Warehouse Location Layout Optimization Method and System Based on Big Data Visual Analysis

Abstract

The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for optimizing the layout of warehouse storage locations based on big data visualization analysis. The method includes: obtaining a heat map of the access probability of goods in the warehouse in the recent period and the data of the same batch of outbound product categories, and based on the highly correlated goods combinations determined from the data of the same batch of outbound product categories, adjusting the goods layout in multiple high-frequency return routes determined based on the heat map of the access probability. After the adjustment is completed, based on the smoothness of each discharge order, screening the target order of the goods placement in each high-frequency return route, and based on the smoothness of the target order and the length of the high-frequency return route, calculating the optimization degree of each high-frequency return route, so as to adjust the layout of the warehouse storage locations based on the target order of the high-frequency return route with a larger preference degree. The present invention can improve the picking efficiency of pickers in the warehouse.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for optimizing the layout of warehouse storage locations based on big data visualization analysis. Background Art

[0002] With the rapid development of the e-commerce and logistics industries, the operation mode of a large number of product types and high-frequency inbound and outbound orders in large-scale warehousing scenarios has become the norm. However, there are some problems in the actual application of traditional warehousing management technologies: for example, congestion and blockage often occur in high-heat areas (areas with a higher pick-up frequency) or high-heat paths, and there is a situation where goods are scattered, resulting in an increase in the ineffective moving distance and thus a lower picking efficiency.

[0003] In related technologies, the above problems are often solved by combining big data visualization processing with intelligent algorithms. Specifically, on the one hand, based on a large amount of data related to the inbound and outbound of goods, the demand pattern of a certain product is predicted, and the goods are classified into ABC categories according to the inventory value and turnover rate of the goods themselves. The inventory items are divided into three levels: particularly important inventory (category A), generally important inventory (category B), and unimportant inventory (category C) according to the variety and the amount of occupied funds, and then management and control are carried out for different levels respectively. On the other hand, the picking density is displayed through a warehouse heat map, and combined with a hybrid optimization genetic algorithm, the goods are stored in a concentrated manner to reduce the picking time of the picker.

[0004] However, the ABC classification method only relies on a single-dimensional index, ignoring the order co-occurrence law between goods, resulting in a low correlation between goods on the same picking path, so that the picker needs to pass through multiple paths to complete the goods extraction, and the picking path is redundant; and the hybrid optimization algorithm (such as the genetic algorithm) mostly generates a fixed layout based on a static data set and cannot respond to the impact of order fluctuations in real time, which may concentrate highly correlated goods, resulting in congestion when the picker picks up goods in the warehouse, and thus affecting the picking efficiency. Summary of the Invention

[0005] In order to solve the problem of low picking efficiency caused by redundant picking paths and concentrated storage of high-frequency goods, the present invention provides a method and system for optimizing the layout of warehouse storage locations based on big data visualization analysis.

[0006] According to a first aspect of the present invention, there is provided a method for optimizing the layout of warehouse storage locations based on big data visualization analysis, including:

[0007] Obtaining the access probability heat map of goods in a target warehouse in the recent period and the data of the outbound product categories in the same batch;

[0008] Based on the access probability heat map, multiple high-frequency return routes are generated, and according to the data of the outbound categories in the same batch, multiple highly correlated product combinations are generated. The high-frequency return routes are combined with the highly correlated product combinations to adjust the product layout in each high-frequency return route so that the highly correlated products are distributed in the same high-frequency return route;

[0009] Obtain all the permutation orders of the products in the same high-frequency return route after adjusting the product layout, and calculate the smoothness of each permutation order according to the dispersion degree of the products with similar outbound frequencies, so as to take the permutation order with the maximum smoothness as the target order of the corresponding high-frequency return route;

[0010] Calculate the optimization degree of each high-frequency return route. The optimization degree is positively correlated with the smoothness of the target order of the corresponding high-frequency return route and negatively correlated with the length of the corresponding high-frequency return route, so as to adjust the warehouse location layout according to the target order of the high-frequency return route with an optimization degree greater than the preset threshold.

[0011] The present invention combines the high-frequency return route with the highly correlated product combination and adjusts the product layout in each high-frequency return route, which can adjust the highly correlated products to the same high-frequency return route, reduce the redundancy of the picking path of the picker, and thus improve the picking efficiency; moreover, the present invention selects the target order by evaluating the smoothness of different permutation orders of the products in each high-frequency return route, which can avoid concentrating the high-frequency products and reduce the congestion degree, thereby further improving the picking efficiency. After adjusting the warehouse location layout according to the target order of the high-frequency return route selected based on the optimization degree, the picking effect can be significantly improved.

[0012] Preferably, calculating the smoothness of each permutation order according to the dispersion degree of the products with similar outbound frequencies includes:

[0013] Take any high-frequency return route after adjusting the product layout as the target route, number the products distributed in any permutation order in the target route, and after numbering, cluster the numbered products based on the outbound frequencies of the products to obtain multiple clustering clusters;

[0014] Calculate the variance of the data point numbers in each clustering cluster and sum them up, and take the normalized value of the obtained sum as the smoothness of any permutation order.

[0015] Through the clustering algorithm, the present invention can quickly and accurately find the products with similar outbound frequencies, thus providing a reliable data basis for calculating the smoothness of each permutation order.

[0016] Preferably, take the variance of the data point numbers in each clustering cluster as the dispersion degree of the corresponding clustering cluster, and the smoothness of any permutation order satisfies the following relational expression:

[0017] ;

[0018] wherein, is the smoothness of the th permutation order in the th high-frequency return route after adjusting the layout of goods; is the dispersion degree of the th cluster obtained when clustering the goods numbered according to the th permutation order in the high-frequency return route; is the number of clusters in the clustering result; is the normalization function.

[0019] Preferably, combining the high-frequency return route with the high-correlation goods combination and adjusting the layout of goods in each high-frequency return route, including:

[0020] Mark the goods with the recent outbound frequency greater than the preset frequency threshold in each high-frequency return route, and based on the high-correlation goods combination, obtain one or more associated goods of each marked good;

[0021] For any marked good in any high-frequency return route, determine whether all the associated goods of any marked good exist in any high-frequency return route. If so, continue to judge the next marked good. Otherwise, record the missing associated goods as the goods to be moved in;

[0022] Based on the comparison between the total number of goods to be moved in and the total number of vacant storage locations in the same high-frequency return route, adjust the layout of goods in each high-frequency return route according to the preset rules.

[0023] The method for adjusting the layout of goods in each high-frequency return route according to the present invention can place the high-correlation goods in the same high-frequency return route, thereby reducing the time spent on picking goods.

[0024] Preferably, adjusting the layout of goods in each high-frequency return route according to the preset rules, including:

[0025] If the total number of goods to be moved in any high-frequency return route is greater than or equal to the total number of vacant storage locations, adjust the layout of goods in any high-frequency return route according to the first preset rule;

[0026] If the total number of goods to be moved in any high-frequency return route is less than the total number of vacant storage locations, adjust the layout of goods in any high-frequency return route according to the second preset rule;

[0027] Among them, the first preset rule is to move out the low-frequency goods in any high-frequency return route and completely move the goods to be moved in into any high-frequency return route; the second preset rule is to directly move the goods to be moved in into any high-frequency return route.

[0028] The present invention performs the moving-in and moving-out operations in different ways, which can ensure that the goods to be moved in each high-frequency return route are completely moved into the corresponding high-frequency return route, thereby reducing the number of goods with low correlation in each high-frequency return route.

[0029] Preferably, adjusting the layout of goods in any high-frequency return route according to the first preset rule includes:

[0030] Calculating the difference between the total quantity of goods to be moved in and the total quantity of vacant storage locations in any high-frequency return route;

[0031] Sequentially moving out a number of goods from any high-frequency return route in the order of increasing outbound frequency until the quantity of the moved-out goods is the same as the difference, so as to move the goods to be moved in into any high-frequency return route.

[0032] Preferably, calculating the optimization degree of each high-frequency return route satisfies the following relational expression:

[0033] ;

[0034] In the formula, is the optimization degree of the th high-frequency return route; is the length of the th high-frequency return route; is the smoothness of the target order in the th high-frequency return route after adjusting the layout of goods; is the ordinal number of the target order; is the normalization function.

[0035] The present invention adds 1 to the denominator, which can avoid the denominator being zero and ensure the validity of the optimization degree calculation formula.

[0036] Preferably, generating multiple high-frequency return routes based on the access probability heat map includes:

[0037] Regarding each position in the access probability heat map as a node, and mapping the access probability of each node into a weight, where the weight is negatively correlated with the access probability;

[0038] Based on the weights of each node, using the Dijkstra algorithm to obtain a shortest high-frequency return path from the starting point to the ending point, and combining with the genetic algorithm to generate multiple paths to obtain multiple high-frequency return routes.

[0039] Preferably, according to the same batch of outbound category data, multiple highly correlated product combinations are generated, including:

[0040] According to the same batch of outbound category data, the Apriori algorithm is used to generate multiple highly correlated product combinations.

[0041] According to the second aspect of the present invention, a warehouse location layout optimization system based on big data visualization analysis is provided. The system includes a memory and a processor. A computer program is stored on the memory, and the processor executes the computer program to implement the steps of the first aspect of the present invention.

[0042] The present invention has the following effects:

[0043] On the one hand, by adjusting the product categories in the high-frequency return route and concentrating the highly correlated products in the same route, the present invention can reduce the picking time and improve the picking efficiency. On the other hand, by optimizing the product placement order and avoiding the concentrated distribution of products with similar frequencies, the picking efficiency can be further improved while ensuring the layout of highly correlated products. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic flowchart of the steps of the warehouse location layout optimization method based on big data visualization analysis according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following will describe in detail the specific embodiments of the present invention with reference to the accompanying drawings.

[0046] Refer to Figure 1 , the warehouse location layout optimization method based on big data visualization analysis includes steps S1 - S5, specifically as follows:

[0047] S1: Obtain the access probability heat map of the products in the target warehouse in the recent period and the same batch of outbound category data.

[0048] Among them, the target warehouse refers to any warehouse selected for analysis; the access probability heat map is generated by analyzing the access probability and picking distribution of the products in the warehouse. The color of the heat map represents the size of the access probability, and the darker the color, the higher the access probability; the same batch of outbound category data refers to the number of categories included in the products in any outbound order. For example, when products A, B, and C are on the same outbound order, products A, B, and C are used as the same batch of outbound category data.

[0049] Specifically, the picking data and access data of goods in the target warehouse in a recent period, such as within the recent month, can be collected. Then, calculate the access probability of each good (i.e., the proportion of the number of times each good is accessed to the total number of accesses) and the picking distribution (i.e., the proportion of the number of picking times at each good location to the total number of picking times), and synthesize them into a heat index (such as through a weighted method). Finally, generate a heat map based on each heat index to obtain the access probability heat map of the goods in the target warehouse in the recent period. It should be noted that the construction process of the heat map is a prior art, and this embodiment will not elaborate on it here.

[0050] Optionally, the data of the same batch of outbound categories that have been shipped out of the target warehouse in the recent period, such as within a month, can be obtained by counting the outbound order information in the target warehouse; alternatively, the data of the same batch of outbound categories that have been shipped out of the target warehouse in the recent period can be obtained through the warehouse management system. Among them, the time range for counting the data of the same batch of outbound categories is the same as the time range when constructing the access probability heat map, and this embodiment does not particularly limit the method for determining the data of the same batch of outbound categories.

[0051] S2: Generate multiple high-frequency return routes based on the access probability heat map, and generate multiple high-correlation goods combinations according to the data of the same batch of outbound categories.

[0052] Among them, the high-frequency return route refers to the route with a relatively high access probability of goods; the high-correlation goods combination refers to the goods combination with a strong correlation. For example, when the correlation between goods A, goods B, and goods C is relatively high, then goods A, goods B, and goods C are used as a group of high-correlation goods combinations.

[0053] In an exemplary embodiment of the present invention, the determination of the high-frequency return route can be achieved through the following steps:

[0054] Take each position in the access probability heat map as a node, and map the access probability of each node as a weight, where the weight is negatively correlated with the access probability; based on the weights of each node, use the Dijkstra algorithm to obtain a high-frequency return shortest path from the starting point to the ending point, and combine it with the genetic algorithm to generate multiple paths to obtain multiple high-frequency return routes.

[0055] It should be noted that since the access probability heat map is determined based on the access probability and picking distribution of goods, the obtained access probability heat map can reflect the access probability of goods at each position in the warehouse. Therefore, the present invention takes each position in the access probability heat map as a node.

[0056] It should be further explained that the Dijkstra algorithm itself can only generate one shortest path and cannot directly generate multiple routes. However, in practical applications, by combining other algorithms (such as A*, genetic algorithms, etc.) or improved strategies (such as dynamic adjustment of weights, bidirectional search, etc.), the generation of multiple routes can be indirectly achieved.

[0057] Specifically, the access probability of each node in the access probability heat map can be mapped to a weight (for example, the inverse of the access probability is used as the weight, so that nodes with high access probability have lower weights). Then, based on the determined weights, the Dijkstra algorithm is used to find a shortest path from the node at the warehouse entrance (starting point) in the access probability heat map to the node at the warehouse exit (end point) in the access probability heat map, thereby obtaining a high-frequency return shortest path.

[0058] Furthermore, based on the obtained high-frequency return shortest path, a genetic algorithm or an A* algorithm can be combined to generate multiple high-frequency return routes. This process is a prior art and will not be described in detail in this embodiment.

[0059] In an exemplary embodiment of the present invention, the determination of a highly correlated product combination can be achieved by the following steps:

[0060] Based on the category data of the same batch of outbound products, the Apriori algorithm is used to generate multiple highly correlated product combinations.

[0061] Specifically, all the category data of the same batch of outbound products collected in S1 can be used as input, and then the minimum support is set to the minimum proportion of the product combination in all orders (such as 20%), and the minimum confidence is set to 50%. After that, based on the two set thresholds (minimum support and minimum confidence), the Apriori algorithm is used to iteratively generate frequent item sets. After several (such as 3) iterations, the last generated frequent item set is used as a high-correlation product combination, thereby obtaining multiple high-correlation product combinations.

[0062] It should be noted that the minimum support and the minimum confidence are professional terms in the Apriori algorithm, and the process of iteratively generating frequent item sets using the Apriori algorithm is a prior art, which will not be described in detail in this embodiment.

[0063] S3: Combine high-frequency return routes with high-correlation goods, and adjust the layout of goods in each high-frequency return route to distribute high-correlation goods in the same high-frequency return route.

[0064] It should be noted that the goods in the high-frequency return route are not necessarily all high-correlation goods combinations. There may also be some goods with relatively low outbound frequencies or vacant storage locations. In order to shorten the relevant picking time of high-correlation goods combinations, the present invention adjusts the layout of the goods in each high-frequency return route so as to place high-correlation goods in the same high-frequency return route.

[0065] In an exemplary embodiment of the present invention, the adjustment of the layout of the goods in each high-frequency return route can be achieved through the following steps:

[0066] Step 1: Mark the goods in each high-frequency return route with an outbound frequency in the recent period greater than a preset frequency threshold, and based on the high-correlation goods combination, obtain one or more associated goods of each marked good.

[0067] It should be noted that, for the convenience of screening marked goods, the present invention normalizes the outbound frequency of the goods in each high-frequency return route in the recent period (such as within one month), so as to screen the goods to be marked based on the normalized value. The outbound frequency here refers to the number of outbound times of any good in the recent period.

[0068] Optionally, the frequency threshold can be set to 0.7. Thus, based on this frequency threshold and the normalized value of the outbound frequency of the goods in each high-frequency return route in the recent period, the goods to be marked can be screened and marked. Then, based on the high-correlation goods combination determined in step S2, one or more associated goods of each marked good are obtained.

[0069] Step 2: For any marked good in any high-frequency return route, determine whether all the associated goods of any marked good exist in any high-frequency return route. If so, continue to judge the next marked good. Otherwise, mark the missing associated goods as goods to be moved in.

[0070] Exemplarily, assume that the associated goods of good A are good B and good C, and good A is a marked good in the th high-frequency return route. At this time, if good B is in the th high-frequency return route, but good C is not in the th high-frequency return route, then mark good C as the good to be moved into the th high-frequency return route, so that all the goods to be moved into each high-frequency return route can be determined.

[0071] Step 3: Based on the comparison between the total number of goods to be moved into in the same high-frequency return route and the total number of vacant storage locations, adjust the layout of the goods in each high-frequency return route according to a preset rule.

[0072] Among them, the total quantity of goods to be moved in refers to the sum of the inventory quantities of all goods to be moved in any high-frequency return route; the total quantity of available storage locations refers to the quantity of goods that can be accommodated in any high-frequency return route. And for the convenience of calculation, in the present invention, goods of different categories are regarded as the same independent unit, without considering the size and dimensions of the goods.

[0073] In an exemplary embodiment of the present invention, the layout of goods on each high-frequency return route can be adjusted according to a preset rule through the following steps:

[0074] (1) If the total quantity of goods to be moved in any high-frequency return route is greater than or equal to the total quantity of available storage locations, then adjust the layout of goods on any high-frequency return route according to the first preset rule; wherein, the first preset rule is to move out the goods with low frequency of outbound in any high-frequency return route and completely move the goods to be moved in into any high-frequency return route;

[0075] In an exemplary embodiment of the present invention, the layout of goods can be adjusted according to the first preset rule through the following steps:

[0076] Calculate the difference between the total quantity of goods to be moved in and the total quantity of available storage locations in any high-frequency return route; screen and move out several marked goods from the marked goods in any high-frequency return route in ascending order of the outbound frequency until the quantity of the moved-out goods is the same as the difference, so as to move the goods to be moved in into any high-frequency return route.

[0077] Exemplarily, the total quantity of goods to be moved in the th high-frequency return route can be denoted as , and the total quantity of available storage locations in this high-frequency return route can be denoted as , then the difference of this high-frequency return route is .

[0078] Then, the outbound frequencies of each good in the th high-frequency return route in the most recent month can be normalized, and several goods are moved out in ascending order until the total quantity of the moved-out goods is so as to make the total quantity of available storage locations in this high-frequency return route the same as the total quantity of goods to be moved in this high-frequency return route, and move the goods to be moved in into this high-frequency return route to realize the adjustment of the layout of goods in this high-frequency return route.

[0079] (2) If the total quantity of goods to be moved in any high-frequency return route is less than the total quantity of available storage locations, then adjust the layout of goods on any high-frequency return route according to the second preset rule; wherein, the second preset rule is to directly move the goods to be moved in into any high-frequency return route.

[0080] Optionally, after completing the operation of moving goods into or out of each high-frequency return route according to the first preset rule or the second preset rule, goods with high correlation can be distributed to the same high-frequency return route, so as to adjust the layout of goods in each high-frequency return route.

[0081] S4: Obtain all the permutation orders of the goods in the same high-frequency return route after adjusting the goods layout, and calculate the smoothness of each permutation order according to the dispersion degree of the goods with similar outbound frequencies, so as to take the permutation order with the maximum smoothness as the target order of the corresponding high-frequency return route.

[0082] It should be noted that the placement order of the goods will also affect the picking efficiency of the pickers. For example, when the high-frequency goods are placed too densely, it will cause multiple pickers to gather in this area to pick up goods at the same time, resulting in congestion. Therefore, the present invention evaluates the smoothness of each permutation order, so that the goods placement order with the maximum smoothness can be selected to adjust the placement method of the goods in each high-frequency return route after adjusting the goods layout.

[0083] In an exemplary embodiment of the present invention, the determination of the smoothness of each goods placement order can be achieved through the following steps:

[0084] (1) Take any high-frequency return route after adjusting the goods layout as the target route, number the goods distributed in any permutation order in the target route, and after numbering, cluster the numbered goods based on the outbound frequencies of the goods to obtain multiple clustering clusters;

[0085] Specifically, clustering algorithms such as the K-means algorithm and the DBSCAN algorithm can be used to cluster all the numbered goods in the target route, so that the goods with similar outbound frequencies can be clustered into a clustering cluster, and multiple (such as 3) clustering clusters can be obtained. The number of clustering clusters obtained in this embodiment can be adjusted based on the actual scenario. Among them, the process of clustering using the clustering algorithm is prior art, and this embodiment will not be described in detail here.

[0086] It should be noted that the present invention numbers the goods in the target route in order to evaluate the distance between different goods through the number difference, so as to evaluate the dispersion degree of the goods with similar outbound frequencies based on the distribution of the numbers of the data points in each clustering cluster.

[0087] (2) Calculate the variance of the numbers of the data points in each clustering cluster and sum them up, and take the normalized value of the obtained cumulative sum as the smoothness of any permutation order.

[0088] In an exemplary embodiment of the present invention, the variance of the numbers of data points within each clustering cluster is used as the degree of dispersion of the corresponding clustering cluster. Then, the smoothness of each permutation order satisfies the following relational expression:

[0089] ;

[0090] In the formula, is the smoothness of the th high-frequency return route after adjusting the layout of goods. For the th permutation order; is the degree of dispersion of the th clustering cluster obtained when clustering the goods numbered in the th permutation order in this high-frequency return route; is the number of clustering clusters in the clustering result. In this embodiment, ; is the normalization function.

[0091] Among them, The smaller the value of, the closer the numbers of data points within the corresponding clustering cluster are, which further indicates that the high-frequency goods are distributed more densely in the corresponding permutation order. If the goods are placed in this permutation order, the congestion situation of the corresponding high-frequency return route is more serious, and the corresponding smoothness is lower. On the contrary, the larger the value, the more dispersed the high-frequency goods are distributed in the corresponding permutation order. If the goods are placed in this permutation order, the probability of congestion in the corresponding high-frequency return route is lower, and the corresponding smoothness is higher.

[0092] Optionally, the standard deviation of the numbers of data points within the clustering cluster can also be calculated to evaluate the degree of dispersion of the clustering cluster.

[0093] In another embodiment, the method of clustering may not be adopted to obtain the goods with similar outbound frequencies. Instead, by directly setting a threshold, the goods with an outbound frequency difference less than the set threshold are regarded as the goods with similar outbound frequencies.

[0094] Furthermore, the smoothness calculation formula can be used to calculate the smoothness of each goods arrangement order in each high-frequency return route. Then, the goods arrangement order with the maximum smoothness is used as the target order of the corresponding high-frequency return route. The high-frequency return route here is the high-frequency return route after adjusting the layout of goods through step S3.

[0095] S5: Calculate the optimization degree of each high-frequency return route. The optimization degree is positively correlated with the smoothness of the target order of the corresponding high-frequency return route and negatively correlated with the length of the corresponding high-frequency return route. Adjust the layout of warehouse storage locations according to the target order of the high-frequency return route with an optimization degree greater than the preset threshold.

[0096] Specifically, the optimization degree of each high-frequency return route satisfies the following relational expression:

[0097] ;

[0098] In the formula, is the optimization degree of the th high-frequency return route; is the length of the th high-frequency return route; is the smoothness degree of the target order in the th high-frequency return route after adjusting the layout of goods; is the ordinal number of the target order; is a normalization function.

[0099] Among them, a smaller value indicates that the time spent by the picker in picking goods on the th high-frequency return route is smaller, then the picker has a higher picking efficiency on this high-frequency return route, and the corresponding optimization degree is higher; a larger value indicates that it is not easy to cause congestion when the picker picks goods on the th high-frequency return route after adjusting the layout of goods, and the corresponding optimization degree of this high-frequency return route is higher.

[0100] Furthermore, when determining the optimization degree of each high-frequency return route, high-frequency return routes with an optimization degree greater than a preset threshold, such as 0.8, can be screened out, and then according to the target order of these screened high-frequency return routes (high-frequency return routes with an optimization degree greater than 0.8), the placement order of goods in the corresponding high-frequency return routes can be adjusted, so as to optimize the layout of warehouse storage locations.

[0101] The present invention also provides a warehouse storage location layout optimization system based on big data visual analysis. The system includes a memory and a processor, and a computer program is stored on the memory. The computer program integrates the functions of the warehouse storage location layout optimization method based on big data visual analysis. When the computer program is executed, the picking efficiency of the picker in the warehouse can be improved through the warehouse storage location layout optimization method based on big data visual analysis.

[0102] It should be understood that in the process of practicing the present invention, various alternative solutions to the embodiments of the present invention described herein can be adopted.

Claims

1. A method for optimizing the layout of warehouse storage locations based on big data visualization analysis, characterized in that, Including: Obtain the access probability heat map of the goods in the target warehouse in the recent period and the data of the same batch of outbound product categories; Based on the access probability heat map, generate multiple high-frequency return routes, and based on the data of the same batch of outbound product categories, generate multiple highly correlated goods combinations. Combine the high-frequency return routes with the highly correlated goods combinations, and adjust the goods layout in each high-frequency return route so that the highly correlated goods are distributed in the same high-frequency return route; Obtain all the permutation orders of the goods in the same high-frequency return route after adjusting the goods layout, and calculate the smoothness of each permutation order according to the dispersion degree of the goods with similar outbound frequencies, so as to take the permutation order with the maximum smoothness as the target order of the corresponding high-frequency return route; Calculate the optimization degree of each high-frequency return route. The optimization degree is positively correlated with the smoothness of the target order of the corresponding high-frequency return route and negatively correlated with the length of the corresponding high-frequency return route, so as to adjust the warehouse storage location layout according to the target order of the high-frequency return route with the optimization degree greater than the preset threshold.

2. The warehouse location layout optimization method based on big data visualization analysis according to claim 1, wherein The calculating the smoothness of each permutation order according to the dispersion degree of the goods with similar outbound frequencies includes: Take any high-frequency return route after adjusting the goods layout as the target route, number the goods distributed in any permutation order in the target route. After numbering, cluster the numbered goods based on the outbound frequencies of the goods to obtain multiple clustering clusters; Calculate the variance of the data point numbers in each clustering cluster and sum them up. Take the normalized value of the obtained sum as the smoothness of any permutation order.

3. The method for optimizing the warehouse location layout based on big data visualization analysis according to claim 2, wherein Take the variance of the data point numbers in each clustering cluster as the dispersion degree of the corresponding clustering cluster. The smoothness of any permutation order satisfies the following relationship: ； In the formula, is the smoothness of the th high-frequency return route after adjusting the layout of goods, and is the th permutation order; is the discreteness of the th cluster obtained when clustering the goods numbered in the th permutation order in the high-frequency return route; is the number of clusters in the clustering result; is the normalization function.

4. The method for optimizing the layout of warehouse storage locations based on big data visualization analysis according to claim 1, wherein The combining the high-frequency return route with the highly correlated goods combination and adjusting the goods layout in each high-frequency return route includes: Mark the goods with the outbound frequency greater than the preset frequency threshold in each high-frequency return route, and based on the highly correlated goods combination, obtain one or more associated goods of each marked goods; For any marked goods in any high-frequency return route, judge whether all the associated goods of the any marked goods exist in the any high-frequency return route. If so, continue to judge the next marked goods. Otherwise, record the missing associated goods as the goods to be moved in; Based on the size of the total number of goods to be moved in and the total number of empty storage locations in the same high-frequency return route, adjust the goods layout of each high-frequency return route according to the preset rules.

5. The optimized method for warehouse location layout based on big data visualization analysis according to claim 4, characterized in that The adjusting the goods layout of each high-frequency return route according to the preset rules includes: If the total number of goods to be moved in any high-frequency return route is greater than or equal to the total number of empty storage locations, adjust the goods layout of the any high-frequency return route according to the first preset rule; If the total number of goods to be moved in any high-frequency return route is less than the total number of empty storage locations, adjust the goods layout of the any high-frequency return route according to the second preset rule; Among them, the first preset rule is to move out the low-frequency goods in any one of the high-frequency return routes and completely move the goods to be moved in into any one of the high-frequency return routes; the second preset rule is to directly move the goods to be moved in into any one of the high-frequency return routes.

6. The warehouse location layout optimization method based on big data visualization analysis according to claim 5, characterized in that Adjusting the goods layout of any one of the high-frequency return routes according to the first preset rule includes: Calculating the difference between the total quantity of the goods to be moved in and the total quantity of available storage locations in any one of the high-frequency return routes; Sequentially moving out a number of goods from any one of the high-frequency return routes in ascending order of the outbound frequency until the quantity of the moved-out goods is the same as the difference, so as to move the goods to be moved in into any one of the high-frequency return routes.

7. The method for optimizing the layout of warehouse storage locations based on big data visualization analysis according to claim 1, wherein Calculating the optimization degree of each high-frequency return route satisfies the following relational expression: ； In the formula, is the optimization degree of the th high-frequency return route; is the length of the th high-frequency return route; is the smoothness of the target order in the th high-frequency return route after adjusting the layout of goods; is the ordinal number of the target order; is the normalization function.

8. The method for optimizing the layout of warehouse storage locations based on big data visualization analysis according to claim 1, characterized in that, Generating multiple high-frequency return routes based on the access probability heat map, including: Regarding each position in the access probability heat map as a node, and mapping the access probability of each node to a weight, where the weight is negatively correlated with the access probability; Based on the weights of each node, using the Dijkstra algorithm to obtain a shortest high-frequency return path from the starting point to the ending point, and combining with the genetic algorithm to generate multiple paths to obtain multiple high-frequency return routes.

9. The method for optimizing the layout of warehouse storage locations based on big data visualization analysis according to claim 1, wherein, Generating multiple highly correlated goods combinations according to the same-batch outbound category data, including: Using the Apriori algorithm to generate multiple highly correlated goods combinations according to the same-batch outbound category data.

10. The warehouse location layout optimization system based on big data visualization analysis is characterized in that The warehouse storage location layout optimization system based on big data visualization analysis includes a memory and a processor. A computer program is stored on the memory, and the processor executes the computer program to implement the steps of the warehouse storage location layout optimization method according to any one of claims 1-9.

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