CN116976789A - Intelligent logistics warehouse system resource allocation method based on big data - Google Patents
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Abstract
The invention relates to the technical field of warehouse resource allocation. The invention relates to a resource allocation method of an intelligent logistics warehouse system based on big data. The method comprises the following steps: s1, collecting cargo information in a warehouse and cargo circulation information in the market; s2, storing the information acquired in the step S1. According to the method, the sales conditions of the goods in each area are obtained according to the whole and partial information of the market, the sales conditions of the goods in each area in the past year are recorded, the inflection point position of the sales quantity change is analyzed, factors and influences influencing the sales of the goods are stored, the sales conditions of the goods in the later period are predicted according to the information, the goods storage of each warehouse is managed and allocated according to the prediction results, the situation that the warehouses do not have locally needed goods is prevented, meanwhile, the phenomenon that a large amount of goods are accumulated in the warehouses and cannot be processed is avoided, meanwhile, the accuracy of each time is judged, and the accuracy is optimized according to the accuracy, so that the accuracy is improved.
Description
Technical Field
The invention relates to the technical field of warehouse resource allocation, in particular to a large data-based intelligent logistics warehouse system resource allocation method.
Background
The online shopping is the most mainstream shopping method in the society, after the consumer places an order on a shopping platform, the goods are transported by a logistics company, the way connects the areas of the south and the north, compared with the prior shopping way, the way has more types of goods which can be selected by the consumer, but the way consumes a great deal of time, manpower and material resources, and the consumer has the possibility of returning goods or changing goods after receiving the goods, so the consumed time, manpower and material resources are further consumed, so that the way is now to arrange warehouses in various places and store certain goods in the warehouses, but the way can not allocate the inventory of the warehouses according to the specific situations of the market and the area, and therefore, the intelligent logistics warehouse system resource allocation method based on big data is provided.
Disclosure of Invention
The invention aims to provide a large data-based intelligent logistics warehouse system resource allocation method so as to solve the problems in the background technology.
In order to achieve the above purpose, the method for configuring the resources of the intelligent logistics warehouse system based on big data is provided, and comprises the following steps:
s1, collecting cargo information in a warehouse and cargo circulation information in the market;
s2, storing the information acquired in the step S1;
s3, when the sales volume of the goods in the market is changed, analyzing factors which cause the sales volume change, predicting the influence of the factors on the later sales volume, and finally controlling the storage of the goods in each warehouse according to the prediction result;
s4, judging the accuracy of the prediction result of the S3, optimizing the prediction of the S3 according to the accuracy, and re-analyzing factors influencing the sales of goods if the accuracy is lower than a preset value, so that the accuracy of the prediction is improved.
As a further improvement of the technical scheme, the step of collecting the cargo information in the warehouse by the S1 is as follows:
s1.1, collecting type information of goods in a warehouse;
s1.2, collecting the quantity information of various cargoes in the warehouse.
As a further improvement of the technical scheme, the step S1 for collecting the circulation information of the goods in the market is as follows:
s1.3, counting the sales conditions of goods in the market to obtain sales trends of various types of goods;
and S1.4, matching according to sales conditions of various cargoes in all areas and warehouse storage conditions in all areas in S1.2.
As a further improvement of the technical scheme, the step of storing the information in S2 is as follows:
s2.1, storing the change information of the types and the quantity of goods in warehouses all over the years;
s2.2, storing information of goods sales in the market of the past year.
As a further improvement of the technical scheme, the step of analyzing the cargo demand in the warehouse by the S3 is as follows:
s3.1, retrieving the cargo information in each warehouse stored in the S2.1, and drawing a warehouse histogram according to the information;
s3.2, collecting reasons for inflection points in the warehouse histogram, finding similar conditions in S2.1 and S2.2, and calculating the ratio of the original sales of the goods and the sales of the goods after change in each region when the factors occur in S2.2.
As a further improvement of the technical scheme, the analysis steps of the market demand of the goods in the S3 are as follows:
s3.3, retrieving the information stored in the S2.2 to draw information of the sales volume of the goods on the market, and drawing a goods sales volume histogram according to the information;
and S3.4, collecting reasons for inflection points in the cargo sales histogram, finding similar conditions in S2.1 and S2.2, and calculating the ratio of the original cargo sales to the changed cargo sales in the whole market when the factors appear in S2.2.
As a further improvement of the technical scheme, the predicting step of the sales volume of the market goods in the step S3 is as follows:
s3.5, collecting information which possibly influences the sales volume of the goods in the market, finding out the change ratio of the sales volume of the goods in the market when the information appears in the past in S3.4, and predicting the overall sales volume of the goods in the market according to the ratio and the overall sales trend of the goods in S1.3;
and S3.6, collecting information which can influence the sales of the cargos in each region, finding out the change ratio of the sales of the cargos in each region when the information appears in the past in S3.2, and predicting the sales of the cargos in each region by combining the ratio, the prediction result of S3.5 and the sales of the cargos in each region in S1.4.
As a further improvement of the technical scheme, the control steps of the type and the number of the cargoes in the warehouse in the step S3 are as follows:
s3.7, comparing the forecast result of the sales condition of the goods in each region with the storage condition of the goods in the warehouse in each region by the S3.6;
and S3.8, managing the cargo reserves in the warehouses in each region according to the comparison result of the step S3.7.
As a further improvement of the present technical solution, the step of determining the accuracy of the S3 prediction result in S4 is as follows:
s4.1, transmitting the predicted result of the S3.6 to a judging module;
s4.2, comparing according to actual sales conditions and predicted results, and further obtaining the accuracy of prediction.
Compared with the prior art, the invention has the beneficial effects that:
according to the intelligent logistics warehouse system resource allocation method based on big data, sales conditions of goods in all areas are obtained according to the whole and partial information of the market, the sales conditions of the goods in all the year around are recorded, the inflection point positions of sales quantity changes are analyzed, factors and influences affecting the sales of the goods are stored, later sales conditions of the goods are predicted according to the information, goods storage of all the warehouses are managed and allocated according to the prediction results, the situation that the warehouses do not have locally needed goods is prevented, meanwhile, the phenomenon that a large amount of goods are accumulated in the warehouses and cannot be processed is avoided, meanwhile, accuracy of each time is judged, optimization is carried out according to accuracy, and therefore accuracy is improved.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a block flow diagram of the present invention for collecting information on goods and information on marketing in a warehouse;
FIG. 3 is a flow chart of storing information according to the present invention;
FIG. 4 is a block flow diagram of the prediction and analysis of sales changes of the present invention;
FIG. 5 is a block flow diagram of the present invention for determining the accuracy of a prediction result.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1-5, the present embodiment is directed to providing a method for configuring resources of an intelligent logistics warehouse system based on big data, comprising the following steps:
s1, collecting cargo information in a warehouse and cargo circulation information in the market;
s2, storing the information acquired in the step S1;
s3, when the sales volume of the goods in the market is changed, analyzing factors which cause the sales volume change, predicting the influence of the factors on the later sales volume, and finally controlling the storage of the goods in each warehouse according to the prediction result;
s4, judging the accuracy of the prediction result of the S3, optimizing the prediction of the S3 according to the accuracy, and re-analyzing factors influencing the sales of goods if the accuracy is lower than a preset value, so that the accuracy of the prediction is improved.
S1, the steps for collecting cargo information in a warehouse are as follows:
s1.1, collecting type information of cargoes in a warehouse, and counting according to records of warehouse entry and warehouse exit in the warehouse to obtain the number of the cargoes in each warehouse;
s1.2, collecting the quantity information of various cargoes in the warehouse, counting according to the warehouse entry and warehouse exit records in the warehouse, and integrating the quantity information with the information of S1.1 to obtain the quantity of various cargoes in each warehouse.
S1, the acquisition steps of circulation information of goods in the market are as follows:
s1.3, counting the sales conditions of goods in the market to obtain sales trends of various types of goods, obtaining sales conditions of various goods according to transaction information of a platform, and obtaining sales trends of various goods in the quarter by combining the sales information of the goods stored in the past year, so that the goods in the warehouse can be managed according to the information;
s1.4, matching according to sales conditions of various cargoes in all areas and warehouse storage conditions in all areas in S1.2, wherein sales of a lot of cargoes in different areas can be diversified, sales are different due to influences of factors such as temperature, environment and humanity, therefore, requirements of various cargoes in the warehouse are different in all areas, and warehouse cargo change trend can be conveniently obtained according to sales conditions of cargoes in all areas and warehouse cargo storage conditions.
S2, the information is stored as follows:
s2.1, storing the change information of the types and the quantity of the cargoes in the warehouses in each place in the past year, wherein the quantity and the types of the cargoes stored in each place are changed because the urban construction around the warehouses is changed in real time in each warehouse in each region or in each city, and storing the information in the past year is convenient for analyzing the change trend of the cargoes in each warehouse in the later period;
s2.2, storing information of goods sales in the market in the past year, wherein the market is influenced by various factors in the society, such as weather, economic conditions, humanity and the like, and sales of various goods in the market are influenced by different degrees, so that the information of the market in the past year is stored, and the market sales can be conveniently grasped according to the current environment.
S3, analyzing the cargo demand in the warehouse, wherein the method comprises the following steps of:
s3.1, retrieving the cargo information in each warehouse stored in the S2.1, drawing a warehouse histogram according to the information, and clearly knowing the variation trend of the cargo in-and-out quantity of each region by using the histogram, so that the warehouse can be managed in the later period;
s3.2, collecting reasons for inflection points in a warehouse histogram, finding similar conditions in S2.1 and S2.2, calculating the ratio of the original sales volume of the goods to the sales volume of the goods after the change in the areas when the factors occur in S2.2, wherein the factors for causing the sales change of the goods are quite large, such as weather influences the sales volume of clothes and heating equipment, warehouses in the region with suddenly lowered air temperature can receive a great amount of goods similar to clothes and heating equipment, the situation that the sales volume trend of the goods is the same is likely to occur later if the situation occurs, and the types of the goods and the quantity of the goods of the warehouses are also likely to be changed along with the change of urban construction around the warehouses.
The analysis steps of the market demand of the goods in the S3 are as follows:
s3.3, retrieving the information stored in the S2.2 to draw information of sales of cargos on the market, drawing a cargo sales histogram according to the information, and clearly knowing sales change trends of cargos of various types according to the histogram, so that the management of various cargos in the later period is facilitated;
s3.4, collecting reasons for inflection points in a cargo sales histogram, finding similar conditions in S2.1 and S2.2, calculating the ratio of the original cargo sales and the changed cargo sales in the whole market when the factors in S2.2 occur, causing sales changes of various cargoes in the whole market, generally affecting a plurality of areas or affecting one or more events in the whole country, finding the reasons for influencing the market sales by adopting a market research method, adjusting cargo distribution in a warehouse when the problem occurs again in the later stage, comparing the reasons with the reasons in S3.2, further obtaining influences of the factors for influencing the whole market on the areas, allocating the cargo storage of the warehouse in the whole market and the local areas when the situation occurs in the later stage, recording the factors for influencing the sales obtained by research, and automatically identifying the factors for influencing the sales when the similar conditions occur in the later stage.
The step of predicting the sales of the market goods in the S3 is as follows:
s3.5, collecting information which possibly influences the sales volume of the goods in the market, finding out the change ratio of the sales volume of the goods in the market when the information appears in the past in S3.4, predicting the whole sales volume of the goods in the market according to the ratio and the whole sales trend of the goods in S1.3, wherein the influence on the sales volume of the goods is different when the same situation appears in society all the time, so that the sales volume is predicted only by the increase and decrease amount of the goods, but the trend of the sales volume of the goods is the same when the same situation appears in each time, and predicting the sales volume by adopting the change ratio of the whole sales volume, thereby ensuring that the predicted result is attached to the actual sales volume;
s3.6, collecting information of the sales of cargos in each region, finding out the change ratio of the sales of cargos in each region when the information appears in the past in S3.2, predicting the sales of cargos in each region by combining the ratio, the prediction result of S3.5 and the sales of cargos in each region in S1.4, wherein the overall trend in the market represents the trend of the whole market, the conditions of each region are different, even part of regions can appear in the opposite condition to the overall sales trend of the market, so that analysis is required to be performed by combining the specific conditions of each region, and the accuracy of sales prediction of each region is ensured.
The control steps of the types and the quantity of the cargoes in the warehouse in the S3 are as follows:
s3.7, comparing the predicted result of the goods sales condition of each region with the storage condition of each goods in the warehouse of each region by the S3.6, wherein the predicted result of the S3.6 represents the change trend of the goods sales in a period of time in the future, and the actual storage information in the warehouse of each region is combined, so that the goods in the warehouse can be managed in the later period;
and S3.8, managing the cargo reserves in the warehouses in each region according to the comparison result of S3.7, wherein the sales of certain cargoes in a certain region predicted by S3.6 can be greatly increased, when the storage of the cargoes in the warehouse is not large, the quantity of the cargoes is supplemented, otherwise, the cargo is stopped, the warehouses lacking the cargoes are allocated, the problem that the warehouses do not have locally needed cargoes is solved, and meanwhile, the phenomenon that a large amount of piled cargoes in the warehouses cannot be processed is avoided.
The step of judging the accuracy of the S3 prediction result in S4 is as follows:
s4.1, transmitting the predicted result of the S3.6 to a judging module, wherein the judging module judges the predicted result by manpower, if the predicted quantity is far greater than the normal quantity, the operation in the S3.6 or the S3.5 is problematic, and the operation is optimized by manpower;
s4.2, comparing according to actual sales conditions and predicted results, further obtaining the accuracy of prediction, and if the accuracy is high, continuing normal use of the method; if the accuracy is low, the prediction of sales changes also needs to be optimized, and if the accuracy is far below normal at a certain time or occasionally, the previous data is calibrated.
The comparison method of S3.7 is as follows:
A＝{a 1 ，a 2 ，...，a n }；
B＝{b 1 ，b 2 ，...，b n }；
a is a collection of predicted sales of each commodity in each warehouse; b is a set of actual sales of each commodity in each warehouse; f (x) is the average of the ratios between the set of predicted sales of each item in each warehouse and the set of actual sales of each item in each warehouse; k is the range of the ratio between the predicted sales volume and the actual sales volume of the warehouse under normal conditions; when f (x) epsilon kn, the prediction accuracy of S3.6 is in a normal range; when (when)When this is the case, the prediction accuracy of S3.6 is lower than the normal range.
The method comprises the steps of firstly collecting cargo information in each warehouse, collecting sales information of each cargo in the market to obtain the whole sales and sales of the cargo in local areas, storing the collected information, extracting historical information of the past year, generating a histogram according to the just-collected information and the information of the past year, analyzing inflection point positions in the histogram in combination with information in actual conditions, recording factors influencing the sales of the cargo, predicting the sales of the whole market and sales of each area, managing and allocating the cargo in each area according to prediction results, preventing the situation that a certain object is extremely large in certain area, but no cargo is present in the local warehouse, simultaneously avoiding stacking a large amount of cargo with small requirements in the warehouse, finally judging the prediction accuracy according to the prediction and the actual sales, re-evaluating the influence of various factors on the cargo if the accuracy is low, optimizing the calculation method of the prediction, and improving the prediction accuracy.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. The intelligent logistics warehouse system resource allocation method based on big data is characterized by comprising the following steps of: the method comprises the following steps:
s1, collecting cargo information in a warehouse and cargo circulation information in the market;
s2, storing the information acquired in the step S1;
s3, when the sales volume of the goods in the market is changed, analyzing factors which cause the sales volume change, predicting the influence of the factors on the later sales volume, and finally controlling the storage of the goods in each warehouse according to the prediction result;
s4, judging the accuracy of the prediction result of the S3, optimizing the prediction of the S3 according to the accuracy, and re-analyzing factors influencing the sales of goods if the accuracy is lower than a preset value, so that the accuracy of the prediction is improved.
2. The big data-based intelligent logistics warehouse system resource allocation method as set forth in claim 1, wherein: the step of collecting the cargo information in the warehouse is as follows:
s1.1, collecting type information of goods in a warehouse;
s1.2, collecting the quantity information of various cargoes in the warehouse.
3. The big data-based intelligent logistics warehouse system resource allocation method as claimed in claim 2, wherein the method comprises the steps of: the step S1 of collecting circulation information of goods in the market comprises the following steps:
s1.3, counting the sales conditions of goods in the market to obtain sales trends of various types of goods;
and S1.4, matching according to sales conditions of various cargoes in all areas and warehouse storage conditions in all areas in S1.2.
4. The intelligent logistics warehouse system resource allocation method based on big data as set forth in claim 3, wherein: the step of storing the information in S2 is as follows:
s2.1, storing the change information of the types and the quantity of goods in warehouses all over the years;
s2.2, storing information of goods sales in the market of the past year.
5. The big data-based intelligent logistics warehouse system resource allocation method as set forth in claim 4, wherein: the step of S3 is to analyze the cargo demand in the warehouse as follows:
s3.1, retrieving the cargo information in each warehouse stored in the S2.1, and drawing a warehouse histogram according to the information;
s3.2, collecting reasons for inflection points in the warehouse histogram, finding similar conditions in S2.1 and S2.2, and calculating the ratio of the original sales of the goods and the sales of the goods after change in each region when the factors occur in S2.2.
6. The big data-based intelligent logistics warehouse system resource allocation method as set forth in claim 5, wherein: the analysis steps of the market demand of the goods in the S3 are as follows:
s3.3, retrieving the information stored in the S2.2 to draw information of the sales volume of the goods on the market, and drawing a goods sales volume histogram according to the information;
and S3.4, collecting reasons for inflection points in the cargo sales histogram, finding similar conditions in S2.1 and S2.2, and calculating the ratio of the original cargo sales to the changed cargo sales in the whole market when the factors appear in S2.2.
7. The big data-based intelligent logistics warehouse system resource allocation method as set forth in claim 6, wherein: the step of predicting the sales of the market goods in the S3 is as follows:
s3.5, collecting information which possibly influences the sales volume of the goods in the market, finding out the change ratio of the sales volume of the goods in the market when the information appears in the past in S3.4, and predicting the overall sales volume of the goods in the market according to the ratio and the overall sales trend of the goods in S1.3;
and S3.6, collecting information which can influence the sales of the cargos in each region, finding out the change ratio of the sales of the cargos in each region when the information appears in the past in S3.2, and predicting the sales of the cargos in each region by combining the ratio, the prediction result of S3.5 and the sales of the cargos in each region in S1.4.
8. The big data-based intelligent logistics warehouse system resource allocation method of claim 7, wherein the method comprises the steps of: the control steps of the types and the quantity of the cargoes in the warehouse in the S3 are as follows:
s3.7, comparing the forecast result of the sales condition of the goods in each region with the storage condition of the goods in the warehouse in each region by the S3.6;
and S3.8, managing the cargo reserves in the warehouses in each region according to the comparison result of the step S3.7.
9. The big data-based intelligent logistics warehouse system resource allocation method of claim 8, wherein the method comprises the steps of: the step of judging the accuracy of the S3 prediction result in the S4 is as follows:
s4.1, transmitting the predicted result of the S3.6 to a judging module;
s4.2, comparing according to actual sales conditions and predicted results, and further obtaining the accuracy of prediction.
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