CN112330246A - Order summarizing method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of warehousing, in particular to an order summarizing method and device, computer equipment and a storage medium. The method comprises the steps of obtaining warehouse operation data corresponding to the current order processing wave; the warehouse operation data comprises current environment variables, warehouse operation parameters and customer orders; determining a target operation environment of the current order processing wave number according to the current environment variable; processing the customer order by adopting a target order model corresponding to the target operation environment based on the warehouse operation parameters, and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the order. The method can switch different bill models according to different environment variables to carry out intelligent summarization, has strong pertinence, and can effectively improve the warehouse operation efficiency.
Description
Technical Field
The invention relates to the technical field of warehousing, in particular to an order summarizing method and device, computer equipment and a storage medium.
Background
With the rapid development of modern electronic commerce and logistics industry, various goods are various in types and large in quantity, the work load of ex-warehouse is heavy, and the ex-warehouse efficiency is low. At present, a batch of customer orders to be delivered are collected together according to a certain standard or rule to be delivered in a mode of order wave collection during delivery operation, so that the operation efficiency is improved. However, in the conventional order sorting mode, orders meeting the same screening conditions are mainly collected into a picking order and printed according to the system screening conditions (customer requirements, different expressages, other delivery rules and the like) by an order maker according to field operation conditions, the picking order is manufactured, then the picking order is guided by the picking order by the picking clerk, the screening conditions for order collection are not comprehensive enough, the picking order distribution is unreasonable, the subsequent picking efficiency of the picking clerk is low, and the delivery efficiency is affected.
Disclosure of Invention
The embodiment of the invention provides an order summarizing method and device, computer equipment and a storage medium, and aims to solve the problem of low efficiency of the traditional ex-warehouse operation at present.
An order aggregation method comprising:
acquiring warehouse operation data corresponding to the current order processing wave number; the warehouse operation data comprises current environment variables, warehouse operation parameters and customer orders;
determining a target operation environment of the current order processing wave number according to the current environment variable;
processing the customer order by adopting a target order model corresponding to the target operation environment based on the warehouse operation parameters, and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the order.
An order aggregation apparatus comprising:
the operation data acquisition module is used for acquiring warehouse operation data corresponding to the current order processing frequency; the warehouse operation data comprises current environment variables, warehouse operation parameters and customer orders;
the target operation environment determining module is used for determining the target operation environment of the current order processing frequency according to the current environment variable;
the order summarizing module is used for processing the customer order by adopting a target order summarizing model corresponding to the target operation environment based on the warehouse operation parameters and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the order.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the order aggregation method described above when executing the computer program.
A computer storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned order aggregation method.
The invention can determine the target operation environment by monitoring the environment variable corresponding to each order processing wave, so as to switch different target order model client orders according to different environment variables for intelligent summarization, output order summarization results and expected operation indexes, have strong pertinence and can effectively improve the warehouse operation efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a flow chart of an order aggregation method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a warehouse operation timeline in one embodiment of the present invention;
FIG. 3 is a detailed flowchart of step S103 in FIG. 1;
FIG. 4 is a detailed flowchart of step S102 in FIG. 1;
FIG. 5 is a detailed flowchart of step S402 in FIG. 4;
FIG. 6 is a flow chart of an order aggregation method according to an embodiment of the invention;
FIG. 7 is a flowchart of an order aggregation method according to an embodiment of the invention;
FIG. 8 is a schematic diagram of an order aggregation apparatus according to an embodiment of the invention;
FIG. 9 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described implementations
Examples are a subset of the examples of the invention, and not all examples. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In one embodiment, as shown in fig. 1, an order aggregation method is provided, which includes the following steps:
s101: acquiring warehouse operation data corresponding to the current order processing wave number; the warehouse operational data includes current environmental variables and customer orders.
The warehouse operation data is a CSV format file, and the CSV format file is an actual operation data file of the warehouse acquired from a WMS (intelligent warehouse management system) system. The warehouse operational data includes current environmental variables, historical job metrics for a previous order processing wave (i.e., a previous order processing wave adjacent to the current order processing wave), warehouse operational parameters, and customer orders. The order processing order is to divide the customer orders read from the order pool into different processing orders, then the orders are summarized for each processing order to generate a plurality of picking orders, so that the picking personnel can pick the orders according to the picking orders.
Current environmental variables include, but are not limited to, predicted order volume for the day (estimated from human experience values), order arrival frequency (number of customer orders per order processing wave), order structure ratio, and SKU structure ratio (ratio of 1SKU to multiple SKUs). Historical job metrics include, but are not limited to, the sum of order processing times (including the time for job preparation or waiting) for the last order processing wave, the order processing time span (the time span from the beginning of the first customer order processing to the end of the last order processing), the human effectiveness (i.e., the workload/working time) of the picker and packager, and the number of pickers and packaging stations suggested for use by the last order processing wave.
The warehouse operating parameters include, but are not limited to, warehouse pick-up area size, pick-up area pick-up lane number, upper limit of number of customer orders processed per pass, upper limit of SKU capacity in each pick-up order, vertical travel distance of pick-up path, average length of pick-up lane in pick-up area, preparation time of each pick-up order, speed of picker travel, speed of picker finding goods, time required for picker to take each goods, speed of picker to sort goods, packing speed of packer, upper limit of packing waiting time, and average packing time of each goods.
The order structure includes a plurality of categories, such as singleton (each customer order contained in the pick order contains 1SKU, each SKU contains 1 item), singleton (each customer order contained in the pick order contains 1SKU, each SKU contains at least 2 items), singleton (each customer order contained in the pick order contains multiple SKUs, each SKU contains 1 item), multiple singleton (each customer order contained in the pick order contains multiple SKUs, each SKU contains at least 2 items), and tail waves (a small number of customer orders not finally consolidated into the pick order, which may contain any of the above categories). Multiple items may be divided into two groups of strategies, one containing 2-5 SKUs for each customer order contained in the pick order, each SKU containing 2-20 items; another group contains at least 5 for each customer order contained in the pick-up order, with each SKU containing at least two items. As can be understood, the generalization of order processing is ensured by refining the decision strategy of each category in the order structure, orders with different structures can be processed compatibly, and the accuracy of model training is further ensured.
S102: and determining the target operation environment of the current order processing wave number according to the current environment variable.
In this embodiment, a configuration file is created in advance, different operating environments and corresponding environment variables and operation indexes thereof are stored in the configuration file, the configuration file is called to perform environment matching according to a current environment variable and a historical operation index of a previous order, and if the current environment variable meets the corresponding environment variable and operation index corresponding to a certain operating environment, the operating environment is used as a target operating environment of a current order processing order.
Illustratively, the structure of the configuration file is shown in the following table:
s103: processing the customer order by adopting a target order model corresponding to the target operation environment based on the warehouse operation parameters, and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the goods.
The order summary result is used for describing a plurality of picking orders, and the picking orders are used for indicating the pickers to pick. The pick order includes, but is not limited to, the name of the item, the quantity of the item required for each customer order, and the location of each item.
In one embodiment, the target order model includes a first order model and a second order model; the first bill model is obtained by adopting mixed integer nonlinear programming modeling in advance; the second bill model is obtained by training historical warehouse operation data in advance by adopting a machine learning algorithm or a deep learning algorithm.
It can be understood that the first order model is a mathematical model that is used for optimizing warehouse operation time and building corresponding constraint conditions to obtain a mixed integer nonlinear programming.
For the first order model: specifically, as shown in fig. 2, the order picking operation time axis is composed of two operations of order picking operation and packing operation in series, so in this embodiment, the optimization modes of the first order model include, but are not limited to, two, one is only to optimize the order picking operation; the other is to optimize the goods picking operation and the packing operation simultaneously; to establish a mathematical model of a corresponding mixed integer nonlinear programming, by either embodiment.
For ease of understanding, each embodiment is described below.
In one embodiment, a mathematical model of a mixed integer nonlinear programming is created for the purpose of optimizing picking operations, i.e., the decision variables of the mathematical model of the mixed integer nonlinear programming may be determined as xijAnd yjpkWherein x isijIndicating whether the customer order I e I is assigned to the pick slip J e J (if, x)ijX is 1, if notij＝0)；yjpkIndicating whether the picking order J belongs to the picking order storage position K belonging to the picking order of the picking person P belonging to the picking order storage position K belonging to the picking order storage position (if yes, yjpkIf not, yjpk＝0)。
In particular, the objective function of the mathematical model of the mixed integer nonlinear programming is
The goal is to minimize the sum of order processing time and the span of order processing time; the order processing time sum refers to the sum of all customer order processing times in the current order processing wave, including pick preparation or wait time. The order processing time span refers to the time span from the beginning of the first customer order pick to the end of the last customer order pick in the current order processing wave.
Wherein the content of the first and second substances,
represents the sum of order processing time;
indicating a pick completion time for a last customer order; t is tenter-pickIndicating a pick start time for the first customer order; j represents a pick order; p represents the picker; k represents the last pick-up stock of the pickerAnd (4) placing units.
Specifically, the model constraint conditions corresponding to the objective function are as follows:
it can be understood that, as can be seen from the above objective function and model constraints, the objective function is a non-linear objective function, so that the objective function and the constraints need to be linearized to convert the non-linear objective function into a linear objective function.
In particular, the non-linear objective function is linearly transformed, i.e. the non-linear objective function
Is equivalent to
And updating the constraints, i.e. adding to the above constraints
tdmakespan-pick≥0。
In another embodiment, a mathematical model of a mixed integer non-linear programming is created with the purpose of simultaneously optimizing ex-warehouse and packing jobs, i.e. the decision variables of the mathematical model of the mixed integer non-linear programming can be determined as xij、yjpkAnd zjrlWherein x isijIndicating whether the customer order I e I is assigned to the pick slip J e J (if, x)ijX is 1, if notij＝0)；yjpkIndicating whether a pick order J e J is assigned to the pickThe order picking storage position K belonging to the member P belonging to the group P belongs to the group K, (if yes, yjpkIf not, yjpk＝0)；zjrlIndicating whether the picking order J belongs to the picking order storage position L belonging to the packing table R belonging to the picking order storage position L belonging to the packing table R or not (if yes, zjrl1, if not, zjrl＝0)。
The objective function of the mathematical model for mixed integer nonlinear programming is
The goal is to minimize the sum of order processing times and the order processing time span. The order processing time sum refers to the sum of all customer order processing times, including the waiting time for picking. The order processing time span refers to the time span from the beginning of the first customer order pick to the end of the last customer order pick.
Wherein the content of the first and second substances,
represents the sum of order processing time;
indicating a packaging completion time of a last customer order; t is tenter-pickIndicating a pick start time for the first customer order; j represents a pick order; r denotes a packing table; l denotes the last order deposit location of the packed table.
Specifically, the model constraint conditions corresponding to the objective function are as follows:
specifically, the meanings of the parameters corresponding to the model constraints in the above two embodiments are as follows:
it can be understood that, as can be seen from the above objective function and model constraint conditions, the objective function is a nonlinear objective function, and the model constraint conditions include nonlinear constraint conditions and linear constraint conditions, so it is necessary to linearize the objective function and the constraint conditions to convert the nonlinear objective function into a linear objective function and convert the nonlinear constraint conditions into linear constraint conditions, so that only linear constraint conditions are included in the model constraint conditions.
In particular, the non-linear objective function is linearly transformed, i.e. the non-linear objective function
Is equivalent to
And updating the constraints, i.e. adding to the above constraints
tdmakespan-pack≥0。
Further, the nonlinear constraints are linearized, i.e. according to the theorem
Can be combined with
l.epsilon./L \ 1} conversion
arle {0, 1}, where arlIs a binary variable 0 or 1.
Further, the nonlinear constraint condition is linearized, namely the following nonlinear constraint condition
The absolute value item in L epsilon/L/1 is expanded,
l is an element of L \ 1, and
l∈L\{1}。
for the second order model: the second order model can be obtained by training historical warehouse operation data by adopting a machine learning algorithm or a deep learning algorithm. The historical warehouse operational data includes, but is not limited to, the number of SKUs in the order, the number of items contained in each SKU, and the like.
In one embodiment, as shown in FIG. 3, the target operating environment includes a promotional environment and a conventional environment; in step S103, processing the customer order by using the target order model corresponding to the target operating environment based on the warehouse operating parameters, and outputting an order summary result specifically includes the following steps:
s301: and when the target operation environment is a conventional environment, processing the customer order by adopting a first order model based on the warehouse operation parameters, outputting an order summarizing result, and calculating an expected operation index according to the order summarizing result.
The conventional environment can be divided into different conventional environments according to different combinations of environment variable data, such as conventional environment 1 and conventional environment 2 …. The promotional environments can also be divided into different promotional environments according to different combinations of environment variable data, such as promotional environment 1, promotional environment 2 ….
Specifically, since the first order model is a mathematical model and the solution time of the first order model is longer than that of the first order model, the first order model can be preferentially adopted for order summarization to output an order summarization result and calculate an expected operation index according to the order summarization result, compared with a conventional environment.
S302: and when the target operation environment is a sales promotion environment, processing the customer order by adopting a second order model based on the warehouse operation parameters, and outputting an order summarizing result and an expected operation index.
Specifically, since the first order model is a mathematical model and the solution time of the first order model is longer than that of the first order model, the first order model can be preferentially adopted for order summarization to output an order summarization result and calculate an expected operation index according to the order summarization result, compared with a conventional environment. And inputting the warehouse operation parameters and the customer orders into the second order model for order summarization, so that an order summarization result and expected operation indexes can be output.
Specifically, the method can monitor the environment variable corresponding to each order processing wave to determine the target operation environment, and when the target operation environment is monitored to be a conventional environment, the time effect requirement under the environment is lower compared with that of a sales promotion environment, the solving time of the first order model is relatively longer, and the processing time of the second order model is relatively shorter, so that the first order model can be adopted to process the customer orders and output order summarizing results; when the target operation environment is monitored to be a sales promotion environment, the current target order model can be switched to the second order model due to the fact that the requirement on the timeliness under the environment is high, so that the second order model can process the customer orders and output order summarizing results. It can be understood that, in this embodiment, different order models can be switched to process a customer order by monitoring different environment variables, and the pertinence is strong.
It should be noted that the correspondence relationship between different environment variables and the selected bill of exchange model can be determined by the effect of the actual test, and is not limited herein.
Specifically, based on the warehouse operating parameters, the first order model is adopted to process the customer order, and outputting the order summary result specifically includes: (1) the first order model is initialized with warehouse operational parameters. (2) And solving the first order model by adopting a defined algorithm, and outputting an order summarizing result.
Specifically, according to the above mathematical model, some parameters in the model, such as SKU capacity upper limit in each picking order, need to be initialized for subsequent model solution, so in this embodiment, the warehouse operation parameters are used to initialize the target order model, and by means of the Gurobi optimizer, the model is implemented by using a defined algorithm to efficiently solve, and the order summary result is output.
Specifically, the warehouse operating parameters include, but are not limited to, warehouse pick-up area size, pick-up area goods pick-up channel number, upper limit of the number of customer orders processed per pass, upper limit of SKU capacity in each pick-up order, vertical travel distance of pick-up path, average length of pick-up channel in pick-up area, preparation time per pick-up order, speed of pick-up person walking, speed of pick-up person looking for goods, time required for pick-up person to take each goods, speed of pick-up person to arrange goods, packing speed of packing person, upper limit of packing waiting time, and average packing time per goods.
In an embodiment, as shown in fig. 4, in step S102, that is, according to the current environment variable, the target operation environment of the current order processing wave is determined, which specifically includes the following steps:
s401: acquiring a running environment configuration file; the running environment configuration file is used for describing the mapping relation between the running environment variables and the bill of exchange model.
S402: and matching the current environment variables according to the operating environment configuration file, and determining the target operating environment of the current order processing wave number.
Specifically, different target statement models corresponding to different target operating environments are different and can be determined by the mapping relationship between the operating environment variables in the operating environment configuration file and the statement models. Understandably, the binding relationship between the environment variables and the bill model is unified through the configuration files, and the expandability is strong.
In an embodiment, as shown in fig. 5, in step S402, that is, matching the current environment variables according to the operating environment configuration file, and determining the target operating environment of the current order processing wave number includes:
s501: and if the current environment variable is matched with the operation environment variable in the operation environment configuration file, taking the bill model corresponding to the operation environment variable as a target bill model.
S502: and if the current environment variable is not matched with the operation environment variable in the operation environment configuration file, taking the default bill model as a target bill model, and storing the current environment variable into an operation environment management library.
It can be understood that, if the current environment variable is not matched with the environment variable set in the operating environment configuration file, a default order model may be adopted for processing to ensure normal execution of the program, and at the same time, the current environment variable is stored in the operating environment management library so as to further test the environment variable in the following, and the order model corresponding to the environment variable is determined, so as to record the environment variable and the corresponding order model in the operating environment configuration file, thereby facilitating the use of the following order aggregation.
In an embodiment, as shown in fig. 6, before step S101, the order aggregation method further includes:
s601: obtaining historical warehouse operating data; the historical warehouse operation data comprises customer orders, order information, warehouse operation parameters and historical operation indexes.
Historical warehouse operational data includes, but is not limited to, customer orders and order information for a certain order processing wave. The customer order information includes, but is not limited to, the number of SKUs, and the number of items for each SKU.
S602: and classifying the customer orders according to a preset order classification strategy to obtain order categories corresponding to the customer orders.
Specifically, order categories include, but are not limited to, single item, multiple item strategy 1, multiple item strategy 2 …, multiple item strategy N, and others. N may be a positive integer, and may be determined according to an actual situation, so that a plurality of different multi-product strategies may be set, which is not limited herein.
For example, the description information of different categories is shown in the following table, and it should be noted that the descriptions of different categories can be customized according to actual needs, which is only shown as an example here, and does not limit the implementation of the present solution.
As an embodiment, the customer orders may be classified according to the classification policy in the table, the order category corresponding to the customer order is obtained, and each customer order is labeled by using the order category.
As another embodiment, after classifying the customer orders according to the classification policy, a plurality of class clusters containing the same class of customer orders are obtained, and each class cluster corresponds to an order class. Since the classification policy is based on the number of SKUs and the number of commodities corresponding to each SKU, in order to further reduce the work time of warehouse workers and improve the work efficiency of warehouse workers, in this embodiment, customer orders with the same order type and similar SKU positions can be further screened out for the SKU positions included in each customer order in each category of clusters as picking orders corresponding to the order type.
Specifically, in this embodiment, when the SKUs are determined to be close, the elements in each cluster, i.e. the customer orders, can be determined by obtaining the stock location information, such as 1,2,3 …. Illustratively, if a certain cluster comprises a customer order 1 and a customer order 1, because the SKUs corresponding to the two customer orders are consistent in quantity, the two customer orders can be used as elements of the cluster by using quantities or proportions which are closer to the SKU positions in the two customer orders and are greater than a preset threshold value; conversely, customer orders with different locations may be shifted to other order categories. Specifically, when the SKUs are judged to be close in position, the absolute value of the difference value of the stock location information of each two SKUs is compared to be smaller than a preset value, if so, the two SKUs are proved to be close in position, otherwise, the two SKUs are considered to be far in position.
S603: and vectorizing the order information, the warehouse operation parameters and the historical operation indexes to obtain target characteristic data.
The order information includes, but is not limited to, the number of SKUs, the number of items contained in each SKU, and the like. The warehouse operation parameters include, but are not limited to, warehouse pick-up area size, pick-up area goods pick-up channel number, upper limit of the number of customer orders processed per pass, upper limit of SKU capacity in each pick-up order, vertical travel distance of pick-up path, average length of pick-up channel in pick-up area, preparation time of each pick-up order, speed of the picker walking, speed of the picker searching for goods, time required for the picker to take each goods, speed of the picker sorting goods, packing speed of the packer, upper limit of packing waiting time, and average packing time of each goods.
Specifically, multiple dimensions in the order information may be converted into numerical representations according to a preset strategy, for example, the SKU quantity may be directly expressed as an actual numerical value for the feature or expressed as a numerical value for the feature by specifying it as a range, and the feature value corresponding to the range is not limited herein. It should be understood that any way of extracting information features can be applied in the present application as long as the numerical expression of the features is realized.
It can be understood that, by performing numerical expression on each feature, a feature vector composed of a plurality of values is obtained, and the feature vector is the target feature data corresponding to the order information.
S604: and taking the target characteristic data and the order category as training samples.
The target characteristic data comprises order information, warehouse operation parameters, historical operation indexes and order types. It is understood that the X value in the training sample (X, Y) includes dimensions such as warehouse operation parameters and order information, and the Y value is the order category and the corresponding historical job index.
S605: and training the training sample by adopting a machine learning algorithm or a deep learning algorithm to obtain a second bill of exchange model.
Specifically, in this embodiment, a machine learning algorithm (e.g., a random forest) or a deep learning algorithm (e.g., an LSTM neural network) may be used to train the training samples to obtain the second bill of lading model.
Illustratively, taking a deep learning algorithm as an example for explanation, training is performed by inputting training samples including warehouse operating parameters, order information, order categories and corresponding actual operation indicators (KPIs) as training samples into a multilayer neural network, calculating output values of a model through a forward propagation algorithm, and calculating model losses and gradients through a backward propagation algorithm to update the multilayer neural network until the model converges to obtain a second order model.
In an embodiment, as shown in fig. 7, after step S103, the order aggregation method further includes:
s701: and acquiring actual operation indexes for warehouse operation according to the order summarizing result.
The actual operation index and the expected operation index both comprise two dimensions of warehouse operation time and employee work efficiency. The employee work efficiency is the ratio of the total order quantity for processing the current order processing wave to the speed of the employee in performing warehouse operations. The warehouse operations include picking operations and packing operations. The time for the warehouse operation comprises the total time of the warehouse operation corresponding to the current order processing wave and the order processing time span. The order processing time span refers to the time span from the beginning of the first customer order pick to the end of the last customer order pick.
S702: and analyzing the actual operation index and the expected operation index to determine a target sample.
Specifically, after the order summarizing result and the expected operation index are output, the order summarizing result can be applied to actual warehouse operation, the actual operation index is calculated, then the actual operation index and the expected operation index are compared, and if the error is larger than a preset threshold value, the warehouse operation data can be used as a target sample so as to carry out incremental training.
S703: and performing incremental training on the second bill of exchange model by adopting the target sample, and updating the second bill of exchange model.
It can be understood that the model precision can be effectively improved by taking the target sample as a training sample for incrementally training the second bill model, namely, incrementally training the second bill model through actual warehouse operating data, so that the model training is continuously integrated.
Specifically, a software package in the python tool can be called, network training can be realized by initializing network parameters such as initial weight, network layer number, neuron number and the like, and inputting training sample data into a network, and a second bill of exchange model can be obtained when the model reaches a convergence condition; during incremental training, the determined model parameters (such as model weights) in the trained second order model can be directly loaded, so that the second order model is optimally updated by performing incremental training by using the target sample on the basis of the trained model.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, an order summarizing device is provided, and the order summarizing device corresponds to the order summarizing method in the embodiment one to one. As shown in fig. 8, the order summarizing apparatus includes an operation data acquiring module 10, a target operation environment determining module 20, and an order summarizing module 30. The functional modules are explained in detail as follows:
the operation data acquisition module 10 is configured to acquire warehouse operation data corresponding to a current order processing frequency; the warehouse operating data comprises current environment variables, warehouse operating parameters and customer orders.
And a target operation environment determination module 20, configured to determine a target operation environment of the current order processing frequency according to the current environment variable.
The order summarizing module 30 is used for processing the customer order by adopting a target order summarizing model corresponding to a target operation environment based on the warehouse operation parameters and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the goods.
Specifically, the target operating environment determination module includes an operating environment configuration file acquisition unit and an environment variable matching unit.
An operating environment configuration file obtaining unit, configured to obtain an operating environment configuration file; the running environment configuration file is used for describing the mapping relation between the running environment variables and the bill of exchange model.
And the environment variable matching unit is used for matching the current environment variables according to the operating environment configuration file and determining the target operating environment of the current order processing wave number.
Specifically, the target operating environment includes a promotion environment and a conventional environment; the order summarizing module comprises a first summarizing unit and a second summarizing unit.
And the first summarizing unit is used for processing the customer orders by adopting a first summarizing model based on the warehouse operation parameters when the target operation environment is a conventional environment, outputting order summarizing results and calculating expected operation indexes according to the order summarizing results.
And the second summarizing unit is used for processing the customer order by adopting a second remittance model based on the warehouse operation parameters and outputting an order summarizing result and an expected operation index when the target operation environment is a promotion environment.
Specifically, the order summarizing device further comprises a historical warehouse operation data acquisition module, an order classification module, a vector conversion module, a training sample determination module and a model training module.
The historical warehouse operation data acquisition module is used for acquiring historical warehouse operation data; the historical warehouse operation data comprises customer orders, order information, warehouse operation parameters and historical operation indexes.
And the order classification module is used for classifying the customer orders according to a preset order classification strategy to obtain order categories corresponding to the customer orders.
And the vector conversion module is used for vectorizing and representing the order information, the warehouse operation parameters and the historical operation indexes to acquire target characteristic data.
And the training sample determining module is used for taking the target characteristic data and the order type as training samples.
And the model training module is used for training the training samples by adopting a machine learning algorithm or a deep learning algorithm to obtain a second bill of exchange model.
Specifically, the order summarizing device further comprises an actual operation index obtaining module, an operation index analyzing module and a model increment training module.
And the actual operation index acquisition module is used for acquiring the actual operation index of warehouse operation according to the order summarizing result based on the warehouse operation parameters.
And the operation index analysis module is used for analyzing the actual operation index and the expected operation index to determine a target sample.
And the model increment training module is used for carrying out increment training on the second bill model by adopting the target sample and updating the second bill model.
Specifically, the environment variable matching unit includes a first matching unit and a second matching unit.
And the first matching unit is used for taking the bill model corresponding to the operating environment variable as the target bill model when the current environment variable is matched with the operating environment variable in the operating environment configuration file.
And the second matching unit is used for taking the default bill model as the target bill model and storing the current environment variable into the operation environment management library when the current environment variable is not matched with the operation environment variable in the operation environment configuration file.
For the specific limitations of the apparatus, reference may be made to the above limitations of the order aggregation method, which are not described herein again. The various modules in the above-described apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a computer storage medium and an internal memory. The computer storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the computer storage media. The database of the computer device is used to store data generated or obtained during execution of the order aggregation method, such as a target order model. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an order aggregation method.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the steps of the order aggregation method in the above embodiments are implemented, for example, steps S101 to S103 shown in fig. 1 or steps shown in fig. 2 to 7. Alternatively, the functions of each module/unit in this embodiment of the apparatus, for example, the functions of each module/unit shown in fig. 8, are realized when the processor executes the computer program, and are not described here again to avoid repetition.
In an embodiment, a computer storage medium is provided, where a computer program is stored on the computer storage medium, and when executed by a processor, the computer program implements the steps of the order summarizing method in the foregoing embodiments, such as steps S101 to S103 shown in fig. 1 or steps shown in fig. 3 to fig. 7, which are not repeated herein for avoiding repetition. Alternatively, the computer program, when executed by the processor, implements the functions of each module/unit in the embodiment of the apparatus, for example, the functions of each module/unit shown in fig. 8, and is not described here again to avoid repetition.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An order aggregation method, comprising:
acquiring warehouse operation data corresponding to the current order processing wave number; the warehouse operation data comprises current environment variables, warehouse operation parameters and customer orders;
determining a target operation environment of the current order processing wave number according to the current environment variable;
processing the customer order by adopting a target order model corresponding to the target operation environment based on the warehouse operation parameters, and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the order.
2. The order aggregation method as claimed in claim 1, wherein the determining the target operating environment of the current order processing wave based on the current environment variable comprises:
acquiring a running environment configuration file; the operating environment configuration file is used for describing the mapping relation between the operating environment variables and the bill of exchange model;
and matching the current environment variables according to the operating environment configuration file, and determining the target operating environment of the current order processing wave.
3. The order aggregation method of claim 1, wherein the target bill of lading model comprises a first bill of lading model and a second bill of lading model; the first bill model is obtained by adopting mixed integer nonlinear programming modeling in advance; the second bill model is obtained by training historical warehouse operation data in advance by adopting a machine learning algorithm or a deep learning algorithm.
4. The order aggregation method of claim 3, wherein the target operating environment comprises a promotional environment and a regular environment;
the processing the customer order by adopting the target order model corresponding to the target operation environment based on the warehouse operation parameters and outputting an order summarizing result and expected operation indexes comprises the following steps:
when the target operation environment is a conventional environment, processing the customer order by adopting the first order model based on the warehouse operation parameters, outputting an order summarizing result, and calculating the expected operation index according to the order summarizing result;
and when the target operation environment is a promotion environment, processing the customer order by adopting the second order model based on the warehouse operation parameters, and outputting the order summary result and the expected operation index.
5. The order aggregation method of claim 3, wherein prior to the obtaining warehouse operational data corresponding to a current order processing wave, the order aggregation method further comprises:
obtaining historical warehouse operating data; the historical warehouse operation data comprises a customer order, order information, warehouse operation parameters and historical operation indexes;
classifying the customer orders according to a preset order classification strategy to obtain order categories corresponding to the customer orders;
vectorizing the order information, the warehouse operation parameters and the historical operation indexes to obtain target characteristic data;
taking the target characteristic data and the order type as training samples;
and training the training sample by adopting a machine learning algorithm or a deep learning algorithm to obtain the second bill of lading model.
6. The order summarization method according to claim 3, wherein after the target order model corresponding to the target operating environment is used to process the customer order and an order summarization result is output, the order summarization method further comprises:
acquiring actual operation indexes for warehouse operation according to the order summarizing result;
analyzing the actual operation index and the expected operation index to determine a target sample;
and performing incremental training on the second bill of exchange model by adopting the target sample, and updating the second bill of exchange model.
7. The order aggregation method as claimed in claim 2, wherein the matching the current environment variable according to the operating environment configuration file to determine the target operating environment of the current order processing wave comprises:
if the current environment variable is matched with the operation environment variable in the operation environment configuration file, taking a bill model corresponding to the operation environment variable as the target bill model;
and if the current environment variable is not matched with the operation environment variable in the operation environment configuration file, taking a default bill model as the target bill model, and storing the current environment variable into an operation environment management library.
8. An order aggregation apparatus, comprising:
the operation data acquisition module is used for acquiring warehouse operation data corresponding to the current order processing frequency; the warehouse operation data comprises current environment variables, warehouse operation parameters and customer orders;
the target operation environment determining module is used for determining the target operation environment of the current order processing frequency according to the current environment variable;
the order summarizing module is used for processing the customer order by adopting a target order summarizing model corresponding to the target operation environment based on the warehouse operation parameters and outputting an order summarizing result and expected operation indexes; the order summarizing result is used for indicating a picker to pick the order.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the order aggregation method according to any of claims 1 to 7 are implemented by the processor when executing the computer program.
10. A computer storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the order aggregation method according to any one of claims 1 to 7.
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