corresponding to each shift in the outlet according to the shift sub-data corresponding to each outlet, and determines the shift label corresponding to each shift in the outlet according to the shift time corresponding to each shift in the outlet.

[0070] In one embodiment, the server determines the shift labels corresponding to each shift within the ticket clearing period according to the shift time corresponding to each shift and the preset ticket clearing period, that is, determines the sweeping shift and non-sweeping shift within each ticket clearing period. The sweeping shift within each ticket clearing period refers to the shift with the largest shift number within the ticket clearing period, that is, the last shift within the ticket clearing period, and the non-sweeping shift refers to each shift other than the sweeping shift within the ticket clearing period. The ticket clearing period refers to the time interval between two adjacent ticket clearing processes, which can be customized according to actual conditions, such as one day or one week. Taking the ticket clearing period as an example of one day, it is necessary to determine the shift label of each shift within the day according to the shift time of each shift within the day, that is, determine the sweeping shift and non-sweeping shift within the day.

[0071] Step 106, obtaining first ticket preprocessing data according to the ticket routing data, the ticket information data and the special customer list data.

[0072] The first pre-processed data is the data obtained by processing the route data, information data and special customer list data, which may include the warehouse entry time, target variables and customer labels corresponding to each ticket. The target variables corresponding to the ticket include the sender ID, customer card number, ticket weight and product type label corresponding to the ticket. The customer label refers to the type label of the customer, which is used to characterize the type of the customer, and may include special customers and non-special customers.

[0073] Specifically, the server processes the ticket routing data, the ticket information data and the special customer list data respectively, and obtains the first ticket pre-processing data based on the data obtained from the respective processing.

[0074] In one embodiment, the server processes the ticket routing data to obtain the warehouse entry time of each ticket, obtains the target variable corresponding to each ticket based on the ticket information data, determines the customer tag corresponding to each ticket based on the special customer list data, and obtains the corresponding first ticket preprocessing data based on the warehouse entry time, target variable and customer tag corresponding to each ticket.

[0075] Step 108, performing variable integration on the first ticket preprocessing data to obtain second ticket preprocessing data.

[0076] Specifically, the server integrates the variable data in the obtained first ticket preprocessing data to obtain variable integration data, and obtains corresponding second ticket preprocessing data according to the first ticket preprocessing data and the variable integration data. The variable data is, for example, the ticket weight.

[0077] In one embodiment, the server groups and summarizes the ticket weights of each ticket according to the entry time of each ticket in the first ticket preprocessing data to obtain the total ticket weight corresponding to each ticket group, obtains the cumulative ticket weight corresponding to each ticket group according to the total ticket weight corresponding to each ticket group, and obtains the corresponding second ticket preprocessing data according to the total ticket weight and cumulative ticket weight corresponding to each ticket group and the first ticket preprocessing data. Each ticket group can be understood as a slice unit, and multiple tickets in each ticket group are in the same slice unit. Therefore, the variable integration data includes the total ticket weight and cumulative ticket weight corresponding to each ticket group.

[0078] In one embodiment, the server determines the total number of tickets in each ticket group and the cumulative number of tickets corresponding to each ticket group in a similar manner as described above. Therefore, the second ticket preprocessing data may also include the total number of tickets and the cumulative number of tickets corresponding to each ticket group. The total number of tickets refers to the total number of tickets belonging to the same ticket group. The cumulative number of tickets refers to the cumulative number obtained by accumulating the total number of tickets corresponding to one or more ticket groups arranged in time sequence. Therefore, the variable integration data also includes the total number of tickets and the cumulative number of tickets corresponding to each ticket group.

[0079] Step 110, extracting target data with a customer tag of a special customer from the first ticket preprocessing data, and determining the direct train number and direct load capacity of the special customer based on the target data.

[0080] The target data includes the weight of the parcel, and may also include the distance of the parcel directly shipped, or the address of the consignee and the address of the consignee. The distance of the parcel directly shipped is determined by the address of the consignee and the address of the consignee, and may specifically refer to the distance from the special customer to the next outlet. The number of direct shipments from special customers refers to the number of vehicles that directly ship from special customers. The direct shipment weight from special customers refers to the total weight of the parcels directly shipped from special customers.

[0081] Specifically, the server screens tickets with customer tags of special customers from the first ticket preprocessing data, and extracts corresponding target data from the first ticket preprocessing data according to each screened ticket. The server determines the special customer direct shipment trip and the special customer direct shipment load according to the target data corresponding to each screened ticket and the standard load and loading rate threshold preset for each direct shipment.

[0082] In one embodiment, the server determines the load threshold for each direct shipment based on the standard load and loading rate thresholds preset for each direct shipment. The server determines the ticket weight and ticket direct shipment distance corresponding to each ticket based on the target data of each screened ticket, and determines the direct shipment number for a special customer based on the load threshold for each direct shipment, and the ticket weight and ticket direct shipment distance for each ticket. Further, the server determines the direct shipment load for a special customer based on the direct shipment number for a special customer, and the standard load and actual loading rate for each direct shipment.

[0083] In one embodiment, the loading rate threshold includes a first loading rate threshold and a second loading rate threshold. The server loads and summarizes each ticket according to the target data of each ticket price selected, thereby summarizing and loading multiple tickets with the same starting position and ending position of direct shipment into the same direct shipment, that is, summarizing and loading multiple tickets with the same sender address and the next outlet into the same direct shipment vehicle. In this way, the ticket direct shipment distances corresponding to each ticket on the same direct shipment are consistent, and the server loads and summarizes each ticket in the above manner, and dynamically determines the actual loading rate. When the ticket direct shipment distance is greater than or equal to the direct shipment distance threshold, and the actual loading rate is greater than or equal to the first loading rate threshold, the server records a special customer direct shipment. When the ticket direct shipment distance is less than the direct shipment distance threshold, and the actual loading rate is greater than or equal to the second loading rate threshold, the server records a special customer direct shipment. Therefore, the server determines the special customer direct shipment number in the above manner according to the target data of each ticket selected. Among them, the direct distance threshold can be customized, such as 800, and the first loading rate threshold and the second loading rate threshold can be customized, such as 60% and 80% respectively.

[0084] In one embodiment, the server determines the special customer direct shipment weight according to the special customer direct shipment number, the standard load weight of each direct shipment, and the actual loading rate according to a preset mapping relationship. For example, the special customer direct shipment weight = special customer direct shipment number * standard load weight of each direct shipment * actual loading rate.

[0085] Step 112, determining the hub point direct shipping document according to the direct shipping load of the special customer and the second document pre-processing data.

[0086] Among them, the hub point direct invoice refers to the invoice that is directly shipped from the hub point. It can be understood that after removing the special customer direct invoice from the invoices to be directly shipped, the remaining invoices are the hub point direct invoices, among which the special customer direct invoice refers to the invoice that is directly shipped from the special customer.

[0087] Specifically, the server determines the hub point direct shipment document from the documents to be directly shipped according to the direct shipment load of the special customer and the variable integration data in the second document pre-processing data.

[0088] In one embodiment, the server determines a target ticket group from each ticket group based on the direct shipment load of a special customer and the cumulative ticket weight corresponding to each ticket group in the second ticket preprocessing data, and determines the hub point direct shipment documents from the tickets to be directly shipped based on the warehouse entry time of each ticket in the target ticket group and the warehouse entry time of each ticket to be directly shipped.

[0089] Step 114, determine the tickets for each shift based on the hub point direct invoices and the shift base table.

[0090] Specifically, the server associates the direct invoices from each hub with each shift in the shift base table through the affiliated outlets, so as to group the tickets and obtain the tickets corresponding to each shift in each outlet.

[0091] In one embodiment, since each hub point direct invoice document has a corresponding affiliated outlet, correspondingly, each shift in the shift base table also has a corresponding affiliated outlet, thus, the hub point direct invoice document can be associated with the shift in the outlet to which it belongs through the affiliated outlet. Further, each hub point direct invoice document has a corresponding warehouse entry time, and each shift in each outlet has a corresponding shift time. Thus, according to the association between the hub point direct invoice document and the shift, according to the warehouse entry time of the hub point direct invoice document, and the shift time corresponding to each associated shift, the shift to which each hub point direct invoice document belongs is determined from the outlet to which the hub point direct invoice document belongs.

[0092] Step 116, determine the direct train number from the hub point within the corresponding shift according to the ticket items of each shift and the direct train number determination method corresponding to the corresponding shift label.

[0093] Among them, the direct train determination method is a method for determining the direct trains from the hub within each shift. When the shift label is a non-sweep-up shift, the direct train determination method corresponding to the shift label is, for example, based on the tickets with the product type label of non-special products in the corresponding shift, the direct trains from the hub within the shift are determined. When the shift label is a sweep-up label, the direct train determination method corresponding to the shift label is, for example, based on each ticket in the corresponding shift and the corresponding inventory quantity of each shift with the shift label as a non-sweep-up shift during the ticket clearance cycle to which the shift belongs, the direct trains from the hub within the shift are determined, that is, based on each ticket in the shift and the accumulated inventory quantity during the ticket clearance cycle to which the shift belongs, the direct trains from the hub within the shift are determined.

[0094] Specifically, the server traverses each shift in sequence according to the shift sequence number in the shift base table, and for the shifts traversed in sequence, according to the ticket corresponding to the currently traversed shift and the direct train determination method corresponding to the shift label corresponding to the currently traversed shift, determines the direct train from the hub point within the currently traversed shift.

[0095] In one embodiment, the server determines the shifts in each outlet and the shift numbers corresponding to each shift based on the shift base table, traverses each shift in the outlet in sequence according to the shift numbers of each shift in the outlet, and determines the direct departure times from the hub point in the currently traversed shift.

[0096] The train configuration method for the above-mentioned mixed scenario processes the acquired shift data to obtain the shift time and shift label corresponding to each shift, automatically generates a shift base table according to the shift time and shift label of each shift, obtains the first ticket preprocessing data based on the acquired ticket routing data, ticket information data and special customer list data, extracts target data with customer labels as special customers from the first ticket preprocessing data, automatically determines the special customer direct train and the special customer direct load according to the target data, determines the hub point direct ticket according to the second preprocessed ticket data obtained by further processing the first ticket preprocessing data, and the special customer direct load, determines the hub point direct ticket according to the hub point direct ticket and the pre-generated shift base table, and determines the hub point direct ticket within each shift in turn according to the ticket of each shift and the direct train determination method corresponding to the corresponding shift label. In this way, direct trains for special customers are automatically determined based on the shift data, ticket routing data, ticket information data and special customer list data, and the direct trains from the hub point within each shift are automatically determined based on the determined direct trains for special customers. Compared with manual estimation, this can save a lot of manpower and material resources and estimation time and reduce estimation errors, thereby improving the efficiency and accuracy of train configuration in mixed scenarios.

[0097] In one embodiment, the shift time and shift label corresponding to each shift are determined according to the shift data, including: according to the initial start time and the initial cut-off time of each shift in the shift data, the initial cut-off time is processed to obtain the corresponding shift end time; the shift end time of the previous shift is used as the shift start time of the current shift; the corresponding shift time is obtained according to the shift start time and the shift end time corresponding to each shift; the shifts are grouped according to the shift start time of each shift; the shifts are sorted according to the shift start time in each shift group, and the shift label of each shift is determined according to the sorting.

[0098] Among them, the shift time includes the shift start time and the shift end time, and may also include the shift departure time. The shift time corresponding to the shift is obtained by processing the initial start time, initial cut-off time and initial departure time corresponding to each shift in the shift data. The shift time is time data expressed in the format of YYYY-MM-DDHH:mm:ss (YYYY represents year, MM represents month, DD represents day, HH represents hour, mm represents minute, ss represents second). For example, the shift start time is 9:10:00 on July 21, 2020.

[0099] Specifically, the server compares the initial start time of each shift in the shift data with the initial cut-off time, and processes the initial cut-off time according to the comparison result based on the date of the initial start time to obtain the corresponding shift end time of the shift. After obtaining the corresponding shift end time of each shift, the server traverses the shifts in each outlet according to the shift sequence number of each shift in each outlet in the shift data, and uses the shift end time of the previous shift as the shift start time of the current shift. The server obtains the corresponding shift time of the shift based on the shift start time and shift end time corresponding to each shift. Further, the server groups the shifts in each outlet according to the shift start time of each shift in the outlet, and sorts the shifts in each shift group according to the shift start time of each shift in each shift group to obtain the corresponding shift sequence of the shift group, and then determines the shift label of each shift in the corresponding shift group according to the sorting of each shift in the shift sequence.

[0100] In one embodiment, for each shift in the shift data, the server compares the initial start time of the shift with the initial cut-off time. When the initial start time is less than or equal to the initial cut-off time, the date of the initial start time is concatenated with the initial cut-off time to obtain the corresponding shift end time. When the initial start time is greater than the initial cut-off time, the date of the initial start time is added with one day, and the added date is concatenated with the initial cut-off time to obtain the corresponding shift end time. For example, assuming that the initial start time and initial cut-off time of the shift are 9:10 and 10:50 respectively, the effective date of the shift is Monday to Friday, the current date is July 21, 2020, and July 21, 2020 is Tuesday, therefore, the date of the initial start time is July 21, 2020. Since the initial start time is less than the initial cut-off time, the end time of the shift determined in the above manner is 10:50 on July 21, 2020. Assuming that the initial start time and initial cut-off time of the shift are 12:10 and 1:30 respectively, the effective date of the shift remains unchanged with the current date. Since the initial start time is greater than the initial cut-off time, the end time of the shift determined in the above manner is 1:30 on July 22, 2020. Among them, the current date refers to the date on which the train configuration method for the hybrid scenario provided by this application is executed.

[0101] In one embodiment, the server groups the shifts of each outlet according to the start time of each shift in each outlet and the preset ticket clearance cycle to obtain multiple corresponding shift groups. Taking the ticket clearance cycle as one day as an example, for each outlet, the server groups the shifts in the outlet according to the date of the start time of each shift, and classifies the shifts with the same start time on the same day into the same shift group.

[0102] In one embodiment, the server sorts the shifts in each shift group in reverse order according to the shift start time, that is, sorts the shifts in order from the latest to the earliest start time, to obtain a shift sequence corresponding to the shift group. The server determines the shift label corresponding to the shift with the highest sorting in each shift sequence as a finishing shift, and determines the shift label corresponding to each other shift in the shift sequence as a non-finishing shift.

[0103] In one embodiment, after the server divides the shift groups in the above manner, it determines the shift with the latest start time in each shift group as the finishing shift according to the start time of each shift in the shift group, and determines each other shift in the shift group as a non-finishing shift.

[0104] In one embodiment, the server compares the initial start time of each shift in the shift data with the initial departure time, and processes the initial departure time according to the comparison result based on the date of the initial start time to obtain the corresponding shift departure time of the shift. The specific processing flow for the initial departure time is similar to the specific processing flow for the initial cut-off time, which will not be repeated here.

[0105] In the above embodiment, the corresponding shift time is obtained by processing the initial start time and the initial cut-off time corresponding to each shift in the shift data, the shifts are grouped and sorted based on the shift times of each shift, and the shift labels of each shift are determined according to the sorting, which can improve the processing efficiency and accuracy of the shift time and shift labels.

[0106] In one embodiment, step 106 includes: determining the warehouse entry time of each ticket based on the ticket routing data; extracting the target variable corresponding to each ticket from the ticket information data; determining the customer tag corresponding to each ticket based on the special customer list data; and obtaining the first ticket preprocessing data based on the warehouse entry time, target variable and customer tag corresponding to each ticket.

[0107] Specifically, the ticket routing data includes ticket routing sub-data corresponding to each ticket, and the ticket information data includes ticket information sub-data corresponding to each ticket. The server obtains the ticket routing sub-data corresponding to each ticket based on the ticket routing data, and processes the ticket routing sub-data corresponding to each ticket to obtain the entry time of the ticket. The server obtains the ticket information sub-data corresponding to each ticket based on the ticket information data, and extracts the target variable corresponding to the ticket from the ticket information sub-data corresponding to each ticket. The server matches the customer card number corresponding to each ticket with the special customer list data, and determines the customer tag corresponding to the ticket based on the matching result. Further, the server obtains the first ticket preprocessing data based on the entry time, target variable and customer tag corresponding to each ticket.

[0108] In one embodiment, if the ticket routing sub-data includes the warehouse entry operation and the corresponding warehouse entry time of the ticket, the warehouse entry time in the ticket routing sub-data is determined as the warehouse entry time of the ticket. If the ticket routing sub-data does not include the warehouse entry operation of the ticket, but includes the departure time of the ticket, the departure time is subtracted by 30 minutes to obtain the warehouse entry time of the ticket. If the ticket routing sub-data does not include the warehouse entry operation and departure time of the ticket, but includes the ticket pickup operation and the corresponding pickup time, the ticket entry time is obtained by adding 1 hour to the pickup time.

[0109] In one embodiment, the server determines the warehouse entry time of each ticket according to the ticket routing data, and after extracting the target variable corresponding to each ticket from the ticket information data, associates the warehouse entry time of each ticket with the target variable through the waybill number of each ticket. Accordingly, the server associates the target variable corresponding to each ticket with the special customer list data based on the customer card number to determine the customer tag corresponding to each ticket, thereby obtaining the warehouse entry time, target variable and customer tag corresponding to each ticket.

[0110] In the above embodiment, based on the ticket routing data, ticket information data and special customer list data, the first ticket preprocessing data including the warehousing time, target variables and customer labels corresponding to each ticket is determined, so that the train configuration can be performed quickly and accurately based on the first ticket preprocessing data.

[0111] In one embodiment, step 108 includes: slicing the tickets in the first ticket preprocessing data according to the warehouse entry time to obtain multiple slice units; summarizing and analyzing the ticket weights of each ticket in each slice unit to obtain the corresponding total ticket weight; accumulating the total ticket weights of each slice unit in time sequence to obtain the cumulative ticket weight corresponding to each slice unit; and obtaining the second ticket preprocessing data based on the first ticket preprocessing data, the total ticket weight corresponding to each slice unit and the cumulative ticket weight.

[0112] Among them, the slice unit is obtained by slicing the entry time of each ticket. Through slicing, one or more tickets with the same entry time are divided into the same slice unit, for example, all tickets with the entry time in the time range of 8:11 to 8:20 are divided into the 8:20 slice unit. The total weight of tickets corresponding to the slice unit refers to the total weight obtained by summarizing the weights of each ticket in the slice unit. The cumulative ticket weight corresponding to the slice unit refers to the total weight obtained by summarizing the total weights of tickets corresponding to each slice unit before the slice unit in time sequence. For example, the total weights of tickets corresponding to the slice units 8:20, 8:30 and 8:40 are 0.5 tons, 1 ton and 0.8 tons respectively, then the cumulative ticket weight corresponding to the slice unit 8:40 is 0.5+1+0.8=2.3 tons.

[0113] Specifically, the server performs backward compression processing on the warehouse entry time of each ticket in the first ticket preprocessing data, and determines the slice unit to which each ticket belongs based on the compression result, that is, the tickets with the same compression result are divided into the same slice unit, and multiple slice units corresponding to the first ticket preprocessing data are obtained, thereby slicing each ticket in the first ticket preprocessing data according to the warehouse entry time to obtain corresponding multiple slice units. For each slice unit, the server sums the ticket weights of each ticket in the slice unit to obtain the total ticket weight corresponding to the slice unit. Further, for each slice unit, the server accumulates and sums the total ticket weights corresponding to the slice unit and each slice unit before it in time sequence to obtain the cumulative ticket weight corresponding to the slice unit. The server obtains the corresponding second ticket preprocessing data based on the first ticket preprocessing data, the total ticket weight corresponding to each slice unit, and the cumulative ticket weight. It can be understood that the total ticket weight and the cumulative ticket weight corresponding to each slice unit can be understood as the variable integration data corresponding to the slice unit. In this way, the second ticket preprocessing data includes the first ticket preprocessing data and the variable integration data corresponding to each slicing unit.

[0114] In one embodiment, the server can also calculate the total number of tickets and the cumulative number of tickets corresponding to each slice unit in a similar manner as described above, so that the variable integration data corresponding to each slice unit also includes the total number of tickets and the cumulative number of tickets.

[0115] In one embodiment, the server calls a preconfigured time conversion function to process the entry time of each ticket into a corresponding target time, and performs backward compression on the corresponding entry time according to the preset mapping relationship based on the target time corresponding to each ticket and the preset compression granularity to obtain a corresponding compression result. The preset mapping relationship is: compression result = ceiling(unix_timestamp(t) / (60*k)), where ceiling is a rounding up operation, k is the preset compression granularity, t is the entry time of the ticket, and unix_timestamp(t) refers to calling the timestamp function unix_timestamp to process the entry time into the corresponding target function. For example, assuming that the compression granularity is 10 minutes, each time point in the time range of 8:11 to 8:20 can be compressed to 8:20 in the above manner.

[0116] In the above embodiment, the entry time of each ticket is sliced ​​to obtain multiple slice units, and the ticket weight of each ticket is summarized and analyzed according to the slice unit to obtain the total ticket weight and cumulative ticket weight corresponding to each slice unit, and then the second ticket preprocessing data including the total ticket weight and the cumulative ticket weight is obtained, so that the hub point direct issuance document can be quickly and accurately determined according to the second ticket preprocessing data.

[0117] In one embodiment, step 112 includes: determining a target slice unit whose cumulative ticket weight is greater than or equal to the direct delivery weight for a special customer from the second ticket preprocessing data; extracting the warehousing time corresponding to the ticket with the earliest warehousing time from the target slice unit; and determining the ticket whose warehousing time in the second ticket preprocessing data is greater than the extracted warehousing time as a hub point direct delivery document.

[0118] Specifically, the server compares the direct shipment weight of the special customer with the cumulative ticket weight corresponding to each slice unit in the second ticket preprocessing data, so as to screen out the target slice unit whose cumulative ticket weight is greater than or equal to the direct shipment weight of the special customer from each slice unit. The server screens out the ticket with the earliest warehouse entry time from the target slice unit, and extracts the warehouse entry time corresponding to the screened ticket from the second ticket preprocessing data. Further, the server compares the warehouse entry time corresponding to each ticket in the second ticket preprocessing data with the extracted warehouse entry time, so as to select the ticket with the warehouse entry time greater than or equal to the extracted warehouse entry time from the second ticket preprocessing data, and determine the selected ticket as the hub point direct shipment ticket.

[0119] In one embodiment, for tickets of special customers, if the direct shipment conditions are met, they are shipped directly from the special customers in chronological order, and the remaining tickets are transferred to the hub for direct shipment. Therefore, in order to separate the tickets that are directly shipped from special customers and the tickets that are directly shipped from the hub from the tickets that meet the direct shipment conditions (that is, the tickets to be directly shipped), it is necessary to calculate the direct shipment load of special customers shipped directly from special customers in the above manner, and use the direct shipment load of special customers as the load threshold to determine the hub direct shipment from the tickets to be directly shipped.

[0120] In the above embodiment, the target slice unit is determined based on the direct shipment weight of a special customer and the cumulative ticket weight corresponding to each slice unit, and the hub point direct shipment documents are determined based on the earliest warehousing time in the target slice unit and the warehousing time of each ticket to be directly shipped. The hub point direct shipment documents can be determined quickly and accurately, so that the hub point direct shipment trips can be determined quickly and accurately based on the hub point direct shipment documents.

[0121] In one embodiment, step 114 includes: determining the outlets to which each hub point direct invoice document belongs; and determining the tickets of each shift from the hub point direct invoice documents according to the shift times of each shift in each outlet and the warehousing time of each hub point direct invoice document.

[0122] Specifically, the server determines the outlet to which each hub point direct invoice belongs based on the ticket information data, so as to associate each hub point direct invoice with each shift in the outlet to which it belongs, that is, to determine the hub point direct invoice document set corresponding to each outlet. For each outlet, the server compares the warehouse entry time of each hub point direct invoice document in the hub point direct invoice document set corresponding to the outlet with the shift time corresponding to each shift in the outlet, so as to determine the shift to which each hub point direct invoice document belongs in the outlet according to the comparison result, that is, to determine the ticket corresponding to each shift in the outlet from the hub point direct invoice document set.

[0123] In one embodiment, the server determines the outlet to which the direct invoice from each hub point belongs based on the sender address corresponding to the direct invoice from each hub point and the preset outlet address of each outlet.

[0124] In one embodiment, according to the above comparison method, when the warehouse entry time of the hub point direct invoice document is greater than or equal to the start time of the shift, and less than or equal to the end time of the shift, the server determines that the hub point direct invoice document belongs to the shift.

[0125] In one embodiment, for each shift in each outlet, the server regards the tickets that enter the warehouse between the cut-off time of the previous shift and the cut-off time of the current shift as the tickets belonging to the current shift. Since the end time of each shift in the shift base table is obtained by processing the initial cut-off time corresponding to the shift in the shift data, and the start time of each shift is the end time of the previous shift in the outlet to which it belongs, the tickets whose entry time is between the start time and the end time of the shift are determined as the tickets belonging to the shift.

[0126] In the above embodiment, based on the outlet to which the ticket belongs and the corresponding warehousing time, as well as the shift time corresponding to each shift in each outlet, the ticket corresponding to each shift in each outlet is determined from the tickets directly issued from the hub point.

[0127] In one embodiment, step 116 includes: traversing the shifts in the shift base table; when the shift label corresponding to the currently traversed shift is a non-sweep-up shift, filtering out tickets with product type labels of non-special products from the currently traversed shift, and determining the number of direct departures from the hub point in the currently traversed shift and the accumulated inventory quantity based on the filtered tickets; when the shift label corresponding to the currently traversed shift is a sweep-up shift, determining the number of direct departures from the hub point in the currently traversed shift based on the tickets in the currently traversed shift and the accumulated inventory quantity.

[0128] Among them, the cumulative inventory quantity corresponding to a shift refers to the cumulative value of the inventory quantity corresponding to each shift in the outlet to which the shift belongs, whose shift time is before the shift time of the shift. The cumulative inventory quantity corresponding to a shift may specifically refer to the cumulative value of the inventory quantity corresponding to each shift in the outlet to which the shift belongs, whose shift time is before the shift time of the shift and which is in the same ticket clearance cycle as the shift. The shift in the same outlet whose shift time is before the shift time of the current shift refers to the shift whose shift number in the outlet is smaller than the shift number of the current shift. The number of direct departures from the hub point within a shift refers to the number of vehicles directly dispatched from the hub point within the shift, specifically refers to the number of vehicles directly dispatched from the corresponding outlet within the shift.

[0129] Specifically, the server traverses each shift in the shift base table in sequence. When the shift label corresponding to the currently traversed shift is a non-sweep-up shift, the server screens out tickets with product type labels of special products and non-special products from the currently traversed shift, and determines the number of direct departures from the hub point in the currently traversed shift based on the screened tickets with product type labels of non-special products, and determines the load of the tickets to be filled in the currently traversed shift based on the determined direct departures from the hub point and the tickets with the product type label of non-special products. The server determines the stock volume in the currently traversed shift based on the screened tickets with special product types and the load volume of the tickets to be filled, and sums the stock volume in the currently traversed shift with the cumulative stock volume corresponding to the previous traversed shift to obtain the cumulative stock volume corresponding to the currently traversed shift. The load of tickets to be filled refers to the load of tickets that can be loaded for filling after the direct trains from each hub point in the shift have loaded tickets with product type labels of non-special products. The load of tickets for filling refers to the total weight of tickets with product type labels of special products used for filling. When the shift label corresponding to the currently traversed shift is a sweeping shift, the server determines the direct trains from the hub point in the currently traversed shift based on the tickets in the currently traversed shift and the accumulated inventory corresponding to the non-sweeping shifts of the previous traversal and belonging to the same outlet as the currently traversed shift.

[0130] In one embodiment, when the shift label corresponding to the currently traversed shift is a non-sweep-up shift, the server obtains the corresponding total weight of the tickets according to the ticket weights of the tickets with product type labels of non-special products in the shift, as the total load of non-special products in the shift, and determines the equivalent standard container quantity of non-special products in the shift according to the total load of non-special products and the preset standard container load for each direct dispatch from the hub point, and rounds up the equivalent standard container quantity of non-special products to obtain the number of direct dispatches from the hub point in the shift. The server determines the equivalent standard container quantity of the theoretical direct dispatch of special products in the shift according to the tickets with product type labels of special products in the shift and the standard container load in a similar manner as described above. The server obtains the equivalent standard container quantity of special product filling pieces in the shift according to the direct dispatches from the hub point in the shift and the equivalent standard container quantity of non-special products, that is, determines the load of the tickets to be filled in the shift. The server determines the stocking quantity within the shift based on the theoretical standard container quantity equivalent to the special product direct shipment within the shift and the standard container quantity equivalent to the special product warehouse filling quantity.

[0131] Furthermore, for each shift, the number of direct departures from the hub is recorded as x0, the equivalent standard container quantity of non-special products is recorded as x1, the equivalent standard container quantity of special product theoretical direct shipments is recorded as x2, the equivalent standard container quantity of special product filling warehouses is recorded as x3, the stock volume, that is, the net remaining special product equivalent container quantity, is recorded as x4, and the cumulative stock volume, that is, the cumulative stock equivalent standard container quantity, is recorded as z, then x1 = total load of non-special products / standard container load, x2 = total load of special products / standard container load, x0 = ceiling (x1), ceiling is rounded up, x3 = x0-x1, x4 = x2-x3, z = x4 + the cumulative stock volume corresponding to the previous traversed shift. Among them, the initial cumulative stock volume is 0. If the cumulative stock volume corresponding to the currently traversed shift is less than or equal to zero, the value of the cumulative stock volume z is assigned to 0. The standard container load is the preset standard load for a single direct shipment of a transport tool, which can be customized according to actual conditions. After the server determines the direct shipment number and accumulated stock volume corresponding to the hub point of the shift in the above manner, it continues to traverse the next shift until all the shifts in the shift base table are traversed.

[0132] In one embodiment, when the shift label corresponding to the currently traversed shift is a sweeping shift, the server calculates the equivalent standard container quantity of non-special products in the shift and the theoretical direct-shipment equivalent standard container quantity of special products based on the tickets in the shift in a similar manner as described above, and sums the equivalent standard container quantity of non-special products in the shift, the theoretical direct-shipment equivalent standard container quantity of special products, and the cumulative inventory quantity corresponding to the previous traversal shift that is in the same outlet as the shift, and rounds up the sum to obtain the direct-shipment number of hubs in the shift.

[0133] In one embodiment, the server traverses each shift in each outlet in turn according to the shift base table. If a corresponding ticket clearance cycle is pre-configured for each outlet, the ticket clearance cycle to which the shift belongs can be determined according to the shift time of each shift in the outlet. In this way, one or more shifts in each outlet belong to the same ticket clearance cycle, and there is a sweeping shift in each ticket clearance cycle, and there may also be one or more non-sweeping shifts. For each ticket clearance cycle in each outlet, the server calculates the number of direct departures from the hub and the accumulated stockpiling volume corresponding to each non-sweeping shift in the ticket clearance cycle, as well as the number of direct departures from the hub corresponding to the sweeping shift in the above manner, thereby clearing the stockpiling volume of the outlet in each ticket clearance cycle.

[0134] In one embodiment, the server determines the direct departure times of the hub point in each shift in the shift base table in the above manner, and obtains the direct departure results of the hub point shift based on the direct departure times of the hub point in each shift.

[0135] In the above embodiment, the shifts in the shift base table are traversed, and the direct departure trains from the hub points in each shift are determined according to the direct departure train determination method corresponding to the shift label corresponding to the shift, which can improve the accuracy and efficiency of determining the direct departure trains from the hub points.

[0136] In one embodiment, a method for configuring train schedules in a mixed scenario is provided, and the method specifically comprises the following steps:

[0137] Step 202, obtaining shift data, ticket routing data, ticket information data and special customer list data.

[0138] Step 204, according to the initial start time and the initial cut-off time of each shift in the shift data, the initial cut-off time is processed to obtain the corresponding shift end time.

[0139] Step 206: Use the end time of the previous shift as the start time of the current shift.

[0140] Step 208, obtaining the corresponding shift time according to the shift start time and shift end time corresponding to each shift.

[0141] Step 210 , grouping the shifts according to their start time.

[0142] Step 212, sort the shifts in each shift group according to the shift start time, determine the shift label of each shift according to the sorting, and obtain the corresponding shift base table according to the shift time and the shift label.

[0143] Step 214, determine the warehouse entry time of each ticket according to the ticket routing data; extract the target variable corresponding to each ticket from the ticket information data; determine the customer label corresponding to each ticket according to the special customer list data.

[0144] Step 216, obtaining the first ticket preprocessing data according to the warehouse entry time, target variable and customer label corresponding to each ticket.

[0145] Step 218, slicing the tickets in the first ticket preprocessing data according to the warehouse entry time to obtain multiple slice units.

[0146] Step 220: The ticket weights of the tickets in each slicing unit are summarized and analyzed to obtain the total weight of the corresponding tickets.

[0147] Step 222, the total weight of the tickets of each slice unit is accumulated and calculated in time sequence to obtain the accumulated ticket weight corresponding to each slice unit.

[0148] Step 224, obtaining second ticket preprocessing data according to the first ticket preprocessing data, the total ticket weight corresponding to each slicing unit, and the accumulated ticket weight.

[0149] Step 226, extracting target data of special customers with customer tags from the first ticket preprocessing data, and determining the special customer direct shipment train number and special customer direct shipment load according to the target data.

[0150] Step 228, determining the target slice unit whose cumulative ticket weight is greater than or equal to the direct delivery load of the special customer from the second ticket pre-processing data.

[0151] Step 230, extracting the entry time corresponding to the ticket with the earliest entry time from the target slice unit.

[0152] Step 232, determine the tickets in the second ticket pre-processing data whose warehouse entry time is greater than the extracted warehouse entry time as hub point direct invoices.

[0153] Step 234, determine the outlets to which each hub point direct invoice belongs.

[0154] Step 236, according to the shift time of each shift in each outlet and the warehouse entry time of the direct invoice documents at each hub point, determine the tickets for each shift from the direct invoice documents at the hub point.

[0155] Step 238, traverse the shifts in the shift base table.

[0156] Step 240, when the flight label corresponding to the currently traversed flight is a non-sweep-up flight, filter out tickets with product type labels of non-special products from the currently traversed flight, and determine the number of direct departures from the hub point within the currently traversed flight and the accumulated inventory quantity based on the filtered tickets.

[0157] Step 242, when the flight label corresponding to the currently traversed flight is a sweeping flight, determine the hub point direct departure times within the currently traversed flight based on the tickets and the accumulated inventory within the currently traversed flight.

[0158] In the above embodiment, a combination of standardization method, parameter method and moving accumulation method is used to respectively determine the direct trains for special customers and the direct trains from hubs, which solves the difficulty of manually estimating and summarizing the stockpiling and filling operations. Moreover, by adopting the atomization method, each ticket is sliced ​​into corresponding slice units according to the warehouse entry time, the ticket weight is accumulated according to the slice unit, and the direct train is configured according to the accumulated ticket weight of each slice unit, which solves the problem of tilt that is easy to exist when calculating large-volume data with second-level granularity. Therefore, the train configuration method under this mixed scenario can help the implementation of the direct dispatch scenario and provide effective reference theoretical values ​​for the trains to be dispatched in each area.

[0159] In one embodiment, the hybrid scenario train configuration system to which the hybrid scenario train configuration method provided by the present application is applied is divided into five layers, namely, the basic data layer, the data processing layer, the variable integration layer, the estimated calculation layer of the trains to be dispatched, and the result integration layer. Among them, in the basic data layer, the shift data, the ticket routing data, the ticket information data and the special customer list data are obtained; in the data processing layer, the shift base table is generated according to the shift data, and the first ticket preprocessing data is obtained according to the ticket routing data, the ticket information data and the special customer list data; in the variable integration layer, the first ticket preprocessing data is integrated with variables to obtain the second ticket preprocessing data; in the estimated calculation layer of the trains to be dispatched, the direct dispatch trains of special customers and the direct dispatch trains of the hub point in each shift in each network point are determined respectively according to the above method; in the result integration layer, the direct dispatch results of special customers are obtained according to the direct dispatch trains of special customers, and the direct dispatch results of the shifts are obtained according to the direct dispatch trains of the hub point in each shift in each network point, and the direct dispatch results of the shifts are distinguished from the direct dispatch results of special customers by labels, and the two are merged and output by aligning the fields.

[0160] It should be understood that although Figure 1-2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-2At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0161] In one embodiment, Figure 3 As shown, a hybrid scenario train configuration device 300 is provided, comprising: an acquisition module 301, a shift base table generation module 302, a first data processing module 303, a second data processing module 304, a first shift configuration module 305, a hub ticket determination module 306, a shift ticket determination module 307, and a second shift configuration module 308, wherein:

[0162] The acquisition module 301 is used to acquire shift data, ticket routing data, ticket information data and special customer list data;

[0163] A shift base table generation module 302 is used to determine the shift time and shift label corresponding to each shift according to the shift data, and obtain the corresponding shift base table according to the shift time and shift label;

[0164] The first data processing module 303 is used to obtain first ticket pre-processing data according to the ticket routing data, the ticket information data and the special customer list data;

[0165] The second data processing module 304 is used to perform variable integration on the first ticket pre-processing data to obtain the second ticket pre-processing data;

[0166] The first shift configuration module 305 is used to extract target data of special customers with customer tags from the first ticket pre-processing data, and determine the special customer direct delivery train number and special customer direct delivery load according to the target data;

[0167] The hub point ticket determination module 306 is used to determine the hub point direct delivery document according to the special customer direct delivery load and the second ticket pre-processing data;

[0168] The flight ticket determination module 307 is used to determine the tickets for each flight according to the hub point direct invoice and the flight base table;

[0169] The second shift configuration module 308 is used to determine the hub point direct train number within the corresponding shift according to the ticket items of each shift in turn and in accordance with the direct train number determination method corresponding to the corresponding shift label.

[0170] In one embodiment, the shift base table generation module 302 is further used to process the initial cut-off time to obtain the corresponding shift end time according to the initial start time and the initial cut-off time of each shift in the shift data; use the shift end time of the previous shift as the shift start time of the current shift; obtain the corresponding shift time according to the shift start time and the shift end time corresponding to each shift; group the shifts according to the shift start time of each shift; sort the shifts according to the shift start time in each shift group, and determine the shift label of each shift according to the sorting.

[0171] In one embodiment, the first data processing module 303 is also used to determine the warehouse entry time of each ticket based on the ticket routing data; extract the target variable corresponding to each ticket from the ticket information data; determine the customer label corresponding to each ticket based on the special customer list data; and obtain the first ticket preprocessing data based on the warehouse entry time, target variable and customer label corresponding to each ticket.

[0172] In one embodiment, the second data processing module 304 is also used to slice the tickets in the first ticket preprocessing data according to the warehouse entry time to obtain multiple slice units; summarize and analyze the ticket weights of each ticket in each slice unit to obtain the corresponding total ticket weight; accumulate the total ticket weights of each slice unit in time sequence to obtain the cumulative ticket weight corresponding to each slice unit; and obtain the second ticket preprocessing data based on the first ticket preprocessing data, the total ticket weight corresponding to each slice unit and the cumulative ticket weight.

[0173] In one embodiment, the hub ticket determination module 306 is also used to determine a target slice unit whose cumulative ticket weight is greater than or equal to the direct delivery weight for a special customer from the second ticket preprocessing data; extract the warehousing time corresponding to the ticket with the earliest warehousing time from the target slice unit; and determine the ticket whose warehousing time in the second ticket preprocessing data is greater than the extracted warehousing time as a hub direct delivery document.

[0174] In one embodiment, the shift ticket determination module 307 is also used to determine the outlets to which each hub point direct invoice document belongs; based on the shift times of each shift in each outlet and the warehousing time of each hub point direct invoice document, the tickets of each shift are determined from the hub point direct invoice documents.

[0175] In one embodiment, the second shift configuration module 308 is also used to traverse the shifts in the shift base table; when the shift label corresponding to the currently traversed shift is a non-sweep-up shift, tickets with product type labels of non-special products are screened from the currently traversed shift, and the hub point direct departure times and accumulated inventory quantities within the currently traversed shift are determined based on the screened tickets; when the shift label corresponding to the currently traversed shift is a sweep-up shift, the hub point direct departure times within the currently traversed shift are determined based on the tickets and accumulated inventory quantities within the currently traversed shift.

[0176] For the specific limitations of the train number configuration device for the hybrid scenario, please refer to the limitations of the train number configuration method for the hybrid scenario above, which will not be repeated here. Each module in the above-mentioned train number configuration device for the hybrid scenario can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0177] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store shift data, ticket routing data, ticket information data and special customer list data, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hybrid scenario train configuration method is implemented.

[0178] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0179] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0180] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0181] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0182] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A mixed scenario train configuration method, characterized in that: The method comprises: Obtain shift data, ticket routing data, ticket information data and special customer list data; Determine the shift time and shift label corresponding to each shift according to the shift data, and obtain a corresponding shift base table according to the shift time and the shift label; Obtaining first ticket preprocessing data according to the ticket routing data, the ticket information data and the special customer list data; Slicing the tickets in the first ticket preprocessing data according to the warehouse entry time to obtain a plurality of slice units; Summarize and analyze the weight of each ticket in each slicing unit to obtain the total weight of the corresponding ticket; The total weight of the tickets of each slice unit is accumulated and calculated in time sequence to obtain the accumulated ticket weight corresponding to each slice unit; Obtaining second ticket preprocessing data according to the first ticket preprocessing data, the total ticket weight corresponding to each slicing unit, and the accumulated ticket weight; Extract target data with a customer tag of a special customer from the first ticket preprocessing data, and determine the special customer direct delivery train number and the special customer direct delivery load according to the target data; the target data includes the ticket weight and the ticket direct delivery distance; Determining the hub point direct delivery of documents according to the direct delivery load of the special customer and the second document pre-processing data; Determine the tickets for each flight according to the direct invoices from the hub point and the flight base table; According to the tickets of each flight, the direct train number from the hub point within the corresponding flight is determined according to the direct train number determination method corresponding to the corresponding flight label.

2. The method according to claim 1, characterized in that Determining the shift time and shift label corresponding to each shift according to the shift data includes: According to the initial start time and initial cut-off time of each shift in the shift data, the initial cut-off time is processed to obtain the corresponding shift end time; The end time of the previous shift is used as the start time of the current shift; Get the corresponding shift time according to the start time and end time of each shift; grouping the shifts according to their shift start times; The shifts are sorted according to the shift start time within each shift group, and the shift label of each shift is determined according to the sorting.

3. The method according to claim 1, characterized in that: The step of obtaining the first ticket preprocessing data according to the ticket routing data, the ticket information data and the special customer list data includes: Determine the warehouse entry time of each ticket according to the ticket routing data; Extracting target variables corresponding to each ticket from the ticket information data; the target variables include the sender ID, customer card number, ticket weight and product type label corresponding to the ticket; Determine the customer label corresponding to each ticket according to the special customer list data; The first ticket preprocessing data is obtained according to the warehouse entry time, target variable and customer label corresponding to each of the tickets.

4. The method according to claim 1, characterized in that: The step of determining the hub point direct delivery of documents according to the direct delivery load of the special customer and the second document pre-processing data includes: Determine, from the second ticket preprocessing data, a target slice unit whose cumulative ticket weight is greater than or equal to the direct shipping weight for the special customer; Extracting the entry time corresponding to the earliest ticket entry time from the target slice unit; The tickets in the second ticket pre-processing data whose warehousing time is greater than the extracted warehousing time are determined as hub point direct invoices.

5. The method according to claim 4, characterized in that The step of determining the tickets for each shift based on the direct invoices from the hub point and the shift base table includes: Determine the outlets to which each hub point direct invoice belongs; According to the shift times of each shift in each outlet and the warehousing time of the invoices issued directly at each hub point, the invoices of each shift are determined from the invoices issued directly at the hub point.

6. The method according to any one of claims 1 to 5, characterized in that: The method of determining the direct departure train number from the hub point within the corresponding flight according to the ticket pieces of each flight and the direct departure train number determination method corresponding to the corresponding flight label includes: Traversing the shifts in the shift base table; When the flight label corresponding to the currently traversed flight is a non-sweep-up flight, filter out tickets with product type labels of non-special products from the currently traversed flight, and determine the number of direct departures from the hub point within the currently traversed flight and the accumulated stockpiling quantity based on the filtered tickets; When the flight label corresponding to the currently traversed flight is a sweeping flight, the direct departure train number from the hub point in the currently traversed flight is determined according to each ticket in the currently traversed flight and the accumulated stock volume.

7. A mixed scenario train number configuration device, characterized in that: The device comprises: The acquisition module is used to obtain shift data, ticket routing data, ticket information data and special customer list data; A shift base table generation module is used to determine the shift time and shift label corresponding to each shift according to the shift data, and obtain a corresponding shift base table according to the shift time and the shift label; A first data processing module, used for obtaining first ticket pre-processing data according to the ticket routing data, the ticket information data and the special customer list data; The second data processing module is used to slice the tickets in the first ticket preprocessing data according to the warehouse entry time to obtain multiple slice units; summarize and analyze the ticket weights of each ticket in each slice unit to obtain the corresponding total ticket weight; accumulate the total ticket weights of each slice unit according to the time sequence to obtain the accumulated ticket weight corresponding to each slice unit; and obtain the second ticket preprocessing data according to the first ticket preprocessing data, the total ticket weight corresponding to each slice unit and the accumulated ticket weight; A first shift configuration module is used to extract target data with a customer tag of a special customer from the first ticket preprocessing data, and determine a direct train number and a direct load capacity for the special customer according to the target data; the target data includes a ticket weight and a direct ticket distance; A hub point ticket determination module, used to determine the hub point direct delivery document according to the special customer direct delivery load and the second ticket pre-processing data; A flight ticket determination module, used to determine the tickets for each flight according to the hub point direct invoice and the flight base table; The second shift configuration module is used to determine the direct departure trains from the hub within the corresponding shift according to the tickets of each shift and the direct departure train determination method corresponding to the corresponding shift label.

8. The device according to claim 7, characterized in that The shift base table generation module is also used to: process the initial cut-off time to obtain the corresponding shift end time according to the initial start time and the initial cut-off time of each shift in the shift data; use the shift end time of the previous shift as the shift start time of the current shift; obtain the corresponding shift time according to the shift start time and the shift end time corresponding to each shift; group the shifts according to the shift start time of each shift; sort the shifts according to the shift start time in each shift group, and determine the shift label of each shift according to the sorting.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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