CN116384713A - Information processing method and device and storage medium - Google Patents
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Abstract
The embodiment of the application discloses an information processing method, an information processing device and a storage medium, wherein the information processing method comprises the following steps: under the condition that an in-bin layout determining instruction is received, determining a template bin corresponding to a target bin according to the in-bin layout determining instruction, and acquiring historical commodity information in the template bin; the template bin is a historical warehouse in an application state; inputting historical commodity information into a bin prediction model to obtain bin storage information; and inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain the bin layout information of the target bin, and laying out the target bin according to the bin layout information.
Description
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to an information processing method and apparatus, and a storage medium.
Background
In recent years, the logistics industry has rapidly developed due to the influence of external environment and the change of shopping modes of people. The storage link in the logistics influences the response time and cost of the logistics, the reasonable warehouse layout can improve the warehouse-in and warehouse-out efficiency of goods, and the goods picking time is shortened.
In the related art, when a new bin or an old bin is updated, the storage requirement of the new bin or the updated bin is set first, a bin similar to the storage requirement is searched in the existing bin, the new bin or the updated bin is planned according to the process configured in the similar bin, and the process planning in the bin may not be in line with the current production development planning, so that the accuracy of planning the new bin or the updated bin is reduced, namely the accuracy of layout of the bin is reduced.
Disclosure of Invention
In order to solve the above technical problems, it is desirable in the embodiments of the present application to provide an information processing method, an information processing device, and a storage medium, which can improve accuracy when a warehouse is laid out.
The technical scheme of the application is realized as follows:
an embodiment of the present application provides an information processing method, including:
under the condition that an in-bin layout determining instruction is received, determining a template bin corresponding to a target bin according to the in-bin layout determining instruction, and acquiring historical commodity information in the template bin; the template bin is a historical warehouse in an application state;
inputting the historical commodity information into a bin prediction model to obtain bin storage information;
and inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain the bin layout information of the target bin, and laying out the target bin according to the bin layout information.
An embodiment of the present application provides an information processing apparatus, including:
the determining unit is used for determining a template bin corresponding to the target bin according to the in-bin layout determining instruction under the condition that the in-bin layout determining instruction is received; the template bin is a historical warehouse in an application state;
The acquisition unit is used for acquiring historical commodity information in the template bin;
the input unit is used for inputting the historical commodity information into a bin prediction model to obtain bin storage information; inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain bin layout information of the target bin;
and the layout unit is used for laying out the target bin according to the in-bin layout information.
An embodiment of the present application provides an information processing apparatus, including:
the information processing device comprises a memory, a processor and a communication bus, wherein the memory is communicated with the processor through the communication bus, the memory stores an information processing program executable by the processor, and the information processing method is executed by the processor when the information processing program is executed.
An embodiment of the present application provides a storage medium having stored thereon a computer program for use in an information processing apparatus, wherein the computer program when executed by a processor implements the above-described information processing method.
The embodiment of the application provides an information processing method, an information processing device and a storage medium, wherein the information processing method comprises the following steps: under the condition that an in-bin layout determining instruction is received, determining a template bin corresponding to a target bin according to the in-bin layout determining instruction, and acquiring historical commodity information in the template bin; the template bin is a historical warehouse in an application state; inputting historical commodity information into a bin prediction model to obtain bin storage information; and inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain the bin layout information of the target bin, and laying out the target bin according to the bin layout information. According to the method, when the in-bin layout determining instruction is received and obtained, the information processing device inputs historical commodity information of a template bin corresponding to the in-bin layout determining instruction into the bin prediction model, bin storage information of a target bin, namely capacity and energy storage of the target bin, is determined by the bin layout determining model under the constraint condition that the bin storage information is met, in-bin layout information of the target bin is determined according to the historical commodity information, consumption caused by excessive investment is avoided, in-bin layout information of accurate layout is obtained, the target bin is laid out according to the in-bin layout information, and accuracy in layout of the target bin is improved.
Drawings
Fig. 1 is a flowchart of an information processing method provided in an embodiment of the present application;
FIG. 2 is a schematic diagram of an exemplary clustering flow provided in an embodiment of the present application;
FIG. 3 is a schematic diagram of an exemplary in-bin layout determination method according to an embodiment of the present application;
fig. 4 is a schematic diagram of a composition structure of an information processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic diagram of a second component structure of an information processing apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
An embodiment of the present application provides an information processing method, where the information processing method is applied to an information processing apparatus, and fig. 1 is a flowchart of the information processing method provided in the embodiment of the present application, and as shown in fig. 1, the information processing method may include:
s101, under the condition that an in-bin layout determining instruction is received, determining a template bin corresponding to a target bin according to the in-bin layout determining instruction, and acquiring historical commodity information in the template bin; the template bins are historical and application state warehouses.
The information processing method provided by the embodiment of the application is suitable for a scene of laying out the target bin.
In the embodiments of the present application, the information processing apparatus may be implemented in various forms. For example, the information processing apparatus described in the present application may include apparatuses such as a mobile phone, a camera, a tablet computer, a notebook computer, a palm top computer, a personal digital assistant (Personal Digital Assistant, PDA), a portable media player (Portable Media Player, PMP), a navigation apparatus, a wearable device, a smart bracelet, a pedometer, and the like, as well as apparatuses such as a digital TV, a desktop computer, a server, and the like.
In the embodiment of the application, the in-bin layout determining instruction carries template bin information, and the information processing device can directly acquire the information of the template bin from the in-bin layout determining instruction.
In the embodiment of the application, the information processing device can directly acquire the historical commodity information in the template bin from the database, and can also acquire the historical commodity information in the template bin from other equipment; the specific manner in which the information processing apparatus obtains the historical merchandise information in the template bin may be determined according to the actual situation, which is not limited in the embodiment of the present application.
It should be noted that the historical commodity information may be SKU data of a commodity stored in a history in the template bin. Specific SKU data includes historical SKU sales data, SKU reserves data, SKU size data, SKU inventory, etc. within the template bin.
In the embodiment of the application, the target bin and the template bin are both e-commerce warehouses. The template bin is a warehouse existing in the prior art. The target warehouse is a newly built warehouse or a warehouse after upgrading and reforming the old warehouse.
In the embodiment of the application, the e-commerce warehouse comprises a warehouse entry and inspection, loading, picking and rechecking packaging process, and mainly comprises elements such as goods, shelves, personnel, a packaging table and the like, wherein the content for determining the in-warehouse layout of the target warehouse comprises the shelf type and the number of the target warehouse; production line selection (rechecking and packing table), quantity; number of people, etc. Namely, the in-bin layout determining instruction is the type and the quantity of bin shelves in the target bin; production line selection (rechecking and packing table), quantity; the number of people, etc.
In the embodiment of the present application, the number of the historical merchandise information may be plural, which is not limited in the embodiment of the present application.
S102, inputting historical commodity information into a bin prediction model to obtain bin storage information.
In the embodiment of the application, under the condition that the in-bin layout determining instruction is received, the information processing device determines the template bin corresponding to the target bin according to the in-bin layout determining instruction, and after acquiring the historical commodity information in the template bin, the information processing device can input the historical commodity information into the bin prediction model to obtain bin storage information.
In the embodiment of the present application, the bin prediction model may be a model configured in the information processing apparatus; the bin prediction model may be a model obtained by the information processing apparatus in other manners; the determination may be specifically determined according to actual situations, which is not limited in the embodiment of the present application.
In the embodiment of the application, the bin prediction model may be an integrated moving average autoregressive model (Autoregressive Integrated Moving Average model, ARIMA), or may be another model for predicting and obtaining bin storage information according to historical commodity information; the specific bin prediction model may be determined according to practical situations, which is not limited in the embodiments of the present application.
In the embodiment of the present application, the python's pdarima module may be used to build a bin prediction model, or other programming languages may be used to build a bin prediction model, which may specifically be determined according to the actual situation, which is not limited in the embodiment of the present application.
In embodiments of the present application, the bin storage information includes capacity and energy storage for the support year.
In an embodiment of the present application, a process for inputting historical commodity information into a bin prediction model by an information processing device to obtain bin storage information includes: performing stability detection on historical commodity information to obtain a detection result; under the condition that the detection result identifies that the historical commodity information is non-stabilized data, stabilizing the historical commodity information to obtain processed historical commodity information; and inputting the processed historical commodity information into a warehouse prediction model to obtain warehouse storage information.
In the embodiment of the present application, the manner of performing stationarity detection on the historical merchandise information to obtain a detection result may be that the historical merchandise information is stationarity detected by using an Augmented digital-fuse (ADF) manner to obtain a detection result; the historical commodity information can be subjected to stability detection by using other stability detection modes to obtain a detection result; the specific stability detection mode may be determined according to practical situations, which is not limited in the embodiment of the present application.
Note that the ARIMA model requires that the time series be smooth. I.e. the statistical law that determines the characteristics of the process does not change over time. For everything 
，
And
sum time interval of covariance of (c) over time
Related to the actual moment
And
irrespective of the fact that the first and second parts are. There may be some subjectivity if viewed with the naked eye through a time series plot. If the bin prediction model is an ARIMA model, carrying out stability detection on historical commodity information to obtain a detection result; in the case that the detection result identifies the historical commodity information as non-stabilized data, the historical commodity information is neededAnd performing stabilization treatment to obtain the treated historical commodity information.
It should be noted that ADF test is a relatively common strict statistical test method.
It should be noted that the detection result includes stationary data or non-stationary data.
In the embodiment of the application, the method for stabilizing the historical commodity information to obtain the processed historical commodity information can be that d-order differential operation processing is performed on the historical commodity information to change the historical commodity information into a stable sequence, so that the processed historical commodity information is obtained; the historical commodity information may be subjected to a stabilizing treatment by using other stabilizing treatment modes, so as to obtain the processed historical commodity information, and the specific stabilizing treatment modes may be treated according to actual conditions, which is not limited in the embodiment of the present application.
D is a positive integer of 1 or more.
In the embodiment of the application, the information processing device performs stability detection on the historical commodity information, and after a detection result is obtained, the historical commodity information is input into a bin prediction model under the condition that the detection result identifies that the historical commodity information is stabilized data, so as to obtain bin storage information.
In the embodiment of the application, before the information processing device inputs the historical commodity information into the bin prediction model to obtain bin storage information, historical sample commodity information in a sample bin and historical sample bin storage information corresponding to the historical sample commodity information are also obtained; and training an initial bin prediction model by utilizing the historical sample commodity information and the historical sample bin storage information to obtain a bin prediction model.
In the embodiment of the present application, the initial bin prediction model may also be a seasonal ARIMA model, or may also be a network model, and a specific initial bin prediction model may be determined according to an actual situation, which is not limited in the embodiment of the present application.
In the embodiment of the application, the process of training the initial bin prediction model by utilizing the historical sample commodity information and the historical sample bin storage information to obtain the bin prediction model can be used for outputting the historical sample commodity information to the initial bin prediction model to obtain the output sample bin storage information; determining a first model loss of an initial bin prediction model according to the output sample bin storage information and the historical sample bin storage information, and taking the initial bin prediction model as a bin prediction model under the condition that the first model loss is less than or equal to a preset model loss; and under the condition that the first model loss is larger than the preset model loss, continuing to train the initial bin prediction model by utilizing the historical sample commodity information and the historical sample bin storage information to obtain a first training model, and taking the first training model as the bin prediction model under the condition that the first training model loss corresponding to the first training model is smaller than or equal to the preset model loss.
In the embodiment of the application, the ARIMA model is shown in formula (1):
wherein:
as an auto-regressive order of the trend,
for the trend to be of a differential order,
as the trend moving average order number,
for the seasonal auto-regressive order,
for the seasonal differential order to be the same,
for the seasonal moving average order,
is the number of time steps during a single season.
S103, inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain the bin layout information of the target bin, and laying out the target bin according to the bin layout information.
In the embodiment of the application, after the information processing device inputs the historical commodity information into the bin prediction model to obtain bin storage information, the historical commodity information and the bin storage information can be input into the in-bin layout determination model to obtain in-bin layout information of the target bin, and the target bin is laid out according to the in-bin layout information.
In the embodiment of the application, the in-bin layout determining model may be a model configured in the information processing device, or may be a model obtained by the information processing device in other manners; the determination may be specifically determined according to actual situations, which is not limited in the embodiment of the present application.
In the embodiment of the application, the in-bin layout information comprises bin shelf types and quantity in a target bin; production line selection (rechecking and packing table), quantity; information such as the number of people.
In this embodiment of the present application, the process of determining, by the information processing apparatus, the template bin corresponding to the target bin according to the in-bin layout determining instruction further includes: acquiring layout mode information from a bin layout determining instruction; correspondingly, the process of inputting the historical commodity information and the bin storage information into the in-bin layout determining model to obtain the in-bin layout information of the target bin comprises the following steps: under the condition that the layout mode information is an old mode, acquiring an old bin identifier; determining historical goods shelf information used in an old bin corresponding to the old bin identifier; and inputting the historical goods shelf information, the historical goods information and the warehouse storage information into a warehouse layout determining model to obtain warehouse layout information.
In the embodiment of the present application, the layout pattern information includes an old pattern and a non-old pattern.
It should be noted that the old mode is the mode of upgrading and reforming the old bin. The non-beneficial old mode is the mode of the newly built bin.
In the case of a newly built bin, a template bin corresponding to the target bin (i.e., a newly built bin) is determined, and historical commodity information (SKU historical data) in the template bin is obtained from the template bin. Under the condition of upgrading and reforming an old bin, taking the old bin as a template bin, and acquiring historical commodity information (SKU historical data) in the template bin.
The historical shelf information is historical shelf parameter information, and comprises information such as shelf type, number, length, width, height and the like of the shelf.
In the embodiment of the present application, the layout mode information carries the old bin identifier. The old bin identifier may be a digital identifier, or may be a character identifier, and may also be represented by other identifiers, specifically may be determined according to actual situations, which is not limited in this embodiment of the present application.
It will be appreciated that there are hundreds to thousands of SKUs in a bin, and if each SKU is considered as a unit for placement and shelf selection, the optimization model (in-bin layout determination model) will have hundreds or even thousands of decision variables, greatly increasing the optimization model size and solution difficulty. And clustering the SKUs according to the attributes of the SKUs in a data prediction processing stage, and processing a plurality of SKU sets into a SKU aggregation class to reduce the scale of an optimization model and the solving speed of the model, so that the in-bin layout information is obtained rapidly, namely the speed of determining the in-bin layout information is improved.
In this embodiment of the present application, a process of inputting historical shelf information, historical commodity information, and bin storage information into a in-bin layout determination model by an information processing apparatus to obtain in-bin layout information includes: clustering the historical commodity information to obtain clustered historical commodity information; and inputting the historical goods shelf information, the clustered historical goods information and the bin storage information into a bin layout determining model to obtain bin layout information.
In the embodiment of the present application, a method for clustering historical commodity information to obtain clustered historical commodity information includes: clustering the historical commodity information by using a k-means clustering method to obtain clustered historical commodity information; the historical commodity information can also be clustered by using other clustering modes to obtain clustered historical commodity information; the specific manner of clustering the historical commodity information to obtain clustered historical commodity information can be determined according to actual conditions, and the embodiment of the application is not limited to this.
The data preprocessing (i.e. clustering the historical commodity information to obtain clustered historical commodity information) is followed by bin process optimization, and the input data includes SKU aggregation information (clustered historical commodity information), input parameters such as shelves and capacity (bin storage information), old shelf information (historical shelf information) and the like. When the old bin is required to be updated, the old bin is used as an old mode, and the existing shelf information (historical shelf information) is required to be input into a bin process planning model (in-bin layout determining model) during optimization; when a new bin is created, no existing shelf information is entered. The optimization model (in-bin layout determining model) is output as in-bin layout information, including the number of shelves, the number of personnel, the number of review packing tables and the like.
In the embodiment of the application, the main workflow in the warehouse is cargo warehousing, cargo inspection, shelf loading, cargo picking, rechecking and packaging, and the main elements in one warehouse are a factory building, cargo, a shelf, a rechecking and packaging table and people. The main area in the factory building is a storage area, which generally occupies 70% of the total area, the area of the storage area limits the maximum energy storage and capacity of the warehouse, the storage area is divided into a goods picking area and a goods preparation area, and different areas use different goods shelves. The goods picking area uses goods shelves with lower layer height, and the goods preparation area uses beam goods shelves with higher layer height. According to the operation experience, the ratio of one SKU goods to be placed in the goods picking area and the goods storage area is 3:7. The order picking person picks the order from the order picking area according to the order information, sends the order picking information to a rechecking and packaging table, and the packaging person checks the information and packages the order. When the goods in the goods picking area is insufficient, the goods supplementing person supplements goods from the goods preparing area to the goods picking area.
In this application embodiment, when goods shelves are selected unreasonably and goods are placed incorrectly, can lead to the warehouse goods shelves utilization ratio not high, pick goods inefficiency, increase goods shelves cost and personnel's cost, and then reduce the storage level ground effect and the production level ground effect in storehouse. Therefore, the in-house process optimization model (in-house layout determination model) comprehensively considers the planning (layout) methods of the goods shelves, personnel and production lines in the house with old goods shelves. By deciding the types of the goods shelves and the number of different goods shelves, the personnel number and the number of the rechecking and packing tables enable the warehouse to have the minimum cost of goods shelves, personnel cost, rechecking and packing tables and warehouse renting cost under the condition of meeting the peak capacity and peak energy storage of the supporting year. Enterprises generally have a large number of warehouses, and a large number of old goods shelf resources can be utilized in the warehouses. With the development of enterprises, the old warehouse needs to be upgraded, if the old goods shelf resources are considered in the process of upgrading and optimizing the warehouse, the investment and cost of the resources are reduced, and according to historical data, the cost of the goods shelf in warehouse construction is about 40%. Therefore, the old goods shelf information is added during optimization, the use amount of the old goods shelf is decided, the existing resources are fully utilized, and the investment of the new goods shelf is reduced. The information processing method is suitable for both new bin construction and old bin upgrading.
In the embodiment of the application, the in-bin layout determination model is shown as formula (2) -formula (9):
wherein, the parameter symbol descriptions in the formula (2) -formula (9) are shown in the table 1:
table 1: parameter symbol description
In the embodiment of the application, the optimization target of the model (in-bin layout determination model) is the minimum cost, and the cost is the sum of the newly added goods shelf cost, the wages of the pickers and the replenishment staff and the rechecking packaging table cost. The old goods shelf is put into the prior art, and the cost is not increased when the old goods shelf is used in the new warehouse and the old warehouse upgrading, so that the cost of the old goods shelf does not exist in the objective function.
In the embodiment of the application, the formula (2) determines an objective function of the model for the in-bin layout, and the formulas (3) - (9) are constraints for optimizing the model: equation (3) shows that the i-th SKU sub-pick zone shelf volume is greater than the i-th SKU sub-pick zone cargo volume; equation (4) shows that the shelf volume of the ith SKU sub-class cargo area is greater than the cargo volume of the ith SKU sub-class cargo area; equation (5) shows that the area of the picking area plus the area of the stock area is smaller than the area of the warehouse storage area; equation (6) is used to calculate the number of pickers needed to process a given daily inventory, where 
For picking efficiency, the picking efficiency is related to the area of the picking area, and the relation is shown in formula (7); equation (8) is used to calculate the number of restocking staff needed to restock a given restock volume, where
For the replenishment efficiency, the replenishment efficiency is related to the area of the stock area, and the relation is shown in formula (9).
In the embodiment of the present application, a process of clustering historical commodity information by an information processing device to obtain clustered historical commodity information includes: acquiring sales information corresponding to historical commodity information; classifying the historical commodity information according to the sales volume information to obtain multi-class classification data; and clustering each class of classified data in the multiple classes of classified data respectively to obtain clustered historical commodity information.
In the embodiment of the application, the historical commodity information can be classified according to sales volume information to obtain multi-class classification data; the historical commodity information can be classified according to the size information of the historical commodity information to obtain multi-class classification data; the historical commodity information can be classified according to the size information and sales volume information of the historical commodity information, and multi-class classification data are obtained; the specific manner of classifying the historical commodity information to obtain the multi-class classification data can be determined according to actual conditions, and the embodiment of the application is not limited to this.
In the embodiment of the application, for SKUs (historical commodity information) in an e-commerce warehouse, sales volume is the most important variable, positions, replenishment periods and the like placed in a picking area and a standby area are determined, so SKUs (historical commodity information) are pre-classified according to sales volume (sales volume information), SKUs in subclasses are further clustered by using a k-means++ algorithm for each SKU Band (classification data in the subclasses after classification is completed, the number of clustering points is a set value, and clustering center point size data is used as size data of the class, and volume cargo volume and the like are summary of the SKUs. And reducing the dimension of the original SKU data to tens of SKU subclasses through a clustering algorithm.
In the embodiment of the present application, the process of performing clustering processing on each class of classified data in the classes of classified data by the information processing device to obtain clustered historical commodity information includes: acquiring first class classification data from the multiple classes of classification data; acquiring preset quantity of commodity information from the first class of classified data; clustering the first class of classified data by taking a preset number of commodity information as a clustering center to obtain a first group of clustered historical commodity information; and obtaining clustered historical commodity information according to the multi-class classification data.
In the embodiment of the application, first class classification data can be obtained from multiple classes of classification data, preset quantity of commodity information is obtained from the first class classification data, the first class classification data is clustered by taking the preset quantity of commodity information as a clustering center, and historical commodity information after the first group of clustering is obtained; then obtaining second class classification data from the multi-class classification data, obtaining preset quantity of commodity information from the second class classification data, and clustering the second class classification data by taking the preset quantity of commodity information as a clustering center to obtain second group of clustered historical commodity information; …; acquiring the last class of classified data from the multiple classes of classified data, acquiring preset quantity of commodity information from the last class of classified data, and clustering the last class of classified data by taking the preset quantity of commodity information as a clustering center to obtain a last group of clustered historical commodity information; the first group of clustered historical commodity information, the second group of clustered historical commodity information, … and the last group of clustered historical commodity information are clustered historical commodity information.
In this embodiment of the present application, the preset number may be the number configured in the information processing apparatus, or the number that other devices transmit to the information processing apparatus, or the number that the information processing apparatus obtains in other manners, and the manner in which the specific information processing apparatus obtains the preset number may be determined according to the actual situation, which is not limited in this embodiment of the present application.
In the embodiment of the present application, an exemplary clustering process is performed on historical commodity information, and a flow of obtaining clustered historical commodity information is shown in fig. 2: classifying historical commodity information (SKU historical data) according to sales volume information (pre-classifying SKU historical data based on sales volume) to obtain multi-class classification data (Band a, band B, …); and clustering (K-means++ algorithm clustering) is carried out on each class of classified data in the multiple classes of classified data to obtain clustered historical commodity information (Band A, band 2, band B, band 1, band B, band 2 and …).
In this embodiment of the present application, a process of inputting historical shelf information, historical commodity information, and bin storage information into a in-bin layout determination model by an information processing apparatus to obtain in-bin layout information includes: under the condition that the layout mode information is in a non-favorable old mode, the historical commodity information and the bin storage information are input into a bin layout determining model, and the bin layout information is obtained.
Illustratively, taking the data of the already-used bin as an example, the information processing method in the present application is used to target the in-bin shelf. The warehouse is a hundred-cargo warehouse, the warehouse area is 28000 square meters, the current cargo amount in the warehouse is 682080, and the current storage plateau effect is 24.36. The final optimization results are shown in table 2:
TABLE 2
The shelves include medium-sized shelves (4 and 5 layers), stack-type shelves, small-sized shelves (5 and 6 layers), and beam-type shelves (4 and 8 layers). As can be seen from Table 2, the total area of the shelves is 27019 and the storable quantity is 803717, so that the current storage plateau effect is 29.75, which can be improved by 22.12% compared with the current storage plateau effect (24.36).
Exemplary, as shown in fig. 3: under the condition that an in-bin layout determining instruction is received, determining a template bin corresponding to a target bin and layout mode information (bin planning mode selection) according to the in-bin layout determining instruction, and acquiring historical commodity information (in-bin SKU historical data) in the template bin; performing stationarity detection (stationarity inspection) on the historical commodity information to obtain a detection result; under the condition that the detection result identifies that the historical commodity information is non-stabilized data (not passed), stabilizing the historical commodity information to obtain processed historical commodity information; under the condition that the processed historical commodity information is stabilized data, inputting the processed historical commodity information into a warehouse prediction model (fitting a Seasonal ARIMA model) to obtain warehouse storage information (supporting year capacity and energy storage); and under the condition that the detection result identifies that the historical commodity information is the stabilized data (passing), inputting the historical commodity information into a warehouse prediction model (fitting a Seasonal ARIMA model) to obtain warehouse storage information (supporting year capacity and energy storage). Under the condition that the layout mode information is an old mode (old goods shelf information is utilized), an old bin identifier is obtained; determining historical goods shelf information (old goods shelf information) used in the old bin corresponding to the old bin identification; clustering (data preprocessing) is carried out on historical commodity information (SKU historical data in a bin) to obtain clustered historical commodity information; inputting the historical goods shelf information, clustered historical goods information and bin storage information into a bin layout determining model (a bin process optimizing model) to obtain bin layout information (result output); under the condition that the layout mode information is in a non-beneficial old mode (old goods shelf information is not utilized), inputting historical goods information and bin storage information into a bin layout determining model (a bin process optimizing model) to obtain bin layout information (result output); and the target bin is laid out according to the in-bin layout information.
It can be understood that under the condition that the in-bin layout determining instruction is received and obtained, the information processing device inputs historical commodity information of the template bin corresponding to the in-bin layout determining instruction into the bin prediction model, the bin storage information of the target bin, namely the capacity and energy storage of the target bin, is determined by using the bin layout prediction model, and the in-bin layout information of the target bin is determined according to the historical commodity information under the constraint condition that the bin storage information is met by using the in-bin layout determining model, so that consumption caused by excessive investment is avoided, the in-bin layout information of accurate layout is obtained, the target bin is laid out according to the in-bin layout information, and the accuracy of the target bin in layout is improved.
Based on the same inventive concept as the above-described information processing method, the present embodiment provides an information processing apparatus 1, corresponding to an information processing method; fig. 4 is a schematic diagram of a composition structure of an information processing apparatus according to an embodiment of the present application, where the information processing apparatus 1 may include:
a determining unit 11, configured to determine, when an intra-bin layout determining instruction is received, a template bin corresponding to a target bin according to the intra-bin layout determining instruction; the template bin is a historical warehouse in an application state;
An acquiring unit 12, configured to acquire historical commodity information in the template bin;
an input unit 13, configured to input the historical commodity information into a bin prediction model to obtain bin storage information; inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain bin layout information of the target bin;
and a layout unit 14 for laying out the target bins according to the in-bin layout information.
In some embodiments of the present application, the apparatus further comprises a detection unit and a processing unit;
the detection unit is used for carrying out stability detection on the historical commodity information to obtain a detection result;
the processing unit is used for stabilizing the historical commodity information to obtain processed historical commodity information under the condition that the detection result indicates that the historical commodity information is non-stabilized data;
the input unit 13 is configured to input the processed historical commodity information into the bin prediction model to obtain the bin storage information.
In some embodiments of the present application, the input unit 13 is configured to input the historical commodity information into the bin prediction model to obtain the bin storage information when the detection result identifies that the historical commodity information is the stabilizing data.
In some embodiments of the present application, the acquiring unit 12 is configured to acquire layout mode information from the intra-bin layout determining instruction;
correspondingly, the acquiring unit 12 is configured to acquire an old bin identifier when the layout mode information is an old mode;
the determining unit 11 is configured to determine historical shelf information used in the old bin corresponding to the old bin identifier;
the input unit 13 is configured to input the historical shelf information, the historical commodity information and the bin storage information into an in-bin layout determination model, and obtain the in-bin layout information.
In some embodiments of the present application, the apparatus further comprises a clustering unit;
the clustering unit is used for carrying out clustering processing on the historical commodity information to obtain clustered historical commodity information;
the input unit 13 is configured to input the historical shelf information, the clustered historical commodity information and the bin storage information into a bin layout determining model, and obtain the bin layout information.
In some embodiments of the present application, the apparatus further comprises a classification unit;
the acquiring unit 12 is configured to acquire sales information corresponding to the historical commodity information;
The classifying unit is used for classifying the historical commodity information according to the sales volume information to obtain multi-class classified data;
and the clustering unit is used for respectively clustering each class of classified data in the classes of classified data to obtain clustered historical commodity information.
In some embodiments of the present application, the obtaining unit 12 is configured to obtain the first class classification data from the multiple classes of classification data; acquiring preset quantity of commodity information from the first class of classified data;
the clustering unit is used for clustering the first class of classified data by taking the preset number of commodity information as a clustering center to obtain a first group of clustered historical commodity information; and obtaining the clustered historical commodity information according to the multi-class classification data.
In some embodiments of the present application, the input unit 13 is configured to input the historical merchandise information and the bin storage information into an in-bin layout determining model to obtain the in-bin layout information when the layout mode information is in a non-legacy mode.
In some embodiments of the present application, the apparatus further comprises a training unit;
the acquiring unit 12 is configured to acquire historical sample commodity information in a sample bin and historical sample bin storage information corresponding to the historical sample commodity information;
The training unit is used for training an initial bin prediction model by utilizing the historical sample commodity information and the historical sample bin storage information to obtain the bin prediction model.
It should be noted that, in practical applications, the determining unit 11, the acquiring unit 12, the input unit 13, and the layout unit 14 may be implemented by the processor 15 on the information processing apparatus 1, specifically, a CPU (Central Processing Unit ), an MPU (Microprocessor Unit, microprocessor), a DSP (Digital Signal Processing, digital signal processor), or a field programmable gate array (FPGA, field Programmable Gate Array); the above-described data storage may be realized by the memory 16 on the information processing apparatus 1.
The embodiment of the application also provides an information processing apparatus 1, as shown in fig. 5, the information processing apparatus 1 includes: a processor 15, a memory 16, and a communication bus 17, the memory 16 being in communication with the processor 15 via the communication bus 17, the memory 16 storing a program executable by the processor 15, the information processing method as described above being executed by the processor 15 when the program is executed.
In practical applications, the Memory 16 may be a volatile Memory (RAM), such as a Random-Access Memory (RAM); or a nonvolatile Memory (non-volatile Memory), such as a Read-Only Memory (ROM), a flash Memory (flash Memory), a Hard Disk (HDD) or a Solid State Drive (SSD); or a combination of memories of the above kind and providing instructions and data to the processor 15.
The present embodiment provides a computer-readable storage medium having thereon a computer program which, when executed by the processor 15, implements the information processing method as described above.
It can be understood that under the condition that the in-bin layout determining instruction is received and obtained, the information processing device inputs historical commodity information of the template bin corresponding to the in-bin layout determining instruction into the bin prediction model, the bin storage information of the target bin, namely the capacity and energy storage of the target bin, is determined by using the bin layout prediction model, and the in-bin layout information of the target bin is determined according to the historical commodity information under the constraint condition that the bin storage information is met by using the in-bin layout determining model, so that consumption caused by excessive investment is avoided, the in-bin layout information of accurate layout is obtained, the target bin is laid out according to the in-bin layout information, and the accuracy of the target bin in layout is improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the present application.
Claims (12)
1. An information processing method, characterized in that the method comprises:
under the condition that an in-bin layout determining instruction is received, determining a template bin corresponding to a target bin according to the in-bin layout determining instruction, and acquiring historical commodity information in the template bin; the template bin is a historical warehouse in an application state;
Inputting the historical commodity information into a bin prediction model to obtain bin storage information;
and inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain the bin layout information of the target bin, and laying out the target bin according to the bin layout information.
2. The method of claim 1, wherein inputting the historical commodity information into a bin prediction model to obtain bin storage information comprises:
performing stability detection on the historical commodity information to obtain a detection result;
under the condition that the detection result identifies that the historical commodity information is non-stabilized data, stabilizing the historical commodity information to obtain processed historical commodity information;
and inputting the processed historical commodity information into the bin prediction model to obtain bin storage information.
3. The method according to claim 2, wherein after the stationarity detection is performed on the historical merchandise information, the method further comprises:
and under the condition that the detection result identifies that the historical commodity information is stabilized data, inputting the historical commodity information into the bin prediction model to obtain the bin storage information.
4. The method of claim 1, wherein the determining a template bin corresponding to a target bin according to the intra-bin layout determination instruction further comprises:
acquiring layout mode information from the in-bin layout determining instruction;
correspondingly, the step of inputting the historical commodity information and the bin storage information into an in-bin layout determining model to obtain the in-bin layout information of the target bin comprises the following steps:
under the condition that the layout mode information is an old utilization mode, an old bin identification is obtained;
determining historical goods shelf information used in the old bin corresponding to the old bin identifier;
and inputting the historical goods shelf information, the historical goods information and the bin storage information into a bin layout determining model to obtain the bin layout information.
5. The method of claim 4, wherein said inputting said historical shelf information, said historical merchandise information, and said bin storage information into an in-bin layout determination model to obtain said in-bin layout information comprises:
clustering the historical commodity information to obtain clustered historical commodity information;
and inputting the historical goods shelf information, the clustered historical goods information and the bin storage information into a bin layout determining model to obtain the bin layout information.
6. The method of claim 5, wherein clustering the historical merchandise information to obtain clustered historical merchandise information comprises:
acquiring sales information corresponding to the historical commodity information;
classifying the historical commodity information according to the sales volume information to obtain multi-class classification data;
and clustering each class of classified data in the classes of classified data respectively to obtain clustered historical commodity information.
7. The method of claim 6, wherein clustering each class of classified data to obtain the clustered historical commodity information comprises:
acquiring first class classification data from the multiple classes of classification data;
acquiring preset quantity of commodity information from the first class of classified data;
clustering the first class of classified data by taking the preset number of commodity information as a clustering center to obtain a first group of clustered historical commodity information; and obtaining the clustered historical commodity information according to the multi-class classification data.
8. The method of claim 4, wherein said inputting said historical shelf information, said historical merchandise information, and said bin storage information into an in-bin layout determination model to obtain said in-bin layout information comprises:
And under the condition that the layout mode information is in a non-favorable old mode, inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain the bin layout information.
9. The method of claim 1, wherein before inputting the historical commodity information into a bin prediction model to obtain bin storage information, the method further comprises:
acquiring historical sample commodity information in a sample bin and historical sample bin storage information corresponding to the historical sample commodity information;
and training an initial bin prediction model by utilizing the historical sample commodity information and the historical sample bin storage information to obtain the bin prediction model.
10. An information processing apparatus, characterized in that the apparatus comprises:
the determining unit is used for determining a template bin corresponding to the target bin according to the in-bin layout determining instruction under the condition that the in-bin layout determining instruction is received; the template bin is a historical warehouse in an application state;
the acquisition unit is used for acquiring historical commodity information in the template bin;
the input unit is used for inputting the historical commodity information into a bin prediction model to obtain bin storage information; inputting the historical commodity information and the bin storage information into a bin layout determining model to obtain bin layout information of the target bin;
And the layout unit is used for laying out the target bin according to the in-bin layout information.
11. An information processing apparatus, characterized in that the apparatus comprises:
a memory, a processor and a communication bus, the memory being in communication with the processor via the communication bus, the memory storing a program of information processing executable by the processor, the program of information processing, when executed, performing the method of any one of claims 1 to 9 by the processor.
12. A storage medium having stored thereon a computer program for application to an information processing apparatus, characterized in that the computer program, when executed by a processor, implements the method of any of claims 1 to 9.
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