Method, device and equipment for determining delivery duration information and storage medium

Abstract

The application discloses a kind of method for determining distribution time length information, device, equipment and storage medium, the method includes: obtaining the historical order information and historical maximum radiation distance of current merchant in original distribution area, and the target order information and target maximum radiation distance of the current merchant in target distribution area;Determine the target distribution time length probability distribution information corresponding to the target distribution area of the current merchant in the target distribution area based on the historical order information, the historical maximum radiation distance, the target order information and the target maximum radiation distance;Distribution time length information of the current merchant in the target distribution area is determined based on the target distribution time length probability distribution information.The application can realize accurate evaluation of the distribution time length information of the current merchant in the target distribution area, and then accurate basis can be provided for subsequent judgment whether to expand distribution area, and the distribution area evaluation needs of the current merchant can be met.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, and storage medium for determining delivery time information. Background Technology

[0002] Typically, food delivery merchants have fixed delivery areas. Some merchants hope to increase orders and total revenue by expanding their delivery areas. However, continuously expanding the delivery area will increase delivery time and reduce customer satisfaction. Therefore, how to reasonably evaluate the delivery time information after expanding the delivery area, and thus provide an accurate basis for subsequent judgments on whether to expand the delivery area, is one of the urgent technical problems to be solved. Summary of the Invention

[0003] In view of this, the present invention proposes a method, apparatus, device and storage medium for determining delivery time information to solve the above-mentioned technical problems.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] According to a first aspect of the present invention, a method for determining delivery time information is provided, comprising:

[0006] Obtain the historical order information and historical maximum coverage distance of the current merchant in the original delivery area, as well as the target order information and target maximum coverage distance of the current merchant in the target delivery area;

[0007] Based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance, determine the probability distribution information of the target delivery time for the current merchant in the target delivery area;

[0008] The delivery time information of the current merchant within the target delivery area is determined based on the target delivery time probability distribution information.

[0009] In some embodiments, determining the probability distribution information of the target delivery time for the current merchant in the target delivery area based on the historical order information, the historical maximum coverage distance, the target order information, and the target maximum coverage distance includes:

[0010] Based on the historical order information and the historical maximum radiation distance, determine the original delivery time probability distribution information of the current merchant in the original delivery area;

[0011] Based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information, the target delivery time probability distribution information corresponding to the current merchant in the target delivery area is determined.

[0012] In some embodiments, the original delivery time probability distribution information includes an original delivery time probability density model, and the target delivery time probability distribution information includes a target delivery time probability density model.

[0013] In some embodiments, determining the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum coverage distance, the target order information, the target maximum coverage distance, and the original delivery time probability distribution information includes:

[0014] Based on the historical order information and the first position parameter of the original delivery time probability density model, the second position parameter of the target delivery time probability density model is determined.

[0015] Based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model, the second scale parameter of the target delivery time probability density model is determined.

[0016] The target delivery time probability density model is determined based on the second location parameter and the second scale parameter.

[0017] In some embodiments, the historical order information includes the number of historical orders and the historical delivery distance, and the target order information includes the number of target orders and the target delivery distance of the target orders;

[0018] The process of determining the second position parameter of the target delivery time probability density model based on the historical order information and the first position parameter of the original delivery time probability density model includes:

[0019] Obtain the target order quantity and target delivery distance of the target orders for the current merchant within the target delivery area;

[0020] The second location parameter is determined based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter.

[0021] In some embodiments, both the original delivery area and the target delivery area are divided into multiple grids of the same size;

[0022] The step of determining the second location parameter based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter includes:

[0023] The first merchant radiation weight distance of the current merchant in the original delivery area is determined based on the number of historical orders and the historical delivery distance in multiple grids within the original delivery area.

[0024] The second merchant radiation weight distance of the current merchant in the target delivery area is determined based on the number of target orders and the target delivery distance in multiple grids within the target delivery area, as well as the radiation weight distance of the first merchant.

[0025] The second location parameter is determined based on the first location parameter, the first merchant radiation weight distance, and the second merchant radiation weight distance.

[0026] In some embodiments, determining the second scale parameters of the target delivery time probability density model based on the historical maximum radiation distance and the first scale parameters of the original delivery time probability density model includes:

[0027] Obtain the maximum target coverage distance of the current merchant within the target delivery area;

[0028] The second scale parameter is determined based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter.

[0029] In some embodiments, both the original delivery area and the target delivery area are divided into multiple grids of the same size;

[0030] Before determining the second scale parameter based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter, the method further includes:

[0031] The historical maximum radiation distance is determined based on the distance between the grid furthest from the current merchant within the original delivery area and the current merchant.

[0032] The maximum radiation distance of the target is determined based on the distance between the grid furthest from the current merchant within the target delivery area and the current merchant.

[0033] In some embodiments, the delivery time information includes: the proportion of a specific order of the current merchant in the target delivery area to the total number of orders, wherein the specific order includes orders with a delivery time within a preset delivery time;

[0034] The step of determining the delivery time information of the current merchant within the target delivery area based on the target delivery time probability distribution information includes:

[0035] Obtain at least one preset delivery time currently input;

[0036] Based on the target delivery time probability distribution information, determine the proportion of the specific order of the current merchant in the target delivery area to the total number of orders.

[0037] According to a second aspect of the present invention, an apparatus for determining delivery time information is provided, comprising:

[0038] The information distance acquisition module is used to acquire the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area;

[0039] The distribution information determination module is used to determine the probability distribution information of the target delivery time of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance;

[0040] The duration information determination module is used to determine the delivery duration information of the current merchant in the target delivery area based on the target delivery duration probability distribution information.

[0041] In some embodiments, the distribution information determination module includes:

[0042] The original information determination unit is used to determine the original delivery time probability distribution information of the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance;

[0043] The target information determination unit is used to determine the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information.

[0044] In some embodiments, the original delivery time probability distribution information includes an original delivery time probability density model, and the target delivery time probability distribution information includes a target delivery time probability density model.

[0045] In some embodiments, the target information determining unit is further configured to:

[0046] Based on the historical order information and the first position parameter of the original delivery time probability density model, the second position parameter of the target delivery time probability density model is determined.

[0047] Based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model, the second scale parameter of the target delivery time probability density model is determined.

[0048] The target delivery time probability density model is determined based on the second location parameter and the second scale parameter.

[0049] In some embodiments, the historical order information includes the number of historical orders and the historical delivery distance, and the target order information includes the number of target orders and the target delivery distance of the target orders;

[0050] The target information determination unit is further configured to:

[0051] Obtain the target order quantity and target delivery distance of the target orders for the current merchant within the target delivery area;

[0052] The second location parameter is determined based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter.

[0053] In some embodiments, both the original delivery area and the target delivery area are divided into multiple grids of the same size;

[0054] The target information determination unit is further configured to:

[0055] The first merchant radiation weight distance of the current merchant in the original delivery area is determined based on the number of historical orders and the historical delivery distance in multiple grids within the original delivery area.

[0056] The second merchant radiation weight distance of the current merchant in the target delivery area is determined based on the number of target orders and the target delivery distance in multiple grids within the target delivery area, as well as the radiation weight distance of the first merchant.

[0057] The second location parameter is determined based on the first location parameter, the first merchant radiation weight distance, and the second merchant radiation weight distance.

[0058] In some embodiments, the target information determining unit is further configured to:

[0059] Obtain the maximum target coverage distance of the current merchant within the target delivery area;

[0060] The second scale parameter is determined based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter.

[0061] In some embodiments, both the original delivery area and the target delivery area are divided into multiple grids of the same size;

[0062] The target information determination unit is further configured to:

[0063] The historical maximum radiation distance is determined based on the distance between the grid furthest from the current merchant within the original delivery area and the current merchant.

[0064] The maximum radiation distance of the target is determined based on the distance between the grid furthest from the current merchant within the target delivery area and the current merchant.

[0065] In some embodiments, the delivery time information includes: the proportion of a specific order of the current merchant in the target delivery area to the total number of orders, wherein the specific order includes orders with a delivery time within a preset delivery time;

[0066] The duration information determination module includes:

[0067] The duration acquisition unit is used to acquire at least one preset delivery duration currently input;

[0068] An information determination unit is used to determine, based on the target delivery time probability distribution information, the proportion of the specific order of the current merchant in the target delivery area to the total number of orders.

[0069] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0070] processor;

[0071] Memory used to store computer programs;

[0072] The processor is configured to implement the method for determining delivery time information as described in any of the first aspects when executing the computer program.

[0073] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when processed by a processor, implements the method for determining delivery time information as described in any one of the first aspects above.

[0074] Compared with existing technologies, this invention obtains historical order information and historical maximum coverage distance of the current merchant within the original delivery area, as well as target order information and target maximum coverage distance of the current merchant within the target delivery area. Based on the historical order information, the historical maximum coverage distance, the target order information, and the target maximum coverage distance, it determines the target delivery time probability distribution information of the current merchant within the target delivery area. Furthermore, based on the target delivery time probability distribution information, it determines the delivery time information of the current merchant within the target delivery area. This allows for an accurate assessment of the current merchant's delivery time within the target delivery area based on the determined target delivery time probability distribution information. Consequently, it provides an accurate basis for subsequent decisions on whether to expand the original delivery area to the target delivery area, thus meeting the current merchant's delivery area assessment needs. Attached Figure Description

[0075] Figure 1 A flowchart illustrating a method for determining delivery time information according to an exemplary embodiment of the present invention is shown;

[0076] Figure 2A A flowchart illustrating how, according to the present invention, the probability distribution information of the target delivery time of the current merchant in the target delivery area is determined based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance;

[0077] Figure 2B A schematic diagram of the normal distribution probability density model of order delivery time according to the present invention is shown;

[0078] Figure 3 A flowchart illustrating how, according to the present invention, the target delivery time probability distribution information of the current merchant in the target delivery area is determined based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information;

[0079] Figure 4 A flowchart illustrating how, according to the present invention, a second location parameter of the target delivery time probability density model is determined based on the historical order information and a first location parameter of the original delivery time probability density model;

[0080] Figure 5A A flowchart illustrating how, according to the present invention, the second location parameter is determined based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter;

[0081] Figure 5BA schematic diagram of the grid division of the delivery area according to the present invention is shown;

[0082] Figure 5C A schematic diagram illustrating the calculation method of merchant radiation weight distance according to the present invention is shown;

[0083] Figure 6A A flowchart illustrating how, according to the present invention, a second scale parameter of the target delivery time probability density model is determined based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model;

[0084] Figure 6B A schematic diagram illustrating the calculation method for the maximum radiation distance of merchants according to the present invention is shown;

[0085] Figure 7 A flowchart illustrating how, according to the present invention, the delivery time information of the current merchant within the target delivery area is determined based on the target delivery time probability distribution information;

[0086] Figure 8 A structural block diagram of an apparatus for determining delivery time information according to an exemplary embodiment of the present invention is shown;

[0087] Figure 9 A structural block diagram of an apparatus for determining delivery time information according to another exemplary embodiment of the present invention is shown;

[0088] Figure 10 A structural block diagram of an electronic device according to an exemplary embodiment of the present invention is shown. Detailed Implementation

[0089] The present invention will now be described in detail with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the scope of protection of the present invention.

[0090] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular forms “a,” “the,” and “the” used in this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0091] It should be understood that although the terms first, second, etc. may be used in this invention to describe various structures, these structures should not be limited to these terms. These terms are only used to distinguish structures of the same type from each other.

[0092] Nowadays, food delivery merchants typically have fixed delivery areas. Some merchants hope to increase orders and thus increase total revenue by expanding their delivery areas. However, continuously expanding the delivery area will increase delivery time and reduce customer satisfaction. Therefore, accurately determining whether to expand the delivery area is of great importance to food delivery merchants.

[0093] The existing solutions in related technologies assume that the average delivery time for takeout is directly proportional to the product of the area of ​​the business district and the total number of orders; that is, the larger the area of ​​the business district and / or the total number of orders, the longer the average delivery time. Therefore, when determining whether to expand the delivery area, the average delivery time of the new business district can be calculated based on the product of the area of ​​the original business district and the total number of orders, the area of ​​the new business district and the total number of orders, and the average delivery time of the original business district. This average delivery time is then analyzed to determine whether to expand the delivery area. However, this approach only reflects the delivery situation after the business district is expanded based on the "average" delivery time, providing a rather one-sided view of the information. Furthermore, the average delivery time is calculated based on the assumption that "the average delivery time for takeout is directly proportional to the product of the area of ​​the business district and the total number of orders," which cannot guarantee the accuracy of the average delivery time and thus reduces the accuracy of takeout merchants' decisions on whether to expand the delivery area. In view of this, the embodiments of the present invention provide the following methods, apparatus, devices, and storage media for determining delivery time information to solve the above-mentioned problems in the related technologies.

[0094] Figure 1 A flowchart illustrating a method for determining delivery time information according to an exemplary embodiment of the present invention is shown. This method can be applied to terminal devices with data processing capabilities (e.g., smartphones, tablets, or desktop computers), or to a server (e.g., a single server or a server cluster). The following description uses a terminal device as an example.

[0095] like Figure 1 As shown, the method includes the following steps S101-S103:

[0096] In step S101, the historical order information and historical maximum radiation distance of the current merchant in the original delivery area are obtained, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area.

[0097] In this embodiment, when it is necessary to evaluate the delivery time information of the current merchant in the target delivery area, the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area can be obtained.

[0098] The original delivery area can include the delivery area of ​​the current merchant before the delivery area was expanded, while the target delivery area can include the delivery area of ​​the current merchant after the delivery area was expanded.

[0099] The aforementioned historical order information may include the number of historical orders completed by the current merchant within the original delivery area and / or the delivery distance of historical orders, etc. This embodiment does not limit this.

[0100] In this embodiment, the radiation distance can be understood as the distance that the current merchant can deliver within the corresponding delivery area, or the distance that the current merchant's delivery range within the corresponding delivery area can cover (i.e., the distance that the merchant's delivery can reach), or the distance between the current merchant and the deliverable locations within the administrative region where the corresponding delivery area is located, or the distance between the current merchant and the deliverable locations within the custom area where the corresponding delivery area is located. This embodiment does not limit this.

[0101] Therefore, the aforementioned maximum historical coverage distance can be understood as the farthest distance that the current merchant can deliver within the original delivery area, or the farthest distance that the current merchant's delivery range within the original delivery area can cover (i.e., the farthest distance that the merchant's delivery can reach), or the farthest distance between the current merchant and deliverable locations within the administrative region where the original delivery area is located, or the farthest distance between the current merchant and deliverable locations within a custom area where the original delivery area is located. This embodiment does not limit this. It is understood that the maximum coverage distance can be used to characterize the current merchant's ability to deliver within the corresponding delivery area, and is not necessarily the farthest distance actually delivered. The aforementioned target order information may include the number of target orders that the current merchant is expected to transact within the target delivery area and / or the delivery distance of the target orders, etc. This embodiment does not limit this.

[0102] The maximum coverage distance mentioned above can include the farthest distance that the merchant can deliver within the target delivery area.

[0103] In step S102, the probability distribution information of the target delivery time of the current merchant in the target delivery area is determined based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance.

[0104] In this embodiment, after obtaining the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area, the target delivery time probability distribution information of the current merchant in the target delivery area can be determined based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance.

[0105] The aforementioned target delivery time probability distribution information can be used to evaluate the current merchant's delivery time information within the target delivery area. For example, when expanding the delivery area from the original delivery area to the target delivery area, but keeping the delivery capacity unchanged, the current merchant's delivery time and related information within the target delivery area can be evaluated based on the aforementioned target delivery time probability distribution information. Exemplarily, this delivery time and related information may include the proportion of orders with delivery times within any given delivery time to the total number of orders. Specifically, in this embodiment, a time probability delivery index PT (Probabilistic time) for the food delivery merchant can be defined. This index can be used to characterize the proportion of the number of orders placed by the food delivery merchant within any given delivery time in a defined area to the total number of orders. For example, a PT index "9030" can represent that orders with delivery times within 30 minutes account for 90% of the total orders. In some embodiments, the aforementioned PT index "9030" can also be expressed as: P(T≤30)=90%, which is not limited in this embodiment.

[0106] In this embodiment, after obtaining the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, and the target order information and target maximum radiation distance of the current merchant in the target delivery area, the original delivery time probability distribution information of the current merchant in the original delivery area can be determined based on the historical order information and the historical maximum radiation distance. This original delivery time probability distribution information can be used to describe the delivery time information of the current merchant in the original delivery area, such as the proportion of the number of orders within any delivery time period in the original delivery area to the total number of orders. Based on this, further analysis can be performed based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information to obtain the target delivery time probability distribution information of the current merchant in the target delivery area.

[0107] In some embodiments, the method for determining the probability distribution information of the target delivery time of the current merchant in the target delivery area can be found in the following. Figure 2A The embodiments shown will not be described in detail here.

[0108] In step S103, the delivery time information of the current merchant in the target delivery area is determined based on the target delivery time probability distribution information.

[0109] In this embodiment, after determining the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance, the delivery time information of the current merchant in the target delivery area can be determined based on the target delivery time probability distribution information. For example, it can assess the proportion of orders with delivery times within any given delivery time range to the total orders if the current merchant expands its delivery area from the original delivery area to the target delivery area. This can be achieved by obtaining PT indicators such as "9040", "7835", and "6330", which respectively indicate that orders with delivery times within 40 minutes account for 90% of the total orders, orders with delivery times within 35 minutes account for 78% of the total orders, and orders with delivery times within 30 minutes account for 63% of the total orders. Based on this information, the current merchant can determine whether to expand its delivery area. Compared to related technologies that only use "average" delivery times to reflect the delivery situation after the business area is expanded, the delivery time information assessed in this embodiment is more comprehensive, thus providing an accurate basis for determining whether to expand the original delivery area to the target delivery area.

[0110] In another embodiment, the method for determining the delivery time information of the current merchant within the target delivery area based on the target delivery time probability distribution information can also be found in the following: Figure 7 The embodiments shown will not be described in detail here.

[0111] As described above, the method of this embodiment obtains the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area. Based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance, it determines the target delivery time probability distribution information of the current merchant in the target delivery area. Then, based on the target delivery time probability distribution information, it determines the delivery time information of the current merchant in the target delivery area. This can accurately assess the delivery time information of the current merchant in the target delivery area based on the determined target delivery time probability distribution information, thereby providing an accurate basis for subsequent judgment on whether to expand the original delivery area to the target delivery area, and meeting the current merchant's delivery area assessment needs.

[0112] Figure 2AA flowchart illustrating how to determine the probability distribution information of the target delivery time of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance, according to the present invention, is provided. This embodiment, based on the above embodiment, takes the determination of the probability distribution information of the target delivery time of the current merchant in the target delivery area as an example for illustrative explanation.

[0113] like Figure 2A As shown, the step S102 above, which involves determining the probability distribution information of the target delivery time of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance, may include the following steps S201-S202:

[0114] In step S201, the probability distribution information of the original delivery time of the current merchant in the original delivery area is determined based on the historical order information and the historical maximum radiation distance.

[0115] The aforementioned original delivery time probability distribution information may include an original delivery time probability density model.

[0116] The aforementioned historical order information may include the number of historical orders and the corresponding historical delivery distance for each historical order. Furthermore, a probabilistic time (PT) delivery index for food delivery merchants can be defined. This index characterizes the proportion of orders placed within any delivery time period in a defined area to the total number of orders. For example, a PT index "9030" can indicate that orders with a delivery time of less than 30 minutes account for 90% of the total orders. In some embodiments, the PT index "9030" can also be expressed as: P(T≤30)=90%, which is not limited in this embodiment. Based on this, by analyzing the PT index, it can be determined that the delivery time of historical orders conforms to a normal distribution, and thus the order delivery time probability density model described in equation (2-1) can be constructed:

[0117]

[0118] Where t is the delivery time; the location parameter μ (mean) can be used to control the center line of the distribution, that is, the larger μ is, the more the distribution is to the right; the scale parameter σ (standard deviation) can be used to control the span of the distribution, that is, the larger σ is, the more the distribution extends to both sides and the flatter it is overall. For example, the probability density model of this normal distribution can be found in [reference needed]. Figure 2B .like Figure 2BAs shown, the location parameter μ and scale parameter σ of the above probability density model have the following temporal and spatial effects on the order delivery of food delivery merchants:

[0119] In terms of time: an increase in order volume means a longer average delivery time, which in turn leads to a change in the location parameter μ (i.e., Figure 2B An increase in the mean μ in the probability density model will cause the probability density model to shift to the right on the coordinate axis as a whole;

[0120] Spatially: An increase in the merchant's delivery area means a longer delivery time for the furthest order, which in turn leads to an increase in the scale parameter σ, causing the probability density model to extend to both sides of the coordinate axis. Specifically, for example... Figure 2B As shown, the proportion of values ​​within one standard deviation σ of the mean μ is 68% of all values, the proportion of values ​​within two standard deviations σ of the mean μ is 95.4% of all values, and the proportion of values ​​within three standard deviations σ is 99.7% of all values. Therefore, as σ increases, the probability density model will extend to both sides of the coordinate axis.

[0121] Based on this, a probability density model of the original delivery time corresponding to the current merchant in the original delivery area can be determined based on historical order information and the historical maximum radiation distance. For example, the location parameter μ can be determined based on the average delivery time of historical orders. ori =20, while the scale parameter σ is determined based on the historical maximum radiation distance. ori =5, thus the original delivery time probability density model can be obtained, as shown in equation (2-2) below:

[0122]

[0123] Furthermore, based on this original delivery time probability density model, P can be determined. ori (T≤30)=90%, which means that within the original delivery area, 90% of the merchant's orders have a delivery time of less than 30 minutes.

[0124] In step S202, based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information, the target delivery time probability distribution information corresponding to the current merchant in the target delivery area is determined.

[0125] The aforementioned target delivery time probability distribution information may include a target delivery time probability density model.

[0126] In this embodiment, after determining the original delivery time probability density model of the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance, the target delivery time probability density model of the current merchant in the target delivery area can be determined based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability density model.

[0127] In some embodiments, after determining the original delivery time probability density model corresponding to the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance, relevant parameters of the target delivery time probability density model, such as the location parameter μ, can be determined based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability density model. new and scale parameter σ new Then, these two parameters can be substituted into the formula shown in (2-1) above to obtain the probability density model of the target delivery time.

[0128] In another embodiment, the method for determining the probability distribution information of the target delivery time of the current merchant in the target delivery area can also be found in the following: Figure 3 The embodiments shown will not be described in detail here.

[0129] Figure 3 A flowchart illustrating how, according to the present invention, the target delivery time probability distribution information of the current merchant in the target delivery area is determined based on the historical order information, the historical maximum coverage distance, the target order information, the target maximum coverage distance, and the original delivery time probability distribution information; this embodiment, based on the above embodiment, provides an exemplary description using the example of how to determine the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum coverage distance, the target order information, the target maximum coverage distance, and the original delivery time probability distribution information. Figure 3 As shown, the step S202 above, which involves determining the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information, may include the following steps S301-S303:

[0130] In step S301, based on the historical order information and the first position parameter of the original delivery time probability density model, the second position parameter of the target delivery time probability density model is determined.

[0131] In this embodiment, after determining the original delivery time probability density model of the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance, the second position parameter of the target delivery time probability density model can be determined based on the historical order information and the first position parameter of the original delivery time probability density model.

[0132] For example, after determining the original delivery time probability density model as shown in (2-2) above based on the historical order information and the historical maximum radiation distance, the model can be based on the historical order information and the first location parameter (i.e., μ) of the original delivery time probability density model. ori ), determine the second location parameter (i.e., μ) of the target delivery time probability density model. new ).

[0133] It is worth noting that this embodiment presupposes that the delivery capacity of the merchant after expanding the delivery area is the same. Therefore, the ratio of the second location parameter of the target delivery area to the first location parameter of the original delivery area should be proportional to the ratio of the second merchant radiation weight distance of the target delivery area to the first merchant radiation weight distance of the original delivery area. The merchant radiation weight distance can be the sum of the products of the number of orders a merchant places in the corresponding delivery area and the delivery distance of each order. Therefore, the second location parameter μ can be determined based on the following formula (3-1). new :

[0134]

[0135] Where, d new d represents the radius weight distance of the second merchant. ori The radius weight distance for the first merchant is denoted by k1, which is an adjustable coefficient with a default value of 1. For example, this coefficient can be determined based on actual order data; this embodiment does not limit this determination.

[0136] In another embodiment, the method for determining the second location parameter of the target delivery time probability density model described above can refer to the following... Figure 4 The embodiments shown will not be described in detail here.

[0137] In step S302, based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model, the second scale parameter of the target delivery time probability density model is determined.

[0138] In this embodiment, after determining the original delivery time probability density model of the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance, the second scale parameter of the target delivery time probability density model can be determined based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model.

[0139] For example, after determining the original delivery time probability density model as shown in (2-2) above based on the historical order information and the historical maximum radiation distance, the model can be based on the historical maximum radiation distance (e.g., r). ori ) and the first scale parameter (i.e., σ) of the original delivery time probability density model. ori ), determine the second scale parameter (i.e., σ) of the target delivery time probability density model. new ).

[0140] It is worth noting that this embodiment pre-assuming that the delivery capacity of the merchant after expanding the delivery area is the same. Therefore, the ratio of the second scale parameter of the target delivery area to the first scale parameter of the original delivery area, and the target maximum radiation distance of the target delivery area (e.g., r) are considered. new The ratio of the merchant's maximum historical radiation distance to the original delivery area should be proportional. Here, the merchant's maximum radiation distance can be the furthest delivery distance for orders within the corresponding delivery area. Therefore, the second scale parameter σ can be determined based on the following formula (3-2). new :

[0141]

[0142] Wherein, k2 is an adjustable coefficient, whose default value can be 1. For example, this coefficient can be determined based on actual order data, and this embodiment does not limit it in this way.

[0143] In another embodiment, the method for determining the second scale parameter of the target delivery time probability density model described above can refer to the following... Figure 6A The embodiments shown will not be described in detail here.

[0144] In step S303, the target delivery time probability density model is determined based on the second location parameter and the second scale parameter.

[0145] In this embodiment, when the second location parameter and the second scale parameter of the target delivery time probability density model are determined, i.e., the location parameter μ new and scale parameter σ new Then, these two parameters can be substituted into the formula shown in (2-1) above to obtain the probability density model of the target delivery time.

[0146] For example, if the position parameter μ is calculated new =24, scale parameter σ new =8, then substituting into the formula shown in (2-1) above, we can obtain the target delivery time probability density model as follows:

[0147]

[0148] Figure 4 A flowchart illustrating how to determine the second position parameter of the target delivery time probability density model based on the historical order information and the first position parameter of the original delivery time probability density model according to the present invention is provided. This embodiment, based on the above embodiment, takes how to determine the second position parameter of the target delivery time probability density model based on the historical order information and the first position parameter of the original delivery time probability density model as an example for illustrative explanation.

[0149] In this embodiment, historical order information may include the number of historical orders and the historical delivery distance, and target order information may include the number of target orders and the target delivery distance of the target orders.

[0150] Based on this, such as Figure 4 As shown, the step S301 above, which involves determining the second position parameter of the target delivery time probability density model based on the historical order information and the first position parameter of the original delivery time probability density model, may include the following steps S401-S402:

[0151] In step S401, the target order quantity and target delivery distance of the target orders of the current merchant within the target delivery area are obtained.

[0152] In this embodiment, when it is necessary to determine the second location parameter of the target delivery time probability density model, the target order quantity of the current merchant in the target delivery area and the target delivery distance of the target order can be obtained first.

[0153] For example, the target order quantity and target delivery distance can be estimated based on the historical order information of other merchants in the target delivery area, or the target order quantity and target delivery distance can be estimated based on the number of mobile terminal users in the target delivery area. This embodiment does not limit this.

[0154] In step S402, the second location parameter is determined based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter.

[0155] Figure 5A A flowchart illustrating how, according to the present invention, a second location parameter is determined based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter. In this embodiment, both the original delivery area and the target delivery area are divided into multiple grids of the same size. Furthermore, as... Figure 5A As shown, the step S402 above, which involves determining the second location parameter based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter, may include the following steps S501-S503:

[0156] In step S501, the first merchant radiation weight distance of the current merchant in the original delivery area is determined based on the number of historical orders and the historical delivery distance in multiple grids within the original delivery area;

[0157] In step S502, the second merchant radiation weight distance of the current merchant in the target delivery area is determined based on the number of target orders and the target delivery distance in multiple grids within the target delivery area, as well as the first merchant radiation weight distance.

[0158] In step S503, the second location parameter is determined based on the first location parameter, the first merchant radiation weight distance, and the second merchant radiation weight distance.

[0159] For example, in this embodiment, the current merchant's target delivery area and historical delivery area can be pre-divided into grids of the same size (e.g., 80m*80m). Then, based on the grid, the historical order quantity and historical delivery distance of the historical delivery area, as well as the target order quantity and target delivery distance of the target delivery area, can be determined. Then, combined with the first location parameter, the second location parameter can be determined.

[0160] It should be noted that the above-mentioned 80m*80m grid size is only for illustrative purposes. In practical applications, it can be set to other sizes as needed, and this embodiment does not limit this.

[0161] Figure 5B A schematic diagram of the grid division of the delivery area according to the present invention is shown; Figure 5C A schematic diagram illustrating the calculation method of merchant radiation weight distance according to the present invention is shown.

[0162] like Figure 5B As shown, each mesh g can be defined. ij Therefore, the number of orders x within each grid can be determined based on historical order information. ij(That is, the numbers marked in each grid in the diagram). It's worth noting that since the number of orders within each grid is an estimate, it's accurate to one decimal place. In practical applications, integers can also be used; this embodiment does not impose this limitation. Furthermore, the distance d between the grid and the merchant (e.g., navigation distance) can also be defined. ij The probability density model of the original delivery time within a preset time period is known, i.e., the first location parameter μ is known. ori and the first scale parameter σ ori In addition, the following parameter a is defined to indicate whether the grid is within the original delivery area. ij :

[0163]

[0164] Depend on Figure 5B As can be seen from the content, the original delivery area of ​​the current merchant can be the area within the larger rectangle in the figure (that is, the grid with the top left corner of 47.4 and the grid with the bottom right corner of 46.1), while the grid where the current merchant is located is the smaller rectangle in the figure, that is, the grid with the value of 66.7.

[0165] Furthermore, the following parameter b is defined to indicate whether the grid is within the target delivery area. ij :

[0166]

[0167] Based on this, the first merchant's radiation weight distance d in the original delivery area can be defined and calculated. ori .

[0168] It is understandable that the more orders within each grid, the higher the probability of food being prepared for that grid, and consequently, the more times delivery riders will access it. In this embodiment, the number of orders is used as the weight, so the order delivery distance (e.g., navigation distance) d ij x number of orders within the grid ij The product x ij d ij That is, merchants in grid g ij Weighted distance (e.g., Figure 5C (Examples include 52.7*320, 59.3*160, and 46.1*560, etc.). Based on this, the sum of the weighted distances of the merchant to all its grids can be calculated, which is the first merchant's radiation weighted distance d in the original delivery area. ori :

[0169] d ori =∑∑x ij d ij a ij (5-3)

[0170] Similarly, the radiation weight distance d of the second merchant in the target delivery area can be calculated. new :

[0171] d new =d ori +∑∑x ij d ij (1-a ij )b ij (5-4)

[0172] Based on this, the second location parameter μ of the probability density model for the target delivery time of the current merchant in the target delivery area can be calculated. new :

[0173]

[0174] Where, d new d represents the radius weight distance of the second merchant. ori The radius weight distance for the first merchant is denoted by k1, which is an adjustable coefficient with a default value of 1. For example, this coefficient can be determined based on actual order data; this embodiment does not limit this determination.

[0175] Figure 6A A flowchart illustrating how, according to the present invention, a second scale parameter of the target delivery time probability density model is determined based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model.

[0176] This embodiment, based on the above embodiments, provides an illustrative example of how to determine the second scale parameters of the target delivery time probability density model based on the historical maximum radiation distance and the first scale parameters of the original delivery time probability density model. For example... Figure 6A As shown, the determination of the second scale parameters of the target delivery time probability density model based on the historical maximum radiation distance and the first scale parameters of the original delivery time probability density model in step S302 above may include the following steps S601-S602:

[0177] In step S601, the maximum target coverage distance of the current merchant within the target delivery area is obtained;

[0178] In step S602, the second scale parameter is determined based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter.

[0179] In this embodiment, the merchant's target delivery area and historical delivery area can be divided into grids of the same size (e.g., 80m*80m) in advance, and then the maximum target radiation distance of the current merchant in the target delivery area can be obtained based on the grid.

[0180] It should be noted that the above-mentioned 80m*80m grid size is only for illustrative purposes. In practical applications, it can be set to other sizes as needed, and this embodiment does not limit this.

[0181] In some embodiments, the historical maximum radiation distance can be determined based on the distance between the grid furthest from the current merchant in the original delivery area and the current merchant, and then the target maximum radiation distance can be determined based on the distance between the grid furthest from the current merchant in the target delivery area and the current merchant.

[0182] For example, Figure 6B A schematic diagram illustrating the calculation method for the maximum radiation distance of merchants according to the present invention is shown. As described above. Figure 6B As shown in the embodiment, this embodiment also defines each grid g. ij The distance between the grid and the merchant (e.g., navigation distance, etc.) d ij The probability density model of the original delivery time within a preset time period is known, i.e., the first location parameter μ is known. ori and the first scale parameter σ ori In addition, the following parameter a is defined to indicate whether the grid is within the original delivery area. ij :

[0183]

[0184] Furthermore, the following parameter b is defined to indicate whether the grid is within the target delivery area. ij :

[0185]

[0186] Based on this, the historical maximum coverage distance r of the original delivery area can be defined and calculated. ori :

[0187] r ori =max{d ij a ij}; (6-3)

[0188] As shown in equation (6-3) above, in this embodiment, the grid furthest from the current merchant within the original delivery area is selected, and the distance between this grid and the current merchant is calculated as the historical maximum radiation distance (i.e., Figure 6B The r shown ori=630).

[0189] Similarly, the maximum target coverage distance r of the target delivery area can be defined and calculated. new :

[0190] r new =max{d ij (1-a ij )b ij ,r ori}; (6-4)

[0191] In other words, in this embodiment, the grid furthest from the current merchant within the target delivery area is selected, and the distance between that grid and the current merchant is calculated as the target's maximum radiation distance (i.e., Figure 6B The r shown new =770).

[0192] Based on this, the second scale parameter σ of the probability density model of the target delivery time for the current merchant in the target delivery area can be calculated. new :

[0193]

[0194] Wherein, k2 is an adjustable coefficient, whose default value can be 1. For example, this coefficient can be determined based on actual order data, and this embodiment does not limit it in this way.

[0195] Figure 7 A flowchart illustrating how, according to the present invention, the delivery time information of the current merchant within the target delivery area is determined based on the target delivery time probability distribution information;

[0196] In this embodiment, the delivery time information may include: the proportion of specific orders of the current merchant within the target delivery area to the total number of orders, wherein the specific orders include orders with delivery times within a preset delivery time. Furthermore, this embodiment, based on the above embodiments, provides an illustrative example of how to determine the delivery time information of the current merchant within the target delivery area based on the target delivery time probability distribution information. Figure 7 As shown, the step S103 above, which involves determining the delivery time information of the current merchant within the target delivery area based on the target delivery time probability distribution information, may include the following steps S701-S702:

[0197] In step S701, at least one preset delivery time currently input is obtained.

[0198] For example, when a user needs to determine the proportion of a specific order from a merchant within the target delivery area to the total number of orders, they can input at least one preset delivery time into the terminal device, and the terminal device can then obtain the currently input at least one preset delivery time.

[0199] Among them, at least one of the above-mentioned preset delivery times can be set by the user according to actual needs, such as 30 minutes, 35 minutes and / or 40 minutes.

[0200] It is understood that the users mentioned above can include the current merchant's management personnel, etc., and this embodiment does not limit this.

[0201] In step S702, the proportion of the specific order of the current merchant in the target delivery area to the total number of orders is evaluated based on the target delivery time probability density model.

[0202] In this embodiment, after the terminal device receives at least one preset delivery time currently input, it can evaluate the proportion of the specific order of the current merchant in the target delivery area to the total number of orders based on the target delivery time probability density model.

[0203] In some embodiments, the target delivery time probability density model described above may include a target delivery time probability density model, as shown in equation (7-1):

[0204]

[0205] Based on this, the proportion of the specific order of the current merchant in the target delivery area to the total number of orders can be evaluated based on the target delivery time probability density model shown in (7-1) above, as shown in equations (7-2) to (7-4) below:

[0206] P new (T≤40)=90%; (7-2)

[0207] P new (T≤35)=78%; (7-3)

[0208] P new (T≤30)=63%; (7-4)

[0209] For example, the above ratio can be determined based on the integral operation of the probability density model of the target delivery time, as shown in equation (7-5) below:

[0210]

[0211] P can then be calculated. new(T≤40)=90%. Similarly, the proportions of T≤35 and T≤40 can be calculated, which will not be elaborated here.

[0212] As described above, the embodiments of the present invention, by defining a time probability delivery index (PT) for delivery areas, can characterize the delivery time of delivery areas from a probability distribution perspective, thereby improving the accuracy of information characterization. Furthermore, by combining the change in the total number of orders with the location parameter μ of the normal distribution function and the change in delivery range with the scale parameter σ of the normal distribution function, a probability density model of the delivery time of different delivery areas is constructed. This allows the proportion of orders with arbitrary delivery times to be obtained based on this function, providing an accurate basis for subsequent judgments on whether to expand the delivery area and meeting the current delivery area assessment needs of merchants.

[0213] Figure 8 A structural block diagram of an apparatus for determining delivery time information according to an exemplary embodiment of the present invention is shown. The apparatus of this embodiment can be applied to terminal devices with data processing capabilities (e.g., smartphones, tablets, or desktop computers), or to a server (e.g., a single server or a server cluster consisting of multiple servers). Figure 8 As shown, the device includes: an information distance acquisition module 110, a distribution information determination module 120, and a duration information determination module 130, wherein:

[0214] The information distance acquisition module 110 is used to acquire the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area;

[0215] The distribution information determination module 120 is used to determine the probability distribution information of the target delivery time of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance.

[0216] The duration information determination module 130 is used to determine the delivery duration information of the current merchant in the target delivery area based on the target delivery duration probability distribution information.

[0217] As described above, the device in this embodiment obtains the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area. Based on the historical order information, the historical maximum radiation distance, the target order information, and the target maximum radiation distance, it determines the target delivery time probability distribution information of the current merchant in the target delivery area. Then, based on the target delivery time probability distribution information, it determines the delivery time information of the current merchant in the target delivery area. This allows for an accurate assessment of the delivery time information of the current merchant in the target delivery area based on the determined target delivery time probability distribution information. This provides an accurate basis for subsequent judgments on whether to expand the original delivery area to the target delivery area, thus meeting the current merchant's delivery area assessment needs.

[0218] Figure 9 A structural block diagram of an apparatus for determining delivery time information according to another exemplary embodiment of the present invention is shown. The apparatus of this embodiment can be applied to a terminal device with data processing capabilities (e.g., a smartphone, tablet computer, or desktop computer), or to a server (e.g., a server or a server cluster consisting of multiple servers). The information distance acquisition module 210, distribution information determination module 220, and time information determination module 230 are as described above. Figure 8 The information distance acquisition module 110, distribution information determination module 120, and duration information determination module 130 in the illustrated embodiment have the same functions, which will not be described in detail here.

[0219] like Figure 9 As shown, the distribution information determination module 220 includes:

[0220] The original information determination unit 221 is used to determine the original delivery time probability distribution information of the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance;

[0221] The target information determination unit 222 is used to determine the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information.

[0222] In some embodiments, the original delivery time probability distribution information includes an original delivery time probability density model, and the target delivery time probability distribution information includes a target delivery time probability density model.

[0223] In some embodiments, the target information determination unit 222 is further configured to:

[0224] Based on the historical order information and the first position parameter of the original delivery time probability density model, the second position parameter of the target delivery time probability density model is determined.

[0225] Based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model, the second scale parameter of the target delivery time probability density model is determined.

[0226] The target delivery time probability density model is determined based on the second location parameter and the second scale parameter.

[0227] In some embodiments, the historical order information includes the number of historical orders and the historical delivery distance, and the target order information includes the number of target orders and the target delivery distance of the target orders;

[0228] The target information determination unit 222 is further configured to:

[0229] Obtain the target order quantity and target delivery distance of the target orders for the current merchant within the target delivery area;

[0230] The second location parameter is determined based on the historical order quantity, the historical delivery distance, the target order quantity, the target delivery distance, and the first location parameter.

[0231] In some embodiments, both the original delivery area and the target delivery area are divided into multiple grids of the same size;

[0232] The target information determination unit 222 is further configured to:

[0233] The first merchant radiation weight distance of the current merchant in the original delivery area is determined based on the number of historical orders and the historical delivery distance in multiple grids within the original delivery area.

[0234] The second merchant radiation weight distance of the current merchant in the target delivery area is determined based on the number of target orders and the target delivery distance in multiple grids within the target delivery area, as well as the radiation weight distance of the first merchant.

[0235] The second location parameter is determined based on the first location parameter, the first merchant radiation weight distance, and the second merchant radiation weight distance.

[0236] In some embodiments, the target information determination unit 222 is further configured to:

[0237] Obtain the maximum target coverage distance of the current merchant within the target delivery area;

[0238] The second scale parameter is determined based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter.

[0239] In some embodiments, both the original delivery area and the target delivery area are divided into multiple grids of the same size;

[0240] The target information determination unit 222 is further configured to:

[0241] The historical maximum radiation distance is determined based on the distance between the grid furthest from the current merchant within the original delivery area and the current merchant.

[0242] The maximum radiation distance of the target is determined based on the distance between the grid furthest from the current merchant within the target delivery area and the current merchant.

[0243] In some embodiments, the above delivery time information includes: the proportion of a specific order of the current merchant in the target delivery area to the total number of orders, wherein the specific order includes orders with a delivery time within a preset delivery time;

[0244] Furthermore, the duration information determination module 230 may include:

[0245] The duration acquisition unit 231 is used to acquire at least one preset delivery duration currently input;

[0246] Information determination unit 232 is used to determine the proportion of the specific order of the current merchant in the target delivery area to the total number of orders based on the target delivery time probability distribution information.

[0247] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0248] Embodiments of the device for determining delivery time information according to the present invention can be applied to network devices. The device embodiments can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of the device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 10 The diagram shown is a hardware structure diagram of an electronic device containing the device for determining delivery time information according to the present invention. (Except for...) Figure 10 In addition to the processor, network interface, memory, and non-volatile memory shown, the device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets; from a hardware structure perspective, the device may also be a distributed device, which may include multiple interface cards to extend packet processing at the hardware level.

[0249] This invention also provides a computer-readable storage medium having a computer program stored thereon, which, when processed by a processor, implements the above-described... Figures 1 to 7 Any method for determining delivery time information.

[0250] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.

[0251] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for determining delivery time information, characterized in that, include: The system obtains the merchant's historical order information and historical maximum coverage distance within the original delivery area, as well as the merchant's target order information and target maximum coverage distance within the target delivery area. The historical order information includes the number of historical orders and the historical delivery distance, while the target order information includes the number of target orders and the target delivery distance for each target order. Both the original delivery area and the target delivery area are divided into multiple grids of the same size. Based on the historical order information and the historical maximum radiation distance, determine the original delivery time probability distribution information of the current merchant in the original delivery area; Based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information, the target delivery time probability distribution information corresponding to the current merchant in the target delivery area is determined; the original delivery time probability distribution information includes an original delivery time probability density model, and the target delivery time probability distribution information includes a target delivery time probability density model. The delivery time information of the current merchant within the target delivery area is determined based on the target delivery time probability distribution information; The step of determining the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum coverage distance, the target order information, the target maximum coverage distance, and the original delivery time probability distribution information includes: Obtain the target order quantity and target delivery distance of the target orders for the current merchant within the target delivery area; The first merchant radiation weight distance of the current merchant in the original delivery area is determined based on the number of historical orders and the historical delivery distance in multiple grids within the original delivery area. The second merchant radiation weight distance of the current merchant in the target delivery area is determined based on the number of target orders and the target delivery distance in multiple grids within the target delivery area, as well as the radiation weight distance of the first merchant. Based on the first location parameter of the original delivery time probability density model, the first merchant radiation weight distance, and the second merchant radiation weight distance, the second location parameter of the target delivery time probability density model is determined; the first location parameter is the mean of the original delivery time probability density model, and the second location parameter is the mean of the target delivery time probability density model. Based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model, the second scale parameter of the target delivery time probability density model is determined; the first scale parameter is the standard deviation of the original delivery time probability density model, and the second scale parameter is the standard deviation of the target delivery time probability density model. The target delivery time probability density model is determined based on the second location parameter and the second scale parameter.

2. The method according to claim 1, characterized in that, The determination of the second scale parameters of the target delivery time probability density model based on the historical maximum radiation distance and the first scale parameters of the original delivery time probability density model includes: Obtain the maximum target coverage distance of the current merchant within the target delivery area; The second scale parameter is determined based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter.

3. The method according to claim 2, characterized in that, Both the original delivery area and the target delivery area are divided into multiple grids of the same size; Before determining the second scale parameter based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter, the method further includes: The historical maximum radiation distance is determined based on the distance between the grid furthest from the current merchant within the original delivery area and the current merchant. The maximum radiation distance of the target is determined based on the distance between the grid furthest from the current merchant within the target delivery area and the current merchant.

4. The method according to claim 1, characterized in that, The delivery time information includes: the proportion of specific orders of the current merchant in the target delivery area to the total number of orders, and the specific orders include orders with a delivery time within the preset delivery time. The step of determining the delivery time information of the current merchant within the target delivery area based on the target delivery time probability distribution information includes: Obtain at least one preset delivery time currently input; Based on the target delivery time probability distribution information, determine the proportion of the specific order of the current merchant in the target delivery area to the total number of orders.

5. A device for determining delivery time information, characterized in that, include: The information distance acquisition module is used to acquire the historical order information and historical maximum radiation distance of the current merchant in the original delivery area, as well as the target order information and target maximum radiation distance of the current merchant in the target delivery area; the historical order information includes the number of historical orders and the historical delivery distance, and the target order information includes the number of target orders and the target delivery distance of the target orders; both the original delivery area and the target delivery area are divided into multiple grids of the same size; The distribution information determination module is used to determine the probability distribution information of the original delivery time of the current merchant in the original delivery area based on the historical order information and the historical maximum radiation distance; Based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information, the target delivery time probability distribution information corresponding to the current merchant in the target delivery area is determined; the original delivery time probability distribution information includes an original delivery time probability density model, and the target delivery time probability distribution information includes a target delivery time probability density model. The duration information determination module is used to determine the delivery duration information of the current merchant in the target delivery area based on the target delivery duration probability distribution information; The process by which the distribution information determination module determines the target delivery time probability distribution information of the current merchant in the target delivery area based on the historical order information, the historical maximum radiation distance, the target order information, the target maximum radiation distance, and the original delivery time probability distribution information includes: Obtain the target order quantity and target delivery distance of the target orders for the current merchant within the target delivery area; The first merchant radiation weight distance of the current merchant in the original delivery area is determined based on the number of historical orders and the historical delivery distance in multiple grids within the original delivery area. The second merchant radiation weight distance of the current merchant in the target delivery area is determined based on the number of target orders and the target delivery distance in multiple grids within the target delivery area, as well as the radiation weight distance of the first merchant. Based on the first location parameter of the original delivery time probability density model, the first merchant radiation weight distance, and the second merchant radiation weight distance, the second location parameter of the target delivery time probability density model is determined; the first location parameter is the mean of the original delivery time probability density model, and the second location parameter is the mean of the target delivery time probability density model. Based on the historical maximum radiation distance and the first scale parameter of the original delivery time probability density model, the second scale parameter of the target delivery time probability density model is determined; the first scale parameter is the standard deviation of the original delivery time probability density model, and the second scale parameter is the standard deviation of the target delivery time probability density model. The target delivery time probability density model is determined based on the second location parameter and the second scale parameter.

6. The apparatus according to claim 5, characterized in that, The distribution information determination module is also used for: Obtain the maximum target coverage distance of the current merchant within the target delivery area; The second scale parameter is determined based on the historical maximum radiation distance, the target maximum radiation distance, and the first scale parameter.

7. The apparatus according to claim 6, characterized in that, Both the original delivery area and the target delivery area are divided into multiple grids of the same size; The distribution information determination module is also used for: The historical maximum radiation distance is determined based on the distance between the grid furthest from the current merchant within the original delivery area and the current merchant. The maximum radiation distance of the target is determined based on the distance between the grid furthest from the current merchant within the target delivery area and the current merchant.

8. The apparatus according to claim 5, characterized in that, The delivery time information includes: the proportion of specific orders of the current merchant in the target delivery area to the total number of orders, and the specific orders include orders with a delivery time within the preset delivery time. The duration information determination module includes: The duration acquisition unit is used to acquire at least one preset delivery duration currently input; An information determination unit is used to determine, based on the target delivery time probability distribution information, the proportion of the specific order of the current merchant in the target delivery area to the total number of orders.

9. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store computer programs; The processor is configured to implement the method for determining delivery time information as described in any one of claims 1-4 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is processed by a processor, it implements the method for determining delivery time information as described in any one of claims 1-4.

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