Intelligent tool warehouse-in and warehouse-out management method based on internet of things

Abstract

This invention discloses an intelligent tool storage and warehousing management method based on the Internet of Things (IoT). This invention relates to the field of tool storage management technology. It acquires the specification information of tools to be stored using IoT technology and generates a unique tool ID for each. This specification information is then combined to form a storage record to be completed. Within a preset monitoring period, the usage frequency and duration of tools of the same specification as the tools to be stored are evaluated, and a comprehensive load index is calculated. Based on this, a suitable storage location is determined in the physical tool storage warehouse. Simultaneously, the wear index of the tools to be stored is measured, the storage record is completed, and stored in a database. A smart robot completes the physical storage operation. Upon retrieval, the system retrieves the optimal matching tool with the lowest wear index from the database based on the specification information of the retrieval request, extracts its storage location and tool ID, and executes the retrieval operation through a smart robot. This achieves intelligent management of tool storage and warehousing, improving efficiency and optimizing storage layout.

Description

Technical Field

[0001] This invention belongs to the field of tool storage management technology, specifically, it relates to an intelligent tool entry and exit management method based on the Internet of Things. Background Technology

[0002] With rapid economic development and continuous upgrading of the manufacturing industry, the use and management of cutting tools have become increasingly important in the field of electromechanical processing. In modern production, efficient management of the entry and exit of cutting tools directly affects production efficiency and processing quality.

[0003] Existing tool management methods typically rely on manual operation and simple record-keeping systems, lacking integration with IoT technology. This leads to numerous drawbacks in tool inbound and outbound processes. Existing technologies often cannot be effectively managed based on manual records or basic spreadsheets, easily resulting in data confusion or loss. This is particularly problematic when handling large numbers of tools, where manually binding specification information is inefficient, error-prone, and fails to ensure the accuracy and uniqueness of each record. Secondly, the lack of dynamic monitoring and evaluation of tool usage prevents analysis of usage frequency and duration based on preset cycles. This leads to unreasonable storage location allocation, with tools often stored haphazardly, failing to optimize storage locations based on actual load. This increases retrieval time and operational costs, reducing overall warehousing efficiency. Furthermore, existing technologies often ignore tool wear conditions, lacking integrated wear monitoring mechanisms. Only basic specifications are recorded upon entry, while tool selection for outbound relies on manual experience. This may result in the selection of severely worn tools, affecting machining quality and tool life. Simultaneously, it fails to optimize resource allocation, leading to unscientific inbound and outbound planning and an inability to respond to outbound requests in real time and quickly match the optimal tool.

[0004] To address the aforementioned issues, this invention proposes an intelligent tool entry and exit management method based on the Internet of Things (IoT). Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent tool entry and exit management method based on the Internet of Things, which solves the problems of low management efficiency and waste of warehousing resources in the management of band saws in existing technologies.

[0006] The objective of this invention can be achieved through the following technical solutions: A smart tool inventory management method based on the Internet of Things, the method comprising: Step 1: Obtain the specification information of the tools to be put into storage based on the Internet of Things, generate a unique tool ID for the tools to be put into storage and bind it with the specification information of the tools to be put into storage, and determine a storage record to be improved. Step 2: Based on the preset monitoring period, determine the usage frequency and usage duration of the tools with the same specifications as the tools to be put into storage, evaluate the comprehensive load index associated with the tools to be put into storage, and determine the corresponding storage location in the physical tool storage warehouse pre-built by the corresponding electromechanical processing plant based on the comprehensive load index associated with tools with different specifications. Step 3: Determine the wear index of the tool to be put into storage, improve the storage record to be improved by the joint storage location, obtain the storage record associated with the tool to be put into storage, and store it in the pre-built tool storage database. Perform the physical tool storage storage operation to put the tool into storage. Step 4: Based on the Internet of Things, obtain the tool outbound request, extract the specification information of the tool to be outbound, retrieve the best matching tool from the tool storage database, extract the tool ID and storage location of the best matching tool, extract the best matching tool with the corresponding tool ID from the corresponding storage location in the physical tool storage repository, and execute the outbound process.

[0007] As a further aspect of the present invention, the specific method for determining a storage record to be improved in step one is as follows: Based on the Internet of Things, the warehousing information of any tool to be put into storage is obtained, and the tool to be put into storage is extracted and denoted as A; The specification information of the cutting tools to be put into storage is obtained based on the storage information and recorded as M_A; The specification information M_A includes at least the tool type, tool diameter, tool length, and tool material; A unique tool ID, denoted as ID_A, is generated based on the UUID and associated with the tool A to be put into storage. Combine ID_A, M_A, and the tool A to be put into storage to form a storage record, denoted as R_A, and mark the storage status of storage record R_A as pending completion.

[0008] As a further aspect of the present invention, the specific method for evaluating the comprehensive load index associated with the tool to be put into storage in step two is as follows: Obtain the preset monitoring period T, where the monitoring period T is a time interval in days; Starting from the current time, trace back to a complete monitoring cycle T. Based on the Internet of Things, all outgoing and used tools with the same specifications as the tools to be put into storage in the electromechanical processing plant within the monitoring period T are obtained and randomly arranged into a sequence of outgoing and used tools L1, L2, ..., Lj, where j represents the total number of outgoing and used tools within the monitoring period T. Based on the Internet of Things, the total number of usage records of any out-of-warehouse tool Li within the monitoring period T is obtained, denoted as m. The usage record includes tool ID, usage duration, and i is the counting index, with a value range from 1 to j. The total number of usage records m is recorded as the number of times the tool Li has been used and has been released from the warehouse. Summarize the usage duration from m usage records and record it as the total usage duration of the tool Li that has been released from the warehouse. Similarly, determine the usage count associated with each of the j tools in the sequence of tools that have been issued and used, L1, L2, ..., Lj, and summarize them as the cumulative usage count CUC; Similarly, determine the total usage time associated with each of the j tools in the sequence of tools that have been issued and used, L1, L2, ..., Lj, and summarize it as the cumulative usage time AUD; The usage frequency F of the tool with specification information M_A is determined by F=AUD / T; The average service time H of the tool with specification information M_A is determined by H=AUD / j; The comprehensive load index CL of a tool with specification information M_A is determined by CL = α*F_g + β*H_g, where F_g is the numerical part of the usage frequency F, and H_g is the average usage time. In the numerical part, α and β are preset calculation weights, both α and β are greater than 0, and α+β=1.

[0009] As a further aspect of the present invention, the specific method for determining the corresponding storage location in step two is as follows: Obtain the pre-built physical tool repository and divide it into k repository slots, where k is a preset integer and is greater than the total number of specification information types of all tools stored in the physical tool repository; The storage locations are sorted from high to low based on the physical accessibility efficiency between the storage locations and entrances / exits, generating a storage location sequence S1, S2, ..., Sk; The steps involve determining the specification information of all stored tools in the physical tool repository, and determining the comprehensive load index CL of the tool with specification information M_A, and the comprehensive load index associated with each tool specification information. Sort all specifications in descending order based on the comprehensive load index value to generate a specification priority sequence; Match the priority sequence of specification information with the storage location sequence S1, S2, ..., Sk one by one, and determine the storage location associated with the tool corresponding to the specification information; Obtain the storage location Su of the tool A to be put into storage with specification information M_A, where u is the counting index, and the value range is from 1 to k.

[0010] As a further aspect of the present invention, in step two, the method for determining the physical reachability efficiency of the repository entrance and exit is as follows: The straight-line distance from any storage location Su to the entrance / exit of the physical tool storage is denoted as E; Get the total number of obstacles in the shortest path from the storage location Su to the entrance / exit of the physical tool storage, denoted as sum, where the total number of obstacles represents the number of times the straight path is changed; The straight-line distance E and the total number of obstacles sum are normalized using E_g=E / E_max and sum_g=sum / sum_max to obtain the normalized straight-line distance E_g and the normalized total number of obstacles sum_g. Here, E_max is the maximum straight-line distance from all storage locations to the entrance and exit, and sum_max is the maximum total number of obstacles in the shortest path from all storage locations to the entrance and exit. The physical reachability efficiency value PAE_u of the storage location Su is calculated using PAE_u=[1-(ω1×E_g+ω2×sum_g)]*100%, where ω1 and ω2 are preset weight coefficients that satisfy ω1+ω2=1, ω1>0, and ω2>0. Similarly, determine the physical reachability efficiency of all storage locations and entrances / exits.

[0011] As a further aspect of the present invention, the specific method for obtaining the storage record associated with the tool to be put into storage in step three is as follows: A tool wear measuring instrument is used to test the tool A to be put into storage, and the wear index of the tool A to be put into storage is determined and denoted as W_A; Obtain the storage record R_A marked as "to be improved" for the tool A to be put into storage. Extract the storage location Su of the tool A to be put into storage. Improve the storage record R_A in conjunction with the wear index W_A. Mark the storage status of the storage record R_A as "improved". Store the storage record R_A in the tool storage database.

[0012] As a further aspect of the present invention, the specific method for performing the physical tool storage storage operation on the tools to be stored in step three is as follows: Extract the storage location Su from the storage record R_A of the tool A to be stored, retrieve the storage location Su from the physical tool storage repository, and use a pre-built intelligent robot to adaptively construct the shortest path to store the tool A to be stored in the storage location Su of the physical tool storage repository.

[0013] As a further aspect of the present invention, in step four, the specific method for obtaining the tool outbound request based on the Internet of Things, extracting the specification information of the tool to be outbound, and retrieving the optimal matching tool from the tool storage database is as follows: Obtain any tool outbound request and parse it to determine the specifications of the tool to be outbound; Extract all storage records with the same specifications as the tool to be shipped from the tool storage database, and select the tool with the lowest wear index as the optimal matching tool.

[0014] As a further aspect of the present invention, the specific method for retrieving the optimal matching tool with the corresponding tool ID from the corresponding storage location in the physical tool repository in step four is as follows: Extract the storage record associated with the best matching tool, and separate the tool ID and storage location; Based on the determined tool ID and storage location, a search is performed in the physical tool storage repository. The corresponding tool retrieved is taken as the optimal physical matching tool, and an execution order for the out-of-repository is given to the intelligent robot.

[0015] The beneficial effects of this invention are: This invention improves the efficiency and accuracy of tool management through automated data acquisition and intelligent decision-making. By generating a unique ID for each tool and binding it with specification information, it ensures full lifecycle tracking of tools, reducing human error and confusion. Secondly, by evaluating the comprehensive load index of tools, it intelligently allocates storage locations, optimizes inventory layout, and shortens storage and retrieval time. It also improves storage records by combining wear index. When tools are issued, the optimal matching tool is quickly retrieved from the database, improving response speed while ensuring processing quality. Based on the refined and intelligent management of tool resources, it reduces operating costs and improves the production efficiency and reliability of electromechanical processing plants. This invention automatically acquires the warehousing information of cutting tools to be put into storage through Internet of Things (IoT) technology and extracts key specification information to ensure the comprehensiveness and accuracy of the data. At the same time, it uses UUID to generate a unique tool ID, combines the information into a storage record and marks it as pending completion, thereby improving the efficiency and automation level of warehousing management, reducing errors and duplication caused by manual intervention, ensuring the unique identification and traceability of each tool, laying the foundation for subsequent inventory query, maintenance and intelligent management, and enhancing the digital level of intelligent tool management. This invention achieves efficient and optimized warehouse management by evaluating the comprehensive load index of cutting tools and intelligently allocating storage locations. Its advantages lie in dynamically quantifying the usage frequency and average usage time of cutting tools using real-time monitoring data, thereby calculating the comprehensive load index. This ensures that tools with high usage rates are prioritized for allocation to storage locations with high physical accessibility, reducing tool retrieval time and operational costs. Simultaneously, by normalizing straight-line distances and the number of obstacles to calculate the accessibility efficiency of storage locations and matching it with tool priorities, it improves inventory turnover and overall system efficiency. While enhancing the accuracy and automation of tool management, data-driven decision-making optimizes resource allocation, reduces human intervention errors, and indirectly extends tool life, bringing higher production efficiency and cost-effectiveness to electromechanical processing plants. This invention uses a tool wear measuring instrument to accurately detect incoming tools and obtain the wear index. This index is then combined with the storage location information to update and improve the storage records, achieving dynamic linkage between tool status data and storage location management. This enhances the completeness of tool information management. At the same time, intelligent robots achieve rapid and accurate warehousing based on the shortest path planning, optimizing the efficiency of unmanned warehouse operations and reducing errors and delays caused by human intervention. This invention improves the accuracy of tool management by automatically parsing tool release requests and intelligently selecting the best matching tools from the database based on specification information and wear index. Its advantages include prioritizing the selection of tools of the same specification with the lowest wear, significantly extending the overall lifespan of the tool cluster, reducing tool wear and replacement costs, and improving product quality. Secondly, by associating tool IDs with storage location information and driving intelligent robots to execute releases, it achieves seamless integration of warehouse positioning and physical retrieval, improving the automation level and operational reliability of release operations, and providing strong support for lean tool management in modern intelligent manufacturing environments. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart illustrating the method described in this invention; Figure 2 This is a flowchart illustrating the method described in Embodiment 2 of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1

[0019] IoT-based intelligent tool inbound and outbound management methods, such as Figure 1 As shown, this system includes the following: This method, an IoT-based intelligent tool inbound / outbound management method, utilizes IoT technology to automate and intelligently manage tools, improving warehousing efficiency, reducing human error, and optimizing the tool lifecycle. It is primarily implemented through the following steps: The first step is to acquire the specification information of the tools to be put into storage based on the Internet of Things, and generate a unique tool ID for each tool and bind it with the specification information of the tool to be put into storage, thus determining a storage record to be completed. Specifically: This step uses the Internet of Things to obtain the specification information of the tools to be put into storage. The methods for obtaining the specification information include RFID tags, QR codes or laser scanners. The operator shall determine the appropriate method based on the actual situation. Next, a unique tool ID is generated for the tool to be put into the warehouse based on the UUID, which ensures global uniqueness, avoids tool ID conflicts, and facilitates subsequent tracking and management of tools. Finally, the tool ID is bound to the specification information to form an initial storage record, which is marked as to be improved. This is because more dynamic information, including storage location and wear status, will be added in subsequent steps. The cutting tools mentioned in this plan refer to the machining tools used by the electromechanical processing plant, such as drill bits and milling cutters.

[0020] For example, an initial storage record can be represented as: { "Tool ID": "ID_A", Specifications: { "Tool Type": "Drill Bit" "Tool diameter": "10mm", "Tool length": "100mm", "Tool Material": "Carbide" }, Storage Status: "To be improved" }

[0021] The second step involves determining the usage frequency and duration of tools with the same specifications as the tools to be stored within a preset monitoring period, evaluating the comprehensive load index associated with each tool, and determining the corresponding storage location in the pre-built physical tool storage warehouse of the corresponding electromechanical processing plant based on the comprehensive load index associated with tools of different specifications. Specifically: The comprehensive load index associated with the cutting tool to be put into storage is determined based on the usage frequency and usage duration of the cutting tool with corresponding specifications in the usage records of the past monitoring period. If the cutting tool to be put into storage is a new cutting tool that has never been used, then the comprehensive load index is considered to be a minimum value. Next, based on the comprehensive load index of tools with different specifications, the corresponding storage location of the tools with the corresponding specifications in the physical tool storage warehouse pre-built by the operator is determined. The reason for determining the storage location is to centrally store tools of the same specifications, which facilitates batch management and reduces the retrieval time of the corresponding tools, thereby improving production efficiency.

[0022] The third step is to determine the wear index of the tool to be put into storage, refine the storage record to be improved by the joint storage location, obtain the storage record associated with the tool to be put into storage, and store it in the pre-built tool storage database. Then, perform the physical tool storage storage operation to put the tool into storage. Specifically: The wear index of the tool to be put into storage is determined by using existing measuring instruments (the parts covered by existing technology will not be elaborated in this solution). Combined with the method described in the second step, the storage location of the tool to be put into storage is determined. The wear index is added to the storage record to be improved to form a complete storage record. The storage status is then changed to improved. The tool to be put into storage is put into storage according to the complete storage record associated with it. The storage operation is put into the physical tool storage repository. After the storage operation, the storage record also needs to be added with the storage time.

[0023] For example, a complete storage record can be represented as: { "Tool ID": "ID_A", Specifications: {...} "Storage location": "A-01-02", Wear Index: 0.2 "Inbound Time": "2023-10-01 10:00:00" Storage Status: "Complete" }

[0024] The fourth step involves obtaining a tool outbound request based on the Internet of Things (IoT), extracting the specification information of the tool to be outbound, retrieving the optimal matching tool from the tool storage database, extracting the tool ID and storage location of the optimal matching tool, retrieving the optimal matching tool with the corresponding tool ID from the corresponding storage location in the physical tool storage repository, and executing the outbound process. Specifically: Tool outbound requests are typically obtained through the Internet of Things (IoT) from production orders or maintenance plans. Then, specification information is extracted, and the best matching tool is retrieved from the tool storage database. By combining the best matching tool storage record, the storage location and tool ID are determined. In this way, an intelligent robot can retrieve the best matching tool with the corresponding tool ID from the physical tool storage warehouse and execute the outbound operation. This embodiment is based on the cloud-edge-device architecture of the Internet of Things. Through real-time data acquisition and intelligent algorithms, it realizes refined and dynamic management of cutting tools, fully automates the process, and reduces human error. In this way, electromechanical processing plants can realize the digitalization and intelligentization of cutting tool management and adapt to the needs of Industry 4.0. Example 2

[0025] This embodiment, based on embodiment 1, further discloses a method for determining the storage location of the tool to be stored in the physical tool storage repository, such as... Figure 2 As shown, it specifically includes the following: Before determining the storage location of the tool to be stored in the physical tool storage repository, it is necessary to identify the tool to be stored and the storage record to be completed, because subsequent inbound and outbound operations are entirely based on the completed storage record associated with the storage record to be completed.

[0026] First, based on the Internet of Things described in Example 1, the entry information of any tool to be put into storage is obtained, and the tool to be put into storage is extracted from the entry information and marked as A. Obtain the specification information of tool A to be put into storage through the storage information of tool A to be put into storage, and record it as M_A. The specification information M_A includes at least tool type, tool diameter, tool length and tool material, because they are the most critical dimensions to distinguish tool function and applicable processing scenarios.

[0027] Next, the tool ID of the tool A to be put into storage is generated using UUID and recorded as ID_A. This ensures accurate traceability of the tool throughout its entire lifecycle, from storage, use, maintenance to scrapping, and avoids data chaos caused by duplicate tool IDs.

[0028] Finally, the tool ID: ID_A, specification information M_A, and tool A to be put into storage are combined to obtain a storage record denoted as R_A. At this time, the storage status of storage record R_A is "to be completed".

[0029] When the storage status of the storage record R_A is "pending completion", physical data entry should not be performed to prevent the automated process from executing subsequent operations with incomplete information.

[0030] Next, the monitoring period T preset by the operator based on the actual situation is obtained. It should be noted that the monitoring period T is a time interval in days. Next, determine the current time and use it as the end time of a monitoring period T. Then, trace back the duration of a monitoring period T to the past. In this way, the start time and end time of the monitoring period T are determined.

[0031] Next, the Internet of Things is used to obtain information on all outgoing and used tools in the electromechanical processing plant that are of the same specifications as the tools to be put into storage within the monitoring period T. All tools that have been outgoing and used within the monitoring period T are extracted, regardless of whether they are eventually put back into storage.

[0032] All extracted tools that have been taken out of the warehouse are randomly arranged to obtain the sequence of tools that have been taken out of the warehouse, denoted as: L1, L2, ..., Lj, where j represents the total number of tools that have been taken out of the warehouse within the monitoring period T.

[0033] Next, select any one of the outgoing tools Li from the sequence of outgoing tools L1, L2, ..., Lj and process it as shown in the example below. All other outgoing tools in the sequence of outgoing tools L1, L2, ..., Lj are processed in the same way as the outgoing tools Li.

[0034] Then, the usage records of the outgoing and used tools Li within the monitoring period T are obtained through the Internet of Things, as well as the total number of usage records, denoted as m. It should be explained that the usage record includes the tool ID and usage duration, and i is the counting index, with a value range from 1 to j.

[0035] Among them, the total number of usage records m is the number of times the tool Li that has been released from the warehouse has been used within the monitoring period T. Retrieve all usage records of tool Li that has been shipped and used, totaling m records. Extract the usage duration from each of the m records and sum them up. The summed result is recorded as the total usage duration of tool Li that has been shipped and used.

[0036] By repeating the above steps, the usage count associated with each of the outgoing tools in the sequence L1, L2, ..., Lj can be determined, and the usage count associated with each of the outgoing tools can be summarized again. The summation result is recorded as the cumulative usage count CUC. By repeating the above steps, the total usage time associated with each of the outgoing and used tools in the sequence L1, L2, ..., Lj can be determined. The total usage time associated with each of the outgoing and used tools is then summarized again, and the summation result is recorded as the cumulative usage time AUD.

[0037] Next, the usage frequency F and average usage time H of the tool with specification information M_A are calculated, as follows: The usage frequency F of the tool with specification information M_A is determined by F=AUD / T. The higher the value of the usage frequency F, the longer the tool of this specification works on the machine tool on average every day, and it is the main tool in the workshop. The average usage time H of the tool with specification information M_A is determined by H=AUD / j. A higher average usage time H indicates that the tool with specification information will perform a long machining task each time, which may be used for complex or large workpieces. Then, the comprehensive load index CL of the tool with specification information M_A is determined by using: CL=α*F_g+β*H_g, where F_g is the numerical part of the usage frequency F, and H_g is the average usage time. The numerical part therefore does not have a dimensionless problem, and is only a numerical calculation. α and β are calculation weights preset by the operator. Both α and β are greater than 0, and α+β=1. The tool with a higher comprehensive load index CL value means that it is more important and busier in the actual production environment.

[0038] Then, the physical tool repository pre-built by the operator for storing physical tools is obtained, and the physical tool repository is divided into k storage locations (or not equally divided, depending on the actual situation and the operator's allocation). Here, k is an integer preset by the operator, and k is greater than the total number of specification information types of all tools stored in the physical tool repository. The physical reachability efficiency of each storage location associated with its entry and exit points is obtained as follows: First, obtain any one of the k storage locations and label it Su, where u is the counting index, with a value ranging from 1 to k; Next, determine the straight-line distance from the storage location Su to the entrance / exit of the physical tool storage location, and mark it as E; Next, obtain the total number of obstacles in the shortest path from storage location Su to the entrance / exit of the physical tool storage, denoted as sum. The total number of obstacles represents the number of times the straight-line travel route is changed, for example, the straight-line travel route is changed because the shelf is blocked and a turn is required. Next, the straight-line distance E and the total number of obstacles sum are normalized by E_g=E / E_max and sum_g=sum / sum_max respectively. After normalization, the dimensionless normalized straight-line distance E_g and the normalized total number of obstacles sum_g are obtained, where E_max is the maximum value of the straight-line distance from all storage locations to the entrance and exit, and sum_max is the maximum value of the total number of obstacles in the shortest path from all storage locations to the entrance and exit.

[0039] Then by adopting: Calculate the physical accessibility efficiency value PAE_u of storage location Su, where ω1 and ω2 are weight coefficients preset by the operator, and ω1 and ω2 satisfy ω1+ω2=1, ω1>0, ω2>0. It should be explained that in an open warehouse with mainly straight-line walking, setting ω1>ω2 emphasizes distance, while in a warehouse with dense shelves and winding aisles, setting ω2>ω1 emphasizes the smoothness of the path. The specific weight coefficient values ​​are set by the operator according to the actual situation. By repeating the above steps, the physical accessibility efficiency of all storage locations and entrances / exits can be determined.

[0040] Next, the k storage locations are sorted from high to low according to their physical accessibility efficiency with entrances and exits, resulting in the storage location sequence S1, S2, ..., Sk. Next, obtain the specification information of all stored tools in the physical tool repository. Following the steps of determining the comprehensive load index CL of the tool with specification information M_A, the comprehensive load index associated with each specification information can be determined. Then, sort all tool specifications in descending order according to the comprehensive load index associated with each type of tool specification to obtain a specification priority sequence. Match the specification information priority sequence with the storage location sequence S1, S2, ..., Sk one by one. For example, the specification information that is first in the priority sequence corresponds to the first storage location S1 in the storage location sequence S1, S2, ..., Sk, and so on.

[0041] In this way, the storage location Su of the tool A to be put into storage with specification information M_A can be determined. Example 3

[0042] This embodiment, based on embodiment 2, further discloses a method for determining the storage record associated with the tool to be put into storage and performing the storage operation, specifically including the following: Based on the method described in Example 3, the storage location Su of the tool A to be stored is extracted; Then, the tool wear measuring instrument mentioned in Example 1 is used to detect the tool A to be put into storage, and the wear index of the tool A to be put into storage is determined and recorded as W_A. If the tool A to be put into storage is a new tool, the detection step of the tool wear measuring instrument is skipped, and the wear index W_A of the tool A to be put into storage is directly assigned to 0. The larger the value of the wear index, the worse the health condition of the corresponding tool. The quantified value of the wear index is determined by the characteristics of the tool wear measuring instrument itself.

[0043] Based on the determined wear index W_A of the tool A to be put into storage and the storage location Su, the storage record R_A associated with the tool A to be put into storage is supplemented, and the storage status of the supplemented storage record R_A is marked as complete. At the same time, it is stored in the tool storage database for persistent storage.

[0044] Next, the storage location Su is extracted from the storage record R_A of the tool A to be put into storage. This step must be performed after the storage record R_A is stored in the tool storage database. The intelligent robot, combined with the Internet of Things, extracts the tool from the tool storage database. The intelligent robot adaptively constructs the shortest path from the entrance / exit of the physical tool storage warehouse to the storage location Su, and navigates along this shortest path to put the tool A to be put into storage into the storage location Su in the physical tool storage warehouse, thus completing the storage operation. Example 4

[0045] This embodiment further discloses a method for performing the outbound operation of a tool to be taken out of the warehouse, based on embodiment 3, specifically including the following: The system obtains any tool outbound request from the Internet of Things and extracts the tool to be outbound and its specifications from the request. Then, it searches the tool storage database for the specifications of the tool to be outbound and extracts all storage records with the same specifications as the tool to be outbound. If no storage record with the same specifications as the tool to be outbound exists, it returns that there is no corresponding tool to be outbound. Then, from all the storage records with the same specifications as the tools to be dispatched, the storage record with the smallest wear index is parsed and filtered out, and the tool corresponding to the storage record with the smallest wear index is taken as the optimal matching tool. At the same time, the tool ID and storage location of the optimal matching tool are extracted from the storage record and transmitted to the intelligent robot through the Internet of Things. After receiving the tool ID and storage location, the intelligent robot searches the physical tool storage repository, determines the corresponding physical storage location, arrives at the storage location through adaptive navigation, obtains the optimal matching tool for the corresponding tool ID, and performs the outbound operation for this optimal matching tool.

[0046] In this embodiment, the dual verification mechanism of tool ID and storage location minimizes misoperation, making the entire outbound process unmanned, precise, and traceable. It seamlessly connects with the aforementioned intelligent inbound process, forming a complete closed-loop management system. This enables the transformation from traditional inventory management to modern asset efficiency operation, providing a solid and reliable underlying support for intelligent manufacturing.

[0047] All data in the formulas described above are numerical calculations performed with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0048] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0049] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A method for intelligent inbound and outbound management of cutting tools based on the Internet of Things, characterized in that, The method includes: Step 1: Obtain the specification information of the tools to be put into storage based on the Internet of Things, generate a unique tool ID for the tools to be put into storage and bind it with the specification information of the tools to be put into storage, and determine a storage record to be improved. Step 2: Based on the preset monitoring period, determine the usage frequency and usage duration of the tools with the same specifications as the tools to be put into storage, evaluate the comprehensive load index associated with the tools to be put into storage, and determine the corresponding storage location in the physical tool storage warehouse pre-built by the corresponding electromechanical processing plant based on the comprehensive load index associated with tools with different specifications. Step 3: Determine the wear index of the tool to be put into storage, improve the storage record to be improved by the joint storage location, obtain the storage record associated with the tool to be put into storage, and store it in the pre-built tool storage database. Perform the physical tool storage storage operation to put the tool into storage. Step 4: Based on the Internet of Things, obtain the tool outbound request, extract the specification information of the tool to be outbound, retrieve the best matching tool from the tool storage database, extract the tool ID and storage location of the best matching tool, extract the best matching tool with the corresponding tool ID from the corresponding storage location in the physical tool storage repository, and execute the outbound process.

2. The method according to claim 1, characterized in that, In step one, the specific method for determining a storage record to be improved is as follows: Based on the Internet of Things, obtain the entry information of any tool to be put into storage, and extract the tool to be put into storage, denoted as A; The specification information of the cutting tools to be put into storage is obtained based on the storage information and recorded as M_A; The specification information M_A includes at least the tool type, tool diameter, tool length, and tool material; A unique tool ID, denoted as ID_A, is generated based on the UUID associated with the tool A to be put into storage. Combine ID_A, M_A, and the tool A to be put into storage to form a storage record, denoted as R_A, and mark the storage status of storage record R_A as pending completion.

3. The method of claim 1, wherein, In step two, the specific method for evaluating the comprehensive load index associated with the cutting tools to be put into storage is as follows: Obtain the preset monitoring period T, where the monitoring period T is a time interval in days; Starting from the current time, trace back to a complete monitoring cycle T. Based on the Internet of Things, all outgoing and used tools with the same specifications as the tools to be put into storage in the electromechanical processing plant within the monitoring period T are obtained and randomly arranged into a sequence of outgoing and used tools L1, L2, ..., Lj, where j represents the total number of outgoing and used tools within the monitoring period T. Based on the Internet of Things, the total number of usage records of any out-of-warehouse tool Li within the monitoring period T is obtained, denoted as m. The usage record includes tool ID, usage duration, and i is the counting index, with a value range from 1 to j. The total number of usage records m is recorded as the number of times the tool Li has been used and has been released from the warehouse. Summarize the usage duration from m usage records and record it as the total usage duration of the tool Li that has been released from the warehouse. Similarly, determine the usage count associated with each of the j tools in the sequence of tools that have been issued and used, L1, L2, ..., Lj, and summarize them as the cumulative usage count CUC; Similarly, determine the total usage time associated with each of the j tools in the sequence of tools that have been issued and used, L1, L2, ..., Lj, and summarize it as the cumulative usage time AUD; The usage frequency F of the tool with specification information M_A is determined by F=AUD / T; The average service time H of the tool with specification information M_A is determined by H=AUD / j; The comprehensive load index CL of a tool with specification information M_A is determined by CL = α*F_g + β*H_g, where F_g is the numerical part of the usage frequency F, and H_g is the average usage time. In the numerical part, α and β are preset calculation weights, both α and β are greater than 0, and α+β=1.

4. The method of claim 3, wherein, In step two, the specific method for determining the corresponding storage location is as follows: Obtain a pre-built physical tool repository and divide it evenly into k repository slots, where, k is a preset integer, which is greater than the total number of specification information types of all stored tools in the physical tool repository; The storage locations are sorted from high to low based on the physical accessibility efficiency between the storage locations and entrances / exits, generating a storage location sequence S1, S2, ..., Sk; The steps involve determining the specification information of all stored tools in the physical tool repository, and determining the comprehensive load index CL of the tool with specification information M_A, and the comprehensive load index associated with each tool specification information. Sort all specifications in descending order based on the comprehensive load index value to generate a specification priority sequence; Match the priority sequence of specification information with the storage location sequence S1, S2, ..., Sk one by one, and determine the storage location associated with the tool corresponding to the specification information; Obtain the storage location Su of the tool A to be put into storage with specification information M_A, where u is the counting index, and the value range is from 1 to k.

5. The method of claim 4, wherein, In step two, the physical reachability efficiency of the repository entrance and exit is determined as follows: The straight-line distance from any storage location Su to the entrance / exit of the physical tool storage is denoted as E; Get the total number of obstacles in the shortest path from the storage location Su to the entrance / exit of the physical tool storage, denoted as sum, where the total number of obstacles represents the number of times the straight path is changed; The straight-line distance E and the total number of obstacles sum are normalized using E_g=E / E_max and sum_g=sum / sum_max to obtain the normalized straight-line distance E_g and the normalized total number of obstacles sum_g. Here, E_max is the maximum straight-line distance from all storage locations to the entrance and exit, and sum_max is the maximum total number of obstacles in the shortest path from all storage locations to the entrance and exit. Adopt Calculate the physical accessibility efficiency value PAE_u of the storage location Su, wherein ω1, ω2 are preset weight coefficients, satisfying ω1+ω2=1, ω1>0, ω2>0; Similarly, determine the physical reachability efficiency of all storage locations and entrances / exits.

6. The method of claim 5, wherein, In step three, the specific method for obtaining the storage record associated with the tool to be put into storage is as follows: A tool wear measuring instrument is used to test the tool A to be put into storage, and the wear index of the tool A to be put into storage is determined and denoted as W_A; Obtain the storage record R_A marked as "to be improved" for the tool A to be put into storage. Extract the storage location Su of the tool A to be put into storage. Improve the storage record R_A in conjunction with the wear index W_A. Mark the storage status of the storage record R_A as "improved". Store the storage record R_A in the tool storage database.

7. The method according to claim 6, characterized in that, In step three, the specific method for performing the physical tool storage storage operation on the tools to be stored is as follows: Extract the storage location Su from the storage record R_A of the tool A to be stored, retrieve the storage location Su from the physical tool storage repository, and use a pre-built intelligent robot to adaptively construct the shortest path to store the tool A to be stored in the storage location Su of the physical tool storage repository.

8. The method of claim 7, wherein, In step four, the specific method for obtaining tool outbound requests based on the Internet of Things, extracting the specification information of the tools to be outbound, and retrieving the optimal matching tool from the tool storage database is as follows: Obtain any tool outbound request and parse it to determine the specifications of the tool to be outbound; Extract all storage records with the same specifications as the tool to be shipped from the tool storage database, and select the tool with the lowest wear index as the optimal matching tool.

9. The method according to claim 8, characterized in that, In step four, the specific method for retrieving the optimal matching tool with the corresponding tool ID from the corresponding storage location in the physical tool repository is as follows: Extract the storage record associated with the best matching tool, and separate the tool ID and storage location; Based on the determined tool ID and storage location, a search is performed in the physical tool storage repository. The corresponding tool retrieved is taken as the optimal physical matching tool, and an execution order for the out-of-repository is given to the intelligent robot.

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