CN110414875B - Capacity data processing method and device, electronic equipment and computer readable medium - Google Patents

Capacity data processing method and device, electronic equipment and computer readable medium Download PDF

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CN110414875B
CN110414875B CN201810387346.XA CN201810387346A CN110414875B CN 110414875 B CN110414875 B CN 110414875B CN 201810387346 A CN201810387346 A CN 201810387346A CN 110414875 B CN110414875 B CN 110414875B
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程瑞华
宋佳慧
马翠花
刘旭
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Beijing Jingdong Zhenshi Information Technology Co Ltd
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Abstract

The disclosure relates to a capacity data processing method, a capacity data processing device, an electronic device and a computer readable medium. Relates to the field of computer information processing, and the method comprises the following steps: inputting a delivery package parameter and a delivery person parameter into a first capacity prediction model to determine first capacity data, wherein the first capacity prediction model is established through a linear algorithm and a machine learning algorithm; inputting the site parameters and the aging parameters into a second capacity prediction model to determine second capacity data, wherein the second capacity prediction model is established through a linear algorithm and a machine learning algorithm; and determining the capacity data of the site through the first capacity data and the second capacity data. According to the capacity data processing method and device, the electronic equipment and the computer readable medium, the capacity data of the site can be accurately obtained, and the user experience is improved.

Description

Capacity data processing method and device, electronic equipment and computer readable medium
Technical Field
The disclosure relates to the field of computer information processing, and in particular relates to a capacity data processing method and device, an electronic device and a computer readable medium.
Background
With the rapid development of electronic commerce, online shopping is more and more popular, and particularly in the event of sales promotion of commodities, the sudden increase of the distribution quantity of the commodities can often cause the site to burst, the distribution is not timely, or the commodities are overstocked in a large quantity. How to accurately estimate the capacity and the delivery timeliness of a delivery site is a problem which needs to be solved urgently at present. Since no clear definition exists for site capacity at present, much information cannot be clearly measured. For example, it is not reasonable to determine the output capacity of a site simply from the number of parcels taken because the value must be different for the site to deliver different types of parcels, delivering a fresh parcel with time requirements, and a regular parcel, the value generated for the deliverer is different, but there is still no suitable way to specify the value of this difference.
In the prior art, the main methods for measuring the capacity are defined as follows: TPM (The Total production Model, Total Productivity), CLP (Construction laboratory Labor Productivity), and LP (laboratory Productivity). The above definitions of capacity are not for delivery type sites, so there is a large error in the site capacity calculated using the above method.
Therefore, a new capacity data processing method, apparatus, electronic device and computer readable medium are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of this, the present disclosure provides a productivity data processing method, a productivity data processing apparatus, an electronic device, and a computer readable medium, which can accurately obtain productivity data of a site and improve user experience.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the present disclosure, a capacity data processing method is provided, the method including: inputting delivery package parameters and delivery personnel parameters into a first capacity prediction model to determine first capacity data, wherein the first capacity prediction model is established through a linear algorithm and a machine learning algorithm; inputting the site parameters and the aging parameters into a second capacity prediction model to determine second capacity data, wherein the second capacity prediction model is established through a linear algorithm and a machine learning algorithm; and determining the capacity data of the site through the first capacity data and the second capacity data.
In an exemplary embodiment of the present disclosure, further comprising: and establishing the first capacity prediction model according to the historical delivery package parameters and the historical delivery personnel parameters.
In an exemplary embodiment of the disclosure, the establishing the first capacity forecasting model by the historical delivery package parameters and the historical delivery personnel parameters comprises: performing data training on historical delivery package parameters and historical delivery personnel parameters through a linear algorithm to obtain a first linear model; performing data training on historical delivery package parameters and historical delivery personnel parameters through a machine learning algorithm to obtain a first learning model; and determining the first capacity prediction model by the first linear model, the first learning model and the first model distribution weight.
In an exemplary embodiment of the disclosure, the establishing the first capacity forecasting model by the historical delivery package parameters and the historical delivery personnel parameters comprises: calculating a first model parameter according to the historical distribution package parameter and the historical distribution personnel parameter; establishing a first model attribute vector by using the first model parameter; and establishing the first productivity prediction model through the first model attribute vector.
In an exemplary embodiment of the present disclosure, further comprising: and establishing the second productivity prediction model through the historical site parameters and the historical aging parameters.
In an exemplary embodiment of the disclosure, the establishing the second productivity prediction model by the historical site parameters and the historical aging parameters includes: performing data training on the historical site parameters and the historical aging parameters through a linear algorithm to obtain a second linear model; performing data training on the historical site parameters and the historical aging parameters through a machine learning algorithm to obtain a second learning model; and determining the second capacity prediction model by the second linear model, the second learning model and the second model distribution weight.
In an exemplary embodiment of the disclosure, the establishing the second productivity prediction model by the historical site parameters and the historical aging parameters includes: calculating a second model parameter according to the historical site parameter and the historical aging parameter; establishing a second model attribute vector by using the second model parameter; and establishing the second productivity prediction model through the second model attribute vector.
In an exemplary embodiment of the present disclosure, further comprising: and determining a distribution strategy according to the capacity data of the site.
In an exemplary embodiment of the present disclosure, determining a delivery strategy based on capacity data of the site includes: providing an appointment delivery service when the current capacity of the site is smaller than the capacity value; when the current capacity of the site is smaller than the capacity value, allowing the order to be downloaded; and when the current capacity of the site is smaller than the capacity value, allowing the warehouse to be normally produced.
According to an aspect of the present disclosure, there is provided a capacity data processing apparatus, the apparatus including: the first forecasting module is used for inputting the delivery package parameters and the delivery personnel parameters into a first capacity forecasting model to determine first capacity data, and the first capacity forecasting model is established through a linear algorithm and a machine learning algorithm; the second prediction module is used for inputting the site parameters and the aging parameters into a second capacity prediction model to determine second capacity data, and the second capacity prediction model is established through a linear algorithm and a machine learning algorithm; and the capacity forecasting module is used for determining capacity data of the site according to the first capacity data and the second capacity data.
According to an aspect of the present disclosure, an electronic device is provided, the electronic device including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the disclosure, a computer-readable medium is proposed, on which a computer program is stored, which program, when being executed by a processor, carries out the method as above.
According to the capacity data processing method, the capacity data processing device, the electronic equipment and the computer readable medium, the capacity data of the site can be accurately obtained, and the user experience is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a system block diagram illustrating a capacity data processing method and apparatus according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating a capacity data processing method according to an exemplary embodiment.
FIG. 3 is a flowchart illustrating a capacity data processing method according to another exemplary embodiment.
FIG. 4 is a flowchart illustrating a capacity data processing method according to another exemplary embodiment.
FIG. 5 is a schematic diagram illustrating a capacity data processing method according to another exemplary embodiment.
FIG. 6 is a block diagram of a capacity data processing apparatus according to an exemplary embodiment.
FIG. 7 is a block diagram illustrating a capacity data processing apparatus according to another exemplary embodiment.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 9 is a schematic diagram illustrating a computer-readable storage medium according to an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and, therefore, are not intended to limit the scope of the present disclosure.
The inventor of the application finds that the site capacity problem mainly needs to consider how to measure the maximum capacity of the site during the period of commodity promotion of a merchant because the site rarely has the problem of warehouse burst at ordinary times and all packages can be delivered according to the wave times. After the maximum capacity is determined, the parcel volume which can be distributed on time can be determined according to the maximum capacity, so that the purposes of improving the user experience and reducing the default rate are achieved. And the number of packages which can be delivered at most by the station every day is known, and is compared with the number of currently placed orders, whether the limit of the delivery capacity of the station is reached is judged, and then the number of the packages reaching the station is controlled in advance, so that the pressure of burst in the warehouse is relieved.
The value of the output is different because for each site, different types of packages are delivered. Due to the fact that the order has a time requirement, the value of delivering 1 single time quick-aging fresh parcels can be the same as the value of delivering n common time-aging parcels, and the specific value ratio cannot be obtained from a historical record. Therefore, the site capacity is finally defined according to the man-hours:
Figure BDA0001642563920000061
wherein n refers to the number of the distributors owned by the site on the same day, Delivery person refers to the distributor, and Actual work hours refers to the Actual working time of the distributor.
For how to calculate the actual working time of the distributor, the following calculation formula is given in the application, and for any distributor k:
Awh k ＝Lstm k -Fstm k
wherein Lstm k Refers to the time of last successful commit of the dispatcher k, Fstm k The time that the first order of the dispenser was successfully committed is referred to.
From the above capacity definition, the maximum capacity of a site may be determined, for example, first. In normal times, the number of packages arriving at the station is not large, so that the capacity of the station is difficult to achieve the maximum in normal times. During the period of commodity promotion, the goods are distributed no more than a multiple of times by the distribution personnel, the packages are distributed immediately once arriving at the site, the time of each person is fully utilized, and the distribution capacity of each person can be supposed to be the maximum. From the above assumptions, it can be concluded that site capacity is maximized during the growth period.
Modeling the maximum capacity of a site should use data from deliverers during a large period of time to extract factors that may affect capacity, such as: POI type, distributor road area familiarity, package weight, volume, timeliness requirements, single quantity concentration and other information.
The capacity data processing method of the present application will be described in detail below.
FIG. 1 is a system block diagram illustrating a capacity data processing method and apparatus according to an exemplary embodiment.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 101, 102, 103 to interact with a server 105 over a network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 101, 102, 103. The back-office management server may analyze and otherwise process the received data such as the product delivery request, and feed back the processing result (e.g., whether the product can be delivered in due course, the delivery time can be reserved, etc.) to the terminal device.
The server 105 may, for example, input the package delivery parameters and the operator delivery parameters into a first capacity forecast model established by a linear algorithm and a machine learning algorithm to determine first capacity data; the server 105 may, for example, input the site parameters and the aging parameters into a second capacity forecast model established by a linear algorithm and a machine learning algorithm to determine second capacity data; the server 105 can determine the capacity data of the site, for example, by the first capacity data and the second capacity data.
The server 105 can also establish the first capacity prediction model through historical delivery package parameters and historical delivery personnel parameters; the server 105 can also establish the second capacity prediction model through the historical site parameters and the historical aging parameters.
It should be noted that the capacity data processing method provided by the embodiment of the disclosure can be executed by the server 105, and accordingly, the capacity data processing apparatus can be disposed in the server 105. The web page side for providing the user with a product view and the request side for making a delivery request are generally located in the terminal apparatuses 101, 102, and 103.
According to the capacity data processing method, the package amount which can be delivered on time can be determined according to the maximum capacity after the maximum capacity is determined, so that the purposes of improving user experience and reducing default rate are achieved. And the maximum number of packages which can be delivered by the station every day can be known by utilizing the capacity model, and compared with the number of currently placed orders, whether the limit of the delivery capacity of the station is reached is judged, so that the number of the packages reaching the station is controlled in advance, and the pressure of explosion of the warehouse is relieved.
FIG. 2 is a flow chart illustrating a capacity data processing method according to an exemplary embodiment. As shown in fig. 2, the capacity data processing method 20 of the present disclosure at least includes steps S202 to S206.
In S202, the package delivery parameters and the delivery personnel parameters are input into a first capacity forecasting model to determine first capacity data, wherein the first capacity forecasting model is established by a linear algorithm and a machine learning algorithm.
For each site, the maximum capacity of a site is the sum of the maximum capacities of each dispatcher at the site. Different sites have their own special cases, such as: the site is a prosperous first-line city or a remote area; the property of the district in charge of the site is that the company is more or the resident is more, which directly influences the distribution time and efficiency; the order density of the site, the place with high order density and more deliveries in the same time are obtained; the number of the convenience points is greatly improved because the convenience stores support the centralized appropriate delivery
However, the above characteristics become fixed values when aiming at a certain station, so that the modeling can be performed without consideration, only factors influencing the distributor are considered, and the above factors can be classified into two types: delivery package parameters and delivery personnel parameters:
the package delivery parameters were as follows:
1) the wrapping weight is as follows: the distribution efficiency is directly influenced, and the distribution difficulty is higher when the distribution is more sunken;
2) the volume of the package is as follows: the loading of a distributor is directly influenced;
3) and (3) coating aging: the higher the timeliness is, the more the preferential distribution is needed;
4) the package type: different types of packages have different distribution priority degrees.
The dispenser parameters are as follows:
1) degree of familiarity of the dispenser with the parcel: the more familiar the distribution efficiency will be;
2) weather conditions on the day of distribution: mainly the difference between rainy days and ordinary times;
3) and (3) distribution time: the fatigue degree of the deliverers is different at different time periods.
For the weight and volume of the package, the weight and volume can be discretized, e.g., by statistical analysis, as follows:
Figure BDA0001642563920000081
because the package type is self-contained and the time-efficient for the shipper, both air and ECLP. During the rush period, the goods can be distributed with only a few kinds of time-ages, one is an emergency time-age, the goods can be fresh time-ages, the goods can be ordinary time-ages, and the goods can be wrapped time-ages of other kinds. The present application may, for example, categorize packages for delivery on an agent basis into class 2, one being an emergency-aged class package, and one being a normal-aged class package.
In practical situations, the delivery capacity of the deliverer and the delivery time are related, because the deliveries are delivered in different time periods, the fatigue degree of people is different, the fatigue degree of people can influence the deliveries very much,
according to the method, the delivery amount d of the average time of the deliverers in the history promotion period is calculated, and the relation d between the delivery amount d per unit time and the delivery time period t is fitted to be f (t). The fatigue level can be defined, for example, as fp ═ 1/d.
In one embodiment, a first capacity forecast model is established from historical data based on the above analysis, and first capacity data is determined based on current package delivery parameters and delivery personnel parameters.
In S204, the site parameters and the aging parameters are input into a second capacity prediction model to determine second capacity data, wherein the second capacity prediction model is established by a linear algorithm and a machine learning algorithm.
Although individual factors of a plurality of sites can not be considered in the capacity prediction aiming at the site individuals, the problems of large calculation amount, large calculation amount and weak generalization capability exist. In view of the above problems, the present application further provides another method for capacity prediction: a total capacity prediction method for a site.
In this way, taking the site as a whole as a consideration object, and taking the distributor as a consideration dimension, the sites are firstly classified into the following 2 types:
1) the stations with large order quantity and large order density are easy to distribute.
2) The order quantity is general, but the order intensity is low, namely the distribution difficulty is large.
In the present application, the differences of the sites are described by site parameters and aging parameters:
site parameters were as follows:
1) station throughput at ordinary times: describing the average delivery volume per day during the non-rush hour of the site;
2) throughput during station promotion: describing the average delivery volume per day during the website promotion period;
3) site large pressure increase ratio: the ratio of station stress to peacetime is described.
The aging parameters are as follows:
1) total number of stations on the day;
2) the number of the fresh aged sub-lists of the sites;
3) number of bullets that the station normally ages.
In one embodiment, a second capacity prediction model is built from historical data based on the above analysis, and first capacity data is determined based on current site parameters and aging parameters.
In S206, capacity data of a site is determined according to the first capacity data and the second capacity data.
In one embodiment, the first capacity data and the second capacity data are compared with the historical capacity data of the site, and the forecast capacity data (possibly the first capacity data and possibly the second capacity data) which is closer to the historical capacity data is used as the capacity data of the site.
In one embodiment, the first capacity data and the second capacity data are averaged, and the average is used as the capacity data of the site.
In one embodiment, further comprising: and determining a distribution strategy according to the capacity data of the site. The method specifically comprises the following steps: providing an appointment delivery service when the current capacity of the site is smaller than the capacity value; when the current capacity of the site is smaller than the capacity value, allowing the order to be downloaded; and allowing the warehouse to be normally produced when the current capacity of the site is smaller than the capacity value.
According to the capacity data processing method, the parcel volume which can be delivered on time can be determined according to the maximum capacity after the maximum capacity is determined, so that the purposes of improving user experience and reducing default rate are achieved. The maximum number of packages which can be delivered by the station every day can be obtained through capacity prediction, the number of the packages is compared with the number of the currently placed orders, whether the limit of the delivery capacity of the station is reached is judged, the number of the packages reaching the station is controlled in advance, and the pressure of storehouse explosion is relieved.
It should be clearly understood that this disclosure describes how to make and use particular examples, but the principles of this disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
FIG. 3 is a flowchart illustrating a capacity data processing method according to another exemplary embodiment. Steps S302 to S306 of fig. 3 are detailed descriptions of the first capacity forecast module establishing process in step S202 of fig. 2 of inputting package delivery parameters and delivery personnel parameters into the first capacity forecast model for determining the first capacity data, wherein the first capacity forecast model is established by a linear algorithm and a machine learning algorithm.
In S302, the historical delivery package parameters and the historical delivery personnel parameters are data-trained through a linear algorithm to obtain a first linear model. For example, calculating a first model parameter by using the historical delivery package parameter and the historical delivery personnel parameter; establishing a first model attribute vector according to the first model parameters, and establishing a first linear model through the first model attribute vector.
For all dispatchers k:
calculate k actual hours of operation per day Awh k ＝Lstm k -Fstm k ；
Respectively calculating the distribution parcels with different ages per day to belong to { s1, s2, s3, s4} p The number of pieces in each case is recorded
Figure BDA0001642563920000111
Judging the weather condition every day, and dividing the weather into 3 types: in the weather of ultra-high temperature and heavy snow, the weather of heavy rain is recorded as W-2-3, and the weather of the rest is recorded as W-1;
the length of time that the distributor is responsible for the distribution area is marked as f k ；
Calculating the fatigue degree of the same day
Figure BDA0001642563920000112
Wherein n is the number of all packages distributed on the same day;
constructing an attribute vector:
Figure BDA0001642563920000113
using data pairs
Figure BDA0001642563920000114
The composed data set is trained by a linear algorithm to obtain a first linear model.
In S304, the historical delivery package parameters and the historical delivery personnel parameters are data-trained by a machine learning algorithm to obtain a first learning model. For example, calculating a first model parameter by the historical delivery package parameter and the historical delivery personnel parameter; and establishing a first model attribute vector according to the first model parameters, and establishing a first learning model through the first model attribute vector.
As in the example above, data pairs are used
Figure BDA0001642563920000115
And training the formed data set through a machine learning algorithm to obtain a first learning model.
In one embodiment, the machine learning algorithm includes a gdbt (gradient Boosting Decision tree) algorithm. GBDT is an iterative decision tree algorithm consisting of a number of decision trees, the conclusions of all of which are summed up to make the final answer. It is considered as an algorithm with stronger generalization capability together with the SVM at the beginning of being proposed. GBDT achieves an algorithm to classify or regress data by using an additive model (i.e., a linear combination of basis functions) and continuously reducing the residual errors produced by the training process.
In S306, the first productivity prediction model is determined by assigning weights to the first linear model, the first learning model and the first model.
For example, the weights of the first linear model and the first learning model are respectively set to 50%, that is, when the site capacity forecast is calculated, the values obtained by respectively multiplying 50% of the first forecast data calculated by the first linear model and the second forecast data calculated by the first learning model are used as the output data of the first capacity forecast model.
According to the capacity data processing method, the site capacity data can be reflected more accurately and objectively through the first capacity prediction model established by fully considering the characteristics of the deliverers and the characteristics of the packages in the site.
FIG. 4 is a flowchart illustrating a capacity data processing method according to another exemplary embodiment. Steps S402 to S406 in fig. 4 are detailed descriptions of the second capacity forecast model establishing process in "inputting site parameters and aging parameters into the second capacity forecast model established by the linear algorithm and the machine learning algorithm to determine the second capacity data" in step S204 in fig. 2.
In S402, a second linear model is obtained by performing data training on the historical site parameters and the historical aging parameters through a linear algorithm. The second model parameters may be calculated, for example, from historical site parameters and historical aging parameters; and establishing a second model attribute vector for the second model parameter, and performing data training through the second model attribute vector to obtain a second linear model.
For each site s, respectively calculating:
number of people per day at the site human s ；
Fresh aged sub-singular number fresh distributed by the station every day s ；
Nofresh, stale, partial singular number of daily deliveries by the site s ；
Station daily regular throughput site _ out _ normal s ；
Throughput site _ out during station promotion s This feature may be set, for example, by selecting one of the 4 th) features 2 above for model building.
Site large pressure increase ratio
Figure BDA0001642563920000121
The station calculates the actual total working time of each day
Figure BDA0001642563920000122
Figure BDA0001642563920000123
Constructing attribute vectors
Figure BDA0001642563920000131
As in the example above, data pairs are used
Figure BDA0001642563920000132
The composed data set is trained through a machine learning algorithm to obtain a second linear model.
In S404, a second learning model is obtained by performing data training on the historical site parameters and the historical aging parameters through a machine learning algorithm. The second model parameters may be calculated, for example, from historical site parameters and historical aging parameters; and establishing a second model attribute vector for the second model parameters, and performing data training through the second model attribute vector to obtain a second machine learning algorithm model.
As in the example above, data pairs are used
Figure BDA0001642563920000133
A data set of composition of said data set by a machine learning algorithmThe data set is trained to obtain a first learning model.
In one embodiment, the machine learning algorithm includes a gdbt (gradient Boosting Decision tree) algorithm. GBDT is an iterative decision tree algorithm consisting of a number of decision trees, the conclusions of all of which are summed up to make the final answer. It is considered as an algorithm with stronger generalization capability together with the SVM at the beginning of being proposed. GBDT achieves an algorithm to classify or regress data by using additive models (i.e., linear combinations of basis functions) and continuously reducing the residual errors produced by the training process.
In S406, the second linear model, the second learning model and the second model are assigned weights to determine the second capacity prediction model.
The weights of the first linear model and the first learning model may be set to 50%, respectively, for example, and the weight values may be set, for example, by historical data. Namely, when the site capacity prediction is calculated, the numerical values of the first prediction data calculated by the second linear model and the second prediction data calculated by the second learning model multiplied by 50% of the weight are used as the output data of the second capacity prediction model.
According to the capacity data processing method, the second capacity prediction model established by fully considering the characteristics of the site and the characteristics of the package aging aspect can reflect the site capacity data more accurately and objectively.
FIG. 5 is a schematic diagram of a capacity data processing method according to another exemplary embodiment. In the actual site distribution process, parameters affecting the capacity can be input at regular time intervals, and the capacity model calculates the capacity value of the site. The warehousing system inputs parameters influencing productivity and transmits the parameters to the productivity model, and the productivity model calculates the warehousing productivity value. The site capacity output by the capacity model is used as a reference value, and the station master performs micro adjustment on data.
Wherein, each website, warehouse all have two calendars: the capacity calendar refers to the maximum capacity of a single quantity per day and per wave. The scheduling calendar refers to how many orders are queued for processing in a daily, per-wave queue (per site, warehouse build count queue).
Comparing the production capacity calendar with the scheduling calendar in real time to update the reservation calendar of the foreground, wherein the current time of the schedule calendar is selectable when the current order quantity is less than the production capacity; when the next order quantity reaches the production value, the foreground appoints the calendar to be grayed for the wave, and the client cannot select the wave.
Updating the scheduling calendar, namely updating the queue value of the scheduled calendar in a certain wave when a client clicks the scheduled calendar to select the certain wave; and when the client does not click on the reserved calendar, defaulting to the fastest proper time, and updating the queue value of the time of the schedule calendar.
The indexes of single quantity of station backlog, single quantity in transit and single quantity of warehouse to be produced and delivered need real-time data to be provided for the station, and the station schedules productivity according to the real-time data
Those skilled in the art will appreciate that all or part of the steps to implement the above embodiments are implemented as a computer program executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
FIG. 6 is a block diagram illustrating a capacity data processing apparatus according to an exemplary embodiment. The capacity data processing apparatus 60 shown in fig. 6 includes: a first forecasting module 602, a second forecasting module 604, and a capacity forecasting module 606.
The first forecasting module 602 is configured to input a package delivery parameter and a delivery person parameter into a first capacity forecasting model to determine first capacity data, where the first capacity forecasting model is established by a linear algorithm and a machine learning algorithm;
the second forecasting module 604 is configured to input the site parameters and the aging parameters into a second capacity forecasting model to determine second capacity data, where the second capacity forecasting model is established by a linear algorithm and a machine learning algorithm;
the capacity forecasting module 606 is used for determining the capacity data of the site according to the first capacity data and the second capacity data.
FIG. 7 is a block diagram illustrating a capacity data processing apparatus according to another exemplary embodiment. The capacity data processing apparatus 70 shown in FIG. 7 comprises, in addition to the first forecasting module 602, the second forecasting module 604 and the capacity forecasting module 606 shown in FIG. 6: a first model building module 702, a second model building module 704.
The first model building module 702 is configured to build the first capacity forecasting model according to the historical delivery package parameters and the historical delivery personnel parameters.
The second model building module 704 is configured to build the second productivity prediction model according to the historical site parameters and the historical aging parameters.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 200 according to this embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 200 shown in fig. 8 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, the electronic device 200 is in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 2, 3, 4.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 230 may be any bus representing one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiments of the present disclosure.
Fig. 9 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure.
Referring to fig. 8, a program product 400 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not so limited, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: inputting a delivery package parameter and a delivery person parameter into a first capacity prediction model to determine first capacity data, wherein the first capacity prediction model is established through a linear algorithm and a machine learning algorithm; inputting the site parameters and the aging parameters into a second capacity prediction model to determine second capacity data, wherein the second capacity prediction model is established through a linear algorithm and a machine learning algorithm; and determining the capacity data of the site through the first capacity data and the second capacity data.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus as described in the embodiments, and that corresponding changes may be made in one or more apparatus that are unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

Claims (8)

1. A method for processing capacity data, comprising:
calculating a first model parameter according to the historical delivery package parameter and the historical delivery personnel parameter; establishing a first model attribute vector by using the first model parameter, and using the data pair
Figure FDA0003646604460000011
A composed data set, which is trained by a linear algorithm to obtain a first linear model;
wherein,
Figure FDA0003646604460000012
a vector of attributes of the first model is represented,
Figure FDA0003646604460000013
Figure FDA0003646604460000014
Awh k ＝Lstm k -Fstm k ；
Figure FDA0003646604460000015
wherein k represents a distributor number, p represents a package aging type number, p is p1, p2, p1 represents an emergency aging package number, and p2 represents a common aging package number;
Figure FDA0003646604460000016
indicating that the packages delivered by the different ages p belong to { s1, s2, …, sn } p The number of n cases is counted, and the packages are divided into n cases according to the distribution package volume and the package weight; w denotes weather conditions, f k Indicates the length of time that the distributor k is responsible for his parcel, fp k Representing the current fatigue degree of a distributor k, n' is the number of all packages distributed by the distributor k on the day, i represents the number of the packages distributed by the distributor k on the day, f (t) represents the distribution amount of the average time of the distributor during the historical promotion period, and the fitted relation between the distribution amount per unit time and the distribution time period t; awh k Represents the actual work hours of the deliverer k each day; lstm k Indicating the time of the last successful commit of the distributor k, Fstm k Indicating the time at which the dispatcher k first successfully committed;
performing data training on historical delivery package parameters and historical delivery personnel parameters through a machine learning algorithm to obtain a first learning model; determining a first capacity prediction model by the first linear model, the first learning model and the first model distribution weight;
inputting the delivery package parameters and the delivery personnel parameters into a first capacity prediction model to determine first capacity data; the first capacity data is used for describing the personal capacity of a distributor in any site;
inputting the site parameters and the aging parameters into a second capacity prediction model to determine second capacity data, wherein the second capacity prediction model is established through a linear algorithm and a machine learning algorithm; the second capacity data is used for describing the total capacity of the site; and
determining capacity data of a site according to the first capacity data and the second capacity data;
providing an appointment delivery service when the current capacity of the site is less than the capacity data;
when the current capacity of the site is smaller than the capacity data, allowing the order to be downloaded; and
and when the current capacity of the site is smaller than the capacity data, allowing the warehouse to normally produce.
2. The method of claim 1, wherein the method further comprises:
and establishing the second productivity prediction model according to the historical site parameters and the historical aging parameters.
3. The method of claim 2, wherein building the second capacity prediction model from historical site parameters and historical aging parameters comprises:
performing data training on the historical site parameters and the historical aging parameters through a linear algorithm to obtain a second linear model;
performing data training on the historical site parameters and the historical aging parameters through a machine learning algorithm to obtain a second learning model; and
and determining the second capacity prediction model by the second linear model, the second learning model and the second model distribution weight.
4. The method of claim 2, wherein building the second capacity forecast model from historical site parameters and historical aging parameters comprises:
calculating a second model parameter according to the historical site parameter and the historical aging parameter;
establishing a second model attribute vector for the second model parameter; and
and establishing the second productivity prediction model through the second model attribute vector.
5. The method of claim 1, further comprising:
and determining a distribution strategy according to the capacity data of the site.
6. A capacity data processing apparatus, comprising:
the first model building module is used for calculating first model parameters through historical delivery package parameters and historical delivery personnel parameters; establishing a first model attribute vector by using the first model parameters, and using the data pair
Figure FDA0003646604460000021
A composed data set, which is trained by a linear algorithm to obtain a first linear model; wherein,
Figure FDA0003646604460000022
a vector of attributes of the first model is represented,
Figure FDA0003646604460000031
Figure FDA0003646604460000032
Awh k ＝Lstm k -Fstm k ；
Figure FDA0003646604460000033
wherein k represents a distributor number, and p represents a packageThe package type number is p-1, p2, p1 represents the package number of the emergency aging class, and p2 represents the package number of the common aging class;
Figure FDA0003646604460000034
indicating that the packages delivered by the different ages p belong to { s1, s2, …, sn } p The number of n cases is counted, and the packages are divided into n cases according to the distribution package volume and the package weight; w denotes weather conditions, f k Indicating the length of time that the distributor k is responsible for his parcel,
Figure FDA0003646604460000035
representing the current fatigue degree of a distributor k, n' is the number of all packages distributed by the distributor k on the day, i represents the number of the packages distributed by the distributor k on the day, f (t) represents the distribution amount of the average time of the distributor during the historical promotion period, and the fitted relation between the distribution amount per unit time and the distribution time period t; awh k Represents the actual work hours of the distributor k each day; lstm k Indicates the time of last successful commit of the dispatcher k, Fstm k Indicating the time at which the dispatcher k first successfully committed; the first model building module is also used for carrying out data training on the historical delivery package parameters and the historical delivery personnel parameters through a machine learning algorithm to obtain a first learning model; determining a first capacity prediction model by the first linear model, the first learning model and the first model distribution weight;
the first forecasting module is used for inputting the delivery package parameters and the delivery personnel parameters into the first capacity forecasting model to determine first capacity data; the first capacity data is used for describing the personal capacity of a distributor in any site;
the second prediction module is used for inputting the site parameters and the aging parameters into a second capacity prediction model to determine second capacity data, and the second capacity prediction model is established through a linear algorithm and a machine learning algorithm; the second capacity data is used for describing the total capacity of the site; and
the capacity forecasting module is used for determining capacity data of a site according to the first capacity data and the second capacity data;
the delivery strategy determining module is used for providing appointed delivery service when the current capacity of the site is smaller than the capacity data; when the current capacity of the site is less than the capacity data, allowing the order to be downloaded; and allowing the warehouse to normally produce when the current capacity of the site is smaller than the capacity data.
7. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method recited in any of claims 1-5.
8. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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