A method and device for scheduling distribution capacity, a storage medium and an electronic device

Abstract

The specification discloses a method and device for scheduling delivery capacity, a storage medium and an electronic device. According to at least one of the pickup point and the delivery point of each to-be-delivered order, the to-be-delivered orders are clustered to determine a plurality of order clusters. For each to-be-delivered order, the delivery willingness corresponding to the to-be-delivered order is determined according to the to-be-delivered order. For each order cluster, the supply-demand level of the to-be-delivered order is determined according to the delivery willingness corresponding to the to-be-delivered order in the order cluster. Finally, the delivery capacity is scheduled according to the supply-demand levels of the plurality of order clusters. As can be seen, the to-be-delivered orders are clustered to determine a plurality of order clusters according to the pickup point and the delivery point of each to-be-delivered order, and the supply-demand levels of the plurality of order clusters are determined. The supply-demand levels of each order cluster are more accurately determined, the supply-demand levels of each to-be-delivered order are determined in real time, and each delivery capacity is more accurately scheduled.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of logistics distribution, and in particular, to a method and device for scheduling delivery capacity, a storage medium, and an electronic device. BACKGROUND

[0002] At present, as the pace of people's work and life speeds up, more and more people have no time to cook and are used to ordering takeout. In the prior art, an order platform usually determines the supply-demand relationship of a region according to the number of delivery capacity and the number of orders in the region. Then, the delivery capacity of the region with an unbalanced supply-demand relationship is scheduled according to the supply-demand relationship of each region.

[0003] It can be seen that the supply-demand relationship determined by the order platform is relatively rough, which leads to low accuracy of subsequent capacity scheduling and makes it difficult to further improve the delivery efficiency. SUMMARY

[0004] The present specification provides a method and device for scheduling delivery capacity, a storage medium, and an electronic device to partially solve the above problems existing in the prior art.

[0005] The present specification adopts the following technical solutions:

[0006] The present specification provides a method for scheduling delivery capacity, comprising:

[0007] According to at least one of the pickup point and the delivery point of each to-be-delivered order, the to-be-delivered orders are clustered to determine a plurality of order clusters;

[0008] According to the to-be-selected delivery capacity of each to-be-delivered order, the delivery willingness corresponding to each to-be-delivered order is determined;

[0009] For each order cluster, according to the delivery willingness corresponding to each to-be-delivered order in the order cluster, the supply-demand level of the order cluster is determined;

[0010] According to the supply-demand levels of the plurality of order clusters, the delivery capacity is scheduled.

[0011] Optionally, according to the pickup point of each to-be-delivered order, the to-be-delivered orders are clustered to determine a plurality of order clusters, specifically comprising:

[0012] According to the order information of each to-be-delivered order, the pickup point of each to-be-delivered order is determined;

[0013] According to the location of the pickup point of each to-be-delivered order, the to-be-delivered orders are clustered to determine a plurality of order clusters.

[0014] Optionally, according to the pickup point and the delivery point of each to-be-delivered order, the to-be-delivered orders are clustered to determine a plurality of order clusters, specifically comprising:

[0015] According to the order information of each to-be-delivered order, a pickup point and a delivery point of each to-be-delivered order are determined;

[0016] According to the position information of the pickup point and the delivery point of each to-be-delivered order, an order flow vector of each to-be-delivered order is determined;

[0017] According to the order flow vector, each to-be-delivered order is clustered to determine a plurality of order clusters.

[0018] Optionally, according to the to-be-delivered order, a to-be-delivered order corresponding to the delivery willingness is determined, and the method specifically comprises:

[0019] According to the order information of the to-be-delivered order and the capacity information of each delivery capacity, the to-be-delivered order is determined.

[0020] According to the order information of the to-be-delivered order and the capacity information of each to-be-delivered capacity, the to-be-delivered order is determined.

[0021] According to the sum of the delivery probability of each to-be-delivered capacity to the to-be-delivered order, the to-be-delivered order corresponding to the delivery willingness is determined.

[0022] Optionally, according to the delivery willingness corresponding to each to-be-delivered order in the order cluster, the supply and demand level of the order cluster is determined, and the method specifically comprises:

[0023] The average value of the delivery willingness corresponding to each to-be-delivered order in the order cluster is determined.

[0024] The order quantity of the to-be-delivered order is obtained, and the supply and demand level of the order cluster is determined according to the average value and the order quantity, wherein the average value and the supply and demand level are negatively correlated, the order quantity and the supply and demand level are positively correlated, and the greater the supply and demand level, the more the supply and demand relationship is tight.

[0025] Optionally, the method further comprises:

[0026] According to the supply and demand level of the plurality of order clusters and the position of the plurality of order clusters, a supply and demand map representing the order supply and demand relationship of different positions is determined.

[0027] The supply and demand map is sent to the terminal of each delivery capacity, and the supply and demand map is used to prompt the difference of the supply and demand relationship of different positions of the delivery capacity.

[0028] Optionally, the method further comprises:

[0029] According to the supply and demand level of each order cluster, the order cluster whose supply and demand level exceeds a preset level threshold is determined as a to-be-adjusted order cluster, wherein the greater the supply and demand level, the more the supply and demand relationship is tight.

[0030] For each to-be-delivered order in the to-be-adjusted order cluster, expand the range of the to-be-delivered order to select the to-be-selected delivery capacity, and re-determine the to-be-selected delivery capacity of the to-be-delivered order.

[0031] The specification provides a delivery capacity scheduling device, comprising:

[0032] The clustering module clusters each to-be-delivered order according to at least one of the pickup point and the delivery point of the to-be-delivered order, and determines a plurality of order clusters.

[0033] The delivery willingness module determines the delivery willingness corresponding to each to-be-delivered order according to the to-be-selected delivery capacity of each to-be-delivered order.

[0034] The supply-demand level module determines the supply-demand level of each order cluster according to the delivery willingness corresponding to each to-be-delivered order in the order cluster.

[0035] The scheduling module schedules the delivery capacity according to the supply-demand levels of the plurality of order clusters.

[0036] The specification provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above-mentioned delivery capacity scheduling method.

[0037] The specification provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned delivery capacity scheduling method when executing the program.

[0038] The above-mentioned at least one technical solution adopted by the specification can achieve the following beneficial effects:

[0039] In the delivery capacity scheduling method provided by the specification, each to-be-delivered order can be clustered according to at least one of the pickup point and the delivery point of each to-be-delivered order to determine a plurality of order clusters, and for each to-be-delivered order, the delivery willingness corresponding to the to-be-delivered order can be determined according to the to-be-selected delivery capacity of the to-be-delivered order, and for each order cluster, the supply-demand level of the to-be-delivered order can be determined according to the delivery willingness corresponding to each to-be-delivered order in the order cluster, and finally, the delivery capacity can be scheduled according to the supply-demand levels of the plurality of order clusters.

[0040] As can be seen from the above method, the method starts from the pickup point and the delivery point of each to-be-delivered order, clusters each to-be-delivered order to determine a plurality of order clusters, and determines the supply-demand levels of the plurality of order clusters, more accurately determines the supply-demand levels of each order cluster, realizes real-time determination of the supply-demand levels of each order cluster according to each to-be-delivered order, and more accurately schedules each delivery capacity. BRIEF DESCRIPTION OF DRAWINGS

[0041] The accompanying drawings, which are included to provide a further understanding of the present description and are incorporated in and constitute a part of the present description, illustrate embodiments of the present description and together with the general description of the present description given above and the detailed description of the present description given below, serve to explain the present description. In the drawings:

[0042] Figure 1 A flowchart of a method for scheduling delivery capacity provided in the present description;

[0043] Figure 2 A clustering diagram provided in the present description;

[0044] Figure 3 A clustering diagram provided in the present description;

[0045] Figure 4 A supply-demand map diagram provided in the present description;

[0046] Figure 5 A structural diagram of a device for scheduling delivery capacity provided in the present description;

[0047] Figure 6 An electronic device diagram corresponding to Figure 1 provided in the present description. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the present description clearer, the technical solutions of the present description will be described below in conjunction with specific embodiments of the present description and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present description, not all the embodiments. Based on the embodiments in the present description, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0049] In the present description, the method for scheduling delivery capacity can be applied to instant delivery scenarios such as delivery of take-out food and intra-city express delivery. In the present description, the delivery of take-out food scenario is taken as an example for illustration.

[0050] Generally, when calculating the supply-demand relationship, the order platform divides a plurality of regions in advance, and for each region, according to the number of orders to be delivered in the region, the number of delivery capacities, the delivery punctuality rate, the average delivery time and other parameters, the supply-demand relationship of the region is determined.

[0051] However, some parameters used in determining supply and demand relationships are lagging, requiring the completion of a portion of orders before these parameters can be determined. Examples include on-time delivery rate and average delivery time. This method lacks real-time accuracy. Furthermore, because some parameters used in this method are contingent, it is unsuitable for characterizing localized supply and demand relationships. Localized supply and demand relationships refer to those within a small area. Examples include parameters like average delivery time and on-time delivery rate. When the area is small, the number of orders may be minimal. If, due to limitations in delivery capacity, a number of orders are delayed, it will significantly impact the parameters characterizing the supply and demand relationship in that small area, hindering accurate determination of the supply and demand relationship within that region.

[0052] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0053] Figure 1 This is a flowchart illustrating a method for scheduling delivery capacity as described in this specification, which specifically includes the following steps:

[0054] S100: Cluster each order to be delivered based on at least one of the pickup point and delivery point to determine multiple order clusters.

[0055] Since characterizing supply and demand typically requires significant computing power, this task can be performed by a server. Therefore, in one or more embodiments of this specification, the delivery capacity scheduling method can be executed by the order platform's server. Of course, this specification does not limit whether the server is a single device or a distributed server system composed of multiple devices; it can be configured as needed.

[0056] In one or more embodiments of this specification, the server may employ any one of three clustering methods: clustering based on the pickup points of each order to be delivered, clustering based on the delivery points of each order to be delivered, and clustering based on the order flow vectors of each order to be delivered, to determine multiple order clusters and ultimately determine the supply and demand relationship. For ease of explanation, this description will focus only on clustering based on the pickup points of each order to be delivered.

[0057] Specifically, the server can obtain each order to be delivered and determine the location of the pickup point for each order based on the order information. Based on the location of the pickup points for each order, the servers cluster the orders to determine multiple order clusters.

[0058] like Figure 2 As shown, Figure 2 This is a clustering diagram provided in this specification. The server can determine each order to be delivered into multiple order clusters based on the location of the pickup point corresponding to each order.Figure 2 In the specific embodiment, a~m are the locations of the pickup points of the to-be-delivered orders, and 200, 202, and 204 are the location ranges corresponding to the three order clusters. According to the locations of the pickup points, the server clusters the to-be-delivered orders corresponding to the pickup point locations a, b, c, d, e, and f into an order cluster, and the location range corresponding to the order cluster is 200. The server clusters the to-be-delivered orders corresponding to the pickup point locations g, h, and i into an order cluster, and the location range corresponding to the order cluster is 202. The server clusters the to-be-delivered orders corresponding to the pickup point locations j, k, l, and m into an order cluster, and the location range corresponding to the order cluster is 204.

[0059] In the clustering of the to-be-delivered orders, the k-means clustering algorithm, the Grid-based Clustering algorithm, the GeoHash algorithm, or the like can be used for calculation according to the locations of the pickup points in the to-be-delivered orders. The specific algorithm can be set as needed, and the present specification does not impose any limitation.

[0060] S102: Determine the delivery willingness of each to-be-delivered order according to the to-be-selected delivery capacity of each to-be-delivered order.

[0061] In one or more embodiments of the present specification, after the order clusters are determined, the delivery willingness of each to-be-delivered order can be determined according to the to-be-delivered orders, so as to determine the supply-demand level of each order cluster according to the delivery willingness of each to-be-delivered order included in each order cluster, and to realize the scheduling of each delivery capacity.

[0062] Specifically, first, the server can determine, for each to-be-delivered order, the order flow direction (flow direction information from the pickup point to the delivery point) of the to-be-delivered order, the number of orders being delivered by each delivery capacity, the total overtime of the orders being delivered by each delivery capacity, the on-the-way situation (the difference between the estimated route length of the delivery of the to-be-delivered order by each delivery capacity and the estimated route length of the to-be-delivered order not delivered by each delivery capacity) of each delivery capacity for delivering the to-be-delivered order, and the like according to the order information of the to-be-delivered order and the capacity information of each delivery capacity, for each delivery capacity. Each parameter is input into a preset delivery probability model, and the delivery probability of each delivery capacity for the to-be-delivered order is determined according to the output result of the model. Other parameters can also be input into the delivery probability model, such as the current time parameter (daytime, evening, night), the environmental parameter of the current area (thunderstorm weather, gale weather), the selection of the delivery capacity range parameter (the distance between the delivery capacity and the pickup point), and the like.

[0063] Secondly, the server can determine, according to the delivery probability of each delivery capacity for the to-be-delivered order and the preset delivery probability threshold, a delivery capacity whose delivery probability exceeds the delivery probability threshold, as the to-be-delivered order's to-be-selected delivery capacity.

[0064] Finally, the server can determine the delivery willingness corresponding to the to-be-delivered order according to the sum of the delivery probability of each to-be-selected delivery capacity for the to-be-delivered order.

[0065] The above-mentioned manner of determining the delivery willingness corresponding to the to-be-delivered order can be expressed by the following formula:

[0066]

[0067] Wherein, S is the delivery willingness corresponding to the to-be-delivered order, i is the serial number of the to-be-selected delivery capacity, for example, 1, 2, 3, …, A i is the delivery probability of the to-be-selected delivery capacity corresponding to the serial number i. By using the manner of determining the delivery willingness of the to-be-delivered order according to the delivery probability of each to-be-selected delivery capacity for the to-be-delivered order, for the to-be-delivered order, the more the number of to-be-selected delivery capacities of the to-be-delivered order, the higher the delivery probability of the to-be-selected delivery capacity of the to-be-delivered order, and the higher the delivery willingness corresponding to the to-be-delivered order. In general, the more the number of delivery capacities in a local area, the more the number of to-be-selected delivery capacities of the to-be-delivered order in the local area. The number of orders being delivered by the delivery capacity in the local area is small, and the delivery probability of the to-be-delivered order by the delivery capacity in the local area is high. Therefore, the delivery willingness of the to-be-delivered order determined by the above-mentioned manner can to some extent represent the supply and demand relationship in the local area.

[0068] By using the above-mentioned manner, the server can determine the delivery willingness corresponding to each to-be-delivered order through the number and delivery probability of the to-be-selected delivery capacity corresponding to each to-be-delivered order. When the number of to-be-selected delivery capacities corresponding to a to-be-delivered order is large, the delivery willingness corresponding to the to-be-delivered order determined is high, so that the delivery willingness corresponding to each to-be-delivered order can to some extent represent the supply and demand relationship. In order to subsequently determine the supply and demand relationship level of each order cluster according to the delivery willingness of each to-be-delivered order.

[0069] S104: For each order cluster, determine the supply and demand level of the order cluster according to the delivery willingness corresponding to each to-be-delivered order in the order cluster.

[0070] In one or more embodiments of the present specification, after determining the delivery willingness corresponding to each to-be-delivered order, the supply and demand relationship level of each order cluster can be determined according to the delivery willingness of each to-be-delivered order included in each order cluster, so as to schedule the delivery capacity.

[0071] Specifically, the server can determine, for each order cluster, an average of the delivery willingness corresponding to each to-be-delivered order included in the order cluster according to the delivery willingness corresponding to each to-be-delivered order included in the order cluster. The server can obtain an order quantity of the to-be-delivered order, and determine, according to the order quantity and the average, a quotient of the order quantity divided by the average as a supply-demand relationship parameter. The server can determine, according to a preset supply-demand level range and the supply-demand relationship parameter, a supply-demand level of the order cluster.

[0072] The process of determining the supply-demand relationship parameter of the order cluster can be represented by the following formula:

[0073]

[0074] In the formula, R is the supply-demand relationship parameter of the order cluster, M is the order quantity, and Q is the average. The supply-demand relationship parameter determined in the above manner is positively correlated with the supply-demand level. The greater the supply-demand level, the more tense the supply-demand relationship, that is, the greater the quantity of to-be-delivered orders and the smaller the quantity of delivery capacity.

[0075] For example, when the supply-demand relationship parameter is 0-100, the corresponding supply-demand level is level 1; when the supply-demand parameter is 101-200, the corresponding supply-demand level is level 2; when the supply-demand parameter is 201-300, the corresponding supply-demand level is level 3; and so on. The supply-demand level of 1 indicates that the supply-demand relationship is relaxed, the quantity of to-be-delivered orders is small, and the quantity of delivery capacity is large. The supply-demand level of 2 indicates that the supply-demand relationship is balanced, the quantity of to-be-delivered orders corresponds to the quantity of delivery capacity, and each delivery capacity can complete each to-be-delivered order under small delivery pressure. The supply-demand level of 3 indicates that the supply-demand relationship is tense, the quantity of to-be-delivered orders is large, and the quantity of delivery capacity is small, and the delivery capacity in a local area has large delivery pressure and cannot complete the to-be-delivered orders in the local area.

[0076] In the above manner, the supply-demand relationship of each location where each order cluster is located can be clearly known through the determined supply-demand level of each order cluster, so as to schedule the delivery capacity according to the supply-demand relationship of each location.

[0077] S106: scheduling the delivery capacity according to the supply-demand level of the plurality of order clusters.

[0078] In one or more embodiments of the present specification, the greater the supply-demand level, the more tense the supply-demand relationship, that is, the greater the quantity of to-be-delivered orders and the smaller the quantity of delivery capacity. Therefore, in order to reduce the pressure of the delivery capacity in the area where the order cluster with a tense supply-demand relationship is located, the delivery capacity can be adjusted after the supply-demand level of each order cluster is determined.

[0079] Specifically, the server can determine, according to the supply-demand levels of the order clusters, an order cluster whose supply-demand level exceeds a preset level threshold as a to-be-adjusted order cluster. For each to-be-delivered order in each to-be-adjusted order cluster, the range of the to-be-delivered order for selecting a to-be-selected delivery capacity is expanded, so as to select a to-be-selected delivery capacity for each to-be-delivered order from a larger range. Then, the to-be-selected delivery capacity of the to-be-delivered order is determined again according to the expanded range. Of course, if there is a to-be-delivered order that has determined a delivery capacity (a delivery capacity for delivering the to-be-delivered order), the to-be-selected delivery capacity of the to-be-delivered order can not be determined again.

[0080] In this way, the range of the to-be-delivered order for selecting a to-be-selected delivery capacity in an order cluster with a tight supply-demand relationship can be expanded, so as to dispatch more delivery capacities to the region where the order cluster is located, and reduce the delivery pressure of each delivery capacity in the region.

[0081] Based on Figure 1 As shown in the method for dispatching a delivery capacity, each to-be-delivered order can be clustered according to at least one of the pickup point and the delivery point of the to-be-delivered order, a plurality of order clusters can be determined, for each to-be-delivered order, a delivery willingness corresponding to the to-be-delivered order can be determined according to the to-be-selected delivery capacity of the to-be-delivered order, for each order cluster, a supply-demand level of each to-be-delivered order in the order cluster can be determined according to the delivery willingness corresponding to the to-be-delivered order, and finally, the delivery capacity can be dispatched according to the supply-demand levels of the plurality of order clusters.

[0082] As can be seen from the above method, the method starts from the pickup point and the delivery point of each to-be-delivered order, clusters each to-be-delivered order to determine a plurality of order clusters, and determines the supply-demand levels of the plurality of order clusters, so as to more accurately determine the supply-demand levels of each order cluster, realize real-time determination of the supply-demand levels of each order cluster according to each to-be-delivered order, and more accurately dispatch each delivery capacity.

[0083] In addition, in one or more embodiments of the present specification, since in general, the locations of the shops in the catering industry are relatively concentrated, the supply-demand relationship of the local region where the locations of the shops are concentrated can be accurately determined by clustering each to-be-delivered order according to the pickup point of the to-be-delivered order and determining the supply-demand level. However, since in general, many white-collar workers have a strong demand for takeout, and every time at lunch time or dinner time, a large number of delivery capacities will appear at the entrances of many office buildings to deliver (deliver meals). Therefore, the server can also cluster each to-be-delivered order according to the location of the delivery point of the to-be-delivered order, and determine a plurality of order clusters.

[0084] Specifically, the server can obtain each order to be delivered and determine the location of the delivery point for each order based on its order information. Based on the location of the delivery points for each order, the servers cluster the orders to determine multiple order clusters. When clustering the orders, algorithms such as k-means clustering, direct grid clustering, and geohashing can be used based on the location of the delivery points within each order. The specific method used can be set as needed, and this manual does not impose any restrictions.

[0085] Of course, in one or more embodiments of this specification, the server may also determine the order flow vector of each order to be delivered based on the location of the pickup point and the location of the delivery point, and cluster the orders to be delivered based on the order flow vectors to determine multiple order clusters. Since the start and end points of the order flow vectors of each order to be delivered are the pickup point and the delivery point, respectively, clustering the orders to be delivered using the order flow vectors can more accurately depict the flow direction of each order to be delivered, and the resulting supply and demand map is more intuitive.

[0086] like Figure 3 As shown, Figure 3 This is a clustering diagram provided in this specification. The server can determine each order vector pointing from the pickup point to the delivery point based on the location of the pickup point and the location of the delivery point corresponding to each order to be delivered, and perform vector clustering based on each order vector to determine multiple order clusters. Figure 3 In the diagram, a to f represent the locations of the pickup points for each order to be delivered, A represents the location of the delivery point for each order to be delivered, and 300, 302, and 304 are the order flow vectors corresponding to the three determined order clusters. Based on the location of each pickup point, the server clusters the orders to be delivered with order vectors (A, a), (A, b), and (A, c) into one order cluster, with order flow vector 300 corresponding to this cluster. Based on the location of each pickup point, the server clusters the orders to be delivered with order vectors (A, d) and (A, e) into one order cluster, with order flow vector 302 corresponding to this cluster. Based on the location of each pickup point, the server clusters the orders to be delivered with order vectors (A, f) and (A, g) into one order cluster, with order flow vector 304 corresponding to this cluster.

[0087] In the server, when clustering each to-be-delivered order according to the order flow vector of each to-be-delivered order, a support vector clustering (SVC) or a kernel clustering can be used for calculation. Of course, other methods can also be used for calculation. For example, according to the order flow vector of each to-be-delivered order, it is determined that the order flow vectors with at least two same parameters in the three parameters of the starting point, the ending point and the vector direction of each order flow vector are a kind of clustering, and each order cluster is determined according to each kind of clustering. The specific method for calculation can be set according to the needs, and the present specification is not limited.

[0088] In the server, when clustering each to-be-delivered order according to the order flow vector of each to-be-delivered order, a support vector clustering (SVC) or a kernel clustering can be used for calculation. Of course, other methods can also be used for calculation. For example, according to the order flow vector of each to-be-delivered order, it is determined that the order flow vectors with at least two same parameters in the three parameters of the starting point, the ending point and the vector direction of each order flow vector are a kind of clustering, and each order cluster is determined according to each kind of clustering. The specific method for calculation can be set according to the needs, and the present specification is not limited.

[0089] It should be noted that in one or more embodiments of the present specification, the server can use any one of the three clustering methods of clustering according to the pickup point of each to-be-delivered order, clustering according to the delivery point of each to-be-delivered order, and clustering according to the order flow vector of each to-be-delivered order to determine a plurality of order clusters and finally determine the supply and demand relationship. Of course, in order to improve the accuracy of the determined supply and demand relationship, at least two supply and demand relationships can also be determined by using at least two of the three clustering methods, so as to schedule the delivery capacity.

[0090] In addition, in one or more embodiments of the present specification, in step S102, in order to more accurately determine the delivery willingness corresponding to each to-be-delivered order, the following method can be used to determine the delivery willingness.

[0091] Specifically, first, according to the delivery probability of each to-be-delivered order corresponding to the selected delivery capacity, the number of orders being delivered, and the preset maximum number of delivery orders, the delivery willingness corresponding to the to-be-delivered order is determined.

[0092] The above-mentioned method for determining the delivery willingness corresponding to the to-be-delivered order can be expressed by the following formula:

[0093]

[0094] Wherein, S is the delivery willingness corresponding to the to-be-delivered order, i is the serial number of the selected delivery capacity, for example, 1, 2, 3……, A i is the delivery probability of the selected delivery capacity corresponding to the serial number i, B iB is the number of orders being delivered corresponding to the selected delivery capacity with the serial number i, and B0 is the preset maximum number of delivery orders. When the maximum number of delivery orders of the selected delivery capacity is less than the number of orders being delivered corresponding to the selected delivery capacity, the selected delivery capacity has no impact on the delivery willingness of the delivery order corresponding to the selected delivery capacity, regardless of the order acceptance probability of the selected delivery capacity.

[0095] In addition, in one or more embodiments of the present specification, in step S102, in order to more accurately determine the delivery willingness of each delivery order, the following method can be used to determine the delivery willingness.

[0096] Specifically, first, according to the delivery probability of the selected delivery capacity corresponding to each delivery order, the upper limit of the number of selected delivery capacities, the number of orders being delivered, and the preset maximum number of delivery orders, the delivery willingness of the delivery order is determined.

[0097] The above-mentioned method for determining the delivery willingness of the delivery order can be expressed by the following formula:

[0098]

[0099] Wherein, S is the delivery willingness of the delivery order, i is the serial number of the selected delivery capacity, for example, 1, 2, 3, …, n is the number of selected delivery capacities corresponding to the delivery order, n0 is the upper limit of the number of selected delivery capacities, A i B is the delivery probability corresponding to the selected delivery capacity with the serial number i. i B is the number of orders being delivered corresponding to the selected delivery capacity with the serial number i, and B0 is the preset maximum number of delivery orders. When the maximum number of delivery orders of the selected delivery capacity is less than the number of orders being delivered corresponding to the selected delivery capacity, the selected delivery capacity has no impact on the delivery willingness of the delivery order corresponding to the selected delivery capacity, regardless of the order acceptance probability of the selected delivery capacity.

[0100] In addition, in step S104, for each order cluster, in order to more accurately determine the supply-demand level of the order cluster, the median of each delivery willingness can also be determined according to the delivery willingness of each delivery order in the order cluster, and the supply-demand relationship parameter of the order cluster can be determined according to the order quantity and the median. Of course, the mode of each delivery willingness can also be determined according to the delivery willingness of each delivery order in the order cluster, and the supply-demand relationship parameter of the order cluster can be determined according to the order quantity and the mode. The supply-demand level of the order cluster is determined according to the preset supply-demand level range and the supply-demand relationship parameter.

[0101] In addition, in one or more embodiments of the present disclosure, in step S104, for each order cluster, in order to more accurately determine the supply-demand level of the order cluster, a difference between the order quantity and the average value can be determined as a supply-demand relationship parameter according to the order quantity and the average value. Then, according to a preset supply-demand level range and the supply-demand relationship parameter, the supply-demand level of the order cluster is determined.

[0102] In addition, in one or more embodiments of the present disclosure, in step S104, after determining the order quantity corresponding to the order cluster and the average value, in order to more accurately determine the supply-demand level of the order cluster and avoid the supply-demand level of each order cluster being too different, a preset normalization function can be introduced.

[0103] Specifically, according to the order quantity, the average value and the preset order-preserving normalization function, the supply-demand relationship parameter of the order cluster is determined.

[0104] The above process of determining the supply-demand relationship parameter of the order cluster can be expressed by the following formula:

[0105]

[0106] Wherein, R is the supply-demand relationship parameter of the order cluster, f is the preset order-preserving normalization function, M is the order quantity, and Q is the average value.

[0107] In addition, in one or more embodiments of the present disclosure, in step S104, after determining the order quantity corresponding to the order cluster and the average value, in order to more accurately determine the supply-demand level of the order cluster and avoid the situation that the divisor is zero in the calculation of the supply-demand relationship due to the fact that each order to be delivered in the order cluster has no selected delivery capacity, a preset non-zero parameter can be introduced.

[0108] Specifically, according to the order quantity, the average value and the preset non-zero parameter, the supply-demand relationship parameter of the order cluster is determined.

[0109] The above process of determining the supply-demand relationship parameter of the order cluster can be expressed by the following formula:

[0110]

[0111] Wherein, R is the supply-demand relationship parameter of the order cluster, Q0 is the preset non-zero parameter, M is the order quantity, and Q is the average value.

[0112] In addition, in one or more embodiments of the present specification, in order to more reasonably schedule the delivery capacity, the server can also determine a supply-demand map representing the supply-demand relationship of orders in different locations according to the supply-demand level of each order cluster and the location of each order cluster, and send the supply-demand map to the terminal of each delivery capacity. The supply-demand map is used to prompt the difference of the supply-demand relationship of different locations of the delivery capacity. So that each delivery capacity can spontaneously adjust the location according to the received supply-demand map.

[0113] As shown in Figure 4 , Figure 4 a supply-demand map provided by the present specification, in Figure 4 , the supply-demand level of local area 400 is 0.8, the supply-demand level of local area 402 is 0.2, and the supply-demand level of local area 404 is 0.2. Wherein, the black dots in local areas 400, 402 and 404 are each to-be-delivered order. Each delivery capacity can spontaneously adjust the location according to the supply-demand level of each local area.

[0114] The above is the method of scheduling the delivery capacity provided by one or more embodiments of the present specification, based on the same idea, the present specification also provides a corresponding device for scheduling the delivery capacity, as shown in Figure 5 .

[0115] Figure 5 A device for scheduling the delivery capacity provided by the present specification, specifically comprising:

[0116] The clustering module 500 clusters each to-be-delivered order according to at least one of the pickup point and the delivery point of each to-be-delivered order, and determines a plurality of order clusters;

[0117] The delivery willingness module 502 determines the delivery willingness corresponding to each to-be-delivered order according to the to-be-selected delivery capacity of each to-be-delivered order;

[0118] The supply-demand level module 504 determines the supply-demand level of each order cluster according to the delivery willingness corresponding to each to-be-delivered order in the order cluster;

[0119] The scheduling module 506 schedules the delivery capacity according to the supply-demand level of the plurality of order clusters.

[0120] Optionally, the clustering module 500 determines the pickup point of each to-be-delivered order according to the order information of each to-be-delivered order, and clusters each to-be-delivered order according to the location of the pickup point of each to-be-delivered order, to determine a plurality of order clusters.

[0121] Optionally, the clustering module 500 determines a pickup point and a delivery point of each to-be-delivered order according to order information of each to-be-delivered order, determines an order flow vector of each to-be-delivered order according to position information of the pickup point and the delivery point of each to-be-delivered order, clusters each to-be-delivered order according to each order flow vector, and determines a plurality of order clusters.

[0122] Optionally, the delivery willingness module 502 determines a to-be-selected delivery capacity of the to-be-delivered order according to order information of the to-be-delivered order and capacity information of each delivery capacity, respectively determines a delivery probability of each to-be-selected delivery capacity for the to-be-delivered order according to order information of the to-be-delivered order and capacity information of each to-be-selected delivery capacity, and determines a corresponding delivery willingness of the to-be-delivered order according to a sum of the delivery probabilities of each to-be-selected delivery capacity for the to-be-delivered order.

[0123] Optionally, the supply-demand level module 504 determines an average value of the delivery willingness corresponding to each to-be-delivered order in the order cluster, acquires an order quantity of the to-be-delivered order, and determines a supply-demand level of the order cluster according to the average value and the order quantity, where the average value is negatively correlated with the supply-demand level, the order quantity is positively correlated with the supply-demand level, and the greater the supply-demand level is, the more tense the supply-demand relationship is.

[0124] Optionally, the scheduling module 506 determines a supply-demand map representing the supply-demand relationship of orders in different positions according to the supply-demand levels of the plurality of order clusters and positions of the plurality of order clusters, and sends the supply-demand map to a terminal of each delivery capacity, where the supply-demand map is used to prompt differences in the supply-demand relationship of different positions of delivery capacities.

[0125] Optionally, the scheduling module 506 determines an order cluster whose supply-demand level exceeds a preset level threshold as a to-be-adjusted order cluster according to the supply-demand levels of the order clusters, where the greater the supply-demand level is, the more tense the supply-demand relationship is, expands a range of to-be-selected delivery capacities for each to-be-delivered order in the to-be-adjusted order cluster, and re-determines to-be-selected delivery capacities of the to-be-delivered order.

[0126] The specification also provides a computer-readable storage medium storing a computer program, where the computer program can be used to execute the above Figure 1 The delivery capacity scheduling method is provided.

[0127] The specification also provides Figure 6 The electronic device is shown in a schematic structural view. As shown in Figure 6At the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and can also include other hardware required by the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the above Figure 1 The method for scheduling the delivery capacity. Of course, in addition to the software implementation, the present specification does not exclude other implementation manners, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or a logic device.

[0128] It should be noted that all the actions of obtaining signals, information or data in this application are carried out in accordance with the corresponding data protection regulations and policies of the country where the device is located, and with the authorization given by the owner of the corresponding device.

[0129] In the 1990s, it was quite obvious to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structures of diodes, transistors, switches, etc.) or in software (improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structures by programming the improved method flow into hardware circuits. Therefore, it cannot be said that an improvement in a method flow cannot be implemented by hardware entity modules. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by user programming of the device. A digital system is "integrated" on a PLD by the designer programming it, rather than by asking a chip manufacturer to design and fabricate a custom integrated circuit chip. Moreover, instead of manually fabricating integrated circuit chips, this programming is now mostly implemented by "logic compiler" software, which is similar to software compilers used in program development, and the original code to be compiled is written in a specific programming language, which is called a hardware description language (HDL), and there are many such languages, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should be aware that, as long as the method flow is logically programmed in the above-mentioned hardware description languages and programmed into an integrated circuit, a hardware circuit implementing the logical method flow can be easily obtained.

[0130] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to being implemented in pure computer readable program code, the controller can equally well be implemented to perform the same functions using logic gates, switches, an application specific integrated circuit, a programmable logic controller and an embedded microcontroller, etc. by means of a logical programming of the method steps. The controller can thus be considered as a hardware component, and the means comprised therein for performing the various functions can be considered as structures within the hardware component. Alternatively, the means for performing the various functions can even be considered as both a software module implementing the method and a structure within the hardware component.

[0131] The systems, apparatuses, modules or units illustrated by the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0132] For the sake of description, the above apparatuses are described in various units by functions respectively. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware in implementing the present specification.

[0133] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0134] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0135] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0136] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. ​ one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0137] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0138] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory. The memory is an example of computer-readable media.

[0139] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0140] It should also be noted that the terms "comprising", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0141] Those skilled in the art will appreciate that embodiments of the present specification can be provided as methods, systems or computer program products. Therefore, the present specification can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] The present specification can be described in the general context of computer-executable instructions, such as program modules, executed by computers. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, including storage devices.

[0143] The various embodiments described in this specification are described using a numbering of embodiments approach: these are each individually related to one another in order to build the overall number of embodiments described in this specification. Each individual embodiment described, however, can stand on its own as a separate inventive concept whether or not it is considered by the applicant to be the best or only embodiment. Each individual embodiment relates to one another and to the entire description herein as indicating different points of emphasis of the overall description. For example, the system embodiments are described with less detail than the method embodiments because they are substantially similar to the method embodiments. The system embodiments are therefore cross-referenced to the method embodiments for relevant portions of the description.

[0144] The above description is embodied in the form of only a few examples of the present description and is not intended to limit the present description. Various modifications and changes can be made by those skilled in the art to which the present description pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present description should be included in the scope of the claims of the present application.

Claims

1. A method for scheduling delivery capacity, characterized in that, include: Each order to be delivered is clustered based on at least one of the pickup and delivery points to determine multiple order clusters; For each order to be delivered, the available delivery capacity is determined based on the order information and the capacity information of each delivery vehicle. Based on the order information of the order to be delivered and the capacity information of each candidate delivery capacity, the delivery probability of each candidate delivery capacity for the order to be delivered is determined. Based on the sum of the delivery probabilities of each candidate delivery capacity for the order to be delivered, the delivery willingness corresponding to the order to be delivered is determined. The delivery probability of the order to be delivered is determined by inputting the determined parameters into a preset delivery probability model. The parameters are determined based on the order information of the order to be delivered and the capacity information of each delivery capacity. The parameters include at least the route information of the delivery capacity for delivering the order to be delivered. For each order cluster, the supply and demand level of the order cluster is determined based on the delivery intentions of each order to be delivered within that cluster. Delivery capacity is scheduled based on the supply and demand levels of the multiple order clusters.

2. The method according to claim 1, characterized in that, Based on the pickup points of each order to be delivered, the orders to be delivered are clustered to determine multiple order clusters, specifically including: Based on the order information of each order to be delivered, determine the pickup point for each order to be delivered; Based on the location of the pickup point for each order to be delivered, the orders to be delivered are clustered to determine multiple order clusters.

3. The method according to claim 1, characterized in that, Based on the pickup and delivery points of each order to be delivered, the orders are clustered to determine multiple order clusters, specifically including: Based on the order information of each order to be delivered, determine the pickup point and delivery point for each order to be delivered; Based on the location information of the pickup and delivery points for each order to be delivered, determine the order flow vector for each order to be delivered; Based on each order flow vector, each order to be delivered is clustered to determine multiple order clusters.

4. The method according to claim 1, characterized in that, Based on the delivery intentions corresponding to each order in the order cluster, the supply and demand level of the order cluster is determined, specifically including: Determine the average delivery willingness for each order in the order cluster; Obtain the number of orders to be delivered, and determine the supply and demand level of the order cluster based on the average value and the number of orders. The average value is negatively correlated with the supply and demand level, and the number of orders is positively correlated with the supply and demand level. The higher the supply and demand level, the tighter the supply and demand relationship.

5. The method according to claim 1, characterized in that, The method further includes: Based on the supply and demand levels of the multiple order clusters and the location of the multiple order clusters, a supply and demand map representing the supply and demand relationship of orders at different locations is determined; The supply and demand map is sent to the terminals of each delivery capacity, and the supply and demand map is used to indicate the differences in the supply and demand relationship at different locations of the delivery capacity.

6. The method according to claim 1, characterized in that, The method further includes: Based on the supply and demand levels of each order cluster, order clusters whose supply and demand levels exceed a preset level threshold are identified as order clusters to be adjusted, wherein the higher the supply and demand level, the tighter the supply and demand relationship. For each order to be delivered in the order cluster to be adjusted, the range of available delivery capacity for that order is expanded, and the available delivery capacity for that order is redefined.

7. A device for dispatching delivery capacity, characterized in that, include: The clustering module clusters each order to be delivered based on at least one of the pickup and delivery points, thus identifying multiple order clusters. The delivery willingness module determines the available delivery capacity for each order to be delivered based on the order information and the capacity information of each delivery vehicle. Based on the order information of the order to be delivered and the capacity information of each candidate delivery capacity, the delivery probability of each candidate delivery capacity for the order to be delivered is determined. Based on the sum of the delivery probabilities of each candidate delivery capacity for the order to be delivered, the delivery willingness corresponding to the order to be delivered is determined. The delivery probability of the order to be delivered is determined by inputting the determined parameters into a preset delivery probability model. The parameters are determined based on the order information of the order to be delivered and the capacity information of each delivery capacity. The parameters include at least the route information of the delivery capacity for delivering the order to be delivered. The supply and demand grading module determines the supply and demand grading of each order cluster based on the delivery intentions of each order to be delivered within that cluster. The scheduling module schedules delivery capacity based on the supply and demand levels of the multiple order clusters.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method described in any one of claims 1 to 6.

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