CN116882902A - Storage management optimization method, system and storage medium based on purchase and sale information of wine - Google Patents
Storage management optimization method, system and storage medium based on purchase and sale information of wine Download PDFInfo
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Abstract
The invention discloses a storage management optimization method, a storage management optimization system and a storage medium based on purchase and sales information of a wine product, which comprise the steps of obtaining purchase and sales data of a target wine product in a preset time period, extracting sales characteristics and generating a corresponding sales characteristic time sequence; constructing a demand prediction model to predict market order demand and judging storage time of a target wine; acquiring optimal storage environment data according to storage time and target wine storage requirements, and clustering through historical order information to acquire the association degree between wine products; screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand. According to the method, an individualized warehousing scheme is provided for the wine according to the optimal warehousing environment data, the storage quality of the wine is guaranteed, and the efficiency of warehousing operation is improved by optimizing the storage allocation of the wine with high order relevance.
Description
Technical Field
The invention relates to the technical field of wine storage management, in particular to a storage management optimization method, a storage management optimization system and a storage medium based on wine purchase and sale information.
Background
With the improvement of living standard, wine market gradually resuscitates, wine enterprises increasingly compete for market, marketing modes are innovated, and wine storage and marketing management have important significance for resource allocation in the wine enterprise marketing process. In recent years, the capacity of wine is rapidly increased, but in the traditional logistics mode, the speed of receiving and delivering is far lower than the production speed of products. Traditional logistics mode and throughput are not matched gradually, and efficiency needs to be improved.
The sales of wines mainly depend on the sales mode of traditional dealers, and the disadvantage of this mode is that the wine manufacturer has very high dependence on dealers and has not strong control and viscosity management for end consumers. The common off-line sales mode is limited by space and time limitation, and can not finish sales anytime and anywhere; at present, online wine sales are commonly used by common electronic commerce, and some wine warehouses related to the electronic commerce lack of reasonable planning, and the operation level in each operation link is relatively low, so that the conditions of low warehouse area utilization rate and disordered arrangement of functional areas exist in part of wine warehouses, thereby greatly preventing the efficiency of wine cargo sorting and vehicle distribution. Therefore, reasonable layout planning is carried out on the wine warehouse, the distribution link of the wine warehouse is optimized, the operation efficiency of enterprises is improved, an important way for reducing the logistics cost of the wine warehouse is provided, a personalized warehouse management scheme is provided for the wine, and the intelligent refined partition of the warehouse position according to the environmental data and the marketing data is one of the problems to be solved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a warehouse management optimization method, a warehouse management optimization system and a storage medium based on purchase and sales information of a wine.
The first aspect of the invention provides a storage management optimization method based on purchase and sales information of wine products, which comprises the following steps:
acquiring purchase and sales data of a target wine in a preset time period, preprocessing the purchase and sales data, extracting sales characteristics of the target wine and generating a corresponding sales characteristic time sequence;
a demand prediction model is constructed, the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, and the storage time of the target wine is judged according to the target wine order demand;
acquiring optimal storage environment data according to the storage time and the target wine storage requirement, and clustering wine information through historical order information to acquire the association degree between the wine;
screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand.
In the scheme, the sales characteristics of the target wine are extracted to generate the corresponding sales characteristic time sequence, which is specifically as follows:
Searching purchase and sales data of a target wine in a preset time step in historical purchase and sales data by utilizing data searching, preprocessing the purchase and sales data, and generating a purchase and sales data sequence by combining an order time stamp;
selecting a target period based on a daily period, a monthly period, a quaternary period and a annual period, dividing the purchase and sale data sequence according to the target period to obtain purchase and sale data subsequences, and calculating purchase and sale data deviation of adjacent purchase and sale data subsequences;
a purchase and sales data deviation threshold is preset, purchase and sales data subsequences with the purchase and sales data deviation larger than the preset purchase and sales data deviation threshold are marked, and sales data change characteristics are extracted from the marked purchase and sales data subsequences;
obtaining wine sales influence factors by a big data method, calculating pearson correlation coefficients of each influence factor and the sales data change characteristics, screening the influence factors based on the pearson correlation coefficients, and setting evaluation indexes according to the screened influence factors;
and acquiring index features according to the evaluation indexes, generating sales features by combining the sales data change features, and constructing corresponding sales feature time sequence according to corresponding order time stamps.
In the scheme, a demand prediction model is constructed, and the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, specifically:
constructing a training sample set according to the sales characteristic time sequence, constructing a demand prediction model based on a Seq2Seq model, training by using the training sample set, and outputting a demand prediction model meeting preset standards;
acquiring index parameters in the current time step according to the evaluation index, carrying out normalization processing in combination with the current sales data of the target wine, importing the demand prediction model for feature coding, and setting attention weights of attention layers for acquiring different hidden state information in the demand prediction model;
according to the attention weight, carrying out weighted average on the input data, realizing summarization of the input data, decoding the summarized input data, and integrating the decoded hidden state information according to a time sequence;
and obtaining market order demand of the target wine after the preset time through feature dimension transformation of the output layer, obtaining the current warehouse storage of the target wine, and obtaining the warehouse storage time of the current warehouse target wine according to the predicted order demand.
In this scheme, according to storage time and target wine storage requirement obtain best storage environment data, specifically do:
according to the big data retrieval, standard storage environment information and fresh-keeping period of the target wine are obtained, the deviation rate of the current storage environment data of the target wine and the standard storage environment information is calculated, and whether the deviation rate is larger than a preset deviation rate threshold value or not is judged;
if the storage time is larger than the standard storage environment information, comprehensively evaluating the combination of the current storage environment data and the storage time, mapping the standard storage environment information and the fresh-keeping period corresponding to the target wine product to a low-dimensional space as standard data, and calculating the mean-squared Manhattan distance between the current storage environment data and the storage time and the standard data in the low-dimensional space;
evaluating the quality influence degree of the current storage environment data on the target wine according to the Manhattan distance, and regulating and controlling the current storage environment data when the quality influence degree is larger than a preset quality influence degree threshold;
and acquiring a corresponding mean squared Manhattan distance difference value according to the deviation value of the current quality influence degree, setting weight information, adjusting current storage environment data according to the weight information, and outputting optimal storage environment data corresponding to the storage time of the target wine.
In this scheme, cluster wine article information through history order information, obtain the association degree between the wine article, specifically be:
acquiring historical order information in a preset time period, extracting order features, matching wine information with the order features, generating a wine information sequence, and clustering the wine information sequence by using the order features by using a clustering algorithm;
randomly selecting a wine information sample from the wine information sequence as an initial clustering center, calculating the Euclidean distance from the residual wine information sample to the initial clustering center, and dividing the residual wine information to the initial clustering center closest to the residual wine information sample to generate an initial clustering result;
constructing a loss function according to the clustering error in the clustering process, performing iterative training based on the loss function, acquiring the mean value of various clusters in each iteration as a new clustering center, and acquiring a clustering result after the clustering is finished;
and taking the wine information in the same class of clusters as a high-association wine, and acquiring the Euclidean distance of the high-association wine as the association.
In this scheme, bind the combination with target wine article and storage associated wine article, carry out the storage optimization according to order demand, specifically do:
Obtaining optimal storage environment data of various types of wine products in a target storage environment, obtaining other types of wine products meeting similarity standards by using similarity calculation, and selecting n other types of wine products with highest similarity for marking;
obtaining a high-relevancy wine of the target wine, screening other types of marked wine in the high-relevancy wine, obtaining other types of wine with highest relevancy according to a screening result, and binding and combining the wine with the target wine to generate a wine combination;
acquiring order demand of the wine combination after preset time, acquiring storage occupation proportion of the wine combination according to the calculated order demand ratio, acquiring current storage of each wine in the wine combination, and acquiring the warehouse-out time of the wine combination according to the current storage;
and when the ex-warehouse time is greater than a preset ex-warehouse time threshold, storing and adjusting the long-ex-warehouse time wine in the wine combination.
The second aspect of the present invention also provides a warehouse management optimization system based on purchase and sales information of wine, the system comprising: the storage comprises a storage and a processor, wherein the storage comprises a storage management optimizing method program based on the purchase and sale information of the wine, and the storage management optimizing method program based on the purchase and sale information of the wine realizes the following steps when being executed by the processor:
Acquiring purchase and sales data of a target wine in a preset time period, preprocessing the purchase and sales data, extracting sales characteristics of the target wine and generating a corresponding sales characteristic time sequence;
a demand prediction model is constructed, the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, and the storage time of the target wine is judged according to the target wine order demand;
acquiring optimal storage environment data according to the storage time and the target wine storage requirement, and clustering wine information through historical order information to acquire the association degree between the wine;
screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand.
The third aspect of the present invention also provides a computer readable storage medium, wherein the computer readable storage medium includes a storage management optimizing method program based on the purchase and sale information of the wine, and when the storage management optimizing method program based on the purchase and sale information of the wine is executed by a processor, the steps of the storage management optimizing method based on the purchase and sale information of the wine are realized.
The invention discloses a storage management optimization method, a storage management optimization system and a storage medium based on purchase and sales information of a wine product, which comprise the steps of obtaining purchase and sales data of a target wine product in a preset time period, extracting sales characteristics and generating a corresponding sales characteristic time sequence; constructing a demand prediction model to predict market order demand and judging storage time of a target wine; acquiring optimal storage environment data according to storage time and target wine storage requirements, and clustering through historical order information to acquire the association degree between wine products; screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand. According to the method, an individualized warehousing scheme is provided for the wine according to the optimal warehousing environment data, the storage quality of the wine is guaranteed, and the efficiency of warehousing operation is improved by optimizing the storage allocation of the wine with high order relevance.
Drawings
FIG. 1 shows a flow chart of a method of optimizing warehouse management based on wine purchase and sales information in accordance with the present invention;
FIG. 2 illustrates a flow chart of the present invention for constructing a demand prediction model to predict demand for an order;
FIG. 3 shows a flow chart of the present application for storage optimization of a target wine bundle with a storage-associated wine bundle;
fig. 4 shows a block diagram of a warehouse management optimization system based on wine purchase and sales information in accordance with the present application.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced in other ways than those described herein, and therefore the scope of the present application is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flowchart of a method for optimizing warehouse management based on purchase and sale information of a wine product according to the present application.
As shown in fig. 1, the first aspect of the present application provides a storage management optimization method based on purchase and sale information of a wine, which includes:
s102, acquiring purchase and sale data of a target wine in a preset time period, preprocessing the purchase and sale data, extracting sales characteristics of the target wine and generating a corresponding sales characteristic time sequence;
S104, constructing a demand prediction model, predicting market order demand of a target wine after preset time according to the demand prediction model and a sales characteristic time sequence, and judging storage time of the target wine according to the target wine order demand;
s106, obtaining optimal storage environment data according to the storage time and the target wine storage requirement, clustering wine information through historical order information, and obtaining the association degree between the wine;
s108, screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand.
It should be noted that, the purchase and sale data of the target wine in the preset time step is searched in the historical purchase and sale data by utilizing data search, the purchase and sale data include but are not limited to sales wine, production date, sales price, sales quantity, sales batch number and the like, the purchase and sale data are preprocessed, and a purchase and sale data sequence is generated by combining an order time stamp; the wine products are also affected by factors such as temperature, advertising promotion, holidays, weather, economic environment and the like, the seasonality and the periodicity are very remarkable, a target period is selected based on a daily period, a monthly period, a quaternary period and a annual period, the purchase and sale data sequence is divided according to the target period, purchase and sale data subsequences are obtained, and purchase and sale data deviation of adjacent purchase and sale data subsequences is calculated; a purchase and sales data deviation threshold is preset, purchase and sales data subsequences with the purchase and sales data deviation larger than the preset purchase and sales data deviation threshold are marked, and sales data change characteristics are extracted from the marked purchase and sales data subsequences; obtaining wine sales influence factors by a big data method, calculating pearson correlation coefficients of each influence factor and the sales data change characteristics, screening the influence factors based on the pearson correlation coefficients, setting evaluation indexes according to the screened influence factors, and obtaining index factors causing sales data fluctuation; and acquiring index features according to the evaluation indexes, generating sales features by combining the sales data change features, and constructing corresponding sales feature time sequence according to corresponding order time stamps.
FIG. 2 illustrates a flow chart of the present invention for constructing a demand prediction model to predict demand for an order.
According to the embodiment of the invention, a demand prediction model is constructed, and the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, specifically:
s202, constructing a training sample set according to the sales characteristic time sequence, constructing a demand prediction model based on a Seq2Seq model, training by using the training sample set, and outputting a demand prediction model meeting preset standards;
s204, acquiring index parameters in the current time step according to the evaluation index, carrying out normalization processing in combination with the current sales data of the target wine, importing the demand prediction model for feature coding, and setting attention weights of attention layers for acquiring different hidden state information in the demand prediction model;
s206, carrying out weighted average on the input data according to the attention weight, collecting the input data, decoding the collected input data, and integrating the decoded hidden state information according to a time sequence;
s208, obtaining market order demand of the target wine after the preset time through feature dimension transformation of the output layer, obtaining current warehouse storage of the target wine, and obtaining warehouse storage time of the current warehouse target wine according to the predicted order demand.
In order to prevent data omission caused by overlong sales data time sequence, the original Seq2Seq model is a GRU encoder and a GRU decoder, the GRU encoder and the GRU decoder are replaced by LSTM units, the encoder and the decoder are arranged by using a three-layer LSTM network, the final hidden state is obtained by the encoder, input data is compressed into vector features with fixed length, and the vector features are decoded and predicted by the decoder. And the attention-drawing mechanism extracts relevant information through self-adaptive attention to the hidden state of the corresponding encoder, so that the order demand is predicted for the decoder better. The attention distribution is calculated on the input data of the demand prediction model, and the weighted average of the input data is calculated from the attention distribution, thereby summarizing the input data.
It should be noted that, obtaining attribute characteristics of a target wine, including brewing modes, wine varieties and the like, dividing according to the attribute characteristics, including but not limited to red wine, champagne, whiskey, sake, white spirit and the like, retrieving and obtaining standard storage environment information and fresh-keeping period of the target wine according to big data based on the attribute characteristics, wherein the standard storage environment information includes average temperature, average humidity, average illumination intensity, average air flow speed and the like, the fresh-keeping period can be one of a wine quality guarantee period or an optimal drinking period, calculating the deviation rate of current storage environment data of the target wine and the standard storage environment information, and judging whether the deviation rate is larger than a preset deviation rate threshold; if the storage time is larger than the standard storage environment information, comprehensively evaluating the combination of the current storage environment data and the storage time, mapping the standard storage environment information and the fresh-keeping period corresponding to the target wine product to a low-dimensional space as standard data, and calculating the mean-squared Manhattan distance between the current storage environment data and the storage time and the standard data in the low-dimensional space; evaluating the quality influence degree of the current storage environment data on the target wine according to the Manhattan distance, and regulating and controlling the current storage environment data when the quality influence degree is larger than a preset quality influence degree threshold; and acquiring a corresponding mean squared Manhattan distance difference value according to the deviation value of the current quality influence degree, setting weight information, adjusting current storage environment data according to the weight information, and outputting optimal storage environment data corresponding to the storage time of the target wine.
FIG. 3 shows a flow chart of the present invention for storage optimization of a target wine bundle in combination with a storage-associated wine bundle.
According to the embodiment of the invention, the target wine is bound and combined with the wine associated with the storage, and the storage optimization is carried out according to the order demand, specifically:
s302, obtaining optimal storage environment data of various types of wine products in a target storage environment, obtaining other types of wine products meeting similarity standards by using similarity calculation, and selecting n other types of wine products with highest similarity for marking;
s304, obtaining a high-association degree wine of the target wine, screening other types of wine marked in the high-association degree wine, obtaining other types of wine with highest association degree according to a screening result, and binding and combining the wine with the target wine to generate a wine combination;
s306, acquiring order demand of the wine combination after the preset time, acquiring storage occupation proportion of the wine combination according to the calculated order demand ratio, acquiring current storage of each wine in the wine combination, and acquiring the delivery time of the wine combination according to the current storage;
and S308, when the ex-warehouse time is greater than a preset ex-warehouse time threshold, storing and adjusting the long-ex-warehouse time wine in the wine combination.
The wine combination may be 3 or more than 3 kinds of wine; when the wine with long ex-warehouse time in the wine combination is subjected to storage position adjustment, the wine with relatively long ex-warehouse time in the wine combination is selected for storage position adjustment, so that the ex-warehouse efficiency of the wine combination in the same order is provided, and the workload of adjusting the storage position is reduced by selecting one to perform adjustment, so that the wine with the adjusted storage position is stored under the same optimal storage environment data.
Acquiring historical order information in a preset time period, extracting order features, matching wine information with the order features, generating a wine information sequence, and clustering the wine information sequence by using the order features by using a clustering algorithm; randomly selecting a wine information sample from the wine information sequence as an initial clustering center, calculating the Euclidean distance from the residual wine information sample to the initial clustering center, and dividing the residual wine information to the initial clustering center closest to the residual wine information sample to generate an initial clustering result; constructing a loss function according to the clustering error in the clustering process, performing iterative training based on the loss function, acquiring the mean value of various clusters in each iteration as a new clustering center, and acquiring a clustering result after the clustering is finished; and taking the wine information in the same class of clusters as a high-association wine, and acquiring the Euclidean distance of the high-association wine as the association.
According to the embodiment of the invention, a promotion scheme is generated according to the inventory information of the wine, and specifically comprises the following steps:
obtaining n corresponding wine combinations of target wine, obtaining market order demand of each wine in the wine combinations according to a demand prediction model, and obtaining wine information with the largest market order demand and the corresponding wine combinations;
reading stock information according to wine information with the largest market order demand, calculating proportion information of the stock information of the target wine, and formulating a sales promotion scheme of the wine combination according to the proportion information;
acquiring inventory change conditions corresponding to wine information with the largest market order demand based on the sales promotion scheme, and judging whether the inventory quantity of the preset time is lower than a preset inventory threshold according to the inventory change conditions;
if the number of the wine sets is lower than the required number of the market order, the sales promotion scheme is adjusted at the preset time, wine sets meeting the required number standard of the market order in the n wine sets are obtained again, and the wine set information with the largest stock quantity and the corresponding wine set are selected to generate a new sales promotion scheme.
Fig. 4 shows a block diagram of a warehouse management optimization system based on wine purchase and sales information in accordance with the present invention.
The second aspect of the present invention also provides a warehouse management optimization system 4 based on purchase and sales information of wine, the system comprising: the storage 41 and the processor 42, wherein the storage comprises a storage management optimizing method program based on the purchase and sale information of the wine, and the storage management optimizing method program based on the purchase and sale information of the wine realizes the following steps when being executed by the processor:
Acquiring purchase and sales data of a target wine in a preset time period, preprocessing the purchase and sales data, extracting sales characteristics of the target wine and generating a corresponding sales characteristic time sequence;
a demand prediction model is constructed, the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, and the storage time of the target wine is judged according to the target wine order demand;
acquiring optimal storage environment data according to the storage time and the target wine storage requirement, and clustering wine information through historical order information to acquire the association degree between the wine;
screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand.
It should be noted that, the purchase and sale data of the target wine in the preset time step is searched in the historical purchase and sale data by utilizing data search, the purchase and sale data include but are not limited to sales wine, production date, sales price, sales quantity, sales batch number and the like, the purchase and sale data are preprocessed, and a purchase and sale data sequence is generated by combining an order time stamp; the wine products are also affected by factors such as temperature, advertising promotion, holidays, weather, economic environment and the like, the seasonality and the periodicity are very remarkable, a target period is selected based on a daily period, a monthly period, a quaternary period and a annual period, the purchase and sale data sequence is divided according to the target period, purchase and sale data subsequences are obtained, and purchase and sale data deviation of adjacent purchase and sale data subsequences is calculated; a purchase and sales data deviation threshold is preset, purchase and sales data subsequences with the purchase and sales data deviation larger than the preset purchase and sales data deviation threshold are marked, and sales data change characteristics are extracted from the marked purchase and sales data subsequences; obtaining wine sales influence factors by a big data method, calculating pearson correlation coefficients of each influence factor and the sales data change characteristics, screening the influence factors based on the pearson correlation coefficients, setting evaluation indexes according to the screened influence factors, and obtaining index factors causing sales data fluctuation; and acquiring index features according to the evaluation indexes, generating sales features by combining the sales data change features, and constructing corresponding sales feature time sequence according to corresponding order time stamps.
According to the embodiment of the invention, a demand prediction model is constructed, and the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, specifically:
constructing a training sample set according to the sales characteristic time sequence, constructing a demand prediction model based on a Seq2Seq model, training by using the training sample set, and outputting a demand prediction model meeting preset standards;
acquiring index parameters in the current time step according to the evaluation index, carrying out normalization processing in combination with the current sales data of the target wine, importing the demand prediction model for feature coding, and setting attention weights of attention layers for acquiring different hidden state information in the demand prediction model;
according to the attention weight, carrying out weighted average on the input data, realizing summarization of the input data, decoding the summarized input data, and integrating the decoded hidden state information according to a time sequence;
and obtaining market order demand of the target wine after the preset time through feature dimension transformation of the output layer, obtaining the current warehouse storage of the target wine, and obtaining the warehouse storage time of the current warehouse target wine according to the predicted order demand.
In order to prevent data omission caused by overlong sales data time sequence, the original Seq2Seq model is a GRU encoder and a GRU decoder, the GRU encoder and the GRU decoder are replaced by LSTM units, the encoder and the decoder are arranged by using a three-layer LSTM network, the final hidden state is obtained by the encoder, input data is compressed into vector features with fixed length, and the vector features are decoded and predicted by the decoder. And the attention-drawing mechanism extracts relevant information through self-adaptive attention to the hidden state of the corresponding encoder, so that the order demand is predicted for the decoder better. The attention distribution is calculated on the input data of the demand prediction model, and the weighted average of the input data is calculated from the attention distribution, thereby summarizing the input data.
It should be noted that, obtaining attribute characteristics of a target wine, including brewing modes, wine varieties and the like, dividing according to the attribute characteristics, including but not limited to red wine, champagne, whiskey, sake, white spirit and the like, retrieving and obtaining standard storage environment information and fresh-keeping period of the target wine according to big data based on the attribute characteristics, wherein the standard storage environment information includes average temperature, average humidity, average illumination intensity, average air flow speed and the like, the fresh-keeping period can be one of a wine quality guarantee period or an optimal drinking period, calculating the deviation rate of current storage environment data of the target wine and the standard storage environment information, and judging whether the deviation rate is larger than a preset deviation rate threshold; if the storage time is larger than the standard storage environment information, comprehensively evaluating the combination of the current storage environment data and the storage time, mapping the standard storage environment information and the fresh-keeping period corresponding to the target wine product to a low-dimensional space as standard data, and calculating the mean-squared Manhattan distance between the current storage environment data and the storage time and the standard data in the low-dimensional space; evaluating the quality influence degree of the current storage environment data on the target wine according to the Manhattan distance, and regulating and controlling the current storage environment data when the quality influence degree is larger than a preset quality influence degree threshold; and acquiring a corresponding mean squared Manhattan distance difference value according to the deviation value of the current quality influence degree, setting weight information, adjusting current storage environment data according to the weight information, and outputting optimal storage environment data corresponding to the storage time of the target wine.
According to the embodiment of the invention, the target wine is bound and combined with the wine associated with the storage, and the storage optimization is carried out according to the order demand, specifically:
obtaining optimal storage environment data of various types of wine products in a target storage environment, obtaining other types of wine products meeting similarity standards by using similarity calculation, and selecting n other types of wine products with highest similarity for marking;
obtaining a high-relevancy wine of the target wine, screening other types of marked wine in the high-relevancy wine, obtaining other types of wine with highest relevancy according to a screening result, and binding and combining the wine with the target wine to generate a wine combination;
acquiring order demand of the wine combination after preset time, acquiring storage occupation proportion of the wine combination according to the calculated order demand ratio, acquiring current storage of each wine in the wine combination, and acquiring the warehouse-out time of the wine combination according to the current storage;
and when the ex-warehouse time is greater than a preset ex-warehouse time threshold, storing and adjusting the long-ex-warehouse time wine in the wine combination.
The wine combination may be 3 or more than 3 kinds of wine; when the wine with long ex-warehouse time in the wine combination is subjected to storage position adjustment, the wine with relatively long ex-warehouse time in the wine combination is selected for storage position adjustment, so that the ex-warehouse efficiency of the wine combination in the same order is provided, and the workload of adjusting the storage position is reduced by selecting one to perform adjustment, so that the wine with the adjusted storage position is stored under the same optimal storage environment data.
Acquiring historical order information in a preset time period, extracting order features, matching wine information with the order features, generating a wine information sequence, and clustering the wine information sequence by using the order features by using a clustering algorithm; randomly selecting a wine information sample from the wine information sequence as an initial clustering center, calculating the Euclidean distance from the residual wine information sample to the initial clustering center, and dividing the residual wine information to the initial clustering center closest to the residual wine information sample to generate an initial clustering result; constructing a loss function according to the clustering error in the clustering process, performing iterative training based on the loss function, acquiring the mean value of various clusters in each iteration as a new clustering center, and acquiring a clustering result after the clustering is finished; and taking the wine information in the same class of clusters as a high-association wine, and acquiring the Euclidean distance of the high-association wine as the association.
The third aspect of the present invention also provides a computer readable storage medium, wherein the computer readable storage medium includes a storage management optimizing method program based on the purchase and sale information of the wine, and when the storage management optimizing method program based on the purchase and sale information of the wine is executed by a processor, the steps of the storage management optimizing method based on the purchase and sale information of the wine are realized.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A storage management optimization method based on wine purchase and sale information is characterized by comprising the following steps of
Acquiring purchase and sales data of a target wine in a preset time period, preprocessing the purchase and sales data, extracting sales characteristics of the target wine and generating a corresponding sales characteristic time sequence;
a demand prediction model is constructed, the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, and the storage time of the target wine is judged according to the target wine order demand;
acquiring optimal storage environment data according to the storage time and the target wine storage requirement, and clustering wine information through historical order information to acquire the association degree between the wine;
screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand.
2. The method for optimizing warehouse management based on the purchase and sales information of wine according to claim 1, wherein the method for extracting sales characteristics of the target wine to generate a corresponding sales characteristic time sequence is specifically as follows:
searching purchase and sales data of a target wine in a preset time step in historical purchase and sales data by utilizing data searching, preprocessing the purchase and sales data, and generating a purchase and sales data sequence by combining an order time stamp;
selecting a target period based on a daily period, a monthly period, a quaternary period and a annual period, dividing the purchase and sale data sequence according to the target period to obtain purchase and sale data subsequences, and calculating purchase and sale data deviation of adjacent purchase and sale data subsequences;
a purchase and sales data deviation threshold is preset, purchase and sales data subsequences with the purchase and sales data deviation larger than the preset purchase and sales data deviation threshold are marked, and sales data change characteristics are extracted from the marked purchase and sales data subsequences;
obtaining wine sales influence factors by a big data method, calculating pearson correlation coefficients of each influence factor and the sales data change characteristics, screening the influence factors based on the pearson correlation coefficients, and setting evaluation indexes according to the screened influence factors;
And acquiring index features according to the evaluation indexes, generating sales features by combining the sales data change features, and constructing corresponding sales feature time sequence according to corresponding order time stamps.
3. The method for optimizing warehouse management based on the purchase and sales information of wine according to claim 1, wherein a demand prediction model is constructed, and market order demand of target wine after a preset time is predicted according to the demand prediction model and a sales characteristic time sequence, specifically:
constructing a training sample set according to the sales characteristic time sequence, constructing a demand prediction model based on a Seq2Seq model, training by using the training sample set, and outputting a demand prediction model meeting preset standards;
acquiring index parameters in the current time step according to the evaluation index, carrying out normalization processing in combination with the current sales data of the target wine, importing the demand prediction model for feature coding, and setting attention weights of attention layers for acquiring different hidden state information in the demand prediction model;
according to the attention weight, carrying out weighted average on the input data, realizing summarization of the input data, decoding the summarized input data, and integrating the decoded hidden state information according to a time sequence;
And obtaining market order demand of the target wine after the preset time through feature dimension transformation of the output layer, obtaining the current warehouse storage of the target wine, and obtaining the warehouse storage time of the current warehouse target wine according to the predicted order demand.
4. The method for optimizing storage management based on wine purchase and sale information according to claim 1, wherein the method is characterized in that the optimal storage environment data is obtained according to the storage time and the target wine storage requirement, and specifically comprises the following steps:
according to the big data retrieval, standard storage environment information and fresh-keeping period of the target wine are obtained, the deviation rate of the current storage environment data of the target wine and the standard storage environment information is calculated, and whether the deviation rate is larger than a preset deviation rate threshold value or not is judged;
if the storage time is larger than the standard storage environment information, comprehensively evaluating the combination of the current storage environment data and the storage time, mapping the standard storage environment information and the fresh-keeping period corresponding to the target wine product to a low-dimensional space as standard data, and calculating the mean-squared Manhattan distance between the current storage environment data and the storage time and the standard data in the low-dimensional space;
evaluating the quality influence degree of the current storage environment data on the target wine according to the Manhattan distance, and regulating and controlling the current storage environment data when the quality influence degree is larger than a preset quality influence degree threshold;
And acquiring a corresponding mean squared Manhattan distance difference value according to the deviation value of the current quality influence degree, setting weight information, adjusting current storage environment data according to the weight information, and outputting optimal storage environment data corresponding to the storage time of the target wine.
5. The method for optimizing warehouse management based on the purchase and sale information of wine according to claim 1, wherein the correlation degree between wine is obtained by clustering the information of wine through the history order information, specifically:
acquiring historical order information in a preset time period, extracting order features, matching wine information with the order features, generating a wine information sequence, and clustering the wine information sequence by using the order features by using a clustering algorithm;
randomly selecting a wine information sample from the wine information sequence as an initial clustering center, calculating the Euclidean distance from the residual wine information sample to the initial clustering center, and dividing the residual wine information to the initial clustering center closest to the residual wine information sample to generate an initial clustering result;
constructing a loss function according to the clustering error in the clustering process, performing iterative training based on the loss function, acquiring the mean value of various clusters in each iteration as a new clustering center, and acquiring a clustering result after the clustering is finished;
And taking the wine information in the same class of clusters as a high-association wine, and acquiring the Euclidean distance of the high-association wine as the association.
6. The method for optimizing storage management based on wine purchase and sale information according to claim 1, wherein the target wine is bound and combined with the wine associated with the storage, and the storage optimization is performed according to the order demand, specifically:
obtaining optimal storage environment data of various types of wine products in a target storage environment, obtaining other types of wine products meeting similarity standards by using similarity calculation, and selecting n other types of wine products with highest similarity for marking;
obtaining a high-relevancy wine of the target wine, screening other types of marked wine in the high-relevancy wine, obtaining other types of wine with highest relevancy according to a screening result, and binding and combining the wine with the target wine to generate a wine combination;
acquiring order demand of the wine combination after preset time, acquiring storage occupation proportion of the wine combination according to the calculated order demand ratio, acquiring current storage of each wine in the wine combination, and acquiring the warehouse-out time of the wine combination according to the current storage;
and when the ex-warehouse time is greater than a preset ex-warehouse time threshold, storing and adjusting the long-ex-warehouse time wine in the wine combination.
7. A warehouse management optimization system based on wine purchase and sale information, the system comprising: the storage comprises a storage and a processor, wherein the storage comprises a storage management optimizing method program based on the purchase and sale information of the wine, and the storage management optimizing method program based on the purchase and sale information of the wine realizes the following steps when being executed by the processor:
acquiring purchase and sales data of a target wine in a preset time period, preprocessing the purchase and sales data, extracting sales characteristics of the target wine and generating a corresponding sales characteristic time sequence;
a demand prediction model is constructed, the market order demand of the target wine after the preset time is predicted according to the demand prediction model and the sales characteristic time sequence, and the storage time of the target wine is judged according to the target wine order demand;
acquiring optimal storage environment data according to the storage time and the target wine storage requirement, and clustering wine information through historical order information to acquire the association degree between the wine;
screening storage-position-related wine products according to the optimal storage environment data of the target wine products and the association degree between the wine products, binding and combining the target wine products and the storage-position-related wine products, and carrying out storage optimization according to the order demand.
8. The system for optimizing storage management based on wine purchase and sale information according to claim 7, wherein the optimal storage environment data is obtained according to the storage time and the target wine storage requirement, specifically:
according to the big data retrieval, standard storage environment information and fresh-keeping period of the target wine are obtained, the deviation rate of the current storage environment data of the target wine and the standard storage environment information is calculated, and whether the deviation rate is larger than a preset deviation rate threshold value or not is judged;
if the storage time is larger than the standard storage environment information, comprehensively evaluating the combination of the current storage environment data and the storage time, mapping the standard storage environment information and the fresh-keeping period corresponding to the target wine product to a low-dimensional space as standard data, and calculating the mean-squared Manhattan distance between the current storage environment data and the storage time and the standard data in the low-dimensional space;
evaluating the quality influence degree of the current storage environment data on the target wine according to the Manhattan distance, and regulating and controlling the current storage environment data when the quality influence degree is larger than a preset quality influence degree threshold;
and acquiring a corresponding mean squared Manhattan distance difference value according to the deviation value of the current quality influence degree, setting weight information, adjusting current storage environment data according to the weight information, and outputting optimal storage environment data corresponding to the storage time of the target wine.
9. The storage management optimization system based on the purchase and sales information of the wine products according to claim 7, wherein the target wine products and the wine products associated with the storage are bound and combined, and the storage optimization is performed according to the order demand, specifically:
obtaining optimal storage environment data of various types of wine products in a target storage environment, obtaining other types of wine products meeting similarity standards by using similarity calculation, and selecting n other types of wine products with highest similarity for marking;
obtaining a high-relevancy wine of the target wine, screening other types of marked wine in the high-relevancy wine, obtaining other types of wine with highest relevancy according to a screening result, and binding and combining the wine with the target wine to generate a wine combination;
acquiring order demand of the wine combination after preset time, acquiring storage occupation proportion of the wine combination according to the calculated order demand ratio, acquiring current storage of each wine in the wine combination, and acquiring the warehouse-out time of the wine combination according to the current storage;
and when the ex-warehouse time is greater than a preset ex-warehouse time threshold, storing and adjusting the long-ex-warehouse time wine in the wine combination.
10. A computer-readable storage medium, characterized by: the storage medium readable by a computer comprises a storage management optimizing method program based on the purchase and sale information of the wine, and when the storage management optimizing method program based on the purchase and sale information of the wine is executed by a processor, the storage management optimizing method steps based on the purchase and sale information of the wine are realized.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117593044A (en) * | 2024-01-18 | 2024-02-23 | 青岛网信信息科技有限公司 | Dual-angle marketing campaign effect prediction method, medium and system |
| CN117726277A (en) * | 2024-02-08 | 2024-03-19 | 酒仙网络科技股份有限公司 | Wine information management method and system based on intelligent storage |
| CN117875841A (en) * | 2024-01-22 | 2024-04-12 | 广州市南粤物流有限公司 | Warehouse system for intelligent logistics |
| CN117593044B (en) * | 2024-01-18 | 2024-05-31 | 青岛网信信息科技有限公司 | Dual-angle marketing campaign effect prediction method, medium and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011096164A (en) * | 2009-11-02 | 2011-05-12 | Seiko Epson Corp | Information processing system, information processor and information processing method |
| CN109559163A (en) * | 2018-11-16 | 2019-04-02 | 广州麦优网络科技有限公司 | A kind of model building method and sales forecasting method based on machine learning |
| CN109886747A (en) * | 2019-02-22 | 2019-06-14 | 网易（杭州）网络有限公司 | Method for Sales Forecast method, medium, device and calculating equipment |
| CN114387037A (en) * | 2022-01-18 | 2022-04-22 | 华院分析技术(上海)有限公司 | Retail commodity sales prediction method based on deep learning |
| CN114971748A (en) * | 2022-07-27 | 2022-08-30 | 阿里健康科技(中国)有限公司 | Prediction data generation method, model training method, computer device, and storage medium |
| US20220292308A1 (en) * | 2021-03-12 | 2022-09-15 | DataRobot, Inc. | Systems and methods for time series modeling |
| CN116596582A (en) * | 2023-05-29 | 2023-08-15 | 深圳闪新软件技术服务有限公司 | Marketing information prediction method and device based on big data |
-
2023
- 2023-09-06 CN CN202311139687.2A patent/CN116882902B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011096164A (en) * | 2009-11-02 | 2011-05-12 | Seiko Epson Corp | Information processing system, information processor and information processing method |
| CN109559163A (en) * | 2018-11-16 | 2019-04-02 | 广州麦优网络科技有限公司 | A kind of model building method and sales forecasting method based on machine learning |
| CN109886747A (en) * | 2019-02-22 | 2019-06-14 | 网易（杭州）网络有限公司 | Method for Sales Forecast method, medium, device and calculating equipment |
| US20220292308A1 (en) * | 2021-03-12 | 2022-09-15 | DataRobot, Inc. | Systems and methods for time series modeling |
| CN114387037A (en) * | 2022-01-18 | 2022-04-22 | 华院分析技术(上海)有限公司 | Retail commodity sales prediction method based on deep learning |
| CN114971748A (en) * | 2022-07-27 | 2022-08-30 | 阿里健康科技(中国)有限公司 | Prediction data generation method, model training method, computer device, and storage medium |
| CN116596582A (en) * | 2023-05-29 | 2023-08-15 | 深圳闪新软件技术服务有限公司 | Marketing information prediction method and device based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 李珍萍;卜晓奇;陈星艺: "基于商品关联度的智能仓库储位分配问题研究", 数学的实践与认识, no. 005 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117593044A (en) * | 2024-01-18 | 2024-02-23 | 青岛网信信息科技有限公司 | Dual-angle marketing campaign effect prediction method, medium and system |
| CN117593044B (en) * | 2024-01-18 | 2024-05-31 | 青岛网信信息科技有限公司 | Dual-angle marketing campaign effect prediction method, medium and system |
| CN117875841A (en) * | 2024-01-22 | 2024-04-12 | 广州市南粤物流有限公司 | Warehouse system for intelligent logistics |
| CN117726277A (en) * | 2024-02-08 | 2024-03-19 | 酒仙网络科技股份有限公司 | Wine information management method and system based on intelligent storage |
| CN117726277B (en) * | 2024-02-08 | 2024-04-26 | 酒仙网络科技股份有限公司 | Wine information management method and system based on intelligent storage |
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