Warehouse storage location allocation method and system based on dynamic analysis

Abstract

The invention relates to the technical field of data processing, in particular to a warehouse storage location allocation method and system based on dynamic analysis, and the method comprises the steps: obtaining the inventory management data of various cargoes needing to be stored at the current moment in each period in a preset time period before the current moment; dividing the preset time period to obtain each time interval of each cargo; obtaining correlation characteristic values and correlation coefficients between various cargos and each kind of cargos in the order where the cargos are located in each time interval, obtaining influence factors of the various cargos in each time interval, and obtaining influence characteristic values and dynamic influence coefficients of the various cargos; and for each cargo needing to be stored at the current moment, the optimal storage location of each cargo is obtained through iteration by using a genetic algorithm. The invention aims to improve the accuracy of warehouse storage location allocation.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a warehouse storage location allocation method and system based on dynamic analysis. Background Technology

[0002] Warehouse management in a logistics system is a key step in improving warehouse space utilization and reducing warehouse costs. In the process of warehouse storage location allocation, by combining automated data collection and analysis technology, the storage space in the warehouse can be scientifically allocated, which can shorten the handling path and time of goods in the warehouse, improve the storage and handling efficiency of goods, and reduce the probability of goods damage and loss, thus ensuring the quality and safety of goods.

[0003] However, even with the integration of automated management and allocation methods, there are still many situations that lead to deviations in warehouse storage allocation. If the dynamic changes in goods cannot be grasped in a timely and accurate manner during warehouse management, the original warehouse storage allocation may become unreasonable, affecting the efficiency of goods storage and handling operations. For example, frequently inbound and outbound goods may be placed deeper in the warehouse, reducing the timeliness of goods handling. Traditional warehouse storage allocation methods do not fully consider the impact of the time-varying characteristics of dynamic information during the actual storage process on warehouse storage allocation, resulting in significant deviations from the optimal allocation and affecting the operational efficiency of logistics warehouses. Summary of the Invention

[0004] In light of the above, it is necessary to provide a warehouse storage location allocation method and system based on dynamic analysis, which improves the accuracy of warehouse storage location allocation compared to traditional dynamic analysis-based methods. In a first aspect, embodiments of this application provide a warehouse storage location allocation method based on dynamic analysis, the method comprising the following steps: Retrieve inventory management data for various goods that need to be stored at the current moment, covering each period within a preset timeframe prior to the current moment; By analyzing the abrupt changes in inventory management data for various goods, we obtain the time intervals for each type of goods. By analyzing the correlation and differences in inventory management data between each type of goods and each type of goods in their respective orders within each time interval, we obtain the correlation characteristic values ​​between each type of goods and each type of goods within each time interval. Combining this with the occurrence of each type of goods, we obtain the correlation coefficients between each type of goods and each type of goods within each time interval, thereby obtaining the influence factors of each type of goods within each time interval. Finally, by combining this with the distribution of inventory management data for each type of goods within each time interval, we obtain the influence characteristic values ​​of each type of goods, thus obtaining the dynamic influence coefficients of each type of goods. The optimal storage location for each item is obtained iteratively using a genetic algorithm. In each iteration, the fitness value of an individual item is obtained based on the inventory management data of each item, the dynamic influence coefficient, the channel occupancy rate of the channel where each item is located, the distance the automated guided vehicle transports the item, and the total weight of the items placed on each shelf after simulated storage.

[0005] In one embodiment, the process of obtaining each time interval is as follows: The inventory management data of various goods within the preset time period are arranged chronologically, and each mutation point is obtained. The period in which each mutation point is located is used as a dividing point, and the preset time period is divided into time intervals for various goods.

[0006] In one embodiment, the process of obtaining the associated feature value is as follows: Each type of goods in the same order as various goods within each time interval is recorded as the associated goods of various goods within each time interval; Calculate the correlation coefficients of inventory management data between various goods and their related goods in each time interval; Calculate the average inventory management data for various goods across different time intervals; Calculate the average value of inventory management data for each type of goods in each time interval; Calculate the difference between the mean and the average. The correlation feature value is directly proportional to the correlation coefficient and inversely proportional to the difference value.

[0007] In one embodiment, the process of obtaining the correlation coefficient is as follows: Obtain the order quantity percentage of each associated product within the orders of various products in each time interval; The correlation coefficient is the product of the correlation feature value and the quantity proportion.

[0008] In one embodiment, the influencing factor is the average of the correlation coefficients between various goods and all their associated goods in each time interval.

[0009] In one embodiment, the process of obtaining the influencing feature value is as follows: Calculate the arithmetic mean of inventory management data for various goods in each time interval, calculate the sum of the arithmetic means of various goods in all time intervals, and take the ratio of the arithmetic mean of various goods in each time interval to the sum as the significance value of the influence of various goods in each time interval. The significant impact value of various goods in each time interval is calculated as the product of the impact factor and the impact characteristic value. The impact characteristic value is obtained by the product value of various goods in all time intervals.

[0010] In one embodiment, the influence characteristic value is the sum of the product values ​​of various goods over all time intervals.

[0011] In one embodiment, the process of obtaining the dynamic influence coefficient is as follows: Inventory management data includes inbound and outbound frequency data and inventory turnover rate data; Obtain the normalized values ​​of the impact characteristic values ​​of various goods calculated based on inbound and outbound frequency data; Obtain the normalized results of the impact characteristic values ​​of various goods calculated based on inventory turnover rate data; The dynamic influence coefficient is the average of the normalized value and the normalization result.

[0012] In one embodiment, the fitness value is expressed as: In the formula, α represents the fitness value of individual v after the nth iteration; α and β both represent preset weight values. This represents the dynamic information change value of all goods based on the individual v during the nth iteration. This represents the device adaptation feature value based on the simulated storage of individual v during the nth iteration; This represents the penalty term used in the nth iteration when simulating storage based on individual v; specifically: Where u represents the total number of goods that need to be stored at the current moment; This represents the inbound and outbound frequency data of goods c in the previous period at the current moment. This represents the dynamic impact coefficient of cargo c; This represents the distance from the storage location of goods c to the warehouse exit; ;in, This indicates the channel occupancy rate of the channel where cargo c is located. This indicates the distance from the automated guided vehicle (AGV) used to transport goods c to goods c. ;in, , These represent the first and second preset constants, respectively; Q represents the total number of shelves currently used for storing goods; max() represents the operation of taking the maximum value; This represents the total weight of the goods placed on the shelf s after the storage simulation based on individual v in the nth iteration. Let represent the load-bearing threshold of shelf s, and R represent the number of automated guided vehicles used in the storage simulation based on individual v in the nth iteration. This represents the distance from the z-th automated guided vehicle to the storage location of the goods to be transported. This represents the maximum range of the z-th automated guided vehicle.

[0013] Secondly, embodiments of this application also provide a warehouse storage location allocation system based on dynamic analysis, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any of the above-described warehouse storage location allocation methods based on dynamic analysis.

[0014] This application has at least the following beneficial effects: This application detects abrupt changes in inventory management data for various goods, enabling the capture of time-varying characteristics of dynamic information for these goods. By dividing the preset time period into multiple time intervals, it allows for more refined analysis of the dynamic changes of various goods. By analyzing the correlation characteristics of dynamic information updates between various goods and their associated goods, as well as the differences in dynamic changes between various goods and their associated goods, it provides a more comprehensive understanding of the correlation characteristics between various goods and their associated goods. This helps in optimizing storage location allocation schemes. Furthermore, by combining the frequency of occurrence of associated goods, it assesses the impact of dynamic information changes on storage location allocation for various goods. This helps in prioritizing goods with a greater impact from dynamic information changes on storage location allocation when optimizing storage location allocation schemes, in order to obtain the optimal storage location allocation scheme. Furthermore, by calculating the dynamic influence coefficient, the dynamic change characteristics of goods can be more accurately reflected, providing a quantitative basis for subsequent storage location allocation. In addition, when using the genetic algorithm to iteratively obtain the optimal storage location allocation scheme, multiple factors can be considered to comprehensively evaluate the merits of the allocation scheme and ensure the rationality and effectiveness of the optimization results. This application can dynamically adjust the storage location allocation scheme according to the dynamic change characteristics of goods, improve the accuracy of warehouse storage location allocation, and thus improve the operational efficiency of logistics warehouses. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a warehouse storage location allocation method based on dynamic analysis, provided in one embodiment of this application; Figure 2 This is a schematic diagram illustrating the calculation process of the dynamic influence coefficient. Figure 3This is a schematic diagram illustrating the process of obtaining the dynamic influence coefficient. Detailed Implementation

[0017] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0019] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the warehouse storage location allocation method and system based on dynamic analysis provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flowchart of a warehouse storage location allocation method based on dynamic analysis according to an embodiment of this application. The method includes the following steps: Step 1: Obtain inventory management data for various goods that need to be stored at the current moment, for each period within a preset time period prior to the current moment.

[0022] Taking the various goods that need to be stored at the current moment as an example, in order to allocate warehouse storage space for the various goods that need to be stored at the current moment, the logistics warehouse management system is used to collect the inventory management data of various goods in each cycle within a preset period before the current moment, including inbound and outbound frequency data and inventory turnover rate data; collect the order number of the order in which the various goods are located within the preset period; collect the load-bearing data of the shelves used to store various goods in real time, that is, the total weight of the goods placed on the shelves; collect the aisle occupancy rate of each aisle in the warehouse, the position, power data and maximum driving distance of each Automated Guided Vehicle (AGV) in the warehouse in real time, where the maximum driving distance refers to the maximum distance that the AGV can travel with the support of the remaining power, and the aisle occupancy rate refers to the proportion of time that the warehouse aisle is occupied.

[0023] In this embodiment, the preset time period refers to the previous year adjacent to the current time. The length of the preset time period is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0024] In this embodiment, the cycle length is 1 day. The cycle length is preset by the user and can be set by the implementer according to the actual situation. This application does not impose any special restrictions.

[0025] Because interference may occur during the data collection, storage and transmission process in the logistics warehouse management system, resulting in data anomalies or data loss, data cleaning is performed on the collected data, including anomaly detection and missing value filling, and the collected data is standardized. The specific data cleaning process is well known to those skilled in the art and will not be described in detail in this application.

[0026] In this embodiment, taking the inbound and outbound frequency data as an example, the maximum value among all the collected inbound and outbound frequency data is statistically analyzed, and the ratio of each inbound and outbound frequency data to the maximum value is used as the result of standardization processing. As for other implementation methods, based on the ability to standardize the inbound and outbound frequency data, implementers can adopt other existing feasible technologies, and this application does not impose any special restrictions.

[0027] Step 2: Divide the preset time period to obtain the time intervals of various goods; obtain the correlation characteristic value and correlation coefficient between various goods and each goods in the order within each time interval, obtain the influence factor of various goods within each time interval, and obtain the influence characteristic value and dynamic influence coefficient of various goods.

[0028] In the process of warehouse storage location allocation and management, due to the dynamic differences in logistics cargo storage and turnover information over time, it is difficult to meet the current actual warehousing allocation needs based solely on the static information of the goods. For example, if the current inbound and outbound frequency and turnover rate of goods are high, the goods need to be allocated to storage locations that are convenient to access and have short handling distances. In addition, if the volume and weight of the goods are large and the handling demand is high, it is necessary to further combine the allocation of storage space in the warehouse to achieve a balance between logistics warehouse storage and handling efficiency.

[0029] Step 2.1: Obtain the time intervals for each type of goods by analyzing the abrupt changes in the inventory management data of various goods; and obtain the correlation characteristic values ​​between each type of goods and each type of goods in each order within each time interval by analyzing the correlation and difference in the inventory management data of each type of goods within each time interval.

[0030] Based on the above analysis, taking inbound and outbound frequency data as an example, the preset time period is divided by analyzing the abrupt changes in the inbound and outbound frequency data of various goods within the preset time period, resulting in different time intervals for each type of goods. Specifically: The data on the frequency of entry and exit of various goods within the preset time period are arranged in chronological order, and each abrupt change point is obtained. The period in which each abrupt change point is located is used as a segmentation point, and the preset time period is divided into time intervals for various goods.

[0031] In this embodiment, the Pettitt mutation point detection algorithm is used to obtain mutation points. The Pettitt mutation point detection algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, based on the ability to obtain mutation points, implementers may use other existing technologies, such as the Bayesian mutation point detection algorithm, the Mann-Kendall mutation point detection algorithm, etc. This application does not impose any special restrictions.

[0032] Based on the method of obtaining various time intervals for various goods from inbound and outbound frequency data, various time intervals for various goods are obtained from inventory turnover rate data.

[0033] Based on the inbound and outbound frequency data, and according to the various time intervals obtained for different types of goods, the dynamic characteristics of the storage and transfer processes of various goods in the warehouse are analyzed, specifically: For each type of goods in the same order as various other goods within each time interval, denoted as each associated goods within each time interval, the correlation coefficient of the inbound and outbound frequency data between each type of goods and its associated goods within each time interval is calculated. The larger the correlation coefficient, the more significant the dynamic update correlation characteristics between each type of goods and its associated goods. The mean of the inbound and outbound frequency data of each type of goods within each time interval is calculated. The average of the inbound and outbound frequency data of each associated goods within each time interval is calculated. The difference between the mean and the average value is calculated to determine the time-varying comparison characteristics of the warehouse storage dynamic information between each type of goods and its associated goods.

[0034] In this embodiment, the correlation coefficient is the Pearson correlation coefficient, which is a well-known technology and will not be described in detail here. As other implementation methods, based on the ability to measure the correlation between inbound and outbound frequency data in time series, implementers may use other existing technologies, such as Spearman correlation coefficient, Kendall rank correlation coefficient, etc. This application does not impose any special restrictions.

[0035] In this embodiment, the difference between the mean and the average is the absolute value of the difference. As another implementation, based on the ability to measure the degree of difference between the mean and the average, the implementer may use other calculation methods, such as ratio, square of difference, etc. This application does not impose any special restrictions.

[0036] Furthermore, by using the correlation coefficient and the difference value, the correlation characteristic value between various goods and their associated goods in each time interval is obtained. Specifically, the correlation characteristic value is directly proportional to the correlation coefficient and inversely proportional to the difference value. The larger the calculated correlation characteristic value, the more significant the correlation between the inbound and outbound frequency data of various goods and the inbound and outbound frequency data of their associated goods in each time interval.

[0037] In this embodiment, the expression for the association characteristic value between various goods and their associated goods in each time interval is as follows: In the formula, This represents the association feature value between the x-th type of goods and its i-th associated goods within the j-th time interval; This represents the correlation coefficient between the inbound and outbound frequency data of the x-th type of goods and its i-th related type of goods in the j-th time interval. This represents the difference between the mean of the x-th type of goods and the mean of its i-th associated type of goods within the j-th time interval; τ represents a preset value greater than 0, used to represent the difference value. The value of τ is preset by the user and can be set by the implementer. In this embodiment, the value of τ is 0.01. There are many ways to map data to positive numbers. The implementer can choose other existing feasible methods. This application does not impose any special restrictions.

[0038] Step 2.2: By using the correlation feature values ​​between various goods and each type of goods in each time interval, and combining the occurrence of each type of goods, obtain the correlation coefficient between various goods and each type of goods in each time interval, and then obtain the influence factor of various goods in each time interval.

[0039] Based on the above analysis, a comprehensive analysis of the dynamic information change characteristics of goods at different time periods is conducted. The more significant the correlation information between various goods and their associated goods regarding the dynamic changes in warehouse storage space at different time periods, the greater the impact of the dynamic information changes of various goods on warehouse storage space allocation. The proportion of the order quantity of each associated good within the orders of various goods in each time interval is denoted as the frequency of occurrence of each associated good within each time interval. Considering that the frequency of occurrence of each associated good varies across time intervals, a higher frequency indicates a greater correlation between the frequency of occurrence of each associated good and the current warehouse storage space allocation of each good.

[0040] Furthermore, by analyzing the correlation characteristic values ​​between various goods and their associated goods within each time interval, and combining this with the occurrence of each associated goods within each time interval, the correlation coefficient between various goods and their associated goods within each time interval is obtained. The expression is as follows: In the formula, This represents the correlation coefficient between the x-th type of goods and its i-th associated goods during the j-th time interval. This represents the association feature value between the x-th type of goods and its i-th associated goods within the j-th time interval; This represents the frequency of occurrence of the i-th associated goods of the x-th type of goods within the j-th time interval.

[0041] It should be noted that the larger the calculated correlation coefficient, the greater the impact of the dynamic information changes of the xth type of goods on the allocation of warehouse storage locations.

[0042] Furthermore, in order to accurately analyze the impact characteristics of the dynamic information changes of various goods at different time periods on warehouse storage location allocation, the average value of the correlation coefficient between various goods and all related goods in each time interval is used as the influence factor of various goods in each time interval. The larger the influence factor, the more serious the impact of the dynamic information changes of various goods on warehouse storage location allocation is when the dynamic information correlation changes of all related goods in the same time period are analyzed.

[0043] Step 2.3: By combining the influencing factors of various goods in each time interval with the inventory management data distribution of various goods in each time interval, the influence characteristic values ​​of various goods are obtained, so as to obtain the dynamic influence coefficient of various goods.

[0044] Based on the above analysis, the dynamic information characteristics of various goods in the logistics warehouse storage and handling process are analyzed by examining the impact of dynamic information changes of various goods in different intervals on warehouse storage location. Based on the analysis results, the dynamic information characteristics of goods affecting warehouse storage location are accurately extracted, specifically: Calculate the arithmetic mean of the inbound and outbound frequency data for various goods in each time interval. Then, calculate the sum of these arithmetic means for all goods across all time intervals. The ratio of the arithmetic mean to the sum for each goods in each time interval is taken as the significance value of the influence of each goods in each time interval. Finally, combining this with the influence factors for each goods in each time interval, the influence characteristic values ​​for each goods are obtained, expressed as follows: In the formula, This represents the influence characteristic value of the x-th type of goods; , Let represent the significance value and influence factor of the x-th type of goods in the j-th time interval, respectively; This represents the number of time intervals for the xth type of goods.

[0045] It should be noted that the larger the calculated influence characteristic value, the greater the impact of the dynamic information changes of the xth type of goods on the allocation of storage locations during the warehouse storage process.

[0046] Following the method of obtaining the influence characteristic values ​​of various goods based on inbound / outbound frequency data, the influence characteristic values ​​of various goods are obtained based on inventory turnover rate data. Normalized values ​​of the influence characteristic values ​​of various goods calculated based on inbound / outbound frequency data are obtained; normalized results of the influence characteristic values ​​of various goods calculated based on inventory turnover rate data are obtained; the mean of the normalized values ​​and the normalized results is used as the dynamic influence coefficient of various goods. A schematic diagram of the calculation process of the dynamic influence coefficient is shown below. Figure 2 As shown in the diagram. The process for obtaining the dynamic influence coefficient is illustrated below. Figure 3 As shown.

[0047] Step 3: Use a genetic algorithm to iteratively obtain the optimal storage location for each item. In each iteration, based on the inventory management data of each item, the dynamic influence coefficient, the channel occupancy rate of each item's channel, the distance the automated guided vehicle transports the items, and the total weight of the items placed on each shelf after simulated storage, obtain the fitness value of each individual item.

[0048] Based on the data collected in step 1, a genetic algorithm is used to accurately allocate warehouse storage locations. Specifically, in the process of allocating warehouse storage locations using the genetic algorithm, the initial population is preset to have 100 individuals, and each individual represents a storage location allocation scheme. Each storage location scheme includes storage locations for each type of goods that need to be stored at the current moment. Here, 100 is merely one embodiment of this application; implementers can set the number of individuals in the initial population according to actual circumstances, and this application does not impose any special restrictions.

[0049] By simulating the storage of corresponding goods in each storage location within an individual, in each iteration, based on the inventory management data of each good, the dynamic influence coefficient, the channel occupancy rate of each good's channel, and the distance the automated guided vehicle (AGV) transports the goods, the fitness value of each individual is obtained, expressed as: In the formula, α represents the fitness value of individual v after the nth iteration; α and β both represent preset weight values, and the sum of α and β is 1. This represents the dynamic information change value of all goods based on the individual v during the nth iteration. This represents the device adaptation feature value based on the simulated storage of individual v during the nth iteration; This represents the penalty term used in the nth iteration when simulating storage based on individual v; specifically: Where u represents the total number of goods that need to be stored at the current moment; This represents the inbound and outbound frequency data of goods c in the previous period at the current moment. This represents the dynamic impact coefficient of cargo c; This represents the distance from the storage location of good c to the warehouse exit. The higher the frequency of good c's entry and exit, and the closer good c is to the warehouse exit, the more convenient the handling of the goods. Simultaneously, the greater the dynamic impact coefficient of changes in the dynamic information of good c on warehouse storage location allocation, the higher the calculated value. The larger the value, the better the storage allocation scheme. ;in, This indicates the channel occupancy rate of the channel where cargo c is located. This indicates the distance from the automated guided vehicle (AGV) used to transport goods c to goods c. ;in, , These represent the first and second preset constants, respectively; Q represents the total number of shelves currently used for storing goods; max() represents the operation of taking the maximum value; This represents the total weight of the goods placed on the shelf s after the storage simulation based on individual v in the nth iteration. Let represent the load-bearing threshold of shelf s, and R represent the number of automated guided vehicles used in the storage simulation based on individual v in the nth iteration. This represents the distance from the z-th automated guided vehicle to the storage location of the goods to be transported. This represents the maximum range of the z-th automated guided vehicle.

[0050] In this embodiment, the values ​​of α and β are 0.6 and 0.4, respectively. The values ​​of α and β are preset by humans, and the implementer can set them according to the actual situation. This application does not impose any special restrictions.

[0051] In this embodiment, the preset values ​​of the first and second constants are 5 and 8, respectively. The preset values ​​of the first and second constants are both preset by humans, and the implementer can set them according to the actual situation. This application does not impose any special restrictions.

[0052] In this embodiment, the distances involved in calculating the fitness value are all Euclidean distances. In order to avoid the influence of dimensions on data analysis, the Sigmoid function is used to normalize the calculated distances. The Sigmoid function is a well-known technique and will not be described in detail in this application.

[0053] In the process of using a genetic algorithm to iteratively obtain the optimal storage location for each cargo, the higher the fitness value of an individual, the better the storage location allocation scheme. After the genetic algorithm iteration is completed, the optimal storage location for each cargo is output. The process of obtaining the optimal storage location based on the genetic algorithm iteratively is a well-known technique and will not be described in detail in this application.

[0054] In this embodiment, the maximum number of iterations is 150, the crossover probability is 0.8, and the mutation probability is 0.01. The values ​​of the maximum number of iterations, crossover probability, and mutation probability are all preset by the user. The implementer can set them according to the actual situation. This application does not impose any special restrictions.

[0055] Based on the same inventive concept as the above methods, this application also provides a warehouse storage location allocation system based on dynamic analysis, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described warehouse storage location allocation methods based on dynamic analysis.

[0056] In summary, this application, by detecting sudden changes in the inventory management data of various goods, can capture the time-varying characteristics of dynamic information of various goods. By dividing the preset time period into multiple time intervals, it can analyze the dynamic changes of various goods in a more refined manner. By analyzing the correlation characteristics of dynamic information updates between various goods and their related goods, as well as the differences in dynamic changes between various goods and their related goods, it can gain a more comprehensive understanding of the correlation characteristics between various goods and their related goods. This helps to optimize the storage location allocation scheme in the future. Furthermore, by combining the frequency of occurrence of related goods, it can assess the degree of impact of dynamic information changes of various goods on storage location allocation. This helps to focus on goods with greater impact of dynamic information changes on storage location allocation when optimizing the storage location allocation scheme in order to obtain the optimal storage location allocation scheme. Furthermore, by calculating the dynamic influence coefficient, the dynamic change characteristics of goods can be more accurately reflected, providing a quantitative basis for subsequent storage location allocation. In addition, when using the genetic algorithm to iteratively obtain the optimal storage location allocation scheme, multiple factors can be considered to comprehensively evaluate the merits of the allocation scheme and ensure the rationality and effectiveness of the optimization results. This application can dynamically adjust the storage location allocation scheme according to the dynamic change characteristics of goods, improve the accuracy of warehouse storage location allocation, and thus improve the operational efficiency of logistics warehouses.

[0057] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0058] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A warehouse storage location allocation method based on dynamic analysis, characterized in that, The method includes the following steps: Retrieve inventory management data for various goods that need to be stored at the current moment, covering each period within a preset timeframe prior to the current moment; By analyzing the abrupt changes in inventory management data for various goods, we obtain the time intervals for each type of goods. By analyzing the correlation and differences in inventory management data between each type of goods and each type of goods in their respective orders within each time interval, we obtain the correlation characteristic values ​​between each type of goods and each type of goods within each time interval. Combining this with the occurrence of each type of goods, we obtain the correlation coefficients between each type of goods and each type of goods within each time interval, thereby obtaining the influence factors of each type of goods within each time interval. Finally, by combining this with the distribution of inventory management data for each type of goods within each time interval, we obtain the influence characteristic values ​​of each type of goods, thus obtaining the dynamic influence coefficients of each type of goods. The optimal storage location for each item is obtained iteratively using a genetic algorithm. In each iteration, the fitness value of an individual item is obtained based on the inventory management data of each item, the dynamic influence coefficient, the channel occupancy rate of the channel where each item is located, the distance the automated guided vehicle transports the item, and the total weight of the items placed on each shelf after simulated storage.

2. The warehouse storage location allocation method based on dynamic analysis as described in claim 1, characterized in that, The process of obtaining each time interval is as follows: The inventory management data of various goods within the preset time period are arranged chronologically, and each mutation point is obtained. The period in which each mutation point is located is used as a dividing point, and the preset time period is divided into time intervals for various goods.

3. The warehouse storage location allocation method based on dynamic analysis as described in claim 1, characterized in that, The process for obtaining the associated feature values ​​is as follows: Each type of goods in the same order as various goods within each time interval is recorded as the associated goods of various goods within each time interval; Calculate the correlation coefficients of inventory management data between various goods and their related goods in each time interval; Calculate the average inventory management data for various goods across different time intervals; Calculate the average value of inventory management data for each type of goods in each time interval; Calculate the difference between the mean and the average. The correlation feature value is directly proportional to the correlation coefficient and inversely proportional to the difference value.

4. The warehouse storage location allocation method based on dynamic analysis as described in claim 3, characterized in that, The process of obtaining the correlation coefficient is as follows: Obtain the order quantity percentage of each associated product within the orders of various products in each time interval; The correlation coefficient is the product of the correlation feature value and the quantity proportion.

5. The warehouse storage location allocation method based on dynamic analysis as described in claim 3, characterized in that, The influencing factor is the average of the correlation coefficients between various goods and all their related goods in each time interval.

6. The warehouse storage location allocation method based on dynamic analysis as described in claim 1, characterized in that, The process for obtaining the influencing feature values ​​is as follows: Calculate the arithmetic mean of inventory management data for various goods in each time interval, calculate the sum of the arithmetic means of various goods in all time intervals, and take the ratio of the arithmetic mean of various goods in each time interval to the sum as the significance value of the influence of various goods in each time interval. The significant impact value of various goods in each time interval is calculated as the product of the impact factor and the impact characteristic value. The impact characteristic value is obtained by the product value of various goods in all time intervals.

7. The warehouse storage location allocation method based on dynamic analysis as described in claim 6, characterized in that, The influence characteristic value is the sum of the product values ​​of various goods over all time intervals.

8. The warehouse storage location allocation method based on dynamic analysis as described in claim 1, characterized in that, The process for obtaining the dynamic influence coefficient is as follows: Inventory management data includes inbound and outbound frequency data and inventory turnover rate data; Obtain the normalized values ​​of the impact characteristic values ​​of various goods calculated based on inbound and outbound frequency data; Obtain the normalized results of the impact characteristic values ​​of various goods calculated based on inventory turnover rate data; The dynamic influence coefficient is the average of the normalized value and the normalization result.

9. The warehouse storage location allocation method based on dynamic analysis as described in claim 8, characterized in that, The expression for the fitness value is: In the formula, α represents the fitness value of individual v after the nth iteration; α and β both represent preset weight values. This represents the dynamic information change value of all goods based on the individual v during the nth iteration. This represents the device adaptation feature value based on the simulated storage of individual v during the nth iteration; This represents the penalty term used in the nth iteration when simulating storage based on individual v; specifically: Where u represents the total number of goods that need to be stored at the current moment; This represents the inbound and outbound frequency data of goods c in the previous period at the current moment. This represents the dynamic impact coefficient of cargo c; This represents the distance from the storage location of goods c to the warehouse exit; ;in, This indicates the channel occupancy rate of the channel where cargo c is located. This indicates the distance from the automated guided vehicle (AGV) used to transport goods c to goods c. ;in, , These represent the first and second preset constants, respectively; Q represents the total number of shelves currently used for storing goods; max() represents the operation of taking the maximum value; This represents the total weight of the goods placed on the shelf s after the storage simulation based on individual v in the nth iteration. Let represent the load-bearing threshold of shelf s, and R represent the number of automated guided vehicles used in the storage simulation based on individual v in the nth iteration. This represents the distance from the z-th automated guided vehicle to the storage location of the goods to be transported. This represents the maximum range of the z-th automated guided vehicle.

10. A warehouse storage location allocation system based on dynamic analysis, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the warehouse storage location allocation method based on dynamic analysis as described in any one of claims 1-9.

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