Delivery difficulty determination method, order allocation method and device, medium and equipment

Abstract

The invention provides a delivery difficulty determination method, an order allocation method, a device, a medium and equipment, and the method comprises the steps: obtaining the historical delivery data of a delivery person in a target region, the target region comprises a plurality of floor types, and the floor types are determined by floor attributes and floor arrival mode attributes; determining a plurality of time periods in a preset time unit according to the historical distribution data of the target area; determining distribution intention information of each floor type in the target area in each time period according to historical distribution data of the target area; and determining distribution difficulty information of each floor type in each time period according to the distribution willingness information of each floor type in each time period. Based on the scheme of the invention, the accuracy and reliability of the distribution difficulty information can be improved, and the rights and interests of distribution personnel can be effectively guaranteed by using the distribution difficulty information.

Description

Technical Field

[0001] The present specification relates to the field of computer technology, and more specifically, to a delivery difficulty determination method, order allocation method, device, medium and equipment in the field of computer technology. Background Art

[0002] When delivery personnel deliver items according to delivery orders, they usually need to reach the corresponding floor to complete the delivery. The process of reaching a specific floor requires the delivery personnel's time and energy. In order to reasonably protect the rights and interests of delivery personnel in the process of generating and allocating delivery orders, a method to determine the delivery difficulty of reaching a specific floor is urgently needed. Summary of the invention

[0003] The present specification provides a method for determining delivery difficulty, an order allocation method, an apparatus, a medium and a device. The method can reflect the influence of three aspects, namely, floor attributes, floor arrival method attributes and time period, in the delivery difficulty information, thereby improving the accuracy and reliability of the delivery difficulty information. Based on the delivery difficulty information, the rights and interests of delivery personnel can be reasonably protected in the process of generating and allocating delivery orders.

[0004] In a first aspect, a method for determining delivery difficulty is provided, the method comprising: Obtain the historical delivery data of the delivery personnel in the target area. The target area includes multiple floor types, and the floor type is determined by the floor attribute and the floor arrival mode attribute; Determine multiple time periods in a preset time unit based on historical delivery data of the target area; According to the historical delivery data of the target area, determine the delivery intention information of each floor type in the target area in each time period; According to the delivery willingness information of each floor type in each time period, the delivery difficulty information of each floor type in each time period is determined.

[0005] In combination with the first aspect, in some possible implementations, the delivery willingness information of each floor type in the target area in each time period is determined based on the historical delivery data of the target area, including: determining the historical delivery data of each floor type in the target area in each time period from the historical delivery data of the target area; traversing multiple time periods; for a target time period among the traversed multiple time periods, determining the target delivery willingness index of each floor type in the target time period based on the historical delivery data of each floor type in the target time period; determining the delivery willingness information of each floor type in the target time period based on the target delivery willingness index of each floor type in the target time period; if the delivery willingness information of each floor type in each time period has been determined, ending the traversal of multiple time periods.

[0006] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, a target delivery willingness index of each floor type in the target time period is determined based on the historical delivery data of each floor type in the target time period, including: determining a statistical data set of each floor type in the target time period based on the historical delivery data of each floor type in the target time period, the statistical data set including statistical data of each first delivery willingness indicator among multiple first delivery willingness indicators; determining a weight of each first delivery willingness indicator in the target time period based on the statistical data set of each floor type in the target time period; performing weighted summation processing on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period, to obtain the target delivery willingness index of each floor type in the target time period.

[0007] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, a weighted summation process is performed on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period to obtain a target delivery willingness index of each floor type in the target time period, including: a weighted summation process is performed on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period to obtain a first delivery willingness index of each floor type in the target time period; multiple floor types are divided to obtain at least one floor type set, and the floor attributes of each floor type in the floor type set are adjacent and the floor arrival method attributes are the same; for each floor type set in at least one floor type set, the first delivery willingness index of each floor type in each floor type set is aggregated to obtain a second delivery willingness index of each floor type set; and the second delivery willingness index of each floor type set is determined as the target delivery willingness index of each floor type in each floor type set in the target time period.

[0008] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, multiple floor types are divided to obtain at least one floor type set, including: determining statistical data of a second delivery willingness indicator, the second delivery willingness indicator being any one of multiple first delivery willingness indicators; determining a first function curve of the statistical data of the second delivery willingness indicator as the floor attributes change under each floor arrival method attribute; respectively deriving each first function curve to obtain a first-order derivative curve corresponding to each first function curve; respectively deriving the first-order derivative curve corresponding to each first function curve to obtain a second-order derivative curve corresponding to each first function curve; dividing multiple floor types based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to each first function curve, and the floor arrival method attribute corresponding to each first function curve, to obtain at least one floor type set.

[0009] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, based on the target delivery willingness index of each floor type in the target time period, the delivery willingness information of each floor type in the target time period is determined, including: determining the maximum delivery willingness index and the minimum delivery willingness index of the target time period from the target delivery willingness index of each floor type in the target time period; determining the benchmark delivery willingness index of the target time period according to the target delivery willingness index of each floor type in the target time period; mapping the target delivery willingness index of each floor type in the target time period to a preset normalized interval according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period to obtain the normalized delivery willingness index of each floor type in the target time period; determining the normalized delivery willingness index of each floor type in the target time period as the delivery willingness information of each floor type in the target time period.

[0010] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period, the target delivery willingness index of each floor type in the target time period is respectively mapped to a preset normalized interval to obtain the normalized delivery willingness index of each floor type in the target time period, including: traversing multiple floor types; for the target floor type among the multiple traversed floor types, if the target delivery willingness index of the target floor type in the target time period is not less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the preset normalized interval. The delivery willingness index is mapped to the first sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period; if the target delivery willingness index of the target floor type in the target time period is less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the second sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period, and the value in the first sub-interval is greater than the value in the second sub-interval; if the normalized delivery willingness index of each floor type in the target time period is obtained, the traversal of multiple floor types is terminated.

[0011] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, multiple time periods in a preset time unit are determined based on the historical delivery data of the target area, including: determining supply-demand ratio statistical data based on the historical delivery data of the target area, the supply-demand ratio statistical data is determined based on the ratio between the number of historical delivery orders and the number of delivery personnel; determining a second function curve of the supply-demand ratio statistical data changing with time in the preset time unit; deriving the second function curve to obtain a first-order derivative curve corresponding to the second function curve; deriving the first-order derivative curve corresponding to the second function curve to obtain a second-order derivative curve corresponding to the second function curve; dividing the preset time unit into time periods based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to the second function curve to obtain multiple time periods in the preset time unit.

[0012] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the delivery difficulty information of each floor type in each time period is determined according to the delivery willingness information of each floor type in each time period, including: based on the linear relationship between the delivery willingness information and the delivery difficulty information, the delivery willingness information of each floor type in each time period is converted to obtain the delivery difficulty information of each floor type in each time period, and the delivery willingness information of each floor type in each time period is negatively correlated with the delivery difficulty information.

[0013] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, before obtaining the historical delivery data of the delivery personnel in the target area, it also includes: obtaining the delivery characteristic data of each sub-area in the preset multiple sub-areas; clustering the multiple sub-areas according to the delivery characteristic data of each sub-area to obtain at least one cluster cluster; respectively clustering the sub-areas of each cluster cluster in at least one cluster cluster to obtain the area corresponding to each cluster cluster, and the target area is the area corresponding to any one of the at least one cluster cluster.

[0014] In a second aspect, an order allocation method is provided, the method comprising: In response to creation of a target delivery order for a target area, determining a delivery time and a floor type of the target delivery order; Determine target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order, and the delivery difficulty information of each floor type in the target area in each time period, wherein the delivery difficulty information of each floor type in the target area in each time period is determined based on the above-mentioned delivery difficulty determination method; According to the target delivery difficulty information, the target delivery orders are assigned to the target delivery personnel.

[0015] In combination with the second aspect, in some possible implementations, the target delivery order is assigned to the target delivery personnel based on the target delivery difficulty information, including: determining multiple candidate delivery personnel corresponding to the target delivery order; determining the delivery preference information of each candidate delivery personnel among the multiple candidate delivery personnel; and determining the target delivery personnel from the multiple candidate delivery personnel based on the delivery preference information of each candidate delivery personnel and the target delivery difficulty information.

[0016] In combination with the second aspect and the above-mentioned implementation methods, in some possible implementation methods, a target delivery personnel is determined from multiple delivery personnel to be selected based on the delivery preference information and target delivery difficulty information of each delivery personnel to be selected, including: determining a set of delivery orders received by each delivery personnel to be selected within a preset time range; determining high-difficulty delivery orders whose delivery difficulty information meets preset conditions in the set of delivery orders received by each delivery personnel to be selected; determining a target delivery personnel from multiple delivery personnel to be selected based on the high-difficulty delivery orders, delivery preference information and target delivery difficulty information of each delivery personnel to be selected.

[0017] In a third aspect, a delivery difficulty determination device is provided, the device comprising: An acquisition unit is used to acquire historical delivery data of the delivery personnel in the target area, where the target area includes multiple floor types, and the floor type is determined by the floor attribute and the floor arrival mode attribute; A first determining unit, configured to determine a plurality of time periods in a preset time unit according to historical delivery data of a target area; A second determination unit is used to determine the delivery intention information of each floor type in the target area in each time period according to the historical delivery data of the target area; The third determining unit is used to determine the delivery difficulty information of each floor type in each time period according to the delivery willingness information of each floor type in each time period.

[0018] In a fourth aspect, an order distribution device is provided, the device comprising: A first determining unit, configured to determine a delivery time and a floor type of a target delivery order in response to creation of the target delivery order of the target area; A second determination unit is used to determine target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order, and the delivery difficulty information of each floor type in the target area in each time period, wherein the delivery difficulty information of each floor type in the target area in each time period is determined based on the above-mentioned delivery difficulty determination method; The allocation unit is used to allocate the target delivery order to the target delivery personnel according to the target delivery difficulty information.

[0019] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code is executed, the above method is implemented.

[0020] In a sixth aspect, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the above method.

[0021] In a seventh aspect, a computer program product is provided, which stores at least one instruction, and when the at least one instruction is executed by a processor, the steps of the above method are implemented.

[0022] The solutions mainly provided in this specification include: first, obtaining historical delivery data of delivery personnel in a target area, where the target area includes multiple floor types, and the floor types are determined by floor attributes and floor arrival method attributes; then, based on the historical delivery data of the target area, determining multiple time periods and the delivery willingness information of each floor type in each time period; finally, determining the delivery difficulty information of each floor type in each time period based on the delivery willingness information, so that the influence of three aspects, namely, floor attributes, floor arrival method attributes and time period, can be reflected in the delivery difficulty information, thereby improving the accuracy and reliability of the delivery difficulty information, and based on the delivery difficulty information, the rights and interests of delivery personnel can be reasonably protected in the process of generating and allocating delivery orders. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of a delivery scene provided by a delivery person in an embodiment of this specification; Figure 2 It is a flowchart of a method for determining delivery difficulty provided in an embodiment of this specification; Figure 3 is an example schematic diagram of a second function curve provided in an embodiment of this specification; Figure 4 is an example schematic diagram of a clustering processing process provided by an embodiment of this specification; Figure 5 It is a flowchart of determining delivery intention information provided by an embodiment of this specification; Figure 6 It is a flowchart of determining a target delivery willingness index provided by an embodiment of this specification; Figure 7 It is a flowchart of determining a target delivery willingness index provided by an embodiment of this specification; Figure 8 This is an example schematic diagram of a floor type set provided in an embodiment of this specification; Fig. 9This is an example schematic diagram of a floor type set provided in an embodiment of this specification; Fig.10 is an example schematic diagram of a first function curve provided in an embodiment of this specification; Fig.11 It is a flowchart of determining delivery intention information provided by an embodiment of this specification; Fig.12 It is a flowchart of an order allocation method provided in an embodiment of this specification; Fig.13 This is a schematic diagram of a process for determining a target delivery person provided by an embodiment of this specification; Fig.14 It is a structural schematic diagram of a delivery difficulty determination device provided in an embodiment of this specification; Fig.15 It is a structural schematic diagram of an order distribution device provided in an embodiment of this specification; Fig.16 It is a structural schematic diagram of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0024] The technical solutions in this specification will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of this specification, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of this specification, "multiple" means two or more than two.

[0025] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0026] See also Figure 1 , is a schematic diagram of a delivery scene provided by a delivery person in an embodiment of this specification. When a delivery person delivers items according to a delivery order, he must reach the floor specified in the delivery order to complete the delivery. In this process, the delivery person needs to spend a certain amount of time and energy to reach the floor specified in the delivery order. Generally, the higher the floor, the more time and energy the delivery person spends.

[0027] In addition, the way to reach the floor also has a significant impact on the difficulty of delivery. For example, in modern building environments, the existence of elevators reduces the burden of vertical movement of delivery personnel, making high-rise delivery relatively convenient. However, not all buildings are equipped with elevators. Especially in some old residential areas or special building structures, delivery personnel may have to walk up the stairs to the designated floor. In this case, even the middle and low floors may significantly increase the difficulty of delivery due to the lack of elevators. Figure 1 As shown in the figure, it may be easier for a delivery person to deliver an item to the 6th floor of a building with an elevator than to the 6th floor of a building without an elevator. In addition to this, there are many possible factors that affect the difficulty of delivering to a specific floor.

[0028] Determining the delivery difficulty to a specific floor is of great significance to reasonably protecting the rights and interests of delivery personnel in the process of generating and allocating delivery orders. For example, the order allocation system can adaptively adjust the delivery revenue of the delivery order according to the delivery difficulty, so that the time and energy of the delivery personnel can be adapted to the delivery revenue; for another example, the order allocation system can allocate to suitable delivery personnel according to the delivery difficulty, thereby improving the delivery personnel's satisfaction with the order allocation results; for another example, the delivery personnel can choose whether to accept a certain delivery order according to their own situation and the delivery difficulty, so that the delivery personnel can complete the delivery more smoothly. Based on the above reasons, there is an urgent need for a method to determine the delivery difficulty to a specific floor.

[0029] The solution mainly provided by the embodiment of the present application includes: first, obtaining historical delivery data of delivery personnel in a target area, where the target area includes multiple floor types, and the floor type is determined by floor attributes and floor arrival method attributes; then, based on the historical delivery data of the target area, determining multiple time periods and delivery intention information of each floor type in each time period; finally, determining delivery difficulty information of each floor type in each time period based on the delivery intention information, so that the influence of three aspects, namely floor attributes, floor arrival method attributes and time period, can be reflected in the delivery difficulty information, thereby improving the accuracy and reliability of the delivery difficulty information, and based on the delivery difficulty information, the rights and interests of delivery personnel can be reasonably protected in the process of generating and allocating delivery orders.

[0030] based on Figure 1 The following will combine Figure 2 - Fig.13 , the delivery difficulty determination method and order allocation method provided in the embodiments of this specification are introduced in detail.

[0031] See also Figure 2 , which is a flow chart of a method for determining the delivery difficulty according to an embodiment of this specification. Figure 2 As shown, the method of the embodiment of this specification may include the following steps S102 to S108.

[0032] S102, obtaining historical delivery data of the delivery personnel in the target area, where the target area includes multiple floor types, and the floor type is determined by floor attributes and floor access mode attributes.

[0033] Specifically, the target area involved in this embodiment refers to an area with a specific geographical range. In some cases, the target area can also be a collection of multiple sub-areas. For example, the target area can be a city, or a collection of multiple cities.

[0034] The target area includes multiple floor types, where floor type refers to the combination of different floors in a building and their access methods. Floor type is determined by floor attributes and floor access method attributes. Floor attributes refer to the specific floor numbers in a building, such as 1st floor, 2nd floor, 3rd floor, etc., which represent the specific height that the delivery personnel need to reach. The floor access method attribute refers to the tool or method used to reach the floor, such as elevator, no elevator, unidentified (not sure whether there is an elevator or no elevator). The elevator here can be a vertical passenger elevator or freight elevator, or an escalator or other type of elevator. In addition, floor access methods can also involve vertical transport vehicles, lifting platforms, walking stairs, etc., which are not listed here one by one.

[0035] The historical delivery data of the delivery personnel in the target area refers to the relevant record data of the delivery orders completed by the delivery personnel in the target area in the past period of time. For example, the historical delivery data may include the original data of multiple delivery orders completed by the delivery personnel in the target area; for another example, the historical delivery data may include the statistical data of multiple delivery orders completed by the delivery personnel in the target area. It should be noted that the delivery personnel here does not refer to a specific person, but to the group of delivery personnel engaged in delivery work in the target area. Therefore, the multiple delivery orders involved in the historical delivery data of the delivery personnel in the target area can actually come from different delivery personnel, so that the historical delivery data of the delivery personnel in the target area can characterize the distribution activities and characteristics of the delivery personnel group in the target area.

[0036] Regarding the process of obtaining the historical delivery data of the delivery personnel in the target area, in a possible implementation, a query statement can be constructed according to the target area to retrieve the historical delivery data of the delivery personnel in the target area from a preset database. If the historical delivery data of the delivery personnel in the target area retrieved from the preset database includes the original data of multiple delivery orders completed by the delivery personnel in the target area, then data statistics can also be performed on the original data of the multiple delivery orders completed by the delivery personnel in the target area to obtain statistical data of the multiple delivery orders completed by the delivery personnel in the target area.

[0037] It should be noted that the statistical data of multiple delivery orders completed by the above-mentioned delivery personnel in the target area may involve multiple delivery willingness indicators. For example, the delivery willingness indicators may be order acceptance rate, on-time completion rate, fast order acceptance rate, order praise rate, etc. Among them, the order acceptance rate refers to the probability statistics of the delivery personnel accepting the delivery order, which represents the degree of acceptance of the delivery personnel to the delivery order. A higher order acceptance rate means that the delivery personnel are more willing to accept the delivery order; the on-time completion rate refers to the probability statistics of the delivery personnel completing the delivery order on time after accepting the delivery order, which represents the delivery personnel's processing ability for the delivery order. A higher on-time completion rate means that the delivery personnel can efficiently process the delivery order; the fast order acceptance rate refers to the probability statistics of the delivery personnel accepting the delivery order within a short time frame (such as 1 minute), which represents the response speed of the delivery personnel to the delivery order. A higher fast order acceptance rate means that the delivery personnel are more willing to accept the delivery order; the order praise rate refers to the probability statistics of the delivery personnel giving a good feedback on the delivery order, which represents the satisfaction and recognition of the delivery personnel to the delivery process of the delivery order. A higher order praise rate means that the delivery personnel are more satisfied with the delivery process of the delivery order.

[0038] It should be noted that the historical delivery data of the delivery personnel in the target area may include not only the data related to the delivery orders, but also the data related to the delivery personnel. For example, how many delivery personnel in the target area were online (i.e., ready to accept delivery orders at any time) at certain times in the past period of time, which is helpful for analyzing the supply-demand ratio statistics.

[0039] S104: Determine a plurality of time periods in a preset time unit according to the historical delivery data of the target area.

[0040] Specifically, in addition to the factors of floor and floor access method, time factors will also affect the delivery difficulty to a certain extent. For example, for a food delivery order of a certain floor type, its floor attribute and floor access method attribute are fixed, but the delivery difficulty of the food delivery order may be different during the peak dining time and non-peak dining time of the day.

[0041] Therefore, considering the influence of time factors, this embodiment proposes to determine multiple time periods in a preset time unit based on the historical delivery data of the target area. The preset time unit refers to the basic time category used to divide the time period, for example, the preset time unit can be a year, month, week, day, etc. The multiple time periods in the preset time unit refer to different time intervals divided within the preset time unit according to the change in delivery difficulty.

[0042] Regarding the process of determining multiple time periods in a preset time unit based on the historical delivery data of the target area, in a possible implementation method, the key nodes of the statistical data changing over time in the preset time unit can be found based on the characteristics of certain statistical data analyzed from the historical delivery data of the target area, and the preset time unit can be divided into multiple different time periods based on these key nodes.

[0043] In one possible implementation, a clustering algorithm (such as K-means, hierarchical clustering, etc.) can be used to perform cluster analysis on the time series data in the historical delivery data of the target area, identify similar patterns and time intervals in the historical delivery data of the target area, and divide the preset time unit into several clusters, each cluster representing a time period with similar delivery difficulty characteristics.

[0044] S106, determining the delivery intention information of each floor type in the target area in each time period according to the historical delivery data of the target area.

[0045] Specifically, after determining a plurality of time periods in the preset time unit, the historical delivery data of the target area can be further filtered out from the historical delivery data of the target area for each floor type in the target area in each time period.

[0046] Among them, the historical delivery data of each floor type in each time period refers to the relevant record data of delivery orders completed by delivery personnel in the target area for specific floor types (such as the 6th floor with elevators, the 3rd floor without elevators, etc.) and specific time periods (such as peak meal times, non-peak times, etc.) in the past period of time. It reflects the delivery activities of delivery personnel in specific floor types and specific time periods, and further characterizes the delivery willingness and preference of delivery personnel when facing different floor types and time periods.

[0047] Then, the historical delivery data of each floor type in each time period is analyzed respectively. The data analysis methods may include but are not limited to statistical analysis, machine learning, data mining, etc. Through data analysis, indicators that can reflect the delivery willingness (such as order acceptance rate, on-time completion rate, fast order acceptance rate, order praise rate, etc.) can be extracted, and the statistical data of multiple indicators can be integrated into a single value as the delivery willingness information through weighted average, normalization, etc. In other words, the delivery willingness information can be expressed in the form of an index to more intuitively compare the delivery willingness of different floor types and time periods.

[0048] S108, determining the delivery difficulty information of each floor type in each time period according to the delivery willingness information of each floor type in each time period.

[0049] Specifically, the delivery difficulty information involved in this embodiment can be expressed in the form of a numerical value. For example, the delivery difficulty information can be specifically expressed as a delivery difficulty coefficient. The higher the delivery difficulty coefficient of a certain floor type in a certain time period, the greater the delivery difficulty of the floor type in the time period, and the delivery personnel need to spend relatively more time and energy to complete the delivery; the lower the delivery difficulty coefficient of a certain floor type in a certain time period, the smaller the delivery difficulty of the floor type in the time period, and the delivery personnel need to spend relatively less time and energy to complete the delivery.

[0050] It is understandable that the delivery intention information and the delivery difficulty information are often negatively correlated. Therefore, the mapping relationship between the delivery intention information and the delivery difficulty information can be determined in advance, and the delivery difficulty information of each floor type in each time period can be determined based on the delivery intention information of each floor type in each time period and the mapping relationship between the delivery intention information and the delivery difficulty information.

[0051] In some possible implementations, the mapping relationship between the delivery willingness information and the delivery difficulty information may be a linear relationship. For example, a linear function may be set, in which the delivery willingness information is used as an independent variable and the delivery difficulty information is used as a dependent variable. Of course, in some possible implementations, the mapping relationship between the delivery willingness information and the delivery difficulty information may also be a nonlinear relationship.

[0052] In this embodiment, first, historical delivery data of delivery personnel in a target area is obtained, and the target area includes multiple floor types, where the floor type is determined by floor attributes and floor access method attributes; then, multiple time periods and delivery intention information of each floor type in each time period are determined based on the historical delivery data of the target area; finally, delivery difficulty information of each floor type in each time period is determined based on the delivery intention information, so that the influence of three aspects, namely, floor attributes, floor access method attributes and time period, can be reflected in the delivery difficulty information, thereby improving the accuracy and reliability of the delivery difficulty information, and based on the delivery difficulty information, the rights and interests of delivery personnel can be reasonably protected in the process of generating and allocating delivery orders.

[0053] In one embodiment, Figure 2 The step S104 of the illustrated embodiment is further refined and may include the following steps: Determine the supply-demand ratio statistics based on the historical delivery data of the target area, where the supply-demand ratio statistics are determined based on the ratio between the number of historical delivery orders and the number of delivery personnel; Determine a second function curve of supply-demand ratio statistical data changing over time in a preset time unit; Deriving the second function curve to obtain a first-order derivative curve corresponding to the second function curve; Derivative the first-order derivative curve corresponding to the second function curve to obtain the second-order derivative curve corresponding to the second function curve; Based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to the second function curve, the preset time unit is divided into time periods to obtain a plurality of time periods in the preset time unit.

[0054] Specifically, the supply-demand ratio statistics involved in this embodiment refer to quantitative indicators that measure the balance between distribution demand and distribution resources in the target area. The supply-demand ratio statistics are determined based on the ratio between the number of historical distribution orders and the number of distribution personnel, reflecting the relative density of distribution orders and the availability of distribution personnel in different time ranges. The level of the supply-demand ratio statistics is directly related to the distribution difficulty and distribution efficiency, and can be used as a basis for the division of analysis time periods.

[0055] Regarding the process of determining the supply-demand ratio statistics based on the historical delivery data of the target area, first, multiple smaller time span units can be determined in the preset time unit, and the historical delivery data of each time span unit can be determined from the historical delivery data of the target area; the number of historical delivery orders and the number of delivery personnel of each time span unit can be determined based on the historical delivery data of each time span unit; the supply-demand ratio statistics of each time span unit can be determined based on the number of historical delivery orders and the number of delivery personnel of each time span unit. For example, if the preset time unit is "day", then 1 day can be divided into 1440 time span units, and the length of each time span unit is 1 minute, and then the supply-demand ratio statistics of each 1 minute in 1 day can be determined based on the historical delivery data of the target area.

[0056] After determining the supply-demand ratio statistics, a second function curve of the supply-demand ratio statistics changing over time in a preset time unit can be further determined. The second function curve refers to a function graph with time as the independent variable and the supply-demand ratio statistics as the dependent variable. By drawing the second function curve, the fluctuation of the supply-demand ratio in different time periods can be observed more clearly, providing an intuitive basis for the subsequent time period division. It can be understood that the supply-demand ratio statistics of each time span unit correspond to a point on the second function curve, and these points are connected to form a complete second function curve, which continuously reflects the change of the supply-demand ratio over time.

[0057] See also Figure 3 , Figure 3 is an example schematic diagram of a second function curve provided in an embodiment of this specification, wherein the second function curve represents the trend and law of the supply-demand ratio statistical data changing over time.

[0058] Furthermore, the second function curve is differentiated to obtain the first-order derivative curve corresponding to the second function curve; the first-order derivative curve corresponding to the second function curve is differentiated to obtain the second-order derivative curve corresponding to the second function curve. The first-order derivative represents the slope of the tangent line of the function at a certain point, reflecting the rate at which the function value changes with the independent variable; the second-order derivative represents the rate at which the first-order derivative changes with the independent variable, reflecting the speed of the function value change.

[0059] Since each inflection point in the first-order derivative curve and the second-order derivative curve corresponding to the second function curve has a corresponding time position, the preset time unit can be divided into time periods based on the time positions of these inflection points to obtain multiple time periods in the preset time unit.

[0060] In this embodiment, by analyzing the supply-demand ratio statistics in the historical distribution data of the target area, and dividing the time periods based on the inflection points in the second function curve and its derivative curve of the supply-demand ratio statistics changing with time, the rationality of the time period division is significantly improved, so that the divided time periods can more finely reflect the fluctuations in distribution demand, and provide a more accurate time dimension reference for the subsequent determination of distribution difficulty.

[0061] In one embodiment, Figure 2 Step S108 of the illustrated embodiment is further refined and may include the following steps: Based on the linear relationship between delivery willingness information and delivery difficulty information, the delivery willingness information of each floor type in each time period is converted to obtain the delivery difficulty information of each floor type in each time period. The delivery willingness information of each floor type in each time period is negatively correlated with the delivery difficulty information.

[0062] Specifically, the linear relationship between delivery willingness information and delivery difficulty information refers to a mathematical relationship in which there is a constant proportional relationship between the two, that is, when the delivery willingness information increases, the delivery difficulty information decreases at a fixed rate, and vice versa.

[0063] The linear relationship can be expressed as a linear function, where the delivery willingness information is the independent variable (assuming it is represented by x) and the delivery difficulty information is the dependent variable (assuming it is represented by y). The general form of the linear function is y = ax + b, where a and b are constants, a represents the slope, and b represents the intercept. Since the delivery willingness information is negatively correlated with the delivery difficulty information, the slope a can be configured as a negative value.

[0064] Then, based on the linear relationship between the delivery willingness information and the delivery difficulty information, the delivery willingness information of each floor type in each time period is converted. Specifically, the delivery willingness information of each floor type in each time period (such as the target delivery willingness index) is substituted into a pre-set linear formula, and the corresponding delivery difficulty information (such as the delivery difficulty coefficient) is obtained by calculation.

[0065] For example, when the delivery willingness information is the target delivery willingness index and the delivery difficulty information is the delivery difficulty coefficient, the following linear formula can be used to realize the conversion between the delivery willingness information and the delivery difficulty information: Delivery difficulty coefficient = 1 - target delivery willingness index In this embodiment, by clarifying the linear relationship between the delivery intention information and the delivery difficulty information, and converting the delivery intention information of each floor type in each time period accordingly, the accuracy and operability of the delivery difficulty information are significantly improved.

[0066] In one embodiment, Figure 2 Before step S102 in the illustrated embodiment, the following steps may also be included: Obtaining distribution characteristic data of each of the preset multiple sub-areas; Clustering the multiple sub-regions according to the distribution characteristic data of each sub-region to obtain at least one cluster; Sub-regions of each cluster in at least one cluster are aggregated respectively to obtain regions corresponding to each cluster, and the target region is a region corresponding to any one cluster in at least one cluster.

[0067] Specifically, the preset multiple sub-regions refer to a series of regions divided based on factors such as geographical location and population distribution.

[0068] The distribution characteristic data of each sub-region refers to a series of quantitative indicators that reflect the status of distribution activities in each sub-region, which can comprehensively and accurately describe the distribution characteristics of each sub-region. For example, the distribution characteristic data may include one or more data such as the number of delivery orders, the number of delivery personnel, supply-demand ratio statistics, the average floor of delivery orders, the average delivery time of delivery orders, and the probability that a delivery order specifies an elevator building. The number of delivery orders indicates the busyness of the distribution activities in each sub-region; the number of delivery personnel indicates the configuration of distribution resources in each sub-region; the supply-demand ratio statistics indicate the balance between distribution demand and distribution resources in each sub-region; the average floor of delivery orders indicates the average floor height involved in the distribution activities in each sub-region; the average delivery time of delivery orders indicates the average time spent on distribution activities in each sub-region; the probability that a delivery order specifies an elevator building indicates the proportion of elevator buildings in the distribution activities in each sub-region, etc.

[0069] The process of clustering multiple sub-regions to obtain at least one cluster according to the distribution feature data of each sub-region can be specifically manifested as using a clustering algorithm (such as K-means, hierarchical clustering, etc.) to process and analyze the distribution feature data of each sub-region, and classify sub-regions with similar distribution feature data into one category to form a cluster. It can be understood that each cluster is composed of at least one sub-region.

[0070] The process of aggregating the sub-regions of each cluster in at least one cluster to obtain the region corresponding to each cluster can be specifically manifested as merging all sub-regions in each cluster into a larger region, which represents the common distribution characteristics of all sub-regions in the cluster. It should be noted that the target region is the region corresponding to any one of the at least one cluster.

[0071] To understand the clustering process of this implementation, see Figure 4 , Figure 4 It is an example schematic diagram of a clustering process provided by an embodiment of this specification. Among them, the sub-region is specifically a city, involving city 1, city 2, city 3, city 4, city 5, and city 6. According to the distribution feature data of each city, multiple cities are clustered to obtain three clusters, namely cluster 1, cluster 2, and cluster 3. It can be understood that cities 1, 4, and 6 in cluster 1 are relatively close in distribution characteristics, and cities 1, 4, and 6 can be regarded as a region; cities 3 and 5 in cluster 3 are relatively close in distribution characteristics, and cities 3 and 5 can be regarded as a region; and cluster 2 only includes city 2, and city 2 can be regarded as a region. Then, the target area may be composed of city 1, city 4, and city 6, it may be composed of city 3 and city 5, or it may be composed of city 2.

[0072] In this embodiment, before obtaining the historical delivery data of the delivery personnel in the target area, the delivery feature data of multiple sub-areas are obtained, and the sub-areas are clustered based on the delivery feature data to form clusters with similar delivery features, and then the sub-areas in each cluster are aggregated to obtain larger areas. These areas not only reflect the regional differences in delivery activities, but also take into account the similarity of delivery features, so that the historical delivery data obtained later is more representative, and at the same time, the applicability of the delivery difficulty information determined later in the specific area is improved.

[0073] See also Figure 5 , which provides a flow chart of determining the delivery intention information in the embodiment of this specification. Figure 5 As shown, the method of the embodiment of this specification may include the following steps S202 to S210, and steps S202 to S210 may be used as Figure 2The detailed steps of step S106 of the illustrated embodiment.

[0074] S202, determining historical delivery data of each floor type in the target area in each time period from the historical delivery data of the target area; S204, traversing multiple time periods; S206, for a target time period among the traversed multiple time periods, determine a target delivery intention index for each floor type in the target time period according to historical delivery data for each floor type in the target time period; S208, determining the delivery intention information of each floor type in the target time period based on the target delivery intention index of each floor type in the target time period; S210: If the delivery intention information of each floor type in each time period has been determined, the traversal of multiple time periods is terminated.

[0075] Specifically, firstly, according to a plurality of time period divisions and a plurality of floor type divisions, the historical delivery data of each floor type in each time period is determined from the historical delivery data of the target area.

[0076] Then, multiple time periods are traversed, specifically: each time period is visited in turn according to the preset time sequence. During the traversal process, subsequent analysis steps (such as calculating the target delivery willingness index, determining delivery willingness information, etc.) are performed on each time period until all time periods are processed.

[0077] For the target time period among the multiple time periods traversed, the target delivery willingness index of each floor type in the target time period is determined according to the historical delivery data of each floor type in the target time period. The process can be expressed as follows: first, the historical delivery data of each floor type in the target time period is analyzed to extract indicators that can reflect the delivery willingness (such as order acceptance rate, on-time completion rate, fast order acceptance rate, order praise rate, etc.), and the statistical data of multiple indicators are combined into a single value as the target delivery willingness index through weighted averaging, normalization and other methods. The target delivery willingness index reflects the delivery personnel's delivery willingness for each floor type in the target time period.

[0078] If the delivery intention information of each floor type in each time period has not been determined, the next time period is traversed and the above steps S206 and S208 are repeated until all time periods are processed. If the delivery intention information of each floor type in each time period has been determined, the traversal of multiple time periods is terminated and the subsequent steps are entered.

[0079] Furthermore, regarding the process of determining the delivery willingness information of each floor type in the target time period based on the target delivery willingness index of each floor type in the target time period, in a possible implementation, the target delivery willingness index of each floor type in the target time period can be directly determined as the delivery willingness information of each floor type in the target time period. In a possible implementation, in order to eliminate the incomparability of the delivery willingness index between different floor types or different time periods, the target delivery willingness index of each floor type in the target time period can be normalized. The purpose of the normalization process is to convert the target delivery willingness index into a unified numerical range (such as between 0 and 1) so as to compare across floor types and across time periods. The value obtained after the normalization process is the normalized delivery willingness index of each floor type in the target time period, which can be used as the delivery willingness information for subsequent analysis.

[0080] In this embodiment, key information is first extracted from historical delivery data, and then multiple time periods are analyzed to ensure that the delivery intention in each time period can be accurately captured. By calculating the target delivery intention index and determining the delivery intention information based on the target delivery intention index, the delivery difficulty information is made more objective.

[0081] See also Figure 6 , which provides a flow chart of determining the target delivery willingness index in the embodiment of this specification. Figure 6 As shown, the method of the embodiment of this specification may include the following steps S302 to S306, and steps S302 to S306 may be used as Figure 5 The illustrated embodiment includes step S206, which is a detailed step of "determining the target delivery intention index of each floor type in the target time period based on the historical delivery data of each floor type in the target time period."

[0082] S302, determining a statistical data set of each floor type in the target time period according to historical delivery data of each floor type in the target time period, the statistical data set including statistical data of each first delivery willingness indicator among a plurality of first delivery willingness indicators; S304, determining the weight of each first delivery willingness indicator in the target time period according to the statistical data set of each floor type in the target time period; S306, performing weighted summation processing on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness index in the target time period, to obtain the target delivery willingness index of each floor type in the target time period.

[0083] Specifically, since the historical delivery data of each floor type in the target time period includes the relevant record data of the delivery orders completed by the delivery personnel for each floor type in the target time period, such as the order quantity, delivery time, delivery results, etc., the statistical data set of each floor type in the target time period can be determined based on these data. The statistical data set is a set including multiple first delivery willingness indicators and their corresponding statistical data, and these first delivery willingness indicators can reflect the delivery willingness and preference of the delivery personnel for each floor type in the target time period.

[0084] Among them, the first delivery willingness indicator can be order acceptance rate, on-time completion rate, fast order acceptance rate, order praise rate, etc. The order acceptance rate indicates the probability that the delivery personnel accepts the delivery order, reflecting the degree of acceptance of the delivery personnel to the delivery order; the on-time completion rate indicates the probability that the delivery personnel completes the delivery order on time after accepting the delivery order, reflecting the execution and efficiency of the delivery personnel to the delivery order; the fast order acceptance rate indicates the probability that the delivery personnel accepts the delivery order in a short time, reflecting the response speed of the delivery personnel; the order praise rate indicates the feedback of the delivery personnel on the satisfaction of the delivery process of the delivery order.

[0085] Furthermore, in order to more accurately calculate the target delivery willingness index of each floor type in the target time period, it is necessary to determine the weight of each first delivery willingness index in the target time period, and the weight reflects the importance of each index in the comprehensive evaluation of delivery willingness. The weight of each first delivery willingness index in the target time period can be determined based on a preset weighting algorithm and a set of statistical data of each floor type in the target time period. Specifically, the weighting algorithm can be a subjective or objective weighting method such as the analytic hierarchy process (AHP), the entropy weight method, and the CRITIC method.

[0086] Finally, according to the weight of each first delivery willingness indicator in the target time period, the statistical data set of each floor type in the target time period is weighted and summed. Weighted summation is the process of multiplying the statistical data of each first delivery willingness indicator by its corresponding weight and then summing them. It can comprehensively consider the influence of each first delivery willingness indicator and obtain a single value that comprehensively reflects the delivery willingness - the target delivery willingness index. Through weighted summation, the dimensional differences and incomparability between different first delivery willingness indicators can be eliminated, so that the target delivery willingness index can more accurately reflect the delivery willingness and preference of the delivery personnel for each floor type in the target time period.

[0087] To facilitate understanding of this implementation scheme, a calculation example based on the CRITIC method is provided below: Assume that the first delivery willingness indicator includes order acceptance rate, on-time completion rate, fast order acceptance rate, and order praise rate. The CRITIC method (Criteria Importance Through Intercriteria Correlation) is an algorithm that comprehensively measures the objective weight of indicators based on the comparative strength of evaluation indicators and the conflict between indicators.

[0088] Indicator variability calculation: For the The standard deviation of the indicators (the first distribution willingness indicators involved in this calculation process are referred to as indicators) is calculated as follows:

[0089] in, It is The sample in The value of an indicator, It is The sample mean of the indicators, is the sample size.

[0090] Calculation of indicator conflict: Indicates indicators and The correlation coefficient between the indicators is For the The overall correlation coefficient of the indicators is calculated as follows:

[0091] It should be noted that the above In the calculation formula , For the The sum of the number of multiple indicators other than indicators.

[0092] Information volume calculation: It is The information content of each indicator takes into account the variability of the indicators and the conflicts between the indicators. The calculation formula is:

[0093] Calculate weights: It is The objective weight of each indicator is calculated by the following formula:

[0094] Weighted sum calculation: The weight of the order acceptance rate is The weight of the on-time completion rate is expressed as Indicates the weight of the quick order acceptance rate. Indicates the weight of the order praise rate Indicates; the statistical data of order acceptance rate is expressed as Statistics on on-time completion rate Statistics of quick order acceptance rate To show the statistical data of order praise rate Target delivery willingness index The calculation formula is as follows:

[0095] In this embodiment, a statistical data set is constructed to cover multiple first delivery willingness indicators, and a weighted summation process is performed based on the weights of these first delivery willingness indicators in the target time period, thereby ensuring the comprehensiveness and objectivity of the target delivery willingness index.

[0096] See also Figure 7 , which provides a flow chart of determining the target delivery willingness index in the embodiment of this specification. Figure 7 As shown, the method of the embodiment of this specification may include the following steps S402 to S408. Steps S402 to S408 may be used as Figure 6 The detailed steps of step S306 of the illustrated embodiment.

[0097] S402, performing weighted summation processing on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness index in the target time period, to obtain the first delivery willingness index of each floor type in the target time period; S404, dividing the multiple floor types to obtain at least one floor type set, in which the floor attributes of the floor types in the floor type set are adjacent and the floor access mode attributes are the same; S406, for each floor type set in at least one floor type set, respectively aggregate the first delivery willingness index of each floor type in each floor type set to obtain a second delivery willingness index of each floor type set; S408, respectively determining the second delivery willingness index of each floor type set as the target delivery willingness index of each floor type in each floor type set in the target time period.

[0098] Specifically, in some cases, the historical delivery data of certain floor types in certain time periods may be sparse, that is, the amount of delivery data for certain floor types or certain time periods is insufficient. In order to improve the accuracy of the target delivery willingness index, it is possible to consider merging the data of some floor types in a specific time period.

[0099] First, according to the weight of each first delivery willingness index in the target time period, the statistical data set of each floor type in the target time period is weighted and summed. This calculation process has been described in detail in the above embodiment, that is, the weight of each first delivery willingness index is determined by the CRITIC method or other weighting algorithm, and then the statistical data of each first delivery willingness index is multiplied by its corresponding weight and summed to obtain the first delivery willingness index of each floor type in the target time period.

[0100] Then, the multiple floor types are divided to form at least one floor type set. The floor attributes of each floor type in the floor type set are adjacent and the floor access method attributes are the same. The floor type set actually represents adjacent floors with the same floor access method attributes.

[0101] Furthermore, for each floor type set in at least one floor type set, the first delivery willingness index of each floor type in each floor type set is aggregated. The aggregation process can adopt an aggregation algorithm such as median calculation or mean calculation, wherein the median calculation can reduce the impact of extreme values ​​on the results and is suitable for the case of uneven data distribution; the mean calculation can directly reflect the average level of the data and is suitable for the case of relatively uniform data distribution. According to the actual situation, a suitable aggregation algorithm is selected to obtain the second delivery willingness index of each floor type set.

[0102] Finally, the second delivery willingness index of each floor type set is determined as the target delivery willingness index of each floor type in each floor type set in the target time period. Through this step, even if the historical delivery data of some floor types or time periods is sparse, a relatively accurate target delivery willingness index can be obtained by data aggregation.

[0103] To facilitate understanding of the division of floor type sets in this embodiment, please refer to Figure 8 and Fig. 9 , Figure 8 and Fig. 9 These are all example schematic diagrams of a floor type set provided in the embodiments of this specification.

[0104] like Figure 8 As shown, for time period 1, in a building with an elevator, the first floor is classified into floor type set 1, the 2nd to 4th floors are classified into floor type set 2, and the 5th to 6th floors are classified into floor type set 3. Similarly, each floor in a building without an elevator or an unidentified building (it is not certain whether it has an elevator or not) is also divided accordingly.

[0105] like Fig. 9As shown, for time period 2, in a building with an elevator, floors 1-2 are classified into floor type set 1, and floors 3-6 are classified into floor type set 2. Similarly, floors in a building without an elevator or an unidentified building (it is not certain whether there is an elevator or not) are also divided accordingly.

[0106] Combination Figure 8 and Fig. 9 It can be seen that for the same floor arrival mode attribute, different floor type set division results may appear in different time periods.

[0107] In this embodiment, the statistical data of each floor type is weighted and summed according to the weight of each first delivery willingness index to obtain a preliminary first delivery willingness index, and then a more representative second delivery willingness index is obtained by dividing and aggregating the floor types. Based on the second delivery willingness index, it is helpful to determine more accurate and objective delivery difficulty information in the future.

[0108] In one embodiment, Figure 7 The step S404 of the illustrated embodiment is further refined and may include the following steps: Determine statistical data of a second delivery willingness indicator, where the second delivery willingness indicator is any one of the plurality of first delivery willingness indicators; Determine a first function curve of the statistical data of the second delivery willingness index changing with the floor attributes under the attributes of the arrival mode of each floor; Deriving each first function curve respectively to obtain a first-order derivative curve corresponding to each first function curve; Deriving the first-order derivative curves corresponding to the first function curves respectively to obtain the second-order derivative curves corresponding to the first function curves; Based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to each first function curve and the floor arrival mode attribute corresponding to each first function curve, multiple floor types are divided to obtain at least one floor type set.

[0109] Specifically, in order to realize the division of floor types, one of the multiple first delivery willingness indicators can be determined as the second delivery willingness indicator, and then the statistical data of the second delivery willingness indicator can be determined. For example, when the first delivery willingness indicator includes the order acceptance rate, the on-time completion rate, the fast order acceptance rate, and the order praise rate, the order acceptance rate can be selected as the second delivery willingness indicator, and its corresponding statistical data can be determined.

[0110] Then, it is necessary to determine the first function curve of the statistical data of the second delivery willingness index under the arrival mode attribute of each floor as the floor attribute changes. The purpose of this step is to observe and analyze how the second delivery willingness index changes with the change of floor attributes (such as increase or decrease of floor number) and the different arrival mode attributes of the floor (such as elevator, no elevator, etc.). By drawing the first function curve, this change trend can be intuitively displayed. It can be understood that a first function curve needs to be drawn separately for each floor arrival mode attribute, and the number of first function curves is determined by the number of floor arrival mode attributes.

[0111] See also Fig.10 , Fig.10 : is an example schematic diagram of a first function curve provided in an embodiment of this specification. The first function curve represents the trend and law of the statistical data of the second delivery willingness index changing with the floor attribute.

[0112] Next, we differentiate each first function curve to obtain the first-order derivative curve corresponding to each first function curve. The first-order derivative represents the slope of the tangent line of the function at a certain point, reflecting the rate at which the function value changes with the independent variable (here, the floor attribute). By taking the derivative, we can further analyze how fast the second delivery willingness index changes with the floor attribute, that is, its rate of change.

[0113] Furthermore, the first function curves are differentiated to obtain the first-order derivative curves corresponding to the first function curves; the first-order derivative curves corresponding to the first function curves are differentiated to obtain the second-order derivative curves corresponding to the first function curves. The first-order derivative represents the slope of the tangent line of the function at a certain point, reflecting the rate at which the function value changes with the independent variable; the second-order derivative represents the rate at which the first-order derivative changes with the independent variable, reflecting the speed of the function value change.

[0114] Since each inflection point in the first-order derivative curve and the second-order derivative curve corresponding to the first function curve has a corresponding floor attribute, multiple floor types can be divided based on the floor attributes of these inflection points to obtain at least one floor type set.

[0115] In this embodiment, by analyzing the trend of the statistical data of the second delivery willingness index changing with the floor attributes and the floor arrival method attributes, and dividing the floors into types based on the inflection points in the first-order derivative curve and the second-order derivative curve, the influence of sparse data of some floor types is effectively eliminated, making the subsequently determined delivery difficulty information more accurate and objective.

[0116] See also Fig.11 , which provides a flow chart of determining the delivery intention information in the embodiment of this specification. Fig.11As shown, the method of the embodiment of this specification may include the following steps S502 to S508, and steps S502 to S508 may be used as Figure 5 The detailed steps of step S208 of the illustrated embodiment.

[0117] S502, determining the maximum delivery willingness index and the minimum delivery willingness index for the target time period from the target delivery willingness indexes of each floor type in the target time period; S504, determining a benchmark delivery willingness index for the target time period according to the target delivery willingness index of each floor type in the target time period; S506, according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index in the target time period, respectively map the target delivery willingness index of each floor type in the target time period to a preset normalized interval to obtain a normalized delivery willingness index of each floor type in the target time period; S508: Determine the normalized delivery willingness index of each floor type in the target time period as the delivery willingness information of each floor type in the target time period.

[0118] Specifically, in order to accurately quantify the delivery difficulty for different floor types in a single time period, the maximum delivery willingness index and the minimum delivery willingness index of the target time period are first determined from the target delivery willingness index of each floor type in the target time period. Among them, the maximum delivery willingness index refers to the highest value of the target delivery willingness index among all floor types in the target time period, which reflects the strongest degree of delivery willingness of the delivery personnel for a certain floor type in the time period; the minimum delivery willingness index refers to the lowest value of the target delivery willingness index among all floor types in the target time period, which reflects the weakest degree of delivery willingness of the delivery personnel for a certain floor type in the time period.

[0119] Furthermore, according to the target delivery willingness index of each floor type in the target time period, the benchmark delivery willingness index of the target time period is determined. In one possible implementation, the median algorithm can be used to sort the target delivery willingness index of each floor type in the target time period, and then the value in the middle position is selected as the benchmark delivery willingness index. The median algorithm can reduce the impact of extreme values ​​on the results, making the benchmark delivery willingness index more representative. Another possible implementation is to use the mean algorithm, that is, to average the target delivery willingness index of all floor types in the target time period to obtain the benchmark delivery willingness index. It can be understood that the benchmark delivery willingness index can represent the average level or median level of the delivery personnel's willingness to deliver floor types during the target time period.

[0120] Then, according to the maximum delivery willingness index, minimum delivery willingness index, and benchmark delivery willingness index in the target time period, the target delivery willingness index of each floor type in the target time period is mapped to a preset normalized interval. The preset normalized interval refers to an interval used to convert values ​​of different dimensions or ranges into a unified numerical range. For example, the preset normalized interval can be [0,1]. After the mapping is completed, the normalized delivery willingness index of each floor type in the target time period can be obtained. The normalized delivery willingness index represents the relative strength of the delivery personnel's delivery willingness for each floor type in the target time period, and the normalized delivery willingness index of all floor types is within the preset normalized interval, which is convenient for comparison across floor types and time periods.

[0121] Finally, the normalized delivery willingness index of each floor type in the target time period is determined as the delivery willingness information of each floor type in the target time period. In this way, through normalization processing, not only the incomparability of the delivery willingness index between different floor types or different time periods is eliminated, but also the delivery willingness information is made more intuitive and easy to understand, providing reliable data support for the subsequent determination of delivery difficulty information.

[0122] In this embodiment, by determining the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index for the target time period, and mapping the target delivery willingness index for each floor type to a preset normalized interval, the incomparability of the delivery willingness indexes between different floor types and different time periods is effectively eliminated, so that the subsequently determined delivery willingness information can more accurately reflect the delivery personnel's delivery willingness for different floor types within a specific time period.

[0123] In one embodiment, Fig.11 Step S506 of the illustrated embodiment is further refined and may include the following steps: Traverse multiple floor types; For the target floor type among the traversed multiple floor types, if the target delivery willingness index of the target floor type in the target time period is not less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the first sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period; If the target delivery willingness index of the target floor type in the target time period is less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the second sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period, and the value in the first sub-interval is greater than the value in the second sub-interval; If the normalized delivery willingness index of each floor type in the target time period is obtained, the traversal of multiple floor types is terminated.

[0124] Specifically, the preset normalized interval involved in this embodiment is divided into a first sub-interval and a second sub-interval, and the value in the first sub-interval is greater than the value in the second sub-interval. For example, if the preset normalized interval is [0,1], then the first sub-interval can be [0.5,1], and the second sub-interval can be [0,0.5). This division method enables the normalized delivery willingness index to more intuitively reflect the delivery personnel's delivery willingness for different floor types.

[0125] First, multiple floor types are traversed, that is, each floor type is visited in turn in a certain order (such as floor number order, random order, etc.).

[0126] During the traversal process, for each traversed floor type (i.e., target floor type), if the target delivery willingness index of the target floor type in the target time period is not less than the benchmark delivery willingness index of the target time period, it means that the delivery personnel have a strong delivery willingness for this floor type, at least reaching the average level or higher. Therefore, according to the maximum delivery willingness index, minimum delivery willingness index, and benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the first sub-interval in the preset normalized interval. Taking [0,1] as the preset normalized interval and [0.5,1] as the first sub-interval as an example, the mapped normalized delivery willingness index will be between 0.5 and 1.

[0127] If the target delivery willingness index of the target floor type in the target time period is less than the benchmark delivery willingness index of the target time period, it means that the delivery personnel's delivery willingness for the floor type is weak and below the average level. Therefore, according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the second sub-interval in the preset normalized interval. Similarly, taking [0,1] as the preset normalized interval and [0,0.5) as the second sub-interval as an example, the mapped normalized delivery willingness index will be between 0 and 0.5.

[0128] Repeat the above steps to traverse all floor types until each floor type has completed the mapping from the target delivery willingness index to the normalized delivery willingness index. If all floor types have completed the mapping, that is, the normalized delivery willingness index of each floor type in the target time period is obtained, then the traversal of multiple floor types is terminated and the subsequent steps are entered.

[0129] To facilitate understanding of the mapping process of this embodiment, a calculation example is provided below: Assume that the target delivery willingness index of the target floor type in the target time period is , the maximum delivery willingness index in the target time period is , the minimum delivery willingness index in the target time period is , the benchmark delivery willingness index for the target time period is .

[0130] If the target delivery willingness index The benchmark delivery willingness index is not less than the target time period , then the target delivery willingness index can be calculated using the following formula Map to the first sub-interval to obtain the normalized distribution willingness index of the target floor type in the target time period :

[0131] If the target delivery willingness index Benchmark delivery willingness index that is less than the target time period , then the target delivery willingness index can be calculated using the following formula Mapped to the second sub-interval to obtain the normalized delivery willingness index of the target floor type in the target time period :

[0132] In this embodiment, it can be ensured that the delivery willingness index of each floor type in the target time period is accurately mapped to the preset normalized interval, and the mapping result can intuitively reflect the strength of the delivery willingness, providing more reliable and intuitive data support for the subsequent determination of delivery difficulty information.

[0133] In this embodiment, by traversing multiple floor types and comparing the target delivery willingness index with the benchmark delivery willingness index, the target delivery willingness index of each floor type in the target time period is mapped to different sub-intervals of a preset normalized interval, which significantly improves the standardization and comparability of delivery willingness information.

[0134] See also Fig.12 , which is a flowchart of an order allocation method provided in the embodiment of this specification. Fig.12 As shown, the method of the embodiment of this specification may include the following steps S602 to S606.

[0135] S602, in response to the creation of a target delivery order for a target area, determining a delivery time and a floor type of the target delivery order.

[0136] Specifically, the target area involved in this embodiment refers to an area with a specific geographical range. In some possible cases, the target area can also be a collection of multiple sub-areas. For example, the target area can be a city, or a collection of multiple cities. The floor type involved in this embodiment is determined by the floor attribute and the floor access method attribute. For details, please refer to Figure 2 The relevant introduction in the illustrated embodiment will not be repeated here.

[0137] In a possible implementation, in response to the creation of a target delivery order for a target area, the delivery time and delivery address can be determined from the target delivery order. The delivery time refers to the time range or specific time point when the target delivery order is expected to be completed. It is usually specified by the user when placing an order to indicate when the delivery personnel need to complete the delivery. The delivery address refers to the delivery location specified in the target delivery order, which contains detailed geographical location information, such as street name, house number, floor number, etc. Further, the floor type of the target delivery order can be determined based on the delivery address of the target delivery order. Specifically, the floor attribute (such as "3rd floor", "5th floor", etc.) can be extracted from the delivery address, and the floor access mode attribute (such as elevator, no elevator, etc.) of the building where the address is located can be combined to determine the floor type. For example, if the delivery address is located in a building with an elevator and the floor attribute is "6th floor", the delivery order can be classified as the floor type of "elevator #6th floor".

[0138] In a possible implementation, information such as delivery time and floor type may have been automatically recorded or attached as part of the target delivery order when the order is created. In this case, the delivery time and floor type can be determined directly from the target delivery order in response to the creation of the target delivery order for the target area, without the need to extract additional information from the delivery address.

[0139] S604: Determine target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order and the delivery difficulty information of each floor type in the target area in each time period.

[0140] Specifically, the delivery difficulty information of each floor type in the target area in each time period involved in this embodiment is determined based on the relevant embodiments of the above-mentioned delivery difficulty determination method. Therefore, when the delivery time and floor type of the target delivery order are determined, the target delivery difficulty information of the target delivery order can be found from the delivery difficulty information of each floor type in the target area in each time period.

[0141] In a possible implementation, the delivery difficulty information of each floor type in the target area in each time period is recorded in a database. Then, by constructing a query statement with the delivery time and floor type of the target delivery order, the target delivery difficulty information of the target delivery order can be queried from the database.

[0142] S606: Allocate the target delivery order to the target delivery personnel according to the target delivery difficulty information.

[0143] Specifically, the target delivery difficulty information provides quantitative guidance on the delivery difficulty of a specific floor type within a specific time period, providing an important decision-making basis for the order allocation process. Therefore, the target delivery order can be assigned to the appropriate target delivery personnel based on the target delivery difficulty information to ensure that the target delivery order can be completed efficiently and smoothly, and the rights and interests of the target delivery personnel are protected.

[0144] In one possible implementation, the delivery personnel's acceptance of the target delivery difficulty information may be considered. Specifically, the target delivery difficulty information may be matched with the delivery personnel's delivery capabilities and preferences. For example, if the target delivery difficulty information indicates that the delivery difficulty is high (such as delivery to high floors of a building without an elevator), the target delivery order may be preferentially assigned to a target delivery personnel with high historical delivery efficiency and rich delivery experience for similar floor types.

[0145] In one possible implementation, the delivery personnel may have already received delivery orders. In order to balance the workload of the delivery personnel and prevent a delivery personnel from receiving too many difficult delivery orders, the number and difficulty of orders currently received by the candidate delivery personnel may be considered when allocating target delivery orders, and the target delivery orders may be allocated to the target delivery personnel with a lower workload.

[0146] In a possible implementation, it is possible to consider adjusting the delivery revenue of the target delivery order so that the delivery revenue of the target delivery order is positively correlated with the target delivery difficulty, and then assigning the target delivery order to the target delivery personnel.

[0147] In addition, there are many application scenarios for the target delivery difficulty information. For example, during the peak order period, orders can be prioritized according to the delivery difficulty information, and orders with lower delivery difficulty can be given priority to relieve delivery pressure and improve overall delivery efficiency. For another example, in the dispatch of delivery personnel, the rest time and working hours of delivery personnel can be reasonably arranged according to the delivery difficulty information to avoid excessive concentration of orders with higher delivery difficulty during peak hours, which may cause excessive fatigue of delivery personnel.

[0148] In some possible implementations, before determining the delivery time and floor type of the target delivery order, the following steps may be performed: in response to creation of the target delivery order, determining a target area corresponding to the target delivery order, where the target area is one of multiple areas.

[0149] In this embodiment, by combining the delivery time, floor type and delivery difficulty information of each floor type in the target area in each time period of the target delivery order, the target delivery order can be assigned to a suitable target delivery personnel, effectively protecting the rights and interests of the target delivery personnel.

[0150] See also Fig.13 , provides a flow chart of determining the target delivery personnel for the embodiment of this specification. Fig.13 As shown, the method of the embodiment of this specification may include the following steps S702 to S706, and steps S702 to S706 may be used as Fig.12 The detailed steps of step S606 of the illustrated embodiment.

[0151] S702, determining multiple delivery personnel to be selected corresponding to the target delivery order; S704, determining the delivery preference information of each of the multiple delivery personnel to be selected; S706: Determine a target delivery personnel from among multiple delivery personnel to be selected based on the delivery preference information of each delivery personnel to be selected and the target delivery difficulty information.

[0152] Specifically, the multiple delivery personnel to be selected corresponding to the target delivery order refer to delivery personnel who are currently online in the target area and can accept new delivery orders.

[0153] Regarding the process of determining multiple delivery personnel to be selected corresponding to the target delivery order, in a possible implementation, firstly, the delivery area is determined according to the delivery time and delivery address of the target delivery order. Then, delivery personnel who are currently online and meet the basic delivery requirements are searched in the delivery area to form a set of delivery personnel to be selected, and the set of delivery personnel to be selected includes multiple delivery personnel to be selected.

[0154] In addition, delivery preference information refers to the preference or tendency of delivery personnel towards specific floor types, time periods, delivery distances, etc. when performing delivery tasks. This information can be collected and recorded by the system in advance, such as through analysis of the delivery personnel's historical delivery data. In addition, delivery preference information may also be set actively by the delivery personnel. Delivery preference information helps the system to more accurately understand the personal characteristics and preferences of delivery personnel, so as to make more reasonable decisions when allocating orders.

[0155] When determining the target delivery personnel, it is necessary to comprehensively consider the delivery preference information of each candidate delivery personnel and the delivery difficulty information of the target delivery order. In a possible implementation, each candidate delivery personnel can be scored or ranked according to the delivery preference information of each candidate delivery personnel and the delivery difficulty information of the target delivery order, and finally the delivery personnel with the highest score or the highest ranking are selected as the target delivery personnel.

[0156] In this embodiment, by comprehensively considering the target delivery difficulty information and the delivery preference information of the selected delivery personnel, the target delivery order is assigned to the target delivery personnel, so that the target delivery order is adapted to the preferences of the target delivery personnel, effectively protecting the rights and interests of the target delivery personnel.

[0157] In one embodiment, Fig.13 Step S706 of the illustrated embodiment is further refined and may include the following steps: Determine the set of delivery orders received by each delivery personnel to be selected within a preset time range; Determine, from the collection of received delivery orders of each delivery personnel to be selected, high-difficulty delivery orders whose delivery difficulty information meets preset conditions; A target delivery personnel is determined from among multiple delivery personnel to be selected based on the high-difficulty delivery orders, delivery preference information and target delivery difficulty information of each delivery personnel to be selected.

[0158] Specifically, the set of delivery orders received by the selected delivery personnel within the preset time range refers to the set of delivery orders that each selected delivery personnel has accepted in the past period of time (such as the last hour, day or week, etc.), and the set of received delivery orders reflects the delivery tasks currently undertaken by the selected delivery personnel. In some cases, the received delivery orders may also specifically refer to the delivery orders that the delivery personnel have accepted but have not yet completed.

[0159] After determining the set of delivery orders received by each delivery personnel to be selected within the preset time range, high-difficulty delivery orders whose delivery difficulty information meets the preset conditions are determined from the set of delivery orders received by each delivery personnel to be selected. The preset conditions refer to the standards or thresholds used to screen high-difficulty delivery orders to ensure that the screened orders do have high delivery difficulty. For example, the preset conditions may include that the delivery difficulty coefficient is greater than a certain specific value (such as 0.8).

[0160] The process of determining high-difficulty delivery orders whose delivery difficulty information meets the preset conditions can be specifically performed as follows: first, the delivery difficulty information (such as the delivery difficulty coefficient) of each received delivery order is extracted from the received delivery order set of each delivery personnel to be selected. Then, the delivery difficulty information of these received delivery orders is screened according to the preset conditions, and the orders that meet the preset conditions are classified as high-difficulty delivery orders. For example, if the preset condition is that the delivery difficulty coefficient is greater than 0.8, then all received delivery orders with a delivery difficulty coefficient greater than 0.8 are screened out as high-difficulty delivery orders.

[0161] Furthermore, based on the high-difficulty delivery orders, delivery preference information, and target delivery difficulty information of each candidate delivery personnel, a target delivery personnel is determined from multiple candidate delivery personnel. In a possible implementation, each candidate delivery personnel may be scored or ranked according to the high-difficulty delivery orders, delivery preference information, and target delivery difficulty information of each candidate delivery personnel, and finally the delivery personnel with the highest score or the highest ranking is selected as the target delivery personnel. In this embodiment, the target delivery order is assigned to the target delivery personnel by comprehensively considering the high-difficulty delivery orders, delivery preference information and target delivery difficulty information in the set of delivery orders received by the selected delivery personnel within a preset time range, so that the target delivery order is adapted to the preferences and order acceptance situation of the target delivery personnel, thereby effectively protecting the rights and interests of the target delivery personnel.

[0162] based on Figure 1 The following will combine Fig.14 , the delivery difficulty determination device provided in the embodiment of this specification is introduced in detail. It should be noted that, Fig.14 The delivery difficulty determination device 1 is used to execute the present specification Figure 2 - Fig.11 For the convenience of explanation, only the part related to the embodiment of this specification is shown. For the specific technical details not disclosed, please refer to this specification. Figure 2 - Fig.11 The delivery difficulty determination device 1 specifically comprises: An acquisition unit 11 is used to acquire historical delivery data of the delivery personnel in a target area, where the target area includes multiple floor types, and the floor type is determined by a floor attribute and a floor arrival mode attribute; A first determining unit 12 is used to determine a plurality of time periods in a preset time unit according to historical delivery data of a target area; A second determining unit 13 is used to determine the delivery intention information of each floor type in the target area in each time period according to the historical delivery data of the target area; The third determining unit 14 is used to determine the delivery difficulty information of each floor type in each time period according to the delivery willingness information of each floor type in each time period.

[0163] Optionally, the second determination unit 13 is further used to: determine the historical distribution data of each floor type in the target area in each time period from the historical distribution data of the target area; traverse multiple time periods; for the target time period among the traversed multiple time periods, determine the target distribution willingness index of each floor type in the target time period according to the historical distribution data of each floor type in the target time period; determine the distribution willingness information of each floor type in the target time period based on the target distribution willingness index of each floor type in the target time period; if the distribution willingness information of each floor type in each time period has been determined, end the traversal of multiple time periods.

[0164] Optionally, the second determination unit 13 is further used to: determine a statistical data set of each floor type in the target time period based on the historical delivery data of each floor type in the target time period, the statistical data set including statistical data of each first delivery willingness indicator among multiple first delivery willingness indicators; determine the weight of each first delivery willingness indicator in the target time period based on the statistical data set of each floor type in the target time period; perform weighted summation processing on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period, so as to obtain the target delivery willingness index of each floor type in the target time period.

[0165] Optionally, the second determination unit 13 is further used to: perform weighted summation processing on the statistical data sets of each floor type in the target time period according to the weights of each first delivery willingness index in the target time period, to obtain the first delivery willingness index of each floor type in the target time period; divide multiple floor types to obtain at least one floor type set, in which the floor attributes of each floor type in the floor type set are adjacent and the floor arrival method attributes are the same; for each floor type set in the at least one floor type set, respectively aggregate the first delivery willingness index of each floor type in each floor type set to obtain the second delivery willingness index of each floor type set; respectively determine the second delivery willingness index of each floor type set as the target delivery willingness index of each floor type in each floor type set in the target time period.

[0166] Optionally, the second determination unit 13 is also used to: determine the statistical data of the second delivery willingness index, where the second delivery willingness index is any one of multiple first delivery willingness indicators; determine the first function curve of the statistical data of the second delivery willingness index as the floor attributes change under the attributes of the arrival method of each floor; respectively derive each first function curve to obtain a first-order derivative curve corresponding to each first function curve; respectively derive the first-order derivative curve corresponding to each first function curve to obtain a second-order derivative curve corresponding to each first function curve; divide multiple floor types based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to each first function curve, and the floor arrival method attributes corresponding to each first function curve, to obtain at least one floor type set.

[0167] Optionally, the second determination unit 13 is also used to: determine the maximum delivery willingness index and the minimum delivery willingness index of the target time period from the target delivery willingness index of each floor type in the target time period; determine the benchmark delivery willingness index of the target time period according to the target delivery willingness index of each floor type in the target time period; map the target delivery willingness index of each floor type in the target time period to a preset normalized interval according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period to obtain the normalized delivery willingness index of each floor type in the target time period; determine the normalized delivery willingness index of each floor type in the target time period as the delivery willingness information of each floor type in the target time period.

[0168] Optionally, the second determination unit 13 is also used to: traverse multiple floor types; for the target floor type among the traversed multiple floor types, if the target delivery willingness index of the target floor type in the target time period is not less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the first sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period; if the target delivery willingness index of the target floor type in the target time period is less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the second sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period, and the value in the first sub-interval is greater than the value in the second sub-interval; if the normalized delivery willingness index of each floor type in the target time period is obtained, the traversal of multiple floor types is terminated.

[0169] Optionally, the first determination unit 12 is further used to: determine supply-demand ratio statistics based on historical delivery data of the target area, the supply-demand ratio statistics being determined based on the ratio between the number of historical delivery orders and the number of delivery personnel; determine a second function curve of the supply-demand ratio statistics changing with time in a preset time unit; derive the second function curve to obtain a first-order derivative curve corresponding to the second function curve; derive the first-order derivative curve corresponding to the second function curve to obtain a second-order derivative curve corresponding to the second function curve; divide the preset time unit into time periods based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to the second function curve to obtain multiple time periods in the preset time unit.

[0170] Optionally, the third determination unit 14 is further used to: based on the linear relationship between the delivery willingness information and the delivery difficulty information, convert the delivery willingness information of each floor type in each time period to obtain the delivery difficulty information of each floor type in each time period, and the delivery willingness information of each floor type in each time period is negatively correlated with the delivery difficulty information.

[0171] Optionally, the delivery difficulty determination device 1 is also used to: obtain delivery feature data of each sub-region among multiple preset sub-regions; cluster the multiple sub-regions according to the delivery feature data of each sub-region to obtain at least one cluster cluster; respectively cluster the sub-regions of each cluster in at least one cluster cluster to obtain the region corresponding to each cluster cluster, and the target region is the region corresponding to any one cluster cluster in at least one cluster cluster.

[0172] The effects that can be achieved by this embodiment can be found in the relevant embodiments of the above-mentioned method for determining the delivery difficulty, which will not be repeated here.

[0173] based on Figure 1 The following will combine Fig.15 , the order distribution device provided in the embodiment of this specification is introduced in detail. It should be noted that, Fig.15 The order distribution device 2 in the embodiment of the present invention is used to execute the order distribution device 2 in the embodiment of the present invention. Fig.12 - Fig.13 For the convenience of explanation, only the part related to the embodiment of this specification is shown. For the specific technical details not disclosed, please refer to this specification. Fig.12 - Fig.13 The order distribution device 2 specifically comprises: A first determining unit 21 is used to determine the delivery time and floor type of the target delivery order in response to the creation of the target delivery order of the target area; The second determining unit 22 is used to determine the target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order, and the delivery difficulty information of each floor type in the target area in each time period, wherein the delivery difficulty information of each floor type in the target area in each time period is based on Figure 2 - Fig.11 The delivery difficulty determination method of the illustrated embodiment determines; The allocating unit 23 is used to allocate the target delivery order to the target delivery personnel according to the target delivery difficulty information.

[0174] Optionally, the allocation unit 23 is also used to: determine multiple delivery personnel to be selected corresponding to the target delivery order; determine the delivery preference information of each delivery personnel to be selected among the multiple delivery personnel to be selected; and determine the target delivery personnel from the multiple delivery personnel to be selected based on the delivery preference information of each delivery personnel to be selected and the target delivery difficulty information.

[0175] Optionally, the allocation unit 23 is further used to: determine the set of received delivery orders for each delivery personnel to be selected within a preset time range; determine the high-difficulty delivery orders whose delivery difficulty information meets preset conditions in the set of received delivery orders for each delivery personnel to be selected; and determine the target delivery personnel from multiple delivery personnel to be selected based on the high-difficulty delivery orders, delivery preference information and target delivery difficulty information of each delivery personnel to be selected.

[0176] The effects that can be achieved by this embodiment can be found in the relevant embodiments of the above-mentioned order allocation method, which will not be repeated here.

[0177] See also Fig.16 , is a schematic diagram of the structure of an electronic device provided in the embodiment of this specification. Fig.16 As shown, the electronic device 1000 may include: at least one processor 1001, such as a CPU, at least one network interface 1004, an input / output interface 1003, a memory 1005, and at least one communication bus 1002. The communication bus 1002 is used to realize the connection and communication between these components. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The memory 1005 may optionally be at least one storage device located away from the aforementioned processor 1001. Fig.16 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, an input and output interface module, a delivery difficulty determination application, and an order allocation application.

[0178] exist Fig.16 In the electronic device 1000 shown, the input-output interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user.

[0179] In one embodiment, the processor 1001 may be used to call a delivery difficulty determination application stored in the memory 1005, and specifically perform the following operations: Obtain the historical delivery data of the delivery personnel in the target area. The target area includes multiple floor types, and the floor type is determined by the floor attribute and the floor arrival mode attribute; Determine multiple time periods in a preset time unit based on historical delivery data of the target area; According to the historical delivery data of the target area, determine the delivery intention information of each floor type in the target area in each time period; According to the delivery willingness information of each floor type in each time period, the delivery difficulty information of each floor type in each time period is determined.

[0180] Optionally, when the processor 1001 determines the delivery willingness information of each floor type in the target area in each time period based on the historical delivery data of the target area, the processor 1001 specifically performs the following operations: determine the historical delivery data of each floor type in the target area in each time period from the historical delivery data of the target area; traverse multiple time periods; for the target time period among the traversed multiple time periods, determine the target delivery willingness index of each floor type in the target time period based on the historical delivery data of each floor type in the target time period; determine the delivery willingness information of each floor type in the target time period based on the target delivery willingness index of each floor type in the target time period; if the delivery willingness information of each floor type in each time period has been determined, end the traversal of multiple time periods.

[0181] Optionally, when processor 1001 determines the target delivery willingness index of each floor type in the target time period based on the historical delivery data of each floor type in the target time period, it specifically performs the following operations: determine a statistical data set of each floor type in the target time period based on the historical delivery data of each floor type in the target time period, the statistical data set including statistical data of each first delivery willingness indicator among multiple first delivery willingness indicators; determine the weight of each first delivery willingness indicator in the target time period based on the statistical data set of each floor type in the target time period; perform weighted summation processing on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period, so as to obtain the target delivery willingness index of each floor type in the target time period.

[0182] Optionally, when processor 1001 performs weighted summation processing on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period to obtain the target delivery willingness index of each floor type in the target time period, the following specific operations are performed: weighted summation processing is performed on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness indicator in the target time period to obtain the first delivery willingness index of each floor type in the target time period; multiple floor types are divided to obtain at least one floor type set, and the floor attributes of each floor type in the floor type set are adjacent and the floor arrival method attributes are the same; for each floor type set in at least one floor type set, the first delivery willingness index of each floor type in each floor type set is aggregated to obtain the second delivery willingness index of each floor type set; the second delivery willingness index of each floor type set is determined as the target delivery willingness index of each floor type in each floor type set in the target time period.

[0183] Optionally, when the processor 1001 performs the following operations when executing the classification of multiple floor types to obtain at least one floor type set: determining the statistical data of the second delivery willingness indicator, where the second delivery willingness indicator is any one of the multiple first delivery willingness indicators; determining the first function curve of the statistical data of the second delivery willingness indicator changing with the floor attributes under the attributes of the arrival method of each floor; respectively deriving each first function curve to obtain a first-order derivative curve corresponding to each first function curve; respectively deriving the first-order derivative curve corresponding to each first function curve to obtain a second-order derivative curve corresponding to each first function curve; dividing the multiple floor types based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to each first function curve, and the floor arrival method attribute corresponding to each first function curve, to obtain at least one floor type set.

[0184] Optionally, when processor 1001 determines the delivery willingness information of each floor type in the target time period based on the target delivery willingness index of each floor type in the target time period, it specifically performs the following operations: determine the maximum delivery willingness index and the minimum delivery willingness index of the target time period from the target delivery willingness index of each floor type in the target time period; determine the benchmark delivery willingness index of the target time period according to the target delivery willingness index of each floor type in the target time period; map the target delivery willingness index of each floor type in the target time period to a preset normalized interval according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period to obtain the normalized delivery willingness index of each floor type in the target time period; determine the normalized delivery willingness index of each floor type in the target time period as the delivery willingness information of each floor type in the target time period.

[0185] Optionally, when the processor 1001 executes the mapping of the target delivery willingness index of each floor type in the target time period to a preset normalized interval according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period to obtain the normalized delivery willingness index of each floor type in the target time period, the following specific operations are performed: multiple floor types are traversed; for the target floor type among the multiple traversed floor types, if the target delivery willingness index of the target floor type in the target time period is not less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the preset normalized interval. The target delivery willingness index is mapped to the first sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period; if the target delivery willingness index of the target floor type in the target time period is less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the second sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period, and the value in the first sub-interval is greater than the value in the second sub-interval; if the normalized delivery willingness index of each floor type in the target time period is obtained, the traversal of multiple floor types is terminated.

[0186] Optionally, when the processor 1001 determines multiple time periods in a preset time unit based on the historical delivery data of the target area, the processor 1001 specifically performs the following operations: determine supply-demand ratio statistical data based on the historical delivery data of the target area, the supply-demand ratio statistical data being determined based on the ratio between the number of historical delivery orders and the number of delivery personnel; determine a second function curve of the supply-demand ratio statistical data changing with time in the preset time unit; derive the second function curve to obtain a first-order derivative curve corresponding to the second function curve; derive the first-order derivative curve corresponding to the second function curve to obtain a second-order derivative curve corresponding to the second function curve; divide the preset time unit into time periods based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to the second function curve to obtain multiple time periods in the preset time unit.

[0187] Optionally, when processor 1001 determines the delivery difficulty information of each floor type in each time period based on the delivery willingness information of each floor type in each time period, it specifically performs the following operations: based on the linear relationship between the delivery willingness information and the delivery difficulty information, the delivery willingness information of each floor type in each time period is converted to obtain the delivery difficulty information of each floor type in each time period, and the delivery willingness information of each floor type in each time period is negatively correlated with the delivery difficulty information.

[0188] Optionally, before executing the acquisition of historical delivery data of delivery personnel in the target area, the processor 1001 specifically performs the following operations: obtaining delivery feature data of each of a plurality of preset sub-areas; clustering the plurality of sub-areas according to the delivery feature data of each sub-area to obtain at least one cluster cluster; respectively clustering the sub-areas of each cluster in at least one cluster cluster to obtain areas corresponding to each cluster cluster, wherein the target area is an area corresponding to any one of the at least one cluster cluster.

[0189] In one embodiment, the processor 1001 may be used to call the order allocation application stored in the memory 1005, and specifically perform the following operations: In response to creation of a target delivery order for a target area, determining a delivery time and a floor type of the target delivery order; Determine target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order, and the delivery difficulty information of each floor type in the target area in each time period, wherein the delivery difficulty information of each floor type in the target area in each time period is determined based on the steps performed by the above-mentioned delivery difficulty determination application; According to the target delivery difficulty information, the target delivery orders are assigned to the target delivery personnel.

[0190] Optionally, when processor 1001 allocates a target delivery order to a target delivery personnel according to target delivery difficulty information, it specifically performs the following operations: determining multiple delivery personnel to be selected corresponding to the target delivery order; determining delivery preference information of each delivery personnel to be selected among the multiple delivery personnel to be selected; and determining the target delivery personnel from the multiple delivery personnel to be selected based on the delivery preference information of each delivery personnel to be selected and the target delivery difficulty information.

[0191] Optionally, when the processor 1001 determines a target delivery personnel from multiple delivery personnel to be selected based on the delivery preference information and target delivery difficulty information of each delivery personnel to be selected, the following specific operations are performed: determining a set of delivery orders received by each delivery personnel to be selected within a preset time range; determining high-difficulty delivery orders whose delivery difficulty information meets preset conditions in the set of delivery orders received by each delivery personnel to be selected; determining a target delivery personnel from multiple delivery personnel to be selected based on the high-difficulty delivery orders, delivery preference information and target delivery difficulty information of each delivery personnel to be selected.

[0192] The effects that can be achieved by this embodiment can be found in the relevant embodiments of the above-mentioned delivery difficulty determination method and order allocation method, which will not be repeated here.

[0193] The embodiment of the present specification also provides a computer storage medium, which computer readable storage medium stores computer program code. When the computer program code is executed, the above Figure 2 - Fig.13 The method of the embodiment shown in the figure can be found in the specific implementation process. Figure 2 - Fig.13 The specific description of the illustrated embodiment will not be repeated here.

[0194] The embodiment of the present specification also provides a computer program product, which stores at least one instruction, and when the at least one instruction is executed by a processor, the above Figure 2 - Fig.13 The delivery difficulty determination method and order allocation method of the embodiment shown in the figure can be found in the specific implementation process. Figure 2 - Fig.13 The specific description of the illustrated embodiment will not be repeated here.

[0195] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0196] The above disclosure is only the preferred embodiment of this specification, which certainly cannot be used to limit the scope of rights of this specification. Therefore, equivalent changes made according to the claims of this specification are still within the scope covered by this specification.

Claims

1. A method for determining delivery difficulty, characterized in that: The method comprises: Acquire historical delivery data of a delivery person in a target area, where the target area includes multiple floor types, and the floor type is determined by a floor attribute and a floor arrival mode attribute; Determining a plurality of time periods in a preset time unit according to historical delivery data of the target area; Determining the delivery intention information of each floor type in the target area in each time period according to the historical delivery data of the target area; The delivery difficulty information of each floor type in each time period is determined according to the delivery willingness information of each floor type in each time period.

2. The method according to claim 1, characterized in that Determining the delivery intention information of each floor type in the target area in each time period according to the historical delivery data of the target area includes: Determining the historical delivery data of each of the floor types in the target area in each of the time periods from the historical delivery data of the target area; Traversing a plurality of the time periods; For a target time period among the traversed multiple time periods, determine a target delivery intention index for each floor type in the target time period according to historical delivery data of each floor type in the target time period; Determining the delivery willingness information of each floor type in the target time period based on the target delivery willingness index of each floor type in the target time period; If the delivery intention information of each floor type in each time period has been determined, the traversal of the multiple time periods is terminated.

3. The method according to claim 2, characterized in that Determining the target delivery willingness index of each floor type in the target time period according to the historical delivery data of each floor type in the target time period includes: Determine a statistical data set of each floor type in the target time period according to the historical delivery data of each floor type in the target time period, wherein the statistical data set includes statistical data of each of the first delivery willingness indicators among a plurality of first delivery willingness indicators; Determining the weight of each of the first delivery willingness indicators in the target time period according to the statistical data set of each of the floor types in the target time period; According to the weight of each of the first delivery willingness indicators in the target time period, a weighted summation process is performed on the statistical data set of each of the floor types in the target time period to obtain a target delivery willingness index for each of the floor types in the target time period.

4. The method according to claim 3, characterized in that The weighted summation process is performed on the statistical data set of each floor type in the target time period according to the weight of each first delivery willingness index in the target time period to obtain the target delivery willingness index of each floor type in the target time period, including: According to the weight of each of the first delivery willingness indicators in the target time period, a weighted summation process is performed on the statistical data set of each of the floor types in the target time period to obtain a first delivery willingness index of each of the floor types in the target time period; Dividing the plurality of floor types to obtain at least one floor type set, wherein the floor attributes of the floor types in the floor type set are adjacent and the floor access mode attributes are the same; For each of the floor type sets in at least one of the floor type sets, respectively aggregating the first delivery willingness index of each floor type in each of the floor type sets to obtain a second delivery willingness index of each of the floor type sets; The second delivery willingness index of each of the floor type sets is respectively determined as the target delivery willingness index of each floor type in each of the floor type sets in the target time period.

5. The method according to claim 4, characterized in that The dividing the plurality of floor types to obtain at least one floor type set includes: Determine statistical data of a second delivery willingness indicator, where the second delivery willingness indicator is any one of the plurality of the first delivery willingness indicators; Determine a first function curve of the statistical data of the second delivery willingness index changing with the floor attribute under each of the floor arrival mode attributes; Deriving each of the first function curves respectively to obtain a first-order derivative curve corresponding to each of the first function curves; Deriving the first-order derivative curves corresponding to the first function curves respectively to obtain the second-order derivative curves corresponding to the first function curves; Based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to each of the first function curves, and the floor arrival mode attributes corresponding to each of the first function curves, the multiple floor types are divided to obtain at least one floor type set.

6. The method according to claim 2, characterized in that The determining of the delivery willingness information of each floor type in the target time period based on the target delivery willingness index of each floor type in the target time period includes: Determine the maximum delivery willingness index and the minimum delivery willingness index of the target time period from the target delivery willingness index of each floor type in the target time period; Determine a benchmark delivery willingness index for the target time period according to the target delivery willingness index of each floor type in the target time period; According to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period, the target delivery willingness index of each floor type in the target time period is mapped to a preset normalized interval to obtain a normalized delivery willingness index of each floor type in the target time period; The normalized delivery willingness index of each floor type in the target time period is determined as the delivery willingness information of each floor type in the target time period.

7. The method according to claim 6, characterized in that According to the maximum delivery willingness index, the minimum delivery willingness index, and the benchmark delivery willingness index of the target time period, the target delivery willingness index of each floor type in the target time period is mapped to a preset normalized interval to obtain the normalized delivery willingness index of each floor type in the target time period, including: Traversing a plurality of the floor types; For a target floor type among the multiple floor types traversed, if the target delivery willingness index of the target floor type in the target time period is not less than the benchmark delivery willingness index of the target time period, then according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the first sub-interval in the preset normalized interval to obtain the normalized delivery willingness index of the target floor type in the target time period; If the target delivery willingness index of the target floor type in the target time period is less than the benchmark delivery willingness index of the target time period, the target delivery willingness index of the target floor type in the target time period is mapped to the second sub-interval in the preset normalized interval according to the maximum delivery willingness index, the minimum delivery willingness index and the benchmark delivery willingness index of the target time period, to obtain the normalized delivery willingness index of the target floor type in the target time period, and the value in the first sub-interval is greater than the value in the second sub-interval; If the normalized delivery willingness index of each floor type in the target time period is obtained, the traversal of the multiple floor types is terminated.

8. The method according to claim 1, characterized in that The step of determining a plurality of time periods in a preset time unit according to the historical delivery data of the target area includes: Determine supply-demand ratio statistics based on historical delivery data of the target area, where the supply-demand ratio statistics are determined based on the ratio between the number of historical delivery orders and the number of delivery personnel; Determine a second function curve of the supply-demand ratio statistical data changing with time in a preset time unit; Deriving the second function curve to obtain a first-order derivative curve corresponding to the second function curve; Deriving a first-order derivative curve corresponding to the second function curve to obtain a second-order derivative curve corresponding to the second function curve; The preset time unit is divided into time periods based on the inflection points in the first-order derivative curve and the second-order derivative curve corresponding to the second function curve to obtain a plurality of time periods in the preset time unit.

9. The method according to claim 1, characterized in that: Determining the delivery difficulty information of each floor type in each time period according to the delivery willingness information of each floor type in each time period includes: Based on the linear relationship between the delivery willingness information and the delivery difficulty information, the delivery willingness information of each floor type in each time period is converted to obtain the delivery difficulty information of each floor type in each time period. The delivery willingness information of each floor type in each time period is negatively correlated with the delivery difficulty information.

10. The method according to claim 1, characterized in that Before obtaining the historical delivery data of the delivery personnel in the target area, the method further includes: Acquire delivery characteristic data of each of the preset multiple sub-areas; Clustering the plurality of sub-regions according to the distribution characteristic data of each sub-region to obtain at least one cluster; Sub-regions of each of the clusters in at least one of the clusters are aggregated respectively to obtain regions corresponding to each of the clusters, and the target region is a region corresponding to any one of the clusters in at least one of the clusters.

11. An order allocation method, characterized in that: The method comprises: In response to creation of a target delivery order for a target area, determining a delivery time and a floor type of the target delivery order; Determine the target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order, and the delivery difficulty information of each floor type in the target area in each time period, wherein the delivery difficulty information of each floor type in the target area in each time period is determined based on the method described in any one of claims 1 to 10; The target delivery order is assigned to a target delivery person according to the target delivery difficulty information.

12. The method according to claim 11, characterized in that The allocating the target delivery order to a target delivery person according to the target delivery difficulty information includes: Determine a plurality of delivery personnel to be selected corresponding to the target delivery order; Determining the delivery preference information of each of the plurality of delivery personnel to be selected; Based on the delivery preference information of each of the candidate delivery personnel and the target delivery difficulty information, a target delivery personnel is determined from the plurality of candidate delivery personnel.

13. The method according to claim 12, characterized in that The step of determining a target delivery person from among a plurality of the delivery persons to be selected based on the delivery preference information of each of the delivery persons to be selected and the target delivery difficulty information comprises: Determine a set of delivery orders received by each of the delivery personnel to be selected within a preset time range; Determine, from the collection of received delivery orders of each of the delivery personnel to be selected, high-difficulty delivery orders whose delivery difficulty information meets preset conditions; A target delivery personnel is determined from among the multiple delivery personnel to be selected based on the high-difficulty delivery orders, delivery preference information and the target delivery difficulty information of each of the delivery personnel to be selected.

14. A delivery difficulty determination device, characterized in that: The device comprises: An acquisition unit, used to acquire historical delivery data of the delivery personnel in a target area, wherein the target area includes a plurality of floor types, and the floor type is determined by a floor attribute and a floor arrival mode attribute; A first determining unit, configured to determine a plurality of time periods in a preset time unit according to historical delivery data of the target area; A second determining unit, configured to determine the delivery intention information of each floor type in the target area in each time period according to the historical delivery data of the target area; The third determining unit is used to determine the delivery difficulty information of each floor type in each time period according to the delivery willingness information of each floor type in each time period.

15. An order distribution device, characterized in that: The device comprises: A first determining unit, configured to determine, in response to creation of a target delivery order for a target area, a delivery time and a floor type of the target delivery order; A second determination unit, configured to determine target delivery difficulty information of the target delivery order according to the delivery time and floor type of the target delivery order, and the delivery difficulty information of each floor type in the target area in each time period, wherein the delivery difficulty information of each floor type in the target area in each time period is determined based on the method described in any one of claims 1 to 10; An allocating unit is used to allocate the target delivery order to a target delivery person according to the target delivery difficulty information.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program code, and when the computer program code is executed, the method according to any one of claims 1 to 13 is implemented.

17. An electronic device, characterized in that: The electronic device comprises: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the method according to any one of claims 1 to 13.

18. A computer program product, characterized in that The computer program product stores at least one instruction, and when the at least one instruction is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

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