Cold-chain logistics warehouse dynamic inventory allocation method based on AI processing

Abstract

The invention discloses a cold-chain logistics warehouse dynamic inventory allocation method based on AI processing, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining a to-be-allocated list, and determining an initial to-be-allocated warehouse set according to the current inventory of each cold-chain logistics warehouse; s2, rejecting the initial to-be-deployed warehouse set by using permission access screening conditions to obtain a latest to-be-deployed warehouse set; generating an access token of the cold-chain logistics warehouse; and S4, when the user inputs the access tokens of the cold-chain logistics warehouses, deployment is carried out. According to the method, the access token of each cold-chain logistics warehouse is determined by calculating the basic number and the access token number, automatic management of inventory allocation is realized, the unique access token ensures that only an authorized user can access the inventory information of a specific warehouse, and the allocation safety is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a dynamic inventory allocation method for cold chain logistics warehouses based on AI processing. Background Art

[0002] With the rapid development of the global cold chain logistics industry, cold chain logistics warehouses, as a key link to ensure the quality and safety of perishable goods such as food and medicine, their operational efficiency and resource utilization rate have become the focus of the industry. In the cold chain logistics system, dynamic inventory allocation is an important means to optimize warehouse resources, reduce operating costs and improve service levels. However, the current inventory allocation in cold chain logistics warehouses faces many challenges. For example, there are risks in permission management and data security. The inventory data of cold chain logistics warehouses involves the core business secrets of enterprises, and there are differences in the access permissions of inventory information for different users (such as warehouse administrators, logistics dispatchers, suppliers, etc.). Traditional inventory allocation methods have deficiencies in data access control, which may lead to the leakage of sensitive information or unauthorized operations, affecting the operational safety of enterprises. Moreover, existing inventory allocation methods mostly rely on manual experience or simple rule engines and are difficult to cope with complex and changing allocation fluctuations. Summary of the Invention

[0003] In order to solve the above problems, the present invention proposes a dynamic inventory allocation method for cold chain logistics warehouses based on AI processing.

[0004] The technical solution of the present invention is: A dynamic inventory allocation method for cold chain logistics warehouses based on AI processing includes the following steps:

[0005] S1. Obtain the list to be allocated, and determine the initial set of warehouses to be allocated according to the current inventory levels of each cold chain logistics warehouse;

[0006] S2. Based on the entries corresponding to the cold chain logistics warehouses, use the permission access screening conditions to eliminate the initial set of warehouses to be allocated, and obtain the latest set of warehouses to be allocated;

[0007] S3. According to the latest entries of the cold chain logistics warehouses in the latest set of warehouses to be allocated, determine the base number, and generate access tokens for the cold chain logistics warehouses;

[0008] S4. When the user inputs the access tokens of each cold chain logistics warehouse, perform the allocation.

[0009] In S1, by obtaining the list to be allocated in real time and matching the current inventory levels of each warehouse, the accuracy of the initial set of warehouses to be allocated is ensured.

[0010] Further, S2 includes the following sub-steps:

[0011] S21. Obtain the global LDAP directory of the cloud platform where all cold chain logistics warehouses are located;

[0012] S22. Use the API interface to call the global LDAP directory to determine the entries corresponding to each cold chain logistics warehouse;

[0013] S23. Construct access screening conditions;

[0014] S24. Input each cold chain logistics warehouse into the access screening conditions, and eliminate the cold chain logistics warehouses that do not meet the access screening conditions to obtain the latest set of warehouses to be allocated.

[0015] The beneficial effects of the above further solution are as follows: In the present invention, by real-time calling the global LDAP directory of the cloud platform, it is ensured that the obtained warehouse information is always in the latest state, avoiding misjudgment of permissions caused by data lag. The screening conditions combine the query time and the response time, and can dynamically adapt to the real-time performance fluctuations of different warehouses. For example, when the response time of a certain warehouse increases due to network congestion, it will be automatically eliminated without manual intervention. After the elimination, if the inventory of the existing warehouses is insufficient, new warehouses can be added in a timely manner.

[0016] Further, in S23, the expression of the access screening conditions is: ; where T f represents the query time of the entry corresponding to the cold chain logistics warehouse in the initial set of warehouses to be allocated, ε represents the set threshold, and T p represents the response time of the entry corresponding to the cold chain logistics warehouse in the initial set of warehouses to be allocated.

[0017] The query time of the entries corresponding to the initial set of warehouses to be allocated is affected by parameters such as data scale, number of warehouses, cache mechanism, and network bandwidth. If the cold chain network contains hundreds to thousands of warehouses, the number of entries to be queried (such as inventory, temperature range, equipment status, etc.) is huge, and the response time increases linearly with the scale.

[0018] Further, S3 includes the following sub-steps:

[0019] S31. Obtain the latest entries of each cold chain logistics warehouse in the latest set of warehouses to be allocated;

[0020] S32. Determine the node weights between the latest entries of the cold chain logistics warehouse and the list to be allocated;

[0021] S33. Determine the self-increasing weights between the latest entries of the cold chain logistics warehouse and the list to be allocated;

[0022] S34. Determine the base number of the cold chain logistics warehouse according to the key-value pairs corresponding to the latest entries of the cold chain logistics warehouse;

[0023] S35. Determine the number of access tokens for the cold chain logistics warehouse based on the base number of the cold chain logistics warehouse, the node weight between the latest entry of the cold chain logistics warehouse and the list to be allocated, and the increment weight.

[0024] S36. Convert the integer part of the number of access tokens into binary form, and combine the binary form integer with the non-integer part of the number of access tokens as the access token for the cold chain logistics warehouse.

[0025] The beneficial effects of the above further solution are as follows: In the present invention, through the calculation of the node weight and the increment weight, the relationship between the warehouse and the list to be allocated can be quantified. By comprehensively generating the number of access tokens based on the base number, node weight, and increment weight, and converting it into binary form and combining it with the non-integer part, this design not only ensures the uniqueness of the token but also improves the security and complexity of the system, making it difficult to be cracked. By generating a unique access token, the permission management is strengthened, ensuring that only authorized users can access the inventory information of specific warehouses, effectively preventing data leakage and unauthorized operations. Even if the token is intercepted, it is difficult for attackers to restore the original information through simple reverse engineering.

[0026] Further, in S32, if there is a longest common substring between the globally unique identifier of the latest entry and the identifier of the list to be allocated, then add 1 to the length of the longest common substring as the node weight; otherwise, set the node weight to 1.

[0027] Further, S33 includes the following sub-steps:

[0028] S331. Insert the list to be allocated into the LADP directory and determine the globally unique identifier of the list to be allocated in the LADP directory.

[0029] S332. Take the ratio between the hash value of the globally unique identifier of the list to be allocated in the LADP directory and the hash value of the globally unique identifier of the latest entry of the cold chain logistics warehouse as the increment weight.

[0030] The beneficial effects of the above further solution are as follows: In the present invention, the increment weight is strongly correlated with the real-time status of the warehouse entry and the allocation list. The LADP directory naturally supports operation logs, which can record the insertion, modification, and deletion operations of the list to be allocated. Combining with the historical snapshot of the hash value ratio, full-link traceability of the allocation decision can be achieved.

[0031] Further, in S34, take the mean value of the values of all key-value pairs corresponding to the latest entry as the base number.

[0032] An entry can contain any number of key-value pairs.

[0033] Further, in S35, the expression for the number of access tokens M is: ; where γ1 Represents the node weight between the latest entry of the cold chain logistics warehouse and the list to be allocated, γ 2 Represents the incremental weight between the latest entry of the cold chain logistics warehouse and the list to be allocated, and B represents the base number of the cold chain logistics warehouse.

[0034] The beneficial effects of the present invention are as follows: According to the real-time inventory data of multiple warehouses, the present invention determines the initial set of warehouses to be allocated, and based on the entries and permission access screening conditions of the cold chain logistics warehouses in the initial set of warehouses to be allocated, dynamically generates the latest set of warehouses to be allocated, ensuring the timeliness of accessing the warehouses and avoiding time delays caused by overly long access times; the present invention also determines the access tokens for each cold chain logistics warehouse by calculating the base number and the number of access tokens, realizing the automated management of inventory allocation. The unique access token ensures that only authorized users can access the inventory information of specific warehouses, enhancing the security of allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Is a flowchart of a dynamic inventory allocation method for cold chain logistics warehouses based on AI processing. DETAILED DESCRIPTION OF THE INVENTION

[0036] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0037] As Figure 1 shown, the present invention provides a dynamic inventory allocation method for cold chain logistics warehouses based on AI processing, including the following steps:

[0038] S1. Obtain the list to be allocated, and determine the initial set of warehouses to be allocated according to the current inventory levels of each cold chain logistics warehouse;

[0039] S2. Based on the entries corresponding to the cold chain logistics warehouses, use the permission access screening conditions to eliminate the initial set of warehouses to be allocated, and obtain the latest set of warehouses to be allocated;

[0040] S3. According to the latest entries of the cold chain logistics warehouses in the latest set of warehouses to be allocated, determine the base number and generate access tokens for the cold chain logistics warehouses;

[0041] S4. When the user inputs the access tokens for each cold chain logistics warehouse, perform the allocation.

[0042] In S1, by obtaining the list to be allocated in real time and matching the current inventory levels of each warehouse, the accuracy of the initial set of warehouses to be allocated is ensured.

[0043] In the embodiments of the present invention, S2 includes the following sub-steps:

[0044] S21. Obtain the global LDAP directory of the cloud platform where all cold chain logistics warehouses are located;

[0045] S22. Use the API interface to call the global LDAP directory to determine the entries corresponding to each cold chain logistics warehouse;

[0046] S23. Construct access screening conditions;

[0047] S24. Input each cold chain logistics warehouse into the access screening conditions, and eliminate the cold chain logistics warehouses that do not meet the access screening conditions to obtain the latest set of warehouses to be allocated.

[0048] In the present invention, by real-time calling the global LDAP directory of the cloud platform, it is ensured that the obtained warehouse information is always in the latest state, avoiding misjudgment of permissions caused by data lag. The screening conditions combine the query time and the response time, and can dynamically adapt to the real-time performance fluctuations of different warehouses. For example, when the response time of a certain warehouse increases due to network congestion, it will be automatically eliminated without manual intervention. After the elimination, if the inventory of the existing warehouses is insufficient, new warehouses can be added in a timely manner.

[0049] In the embodiment of the present invention, in S23, the expression of the access screening condition is: ; where T f represents the query time of the entry corresponding to the cold chain logistics warehouse in the initial set of warehouses to be allocated, ε represents the set threshold, and T p represents the response time of the entry corresponding to the cold chain logistics warehouse in the initial set of warehouses to be allocated.

[0050] In the present invention, the query time of the entries corresponding to the initial set of warehouses to be allocated is affected by parameters such as data scale, number of warehouses, cache mechanism, and network bandwidth. If the cold chain network contains hundreds to thousands of warehouses, the number of entries to be queried (such as inventory, temperature range, equipment status, etc.) is huge, and the response time increases linearly with the scale.

[0051] In the embodiment of the present invention, S3 includes the following sub-steps:

[0052] S31. Obtain the latest entries of each cold chain logistics warehouse in the latest set of warehouses to be allocated;

[0053] S32. Determine the node weights between the latest entries of the cold chain logistics warehouses and the list to be allocated;

[0054] S33. Determine the self-increasing weights between the latest entries of the cold chain logistics warehouses and the list to be allocated;

[0055] S34. Determine the base number of the cold chain logistics warehouse according to the key-value pair corresponding to the latest entry of the cold chain logistics warehouse;

[0056] S35. Determine the number of access tokens for the cold chain logistics warehouse based on the base number of the cold chain logistics warehouse, the node weight between the latest entry of the cold chain logistics warehouse and the list to be allocated, and the increment weight.

[0057] S36. Convert the integer part of the number of access tokens into binary form, and combine the binary form integer with the non-integer part of the number of access tokens as the access token for the cold chain logistics warehouse.

[0058] In the present invention, through the calculation of node weight and increment weight, the relationship between the warehouse and the list to be allocated can be quantified. By comprehensively generating the number of access tokens based on the base number, node weight, and increment weight, and converting it into binary form and combining it with the non-integer part, this design not only ensures the uniqueness of the token but also improves the security and complexity of the system, making it difficult to be cracked. By generating a unique access token, the privilege management is strengthened, ensuring that only authorized users can access the inventory information of specific warehouses, effectively preventing data leakage and unauthorized operations. Even if the token is intercepted, it is difficult for attackers to restore the original information through simple reverse engineering.

[0059] In the embodiment of the present invention, in S32, if there is a longest common substring between the globally unique identifier of the latest entry and the identifier of the list to be allocated, then add 1 to the length of the longest common substring as the node weight; otherwise, set the node weight to 1.

[0060] In the embodiment of the present invention, S33 includes the following sub-steps:

[0061] S331. Insert the list to be allocated into the LADP directory, and determine the globally unique identifier of the list to be allocated in the LADP directory.

[0062] S332. Take the ratio between the hash value of the globally unique identifier of the list to be allocated in the LADP directory and the hash value of the globally unique identifier of the latest entry of the cold chain logistics warehouse as the increment weight.

[0063] In the present invention, the increment weight is strongly associated with the real-time status of the warehouse entry and the allocation list. The LADP directory naturally supports operation logs, which can record the insertion, modification, and deletion operations of the list to be allocated. Combining with the historical snapshot of the hash value ratio, the full-link traceability of the allocation decision can be realized.

[0064] In the embodiment of the present invention, in S34, take the mean value of the values of all key-value pairs corresponding to the latest entry as the base number.

[0065] In the present invention, an entry can contain any number of key-value pairs.

[0066] In the embodiment of the present invention, in S35, the expression for the number of access tokens M is: ; where γ1 represents the node weight between the latest entry in the cold chain logistics warehouse and the list to be allocated, γ 2 represents the incremental weight between the latest entry in the cold chain logistics warehouse and the list to be allocated, and B represents the base number of the cold chain logistics warehouse.

[0067] Those of ordinary skill in the art will realize that the embodiments described herein are for helping the reader understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A dynamic inventory allocation method for cold chain logistics warehouse based on AI processing, characterized in that: The following steps are involved: S1. Obtain the list of warehouses to be allocated and determine the initial set of warehouses to be allocated based on the current inventory of each cold chain logistics warehouse; S2. Based on the entries corresponding to the cold chain logistics warehouses, the initial set of warehouses to be allocated is eliminated using the permission access screening conditions to obtain the latest set of warehouses to be allocated; S3. Determine the base number based on the latest entry of the centralized cold chain logistics warehouse in the latest warehouse to be allocated, and generate an access token for the cold chain logistics warehouse; S4. When the user inputs the access token of each cold chain logistics warehouse, allocation is performed.

2. According to the AI-based cold chain logistics warehouse dynamic inventory allocation method of claim 1, it is characterized in that: The S2 comprises the following sub-steps: S21. Obtain the global LDAP directory of the cloud platform where all cold chain logistics warehouses are located; S22, using the API interface to call the global LDAP directory to determine the entries corresponding to each cold chain logistics warehouse; S23, constructing access screening conditions; S24. Input each cold chain logistics warehouse into the access screening condition, remove the cold chain logistics warehouses that do not meet the access screening condition, and obtain the latest set of warehouses to be deployed.

3. The method for dynamic inventory allocation of cold chain logistics warehouses based on AI processing according to claim 2 is characterized in that: In S23, the expression of the access screening condition is: Where, T f represents the query time of the corresponding entry of the cold chain logistics warehouse in the initial warehouse set to be allocated, ε represents the set threshold, T p Indicates the response time of the corresponding entry of the cold chain logistics warehouse in the initial set of warehouses to be allocated.

4. The method for dynamic inventory allocation of cold chain logistics warehouses based on AI processing according to claim 1 is characterized in that: The S3 comprises the following sub-steps: S31. Obtain the latest entries of each cold chain logistics warehouse in the latest warehouse to be allocated; S32, determining the node weight between the latest entry of the cold chain logistics warehouse and the list to be allocated; S33, determining the self-increasing weight between the latest entry of the cold chain logistics warehouse and the list to be allocated; S34. Determine the basic number of cold chain logistics warehouses according to the key-value pairs corresponding to the latest entries of the cold chain logistics warehouses; S35. Determine the number of access tokens of the cold chain logistics warehouse according to the basic number of the cold chain logistics warehouse, the node weight between the latest entry of the cold chain logistics warehouse and the to-be-allocated list, and the self-increment weight; S36. Convert the integer of the access token number into binary form, and combine the binary integer with the non-integer part of the access token number as the access token for the cold chain logistics warehouse.

5. The method for dynamic inventory allocation of cold chain logistics warehouses based on AI processing according to claim 4 is characterized in that: In S32, if there is a longest common substring between the globally unique identifier of the latest entry and the identifier of the list to be allocated, the length of the longest common substring plus 1 is used as the node weight, otherwise the node weight is set to 1.

6. The method for dynamic inventory allocation of cold chain logistics warehouses based on AI processing according to claim 4 is characterized in that: The S33 comprises the following sub-steps: S331, inserting the list to be deployed into the LADP directory, and determining the globally unique identifier of the list to be deployed in the LADP directory; S332: The ratio between the hash value of the globally unique identifier of the to-be-allocated list in the LADP directory and the hash value of the globally unique identifier of the latest entry in the cold chain logistics warehouse is used as a self-increasing weight.

7. The method for dynamic inventory allocation of cold chain logistics warehouses based on AI processing according to claim 4 is characterized in that: In S34, the average value of all key-value pairs corresponding to the latest entry is used as the base number.

8. The method for dynamic inventory allocation of cold chain logistics warehouses based on AI processing according to claim 4 is characterized in that: In S35, the expression of the number of access tokens M is: ; In the formula, γ1 represents the node weight between the latest entry of the cold chain logistics warehouse and the list to be allocated, γ2 represents the self-increment weight between the latest entry of the cold chain logistics warehouse and the list to be allocated, and B represents the basic number of cold chain logistics warehouses.

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