Data storage method and system based on intelligent warehouse management

Abstract

The invention relates to the technical field of warehouse management, in particular to a data storage method and system based on intelligent warehouse management, and the method comprises the steps: forming a comprehensive data storage structure through creating a three-dimensional shelf index and formulating a storage strategy, and improving the data access speed; by generating the intelligent cargo packaging information, the storage cost can be reduced, and the storage efficiency can be improved; the access efficiency of goods data is improved by adaptively planning a goods picking path and dynamically adjusting goods picking task distribution; through a block chain technology, a unique hash value is generated for each cargo, and transactions are recorded on key nodes, so that the security of data storage is improved, the data is prevented from being maliciously modified or deleted, and meanwhile, a chain storage structure ensures the integrity and historical traceability of the data. The method is used for solving the technical problems of low data storage efficiency, insufficient storage security, lack of efficient order picking paths and task allocation and the like in current warehouse management.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to a data storage method and system based on smart warehouse management. Background Art

[0002] In the current warehouse management field, there are multiple challenges: low storage efficiency. Traditional warehouse management may lack efficient organization and retrieval mechanisms in data storage, resulting in slow data access and low storage efficiency; data integrity is difficult to ensure. In traditional warehouse management, data records in the process of storage, picking and delivery of goods may not be detailed or accurate enough, and there is a lack of effective means to ensure data integrity and historical traceability; insufficient data storage security. Traditional data storage methods may be susceptible to malicious modification or deletion, resulting in data loss or damage, bringing risks to warehouse management; lack of efficient picking paths and task allocation. In traditional warehouse management, the planning of picking paths may not be optimized enough, and the picking task allocation may not be reasonable enough, resulting in long picking time and low efficiency of cargo data access. To this end, the present invention proposes a data storage method and system based on intelligent warehouse management. Summary of the invention

[0003] The purpose of the present invention is to solve the problems in the background technology and to propose a data storage method and system based on smart warehouse management.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: The data storage method based on smart warehouse management includes: Step 1: In the warehouse layout, create a three-dimensional shelf index containing physical location, logical label and timestamp for all goods, and formulate a hierarchical storage strategy; Step 2: Based on the three-dimensional shelf index, the picking frequency, size change and inventory of goods are monitored in real time, and the storage density of goods at each index position is automatically calculated using a formula. The optimal packaging method is determined by controlling the cost, thereby generating intelligent packaging information; Step 3: According to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, adaptively plan the picking path and dynamically adjust the picking task allocation to achieve load balancing; Step 4: Introduce blockchain technology to generate a unique hash value for each product and record corresponding blockchain transactions at key nodes of product entry, storage, picking and delivery. At the same time, use smart contracts to perform automated cargo integrity checks.

[0005] Furthermore, the process of creating a three-dimensional shelf index containing physical locations, logical labels, and timestamps for all goods includes: The shelves, layers and locations in the warehouse are defined by the collection Represented by; where the storage structure of the warehouse is represented by Shelves, each shelf is divided into Each floor has Positions form a three-dimensional storage space, that is, the physical location of the goods ; Represents a collection of shelves, Represents the number of shelves; for each shelf , whose number of layers is ,in It's a shelf Number of layers; for each shelf and , whose location set ,in It's a shelf No. The number of positions on the layer; Using triples represents a three-dimensional shelf index; where They respectively represent the physical location, logical label and timestamp of the goods; the physical location indicates the storage location of the goods, that is, the number of shelves and areas; the logical label is used to uniquely identify each product in the warehouse management system, including the product SKU code, category code, remarks information and any security-related identification; the timestamp is used to record the goods' entry time, picking time, exit time and any operation time related to security checks.

[0006] Furthermore, the process of developing a tiered storage strategy includes: Based on the three-dimensional shelf index, all goods are classified and divided into three levels: weight level, physical property level and value category level. , which is divided into lightweight , Middleweight and heavyweight ,and , physical property layer: Assume that the physical properties of the goods are , which is divided into fragile , moisture resistance , Dustproof , Value category layer: according to the value of the goods , classifying it as low value , medium value and high value ,and ; After the attribute classification and hierarchical division are completed, the attribute classification of the goods' weight layer, physical property layer, and value category layer are combined with the initial three-dimensional shelf index, and the three-dimensional shelf index is updated accordingly. In the updated index, a mark of the level to which each product belongs is added for storage to fully reflect all the hierarchical information to which the goods belong.

[0007] Furthermore, based on the three-dimensional shelf index, the process of real-time monitoring of the picking frequency, size change, and inventory of goods includes: By time period The statistical period is used to count the total number of picking operations recorded during this period. , calculate the picking frequency of goods ; Among them, the time period for a specified period of time, such as a day, week, or quarter; Picking frequency Normalize and define the minimum and maximum values ​​of picking frequency as , then the normalized picking frequency ; Get cargo size changes , where They are vector representations of the changes in the cargo size data in each dimension. When the goods are put into storage, the warehouse manager uses a measuring tool to detect and record the size of the goods to obtain the initial size data of the goods. ; Warehouse managers regularly conduct spot checks on inventory items to check if the size of the items has changed and update the size data of the goods ; Changes in the size of the goods obtained Normalize the size of the cargo by calculating the modulus of change, i.e. ; Mark the reference value of the cargo size change as , then the normalized cargo size changes ; Read inventory information based on the warehouse's automated equipment and automatically update inventory quantities ; For the acquired inventory After normalization, we get , where Respectively represent the minimum and maximum value of the goods inventory; Let the weight coefficient , then the comprehensive impact factor Among them, the comprehensive impact factor The impact of picking frequency, size changes and inventory levels on cargo storage strategy is comprehensively considered. If the picking frequency is higher, the size changes are greater or the inventory level is smaller, a more special storage method or more frequent repackaging is required.

[0008] Furthermore, the attention mechanism is introduced into the adaptive neural network, and the process of optimizing feature representation includes: The formula is used to automatically calculate the storage density of goods at each index position, and the optimal packaging method is determined by controlling the cost. The process of generating intelligent packaging information includes: For 3D shelf indexing The original shape of the goods is defined as a regular cuboid, and the length, width and height of the goods are , the original volume is ; Combined with the size change of the goods, the length, width, height and volume become , ; The total volume of the warehouse is marked as , the occupied volume is marked as , then the remaining available space in the warehouse is ; Calculate storage density using the formula , where storage density reflects the storage volume of goods per unit of remaining space. A higher storage density can make more efficient use of warehouse space; In order to determine the best packaging method and generate intelligent packaging information, suppose there is possible packaging methods, and the length, width, height and volume of each packaging method are and , where An index indicating the packaging method. Indicates the number of packaging methods; Mark the cost of packing goods as ; Among them, the cost of goods packaging includes packaging material cost, labor cost, etc.; Establish the cargo packaging cost function. The specific establishment process is as follows: According to the proportional relationship between packaging material cost and packaging surface area, the packaging material cost is calculated by the formula , where Indicates the packaging material cost ratio coefficient; Since the labor cost is proportional to the complexity of the packaging operation, the labor cost is obtained using the calculation formula , where represents the complexity function ( The larger the volume, the more complicated the packaging, and the higher the labor cost). It represents the labor cost ratio coefficient; Then the total cost of goods packaging is obtained: ; Using the objective function Find the packaging method that minimizes the total cost of packaging goods, where the constraints of the objective function are , where It is the minimum required storage density to ensure that warehouse storage space is utilized reasonably while minimizing costs.

[0009] Furthermore, according to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, the picking path is adaptively planned, and the picking task allocation is dynamically adjusted to achieve load balancing. The process includes: Based on the warehouse Shelves, then The location coordinates of the shelves are ;in, Indicates the shelf number; According to the three-dimensional shelf index and intelligent cargo packaging information, the cargo is The probability of picking on a shelf is ; Among them, the logistic regression model is used to predict the probability of picking , where are the parameters of the logistic regression model, Indicates Comprehensive impact factor of goods on each shelf; Set the picker's initial position to , defined from the initial position to the The distance between the shelves is ; Based on The picking probability of the goods on the shelf and the corresponding picking distance of the picker are used to calculate the total length of the picking path , where the total length formula of the picking path considers the weighted path length of the picking probability, and the shelf with a high picking probability contributes more to the total path length; In order to achieve load balancing, the maximum load capacity of each picker is defined as , has been assigned to the picker The task load is , new picking tasks to the shelf The load is ; Among them, the load is measured according to the weight, volume or complexity of the goods. , where Indicates The weight of the goods on the shelf, Indicates The volume of goods on the shelf, Indicates The picking complexity corresponding to the goods on the shelf; the load balancing constraints are: , where Indicates that it is assigned to the picker The set of shelves for the new task, where the load balancing constraint ensures that the load of each picker does not exceed its maximum load capacity; Dynamically adjust the picking task allocation and introduce the task allocation matrix ,in, Indicates the picker number, Indicates the shelf number; when When The picking task is assigned to The picker, when When , the allocation is stopped; Based on the total length of the picking path, a picking time function is established, and the establishment process is as follows: The walking speed of the picker is recorded as , then the time the picker spends on the path is ; Assume that the additional time cost caused by load imbalance is , the standard deviation of the load is used to measure the degree of load imbalance: the load mean and standard deviation are calculated using the formula , , where Indicates the number of pickers, Indicates shelf of load; assuming that the extra time cost is proportional to the load standard deviation, that is, ,in An adjustment factor that represents the relationship between the extra time cost and the load standard deviation; The picking time is calculated by the formula: ; Then the picking time objective function is obtained as: , where The coefficient that represents the trade-off between the impact of path length and load balancing on picking time. By optimizing this objective function, the picking task allocation can be dynamically adjusted to achieve load balancing and reduce picking time and cost.

[0010] Furthermore, blockchain technology is introduced to generate a unique hash value for each cargo, and the corresponding blockchain transactions are recorded at the key nodes of cargo entry, storage, picking and delivery. At the same time, the process of automated cargo integrity inspection using smart contracts includes: Based on the three-dimensional shelf index of the goods, a hash algorithm is used to generate a unique hash value for the goods; When the goods enter the warehouse, record the goods entry hash value , the time of entry into the warehouse, and the three-dimensional shelf index location of the goods, and use it as a blockchain transaction; among them, this transaction is the initial record of the goods in the warehouse, laying the foundation for subsequent tracking and management; During the storage process, the change of the location of goods is a common operation: when goods are moved from one shelf location to another, the attributes of the goods change, including the change of the three-dimensional shelf index and the overall attribute evaluation of the goods (the overall state of the goods represents the transformation of the physical property layer, because the shelf location may be related to the storage conditions of the goods, such as the guarantee of physical properties such as moisture-proof and dust-proof); re-obtain the hash value according to the new attributes , and record the shelf location and movement time before and after the goods are moved, which can ensure that any changes in the goods during the storage process can leave accurate records on the blockchain, improve the security of goods data storage, and track the storage trajectory of goods through changes in hash values; Picking operations involve direct handling of goods, changing the properties of goods. For example, when the package needs to be opened to pick some goods, the moisture-proof seal may be destroyed, thus affecting the physical properties of the goods. The hash value is re-obtained based on the properties of the goods after picking (specifically including the changed physical properties, the original weight layer and value category layer, and the shelf position at that time). , and record the outbound time and three-dimensional shelf index location; Understandably, through the hash value changes and transaction records throughout the entire warehousing, storage, picking and outbound process, the entire life cycle of the goods in the warehouse can be fully traced; Smart contracts are used to pre-set the rules for each node of the goods operation in the warehouse. When executing the key operations of warehousing, storage, picking and outbound delivery, the hash value of the goods is checked to see if it complies with the contract provisions (i.e. the relevant provisions on hash values ​​and the integrity provisions on transaction records, including the key nodes of warehousing, storage, picking and outbound delivery, and the corresponding warehousing transaction records, storage transaction records, picking transaction records, and outbound delivery link transaction records) to determine whether the attributes of the goods have changed. If the inspection at any node fails, including hash value anomalies (hash value anomalies refer to hash values ​​that do not match historical or expected values, specifically: on the one hand, when compared with historical values, during the entire flow of goods, the hash value should have changed in an orderly manner with the legal changes in the attributes of the goods. For example, in the storage process, if the goods do not undergo any property changes that affect the hash value calculation, but the hash value changes, it does not match the historical hash value, which is a hash value anomaly; on the other hand, when compared with the expected value, the change in the hash value should be consistent with the expected logic of the goods' operating procedures and property changes. For example, in the picking operation, if only part of the goods are picked up but the other properties of the goods are not changed, according to the contract, the hash value should not change, but the actual hash value changes, which does not meet the expected value and is also a hash value anomaly;) or if the transaction record is missing, the smart contract triggers the corresponding alarm or prompt mechanism and immediately notifies the warehouse administrator to handle it, thereby ensuring the accuracy and security of warehouse management.

[0011] Data storage system based on smart warehouse management, including: The index creation and storage strategy formulation module is used to create a three-dimensional shelf index containing physical location, logical label and timestamp for all goods in the warehouse layout, and formulate a hierarchical storage strategy; The intelligent packaging information generation module is used to monitor the picking frequency, size change and inventory of goods in real time based on the three-dimensional shelf index, automatically calculate the storage density of goods at each index position using a formula, and determine the best packaging method by controlling costs, thereby generating intelligent packaging information; The path planning and task allocation module is used to adaptively plan the picking path according to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, and dynamically adjust the picking task allocation to achieve load balancing; The blockchain tracking and contract checking module is used to introduce blockchain technology, generate a unique hash value for each cargo, and record corresponding blockchain transactions at key nodes of cargo entry, storage, picking and delivery; at the same time, smart contracts are used to perform automated cargo integrity checks.

[0012] Compared with the existing technology, the advantages of the data storage method and system based on intelligent warehouse management provided by the present invention are: 1. The present invention combines the physical location, logical label and timestamp of goods by creating a three-dimensional shelf index to form a comprehensive data storage structure, so that the warehouse management system can efficiently organize and retrieve goods data, and improve the data access speed; by classifying and dividing the goods by attributes, a hierarchical storage strategy is formulated, which helps to carry out targeted storage management according to the different characteristics of the goods (such as weight, physical characteristics and value), optimizes the use of storage space, and improves storage efficiency; 2. The present invention monitors the picking frequency, size change and inventory of goods in real time based on the three-dimensional shelf index, and uses a formula to calculate the storage density, which helps to reduce storage costs and improve storage efficiency. It determines the best packaging method by controlling costs, generates intelligent packaging information, and maximizes cost-effectiveness. It provides data support for the adjustment of data storage strategies and helps to achieve more intelligent storage management. 3. The present invention can reduce picking time and improve the access efficiency of cargo data by adaptively planning picking paths and dynamically adjusting picking task allocation. Through blockchain technology, a unique hash value is generated for each cargo, and transactions are recorded at key nodes to ensure the integrity and non-tamperability of cargo data. This distributed storage method improves the security of data storage and prevents data from being maliciously modified or deleted.

[0013] In summary, the present invention constitutes an efficient, intelligent and secure warehouse management system through four processes: index creation and storage strategy formulation, intelligent packaging information generation, path planning and task allocation, and blockchain tracking and contract checking. It helps to optimize the use of storage space, improve storage efficiency, reduce costs, ensure data integrity and security, and thus ensure the normal operation of subsequent data storage methods and systems based on smart warehouse management. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of the data storage method based on intelligent warehouse management proposed by the present invention.

[0015] Figure 2 This is a module diagram of the data storage system based on intelligent warehouse management proposed by the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] See also Figure 1 The present invention provides a data storage method based on smart warehouse management, comprising: Step 1: In the warehouse layout, create a three-dimensional shelf index containing physical location, logical label and timestamp for all goods, and formulate a hierarchical storage strategy; Step 2: Based on the three-dimensional shelf index, the picking frequency, size change and inventory of goods are monitored in real time, and the storage density of goods at each index position is automatically calculated using a formula. The optimal packaging method is determined by controlling the cost, thereby generating intelligent packaging information; Step 3: According to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, adaptively plan the picking path and dynamically adjust the picking task allocation to achieve load balancing; Step 4: Introduce blockchain technology to generate a unique hash value for each product and record corresponding blockchain transactions at key nodes of product entry, storage, picking and delivery. At the same time, use smart contracts to perform automated cargo integrity checks.

[0018] See also Figure 1 The present invention provides a data storage method based on smart warehouse management. In the step 1, in the warehouse layout, a three-dimensional shelf index including physical location, logical label and timestamp is created for all goods, and a hierarchical storage strategy is formulated, which includes: Step 101: Define the shelves, layers and positions in the warehouse by the set Represented by; where the storage structure of the warehouse is represented by Shelves, each shelf is divided into Each floor has Positions form a three-dimensional storage space, that is, the physical location of the goods ; Represents a collection of shelves, Represents the number of shelves; for each shelf , whose number of layers is ,in It's a shelf Number of layers; for each shelf and , whose location set ,in It's a shelf No. The number of positions on the layer; Step 102: Using triples represents a three-dimensional shelf index; where They represent the physical location, logical label and timestamp of the goods respectively; the physical location represents the storage location of the goods, i.e. the number of shelves and areas; the logical label is used to uniquely identify each product in the warehouse management system, including the product SKU code, category code, remarks and any safety-related identification; the timestamp is used to record the time of goods entering the warehouse, picking time, leaving the warehouse and any operation time related to safety inspection; Step 103: Based on the three-dimensional shelf index, all goods are attributed and divided into levels to obtain the following three levels of goods: weight level, physical property level and value category level; wherein, weight level: according to the weight of the goods , which is divided into lightweight , Middleweight and heavyweight ,and , physical property layer: Assume that the physical properties of the goods are , which is divided into fragile , moisture resistance , Dustproof , Value category layer: according to the value of the goods , classifying it as low value , medium value and high value ,and ; Step 104, after the attribute classification and hierarchical division are completed, the attribute classification of the weight layer, physical property layer, and value category layer of the goods are combined with the initial three-dimensional shelf index, and the three-dimensional shelf index is updated accordingly. In the updated index, a mark of the level to which each product belongs is added for storage to fully reflect all the hierarchical information to which the goods belong.

[0019] See also Figure 1 The present invention provides a data storage method based on smart warehouse management. In the second step, based on the three-dimensional shelf index, the picking frequency, size change and inventory of goods are monitored in real time, the storage density of goods at each index position is automatically calculated using a formula, and the optimal packaging method is determined by controlling the cost, so as to generate the smart packaging information. The steps include: Step 201: Time period The statistical period is used to count the total number of picking operations recorded during this period. , calculate the picking frequency of goods ; In step 201, the time period for a specified period of time, such as a day, week, or quarter; Step 202: Picking frequency Normalize and define the minimum and maximum values ​​of picking frequency as , then the normalized picking frequency ; Step 203: Obtain cargo size changes , where They are vector representations of the changes in the cargo size data in each dimension. When the goods are put into storage, the warehouse manager uses a measuring tool to detect and record the size of the goods to obtain the initial size data of the goods. ; Warehouse managers regularly conduct spot checks on inventory items to check if the size of the items has changed and update the size data of the goods ; Step 204: Change the size of the acquired goods Normalize the size of the cargo by calculating the modulus of change, i.e. ; Mark the reference value of the cargo size change as , then the normalized cargo size changes ; Step 205: Read the inventory information according to the warehouse automation equipment and automatically update the inventory quantity ; For the acquired inventory After normalization, we get , where Respectively represent the minimum and maximum value of the goods inventory; Step 206: Let the weight coefficient , then the comprehensive impact factor ; In step 206, the comprehensive impact factor The impact of picking frequency, size variation and inventory on the storage strategy of goods is comprehensively considered. If the picking frequency is higher, the size variation is greater or the inventory is smaller, a more special storage method or more frequent repackaging is required; Step 207: 3D shelf index The original shape of the goods is defined as a regular cuboid, and the length, width and height of the goods are , the original volume is ; Step 208: Combined with the size change of the goods, the length, width, height and volume are changed to , ; Step 209: Mark the total volume of the warehouse as , the occupied volume is marked as , then the remaining available space in the warehouse is ; Calculate storage density using the formula , where storage density reflects the storage volume of goods per unit of remaining space. A higher storage density can make more efficient use of warehouse space; Step 210: In order to determine the best packaging method and generate intelligent packaging information, assume that possible packaging methods, and the length, width, height and volume of each packaging method are and , where An index indicating the packaging method. Indicates the number of packaging methods; Step 211: Mark the goods packaging cost as ; Among them, the cost of goods packaging includes packaging material cost, labor cost, etc.; Step 212: Establish a cargo packaging cost function. The specific establishment process is as follows: According to the proportional relationship between packaging material cost and packaging surface area, the packaging material cost is calculated by the formula , where Indicates the packaging material cost ratio coefficient; Since the labor cost is proportional to the complexity of the packaging operation, the labor cost is obtained using the calculation formula , where represents the complexity function ( The larger the volume, the more complicated the packaging, and the higher the labor cost). It represents the labor cost ratio coefficient; Then the total cost of goods packaging is obtained: ; Using the objective function Find the packaging method that minimizes the total cost of packaging goods, where the constraints of the objective function are , where It is the minimum required storage density to ensure that warehouse storage space is utilized reasonably while minimizing costs.

[0020] See also Figure 1 The present invention provides a data storage method based on smart warehouse management. In step 3, according to the three-dimensional shelf index and intelligent cargo packaging information of the warehouse, the steps of adaptively planning the picking path and dynamically adjusting the picking task allocation to achieve load balancing include: Step 301, based on the warehouse Shelves, then The location coordinates of the shelves are ;in, Indicates the shelf number; Step 302: According to the three-dimensional shelf index and intelligent cargo packaging information, the cargo is The probability of picking on a shelf is ; Among them, the logistic regression model is used to predict the probability of picking , where are the parameters of the logistic regression model, Indicates Comprehensive impact factor of goods on each shelf; Step 303: Set the initial position of the picker to , defined from the initial position to the The distance between the shelves is ; Step 304: Based on The picking probability of the goods on the shelf and the corresponding picking distance of the picker are used to calculate the total length of the picking path ; In step 304, the total length formula of the picking path takes into account the weighted path length of the picking probability, and the shelf with a high picking probability contributes more to the total path length; Step 305: To achieve load balancing, define the maximum load capacity of each picker as , has been assigned to the picker The task load is , new picking tasks to the shelf The load is ; In step 305, the load is measured according to the weight, volume or complexity of the goods. , where Indicates The weight of the goods on the shelf, Indicates The volume of goods on the shelf, Indicates The picking complexity corresponding to the goods on the shelf; the load balancing constraints are: , where Indicates that it is assigned to the picker The set of shelves for the new task, where the load balancing constraint ensures that the load of each picker does not exceed its maximum load capacity; Step 306: Dynamically adjust the picking task allocation and introduce the task allocation matrix ,in, Indicates the picker number, Indicates the shelf number; when When The picking task is assigned to The picker, when When , the allocation is stopped; Step 307: Establish a picking time function based on the total length of the picking path, wherein the establishment process is as follows: The walking speed of the picker is recorded as , then the time the picker spends on the path is ; Assume that the additional time cost caused by load imbalance is , the standard deviation of the load is used to measure the degree of load imbalance: the load mean and standard deviation are calculated using the formula , , where Indicates the number of pickers, Indicates shelf of load; assuming that the extra time cost is proportional to the load standard deviation, that is, ,in An adjustment factor that represents the relationship between the extra time cost and the load standard deviation; The picking time is calculated by the formula: ; Then the picking time objective function is obtained as: , where The coefficient that represents the trade-off between path length and load balancing on picking time; In step 307, by optimizing this objective function, the picking task allocation can be dynamically adjusted to achieve load balancing and reduce picking time and cost.

[0021] See also Figure 1 The present invention provides a data storage method based on smart warehouse management. In step 4, blockchain technology is introduced to generate a unique hash value for each cargo, and the corresponding blockchain transactions are recorded at the key nodes of cargo entry, storage, picking and delivery. At the same time, the steps of using smart contracts to perform automated cargo integrity checks include: Step 401: Based on the three-dimensional shelf index of the goods, a unique hash value of the goods is generated using a hash algorithm; Step 402: When the goods enter the warehouse, record the goods entry hash value , the time of entry into the warehouse, and the three-dimensional shelf index location of the goods, and use it as a blockchain transaction; among them, this transaction is the initial record of the goods in the warehouse, laying the foundation for subsequent tracking and management; Step 403: During the storage process, the change of the location of the goods is a common operation: when the goods are moved from one shelf location to another, the attributes of the goods change, including the change of the three-dimensional shelf index and the overall attribute evaluation of the goods (the overall state of the goods represents the transformation of the physical property layer, because the shelf location may be related to the storage conditions of the goods, such as the guarantee of physical properties such as moisture-proof and dust-proof); re-obtain the hash value according to the new attributes , and record the shelf location and movement time before and after the goods are moved, which can ensure that any changes in the goods during the storage process can leave accurate records on the blockchain, improve the security of goods data storage, and track the storage trajectory of goods through changes in hash values; Step 404: The picking operation involves direct handling of the goods, which changes the properties of the goods. For example, when the package needs to be opened to pick some of the goods, the moisture-proof seal may be damaged, thereby affecting the physical properties of the goods. The hash value is re-obtained based on the properties of the goods after picking (specifically including the changed physical properties, the original weight layer and value category layer, and the shelf position at that time). , and record the outbound time and three-dimensional shelf index location; In step 404, the entire life cycle of the goods in the warehouse can be completely traced through the hash value changes and transaction records during the entire warehousing, storage, picking and outbound process; Step 405: Use smart contracts to pre-set rules for each node of the goods operation in the warehouse. When performing key operations such as warehousing, storage, picking and outbound delivery, check whether the hash value of the goods complies with the contract provisions (i.e., hash value-related provisions and transaction record integrity provisions, including warehousing, storage, picking and outbound delivery key nodes, and corresponding warehousing transaction records, storage transaction records, picking transaction records, and outbound delivery link transaction records) to determine whether the attributes of the goods have changed. If any node fails the check, including hash value anomalies (hash value anomalies refer to hash values ​​that do not match historical or expected values, specifically: on the one hand, when compared with historical values, during the entire flow of goods, the hash value should have changed with the legal changes in the attributes of the goods. Orderly changes. For example, in the storage process, if the goods do not undergo any property changes that affect the hash value calculation, but the hash value changes, it does not match the historical hash value, which is a hash value anomaly. On the other hand, when compared with the expected value, the change in the hash value should be consistent with the expected logic of the goods' operating procedures and property changes. For example, in the picking operation, if only part of the goods are picked up but the other properties of the goods are not changed, the hash value should not change according to the contract, but the actual hash value changes, which does not meet the expected value and is also a hash value anomaly;) or if the transaction record is missing, the smart contract triggers the corresponding alarm or prompt mechanism and immediately notifies the warehouse administrator to handle it, thereby ensuring the accuracy and security of warehouse management.

[0022] In the embodiment of the present invention, by creating a three-dimensional shelf index containing physical location, logical label and timestamp for the goods, accurate management of the goods can be achieved, and the goods can be quickly located and found. By formulating a hierarchical storage strategy, a reasonable layout can be made according to the attributes of the goods (such as weight, physical characteristics and value), and the utilization rate of the warehouse space can be improved. By real-time monitoring of the picking frequency, size change and inventory of the goods, it is helpful to timely understand the status of the goods. By calculating the storage density and packaging cost, the best packaging method can be found to reduce the packaging and transportation costs. By dynamically adjusting the storage strategy according to the change of the goods status, the flexibility of warehouse management is improved. By dynamically adjusting the picking task allocation, it is ensured that the load of each picker does not exceed its maximum capacity, and the overall picking efficiency is improved. The whole life cycle information of the goods is recorded through blockchain technology to ensure the integrity and non-tamperability of data storage. By using smart contracts to perform automated goods integrity checks, the inspection efficiency and accuracy can be improved. In summary, the examples of the present invention involve decisions on data collection and analysis, result generation and performance evaluation, and solve the technical problems of low data storage efficiency, insufficient storage security, lack of efficient picking paths and task allocation in current warehouse management. In actual situations, more data and contextual information may be needed to make specific decisions and optimize solutions.

[0023] See also Figure 2 The present invention provides a data storage system based on intelligent warehouse management, comprising: The index creation and storage strategy formulation module is used to create a three-dimensional shelf index containing physical location, logical label and timestamp for all goods in the warehouse layout, and formulate a hierarchical storage strategy; The intelligent packaging information generation module is used to monitor the picking frequency, size change and inventory of goods in real time based on the three-dimensional shelf index, automatically calculate the storage density of goods at each index position using a formula, and determine the best packaging method by controlling costs, thereby generating intelligent packaging information; The path planning and task allocation module is used to adaptively plan the picking path according to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, and dynamically adjust the picking task allocation to achieve load balancing; The blockchain tracking and contract checking module is used to introduce blockchain technology, generate a unique hash value for each cargo, and record corresponding blockchain transactions at key nodes of cargo entry, storage, picking and delivery; at the same time, smart contracts are used to perform automated cargo integrity checks.

[0024] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0025] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically based on the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0026] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0027] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0028] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0029] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0030] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0031] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data storage method based on intelligent warehouse management, characterized in that: Step 1: In the warehouse layout, create a three-dimensional shelf index containing physical location, logical label and timestamp for all goods, and formulate a hierarchical storage strategy; Step 2: Based on the three-dimensional shelf index, the picking frequency, size change and inventory of goods are monitored in real time, and the storage density of goods at each index position is automatically calculated using a formula. The optimal packaging method is determined by controlling the cost, thereby generating intelligent packaging information; Step 3: According to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, adaptively plan the picking path and dynamically adjust the picking task allocation to achieve load balancing; Step 4: Introduce blockchain technology to generate a unique hash value for each product and record corresponding blockchain transactions at key nodes of product entry, storage, picking and delivery. At the same time, use smart contracts to perform automated cargo integrity checks.

2. The data storage method based on intelligent warehouse management according to claim 1 is characterized in that: In step 1, the process of creating a three-dimensional shelf index including physical location, logical label and time stamp for all goods includes: The shelves, layers and locations in the warehouse are defined by the collection Represented by; where the storage structure of the warehouse is represented by Shelves, each shelf is divided into Each floor has Positions form a three-dimensional storage space, that is, the physical location of the goods ; Represents a collection of shelves, Represents the number of shelves; for each shelf , whose number of layers is ,in It's a shelf Number of layers; for each shelf and , whose location set ,in It's a shelf No. The number of positions on the layer; Using triples represents a three-dimensional shelf index; where They respectively represent the physical location, logical label and timestamp of the goods; the physical location indicates the storage location of the goods, that is, the number of shelves and areas; the logical label is used to uniquely identify each product in the warehouse management system, including the product SKU code, category code, remarks information and any security-related identification; the timestamp is used to record the goods' entry time, picking time, exit time and any operation time related to security checks.

3. The data storage method based on intelligent warehouse management according to claim 2 is characterized in that: In step 1, the process of formulating a tiered storage strategy includes: Based on the three-dimensional shelf index, all goods are classified and divided into three levels: weight level, physical property level and value category level. , which is divided into lightweight , Middleweight and heavyweight ,and , physical property layer: Assume that the physical properties of the goods are , which is divided into fragile , moisture resistance , Dustproof , Value category layer: according to the value of the goods , classifying it as low value , medium value and high value ,and ; After the attribute classification and hierarchical division are completed, the attribute classification of the weight layer, physical property layer, and value category layer of the goods are combined with the initial three-dimensional shelf index, and the three-dimensional shelf index is updated accordingly. In the updated index, a mark of the level to which each product belongs is added for storage.

4. The data storage method based on intelligent warehouse management according to claim 1 is characterized in that: In step 2, the process of real-time monitoring of the picking frequency, size change, and inventory of goods based on the three-dimensional shelf index includes: By time period The statistical period is used to count the total number of picking operations recorded during this period. , calculate the picking frequency of goods ; Picking frequency Normalize and define the minimum and maximum values ​​of picking frequency as , then the normalized picking frequency ; Get cargo size changes , where They are vector representations of the changes in the cargo size data in each dimension. When the goods are put into storage, the warehouse manager uses a measuring tool to detect and record the size of the goods to obtain the initial size data of the goods. ; Warehouse managers regularly conduct spot checks on inventory items to check if the size of the items has changed and update the size data of the goods ; Changes in the size of the goods obtained Normalize the size of the cargo by calculating the modulus of change, i.e. ; Mark the reference value of the cargo size change as , then the normalized cargo size changes ; Read inventory information based on warehouse automation equipment and automatically update inventory quantity ; For the acquired inventory After normalization, we get , where Respectively represent the minimum and maximum value of the goods inventory; Let the weight coefficient , then the comprehensive impact factor .

5. The data storage method based on intelligent warehouse management according to claim 4 is characterized in that: In step 2, the storage density of goods at each index position is automatically calculated using a formula, and the optimal packaging method is determined by controlling the cost. The process of generating intelligent packaging information includes: For 3D shelf indexing The original shape of the goods is defined as a regular cuboid, and the length, width and height of the goods are , the original volume is ; Combined with the size change of the goods, the length, width, height and volume become , ; The total volume of the warehouse is marked as , the occupied volume is marked as , then the remaining available space in the warehouse is ; Calculate storage density using the formula ; In order to determine the best packaging method and generate intelligent packaging information, suppose there is possible packaging methods, and the length, width, height and volume of each packaging method are and , where An index indicating the packaging method. Indicates the number of packaging methods; Mark the cost of packing goods as ; Establish the cargo packaging cost function. The specific establishment process is as follows: According to the proportional relationship between packaging material cost and packaging surface area, the packaging material cost is calculated by the formula , where Indicates the packaging material cost ratio coefficient; Since the labor cost is proportional to the complexity of the packaging operation, the labor cost is obtained using the calculation formula , where represents the complexity function, It represents the labor cost ratio coefficient; Then the total cost of goods packaging is obtained: ； Using the objective function Find the packaging method that minimizes the total cost of packaging goods, where the constraints of the objective function are , where Is the minimum required storage density.

6. The data storage method based on intelligent warehouse management according to claim 1 is characterized in that: In step 3, according to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, the process of adaptively planning the picking path and dynamically adjusting the picking task allocation to achieve load balancing includes: Based on the warehouse Shelves, then The location coordinates of the shelves are ;in, Indicates the shelf number; According to the three-dimensional shelf index and intelligent cargo packaging information, the cargo is The probability of picking on a shelf is ; Among them, the logistic regression model is used to predict the probability of picking , where are the parameters of the logistic regression model, Indicates Comprehensive impact factor of goods on each shelf; Set the picker's initial position to , defined from the initial position to the The distance between the shelves is ; Based on The picking probability of the goods on the shelf and the corresponding picking distance of the picker are used to calculate the total length of the picking path ; In order to achieve load balancing, the maximum load capacity of each picker is defined as , has been assigned to the picker The task load is , new picking tasks to the shelf The load is ; Among them, the load is measured according to the weight, volume or complexity of the goods. , where Indicates The weight of the goods on the shelf, Indicates The volume of goods on the shelf, Indicates The picking complexity corresponding to the goods on the shelf; the load balancing constraints are: , where Indicates that it is assigned to the picker A collection of shelves for new tasks; Dynamically adjust the picking task allocation and introduce the task allocation matrix ,in, Indicates the picker number, Indicates the shelf number; when When The picking task is assigned to The picker, when When , the allocation is stopped; Based on the total length of the picking path, a picking time function is established, and the establishment process is as follows: The walking speed of the picker is recorded as , then the time the picker spends on the path is ; Assume that the additional time cost caused by load imbalance is , the standard deviation of the load is used to measure the degree of load imbalance: the load mean and standard deviation are calculated using the formula , , where Indicates the number of pickers, Indicates shelf of load; assuming that the extra time cost is proportional to the load standard deviation, that is, ,in An adjustment factor that represents the relationship between the extra time cost and the load standard deviation; The picking time is calculated by the formula: ； Then the picking time objective function is obtained as: , where The coefficient that represents the trade-off between path length and load balancing on picking time.

7. The data storage method based on intelligent warehouse management according to claim 1 is characterized in that: In step 4, blockchain technology is introduced to generate a unique hash value for each cargo, and the corresponding blockchain transactions are recorded at the key nodes of cargo entry, storage, picking and delivery. At the same time, the process of automated cargo integrity check using smart contracts includes: Based on the three-dimensional shelf index of the goods, a hash algorithm is used to generate a unique hash value for the goods; When the goods enter the warehouse, record the goods entry hash value , the time of entry into the warehouse, and the three-dimensional shelf index location of the goods, and use it as a blockchain transaction; During the storage process, when goods are moved from one shelf location to another, the attributes of the goods change, including the change of the three-dimensional shelf index and the overall attribute evaluation of the goods; the hash value is re-obtained based on the new attributes , and record the shelf location and moving time before and after the goods are moved; The picking operation involves direct handling of the goods, changing the attributes of the goods; re-obtaining the hash value based on the attributes of the goods after picking , and record the outbound time and three-dimensional shelf index location; Smart contracts are used to pre-set rules for each node in the warehouse operation. When performing key operations such as warehousing, storage, picking and outbound delivery, the hash value of the goods is checked to see if it complies with the contract provisions to determine whether the properties of the goods have changed. If the inspection at any node fails, including abnormal hash value or missing transaction records, the smart contract triggers the corresponding alarm or prompt mechanism and immediately notifies the warehouse administrator for processing.

8. The data storage system based on intelligent warehouse management is characterized by: The data storage method based on intelligent warehouse management as described in any one of claims 1 to 7 comprises: The index creation and storage strategy formulation module is used to create a three-dimensional shelf index containing physical location, logical label and timestamp for all goods in the warehouse layout, and formulate a hierarchical storage strategy; The intelligent packaging information generation module is used to monitor the picking frequency, size change and inventory of goods in real time based on the three-dimensional shelf index, automatically calculate the storage density of goods at each index position using a formula, and determine the best packaging method by controlling costs, thereby generating intelligent packaging information; The path planning and task allocation module is used to adaptively plan the picking path according to the warehouse's three-dimensional shelf index and intelligent cargo packaging information, and dynamically adjust the picking task allocation to achieve load balancing; The blockchain tracking and contract checking module is used to introduce blockchain technology, generate a unique hash value for each cargo, and record corresponding blockchain transactions at key nodes of cargo entry, storage, picking and delivery; at the same time, smart contracts are used to perform automated cargo integrity checks.

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