Intelligent management and control early warning method and system for tool warehouse

Abstract

The invention relates to the technical field of warehouse management and control, and discloses an intelligent management and control early warning method and system for a tool warehouse. The method comprises the following steps: acquiring working condition data through a sensing acquisition network, and forming a control data flow through coupling analysis processing; using frequency and stock are extracted from the control data flow, and scheduling parameters are established according to a dynamic matching principle; carrying out balance operation on an early warning threshold value and a taking time sequence according to the scheduling parameter, and outputting a balance control quantity; analyzing a shift change process and an emergency rule based on the balance control quantity, and constructing a state distribution table; analyzing the abnormity and the maintenance period according to the state distribution table, and forming an early warning instruction set; and carrying out closed-loop optimization on the early warning instruction set, and constructing a management and control execution sequence. By establishing the multi-parameter coupling model and adopting the hierarchical optimization control strategy, multi-parameter dynamic balance control in tool warehouse management is realized, and the control precision and the response speed of the system are improved.

Description

Technical Field

[0001] The present application relates to the field of warehouse management and control, and in particular to an intelligent management and early warning method and system for tool warehouses. Background Art

[0002] In the existing tool warehouse management technology, barcode recognition, RFID tags and other automation technologies are mainly used to manage and track tools. By digitizing tool information, basic functions such as tool in and out management, usage records and inventory counting are realized. At the same time, PLC controllers and various sensors are introduced to automatically control the access process of tools, and the use status of tools is monitored in real time through the management software platform. The application of these technologies has improved the automation level of tool warehouse management and reduced the workload of manual operations.

[0003] However, the existing technology still has obvious shortcomings in the management of tool warehouses. The primary problem is the lack of an effective dynamic balance control mechanism under multi-parameter coupling conditions. Specifically, when multiple parameters such as tool usage frequency, inventory, maintenance cycle, etc. change at the same time, the traditional single PID controller cannot effectively handle this multi-variable coupling relationship, resulting in a lag in system response and reduced control accuracy; especially in scenarios with drastic dynamic changes such as tool handovers and emergency use, the mutual influence between control parameters can cause system oscillations and even control instability; due to the lack of accurate modeling and compensation mechanism for multi-parameter coupling effects, it is difficult to achieve rapid response while ensuring control stability, affecting the management efficiency and safety of tool warehouses. Summary of the invention

[0004] The present application provides an intelligent management and early warning method and system for tool warehouses. By establishing a multi-parameter coupling model and adopting a hierarchical optimization control strategy, multi-parameter dynamic balance control in tool warehouse management is achieved, thereby improving the control accuracy and response speed of the system.

[0005] In a first aspect, the present application provides an intelligent control and early warning method for a tool warehouse, the intelligent control and early warning method for a tool warehouse comprising: acquiring working condition data in the tool warehouse via an industrial sensor collection network, processing the working condition data by coupling analysis and calculation, and generating a tool control data stream; The tool usage frequency and inventory turnover are extracted from the tool control data stream, and a correlation analysis is performed according to the dynamic matching principle to establish tool scheduling parameters; based on the tool scheduling parameters, the tool inventory warning threshold and the retrieval sequence are balanced and calculated to output the tool balance control quantity; based on the tool balance control quantity, a collaborative analysis is conducted on the tool handover process and the emergency retrieval rules to construct a tool status allocation table; according to the tool status allocation table, tool operation abnormalities and maintenance cycles are included in the monitoring scope for analysis to form a tool warning instruction set; based on the tool warning instruction set, a closed-loop optimization analysis is conducted on the tool warehouse management system to construct a tool control execution sequence.

[0006] In a second aspect, the present application provides an intelligent management and early warning system for tool warehouses, the intelligent management and early warning system for tool warehouses comprising: A processing module is used to obtain working condition data in the tool warehouse through an industrial sensor collection network, process the working condition data through coupling analysis and calculation, and generate a tool control data stream; A correlation module is used to extract the tool usage frequency and inventory turnover from the tool control data stream, perform correlation analysis according to the dynamic matching principle, and establish tool scheduling parameters; A balancing module is used to balance the tool inventory warning threshold and the access timing according to the tool scheduling parameters, and output the tool balance control quantity; An allocation module is used to carry out collaborative analysis on the tool handover process and emergency use rules based on the tool balance control quantity, and to construct a tool status allocation table; A monitoring module, for analyzing tool operation abnormalities and maintenance cycles according to the tool status allocation table, and forming a tool early warning instruction set; The analysis module is used to carry out closed-loop optimization analysis on the tool warehouse management system based on the tool warning instruction set and construct a tool control execution sequence.

[0007] In the technical solution provided by the present application, the working condition data is acquired in real time through the industrial sensor collection network, and the coupling analysis algorithm is used to deeply process the data, so as to establish the tool control data stream, realize the comprehensive perception and accurate grasp of the operating status of the tool; by extracting the usage frequency and inventory turnover from the tool control data stream, the dynamic matching algorithm is used for correlation analysis, and the scientific tool scheduling parameters are constructed, which provides reliable data support for management decision-making; according to the tool scheduling parameters, the inventory warning threshold and the access timing are balanced, and the multi-objective optimization algorithm is used to generate the tool balance control quantity. The dynamic balance problem under the condition of multi-parameter coupling was effectively solved; the tool handover process and emergency access rules were analyzed in coordination, and the tool status distribution table was constructed using deep learning algorithms, which improved the intelligent level of tool management and emergency response capabilities; the abnormal operation and maintenance cycle of tools were included in the monitoring scope for analysis, and the tool warning instruction set was formed through the pattern recognition algorithm, which enhanced the fault warning and maintenance capabilities of the system; the closed-loop optimization analysis of the tool warehouse management system was carried out, and the tool control execution sequence was constructed using the recursive optimization algorithm, which achieved continuous optimization and efficiency improvement of the management process. In terms of algorithm features, this method innovatively combines coupling analysis, dynamic matching, multi-objective optimization, deep learning, pattern recognition and recursive optimization algorithms to form a set of tool warehouse intelligent control algorithm system. Each algorithm plays a specific role in its own application link, cooperates with each other, and synergizes to significantly improve the intelligent level and operation efficiency of tool warehouse management. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1 This is a schematic diagram of an embodiment of the intelligent management and early warning method for tool warehouses in the embodiment of the present application; Figure 2 This is a schematic diagram of the tool mapping hierarchy chain in the embodiment of the present application; Figure 3 A timing diagram of closed-loop matching of a tool optimization loop and a tool control trajectory of a tool warehouse management system according to a tool dynamic processing domain in an embodiment of the present application; Figure 4 This is a schematic diagram of an embodiment of the intelligent management and warning system for tool warehouses in the embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method and system for early warning of intelligent management and control of tool warehouses. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the intelligent management and early warning method for tool warehouses includes: Step S101, obtaining working condition data in a tool warehouse through an industrial sensor collection network, processing the working condition data through coupling analysis and calculation, and generating a tool control data stream; Step S102: extract tool usage frequency and inventory turnover from the tool management data stream, perform correlation analysis according to the dynamic matching principle, and establish tool scheduling parameters; Step S103: Based on the tool scheduling parameters, balance the tool inventory warning threshold and the access time sequence, and output the tool balance control quantity; Step S104: Based on the tool balance control quantity, collaborative analysis is performed on the tool handover process and emergency use rules to construct a tool status allocation table; Step S105: According to the tool status allocation table, the tool operation abnormalities and maintenance cycles are included in the monitoring scope for analysis to form a tool warning instruction set; Step S106: Based on the tool warning instruction set, a closed-loop optimization analysis is performed on the tool warehouse management system to construct a tool control execution sequence.

[0012] It is understandable that the execution subject of the present application can be the intelligent management and early warning system for tool warehouses, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0013] Specifically, the working condition data in the tool warehouse is collected through the industrial sensor collection network. Specifically, multiple industrial-grade RFID readers and sensor nodes are deployed in the tool warehouse to collect data such as the storage location, borrowing status, and environmental parameters of the tools in real time. The collected working condition data contains information such as the borrowing right level code and maintenance warning value of the tools. Among them, the borrowing right level code indicates the level of use authority of different staff members to the tools, and the maintenance warning value reflects the service life and maintenance status of the tools. The collected data is transmitted through the industrial communication bus. After data cleaning and preprocessing, the coupling analysis algorithm is used to perform correlation analysis on the relevant parameters in the working condition data to generate a standardized tool management data stream. From the tool management data stream, two key indicators, the frequency of use of tools and inventory turnover, are extracted. The frequency of use is obtained by statistically analyzing the borrowing records of tools, including the number of borrowings per unit time, the length of use, and other information. Inventory turnover is calculated by analyzing the inbound and outbound records of tools and equipment, reflecting the inventory turnover speed of tools and equipment. These two indicators are correlated and analyzed according to the dynamic matching principle to establish tool scheduling parameters. Scheduling parameters include multi-dimensional data such as the tool's timing code value, storage location code, borrowing frequency, etc., which are used for subsequent scheduling optimization.

[0014] Based on the tool scheduling parameters, the tool inventory warning threshold and the access sequence are balanced. The inventory warning threshold is determined according to factors such as the frequency of use and importance of the tool, and the access sequence reflects the borrowing rules and usage patterns of the tool. By balancing these two parameters, the tool balance control quantity is generated. The balance control quantity includes data such as the access safety of the tool, the warning constraint domain, and the balance calibration table, which are used to optimize the inventory management of the tool. Based on the tool balance control quantity, the tool handover process and emergency access rules are collaboratively analyzed. The handover process includes information such as the handover time point, handover method, and handover confirmation of the tool, and the emergency access rules stipulate the rapid deployment mechanism of the tool under special circumstances. Through collaborative analysis, the tool status distribution table is constructed, which includes data such as the time distribution chain of the tool, the handover density value, and the emergency response chain.

[0015] According to the tool status allocation table, tool operation abnormalities and maintenance cycles are included in the monitoring scope for analysis. Operation abnormalities include tool fault information, abnormal use, etc., while maintenance cycles include tool maintenance time, inspection plan, etc. By analyzing these data, a tool early warning instruction set is formed, which includes tool abnormality mark diagram, health assessment table, risk distribution chain and other information. For the tool early warning instruction set, a closed-loop optimization analysis is carried out on the tool warehouse management system. The closed-loop optimization analysis classifies the abnormal mark sequence and maintenance task flow in the early warning instruction set, and then conducts in-depth clustering analysis on the management indicator chain and optimization delimitation points. Through multi-dimensional data reorganization and recursive optimization, the tool control execution sequence is constructed.

[0016] For example, a tool warehouse stores 500 tools of various types. The RFID system collects the average daily borrowing times of tool A as 10 times, and the average usage time is 2 hours / time, and the usage frequency index is obtained; at the same time, the inventory turnover days of tool A are recorded as 3 days, and the inventory turnover is calculated. Combining these two indicators, the scheduling priority of tool A is determined to be level 1. According to the scheduling priority, the inventory warning threshold of tool A is set to 5 pieces, and the balance control quantity is calculated based on the access sequence of the two shift handover time points of 08:00 and 16:00 every day. In actual operation, when it is detected that the inventory of tool A drops to 6 pieces, the system generates a warning message and triggers the replenishment process, thereby ensuring the normal use and management of tools.

[0017] In the embodiment of the present application, the working condition data is acquired in real time through the industrial sensor collection network, and the coupled analysis algorithm is used to deeply process the data, so as to establish the tool control data stream, realize the comprehensive perception and accurate grasp of the operating status of the tool; by extracting the usage frequency and inventory turnover from the tool control data stream, the dynamic matching algorithm is used to perform correlation analysis, and the scientific tool scheduling parameters are constructed, which provides reliable data support for management decision-making; according to the tool scheduling parameters, the inventory warning threshold and the access timing are balanced, and the multi-objective optimization algorithm is used to generate the tool balance control quantity, which effectively solves the problem of tool failure. The dynamic balance problem under multi-parameter coupling conditions was solved; for the collaborative analysis of the tool handover process and emergency access rules, the tool status distribution table was constructed using the deep learning algorithm, which improved the intelligent level of tool management and emergency response capabilities; the abnormal operation and maintenance cycle of tools were included in the monitoring scope for analysis, and the tool warning instruction set was formed through the pattern recognition algorithm, which enhanced the fault warning and maintenance capabilities of the system; the closed-loop optimization analysis of the tool warehouse management system was carried out, and the tool control execution sequence was constructed using the recursive optimization algorithm, which achieved continuous optimization and efficiency improvement of the management process. In terms of algorithm features, this method innovatively combines coupling analysis, dynamic matching, multi-objective optimization, deep learning, pattern recognition and recursive optimization algorithms to form a set of tool warehouse intelligent control algorithm system. Each algorithm plays a specific role in its own application link, cooperates with each other, and synergizes to significantly improve the intelligent level and operation efficiency of tool warehouse management.

[0018] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Intelligently collect the borrowing right level code and maintenance warning value of tools through industrial collection terminals to obtain real-time status information of tools; (2) Calibrate the real-time status information of tools and equipment with the safe prefabrication quantity of tool handover to generate the tool intelligent delivery index; (3) Based on the intelligent delivery index of tools and equipment, the critical life span and emergency deployment rate of tools and equipment are linked and analyzed to obtain the dynamic supervision value of tools and equipment; (4) Using the dynamic supervision value of tools, we can deeply match the tool storage occupancy rate and borrowing saturation to form a tool resource load spectrum; (5) Based on the tool resource load spectrum, the tool borrowing priority chain and storage saturation point are bidirectionally mapped to obtain the tool intelligent scheduling chain; (6) The intelligent dispatching chain of tools and equipment is data-coupled with the tool safety control threshold to obtain the tool control data flow.

[0019] Specifically, the borrowing right level code and maintenance warning value of tools are intelligently collected through industrial collection terminals. The borrowing right level code refers to the digital identification of the level of authority of different staff members to use tools, including multi-dimensional data such as personnel identity information, work level, and use authority; the maintenance warning value reflects the service life and maintenance status of tools, and collects information such as the use time, wear degree, and maintenance records of tools in real time through sensors. These real-time collected data are standardized to form real-time status information of tools.

[0020] Then, the real-time status information of the tools is calibrated with the safe prefabrication quantity of the tools for handover. The safe prefabrication quantity of the tools for handover refers to the safety reference data of the tools during the handover process, including standard handover time, safety storage requirements, handover confirmation rules, etc. Through the data calibration algorithm, the real-time status information is compared and analyzed with the prefabrication quantity to calculate the intelligent delivery index of the tools.

[0021] According to the intelligent delivery index of tools and equipment, the critical life span and emergency deployment rate of tools and equipment are linked and analyzed. The calculation formula is:

[0022] in: Indicates the dynamic supervision value of tools; represents the life weight of the i-th tool; represents the critical life index of the i-th tool; Indicates the urgency of the jth emergency situation; represents the deployment response rate of the jth case; , is the balance coefficient; n is the total number of tools and equipment; m is the number of emergency types.

[0023] Using the dynamic supervision value of tools, the inventory occupancy rate and borrowing saturation of tools are deeply matched. The calculation formula is:

[0024] in: Indicates the tool resource load spectrum; Indicates the occupancy status of the kth storage location; represents the storage capacity of the kth storage location; represents the borrowing frequency of the kth storage location; is the adjustment coefficient; p is the total number of storage locations.

[0025] According to the tool resource load spectrum, the tool borrowing priority chain and storage saturation point are bidirectionally mapped, and the calculation formula is:

[0026] in: It represents the intelligent dispatching chain of tools; Indicates the priority of the t-th type of tool; It represents the borrowing amount of tools of the tth category; Indicates the current storage capacity of the t-th type of tools; represents the storage threshold of the t-th type of tool; q is the number of tool types.

[0027] The intelligent tool scheduling chain is coupled with the tool safety control threshold to obtain the tool control data flow. For example, in a tool warehouse, a batch of electric screwdrivers are managed. The borrowing right level code of this batch of tools is collected as level 3 (indicating that technical workers are available), and the maintenance warning value shows that it has been used for 200 hours. These data are calibrated with the handover safety prefabrication quantity (handover is required twice a day, and integrity must be confirmed each time) to calculate the intelligent delivery index. Then the dynamic supervision value is calculated according to the formula, taking into account that the remaining life of this batch of tools is about 800 hours and the emergency deployment rate is 0.8. Then the resource load spectrum is calculated, the current warehouse occupancy rate is 0.7, and the borrowing saturation is 0.85. The intelligent scheduling chain is obtained through bidirectional mapping, the scheduling priority of this batch of tools is determined, and the control data flow is formed.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Classify and sort the tool borrowing tag information and tool operation status in the tool control data stream to obtain a tool usage record table; (2) The tool timing code values ​​in the tool usage record table are segmented into periods to obtain a tool borrowing intensity graph; (3) According to the tool borrowing intensity diagram, the tool storage location code and borrowing frequency are correlated and calculated to form the tool call density value; (4) Through the tool call density value, the tool location identification and tool delivery time are cross-validated to generate a tool access relationship chain; (5) Match and analyze the tool access relationship chain with the tool safety storage parameters to obtain a tool turnover trend table; (6) According to the tool turnover trend table, the tool usage frequency and inventory turnover are integrated to obtain the tool scheduling parameters.

[0029] Specifically, the borrowing tag information and operating status data in the tool management data stream are classified and sorted. The borrowing tag information contains basic information such as the tool number, borrower, and borrowing time, while the operating status data contains dynamic information such as the tool usage time, operating parameters, and maintenance records. Through the data classification algorithm, this information is standardized and structured to generate a tool usage record table.

[0030] The time code values ​​in the tool usage record table are processed by period segmentation. The time code value refers to the usage status code of the tool in different time periods, including time series information such as borrowing time, return time, and usage time. Through the time series analysis algorithm, the time code value is segmented according to different time dimensions such as hours, shifts, and dates to form a tool borrowing intensity diagram. The borrowing intensity diagram intuitively shows the frequency distribution of tool usage in different time periods. According to the tool borrowing intensity diagram, the tool storage location code and borrowing frequency are associated with analysis. The storage location code is the specific storage location identifier of the tool in the warehouse, and the borrowing frequency reflects the frequency of use of the tool. Through the association analysis algorithm, the relationship between different storage locations and borrowing frequencies is calculated to form the tool call density value. The call density value quantifies the rationality and usage efficiency of the tool storage location.

[0031] Through the tool call density value, the tool storage location identification and delivery time are cross-validated. The storage location identification is the unique code of the tool storage location, and the delivery time includes the borrowing time and return time of the tool. Through the cross-validation algorithm, the matching degree between the storage location allocation and the delivery time is analyzed to generate the tool access relationship chain. The access relationship chain describes the spatiotemporal relationship of the tool access process. The tool access relationship chain is matched and analyzed with the safety storage parameters. The safety storage parameters include the storage environment requirements, protection level, storage period and other information of the tool. Through the matching analysis algorithm, it is evaluated whether the access process meets the safety storage requirements, and the tool turnover trend table is obtained. The turnover trend table reflects the dynamic change law of tool storage and use.

[0032] According to the tool turnover trend table, the tool usage frequency and inventory turnover are fused. The usage frequency reflects the usage pattern of tools, and the inventory turnover reflects the inventory management efficiency of tools. Through the data fusion algorithm, these two key indicators are comprehensively analyzed to obtain the tool scheduling parameters. The scheduling parameters serve as the basis for intelligent scheduling of tools.

[0033] Take the CNC tool management of a tool warehouse as an example: the tool borrowing record is extracted from the control data stream, including information such as tool number T001, operator number W123, borrowing time 09:00, etc. At the same time, the tool speed, feed rate, usage time and other operating status are recorded, and the usage record table is formed. Then the time series data in the usage record table is analyzed, and it is found that the tool is used more frequently in the morning shift, and a borrowing intensity graph is generated based on this. Then the borrowing situation of the tool stored in position 12 in area A is analyzed, and the call density value is calculated. Then the storage location information is matched with the delivery time to determine the access relationship chain. By comparing with the safe storage parameters, a turnover trend table is generated, and the frequency of use and turnover data are integrated to obtain the scheduling parameters to guide the intelligent management of tools.

[0034] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Match the tool period delivery node in the tool scheduling parameter with the tool intelligent storage location code to obtain the tool access security; (2) Compare and analyze the tool access safety level with the tool warning benchmark to obtain the tool warning constraint domain; (3) The tool warning baseline value and tool use index in the tool warning constraint domain are processed in parallel to form a tool balance calibration table; (4) Use the tool usage weights and tool warning levels in the tool balance calibration table to perform dynamic evaluation and generate a tool operation deviation diagram; (5) Balance and calibrate the tool control boundary value in the tool operation deviation diagram with the tool safety threshold to obtain the tool warning balance point; (6) Based on the tool warning balance point, the tool inventory warning threshold and the access time sequence are integrated to obtain the tool balance control quantity.

[0035] Specifically, the tool cycle delivery node is obtained by marking the regular delivery time point of the tool, and the tool intelligent storage location code is the unique identification code of each tool storage location through the RFID tag. The data matching process uses a correlation analysis algorithm to calculate the degree of association between the tool cycle delivery node and the intelligent storage location code, thereby obtaining the tool access security. In specific implementation, the tool cycle delivery node is converted into time series data, a weight coefficient is assigned to each time point, and the tool intelligent storage location code is converted into a spatial distribution matrix. By calculating the cross-correlation function of the time series data and the spatial distribution matrix, a numerical indicator characterizing the tool access security is obtained. For example, in CNC tool management, when the cycle delivery node of a tool is 10 am every Monday, its intelligent storage location code is position 12 in area A. Through calculation, it is known that during the access process of the tool, the stability of the delivery time is 0.92, the fixity of the storage location is 0.88, and the access security is 0.90.

[0036] The comparative analysis of tool access safety and warning benchmarks adopts a difference detection method. The warning benchmark is a safety threshold determined based on statistical analysis of historical data, which includes multiple dimensions such as access frequency and storage location stability. By calculating the deviation between the tool access safety and the warning benchmark, and combining factors such as tool type and importance for weighting, a tool warning constraint domain is formed. The warning constraint domain defines the safety boundary of tool management, and exceeding this boundary triggers a warning of the corresponding level. In practical applications, assuming that the warning benchmark of a certain type of precision measuring tool is set to a storage safety of not less than 0.85, when the actual storage safety is detected to be 0.82, the system calculates a deviation value of -0.03, combined with the importance coefficient of the measuring tool of 1.2, the warning constraint value is -0.036, which indicates that the management status of the measuring tool has entered the warning interval.

[0037] The warning constraint domain of tools and equipment includes warning baseline values ​​and access indicators. The warning baseline value is the minimum safety standard set for various tools and equipment, and the access indicator reflects the frequency and urgency of tool use. By processing these two types of data in parallel, the system constructs a tool balance calibration table. Parallel processing uses a multi-threaded calculation method to normalize the warning baseline value and access indicator at the same time, and obtains the calibration result through weighted average. The calibration table records key parameters such as the safety factor and usage priority of tools and equipment. For frequently used tools, the system will dynamically adjust the safety margin according to its warning baseline value and actual access frequency to ensure the balance of management. The access weight in the tool balance calibration table reflects the importance of tools and equipment in the production process, and the warning level indicates the risk level of the current management status. The dynamic evaluation process uses fuzzy reasoning method to judge the deviation degree of tool and equipment operation status according to the combination of access weight and warning level, and generate tool and equipment operation deviation diagram. The deviation diagram intuitively shows the difference between the tool and equipment management status and the ideal status, helping managers to find potential problems in time.

[0038] The control boundary value in the tool operation deviation diagram defines the upper and lower limits of management, and the safety threshold is a safety standard determined based on experience. The balance correction process adopts a proportional integral algorithm. By adjusting the control parameters, the tool management status is maintained within a safe range to obtain the tool warning balance point. The warning balance point is the optimal working point for tool management, which optimizes management efficiency while ensuring safety. Based on the tool warning balance point, the system integrates and analyzes the inventory warning threshold and the access sequence. The inventory warning threshold is the inventory warning standard set according to the tool usage pattern, and the access sequence records the tool usage time pattern. Data integration uses a time series analysis method to generate tool balance control quantity by calculating the correlation between inventory change trends and usage patterns. The balance control quantity is a comprehensive indicator that reflects the overall balance state of tool management.

[0039] For example, in the management of large tool warehouses, there are many types of tools, such as bench tools, machine tool accessories, and precision measuring tools. For bench tools, the tool scheduling parameters show that their periodic delivery nodes are two shift handover times every morning and evening. The intelligent storage location code is a combination of digital identification and location coordinates. The access safety is calculated through correlation analysis. The warning benchmark points set for bench tools include parameters such as access frequency not exceeding 12 times / day and storage location fixation rate not less than 95%. The warning constraint domain is generated through comparative analysis. The warning baseline value in the warning constraint domain covers indicators such as tool integrity rate and standard storage rate. The access indicators include data such as frequency of use and urgency. After parallel processing, a balance calibration table is formed. In the dynamic evaluation process, the system triggers the warning mechanism based on the real-time data of tool use, such as a sudden increase in the frequency of use of a certain type of special wrench, and determines the new warning balance point through balance correction. According to the warning balance point, the system adjusts the inventory warning threshold and access sequence to ensure the smooth operation of tool management.

[0040] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Integrate the tool balance control quantity with the tool shift handover code to obtain the tool handover sequence table; (2) Perform correlation verification on the tool time distribution chain and tool handover density value in the tool handover sequence table to obtain the tool handover process library; (3) Data mapping is performed between the urgency of tool use and the tool delivery node in the tool handover process library to form a tool emergency response chain; (4) Through the tool emergency response chain, the tool cross-shift allocation rate and tool emergency borrowing value are combined and analyzed to generate a tool coordination planning map; (5) Extract the tool handover standard points and tool emergency disposal levels from the tool coordination planning diagram to obtain the tool scheduling configuration table; (6) According to the tool scheduling configuration table, the tool handover process and emergency use rules are reconstructed to obtain the tool status allocation table.

[0041] Specifically, the tool balance control quantity represents the real-time balance status data in the tool warehouse management, and the tool shift handover code identifies the unique code for the handover of tools between different shifts. The data integration process uses a multidimensional data fusion algorithm to associate and integrate the tool usage status, inventory level and other information in the tool balance control quantity with the time mark, personnel information and other data in the shift handover code to generate a tool handover sequence table containing complete handover information. During data integration, the tool balance control quantity is processed by data standardization, and the data of different dimensions are unified to the same scale, and then a mapping relationship is established with the shift handover code to form a standardized handover sequence data structure. The tool time distribution chain records the use distribution of tools in different time periods, and the tool handover density value reflects the frequency characteristics of the handover behavior. The association verification adopts the time series association analysis method to establish the time series pattern of the handover behavior by calculating the time correlation between the time period distribution chain and the handover density value. During the verification process, the time distribution chain is segmented according to the time window, the handover behavior characteristics in each time window are calculated, and matched and analyzed with the handover density value to generate a tool handover process library that describes the handover behavior rules.

[0042] The urgency of tool access indicates the urgency of tool use, and the tool delivery node is the preset delivery time point. The data mapping process uses a priority mapping algorithm to dynamically adjust the delivery node according to the urgency of access, forming an emergency response chain for tools that reflects the emergency response mechanism. During the mapping process, the urgency of access is graded and quantified, and then the corresponding delivery time adjustment strategy is set according to different urgency levels to establish a corresponding relationship between emergency access and delivery adjustment.

[0043] The tool cross-shift allocation rate reflects the proportion of tools allocated between different shifts, and the tool emergency borrowing value indicates the borrowing frequency in emergency situations. The combined analysis adopts a multi-factor association analysis method to construct a collaborative scheduling model for tools by calculating the correlation between cross-shift allocation and emergency borrowing. During the analysis process, the cross-shift allocation data and the emergency borrowing data are cross-analyzed to identify the scheduling rules and generate a collaborative planning diagram reflecting the tool scheduling strategy. The tool handover specification point defines the key nodes in the standard handover process, and the tool emergency disposal level indicates the disposal level in different emergency situations. The data extraction process adopts a feature extraction algorithm to identify the key handover nodes and emergency disposal requirements from the collaborative planning diagram to form a standardized tool scheduling configuration table. During the extraction process, the pattern recognition method is used to identify the standard nodes in the handover process, and at the same time, combined with the emergency disposal requirements, the configuration relationship between the handover specification and the emergency disposal is established.

[0044] The tool handover process is a standardized handover operation process, and the emergency access rules stipulate the access operation specifications in emergency situations. The data reconstruction process uses a process reorganization algorithm to optimize and reorganize the handover process and emergency access rules based on the standard requirements in the scheduling configuration table to generate a tool status allocation table that meets actual management needs. During the reconstruction process, the standard process and emergency rules are integrated to form a tool management status allocation mechanism.

[0045] For example, in the management of precision measuring tools warehouse, the RFID system collects the tool balance control data, including the usage status, inventory level and other information of various measuring tools, and obtains the handover time, operator and other information recorded in the shift handover code. After data integration, a tool handover sequence table is formed to record the handover information of various measuring tools at each shift handover. By analyzing the time distribution data in the handover sequence table, it is found that a certain type of precision measuring tool is used more frequently during the handover period of the morning shift. Based on this, the corresponding handover specification is set in the handover process library. When there is an emergency demand for use, such as a certain workstation urgently needs a specific model of micrometer, the available measuring tools are quickly located through the emergency response chain, and the optimal allocation plan is determined based on the cross-shift allocation data. In the actual scheduling process, according to the analysis results of the collaborative planning diagram, the handover time and allocation path are reasonably arranged to ensure the timely delivery of the measuring tools. Based on the management mechanism of the tool status allocation table, the efficient management of the precision measuring tool warehouse is realized, which not only meets the normal handover needs, but also can quickly respond to emergency access requests.

[0046] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Extracting data on tool deviation points and tool operation intervals in the tool status distribution table to obtain a tool abnormality marking diagram; (2) Compare and analyze the tool service life values ​​and tool maintenance cycle points in the tool abnormality mark diagram to obtain a tool health assessment table; (3) Cross-validate the tool failure identification and tool maintenance indicators in the tool health assessment table to form a tool risk distribution chain; (4) Data fusion of tool safety factors and tool warning limits is performed through the tool risk distribution chain to generate a tool monitoring and warning map; (5) Extract the abnormal level of tools and the tool maintenance time period from the tool monitoring and early warning map to obtain the tool early warning determination table; (6) According to the tool warning judgment table, the data of tool operation abnormalities and maintenance cycles are normalized to obtain the tool warning instruction set.

[0047] Specifically, the tool deviation point and tool operation interval are identified from the tool status distribution table. The tool deviation point represents the difference between the actual operating state of the tool and the standard state, while the tool operation interval defines the parameter range for the normal operation of the tool. The feature extraction algorithm is used to process these two types of data. Through cluster analysis of the deviation data and boundary detection of the operation interval, a tool abnormality marker map is drawn to intuitively display the abnormal distribution of the tool operation status. The tool service life value reflects the cumulative use time of the tool, and the tool maintenance cycle point is the preset maintenance time node. The comparative analysis uses the time series data analysis method to calculate the deviation between the actual use time of each tool and the standard maintenance cycle, and generate a tool health assessment table. The assessment table contains key indicators such as the use intensity, wear degree, and maintenance requirements of the tool.

[0048] The tool fault identification records the historical fault information of the tool, and the tool maintenance index reflects the execution of maintenance. The cross-validation process uses association rule analysis to calculate the correlation between the occurrence of faults and the execution of maintenance to form a distribution chain that describes the risk status of the tool. The validation process focuses on the temporal relationship between faults and maintenance to identify potential risk factors.

[0049] The following complexity fusion model is used for the data fusion process of tool risk distribution chain, tool safety factor and warning limit:

[0050] in: It represents the risk fusion value of tools; represents the weight of the xth risk factor; represents the intensity value of the x-th risk factor; represents the influence coefficient of the yth safety parameter; represents the threshold ratio of the y-th safety parameter; indicates fusion regulator; represents the total number of risk factors; Indicates the total number of security parameters.

[0051] Based on the fusion model, a tool monitoring and early warning map is generated, including the risk level distribution and early warning threshold boundary. The abnormal level and maintenance time period information of the tool are extracted from the monitoring and early warning map, and the tool early warning judgment table is obtained through the abnormal classification algorithm and time series analysis. The judgment table clearly defines the early warning conditions and disposal requirements corresponding to different abnormal levels. The data normalization processing adopts the standardized method of tool operation abnormality and maintenance cycle, converts indicators of different dimensions into a unified scale space, and forms a standardized tool early warning instruction set. The normalization process ensures that the early warning indicators of different types of tools are comparable, which facilitates unified management and decision-making.

[0052] Taking machine tool management as an example, the deviation point data of the tool comes from the machining accuracy detection, and the operating range is determined by the tool technical specifications. By analyzing the comparative data of the tool usage time and the standard tool change cycle, combined with the tool wear detection results, a health assessment index is generated. When it is found that the usage time of a tool is close to the life limit and the wear index exceeds the standard range, the historical fault record and maintenance execution are comprehensively considered, the risk value is calculated and an early warning signal is generated. After the early warning information is standardized, a clear early warning instruction is formed to guide management personnel to replace or maintain the tool in a timely manner.

[0053] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) The tool abnormality mark sequence and tool maintenance task flow in the tool warning instruction set are hierarchically sorted to obtain the tool optimization basic table; (2) Conduct in-depth cluster analysis on the tool management index chain and tool optimization demarcation points in the tool optimization basic table to form a tool classification optimization library; (3) Based on the tool classification optimization library, the tool execution priority and tool control iteration sequence are associated and mapped to obtain the tool mapping hierarchy chain; (4) Dynamically couple the tool scheduling type code in the tool mapping hierarchy chain and the tool closed-loop processing chain to obtain the tool closed-loop optimization network; (5) Adaptively integrate the tool management constraint domain and the tool operation topology through the tool closed-loop optimization network to generate a tool optimization execution table; (6) From the tool optimization execution table, the tool feedback adjustment value, the tool optimization control chain, and the tool system reconstruction sequence are reorganized in multiple dimensions to obtain the tool closed-loop control set; (7) Recursive optimization is performed using the tool management weight matrix and tool system response chain in the tool closed-loop control to form a tool dynamic processing domain; (8) According to the dynamic processing domain of tools, the tool optimization loop and tool control trajectory of the tool warehouse management system are closed-loop matched to obtain the tool control execution sequence.

[0054] Specifically, the tool abnormality mark sequence records the abnormal conditions and their temporal relationships that occur during tool operation, and the tool maintenance task flow contains the specific content and execution order of the maintenance work. The hierarchical classification method is used for classification, and the initial classification is performed according to the abnormality type and the attributes of the maintenance task. Then, the secondary classification is performed according to the severity and urgency, forming a tool optimization basic table containing complete classification information. Each entry in the basic table contains key information such as the abnormality type code, maintenance task number, and priority identification. The tool management indicator chain is a series of quantitative indicators to measure the tool management effect, and the tool optimization delimitation point defines the target value and limit range of each indicator. The deep clustering analysis uses an improved K-means algorithm to calculate the similarity and correlation between indicators, cluster similar management indicators and optimization delimitation points into the same category, and construct a multi-level tool classification optimization library. In the clustering process, the numerical characteristics and management attributes of the indicators are considered at the same time to ensure that the classification results have practical management significance.

[0055] The tool execution priority reflects the execution order of tool management tasks, and the tool control iteration sequence records the evolution of control measures. The association mapping uses a bidirectional mapping algorithm to establish the correspondence between execution priority and iteration sequence, and generate a multi-level tool mapping hierarchy chain.

[0056] like Figure 2 As shown, it is a schematic diagram of the tool mapping hierarchy chain in the embodiment of the present application, which is divided into four main levels from top to bottom: management task classification layer, task type layer, priority allocation layer and iteration sequence layer. The management task classification layer divides the tool management tasks into three levels: primary, secondary and tertiary, reflecting the importance and urgency of the tasks. In the task type layer, primary tasks include emergency repair tasks and key equipment maintenance, secondary tasks include routine maintenance tasks and regular inspection tasks, and tertiary tasks include preventive maintenance and spare equipment management, realizing the detailed classification of tasks.

[0057] The priority allocation layer assigns a corresponding priority mark to each specific task, decreasing from P1 to P6, to ensure the orderliness of task execution. The iterative sequence layer sets up three rounds of iteration processes, corresponding to scheduling optimization, parameter adjustment, and performance optimization, respectively. The systematic processing of different types of tasks is achieved through the rapid response chain, standard processing chain, and preventive maintenance chain. The connection relationship between the nodes in the figure represents the logical order of task processing and the data flow direction, and the solid arrows indicate the specific paths of task allocation and processing.

[0058] During the mapping process, high-priority tasks are given priority while ensuring the continuity and integrity of the iteration sequence. The tool scheduling type code identifies different scheduling methods, and the tool closed-loop processing chain describes the processing flow. The dynamic coupling process uses an adaptive coupling algorithm to select the appropriate processing flow according to the characteristics of the scheduling type and construct a tool closed-loop optimization network containing multiple optimization loops. Each node in the optimization network corresponds to a specific processing link, and the connection between nodes represents the sequential relationship of the processing flow.

[0059] The tool management constraint domain defines the boundary conditions of the management activities, and the tool operation topology diagram describes the spatial distribution of the tool operation status. The adaptive fusion process uses a multi-objective optimization algorithm to optimize the operation layout of the tool under the premise of meeting the constraints and generate the tool optimization execution table. The execution table contains the optimized management parameters and execution strategies. The tool feedback adjustment value is a correction parameter based on the execution effect, the tool optimization control chain records the evolution of the control strategy, and the tool system reconstruction sequence describes the path of system optimization. Multi-dimensional data reorganization uses the tensor decomposition method to integrate and reconstruct these three types of data to form a tool closed-loop control set. During the reorganization process, attention is paid to maintaining the correlation and timing characteristics between data.

[0060] The tool management weight matrix represents the importance of different management elements, and the tool system response chain records the system's response to management measures. Recursive optimization uses an iterative optimization algorithm to continuously adjust management parameters through multiple rounds of calculations to form a tool dynamic processing domain that can dynamically adapt to management needs. During the recursive process, each round of optimization adjusts parameters based on the results of the previous round until the predetermined optimization goal is achieved. The tool optimization loop represents the optimization process, and the tool control trajectory records the actual control path. The closed-loop matching uses a trajectory matching algorithm to dynamically adjust the control strategy and generate a tool control execution sequence by comparing the differences between the optimization loop and the actual trajectory.

[0061] Taking precision instrument management as an example, the abnormal records of the instrument (such as accuracy deviation, parts wear, etc.) and maintenance tasks (such as calibration, replacement of parts, etc.) are classified and sorted to form a basic optimization table. Then, by analyzing the distribution characteristics of management indicators (such as usage efficiency, maintenance cost, etc.), similar indicators are clustered to form an optimization library. Based on the classification results of the optimization library, the use and maintenance tasks of different instruments are prioritized and a mapping relationship is established. Then, according to the characteristics of different instruments, the appropriate scheduling method is selected, such as priority scheduling for emergency tasks and cyclic scheduling for routine tasks. During the execution process, the management parameters are continuously optimized by analyzing real-time feedback data to ensure the effectiveness of management measures. Through the evaluation and optimization of the execution effect, a control execution sequence is formed to achieve efficient management of precision instruments.

[0062] In a specific embodiment, the process of performing a closed-loop matching step of a tool optimization loop and a tool control trajectory of a tool warehouse management system according to a tool dynamic processing domain may specifically include the following steps: (1) Recursively decompose the system optimization parameter chain and control level identifier in the dynamic processing domain of the tool to obtain the tool optimization sequence diagram; (2) The control node codes and optimization iteration coefficients in the tool optimization sequence diagram are hierarchically separated to obtain a tool optimization classification table; (3) Orthogonally match the closed-loop response values ​​in the tool optimization classification table with the system control variables to form a tool control response chain; (4) Based on the tool control response chain, the management control sequence and the optimization feedback parameters are multi-dimensionally coupled to generate a tool loop mapping diagram; (5) Dynamically reconstruct the system control identification and operation trajectory sequence through the tool loop mapping diagram to obtain the tool management execution table; (6) According to the tool management execution table, the tool optimization loop and the tool control trajectory are integrated to obtain the tool control execution sequence.

[0063] Specifically, Figure 3 As shown, it is a timing diagram of closed-loop matching of the tool optimization loop and tool control trajectory of the tool warehouse management system according to the dynamic processing domain of the tool in the embodiment of the present application. In the timing process, the dynamic processing domain module transfers the system optimization parameter chain and the control level identifier to the optimization sequence module, and the optimization sequence module generates the optimization sequence diagram through the recursive decomposition algorithm. Subsequently, the hierarchical separation module processes the control node encoding and optimization iteration coefficient in the optimization sequence diagram to form an optimization classification table. Then, the orthogonal matching module performs matching analysis based on the closed-loop response value and the system control variable in the optimization classification table to generate a control response chain. After receiving the control response chain, the multidimensional coupling module performs multi-dimensional analysis and processing on the management control sequence and the optimization feedback parameter, and outputs a loop mapping diagram. The dynamic reconstruction module is responsible for identifying the system control identifier and analyzing the running trajectory sequence to generate a management execution table. Finally, the data fusion module fuses the tool optimization loop data and the control trajectory information, continuously adjusts and improves the control strategy through the closed-loop optimization mechanism, and finally forms the tool control execution sequence.

[0064] The system optimization parameter chain contains a set of parameters for optimizing tool management, and the control level identifier defines the characteristic identifiers of different management levels. Recursive decomposition uses a hierarchical iterative algorithm to disassemble the system optimization parameter chain layer by layer according to the control level, and construct a tool optimization sequence diagram that shows the hierarchical relationship of parameters. In the decomposition process, the dependencies between parameters are identified, and then the interrelated parameters are organized into an ordered optimization sequence according to the attribute characteristics of the control level. The control node code represents the unique identifier of the management control point, and the optimization iteration coefficient reflects the convergence characteristics of the optimization process. The hierarchical separation process uses a multi-level decomposition algorithm to separate and reorganize the control elements of different levels by analyzing the hierarchical attributes of the control nodes and the characteristics of the optimization iteration to form a structured tool optimization hierarchical table. Each level in the hierarchical table corresponds to a specific control function and optimization goal.

[0065] The closed-loop response value describes the degree of response of the tool management system to the control input, and the system control variable is the controllable parameter used to adjust the system behavior. The orthogonal matching process adopts the orthogonal experimental design method. By systematically analyzing the influence of different control variables on the response value, the mapping relationship between variables and responses is established, and the tool control response chain reflecting the dynamic characteristics of the system is generated. The management control sequence is a standardized management control process, and the optimization feedback parameters reflect the feedback information of the optimization effect. The multi-dimensional coupling process adopts the tensor decomposition algorithm to correlate the control sequence and feedback parameters in multiple dimensions, and construct a tool loop mapping diagram that describes the system optimization path. Each node in the mapping diagram contains complete information on control decisions and optimization feedback.

[0066] The system control identifier is used to mark different control strategies, and the operation trajectory sequence records the actual path of the system operation. The dynamic reconstruction process uses a trajectory optimization algorithm to optimize and adjust the system operation mode by analyzing the matching degree between the control strategy and the operation trajectory, and generates a tool management execution table to guide the actual management execution. The execution table contains the optimized control strategy and execution path. The tool optimization loop describes the optimization process, and the tool control trajectory records the actual control execution path. Data fusion uses a multi-source information fusion algorithm to integrate and analyze the data of the optimization loop and the control trajectory to form a tool control execution sequence. In the fusion process, it is important to maintain the temporal characteristics and spatial correlation of the data.

[0067] Taking machining tool management as an example, the tool management process involves multiple control levels, including tool inventory management, usage scheduling, maintenance, etc. The system optimization parameter chain contains key parameters of each level, such as inventory warning value, scheduling cycle, maintenance interval, etc. Through recursive decomposition, these parameters are expanded layer by layer according to the management level to form a clear optimization sequence diagram. Then, each control node is encoded, such as encoding the tool access node as Class A and the maintenance node as Class B, and the optimization coefficient of each node is determined according to the characteristics of the optimization iteration. In actual operation, when the usage frequency of a certain type of tool changes, the relevant control parameters, such as tool scheduling frequency, warning threshold, etc., are adjusted through a closed-loop response mechanism. Based on the adjusted parameters, the tool management process is optimized, such as dynamically adjusting the tool storage location according to the usage frequency and optimizing the access path. Through the analysis of the optimization loop and the actual control trajectory, a control execution sequence is formed to achieve dynamic optimization of tool management.

[0068] In a specific embodiment, the process of performing the step of data fusion of the tool optimization loop and the tool control trajectory according to the tool management execution table may specifically include the following steps: (1) The tool hierarchical distribution domain, tool fusion decision chain, and tool execution optimization degree in the tool management execution table are deeply decoupled and analyzed. At the same time, the tool system topology chain and the tool optimization control domain are recursively mapped. Through the multi-dimensional correlation processing of tool control parameters and tool optimization indicators, the tool optimization scheduling table is obtained; (2) Multi-level cross-validation is performed on the tool execution sequence values, tool management optimization chains, and tool response delimiters in the tool optimization scheduling table. Combined with the dynamic matching analysis of tool control nodes and tool execution weights, the tool control optimization domain is obtained through the deep fusion processing of tool distribution characteristics and tool optimization constraints. (3) The tool execution identification chain, tool control delimiter, and tool optimization evaluation value in the tool control optimization domain are systematically deconstructed, and based on the correlation analysis between the tool control constraint domain and the tool iteration sequence, a tool path planning set is formed through recursive mapping of tool optimization parameters and tool execution characteristics; (4) Through the tool path planning set, the tool optimization control domain, the tool management hierarchical chain, and the tool execution mapping point are deeply coupled. At the same time, combined with the dynamic matching of the tool control loop and the tool optimization sequence, the tool optimization control diagram is generated based on the multi-dimensional analysis and processing of the tool system characteristics and tool control indicators; (5) Extract the characteristic sequence of tool management boundary values, tool control optimization chain, and tool execution evaluation domain from the tool optimization control diagram, and adaptively fuse the tool system constraint points and tool optimization parameters. Through the correlation mapping of tool distribution characteristics and tool control indicators, the tool system execution table is obtained. (6) According to the tool system execution table, deep data fusion is performed on the tool optimization loop and the tool control trajectory. At the same time, the tool execution feature chain and the tool system optimization domain are combined for multi-dimensional analysis. Through recursive optimization processing of tool control parameters and tool management indicators, the tool control execution sequence is obtained.

[0069] Specifically, the tool hierarchical distribution domain represents the distribution of tools at different management levels, the tool fusion decision chain records the fusion path of the decision process, and the tool execution optimization degree reflects the optimization degree of the execution effect. The deep decoupling analysis uses the independent component analysis (ICA) algorithm to untie the associations between the three types of data. At the same time, the recursive feature mapping method is used to process the relationship between the tool system topology chain and the optimization control domain. The mapping relationship between the tool control parameters and the optimization indicators is established through multidimensional correlation analysis to form a tool optimization scheduling table. The tool execution sequence value describes the temporal characteristics of the execution process, the tool management optimization chain records the evolution path of the optimization process, and the tool response delimiter defines the boundary conditions of the response. Multi-level cross-validation uses a hierarchical analysis method, combined with the characteristics of the tool control nodes and the dynamic changes of the execution weights, and generates a tool control optimization domain that reflects the system optimization state through distribution feature analysis and optimization constraint fusion. During the verification process, the focus is on the cross-influence and constraint relationship between different levels.

[0070] The tool execution identification chain is used to mark the key nodes of the execution process, the tool control delimiter defines the boundary conditions of the control, and the tool optimization evaluation value reflects the optimization effect. The system deconstruction process adopts a structural decomposition algorithm. Based on the association between control constraints and iterative sequences, the tool path planning set is constructed by recursive mapping of optimization parameters and execution characteristics. The deconstruction process focuses on maintaining the logical association and timing characteristics between data. The tool path planning set records the planned path information, the tool optimization control domain defines the scope of control, and the tool management hierarchical chain describes the hierarchical structure of management. The deep coupling process adopts a multidimensional data fusion algorithm, combined with the dynamic characteristics of the control loop and the optimization sequence, and generates a tool optimization control chart through multidimensional analysis of system characteristics and control indicators. In the coupling process, the focus is on the mutual influence and synergy between the various elements.

[0071] The tool management delimiter sets the boundary parameters of management, the tool control optimization chain records the evolution of optimization, and the tool execution evaluation domain defines the scope of evaluation. The feature extraction adopts the principal component analysis method to adaptively fuse the system constraint points and optimization parameters, and forms the tool system execution table through the mapping analysis of distribution characteristics and management indicators. The extraction process focuses on maintaining the representativeness and integrity of the data. The tool system execution table shows the specific content of the execution, the tool optimization loop describes the cycle process of optimization, and the tool control trajectory records the actual path of control. Deep data fusion adopts a multi-source information fusion algorithm, combined with multi-dimensional analysis of execution characteristics and system optimization, and generates a tool control execution sequence through recursive optimization of control parameters and management indicators.

[0072] Taking CNC tool management as an example, the hierarchical distribution information of the tool (such as spindle tools, turning tools, milling cutters, etc.) is decoupled and analyzed from the decision fusion data (such as replacement cycle, maintenance rules, etc.), and the mapping of tool usage topology and control parameters is processed at the same time. Then, the tool usage sequence, optimization strategy and response characteristics are cross-validated, and matching analysis is performed in combination with the control requirements and usage weights of different workstations. Based on the verification results, the execution identification, control boundary and optimization evaluation of the tool are systematically deconstructed to construct the path planning of tool scheduling. During the execution process, the control strategy is dynamically adjusted by analyzing the tool usage characteristics and optimization indicators, such as adjusting the replacement cycle according to the wear status and adjusting the maintenance frequency based on the processing accuracy. Through the deep fusion and optimization analysis of the execution data, the tool control execution sequence is formed to achieve precise control of tool management.

[0073] The above describes the intelligent control and early warning method for tool warehouses in the embodiment of the present application. The following describes the intelligent control and early warning system for tool warehouses in the embodiment of the present application. Figure 4 In the embodiment of the present application, an embodiment of the intelligent management and early warning system for tool warehouses includes: The processing module 201 is used to obtain the working condition data in the tool warehouse through the industrial sensor collection network, process the working condition data through coupling analysis and calculation, and generate a tool control data stream; The correlation module 202 is used to extract the tool usage frequency and inventory turnover from the tool control data stream, perform correlation analysis according to the dynamic matching principle, and establish tool scheduling parameters; The balancing module 203 is used to balance the tool inventory warning threshold and the access time sequence according to the tool scheduling parameters, and output the tool balance control quantity; The allocation module 204 is used to carry out collaborative analysis on the tool handover process and emergency use rules based on the tool balance control amount, and construct a tool status allocation table; Monitoring module 205, for analyzing abnormal operation and maintenance cycle of tools according to the tool status distribution table, and forming a tool early warning instruction set; The analysis module 206 is used to carry out closed-loop optimization analysis on the tool warehouse management system according to the tool warning instruction set, and construct a tool control execution sequence.

[0074] Through the coordinated cooperation of the above-mentioned components, the working condition data is obtained in real time through the industrial sensor acquisition network, and the coupling analysis algorithm is used to deeply process the data, and the tool management data stream is established, which realizes the comprehensive perception and accurate grasp of the operating status of the tools; by extracting the usage frequency and inventory turnover from the tool management data stream, the dynamic matching algorithm is used for correlation analysis, and a scientific tool scheduling parameter is constructed, which provides reliable data support for management decisions; according to the tool scheduling parameters, the inventory warning threshold and the access timing are balanced, and the multi-objective optimization algorithm is used to generate the tool balance control quantity , effectively solved the dynamic balance problem under multi-parameter coupling conditions; for the collaborative analysis of the tool handover process and emergency access rules, the tool status distribution table was constructed using a deep learning algorithm, which improved the intelligent level of tool management and emergency response capabilities; the tool operation abnormalities and maintenance cycles were included in the monitoring scope for analysis, and the tool warning instruction set was formed through the pattern recognition algorithm, which enhanced the system's fault warning and maintenance capabilities; the closed-loop optimization analysis of the tool warehouse management system was carried out, and the tool control execution sequence was constructed using a recursive optimization algorithm, achieving continuous optimization and efficiency improvement of the management process. In terms of algorithm features, this method innovatively combines coupling analysis, dynamic matching, multi-objective optimization, deep learning, pattern recognition, and recursive optimization algorithms to form a set of tool warehouse intelligent control algorithm system. Each algorithm plays a specific role in its own application link, cooperates with each other, and synergizes to significantly improve the intelligent level and operation efficiency of tool warehouse management.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A tool warehouse intelligent management and early warning method, characterized in that: The intelligent management and early warning method for tool warehouse includes: Acquire the working condition data in the tool warehouse through the industrial sensor collection network, process the working condition data through coupling analysis and calculation, and generate the tool control data stream; Extracting the tool usage frequency and inventory turnover from the tool control data stream, performing correlation analysis according to the dynamic matching principle, and establishing tool scheduling parameters; According to the tool scheduling parameters, balance the tool inventory warning threshold and the access time sequence, and output the tool balance control quantity; Based on the tool balance control quantity, a collaborative analysis is conducted on the tool handover process and emergency use rules to construct a tool status allocation table; According to the tool status allocation table, the tool operation abnormalities and maintenance cycles are included in the monitoring scope for analysis to form a tool early warning instruction set; Based on the tool warning instruction set, a closed-loop optimization analysis is carried out on the tool warehouse management system to construct a tool control execution sequence.

2. The intelligent management and early warning method for tool warehouse according to claim 1 is characterized in that: The method of acquiring the working condition data in the tool warehouse through the industrial sensor collection network, processing the working condition data through coupling analysis and calculation, and generating the tool control data stream includes: Through the industrial collection terminal, the borrowing right level code and maintenance warning value of the tools are intelligently collected to obtain the real-time status information of the tools; Calibrate the real-time status information of the tool with the safe prefabrication quantity of the tool handover to generate a tool intelligent delivery index; According to the intelligent delivery index of tools and equipment, a linkage analysis is performed on the critical life span and emergency deployment rate of tools and equipment to obtain a dynamic supervision value of tools and equipment; By using the dynamic supervision value of the tools, the inventory occupancy rate and borrowing saturation of the tools are deeply matched to form a tool resource load spectrum; According to the tool resource load spectrum, bidirectional mapping is performed on the tool borrowing priority chain and storage saturation point to obtain the tool intelligent scheduling chain; The tool intelligent scheduling chain is data-coupled with the tool safety control threshold to obtain a tool control data stream.

3. The intelligent management and early warning method for tool warehouse according to claim 1 is characterized in that: The method extracts the tool usage frequency and inventory turnover from the tool control data stream, performs correlation analysis according to the dynamic matching principle, and establishes tool scheduling parameters, including: Classify and sort the tool borrowing tag information and tool operation status in the tool control data stream to obtain a tool usage record table; Periodically segmenting the tool timing code values ​​in the tool usage record table to obtain a tool borrowing intensity graph; According to the tool borrowing intensity map, the tool storage location code and the borrowing frequency are correlated and calculated to form a tool calling density value; Through the tool call density value, the tool location identification and tool delivery time are cross-verified to generate a tool access relationship chain; Matching and analyzing the tool access relationship chain with the tool safety storage parameters to obtain a tool turnover trend table; According to the tool turnover trend table, the tool usage frequency and inventory turnover are integrated to obtain tool scheduling parameters.

4. The intelligent management and early warning method for tool warehouse according to claim 1 is characterized in that: According to the tool scheduling parameters, the tool inventory warning threshold and the access timing are balanced and calculated, and the tool balance control quantity is output, including: Data matching is performed between the tool period delivery node in the tool scheduling parameter and the tool intelligent storage location code to obtain the tool access safety degree; Comparative analysis is performed on the tool access safety level and the tool warning reference point to obtain the tool warning constraint domain; Through parallel processing of the tool warning baseline value and the tool use index in the tool warning constraint domain, a tool balance calibration table is formed; Using the tool usage weights and tool warning levels in the tool balance calibration table to perform dynamic evaluation and generate a tool operation deviation diagram; Balance and calibrate the tool control boundary value in the tool operation deviation diagram with the tool safety threshold to obtain a tool warning balance point; Based on the tool warning balance point, data integration is performed on the tool inventory warning threshold and the access timing to obtain the tool balance control quantity.

5. The intelligent management and early warning method for tool warehouse according to claim 1 is characterized in that: Based on the tool balance control quantity, a collaborative analysis is conducted on the tool handover process and emergency use rules to construct a tool status allocation table, including: Integrate the tool balance control quantity with the tool shift handover code to obtain a tool handover sequence table; Performing correlation verification on the tool time distribution chain and the tool handover density value in the tool handover sequence table to obtain a tool handover process library; Data mapping is performed on the tool use urgency and tool delivery nodes in the tool handover process library to form a tool emergency response chain; Through the tool emergency response chain, a combined analysis is performed on the tool cross-shift allocation rate and the tool emergency borrowing value to generate a tool coordination planning diagram; Extract the tool handover standard points and tool emergency disposal levels from the tool coordination planning diagram to obtain a tool scheduling configuration table; According to the tool scheduling configuration table, data of the tool handover process and emergency use rules are reconstructed to obtain a tool status allocation table.

6. The intelligent management and early warning method for tool warehouse according to claim 1 is characterized in that: According to the tool status allocation table, tool operation abnormalities and maintenance cycles are included in the monitoring scope for analysis to form a tool early warning instruction set, including: Extracting data of tool deviation points and tool operation intervals in the tool status distribution table to obtain a tool abnormality marking diagram; Comparative analysis is performed on the tool service life values ​​and tool maintenance cycle points in the tool abnormality mark diagram to obtain a tool health assessment table; Cross-validate the tool fault identification and tool maintenance index in the tool health assessment table to form a tool risk distribution chain; Data fusion of tool safety factor and tool warning limit is performed through the tool risk distribution chain to generate a tool monitoring and warning map; Extracting tool abnormality levels and tool maintenance time periods from the tool monitoring and early warning diagram to obtain a tool early warning determination table; According to the tool warning determination table, data normalization processing is performed on tool operation abnormalities and maintenance cycles to obtain a tool warning instruction set.

7. The intelligent management and early warning method for tool warehouse according to claim 1 is characterized in that: The closed-loop optimization analysis of the tool warehouse management system is carried out for the tool warning instruction set, and the tool control execution sequence is constructed, including: The tool abnormality mark sequence and the tool maintenance task flow in the tool warning instruction set are hierarchically sorted to obtain a tool optimization basic table; Performing in-depth cluster analysis on the tool management index chain and tool optimization delimitation points in the tool optimization basic table to form a tool classification optimization library; Based on the tool classification optimization library, the tool execution priority and the tool management and control iteration sequence are associated and mapped to obtain a tool mapping hierarchy chain; Dynamically coupling the tool scheduling type code in the tool mapping hierarchy chain and the tool closed-loop processing chain to obtain a tool closed-loop optimization network; Adaptively integrate the tool management constraint domain and the tool operation topology map through the tool closed-loop optimization network to generate a tool optimization execution table; From the tool optimization execution table, tool feedback adjustment values, tool optimization control chains, and tool system reconstruction sequences are sequentially reorganized in multiple dimensions to obtain a tool closed-loop control set; Recursive optimization is performed using the tool management weight matrix and tool system response chain in the tool closed-loop control set to form a tool dynamic processing domain; According to the tool dynamic processing domain, a closed-loop matching is performed on the tool optimization loop and the tool control trajectory of the tool warehouse management system to obtain a tool control execution sequence.

8. The intelligent management and early warning method for tool warehouse according to claim 7 is characterized in that: The closed-loop matching of the tool optimization loop and the tool control trajectory of the tool warehouse management system according to the tool dynamic processing domain to obtain the tool control execution sequence includes: Recursively decomposing the system optimization parameter chain and the control level identifier in the tool dynamic processing domain to obtain a tool optimization sequence diagram; Separating the control node codes and optimization iteration coefficients in the tool optimization sequence diagram into layers to obtain a tool optimization grading table; Orthogonally matching the closed-loop response values ​​in the tool optimization classification table with the system control variables to form a tool control response chain; Based on the tool control response chain, multi-dimensional coupling is performed on the management control sequence and the optimization feedback parameters to generate a tool loop mapping diagram; Dynamically reconstructing the system control identifier and the operation trajectory sequence through the tool loop mapping diagram to obtain a tool management execution table; According to the tool management execution table, data fusion is performed on the tool optimization loop and the tool control trajectory to obtain the tool control execution sequence.

9. The intelligent management and early warning method for tool warehouse according to claim 8 is characterized in that: The step of fusing data of the tool optimization loop and the tool control trajectory according to the tool management execution table to obtain the tool control execution sequence includes: The tool hierarchical distribution domain, tool fusion decision chain and tool execution optimization degree in the tool management execution table are deeply decoupled and analyzed, and the tool system topology chain and tool optimization control domain are recursively mapped. Through multi-dimensional correlation processing of tool control parameters and tool optimization indicators, a tool optimization scheduling table is obtained; Perform multi-level cross-validation on the tool execution sequence value, tool management optimization chain and tool response delimiter in the tool optimization scheduling table, and combine the dynamic matching analysis of tool control nodes and tool execution weights to obtain the tool control optimization domain through deep fusion processing of tool distribution characteristics and tool optimization constraints; Systematically deconstruct the tool execution identification chain, tool control delimiter and tool optimization evaluation value in the tool control optimization domain, and form a tool path planning set through recursive mapping of tool optimization parameters and tool execution characteristics based on the correlation analysis between the tool control constraint domain and the tool iteration sequence; Through the tool path planning set, the tool optimization control domain, the tool management hierarchical chain and the tool execution mapping point are deeply coupled, and at the same time, the tool control loop and the tool optimization sequence are dynamically matched, and the tool optimization control diagram is generated based on the multi-dimensional analysis and processing of the tool system characteristics and the tool control indicators; Extracting the tool management delimitation value, the tool control optimization chain and the characteristic sequence of the tool execution evaluation domain from the tool optimization control diagram, and adaptively fusing the tool system constraint points and the tool optimization parameters, and obtaining the tool system execution table through the correlation mapping processing of the tool distribution characteristics and the tool control indicators; According to the tool system execution table, deep data fusion is performed on the tool optimization loop and the tool management trajectory. At the same time, combined with the multi-dimensional analysis of the tool execution feature chain and the tool system optimization domain, the tool management execution sequence is obtained through recursive optimization processing of tool control parameters and tool management indicators.

10. An intelligent control and early warning system for tool warehouses, used to implement the intelligent control and early warning method for tool warehouses as described in any one of claims 1 to 9, characterized in that: The intelligent management and early warning system for tool warehouse includes: A processing module is used to obtain working condition data in the tool warehouse through an industrial sensor collection network, process the working condition data through coupling analysis and calculation, and generate a tool control data stream; A correlation module is used to extract the tool usage frequency and inventory turnover from the tool control data stream, perform correlation analysis according to the dynamic matching principle, and establish tool scheduling parameters; A balancing module is used to balance the tool inventory warning threshold and the access timing according to the tool scheduling parameters, and output the tool balance control quantity; An allocation module is used to carry out collaborative analysis on the tool handover process and emergency use rules based on the tool balance control quantity, and to construct a tool status allocation table; A monitoring module, for analyzing tool operation abnormalities and maintenance cycles according to the tool status allocation table, and forming a tool early warning instruction set; The analysis module is used to carry out closed-loop optimization analysis on the tool warehouse management system based on the tool warning instruction set and construct a tool control execution sequence.

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