A method, device and apparatus for determining target area configuration capability

Abstract

The embodiments of this specification provide a method, device and equipment for determining the configuration capacity of a target area. The technical solution provided in the embodiments of this specification combines the second logistics business data of the reference area before and after the smart cabinets are laid, and calculates the logistics index quantity of the reference area after the smart cabinets are laid, so as to judge the logistics index quantity of the reference area after the smart cabinets are laid; and then determines the configuration capacity of the target area in combination with the first logistics business data of the target area.

Description

Technical Field

[0001] The present invention relates to the field of smart cabinet business technology, and in particular to a method, device and equipment for determining the configuration capability of a target area. Background Art

[0002] At present, all major industries are vigorously promoting the deployment of smart cabinets. With the development of the business, facing thousands of areas in the city, front-line and operation personnel have not found a systematic and effective solution on where smart cabinets should be deployed. How to promote the smart cabinet business is a technical problem that needs to be solved urgently. Summary of the invention

[0003] To overcome the problems existing in the related art, the embodiments of this specification provide a method, an apparatus and a device for determining the configuration capability of a target area.

[0004] According to a first aspect of an embodiment of this specification, a method for determining a target area configuration capability is provided, the method comprising:

[0005] Obtain the first logistics business data of the target area;

[0006] Respectively obtain second logistics business data of at least one reference area, the second logistics business data including first time period logistics business data and second time period logistics business data; wherein the reference area is a smart cabinet service area, the first time period is a time period before the smart cabinet is laid, and the second time period is a time period after the smart cabinet is laid;

[0007] Calculating the logistics index of the reference area according to the second logistics business data;

[0008] The configuration capacity of the target area is determined according to the first logistics business data, the second logistics business data and the logistics indicator.

[0009] According to a second aspect of an embodiment of this specification, there is provided a device for determining a target area configuration capability, the device comprising:

[0010] An acquisition module is used to acquire first logistics business data of a target area, wherein the first logistics business data includes logistics business data of a first time period; and respectively acquire second logistics business data of at least one reference area, wherein the second logistics business data includes logistics business data of a first time period and logistics business data of a second time period; wherein the reference area is a smart cabinet service area, the first time period is a time period before the smart cabinet is laid, and the second time period is a time period after the smart cabinet is laid;

[0011] A calculation module, used for calculating the logistics index of the reference area according to the second logistics business data;

[0012] A determination module is used to determine the configuration capacity of the target area based on the first logistics business data, the second logistics business data and the logistics indicator.

[0013] According to a third aspect of an embodiment of this specification, a device for determining a target area configuration capability is provided, the device comprising:

[0014] processor;

[0015] A memory for storing processor-executable instructions, wherein the processor is configured to implement the method described in any one of the above embodiments.

[0016] The technical solution provided by the embodiments of this specification combines the second logistics business data of the reference area before and after the installation of the smart cabinet to calculate the logistics index of the reference area after the installation of the smart cabinet, so as to judge the logistics index of the reference area after the installation of the smart cabinet; and then combines the first logistics business data of the target area to determine the configuration capacity of the target area. Using multiple data of multiple areas makes the final result more accurate; this method of systematically determining the configuration capacity of the target area does not require a lot of manpower, is more efficient, and has lower costs.

[0017] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and, together with the description, serve to explain the principles of the specification.

[0019] Figure 1 It is a flow chart of a method for determining the configuration capability of a target area provided by an exemplary embodiment.

[0020] Figure 2 It is a flow chart of an embodiment of determining target area configuration capability provided by an exemplary embodiment.

[0021] Figure 3 It is another flow chart of an exemplary embodiment of determining target area configuration capability provided by an exemplary embodiment.

[0022] Figure 4 It is a schematic diagram of the structure of a device for determining target area configuration capability provided by an exemplary embodiment.

[0023] Figure 5 It is a schematic diagram of the structure of a device for determining target area configuration capability provided by an exemplary embodiment. DETAILED DESCRIPTION

[0024] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0025] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] Currently, smart cabinets have been applied in some areas of the market. As for areas where smart cabinets are not installed, which areas should be installed with smart cabinets? Will the installation of smart cabinets in these areas generate positive business benefits, or will it waste hardware and labor costs and eventually turn them into unused zombie cabinets? This is a technical problem that has always existed in this field.

[0028] The existing selection of smart cabinet installation points mainly comes from the reports of the front-line. Business personnel make decisions based on the observations of the front-line to determine which buildings are suitable for installing smart cabinets. However, this method is relatively inefficient, has a large number of subjective factors, and is not supported by specific data. With the support of relevant algorithms, it is improved compared to the above solution. The algorithm can be used to generate a whitelist based on the average number of orders in the area in the past for reference by business personnel. Although there is certain data support, the analysis process has not been systematized, requires a large number of people to invest, is inefficient, and has high costs.

[0029] In response to the above problems, the embodiments of this specification provide a method for determining the configuration capacity of the target area, which is applicable to scenarios where decisions on the installation of smart cabinets need to be made, such as the rapidly developing food delivery industry and express delivery industry. As far as the current food delivery industry is concerned, the food delivery industry has introduced smart cabinets to facilitate more convenient interactions between buyers and riders, which is convenient for riders to save time in delivering food, and for buyers to delay for a while before picking up food. However, the cost of smart cabinets is not low, and it is not advisable to blindly install smart cabinets in a covering manner; and for some areas, smart cabinets are not required. Therefore, the embodiments of this specification provide a method for systematically determining the configuration capacity of the target area. Using the method of the embodiments of this specification, there is no need for a large number of business personnel to conduct front-line observations, and at the same time, there are many aspects of data to support the earliest judgment results, which not only reduces costs, but also improves efficiency and accuracy.

[0030] Of course, the above application scenarios are only used for illustrative purposes and should not be understood as limiting the embodiments of this specification.

[0031] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of this specification, the technical solutions in the embodiments of this specification will be described in detail below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this solution, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field should belong to the scope of protection of the embodiments of this specification.

[0032] See also Figure 1 , Figure 1 A method for determining the configuration capability of a target area provided in an embodiment of this specification includes the following steps:

[0033] S102, obtaining first logistics business data of a target area;

[0034] S104, respectively acquiring second logistics business data of at least one reference area, the second logistics business data including first period logistics business data and second period logistics business data; wherein the reference area is a smart cabinet service area, the first period is a period before the smart cabinet is installed, and the second period is a period after the smart cabinet is installed;

[0035] S106, calculating the logistics index of the reference area according to the second logistics business data;

[0036] S108: Determine the configuration capability of the target area according to the first logistics business data, the second logistics business data, and the logistics indicator.

[0037] According to S102, for the target area, the first logistics business data of the target area must first be obtained. Taking the takeaway scenario as an example, in one embodiment, the first logistics business data of the target area can be obtained based on the information of all historical takeaway orders. Since the target area has not yet been equipped with smart cabinets, the first logistics business data of the target area can be data related to takeaway orders, for example, it can include at least one of the following: the average number of takeaway orders within a specified time period, the number of takeaway orders during each peak period, the average pick-up time for takeaway orders, the average delivery time for takeaway orders, the average time for riders to wait for buyers, and so on. Among them, the average number of takeout orders within the specified time period may be the average daily, weekly, or monthly takeout orders in the target area; the number of takeout orders during peak hours may be the number of takeout orders during meal times every day, for example, the number of takeout orders from 11 a.m. to 13 p.m. every day, the number of takeout orders from 5 p.m. to 7 p.m. every day, and so on; the average pick-up time for takeout orders indicates the average time for all takeout orders in the target area from the moment the order is placed to the time the order is accepted by the takeout rider; the average delivery time for takeout orders indicates the average time it takes for the rider to deliver all takeout orders in the target area; the average time the rider waits for the buyer indicates the average time the rider spends waiting for the buyer to pick up the takeout after arriving at the buyer's destination.

[0038] The above embodiment is for the takeaway scenario. For the express delivery scenario, the first logistics business data associated with the express delivery in the target area may include at least one of the following: the average number of express orders within a specified time period, the average delivery time of express orders, the average time the courier waits for the buyer, etc. Among them, the average number of express orders within the specified time period may be the average number of express orders per day, the average number of express orders per week, the average number of express orders per month, etc. in the target area; the average delivery time of express orders means the average time it takes for the courier to deliver all express orders in the target area; the average time the courier waits for the buyer means the average time the courier spends waiting for the buyer to pick up the express after arriving at the buyer's destination.

[0039] Of course, the first logistics business data cited above only represent the first logistics business data associated with takeout orders and the first logistics business data associated with express orders in a part of the area where smart cabinets are not laid. The first logistics business data associated with takeout orders and the first logistics business data associated with express orders in the area where smart cabinets are not laid can also include other data not listed in the embodiments of this specification; and with the development of the business, there will be more dimensional data that can be used as the first logistics business data associated with takeout orders and the first logistics business data associated with express orders in the area where smart cabinets are not laid. All the data listed above should fall within the scope of the historical logistics big data of the target area in the embodiments of this specification.

[0040] According to S104, the second logistics business data of at least one reference area is obtained, and the second logistics business data includes two types of logistics business data before and after the smart cabinet is laid, that is, the first time period logistics business data and the second time period logistics business data, wherein the reference area is the smart cabinet service area, that is, the area where the smart cabinet has been laid; the first time period is the time period before the smart cabinet is laid, and the second time period is the time period after the smart cabinet is laid. That is, obtaining the second logistics business data of the reference area includes obtaining the first time period logistics business data of the time period before the smart cabinet is laid in the reference area and the second time period logistics business data of the time period after the smart cabinet is laid in the reference area. The first time period logistics business data of the reference area is the same as the first logistics business data type of the target area described in S102, and will not be repeated here. In an embodiment applied to a takeaway scenario, the second time period logistics business data associated with the takeaway orders in the reference area may include at least one of the following: the average number of takeaway orders in a specified time period, the number of takeaway orders in each peak period, the average pick-up time of takeaway orders, the average time for riders to wait for buyers, the rate of cabinet orders in takeaway orders, the average delivery time of cabinet orders for takeaway orders, and the average delivery time of non-cabinet orders for takeaway orders. Among them, the average number of takeout orders within the specified time period can be the average daily number of takeout orders, the average weekly number of takeout orders, the average monthly number of takeout orders, etc. in the target area; the number of takeout orders at each peak period can be the number of takeout orders at meal times every day, such as the number of takeout orders from 11:00 to 13:00 every day, the number of takeout orders from 17:00 to 19:00 every day, etc.; the average pick-up time of takeout orders represents the average time for all takeout orders in the target area from the moment the order is placed to the time the order is accepted by the takeout rider; the average delivery time of takeout orders represents the average time for the rider to deliver all takeout orders in the target area; the average time the rider waits for the buyer represents the average time the rider spends waiting for the buyer to pick up the takeout after arriving at the buyer's destination; the cabinet order rate in takeout orders represents the ratio of takeout orders placed in the smart cabinet to all orders in the area where the smart cabinet has been laid; the average delivery time for cabinet orders for takeout orders represents the average time for the rider to deliver cabinet orders; the average delivery time for non-cabinet orders for takeout orders represents the average time for the rider to deliver non-cabinet orders.

[0041] The above embodiment is for the takeaway scenario. For the express delivery scenario, the logistics business data of the second period of time associated with the express delivery in the reference area may specifically include at least one of the following: the average number of express orders in the specified time period; the average delivery time of express orders; the average time the courier waits for the buyer; the rate of cabinet orders in express orders; the average delivery time of cabinet orders for express orders; the average delivery time of non-cabinet orders for express orders, etc. Among them, the average number of express orders in the specified time period may be the average daily express order volume, weekly express order volume, monthly express order volume, etc. in the target area; the average delivery time of express orders means the average time for the courier to deliver all express orders in the target area; the average time for the courier to wait for the buyer means the average time the courier spends waiting for the buyer to pick up the express after arriving at the buyer's destination; the cabinet order rate in express orders means the ratio of orders placed in the smart cabinet after the smart cabinet is laid in the area where the smart cabinet has been laid to all orders; the average delivery time of cabinet orders for express orders means the average time for the courier to deliver cabinet orders; the average delivery time of non-cabinet orders for express orders means the average time for the courier to deliver non-cabinet orders.

[0042] Of course, the second-time period logistics business data cited above only represent the second-time period logistics business data associated with takeout orders and the second-time period logistics business data associated with express orders in a part of the area where smart cabinets have been installed. The second-time period logistics business data associated with takeout orders and the second-time period logistics business data associated with express orders in the area where smart cabinets have been installed can also include other data not listed in the embodiments of this specification; and with the development of the business, there will be more dimensional data that can be used as the second-time period logistics business data associated with takeout orders and the second-time period logistics business data associated with express orders in the reference area after the smart cabinets are installed. All the first-time period logistics business data and the second-time period logistics business data of the reference area mentioned above should fall within the scope of the second logistics business data of the reference area in the embodiments of this specification.

[0043] According to S106, the logistics index of the reference area is calculated based on the logistics business data of the first time period and the logistics business data of the second time period of the reference area. In a specific embodiment, the logistics index includes at least one of the following: the average increase in orders within the specified time period; the average positive profit of the smart cabinet within the specified time period; the reduction in the average delivery time of the cabinet order, and so on. Among them, the average increase in orders within the specified time period can be the increase in the average daily order volume, the increase in the average weekly order volume, the increase in the average monthly order volume, and so on; the average positive profit of the smart cabinet within the specified time period can be the sum of the reduction in the average waiting time for the rider to wait for the buyer within one month of laying the smart cabinet and the increase in the average monthly order volume, or it can be the net profit of laying the smart cabinet for one month, and so on; the reduction in the average delivery time of the cabinet order represents the average delivery time before the smart cabinet is laid minus the average delivery time of the cabinet order. Of course, the above-mentioned operating data only represents a part of the logistics index, and the logistics index in the embodiment of this specification can also be other logistics indexes that can be calculated and generated based on the second logistics business data of the reference area.

[0044] In a specific embodiment applied to a takeout scenario, reference area A has been equipped with smart cabinets for one month. Based on the logistics business data of the first period before the installation of the smart cabinets in reference area A, such as the average daily takeout order volume a1, and the logistics business data of the second period after the installation of the smart cabinets in reference area A, such as the average daily takeout order volume a2, the logistics indicators of area A, such as the average daily increase in takeout orders, are calculated, and the value is (a2-a1).

[0045] Of course, the above embodiment is for the takeaway scenario, and S106 is also applicable to scenarios such as express delivery. However, since the second logistics business data of the takeaway scenario is different from that of the express delivery industry, the logistics indicators produced for different scenarios may also be different. For example, the takeaway scenario is mainly for meal takeaways, so there is a meal peak every day for the takeaway scenario, so the second logistics business data associated with the takeaway order can also include data on the number of takeaway orders during each peak period, such as the number of takeaway orders from 11:00 to 13:00 every day, which does not exist in the express delivery scenario. For this data, the logistics indicator related to the takeaway order can be the increase in the number of takeaway orders during each peak period. There are also other such things as the average pick-up time for takeaway orders associated with takeaway orders, etc., which are not given one by one here.

[0046] According to S108, the configuration capacity of the target area is determined according to the first logistics business data of the target area, the second logistics business data of the reference area, and the logistics index. That is, the above data are integrated and systematically analyzed to determine the configuration capacity of the target area to configure the smart cabinet, for example, to determine whether the target area is suitable for laying smart cabinets. In an embodiment applied to a takeaway scenario, the reference area A has been laying smart cabinets for up to one month, and the second logistics business data related to the reference area A include: the average daily takeaway orders a11 and the average time the rider waits for buyers a12 in the reference area A before laying the smart cabinets; the average daily takeaway orders a21, the average time the rider waits for buyers a22, and the cabinet order rate in the reference area A after laying the smart cabinets; the target area B is a target area where smart cabinets are not laid, and the first logistics business data related to the target area B include the average daily takeaway orders in the target area B before laying the smart cabinets. b11 and the average time the riders wait for buyers b12; based on the second logistics business data of reference area A, calculate and generate logistics indicators such as the average daily increase in takeout orders and the average reduction in the riders' waiting time for buyers after the smart cabinets are installed in reference area A; based on the first logistics business data of reference area A, the second logistics business data of target area B, and the calculated logistics indicators such as the average daily increase in takeout orders and the average reduction in the riders' waiting time for buyers after the smart cabinets are installed in reference area A, make analysis and decisions to determine the configuration capacity of target area B and whether target area B is suitable for installing smart cabinets. Among them, the algorithm for analysis and decision-making can be a gradient boosting decision tree (GBDT) algorithm, or other algorithms for analysis and decision-making, which are not limited in this embodiment of the specification.

[0047] In an embodiment of a takeaway scenario, after the smart cabinets are installed in the reference area, the average increase in the number of takeaway orders in a specified time period, the increase in the number of takeaway orders during each peak period, the decrease in the average pickup time for takeaway orders, the decrease in the average delivery time of riders, and the decrease in the average waiting time for riders to buy goods all indicate that the benefits of installing smart cabinets in the area are positive. A positive parameter can be set in advance for each of these data. For example: before the smart cabinets were installed in the reference area, the average waiting time for riders to buy goods was 5 minutes; after the smart cabinets were installed in the reference area, the average waiting time for riders to buy goods was reduced by 2 minutes. If the parameter of the average waiting time for riders to buy goods is 50 / minute, then the logistics indicator of the reference area is a positive benefit of 100. If at this time, in the first logistics business data of the target area, the average waiting time for riders to buy goods is also 5 minutes, it can be determined that the configuration capacity of the target area is strong enough and suitable for installing smart cabinets. In another example, before the installation of smart cabinets in the reference area, the average daily volume of takeout orders was 50; after the installation of smart cabinets in the reference area, the average daily volume of takeout orders was 100; if the parameter value of the average daily volume of takeout orders is 100 / order, then the logistics indicator of the reference area is an increase of 50 in the average daily volume of takeout orders, and the positive benefit is 5000. If at this time, the average daily volume of takeout orders in the first logistics business data of the target area is 50, it can be determined that the configuration capability of the target area is strong enough and suitable for the installation of smart cabinets. In another example, before the installation of smart cabinets in the reference area, the average daily volume of takeout orders was 10; after the installation of smart cabinets in the reference area, the average daily volume of takeout orders was still 10. At this time, it is calculated that the logistics indicator of the reference area is 0 positive benefit. At this time, if the average daily volume of takeout orders in the target area is 10, it is determined that the configuration capability of the target area is not strong enough and is not suitable for the installation of smart cabinets. Of course, this is just an example, and the specific algorithm analysis method is not limited here; parameters can be reasonably set according to needs; data selection can be screened according to needs, and at least one data can be selected for analysis. The embodiments of this specification only cite several types of data by way of example, and the embodiments of this specification are not intended to be limiting here.

[0048] In one embodiment, among multiple candidate reference areas, an area whose attributes are similar to those of the target area can be selected as a reference area in the embodiment of this specification. Among them, the attributes include user attributes and / or regional attributes. In a takeaway scenario, the user attributes include at least one of the following: the user's work type, the user's working hours, or the user's order type. The user's work type can be whether the user works in an office building, a school, or a factory; the user's working hours can be the time period when the user orders takeaway; the user's order type can be an indication that the user prefers meal takeaway, snack takeaway, or drink takeaway, etc. Among them, the regional attributes include at least one of the following: the type of building in the area, the number of buildings in the area, or the size of the area. Among them, the average takeaway order volume in the area within a specified time period. Among them, the type of building in the area can be whether the building is a high-rise building, a community building, a factory building, etc.; the average takeaway order volume in the area within a specified time period can be the average daily takeaway order volume, the average weekly takeaway order volume, the average monthly takeaway order volume, etc. The above-mentioned user attributes and regional attribute data can be obtained from the historical takeaway order information of the area, and of course, can also be obtained through other channels, and the embodiments of this specification are not limited here.

[0049] In the above-mentioned embodiment applied to the takeaway scenario, before making an analysis and decision, the target area B can also be predicted based on the reference area A, and the logistics business data of the target area B in the second period after the smart cabinet is installed can be predicted. For example, the reference area A and the target area B are both office buildings with equal areas and numbers of floors, and the work types and working hours of the users in the buildings are also similar. The second logistics business data of the reference area A can be used to predict the logistics business data of the target area B in the second period after the smart cabinet is installed. Since the properties of the reference area A and the target area B are similar, the predicted logistics business data of the target area B in the second period can be similar to the logistics business data of the reference area A in the second period. In addition, the configuration capabilities of the target area B can be further analyzed and decided based on the predicted second-segment logistics business data of the target area B, such as whether the target area B is suitable for installing smart cabinets.

[0050] In an embodiment of a specific takeaway scenario, the first logistics business data of the target area C is obtained, such as the average daily takeaway order volume c11 and the average time the rider waits for the buyer c12. For example, the value of c11 is 30; the value of c12 is 60 seconds; in the second logistics business data of reference area D, the logistics business data of the first period, such as the average daily takeaway order volume d11, is 30, and the average time the rider waits for the buyer d12 is 60 seconds; the logistics business data of the second period of reference area D, such as the average daily takeaway order volume d21, is 60, and the average time the rider waits for the buyer d22 is 30 seconds; and the user attributes and building attributes of the target area C and the reference area D are similar; then it is predicted that the logistics business data of the second period of the target area C after the installation of the smart cabinet is: the average daily takeaway order volume c21 is 60, and the average time the rider waits for the buyer c22 is 30 seconds. According to the predicted logistics business data of target area C in the second period, it is judged that target area C will have positive profit after the installation of smart cabinets. Therefore, it is determined that the configuration capability of target area C is strong enough and target area C is suitable for the installation of smart cabinets.

[0051] In a specific embodiment, the target area can also be evaluated to generate a laying score for the target area; and then the configuration capability of the target area is determined according to the laying score of the target area. The higher the laying score of the target area, the stronger the configuration capability of the target area is determined. Specifically, the laying score of the target area B can be evaluated by combining the first logistics business data of the target area B, the second logistics business data of the reference area A, and the logistics index of the reference area A. All the above data values ​​can be analyzed and calculated according to the linear regression algorithm to evaluate the predicted score of the target area B. Then, the configuration capability of the target area B is determined according to the laying score of the target area B, wherein if the predicted score of the target area B reaches the preset expected value, it is determined that the configuration capability of the target area B is strong and suitable for laying the smart cabinet; if the predicted score of the target area B does not reach the preset expected value, it is determined that the configuration capability of the target area B is weak and not suitable for laying the smart cabinet. In one embodiment, the laying priority of the target area can be determined according to the size of the laying score of the target area, wherein the higher the score, the higher the laying priority of the target area; and then the configuration capability of the target area is determined according to the laying priority of the smart cabinet of the target area. For example, the laying scores of target area C and target area D are 70 and 80 points respectively. Obviously, the laying score of target area D is higher, so the laying priority of target area D is higher, so the configuration capability of target area D is stronger. When smart cabinets are to be laid in both target area C and target area D, target area D is laid first. In another embodiment, the configuration capability of the target area includes the laying specifications of the smart cabinets. The higher the laying score of the target area, the larger the laying specifications of the smart cabinets in the target area. For example, the laying scores of target area E and target area F are 60 and 90 points respectively, then a small smart cabinet can be laid in target area E, and a large smart cabinet can be laid in target area F.

[0052] In the scheme of the embodiments of this specification, different types of smart cabinets can also be set according to the user attributes and / or regional attributes of the target area. In one embodiment, a smart cabinet needs to be laid in a certain area, and the users in the area prefer to order takeout and cold drinks, so the smart cabinet in the area can be set as a smart cabinet with a certain refrigeration effect. In another embodiment, a large community needs to lay smart cabinets, and the community does not allow takeout riders to enter the community, so a large smart cabinet can be laid at the entrance of each community according to demand.

[0053] See also Figure 2 , Figure 2This is a specific flow chart of an embodiment of this specification. According to S201, the first logistics business data, user data and building attributes of the target area M are obtained; according to S202, the second logistics business data, user attributes and building attributes of the reference area N are obtained, wherein the second logistics business data of the reference area N includes the logistics business data of the first period and the logistics business data of the second period; according to S203, based on the second logistics business data of the reference area N, the logistics index of the reference area N is calculated; according to S204, based on the obtained user attributes and building attributes of the target area M and the reference area N, it is determined whether the attributes of the target area M and the reference area N are similar. If they are similar, S205 is executed to predict the target area. The logistics business data of the second time period of domain M after the smart cabinets are laid; if they are not similar, execute S206 and do not make subsequent predictions for the target area M; according to S207, combine the first logistics business data of the target area M, the second logistics business data of the reference area N, the logistics index of the reference area N and the predicted logistics business data of the second time period of the target area M after the smart cabinets are laid, and determine the configuration capacity of the smart cabinets in the target area M; if no prediction is made for the target area M, combine the first logistics business data of the target area M, the second logistics business data of the reference area N and the logistics index of the reference area N to determine the configuration capacity of the target area M.

[0054] See also Figure 3 , Figure 3This is a specific flow chart of an embodiment of this specification. Taking the takeaway scenario as an example, according to the first logistics business data of the target area, statistics are collected from dimensions such as the average number of takeaway orders in a specified time period, the number of takeaway orders in each peak period, the average pickup time of takeaway orders, the average delivery time of takeaway orders, and the average time that riders wait for buyers, to form a set of first logistics business data modules for the target area, which module can be a chart visualization module; according to the second logistics business data of the reference area where the smart cabinets have been laid, the average number of takeaway orders in a specified time period, the number of takeaway orders in each peak period, and the average number of takeaway orders in the first period of logistics business data before the smart cabinets are laid are collected. The dimensions of order quantity, average pick-up time of takeout orders, average delivery time of takeout orders, and average time for riders to wait for buyers, as well as the average order quantity of takeout orders in the specified time period of the second period logistics business data after the installation of smart cabinets, the order quantity of takeout orders in each peak period, the average pick-up time of takeout orders, the average time for riders to wait for buyers, the cabinet order rate of takeout orders, the average delivery time of cabinet orders in takeout orders, and the average delivery time of non-cabinet orders in takeout orders are counted to form a set of reference area second logistics business data modules where smart cabinets have been installed, which can be chart visualization modules. The first logistics business data module of the target area and the second logistics business data module of the reference area should be reusable and expandable; this reusable module can provide the greatest convenience and highest efficiency for future business expansion to other cities; and as the business develops, more dimensional data will be added, so that the logistics indicators after the installation of smart cabinets can be predicted more systematically and accurately. The input data means that from all the logistics business data obtained, some of the data is selected for further analysis. For example, if the input data only has the average daily takeaway order volume, the data of the average daily takeaway order volume is selected from the above two modules for further analysis and decision-making. Combined with the data in the first logistics business data module of the target area and the second logistics business data module of the reference area, the configuration capacity of the target area is summarized. The reference area with similar attributes to the target area can also be analyzed, where the attributes include user belonging and / or regional attributes; the operation trend module combines the data in the above two modules to predict the logistics data of the second period of the target area, and then the analysis and decision module uses decision algorithms such as the gradient boosting decision tree (GBDT) algorithm to determine the configuration capacity of the target area and determine whether the target area is to be laid with a smart cabinet. For the analysis and decision module, it can be expandable, so that different algorithms can be expanded in the future to assist in making decisions; the decision-making method can be to directly determine the strength of the configuration capacity of the target area and whether it is suitable for laying a smart cabinet, or to evaluate the laying score of the target area, further generate a regional priority whitelist based on the laying score, and further determine the configuration capacity of the target area based on the whitelist.

[0055] Of course, all the above embodiments are illustrative examples, and the algorithms therein are only illustrative examples and are not intended to limit the embodiments of this specification. In the embodiments of this specification, at least one algorithm can be used to analyze data, and the embodiments of this specification are not elaborated here. The above embodiments are based on takeaway scenarios. For other scenarios such as express delivery scenarios, the embodiment schemes of this specification are also applicable, and the embodiments for other scenarios such as express delivery scenarios are not elaborated here.

[0056] The above is only a specific implementation of the embodiment of this specification and is not intended to be a limitation of this solution. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of this solution, and these improvements and modifications should also be considered as the protection scope of this solution.

[0057] See also Figure 4 The embodiment of the present specification also provides a device for determining the configuration capability of a target area, the device comprising:

[0058] The acquisition module 402 is used to acquire first logistics business data of a target area, wherein the first logistics business data includes logistics business data of a first time period; and respectively acquire second logistics business data of at least one reference area, wherein the second logistics business data includes logistics business data of a first time period and logistics business data of a second time period; wherein the reference area is a smart cabinet service area, the first time period is a time period before the smart cabinet is installed, and the second time period is a time period after the smart cabinet is installed;

[0059] A calculation module 404, configured to calculate the logistics index of the reference area according to the second logistics business data;

[0060] The determination module 406 is used to determine the configuration capacity of the target area according to the first logistics business data, the second logistics business data and the logistics indicator.

[0061] In one example, the first period logistics business data and the second period logistics business data acquired by the acquisition module 402 include the first period logistics business data and the second period logistics business data associated with a takeaway order.

[0062] The logistics business data for the first period are data before the installation of smart cabinets. The logistics business data for the first period associated with takeaway orders include at least one of the following: the average number of takeaway orders in a specified time period; the number of takeaway orders during each peak period; the average pick-up time for takeaway orders; the average delivery time for takeaway orders and the average time that riders wait for buyers;

[0063] The logistics business data for the second period are the data after the installation of smart cabinets. The logistics business data for the second period associated with takeout orders include at least one of the following: the average number of takeout orders in a specified time period; the number of takeout orders during each peak period; the average pick-up time for takeout orders; the average time the rider waits for the buyer; the cabinet rate of takeout orders; the average delivery time of cabinet orders for takeout orders and the average delivery time of non-cabinet orders for takeout orders.

[0064] In another example, the first period logistics business data and the second period logistics business data acquired by the acquisition module 402 include the first period logistics business data and the second period logistics business data associated with the express order.

[0065] The logistics business data for the first period is data before the installation of the smart cabinets. The logistics business data for the first period associated with the express orders includes at least one of the following: the average number of express orders within a specified time period; the average delivery time of express orders and the average time the courier waits for the buyer;

[0066] The logistics business data for the second period are the data after the installation of smart cabinets. The logistics business data for the second period associated with express orders include at least one of the following: the average number of express orders in a specified time period; the average delivery time of express orders; the average time the courier waits for buyers; the cabinet rate of express orders; the average delivery time of cabinet orders for express orders and the average delivery time of non-cabinet orders for express orders.

[0067] In one example, the logistics indicators of the reference area generated by the calculation module 404 include at least one of the following: the average increase in orders within a specified time period; the average positive profit of smart cabinets and the average reduction in delivery time of cabinet orders within a specified time period.

[0068] In one example, the acquisition module 402 is further used to: acquire attributes of the target area and the reference area, the attributes including: user attributes and / or area attributes;

[0069] The user attributes include at least one of the following: the user's job type; the user's working hours and the user's order type;

[0070] The regional attributes include at least one of the following: the type of buildings in the region; the number of buildings in the region; the size of the region and the average order volume of the region within a specified time period.

[0071] In one example, the determination module 406 is also used to determine whether the attributes of the target area are similar to those of the reference area; the calculation module 404 is also used to predict the logistics business data of the target area in the second time period based on the first logistics business data of the target area and the second logistics business data of the reference area, and the configuration capacity of the target area is also determined based on the predicted logistics business data for the second time period.

[0072] In one example, the determination module 406 is also used to: evaluate the paving score of the target area in combination with the first logistics business data of the target area, the second logistics business data of the reference area, and the logistics index, and determine the configuration capability of the target area according to the paving score of the target area, wherein the higher the paving score, the stronger the configuration capability.

[0073] In one example, the determination module 406 is also used to determine the smart cabinet installation priority of the target area according to the installation score of the target area, wherein the higher the installation score, the higher the smart cabinet installation priority; and then determine the configuration capability according to the smart cabinet installation priority, wherein the higher the installation priority, the stronger the configuration capability.

[0074] In one example, the configuration capability includes smart cabinet installation specifications, and the determination module 406 is further used to determine the smart cabinet installation specifications of the target area according to the installation score of the target area; the higher the installation score, the larger the smart cabinet installation specifications.

[0075] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

[0076] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this specification. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0077] The embodiments of the device for determining the configuration capability of the target area in this specification can be applied to computer devices, such as servers or terminal devices. The device embodiments can be implemented by software, hardware, or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory and running them. From the hardware level, if Figure 5 As shown in the figure, it is a hardware structure diagram of the computer device where the device for determining the configuration capability of the target area in the embodiment of this specification is located, except Figure 5In addition to the processor 510, memory 530, network interface 520, and non-volatile memory 540 shown, the server or electronic device where the device 531 is located in the embodiment may also include other hardware according to the actual function of the computer device, which will not be described in detail.

[0078] The above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results.

[0079] Those skilled in the art will readily appreciate other embodiments of the specification after considering the specification and practicing the invention claimed herein. The specification is intended to cover any variations, uses or adaptations of the specification that follow the general principles of the specification and include common knowledge or customary techniques in the art that are not claimed in the specification. The specification and examples are to be considered exemplary only, and the true scope and spirit of the specification are indicated by the following claims.

[0080] It should be understood that the present description is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.

[0081] The above description is only a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A method for determining a target area configuration capability, the method comprising: Obtain the first logistics business data of the target area; Respectively obtain second logistics business data of at least one reference area, the second logistics business data including first time period logistics business data and second time period logistics business data; wherein the reference area is a smart cabinet service area, the first time period is a time period before the smart cabinet is laid, and the second time period is a time period after the smart cabinet is laid; Predicting the logistics business data of the target area in the second period according to the first logistics business data and the second logistics business data, and determining the configuration capacity of the target area according to the predicted logistics business data of the target area in the second period; or calculating the logistics index of the reference area according to the second logistics business data, evaluating the laying score of the target area according to the first logistics business data, the second logistics business data and the logistics index, and determining the configuration capacity of the target area according to the laying score, wherein the higher the laying score, the stronger the configuration capacity; The configuration capability is used to indicate whether to configure a smart cabinet in the target area.

2. According to the method of claim 1, the logistics business data for the first period includes at least one of the following: The average number of takeaway orders within a specified time period, the number of takeaway orders during each peak period, the average pickup time for takeaway orders, the average delivery time for takeaway orders, or the average time that riders wait for buyers; and / or, The logistics business data for the second period includes at least one of the following: The average number of takeout orders in a specified time period, the number of takeout orders during each peak period, the average pick-up time for takeout orders, the average time that riders wait for buyers, the rate of counter orders in takeout orders, the average delivery time of counter orders for takeout orders, or the average delivery time of non-counter orders for takeout orders.

3. According to the method of claim 1, the logistics business data for the first period includes at least one of the following: The average number of express orders, the average delivery time of express orders, or the average time the courier waits for the buyer within a specified time period; and / or, The logistics business data for the second period includes at least one of the following: The average number of express orders within a specified time period, the average delivery time of express orders, the average time the courier waits for the buyer, the rate of cabinet orders in express orders, the average delivery time of cabinet orders for express orders, or the average delivery time of non-cabinet orders for express orders.

4. According to the method of claim 1, the logistics indicator comprises at least one of the following: The average increase in orders within a specified time period, the average positive revenue of smart cabinets within a specified time period, or the average reduction in delivery time for cabinet orders.

5. The method according to claim 1, further comprising determining the reference region by the following steps: Obtaining the properties of the target area and the properties of the alternative reference area; Determining whether the candidate reference region includes a region with attributes similar to those of the target region; If included, the region with properties similar to those of the target region is determined as the reference region; in, The properties include: User attributes and / or region attributes; The user attributes include at least one of the following: user's job type, user's working hours or user's order type; The regional attributes include at least one of the following: the type of buildings in the region, the number of buildings in the region, the size of the region, or the average order volume in the region within a specified time period.

6. The method according to claim 1, wherein determining the configuration capability according to the laying score comprises: Determine the smart cabinet installation priority of the target area according to the installation score, wherein the higher the installation score, the higher the smart cabinet installation priority; The configuration capability is determined according to the installation priority of the smart cabinet, wherein the higher the installation priority of the smart cabinet, the stronger the configuration capability.

7. According to the method of claim 1, the configuration capability is also used to indicate the smart cabinet installation specifications, and the higher the installation score, the larger the smart cabinet installation specifications.

8. A device for determining a target area configuration capability, the device comprising: An acquisition module, used for acquiring first logistics business data of a target area, wherein the first logistics business data includes logistics business data of a first time period; and respectively acquiring second logistics business data of at least one reference area, the second logistics business data including first time period logistics business data and second time period logistics business data; wherein the reference area is a smart cabinet service area, the first time period is a time period before the smart cabinet is installed, and the second time period is a time period after the smart cabinet is installed; A determination module is used to predict the logistics business data of the target area in the second time period based on the first logistics business data and the second logistics business data, and determine the configuration capability of the target area based on the predicted logistics business data of the target area in the second time period; or calculate the logistics index of the reference area based on the second logistics business data, evaluate the installation score of the target area based on the first logistics business data, the second logistics business data and the logistics index, and determine the configuration capability of the target area based on the installation score, wherein the higher the installation score, the stronger the configuration capability; the configuration capability is used to indicate whether to configure a smart cabinet in the target area.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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