Delivery management method and device

Abstract

The invention discloses a delivery management method and device, and relates to the technical field of warehouse logistics. A specific embodiment of the delivery management method comprises the following steps: in response to a received data acquisition request, acquiring historical purchase data and historical delivery data associated with the historical purchase data; determining a goods quantity conversion coefficient according to the historical purchase data and the historical delivery data; and in response to the received target purchase data, determining pre-delivery data corresponding to the target purchase data according to the goods quantity conversion coefficient. According to the embodiment, the goods quantity conversion coefficient is determined based on the historical purchase data and the historical delivery data, and the delivery data associated with the target purchase data is predicted according to the goods quantity conversion coefficient, so that the delivery data can be accurately predicted, the delivery efficiency is improved, and temporary determination or adjustment of a transportation strategy before delivery is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of warehousing logistics, and in particular to a method and device for shipment management. Background Art

[0002] In the goods procurement process, the purchaser usually sends the purchase data to the manufacturer first, and after the goods are produced, the manufacturer sends the produced goods to the purchaser. For example, in the vehicle parts replenishment business, the regional warehouse that stores the parts sends the purchase data to the central warehouse that produces the parts, and the central warehouse sends the produced parts to the regional warehouse. Since there is a difference between the actual delivery data and the purchase data, and the actual delivery data can only be determined at the time of delivery, it is necessary to temporarily determine the transportation strategy of the goods based on the actual delivery data before delivery, such as the mode of transportation, type of transportation vehicle, etc.

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

[0004] Temporarily determining the transportation strategy for goods consumes a lot of time and results in low delivery efficiency. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method and device for shipment management, which can accurately predict shipment data, improve shipment efficiency, and avoid temporarily determining a transportation strategy before shipment.

[0006] To achieve the above object, according to a first aspect of an embodiment of the present invention, a method for shipment management is provided, comprising:

[0007] In response to receiving the data acquisition request, acquiring historical purchase data and historical shipment data associated with the historical purchase data;

[0008] Determine a cargo volume conversion coefficient based on the historical purchasing data and the historical shipping data;

[0009] In response to receiving the target procurement data, the pre-shipment data corresponding to the target procurement data is determined according to the cargo quantity conversion coefficient.

[0010] Optionally, obtaining historical purchase data and historical delivery data associated with the historical purchase data includes:

[0011] Determine a first data acquisition period for the historical purchase data and a second data acquisition period for the historical delivery data;

[0012] Acquire historical purchase data generated during the first data acquisition cycle;

[0013] Acquire historical delivery data associated with the historical purchase data generated during the second data acquisition cycle.

[0014] Optionally, the first data acquisition cycle and the second data acquisition cycle include a plurality of data acquisition days, and when no historical purchase data or historical shipment data associated with the historical purchase data is generated on the data acquisition day, the method further includes:

[0015] The data acquisition days included in the first data acquisition cycle and / or the second data acquisition cycle are increased.

[0016] Optionally, before determining the cargo volume conversion coefficient according to the historical purchase data and the historical shipment data, the method further includes: determining the single-day historical purchase data generated on each data acquisition day and the single-day historical shipment data associated with the single-day historical purchase data; judging whether the single-day historical purchase data and the single-day historical shipment data meet the preset cargo volume conversion conditions;

[0017] Determining a cargo volume conversion coefficient according to the historical purchasing data and the historical shipping data includes: determining a cargo volume conversion coefficient according to all single-day historical purchasing data and single-day historical shipping data that meet the cargo volume conversion conditions.

[0018] Optionally, before determining the cargo volume conversion coefficient according to the historical purchase data and the historical shipment data, the method further includes:

[0019] Determining the historical documents corresponding to the historical purchase data and the historical shipment data;

[0020] It is determined whether the document type of the historical document matches a preset target document type.

[0021] Optionally, determining the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient includes:

[0022] Determine the document type of the target document corresponding to the target procurement data, use the target procurement data whose document type matches the target document type as the first procurement data, and use the target procurement data whose document type does not match the target document type as the second procurement data;

[0023] Determining first pre-shipment data of the first procurement data according to the cargo quantity conversion coefficient;

[0024] Determining second pre-shipment data of the second procurement data according to a preset fixed conversion coefficient;

[0025] The first pre-shipment data and the second pre-shipment data are summed to obtain the pre-shipment data corresponding to the target procurement data.

[0026] Optionally, the method further comprises:

[0027] The mode of transportation of the goods corresponding to the target procurement data is determined based on the pre-shipment data.

[0028] According to a second aspect of an embodiment of the present invention, there is provided a device for shipment management, including:

[0029] An acquisition module, configured to acquire historical purchase data and historical shipment data associated with the historical purchase data in response to receiving a data acquisition request;

[0030] A determination module, used to determine a cargo volume conversion coefficient based on the historical purchase data and the historical shipment data;

[0031] The prediction module is used to determine the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient in response to receiving the target procurement data.

[0032] Optionally, obtaining historical purchase data and historical delivery data associated with the historical purchase data includes:

[0033] Determine a first data acquisition period for the historical purchase data and a second data acquisition period for the historical delivery data;

[0034] Acquire historical purchase data generated during the first data acquisition cycle;

[0035] Acquire historical delivery data associated with the historical purchase data generated during the second data acquisition cycle.

[0036] Optionally, the first data acquisition cycle and the second data acquisition cycle include a plurality of data acquisition days, and the device further includes:

[0037] An adding module is used to add a data acquisition day included in the first data acquisition cycle and / or the second data acquisition cycle.

[0038] Optionally, the device further comprises: a single-day data module, used to determine the single-day historical purchase data generated on each data acquisition day and the single-day historical shipment data associated with the single-day historical purchase data; a first determination module, used to determine whether the single-day historical purchase data and the single-day historical shipment data meet the preset cargo quantity conversion conditions;

[0039] Determining a cargo volume conversion coefficient according to the historical purchasing data and the historical shipping data includes: determining a cargo volume conversion coefficient according to all single-day historical purchasing data and single-day historical shipping data that meet the cargo volume conversion conditions.

[0040] Optionally, the device further comprises:

[0041] A historical document module, used to determine the historical documents corresponding to the historical purchase data and the historical delivery data;

[0042] The second determination module is used to determine whether the document type of the historical document matches a preset target document type.

[0043] Optionally, determining the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient includes:

[0044] Determine the document type of the target document corresponding to the target procurement data, use the target procurement data whose document type matches the target document type as the first procurement data, and use the target procurement data whose document type does not match the target document type as the second procurement data;

[0045] Determining first pre-shipment data of the first procurement data according to the cargo quantity conversion coefficient;

[0046] Determining second pre-shipment data of the second procurement data according to a preset fixed conversion coefficient;

[0047] The first pre-shipment data and the second pre-shipment data are summed to obtain the pre-shipment data corresponding to the target procurement data.

[0048] Optionally, the device further comprises:

[0049] The transportation mode module is used to determine the transportation mode of the goods corresponding to the target procurement data based on the pre-shipment data.

[0050] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0051] one or more processors;

[0052] a storage device for storing one or more programs,

[0053] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any one of the above embodiments.

[0054] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0055] One embodiment of the above invention has the following advantages or beneficial effects: determining a cargo volume conversion coefficient based on historical purchase data and historical shipment data, and then predicting shipment data associated with target purchase data based on the cargo volume conversion coefficient, can accurately predict shipment data, improve shipment efficiency, and avoid temporarily determining or adjusting transportation strategies before shipment; setting a first data acquisition cycle to acquire historical purchase data, and setting a second data acquisition cycle to acquire historical shipment data, can flexibly acquire data to meet various data acquisition needs; when the data acquisition cycle includes rest days, increasing the data acquisition days included in the data acquisition cycle can ensure the amount of data acquired, and thus ensure the accuracy of the cargo volume conversion coefficient; judging whether the daily historical purchase data and historical shipment data are valid, and using the valid data for Determining the cargo volume conversion coefficient can eliminate the interference of invalid data on the cargo volume conversion coefficient, and further improve the accuracy of the cargo volume conversion coefficient; judging whether the document type of historical procurement data and historical shipment data matches the pre-set target document type, and screening out the shipment documents of the procurement documents applicable to the cargo volume conversion coefficient, can further improve the accuracy of the cargo volume conversion coefficient; classifying the target procurement data according to the document type, and determining the pre-shipment data of different target procurement data according to different calculation methods, can flexibly determine the pre-shipment data, and determine the pre-shipment data based on multiple factors such as document type and data validity, which is scalable; determining the transportation method of the goods according to the pre-shipment data can improve the delivery efficiency, save the preparation time before shipment, increase the vehicle loading rate, and reduce the transportation cost.

[0056] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0058] Figure 1 is a schematic diagram of the main process of the delivery management method according to an embodiment of the present invention;

[0059] Figure 2 is a schematic diagram of the main process of purchasing and delivery according to a reference embodiment of the present invention;

[0060] Figure 3 is a schematic diagram of the main process of generating pre-shipment data according to a reference embodiment of the present invention;

[0061] Figure 4 is a schematic diagram of the main process of obtaining historical purchase data and historical delivery data according to a reference embodiment of the present invention;

[0062] Figure 5 is a schematic diagram of the main process of delivery management according to a reference embodiment of the present invention;

[0063] Figure 6 is a schematic diagram of the main process of delivery management according to another reference embodiment of the present invention;

[0064] Figure 7 is a schematic diagram of main modules of a delivery management device according to an embodiment of the present invention;

[0065] Figure 8 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0066] Fig. 9 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0068] It should be noted that in the technical solution of the present invention, the collection, use, storage, sharing and transfer of user personal information involved are in compliance with the provisions of relevant laws and regulations, and it is necessary to inform the user and obtain the user's consent or authorization. When applicable, the user's personal information is de-identified and / or anonymized and / or encrypted.

[0069] In the goods procurement process, the purchaser usually sends the purchase data to the manufacturer first, and after the goods are produced, the manufacturer sends the produced goods to the purchaser. For example, in the vehicle parts replenishment business, the regional warehouse that stores the parts sends the purchase data to the central warehouse that produces the parts, and the central warehouse sends the produced parts to the regional warehouse. Since there is a difference between the actual delivery data and the purchase data, and the actual delivery data can only be determined at the time of delivery, it is necessary to temporarily determine the transportation strategy of the goods based on the actual delivery data before delivery, such as the mode of transportation, type of transportation vehicle, etc.

[0070] Temporarily determining the transportation strategy for goods consumes a lot of manpower, material resources and time costs, resulting in low delivery efficiency; if the transportation strategy is determined based on procurement data, due to the difference between procurement data and delivery data, it is easy to have a low vehicle loading rate, and there is a lot of idle space in the transportation vehicles, resulting in a waste of resources; originally, smaller capacity vehicles can be used to complete the transportation task, but the larger capacity vehicles determined based on procurement data increase the transportation cost.

[0071] In view of this, according to a first aspect of an embodiment of the present invention, a method for shipment management is provided.

[0072] Figure 1 FIG. 1 is a schematic diagram of the main process of the method for delivery management according to an embodiment of the present invention. Figure 1 As shown, the method for delivery management according to an embodiment of the present invention mainly includes the following steps S101 to S103.

[0073] Step S101, in response to receiving a data acquisition request, acquiring historical purchase data and historical delivery data associated with the historical purchase data.

[0074] After receiving the data acquisition request, the execution subject of the embodiment of the present invention acquires historical purchase data and historical shipment data. Historical purchase data is purchase data that has completed the purchase process. Purchase data is used to indicate the goods and quantity that the user wants to purchase. Purchase data includes: product name, product specification, purchase quantity, unit price of goods, etc. Historical shipment data is shipment data that has completed the shipment process. Shipment data is used to indicate the goods and quantity sent to the user. Shipment data includes: product name, product specification, shipment quantity, transportation method, etc. Each purchase data has an association relationship with one or more shipment data. For example, the user sends a purchase order to the execution subject of the embodiment of the present invention. The execution subject of the embodiment of the present invention parses the purchase order to obtain purchase data, and then sends the purchase data to the production department. The production department produces goods according to the purchase data. After the goods are generated, the execution subject of the embodiment of the present invention generates shipment data according to the produced goods and their quantity, and ships the goods to the user according to the shipment data. When the goods are sent to the user in multiple batches, multiple shipment data will be generated, that is, the purchase data has an association relationship with multiple shipment data.

[0075] Due to the situation that users temporarily modify purchase orders, production raw materials are in short supply, and other production tasks need to be processed first, there is a difference between the purchase data sent to the production department and the actual quantity of goods produced, that is, the purchase quantity in the purchase data and the shipment quantity in the shipment data are often different. There is also a difference between the purchase quantity of the historical purchase data obtained by the execution subject of the embodiment of the present invention and the shipment quantity of the associated historical purchase data, that is, the purchase data can be greater than the shipment quantity, can be equal to the shipment quantity, or can be less than the shipment quantity.

[0076] The historical purchase data may be one or more purchase data, and the historical shipment data may be one or more shipment data. When there is historical shipment data associated with the historical purchase data, the historical purchase data and historical shipment data with the associated relationship are obtained. When there is only historical purchase data but no historical shipment data, the separate historical purchase data is not obtained. Exemplarily, the execution subject of the embodiment of the present invention obtains the historical purchase data of the previous three days. Specifically, a total of 9 purchase orders were received in the previous three days, and 9 historical purchase data were generated, of which 6 have been shipped, that is, there are 6 historical shipment data, and the remaining 3 purchase data are still waiting in line for processing; therefore, only the historical purchase data and historical shipment data corresponding to the 6 shipped goods are obtained. It is ensured that each acquired historical purchase data has corresponding historical shipment data.

[0077] Acquire historical purchasing data and historical delivery data to provide a data basis for determining the cargo volume conversion coefficient; ensure that historical purchasing data and historical delivery data correspond one-to-one, and retain the correlation between historical purchasing data and historical delivery data, so as to facilitate determining the rules between purchasing data and delivery data and improve the accuracy of the cargo volume conversion coefficient.

[0078] According to a reference embodiment of the present invention, when acquiring historical purchase data and historical shipment data associated with the historical purchase data, the first data acquisition cycle of the historical purchase data and the second data acquisition cycle of the historical shipment data are first determined. The data acquisition cycle is the time period for acquiring the historical purchase data and the historical shipment data, that is, the period of time in the past during which the historical purchase data and the historical shipment data are acquired. The data acquisition cycle can be 5 days, a week, half a month, etc. Since there is a time difference between the generation of purchase data and shipment data, that is, the purchase data is generated first and then the shipment data is generated, the first data acquisition cycle of the historical purchase data is different from the second data acquisition cycle of the historical shipment data. The first data acquisition cycle is earlier than the second data acquisition cycle, but the duration of the first data acquisition cycle is equal to the second data acquisition cycle. For example, if the shipment data is generated 12 hours after the purchase data is generated, the first data acquisition cycle starts 12 hours earlier than the second data acquisition cycle, and the second data acquisition cycle ends 12 hours later than the first data acquisition cycle. The duration of the first data acquisition cycle and the second data acquisition cycle are both 24 hours.

[0079] The purchase data includes the generation time, and the historical purchase data generated in the first data acquisition cycle is obtained; the shipment data includes the generation time, and the historical shipment data generated in the second data acquisition cycle is obtained. The acquired historical purchase data is matched with the historical shipment data to determine the association relationship, and the historical purchase data and historical shipment data with the association relationship are further screened out. The historical purchase data has an association relationship with one or more historical shipment data. When all the goods produced according to the purchase order are sent out at one time, the historical purchase data has an association relationship with one historical shipment data. When the above goods are sent out in multiple batches, the historical purchase data has an association relationship with multiple historical shipment data.

[0080] Exemplarily, the generation time of the purchase data is one day earlier than the generation time of the shipment data, the duration of the data acquisition cycle is 6 days, and it is determined that the first data acquisition cycle is from last Monday to last Saturday, then the second data acquisition cycle is from last Tuesday to last Sunday; the historical purchase data generated between last Monday and last Saturday are obtained to obtain historical purchase data A1, historical purchase data A2, and historical purchase data A3; the historical shipment data generated between last Tuesday and last Sunday are obtained to obtain historical shipment data B1 and historical shipment data B2; wherein, the historical purchase data A1 is associated with the historical purchase data B1, the historical purchase data A2 is associated with the historical purchase data B2, and there is no historical shipment data associated with the historical purchase data B3; therefore, the acquired data includes: historical purchase data A1, historical shipment data B1 associated with the historical purchase data A1, historical purchase data A2, and historical shipment data B2 associated with the historical purchase data A2.

[0081] Acquiring historical purchase data according to the first data acquisition cycle and acquiring historical delivery data according to the second data acquisition cycle can ensure that the acquired historical purchase data is associated with the historical delivery data, improve the accuracy and efficiency of data acquisition, and is suitable for situations where there is a generation time difference between purchase data and delivery data, and has certain scalability.

[0082] According to another reference embodiment of the present invention, the first data acquisition cycle and the second data acquisition cycle include multiple data acquisition days, and the length of the data acquisition cycle is determined according to the number of data acquisition days. For example, if the first data acquisition cycle is from last Monday to last Saturday, then the first data acquisition cycle includes 6 data acquisition days, and the length of the first data acquisition cycle is 6 days.

[0083] Some data acquisition days do not generate either purchase data or shipment data. For example, when the data acquisition day is a statutory holiday, or when the system for generating purchase data and shipment data fails, or when no user sends a purchase order to the execution entity of the embodiment of the present invention, there are data acquisition days that do not generate either purchase data or shipment data. When neither historical purchase data nor historical shipment data associated with the historical purchase data is generated on a data acquisition day, it means that the data acquisition day is a rest day when no data is acquired. For example, when the data acquisition duration is 6 days, 6 days of historical purchase data and historical shipment data need to be acquired, but there is a rest day, so that 5 days of historical purchase data and historical shipment data are actually acquired, that is, the acquired historical purchase data and historical shipment data are reduced. Therefore, when there is a rest day, the data acquisition days included in the first data acquisition cycle and / or the second data acquisition cycle are increased. Specifically, when the first data acquisition cycle includes a data acquisition day when no historical purchase data is acquired, the data acquisition days are extended forward or backward by a corresponding number of days; when the second data acquisition cycle includes a data acquisition day when no historical shipment data is acquired, the data acquisition days are extended forward or backward by a corresponding number of days, until the number of days for which historical purchase data is acquired is equal to the duration of the first data acquisition cycle, and the number of days for which historical shipment data is acquired is equal to the duration of the second data acquisition cycle.

[0084] Exemplarily, the first data acquisition cycle includes: February 1, February 2, February 3, February 4, and the second data acquisition cycle includes: February 2, February 3, February 4, wherein no purchase data or delivery data is generated on February 2, therefore, the first data acquisition cycle and the second data acquisition cycle are extended forward by one day, so that the first data acquisition cycle is adjusted to: January 31, February 1, February 2, February 3, and the second data acquisition cycle is adjusted to: February 1, February 2, February 3, February 4.

[0085] When the data acquisition cycle includes rest days when no historical purchasing data and / or historical shipping data are obtained, extending the first data acquisition cycle and / or the second data acquisition cycle can ensure that the number of days on which data is actually obtained is equal to the duration of the data acquisition cycle, so that the amount of historical purchasing data and historical shipping data obtained meets the demand, thereby ensuring the accuracy of the cargo volume conversion coefficient.

[0086] Step S102, determining a cargo volume conversion coefficient based on the historical purchasing data and the historical shipping data.

[0087] After obtaining the historical purchase data and historical delivery data, the purchase quantity is obtained from the historical purchase data, the delivery quantity is obtained from the historical delivery data, and the cargo volume conversion coefficient is determined according to the purchase data and the delivery quantity. For example, the purchase quantity and the delivery quantity are substituted into the preset cargo volume conversion coefficient calculation formula, factor = (bc1 + bc2 + ...) / (tc1 + tc2 + ...), where "factor" represents the cargo volume conversion coefficient, "bc1" represents the purchase quantity included in the first historical purchase data obtained, and the other purchase quantities are analogous, "tc1" represents the delivery quantity included in the first historical delivery data obtained, and the other delivery quantities are analogous; the sum of the purchase quantities of all historical purchase data is divided by the sum of the delivery quantities of all historical delivery data to obtain the cargo volume conversion coefficient. For another example, the purchase quantity and the shipment quantity are brought into the preset cargo quantity conversion coefficient calculation formula, factor = (bc1 / tc1+bc2 / tc2+...bcn / tcn) / N, where "factor" represents the cargo quantity conversion coefficient, "bc1" represents the purchase quantity included in the first acquired historical purchase data, and other purchase quantities are analogous, "tc1" represents the shipment quantity included in the first acquired historical shipment data, and other shipment quantities are analogous, and "N" represents the number of acquired historical purchase data (or historical shipment data); divide each pair of associated purchase quantity and shipment quantity, and then average all division results to obtain the cargo quantity conversion coefficient. The calculation formula is configurable, and the execution subject of the embodiment of the present invention adjusts the calculation formula according to the received configuration request.

[0088] The cargo volume conversion coefficient is a coefficient used to predict the shipment quantity. The cargo volume conversion coefficient is dynamically updated. Different historical purchase data and historical shipment data can be obtained according to different data acquisition cycles to determine different cargo volume conversion coefficients. For example, historical purchase data and historical shipment data are re-obtained at regular intervals. Since the purchase data and shipment data are constantly updated, the cargo volume conversion coefficient determined based on the historical purchase data and historical shipment data is also changing dynamically, and the new cargo volume conversion coefficient is used to replace the old cargo volume conversion coefficient.

[0089] For another example, sum the purchase quantities of the acquired historical purchase data, sum the shipment quantities of the acquired historical shipment data, determine the target purchase quantity range within which the average value of the sum of the purchase quantities falls, determine the target shipment quantity range within which the average value of the sum of the shipment quantities falls, query the pre-set cargo quantity conversion coefficient table, and determine the cargo quantity conversion coefficient that matches the target purchase quantity range and the target shipment quantity range.

[0090] Determine the cargo volume conversion coefficient based on historical purchase data and historical shipment data to provide a data basis for predicting shipment data; bring the purchase quantity and shipment quantity into the pre-set calculation formula to flexibly determine the cargo volume conversion coefficient to meet different calculation needs; search for the cargo volume conversion coefficient according to the cargo volume conversion coefficient table to improve the efficiency of searching for the cargo volume conversion coefficient.

[0091] According to a reference embodiment of the present invention, before determining the cargo volume conversion coefficient based on the historical purchase data and the historical shipment data, the method further includes: determining the single-day historical purchase data generated on each data acquisition day and the single-day historical shipment data associated with the single-day historical purchase data. It is judged whether the single-day historical purchase data and the single-day historical shipment data meet the preset cargo volume conversion conditions. The cargo volume conversion conditions are the screening conditions for the historical purchase data and the historical shipment data used to determine the cargo volume conversion coefficient. The cargo volume conversion conditions include: the result of dividing the purchase quantity of the single-day historical purchase data by the shipment quantity of the associated single-day historical shipment data meets the preset numerical range, the purchase quantity of the single-day historical purchase data is greater than or equal to the preset purchase quantity threshold, the shipment quantity of the single-day historical shipment data is greater than or equal to the preset shipment quantity threshold, and the like. In the case where the historical purchase data and the historical shipment data meet the cargo volume conversion conditions, it means that the historical purchase data and the historical shipment data are valid and can be used to determine the cargo volume conversion coefficient; in the case where the historical purchase data or the historical shipment data does not meet the cargo volume conversion conditions, it means that the historical purchase data or the historical shipment data is invalid, and the invalid historical purchase data and the historical shipment data are discarded.

[0092] When the historical procurement data does not meet the conditions for cargo conversion, the first data acquisition cycle is extended to obtain a sufficient number of historical procurement data that meet the conditions for cargo conversion; when the historical shipment data does not meet the conditions for cargo conversion, the second data acquisition cycle is extended to obtain a sufficient number of historical shipment data that meet the conditions for cargo conversion.

[0093] When determining the cargo volume conversion coefficient based on the historical purchase data and the historical shipment data, obtain the single-day historical purchase data and the single-day historical shipment data that meet the cargo volume conversion conditions, and determine the cargo volume conversion coefficient based on all the single-day historical purchase data and the single-day historical shipment data that meet the cargo volume conversion conditions. The method for determining the cargo volume conversion coefficient based on the historical purchase data and the historical shipment data has been specifically described in the previous embodiment and will not be repeated here.

[0094] Exemplarily, the first data acquisition cycle includes 3 days, and the historical procurement data acquired include: historical procurement data C1 (procurement quantity is 20), historical procurement data C2 (procurement quantity is 30), and historical procurement data C3 (procurement quantity is 40); the second data acquisition cycle includes 3 days, and the historical shipment data acquired include: historical shipment data D1 (shipment quantity is 10), historical shipment data D2 (shipment quantity is 30), and historical shipment data D3 (shipment quantity is 100); wherein, the preset procurement quantity threshold is 20, and the shipment quantity threshold is 20, and the historical procurement data C1 and the historical shipment data D1 do not meet the above conditions; the division result of the single-day procurement quantity and the single-day shipment quantity with an associated relationship is between 0.7 and 2, and the historical procurement data C3 and the historical procurement data D3 do not meet the above conditions; therefore, the historical procurement data that meet the cargo quantity conversion conditions and the historical shipment data with an associated relationship are the historical procurement data C2 and the historical shipment data D2.

[0095] Screening out historical procurement data and historical shipment data that meet the cargo volume conversion conditions from historical procurement data and historical shipment data can ensure the validity of historical procurement data and historical shipment data, improve the accuracy of the cargo volume conversion coefficient, and avoid abnormal data such as excessively high shipment quantities, too low procurement quantities, etc. that affect the accuracy of the cargo volume conversion coefficient.

[0096] According to another reference embodiment of the present invention, before determining the cargo quantity conversion coefficient based on the historical purchasing data and the historical shipment data, the method also includes: determining the historical documents corresponding to the historical purchasing data and the historical shipment data, for example, the historical documents include historical purchasing documents and historical shipment documents, wherein the historical purchasing data is generated based on the historical purchasing documents, and the historical shipment data is generated based on the historical shipment documents; for another example, the historical document is a historical purchasing document, and the historical purchasing data and the historical shipment data associated with the historical purchasing data are both generated by the historical purchasing documents.

[0097] Purchasing documents and shipping documents have document types. For example, document types classified according to the source of goods include: self-operated documents, third-party documents, etc., document types classified according to user types include: personal documents, company documents, collective documents, etc., and document types classified according to the urgency of shipment include: ordinary documents, expedited documents, urgent documents, etc. In the case where historical documents include purchasing documents and shipping documents, the purchasing documents of the historical purchasing data and the shipping documents of the historical shipment data that have an associated relationship have the same document type.

[0098] A target document type suitable for determining the cargo volume conversion coefficient is preset, and the target document type may be one or more document types, and it is determined whether the document type of the historical document matches the target document type. In the case where the target document type includes the document type of the historical document, it is determined that the historical purchase data and historical shipment data generated by the historical document can be used to determine the cargo volume conversion coefficient; in the case where the target document type does not include the document type of the historical document, it is determined that the historical purchase data and historical shipment data generated by the historical document cannot be used to determine the cargo volume conversion coefficient, and the historical purchase data and historical shipment data should be discarded.

[0099] When the document type of the historical document does not match the target document type, the first data acquisition cycle is extended to acquire a sufficient amount of historical purchase data, and the second data acquisition cycle is extended to acquire a sufficient amount of historical delivery data.

[0100] Exemplarily, the target document types include: non-urgent documents (i.e., ordinary documents other than urgent documents, expedited documents, etc.), the document type of the historical documents corresponding to the acquired historical purchase data E1 and historical shipment data F1 is an urgent document, the document type of the historical documents corresponding to the acquired historical purchase data E2 and historical shipment data F2 is an ordinary document, and the document type of the historical documents corresponding to the acquired historical purchase data E3 and historical shipment data F3 is an expedited document; therefore, the document type of the historical documents corresponding to the historical purchase data E2 and historical shipment data F2, and the document type of the historical documents corresponding to the historical purchase data E3 and historical shipment data F3 match the target document type. The historical purchase data E2, historical shipment data F2, historical purchase data E3, and historical shipment data F3 are retained, and the historical purchase data E1 and historical shipment data F1 are discarded.

[0101] Filter out target historical documents that meet the target document type from the historical documents corresponding to the historical procurement data and historical shipment data, and then filter out the historical procurement data and historical shipment data generated by the target historical documents. This can ensure the validity of the historical procurement data and historical shipment data, improve the accuracy of the cargo volume conversion coefficient, and avoid using historical procurement data and historical shipment data generated by historical documents other than the target document type to affect the accuracy of the cargo volume conversion coefficient.

[0102] Step S103, in response to receiving the target procurement data, determining the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient.

[0103] The target purchase data is purchase data without associated delivery data. For example, a user sends a purchase order to the execution subject of the embodiment of the present invention, and the execution subject of the embodiment of the present invention parses the purchase order to obtain purchase data. For another example, the purchase system receives the purchase order sent by the user, and after the purchase system sends the purchase data corresponding to the purchase order to the production department, the purchase data is sent to the execution subject of the embodiment of the present invention. The goods corresponding to the target purchase data have not yet started the delivery process, so the delivery data associated with the target purchase data has not yet been generated.

[0104] The execution subject of the embodiment of the present invention predicts the shipment data of the target procurement data according to the determined cargo volume conversion coefficient to obtain pre-shipment data. The pre-shipment data is used to predict the shipment quantity of the shipment data associated with the target procurement data, and provide data reference for the corresponding commodity storage and commodity transportation. Specifically, the purchase quantity and cargo volume conversion coefficient included in the target procurement data are brought into the pre-set pre-shipment data calculation formula. For example, the pre-shipment data calculation formula is preTransData=factor*targetbc, where "preTransData" represents the pre-shipment data, "factor" represents the cargo volume conversion coefficient, and "targetbc" represents the purchase quantity included in the target procurement data; the purchase quantity is multiplied by the cargo volume conversion coefficient to obtain the pre-shipment data.

[0105] Exemplarily, the cargo quantity conversion coefficient determined based on historical procurement data and historical shipment data is 0.8, the procurement quantity included in the target procurement data is obtained, and the procurement quantity is 100. The procurement data is multiplied by the cargo quantity conversion coefficient, and the estimated shipment data is 100*0.8=80. Therefore, the executing entity of the embodiment of the present invention predicts that the shipment quantity included in the shipment data corresponding to the target procurement data is 80.

[0106] According to the cargo volume conversion coefficient and the target procurement data, the corresponding delivery quantity can be quickly predicted. Since the cargo volume conversion coefficient is obtained based on the historical procurement data and the historical delivery data, the delivery data predicted based on the cargo volume conversion coefficient is relatively accurate, so that the difference between the actual delivery data and the pre-delivery data is very small. The pre-delivery data obtained according to the embodiment of the present invention is suitable for providing data reference for storage, transportation and other processes after procurement and production.

[0107] According to a reference embodiment of the present invention, determining the pre-shipment data corresponding to the target purchase data according to the cargo quantity conversion coefficient includes: judging the document type of the target document corresponding to the target purchase data, taking the target purchase data whose document type matches the target document type as the first purchase data, the first purchase data is the purchase data suitable for calculating the pre-shipment data using the cargo quantity conversion coefficient, taking the target purchase data whose document type does not match the target document type as the second purchase data, the second purchase data is the purchase data not suitable for calculating the pre-shipment data using the cargo quantity conversion coefficient. Determine the first pre-shipment data of the first purchase data according to the cargo quantity conversion coefficient, specifically, multiply the cargo quantity conversion coefficient by the sum of the purchase quantities included in the first purchase data to obtain the first pre-shipment data. Obtain a preset fixed conversion coefficient, the fixed conversion coefficient is a coefficient used to predict the shipment quantity of the purchase data included in other documents except the target document type, the fixed conversion coefficient is fixed and configurable, determine the second pre-shipment data of the second purchase data according to the fixed conversion coefficient, specifically, multiply the fixed conversion coefficient by the sum of the purchase quantities included in the second purchase data to obtain the second pre-shipment data. The first pre-shipment data and the second pre-shipment data are summed to obtain the pre-shipment data corresponding to the target procurement data.

[0108] Exemplarily, the target purchase data includes: purchase data G1, purchase data G2, purchase data G3, and purchase data G4; wherein, the document type of purchase data G1 and purchase data G2 matches the target document type, and the document type of purchase data G3 and purchase data G4 does not match the target document type; therefore, the purchase quantity included in the purchase data is input into a preset calculation formula to obtain preTransData=(h1+h2)*dfactor+*(h3+h4)*sfactor, wherein "preTransData" represents pre-shipment data, "dfactor" represents the cargo quantity conversion coefficient, "h1" and "h2" represent the purchase quantity of purchase data G1 and the purchase quantity of purchase data G2 respectively, therefore, "(h1+h2)*dfactor" represents the first pre-shipment data, "sfactor" represents the fixed conversion coefficient, "h3" and "h4" represent the purchase quantity of purchase data G3 and the purchase quantity of purchase data G4 respectively, therefore, "(h3+h4)*sfactor" represents the second pre-shipment data, the first pre-shipment data and the second pre-shipment data are added to obtain the pre-shipment data of the target purchase data.

[0109] The target procurement data is divided into two categories according to the document type. One category of procurement data uses the cargo quantity conversion coefficient to calculate the pre-shipment data, and the other category of procurement data uses a fixed conversion coefficient to calculate the pre-shipment data. This can flexibly determine the pre-shipment data of the target procurement data and improve the accuracy of the pre-shipment data. It is suitable for various document types and is scalable.

[0110] According to another reference embodiment of the present invention, the method further includes: determining the mode of transportation of the goods corresponding to the target procurement data according to the pre-shipment data of the target procurement data. The modes of transportation of goods include: vehicle transportation, aircraft transportation, ship transportation, etc. Each mode of transportation also includes multiple transportation tool specifications. For example, the transportation tool specifications corresponding to vehicle transportation include: 9.6-meter van, 6.8-meter van, 9.6-meter high-rail new energy vehicle, 9.6-meter van tail truck, 7.6-meter van tail truck, 4.2-meter van tail truck, 9.6-meter flying wing van, etc. The pre-shipment data is the total quantity of all goods. The specific quantity of each type of goods is determined according to the pre-shipment data, and then the weight and volume of each type of goods are queried to calculate the total weight and total volume of each type of goods, and then the material, transportation precautions, etc. of each type of goods are queried. According to the above-mentioned total volume, total weight, material, and transportation precautions, the mode of transportation of the goods is determined.

[0111] Exemplarily, the pre-shipment data of the target procurement data is 100, that is, 100 commodities are sent, including 50 commodities J1, 30 commodities J2 and 20 commodities J3; by querying the volume of the commodities, it can be obtained that the volume of a single commodity J1 is 0.9 square meters, the volume of a single commodity J2 is 0.8 square meters, and the volume of a single commodity J1 is 1.2 square meters; therefore, the total volume of commodity J1 is 45 square meters, the total volume of commodity J2 is 24 square meters, and the total volume of commodity J3 is 24 square meters; the transportation capacity of a 9.6-meter van tailboard truck is 60 square meters, the transportation capacity of a 7.6-meter van tailboard truck is 45 square meters, and the transportation capacity of a 4.2-meter van tailboard truck is 25 square meters; therefore, a 7.6-meter van tailboard truck is used to transport commodity J1, a 4.2-meter van tailboard truck is used to transport commodity J2, and a 4.2-meter van tailboard truck is used to transport commodity J3.

[0112] Determining the mode of transportation of goods based on pre-shipment data can improve delivery efficiency, save preparation time before delivery, increase vehicle loading rate and reduce transportation costs.

[0113] Figure 2 FIG. 1 is a schematic diagram of the main process of purchasing and shipping according to a reference embodiment of the present invention. Figure 2 As shown, first, the order service center receives the purchase order for purchasing auto parts sent by the user, and the order service center can be a virtual service center (VSC); then the order service center sends the purchase plan included in the purchase order to the parts manufacturer, and the parts manufacturer then sends the production task to the headquarters distribution center warehouse (CDC, Central Distribution Center) according to the purchase plan. After generating the auto parts required by the user, the headquarters distribution center warehouse sends the produced goods (i.e., auto parts) to the regional distribution center warehouse (RDC, Regional Distribution Center). The order service center sends the purchase data to the execution subject of the embodiment of the present invention. Before the headquarters distribution center warehouse completes the production, the execution subject of the embodiment of the present invention predicts the corresponding pre-shipment data based on the purchase data. The execution subject of the embodiment of the present invention sends the pre-shipment data to the headquarters distribution center warehouse, and provides the headquarters distribution center warehouse with data references for processes such as commodity storage and commodity transportation. The headquarters distribution center prepares storage space and transportation methods based on the pre-shipment data. After the headquarters distribution center warehouse sends the produced goods to the regional distribution center warehouse, the actual delivery data is generated; after the transportation process is completed, the above-mentioned purchase data and delivery data become historical purchase data and historical delivery data.

[0114] Figure 3FIG. 1 is a schematic diagram of the main process of generating pre-shipment data according to a reference embodiment of the present invention. Figure 3 As shown, multiple modules participate in the generation process of pre-shipment data, including: a big data processing module, a pre-shipment data generation module, a front-end interface, a transportation scheduling module, etc., wherein the execution body of the embodiment of the present invention includes a pre-shipment data generation module and a transportation scheduling module. First, the big data processing module sends the received T-1 day shipment data to the pre-shipment data generation module at 9:00 on T day, and the pre-shipment data generation module stores the T-1 day shipment data in a database or cache, and then obtains 6 days of historical purchase data from T-3 day forward, and obtains 6 days of historical shipment data from T-2 day forward, wherein the purchase data is stored one day earlier than the corresponding shipment data; judge whether there is historical purchase data on each data acquisition day of the first data acquisition cycle, judge whether there is historical shipment data on each data acquisition day of the second data acquisition cycle, and extend the first data acquisition cycle when the first data acquisition cycle includes a data acquisition day on which no historical purchase data exists, and extend the second data acquisition cycle when the second data acquisition cycle includes a data acquisition day on which no historical shipment data exists; judge whether the historical purchase data and the historical shipment data meet the cargo volume conversion conditions, and use the historical purchase data and the historical shipment data that meet the cargo volume conversion conditions as Valid data, otherwise the historical purchase data and historical shipment data that do not meet the cargo volume conversion conditions are ignored; the cargo volume conversion coefficient is determined according to the screened historical purchase data and historical shipment data, and the determined cargo volume conversion coefficient is used to replace the existing cargo volume conversion coefficient; the big data processing module sends the received T-day purchase data (i.e., the target purchase data) to the pre-shipment data generation module at 18:00 on T day, the pre-shipment data generation module classifies the purchase data, uses the cargo volume conversion coefficient to determine the first pre-shipment data, uses the fixed conversion coefficient to determine the second pre-shipment data, sums the first pre-shipment data and the second pre-shipment data to obtain the pre-shipment data of the T-day purchase data, and then sends the pre-shipment data of the T-day purchase data to the front-end interface to generate the cargo volume forecast for T+1 day, the front-end interface sends the cargo volume forecast for T+1 day to the transportation scheduling module, the scheduling module determines the mode of transportation before shipment, thereby receiving the preparation data before shipment, improving the delivery efficiency, reducing the manpower and material costs, and reducing the transportation cost.

[0115] Figure 4 FIG. 1 is a schematic diagram of the main process of obtaining historical purchase data and historical delivery data according to a reference embodiment of the present invention. Figure 4As shown, the first data acquisition cycle of historical purchase data is Monday to Friday, and the second data acquisition cycle of historical shipment data is Tuesday to Saturday. The generation time of historical purchase data is one day earlier than the generation time of historical shipment data. The historical purchase data on Monday is associated with the historical shipment data on Tuesday, and the same applies to other data. The dates marked with shadows are holidays. Since Saturday and Sunday are holidays, there is no historical shipment data and historical purchase data. Therefore, it is necessary to extend the first data acquisition cycle and / or the second data acquisition cycle to ensure that enough days of historical purchase data and historical shipment data are obtained. Specifically, in the forward extension scheme 401, the first data acquisition cycle of historical purchase data is extended forward by three days to obtain the historical purchase data of last Friday, and the second data acquisition cycle of historical shipment data is extended forward by one day to obtain the historical purchase data of Monday, wherein the date represented by the dotted box is the extended date, and the date with an asterisk is the date of obtaining the historical purchase data and historical shipment data. The historical purchase data of last Friday is associated with the historical purchase data of Monday, and since there is no historical shipment data on Saturday, the historical purchase data on Friday is ignored. In the backward extension scheme 402, the second acquisition cycle of the historical delivery data is extended backward by three days to obtain the historical delivery data of next Monday, wherein the date represented by the dotted box is the extended date, and the date with an asterisk is the date for obtaining the historical purchase data and the historical delivery data.

[0116] Figure 5 FIG. 1 is a schematic diagram of the main flow of delivery management according to a reference embodiment of the present invention. Figure 5 As shown, the delivery management method may include:

[0117] Step S501, in response to receiving a data acquisition request, determining a first data acquisition cycle for historical purchase data and a second data acquisition cycle for historical delivery data;

[0118] Step S502, acquiring historical purchase data generated in a first data acquisition cycle;

[0119] Step S503, acquiring historical delivery data associated with historical purchase data generated in the second data acquisition cycle;

[0120] Step S504, determine whether the historical purchase data meets the quantity conversion conditions, if so, jump to step S506, otherwise, jump to step S505;

[0121] Step S505, ignoring historical purchase data and historical shipment data with associated relationships;

[0122] Step S506, determine whether the historical shipment data meets the cargo quantity conversion conditions, if so, jump to step S508, otherwise, jump to step S507;

[0123] Step S507, ignoring historical delivery data and historical purchase data with associated relationships;

[0124] Step S508, determining a cargo volume conversion coefficient based on historical purchase data and historical shipment data;

[0125] Step S509, in response to receiving the target procurement data, determining the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient.

[0126] The specific implementation content of the delivery management method of the above-mentioned reference embodiment of the present invention has been described in detail in the above-mentioned delivery management method, so the repeated content will not be described again here.

[0127] Figure 6 FIG. 1 is a schematic diagram of the main flow of delivery management according to another reference embodiment of the present invention. Figure 6 As shown, the delivery management method may include:

[0128] Step S601, in response to receiving a data acquisition request, acquiring historical purchase data according to a first data acquisition cycle;

[0129] Step S602, determine whether there is a rest day in the first data acquisition cycle, if yes, jump to step S604, otherwise jump to step S603;

[0130] Step S603, extending the first data acquisition cycle;

[0131] Step S604, acquiring historical shipment data according to the second data acquisition cycle;

[0132] Step S605, determining whether there is a rest day in the second data acquisition cycle, if yes, jump to step S607, otherwise, jump to step S606;

[0133] Step S606, extending the second data acquisition cycle;

[0134] Step S607, determining a cargo volume conversion coefficient based on the historical purchase data and the historical shipment data;

[0135] Step S608, in response to receiving the target purchase data, obtaining the document type of the target document corresponding to the target purchase data;

[0136] Step S609, taking the target purchase data whose document type matches the target document type as the first purchase data;

[0137] Step S610, using the target purchase data whose document type does not match the target document type as the second purchase data;

[0138] Step S611, determining first pre-shipment data of first procurement data according to the cargo quantity conversion coefficient;

[0139] Step S612, determining second pre-shipment data of the second procurement data according to a preset fixed conversion coefficient;

[0140] Step S613, summing the first pre-shipment data and the second pre-shipment data to obtain the pre-shipment data corresponding to the target purchase data;

[0141] Step S614, determining the mode of transportation of the goods corresponding to the target procurement data according to the pre-shipment data.

[0142] The specific implementation content of the delivery management method of another reference embodiment of the present invention has been described in detail in the above-mentioned delivery management method, so the repeated content will not be described here.

[0143] Figure 7 is a schematic diagram of the main modules of the device for delivery management according to an embodiment of the present invention, such as Figure 7 As shown, the delivery management device 700 mainly includes:

[0144] An acquisition module 701 is used to acquire historical purchase data and historical shipment data associated with the historical purchase data in response to receiving a data acquisition request;

[0145] A determination module 702, configured to determine a cargo volume conversion coefficient based on the historical purchase data and the historical shipment data;

[0146] The prediction module 703 is used to determine the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient in response to receiving the target procurement data.

[0147] According to a reference embodiment of the present invention, obtaining historical purchase data and historical delivery data associated with the historical purchase data includes:

[0148] Determine a first data acquisition period for the historical purchase data and a second data acquisition period for the historical delivery data;

[0149] Acquire historical purchase data generated during the first data acquisition cycle;

[0150] Acquire historical delivery data associated with the historical purchase data generated during the second data acquisition cycle.

[0151] According to another reference embodiment of the present invention, the first data acquisition cycle and the second data acquisition cycle include multiple data acquisition days, and the delivery management device 700 further includes:

[0152] An adding module is used to add a data acquisition day included in the first data acquisition cycle and / or the second data acquisition cycle.

[0153] According to another reference embodiment of the present invention, the delivery management device 700 further includes: a single-day data module, used to determine the single-day historical purchase data generated on each data acquisition day and the single-day historical delivery data associated with the single-day historical purchase data; a first determination module, used to determine whether the single-day historical purchase data and the single-day historical delivery data meet the preset cargo quantity conversion conditions;

[0154] Determining a cargo volume conversion coefficient according to the historical purchasing data and the historical shipping data includes: determining a cargo volume conversion coefficient according to all single-day historical purchasing data and single-day historical shipping data that meet the cargo volume conversion conditions.

[0155] According to another reference embodiment of the present invention, the delivery management device 700 further includes:

[0156] A historical document module, used to determine the historical documents corresponding to the historical purchase data and the historical delivery data;

[0157] The second determination module is used to determine whether the document type of the historical document matches a preset target document type.

[0158] According to another reference embodiment of the present invention, determining the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient includes:

[0159] Determine the document type of the target document corresponding to the target procurement data, use the target procurement data whose document type matches the target document type as the first procurement data, and use the target procurement data whose document type does not match the target document type as the second procurement data;

[0160] Determining first pre-shipment data of the first procurement data according to the cargo quantity conversion coefficient;

[0161] Determining second pre-shipment data of the second procurement data according to a preset fixed conversion coefficient;

[0162] The first pre-shipment data and the second pre-shipment data are summed to obtain the pre-shipment data corresponding to the target procurement data.

[0163] According to a reference embodiment of the present invention, the delivery management device 700 further includes:

[0164] The transportation mode module is used to determine the transportation mode of the goods corresponding to the target procurement data based on the pre-shipment data.

[0165] It should be noted that the specific implementation content of the device for shipment management in the embodiment of the present invention has been described in detail in the method for shipment management described above, so the repeated content will not be described here.

[0166] According to the technical solution of the embodiment of the present invention, the cargo conversion coefficient is determined based on the historical procurement data and the historical shipment data, and then the shipment data associated with the target procurement data is predicted based on the cargo conversion coefficient, so that the shipment data can be accurately predicted, the shipment efficiency can be improved, and the temporary determination or adjustment of the transportation strategy before shipment can be avoided; the first data acquisition cycle is set to acquire the historical procurement data, and the second data acquisition cycle is set to acquire the historical shipment data, so that data can be flexibly acquired to meet various data acquisition needs; when the data acquisition cycle includes rest days, the data acquisition days included in the data acquisition cycle are increased, so that the amount of data acquired can be guaranteed, and thus the accuracy of the cargo conversion coefficient can be guaranteed; it is judged whether the daily historical procurement data and historical shipment data are valid, and the valid data is used to determine the cargo conversion coefficient. The conversion coefficient can eliminate the interference of invalid data on the cargo conversion coefficient, and further improve the accuracy of the cargo conversion coefficient; determine whether the document type of historical procurement data and historical shipment data matches the pre-set target document type, and screen out the shipment documents of the procurement documents suitable for the cargo conversion coefficient, which can further improve the accuracy of the cargo conversion coefficient; classify the target procurement data according to the document type, and determine the pre-shipment data of different target procurement data according to different calculation methods, which can flexibly determine the pre-shipment data, and determine the pre-shipment data based on multiple factors such as document type and data validity, which is scalable; determine the transportation method of the goods according to the pre-shipment data, which can improve the delivery efficiency, save the preparation time before shipment, increase the vehicle loading rate, and reduce the transportation cost.

[0167] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method provided by the first aspect of the embodiment of the present invention.

[0168] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0169] Figure 8 An exemplary system architecture 800 is shown to which the method or apparatus for shipment management according to an embodiment of the present invention may be applied.

[0170] like Figure 8As shown, system architecture 800 may include terminal devices 801, 802, 803, a network 804, and a server 805. Network 804 is used to provide a medium for communication links between terminal devices 801, 802, 803 and server 805. Network 804 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0171] The user can use the terminal devices 801, 802, 803 to interact with the server 805 through the network 804 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 801, 802, 803, such as delivery management applications, cargo quantity query applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0172] The terminal devices 801 , 802 , and 803 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0173] Server 805 may be a server that provides various services, such as a background management server (only an example) that supports the request for shipment management sent by the upstream terminal devices 801, 802, and 803. In response to receiving the data acquisition request, the background management server may acquire the historical purchase data and the historical shipment data associated with the historical purchase data; determine the cargo volume conversion coefficient based on the historical purchase data and the historical shipment data; in response to receiving the target purchase data, determine the pre-shipment data corresponding to the target purchase data based on the cargo volume conversion coefficient; and feed back the shipment management situation (only an example) to the terminal device.

[0174] It should be noted that the method for shipment management provided in the embodiment of the present invention is generally executed by the server 805, and accordingly, the shipment management device is generally set in the server 805. The method for shipment management provided in the embodiment of the present invention can also be executed by the terminal devices 801, 802, and 803, and accordingly, the shipment management device can be set in the terminal devices 801, 802, and 803.

[0175] It should be understood that Figure 8 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0176] Reference below Fig. 9 , which shows a schematic diagram of the structure of a computer system 900 of a terminal device suitable for implementing an embodiment of the present invention. Fig. 9The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0177] like Fig. 9 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the system 900 are also stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0178] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage section 908 as needed.

[0179] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, the above-mentioned functions defined in the system of the embodiment of the present invention are executed.

[0180] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In an embodiment of the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0181] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0182] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes an acquisition module, a determination module, and a prediction module, wherein the names of these modules do not constitute a limitation on the modules themselves in certain circumstances, for example, the acquisition module may also be described as a "module for acquiring historical purchase data and historical delivery data".

[0183] As another aspect, an embodiment of the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device implements the following method: in response to receiving a data acquisition request, acquiring historical purchase data and historical shipment data associated with the historical purchase data; determining a cargo volume conversion coefficient based on the historical purchase data and the historical shipment data; in response to receiving target purchase data, determining the pre-shipment data corresponding to the target purchase data based on the cargo volume conversion coefficient.

[0184] According to the technical solution of the embodiment of the present invention, the cargo conversion coefficient is determined based on the historical procurement data and the historical shipment data, and then the shipment data associated with the target procurement data is predicted based on the cargo conversion coefficient, so that the shipment data can be accurately predicted, the shipment efficiency can be improved, and the temporary determination or adjustment of the transportation strategy before shipment can be avoided; the first data acquisition cycle is set to acquire the historical procurement data, and the second data acquisition cycle is set to acquire the historical shipment data, so that data can be flexibly acquired to meet various data acquisition needs; when the data acquisition cycle includes rest days, the data acquisition days included in the data acquisition cycle are increased, so that the amount of data acquired can be guaranteed, and thus the accuracy of the cargo conversion coefficient can be guaranteed; it is judged whether the daily historical procurement data and historical shipment data are valid, and the valid data is used to determine the cargo conversion coefficient. The conversion coefficient can eliminate the interference of invalid data on the cargo conversion coefficient, and further improve the accuracy of the cargo conversion coefficient; determine whether the document type of historical procurement data and historical shipment data matches the pre-set target document type, and screen out the shipment documents of the procurement documents suitable for the cargo conversion coefficient, which can further improve the accuracy of the cargo conversion coefficient; classify the target procurement data according to the document type, and determine the pre-shipment data of different target procurement data according to different calculation methods, which can flexibly determine the pre-shipment data, and determine the pre-shipment data based on multiple factors such as document type and data validity, which is scalable; determine the transportation method of the goods according to the pre-shipment data, which can improve the delivery efficiency, save the preparation time before shipment, increase the vehicle loading rate, and reduce the transportation cost.

[0185] The above specific implementations do not constitute a limitation on the protection scope of the embodiments of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the embodiments of the present invention should be included in the protection scope of the embodiments of the present invention.

Claims

1. A method for shipment management, characterized in that: include: In response to receiving the data acquisition request, acquiring historical purchase data and historical shipment data associated with the historical purchase data; Determine a cargo volume conversion coefficient based on the historical purchasing data and the historical shipping data; In response to receiving the target procurement data, the pre-shipment data corresponding to the target procurement data is determined according to the cargo quantity conversion coefficient.

2. The method according to claim 1, characterized in that Obtaining historical purchase data and historical shipment data associated with the historical purchase data, including: Determine a first data acquisition period for the historical purchase data and a second data acquisition period for the historical delivery data; Acquire historical purchase data generated during the first data acquisition cycle; Acquire historical delivery data associated with the historical purchase data generated during the second data acquisition cycle.

3. The method according to claim 2, characterized in that The first data acquisition cycle and the second data acquisition cycle include a plurality of data acquisition days. In the case that no historical purchase data and no historical shipment data associated with the historical purchase data are generated on the data acquisition days, the method further includes: The data acquisition days included in the first data acquisition cycle and / or the second data acquisition cycle are increased.

4. The method according to claim 3, characterized in that Before determining the cargo volume conversion coefficient based on the historical purchase data and the historical shipment data, the method further includes: determining the single-day historical purchase data generated on each data acquisition day and the single-day historical shipment data associated with the single-day historical purchase data; judging whether the single-day historical purchase data and the single-day historical shipment data meet the preset cargo volume conversion conditions; Determining a cargo volume conversion coefficient according to the historical purchasing data and the historical shipping data includes: determining a cargo volume conversion coefficient according to all single-day historical purchasing data and single-day historical shipping data that meet the cargo volume conversion conditions.

5. The method according to claim 1, characterized in that Before determining the cargo volume conversion coefficient according to the historical purchase data and the historical shipment data, the method further includes: Determining the historical documents corresponding to the historical purchase data and the historical shipment data; It is determined whether the document type of the historical document matches a preset target document type.

6. The method according to claim 5, characterized in that Determining the pre-shipment data corresponding to the target procurement data according to the cargo volume conversion coefficient includes: Determine the document type of the target document corresponding to the target procurement data, use the target procurement data whose document type matches the target document type as the first procurement data, and use the target procurement data whose document type does not match the target document type as the second procurement data; Determining first pre-shipment data of the first procurement data according to the cargo quantity conversion coefficient; Determining second pre-shipment data of the second procurement data according to a preset fixed conversion coefficient; The first pre-shipment data and the second pre-shipment data are summed to obtain the pre-shipment data corresponding to the target procurement data.

7. The method according to claim 1, characterized in that The method further comprises: The mode of transportation of the goods corresponding to the target procurement data is determined based on the pre-shipment data.

8. A device for shipment management, characterized in that: include: An acquisition module, configured to acquire historical purchase data and historical shipment data associated with the historical purchase data in response to receiving a data acquisition request; A determination module, used to determine a cargo volume conversion coefficient based on the historical purchase data and the historical shipment data; The prediction module is used to determine the pre-shipment data corresponding to the target procurement data according to the cargo quantity conversion coefficient in response to receiving the target procurement data.

9. An electronic device, characterized in that: include: One or more processors; A storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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