A Tool Life Evaluation Method Combined with Tool Taking Logs and an Intelligent Tool Cabinet

Abstract

The present application provides a tool life evaluation method combined with tool taking logs and an intelligent tool cabinet. The method includes: monitoring and recording tool taking logs when taking tools, where each piece of data includes the tool taking model, tool number, tool taking personnel, tool taking time, and tool returning time; according to the tool taking logs, extracting multiple tool taking time sets and multiple tool taking personnel sets of multiple tools, performing service life loss analysis based on the multiple tool taking time sets, and performing abnormal tool taking life analysis and abnormal installation life analysis based on the multiple tool taking personnel sets; according to the tool taking logs, extracting a repeated tool taking data set and performing abnormal repeated tool taking life analysis; according to multiple abnormal tool taking life information, abnormal installation life information, and abnormal repeated tool taking life information, combining with service life loss information as the tool life evaluation result. The present application solves the technical problem in the prior art that it is difficult for tool life evaluation technology to accurately, timely, and effectively provide a reference for tool maintenance and replacement.

Description

Technical Field

[0001] The present invention relates to the field of tool life assessment, and particularly to a tool life assessment method and an intelligent tool cabinet combined with tool taking logs. Background Art

[0002] In the prior art, tool life assessment is mainly achieved by combining the cumulative usage duration with manual experience judgment. However, this extensive assessment mode has obvious technical limitations: First, relying on manual experience judgment, the assessment accuracy is not high, and early warning cannot be realized; Second, only considering the single variable of usage time, the differential impact of personnel operation behaviors on tool wear is ignored, especially the significant differences in the operation links such as tool taking and installation by technicians with different proficiency levels. Therefore, these technical defects lead to obvious deviations between the tool life assessment results and the actual tool wear status, and it is difficult to accurately, timely, and effectively provide a reference for tool maintenance and replacement. Summary of the Invention

[0003] Aiming at the technical problem that the tool life assessment technology in the prior art is difficult to accurately, timely, and effectively provide a reference for tool maintenance and replacement, the present invention provides a tool life assessment method and an intelligent tool cabinet combined with tool taking logs.

[0004] The technical solutions of the present invention for solving the above technical problems are as follows:

[0005] In the first aspect, the present invention provides a tool life assessment method combined with tool taking logs, including: monitoring and recording tool taking logs when taking tools, wherein each piece of data in the tool taking logs includes the tool taking model, tool number, tool taking personnel, tool taking time, and tool returning time;

[0006] According to the tool taking logs, extract multiple tool taking time sets and multiple tool taking personnel sets of multiple tools, perform service life loss analysis based on the multiple tool taking time sets to obtain multiple service life loss information, and perform tool taking life anomaly analysis and installation life anomaly analysis based on the multiple tool taking personnel sets to obtain multiple tool taking life anomaly information and multiple installation life anomaly information;

[0007] According to the tool taking logs, extract a repeated tool taking data set in which the same type of tool is taken multiple times within a preset time range, extract the corresponding tool taking personnel to obtain a repeated tool taking personnel set, and perform repeated tool taking life anomaly analysis to obtain repeated tool taking life anomaly information;

[0008] According to the multiple tool taking life anomaly information, multiple installation life anomaly information, and repeated tool taking life anomaly information, calculate to obtain multiple life anomaly information, and combine the multiple service life loss information as the tool life assessment result for display.

[0009] In a second aspect, the present invention provides an intelligent tool cabinet, comprising:

[0010] A data acquisition module, configured to monitor and record a tool-taking log when a tool is taken. Each piece of data in the tool-taking log includes the tool-taking model, tool number, tool-taking personnel, tool-taking time, and tool-returning time;

[0011] An evaluation and analysis module, configured to extract multiple tool-taking time sets and multiple tool-taking personnel sets of multiple tools according to the tool-taking log, perform service life loss analysis based on the multiple tool-taking time sets to obtain multiple service life loss information, and perform tool-taking life anomaly analysis and installation life anomaly analysis according to the multiple tool-taking personnel sets to obtain multiple tool-taking life anomaly information and multiple installation life anomaly information;

[0012] A repeated tool-taking analysis module, configured to extract a repeated tool-taking data set in which the same type of tool is taken multiple times within a preset time range according to the tool-taking log, extract the corresponding tool-taking personnel to obtain a repeated tool-taking personnel set, and perform repeated tool-taking life anomaly analysis to obtain repeated tool-taking life anomaly information;

[0013] An integration and output module, configured to calculate and obtain multiple life anomaly information according to the multiple tool-taking life anomaly information, multiple installation life anomaly information, and repeated tool-taking life anomaly information, combine the multiple service life loss information, and display it as a tool life evaluation result.

[0014] The beneficial effects of the present invention are:

[0015] This application first monitors and records the tool-taking log when taking tools, significantly improving the accuracy of data and providing effective data support for subsequent tool life prediction and anomaly detection. Secondly, according to the tool-taking log, multiple tool-taking time sets and multiple tool-taking personnel sets of multiple tools are extracted. The service life loss analysis is carried out according to multiple tool-taking time sets to obtain multiple service life loss information. And according to multiple tool-taking personnel sets, the tool-taking life anomaly analysis and installation life anomaly analysis are carried out to obtain multiple tool-taking life anomaly information and multiple installation life anomaly information, fully considering the differences in the probability of tool damage by technicians with different proficiency levels during tool-taking and installation processes, thus improving the evaluation accuracy and precision. Then, according to the tool-taking log, a repeated tool-taking data set with multiple tool-taking of the same model tool within a preset time range is extracted, and the corresponding tool-taking personnel are obtained to form a repeated tool-taking personnel set. The repeated tool-taking life anomaly analysis is carried out to obtain repeated tool-taking life anomaly information, considering the risk of abnormal tool wear caused by personnel operation factors and improving the accuracy of tool life evaluation. Finally, according to the multiple tool-taking life anomaly information, multiple installation life anomaly information and repeated tool-taking life anomaly information, multiple life anomaly information is calculated and combined with multiple service life loss information as the tool life evaluation result, and the accurate tool life evaluation results of multiple tools are output, providing a reference for tool maintenance and replacement.

[0016] Through the above technical solution, based on the data collected from the tool-taking log, this application fully considers the differential impact of personnel operation behavior on tool wear, analyzes the tool wear situation from multiple dimensions, and thus outputs the tool life evaluation result, providing an accurate, timely and effective reference for tool maintenance and replacement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flowchart of a tool life evaluation method combining tool-taking log provided by the present invention;

[0018] Figure 2 It is a schematic structural diagram of an intelligent tool cabinet provided by the present invention.

[0019] In the drawings, the components represented by each reference numeral are as follows:

[0020] Data acquisition module 11, evaluation and analysis module 12, repeated tool-taking analysis module 13, integration and output module 14. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0022] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0023] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0024] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a tool life evaluation method combined with tool taking logs, including:

[0025] S100: Monitor and record the tool taking logs when taking tools. Each piece of data in the tool taking logs includes the tool taking model, tool number, tool taking personnel, tool taking time, and tool returning time;

[0026] In the prior art, the management of tools is mainly to manually record the tool usage duration and judge its damage situation through manual experience for regular replacement. However, there are problems such as inaccurate data recording and difficult data traceability in manual data recording.

[0027] In response to the above problems, this application records five types of key data, namely the tool taking model, tool number, tool taking personnel, tool taking time, and tool returning time, when taking and returning tools through an intelligent tool cabinet, and integrates the tool taking data sets of multiple tools based on the tool numbers of multiple tools to obtain tool taking logs, providing effective data support for subsequent tool life prediction and anomaly detection analysis.

[0028] Specifically, step S10 in the method includes:

[0029] Through an intelligent tool cabinet, record the tool model, tool number, tool-taking personnel, and tool-taking time when taking tools, and obtain the tool-return time based on the tool number index when returning tools, and record it as tool-taking data;

[0030] Based on the tool numbers of multiple tools, integrate the tool-taking data sets of multiple tools to obtain a tool-taking log.

[0031] In the embodiment of the present application, first, an intelligent tool cabinet records key data when taking and returning tools: when taking tools, record the tool model (such as a turning tool), tool number (such as CD001), tool-taking personnel (such as OP-12), and tool-taking time (such as the starting timestamp accurate to minutes); when returning tools, index the corresponding tool through the tool number to obtain the tool-return time (such as the ending timestamp accurate to minutes). Among them, the intelligent tool cabinet records relevant information when taking and returning tools through Internet of Things technologies (such as RFID scanning, QR code scanning). The tool number (such as CD001) is the ID card of the tool, and through the one-to-one mapping relationship between the tool number and other data during the entire life cycle of the tool, the integrity and traceability of the usage record of each tool are ensured.

[0032] Furthermore, based on the tool numbers of multiple tools, integrate the corresponding tool-taking data sets to obtain a tool-taking log. Among them, each piece of data in the tool-taking log includes the tool model, tool number, tool-taking personnel, tool-taking time, and tool-return time. Exemplarily, a piece of data in the tool-taking log is: tool model (turning tool), tool number (CD001), tool-taking personnel (OP-12), tool-taking time (2025-04-08 13:15), tool-return time (2025-04-10 10:30).

[0033] In summary, compared with the prior art, the present application significantly improves the accuracy and intelligence level of data recording by collecting relevant data through an intelligent tool cabinet, realizes precise tracking of the entire life cycle of tools based on tool numbers, thereby accurately establishing a tool-taking log for the entire life cycle of tools, provides effective data support for subsequent tool life prediction and abnormal detection and analysis, and effectively improves the accuracy and intelligence level of tool management.

[0034] S200: According to the tool-taking log, extract multiple tool-taking time sets and multiple tool-taking personnel sets of multiple tools, perform service life loss analysis based on the multiple tool-taking time sets to obtain multiple service life loss information, and perform tool-taking life abnormal analysis and installation life abnormal analysis according to the multiple tool-taking personnel sets to obtain multiple tool-taking life abnormal information and multiple installation life abnormal information;

[0035] Traditional tool life assessment methods only consider a single variable, namely the usage duration, and ignore the differential impact of personnel operation behaviors on tool wear. In particular, there are significant differences in operations such as tool picking and installation among technicians with different levels of proficiency, resulting in low accuracy and precision of the assessment results.

[0036] To address the above problems, in this application, first, according to the tool picking log, multiple tool picking time sets and multiple tool picking personnel sets of multiple tools are extracted. Then, the multiple total usage times are respectively input into the service life classifier for service life wear analysis to obtain multiple service life wear information. Further, according to the multiple tool picking personnel sets, each cumulative operation time is respectively input into the tool picking abnormal rate classifier and the installation abnormal rate classifier, and multiple tool picking life abnormal rate sets and multiple installation life abnormal rate sets of multiple tools are output, thereby obtaining multiple tool picking life abnormal information and multiple installation life abnormal information.

[0037] Specifically, step S20 in the method includes:

[0038] According to the tool picking log, multiple tool picking time sets and multiple tool returning time sets of multiple tools are extracted, and multiple usage time sets are calculated based on the tool picking time and the corresponding tool returning time;

[0039] According to the tool picking log, multiple tool picking personnel sets of multiple tools are extracted;

[0040] According to the multiple usage time sets, multiple total usage times of multiple tools are calculated;

[0041] The multiple total usage times are respectively input into the service life classifier, and multiple service life wear information is obtained through mapping classification, where the service life classifier includes a mapping table of sample total usage time and sample service life wear information.

[0042] In the embodiments of the present application, first, multiple tool usage time sets are extracted based on the tool taking logs, and the service life loss analysis of the tools is carried out accordingly. Specifically, first, multiple tool taking time sets and multiple tool returning time sets of multiple tools are extracted from the above tool taking logs with the tool numbers as indexes, and then multiple tool usage time sets are calculated based on the tool taking time and the corresponding tool returning time, and the total tool usage times of multiple tools are obtained by cumulative summation. Then, the total tool usage times of the multiple tools are respectively input into the service life classifier, and multiple service life loss information is obtained according to the mapping classification. Among them, the service life classifier includes a mapping table of the sample total tool usage time and the sample service life loss information. Further, the sample service life loss information reflects the tool life loss information through a quantified number. For example, the tool loss rate is reflected by the tool life loss percentage (sample total tool usage time / theoretical total life of the sample). Specifically, first, the theoretical service life of the sample tool is pre-stored in the service life classifier, and then the sample service life loss information is calculated through the mapping relationship between the sample total tool usage time and the theoretical service life of the sample tool. For example, if the sample total tool usage time is 10h and the theoretical total life of the sample is 200h, the service life loss information of the sample is 5%.

[0043] Exemplarily, all the tool taking and returning times of the tool with the tool number CD001 are extracted from the tool taking logs, the exact duration of each use is calculated and the total tool usage time of the tool is obtained by cumulative summation as 86.7h. Then, the total tool usage time of 86.7h is input into the service life classifier. The theoretical total life pre-set in the classifier is 200h. Then, through the mapping relationship between the sample total tool usage time and the sample service life loss information, the service life loss information of the tool is calculated as 43.35%.

[0044] Secondly, according to the tool taking logs, multiple tool taking personnel sets are extracted. The tool taking personnel set includes the cumulative working time of each tool taking personnel at the tool taking time in the corresponding tool taking data. Then, according to the multiple tool taking personnel sets, the tool taking life anomaly analysis and the installation life anomaly analysis are carried out to obtain multiple tool taking life anomaly information and multiple installation life anomaly information.

[0045] Specifically, the "according to the multiple tool taking personnel sets, the tool taking life anomaly analysis and the installation life anomaly analysis are carried out to obtain multiple tool taking life anomaly information and multiple installation life anomaly information" includes:

[0046] Respectively obtain the cumulative working time of each tool taking personnel in the corresponding tool taking data at the tool taking time in the multiple tool taking personnel sets to obtain multiple cumulative working time sets;

[0047] Based on multiple sets of cumulative operation times, perform tool life abnormal analysis and installation life abnormal analysis respectively to obtain multiple tool life abnormal information and multiple installation life abnormal information.

[0048] In the embodiments of the present application, first, based on the aforementioned multiple tool-taking personnel sets, obtain all the personnel who have operated the tool and their total cumulative operation times in the corresponding tool-taking data, and obtain multiple sets of cumulative operation times. Among them, the set of cumulative operation times includes the cumulative operation times of multiple tool-taking personnel, which is calculated by extracting the historical tool-taking and returning records of each operator in the tool-taking log. Specifically, first, extract all the historical tool-taking and returning records of each tool-taking personnel through indexing, calculate the working time at each tool-taking (returning time minus tool-taking time), and then sum up all the single tool-taking times cumulatively to obtain the cumulative operation time of each tool-taking personnel.

[0049] Exemplarily, all the historical tool-taking and returning records of the technician numbered OP-12 are detected in the tool-taking log through indexing: taking CD001 (tool number) at 14:00 on May 5, 2024, and returning the tool at 17:00 on May 5, 2024; taking CD002 (tool number) at 10:00 on May 6, 2024, and returning the tool at 11:00 on May 6, 2024; taking CD003 (tool number) at 15:00 on May 7, 2024, and returning the tool at 18:00 on May 7, 2024... By calculating the working time at each tool-taking (returning time minus tool-taking time) and then summing up cumulatively, the cumulative operation time of OP-12 is obtained as 200h.

[0050] Secondly, based on multiple sets of cumulative operation times, perform tool life abnormal analysis and installation life abnormal analysis respectively to obtain multiple tool life abnormal information and multiple installation life abnormal information. Among them, the tool life abnormal information and installation life abnormal information are obtained by calculating the probability of tool wear during tool-taking and installation processes. This is because the tool may be damaged by collision during tool-taking, and may also be damaged by collision during installation. And the more skilled the technician with longer cumulative working time is, the smaller the probability of damaging the tool during tool-taking and installation; the less skilled the technician with shorter cumulative working time is, the greater the probability of damaging the tool during tool-taking and installation. Therefore, based on multiple tool-taking personnel sets of multiple tools, analyze the probability of tool wear during tool-taking and installation processes of multiple tools respectively, and thus obtain multiple tool life abnormal information and multiple installation life abnormal information.

[0051] Furthermore, the "performing tool life abnormal analysis and installation life abnormal analysis respectively based on multiple sets of cumulative operation times" includes:

[0052] Input each cumulative operation time into the tool-taking abnormal rate classifier, and output multiple sets of tool-taking life abnormal rates for multiple tools. Among them, the tool-taking abnormal rate classifier includes the mapping relationship between the sample cumulative operation time and the sample tool-taking life abnormal rate;

[0053] Respectively calculate multiple sets of tool-taking life normal rates according to the multiple sets of tool-taking life abnormal rates, and calculate multiple tool-taking damage abnormal rates for multiple tools as multiple tool-taking life abnormal information, as shown in the following formula:

[0054] ;

[0055] Among them, is the tool-taking damage abnormal rate, is the first tool-taking life normal rate in the set of tool-taking life normal rates, is the Mth tool-taking life normal rate, and M is the number of tool-taking life normal rates in the set of tool-taking life normal rates;

[0056] Input each cumulative operation time into the installation abnormal rate classifier, and output multiple sets of installation life abnormal rates for multiple tools. Among them, the installation abnormal rate classifier includes the mapping relationship between the sample cumulative operation time and the sample installation life abnormal rate;

[0057] Respectively calculate multiple sets of installation life normal rates according to the multiple sets of installation life abnormal rates, and calculate multiple installation damage abnormal rates for multiple tools as multiple installation life abnormal information.

[0058] In the embodiment of the present application, firstly, the tool-taking life abnormal analysis is carried out through the tool-taking abnormal rate classifier. Specifically, input each cumulative operation time into the tool-taking abnormal rate classifier, and output the corresponding multiple sets of tool-taking life abnormal rates by indexing the sample tool-taking life abnormal rate corresponding to the sample cumulative operation time closest to it. Among them, the tool-taking abnormal rate classifier includes the mapping relationship between the sample cumulative operation time and the sample tool-taking life abnormal rate, and the mapping relationship is established based on the different tool-taking life abnormal rates of technicians with different cumulative operation times during the tool-taking process. This is because the tool may be knocked and damaged during the tool-taking process. The more skilled the technician with a longer cumulative working time, the smaller the probability of damaging the tool during the tool-taking process, and the less skilled the technician with a shorter cumulative working time, the greater the probability of damaging the tool during the tool-taking process. Therefore, the tool-taking life abnormal rate can be represented by collecting the probability of technicians with different cumulative operation times damaging the tool during the tool-taking process, and thus the mapping relationship between the sample cumulative operation time and the sample tool-taking life abnormal rate is established.

[0059] Exemplarily, the probabilities of technicians with different cumulative operation times damaging the tool during tool picking are collected manually: the probability of a technician with a cumulative operation time of 1000 h damaging the tool during tool picking is 1%, and the probability of a technician with a cumulative operation time of 100 h damaging the tool during tool picking is 10%. Thus, a mapping relationship between the cumulative operation time (1000 h) and the tool picking life abnormal rate (1%) and the cumulative operation time (100 h) and the tool picking life abnormal rate (10%) is established.

[0060] Furthermore, according to the multiple tool picking life abnormal rate sets respectively, multiple tool picking life normal rate sets are calculated, and multiple tool picking damage abnormal rates of multiple tools are calculated and obtained as multiple tool picking life abnormal information. Among them, the tool picking life normal rate set is obtained by calculating the complement of the corresponding tool picking life abnormal rate set. Exemplarily, the tool picking life abnormal rate set of a certain tool is [12%, 18%], and its complement is calculated as [88%, 82%], thus obtaining the corresponding tool picking life normal rate set as [88%, 82%]. The tool picking damage abnormal rate is obtained by calculating through the tool picking life normal rate set. Specifically, first, the joint probability of M tool picking life normal rates in the tool picking life normal rate set is calculated by multiplication. This joint probability represents the probability that the tool remains normal during M tool picking operations. Multiplication is used because the tool pickings by different technicians are independent of each other. Then, the tool picking damage abnormal rate is obtained by calculating the complement of the joint probability of M tool picking life normal rates. Exemplarily, if the tool picking life normal rate set of a certain tool is [88%, 82%], then its tool picking damage abnormal rate is: .

[0061] Secondly, installation life abnormal analysis is carried out by the same idea and method as the above tool picking life abnormal analysis. First, each cumulative operation time is input into the installation abnormal rate classifier, and multiple installation life abnormal rate sets of multiple tools are output and obtained. Among them, the installation abnormal rate classifier includes the mapping relationship between the sample cumulative operation time and the sample installation life abnormal rate.

[0062] Specifically, each cumulative operation time is input into the installation anomaly rate classifier, and by indexing the sample installation life anomaly rate corresponding to the sample cumulative operation time closest to it, a corresponding set of multiple installation life anomaly rates is output. Among them, the installation anomaly rate classifier includes the mapping relationship between the sample cumulative operation time and the sample installation life anomaly rate. The mapping relationship is established based on the fact that the installation life anomaly rates of technicians with different cumulative operation times during the installation process are different. This is because the tool may be damaged by bumps during the installation process. The more skilled the technician with a longer cumulative working time, the smaller the probability of damaging the tool during the installation process. The less skilled the technician with a shorter cumulative working time, the greater the probability of damaging the tool during the installation process. Therefore, the installation life anomaly rate can be represented by collecting the probabilities of technicians with different cumulative operation times damaging the tool during the installation process, and based on this, the mapping relationship between the sample cumulative operation time and the sample installation life anomaly rate is established.

[0063] Exemplarily, by manually collecting the probabilities of technicians with different cumulative operation times damaging the tool during the installation: the probability of a technician with a cumulative operation time of 1000h damaging the tool during the installation process is 5%, and the probability of a technician with a cumulative operation time of 100h installing the tool during the tool-taking process is 20%. Based on this, the mapping relationship between the cumulative operation time (1000h) installation life anomaly rate (5%) and the cumulative operation time (100h) installation life anomaly rate (10%) is established.

[0064] Further, according to the multiple sets of installation life anomaly rates respectively, multiple sets of installation life normal rates are calculated, and multiple installation damage anomaly rates of multiple tools are calculated and obtained as multiple installation life anomaly information. Among them, the set of tool-taking life normal rates is obtained by calculating the complement of the corresponding set of installation life anomaly rates. Exemplarily, for a certain tool, the set of installation life anomaly rates is [15%, 20%], and its complement is calculated as [85%, 80%], and thus the corresponding set of installation life normal rates is [85%, 80%]. The installation damage anomaly rate is obtained by calculating the set of installation life normal rates. Specifically, first, the joint probability of M installation life normal rates in the set of installation life normal rates is calculated by multiplication. This joint probability represents the probability that the tool remains normal during M installation operations. Multiplication is used because the installation of the tool by different technicians is independent of each other. Then, the installation damage anomaly rate is obtained by calculating the complement of the joint probability of M installation life normal rates. Exemplarily, for a certain tool, the set of installation life normal rates is [85%, 80%], then its installation damage anomaly rate is: 。

[0065] In summary, compared with the prior art, according to the tool taking log of the present application, multiple tool taking time sets and multiple tool taking personnel sets of multiple tools are extracted. Service life loss analysis is carried out according to multiple tool taking time sets to obtain multiple service life loss information. By outputting the quantified service life loss information, the loss situation of the tool can be intuitively reflected. Then, according to multiple tool taking personnel sets, tool taking life anomaly analysis and installation life anomaly analysis are carried out to obtain multiple tool taking life anomaly information and multiple installation life anomaly information, fully considering the differences in the probability of tool damage by technicians with different proficiency levels during tool taking and installation processes, thus improving the evaluation accuracy and precision.

[0066] S300: According to the tool taking log, extract a repeated taking data set in which the same type of tool is taken multiple times within a preset time range, and extract the corresponding tool taking personnel to obtain a repeated taking personnel set, and conduct repeated taking life anomaly analysis to obtain repeated taking life anomaly information;

[0067] In the prior art, there is a situation where a tool taking personnel takes the same type of tool multiple times in a short period when taking tools. This indicates that the previously taken tool may be damaged, so it is necessary to re-take other tools of the same type. However, there is also a situation where the tool taking personnel makes a misjudgment, that is, the tool is not damaged but is judged to be damaged. The discrimination accuracy rate of the tool damage situation is highly correlated with the proficiency level of the tool taking personnel. That is, the more experienced the tool taking personnel with a longer cumulative working time, the smaller the probability of misjudgment, and the less experienced the tool taking personnel with a shorter cumulative working time, the greater the probability of misjudgment.

[0068] In view of the above problems, according to the tool taking log of the present application, extract a repeated taking data set in which the same type of tool is taken multiple times within a preset time range, and extract the corresponding tool taking personnel to obtain a repeated taking personnel set, and conduct repeated taking life anomaly analysis to obtain repeated taking life anomaly information.

[0069] Specifically, step S30 in the method includes:

[0070] According to the tool taking log, extract the tool taking data in which the same tool taking personnel repeatedly takes the same type of tool within a preset time range to obtain a repeated taking data set;

[0071] Extract the cumulative operation time of the tool taking personnel in the repeated taking data set at the tool taking time of taking the first tool to obtain a repeated cumulative operation time;

[0072] Input the repeated cumulative operation time into a discrimination anomaly rate classifier, output and obtain a tool discrimination anomaly rate, and obtain the repeated taking life anomaly information of the first tool, where the discrimination anomaly rate classifier includes the mapping relationship between the sample repeated cumulative operation time and the sample tool discrimination anomaly rate.

[0073] In the embodiments of the present application, first, according to the tool taking log, tool taking data where the same tool taking personnel repeatedly takes the same type of tool within a preset time range (such as within 10 minutes) is extracted to obtain a repeated taking dataset. The repeated taking dataset includes the tool taking personnel and their historical tool taking and returning records. Exemplarily, within 10 minutes, the tool taking personnel OP-03 takes the same type of turning tool three times. Therefore, the historical tool taking and returning record data of OP-03 is extracted from the tool taking log: taking the tool at 14:00 on May 5, 2024, and returning the tool at 17:00 on May 5, 2024; taking the tool at 10:00 on May 6, 2024, and returning the tool at 11:00 on May 5, 2024... Thus, the repeated taking dataset of the tool taking personnel OP-03 is obtained.

[0074] Secondly, the cumulative operation time of the tool taking personnel in the repeated taking dataset at the tool taking time of the first tool is extracted to obtain the repeated cumulative operation time. The repeated cumulative operation time is the historical cumulative operation time of the tool taking personnel at the first tool taking, which can reflect the proficiency of the tool taking personnel at that time, and the proficiency can reflect the discrimination accuracy rate of the tool taking personnel for tool damage. Exemplarily, the cumulative operation time of the tool taking personnel OP-03 at the first tool taking in the repeated taking dataset is extracted as 50 hours.

[0075] Finally, the repeated cumulative operation time is input into the discrimination abnormality rate classifier, and the tool discrimination abnormality rate is output. One minus the tool discrimination abnormality rate is used as the probability of damage during repeated tool taking, which is used as the abnormal information of the repeated taking life of the first tool. The discrimination abnormality rate classifier includes the mapping relationship between the sample repeated cumulative operation time and the sample tool discrimination abnormality rate. The mapping relationship is established based on the fact that the discrimination error probabilities of tool taking personnel with different cumulative operation times during tool taking are different. This is because the discrimination accuracy rate of tool damage is highly correlated with the proficiency of the tool taking personnel. The tool taking personnel with longer cumulative working hours have more experience and smaller discrimination error probabilities, while the tool taking personnel with shorter cumulative working hours have less experience and larger discrimination error probabilities. Therefore, the discrimination accuracy rate of technicians with different cumulative operation times for tool damage during tool taking can be collected, and then the tool discrimination abnormality rate can be obtained through (tool discrimination abnormality rate = 1 - discrimination accuracy rate of tool damage), and thus the mapping relationship between the sample repeated cumulative operation time and the sample tool discrimination abnormality rate can be established.

[0076] Exemplarily, the discrimination accuracy rates of technicians with different cumulative operation times in identifying tool damage during tool taking are as follows: the discrimination accuracy rate of technicians with a cumulative operation time of 1000h in identifying tool damage during tool taking is 95%, and the discrimination accuracy rate of technicians with a cumulative operation time of 100h in identifying tool damage during tool taking is 60%. Thus, a mapping relationship between the cumulative operation time (1000h) and the tool discrimination abnormal rate (5%) and the cumulative operation time (100h) and the tool discrimination abnormal rate (40%) is established.

[0077] In summary, compared with the prior art, according to the tool taking log, the present application extracts a repeated taking dataset in which the same type of tool is taken multiple times within a preset time range, and extracts the corresponding tool taking personnel to obtain a set of repeated taking personnel, and performs an abnormal analysis of the repeated taking life to obtain abnormal information on the repeated taking life. The accurate identification and probability prediction of abnormal repeated taking behaviors are realized, and the risk of abnormal tool wear caused by human operation factors is considered, improving the accuracy of tool life assessment.

[0078] S400: According to the multiple tool taking life abnormal information, multiple installation life abnormal information, and repeated taking life abnormal information, calculate to obtain multiple life abnormal information, and combine multiple service life loss information as the tool life assessment result for display.

[0079] In the prior art, the assessment of tool life only considers time factors by the method of cumulative use duration + manual regular inspection, while ignoring the risk of abnormal tool wear caused by human operation factors. Therefore, the accuracy and fineness of tool life assessment are not high, and it cannot provide timely and effective reference for tool maintenance and replacement.

[0080] To solve the above problems, the present application integrates the foregoing data: multiple service life loss information, multiple tool taking life abnormal information, multiple installation life abnormal information, and repeated taking life abnormal information, and outputs and displays them as the tool life assessment results of multiple tools.

[0081] Specifically, step S40 in the method includes:

[0082] According to the tool taking life abnormal information, multiple installation life abnormal information, and repeated taking life abnormal information, calculate the total abnormal rate of multiple tools to obtain the total abnormal rate of multiple tools as multiple life abnormal information;

[0083] Combine the multiple life abnormal information and multiple service life loss information as the tool life assessment results of multiple tools for display.

[0084] In the embodiments of the present application, first, according to the abnormal tool-taking life information, multiple installation life abnormal information, and repeated tool-taking life abnormal information, the total abnormal rate of multiple tools is calculated to obtain the total abnormal rate of multiple tools, which is used as multiple life abnormal information. Among them, the total abnormal rate of the multiple tools can be obtained by assigning different weights w1, w2, and w3 (where w1 + w2 + w3 = 1) to the abnormal tool-taking life information, installation life abnormal information, and repeated tool-taking life abnormal information in advance, and then using the weighted geometric mean algorithm. Exemplarily, weights of 0.4, 0.4, and 0.2 are respectively assigned to the abnormal tool-taking life information, installation life abnormal information, and repeated tool-taking life abnormal information of a certain tool, and its total abnormal rate is obtained as 13.3% by using the weighted geometric mean algorithm.

[0085] Secondly, combining the multiple life abnormal information and multiple service life loss information as the tool life evaluation results of multiple tools for display, which is used as the life evaluation results of multiple tools to provide a reference for tool maintenance and replacement. For example, the life evaluation result of a certain tool includes that the life abnormal information is 13.3% and the service life loss information is 43.35%.

[0086] In summary, the present application integrates multiple service life loss information, multiple abnormal tool-taking life information, multiple installation life abnormal information, and repeated tool-taking life abnormal information as the life evaluation results of multiple tools to provide a reference for tool maintenance and replacement.

[0087] In summary, the embodiments of the present application at least have the following technical effects:

[0088] Compared with the prior art, the present application greatly improves the accuracy of data recording by collecting relevant data through an intelligent tool cabinet, thereby establishing a tool-taking log for the entire life cycle of the tool, providing effective data support for subsequent tool life prediction and anomaly detection.

[0089] Secondly, according to the tool-taking log, multiple tool-taking time sets and multiple tool-taking personnel sets of multiple tools are extracted. Service life loss analysis is performed based on the multiple tool-taking time sets to obtain multiple service life loss information. The loss situation of the tool can be intuitively reflected by outputting the quantified service life loss information. Then, according to the multiple tool-taking personnel sets, abnormal tool-taking life analysis and installation life abnormal analysis are performed to obtain multiple abnormal tool-taking life information and multiple installation life abnormal information, fully considering the differences in the probability of tool damage by technical personnel with different proficiency levels during tool-taking and installation, and improving the evaluation accuracy and accuracy.

[0090] Again, according to the tool-taking log, extract the repeated taking dataset in which the same type of tool is taken multiple times within a preset time range, and extract the corresponding tool-taking personnel to obtain the repeated taking personnel set, and conduct an abnormal analysis of the repeated taking life to obtain the abnormal information of the repeated taking life. The accurate identification and probability prediction of abnormal repeated taking behaviors are realized, and the risk of abnormal tool wear caused by personnel operation factors is considered, improving the accuracy of tool life assessment.

[0091] Finally, by integrating multiple service life loss information, multiple tool-taking life abnormal information, multiple installation life abnormal information, and repeated taking life abnormal information, as the life assessment results of multiple tools, it provides a reference for tool maintenance and replacement.

[0092] Through the above technical solution, based on the data collected from the tool-taking log, the present application fully considers the differential impact of personnel operation behaviors on tool wear, analyzes the tool wear situation from multiple dimensions, and outputs the tool life assessment results, providing accurate, timely, and effective reference for tool maintenance and replacement.

[0093] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the tool life assessment method combining the tool-taking log provided in Embodiment 1, the embodiment of the present invention further provides an intelligent tool cabinet, including:

[0094] A data acquisition module 11, configured to monitor and record the tool-taking log when a tool is taken. Each piece of data in the tool-taking log includes the tool-taking model, tool number, tool-taking personnel, tool-taking time, and tool-returning time;

[0095] An evaluation and analysis module 12, configured to extract multiple tool-taking time sets and multiple tool-taking personnel sets of multiple tools according to the tool-taking log, conduct service life loss analysis based on the multiple tool-taking time sets to obtain multiple service life loss information, and conduct tool-taking life abnormal analysis and installation life abnormal analysis according to the multiple tool-taking personnel sets to obtain multiple tool-taking life abnormal information and multiple installation life abnormal information;

[0096] A repeated taking analysis module 13, configured to extract the repeated taking dataset in which the same type of tool is taken multiple times within a preset time range according to the tool-taking log, extract the corresponding tool-taking personnel to obtain the repeated taking personnel set, and conduct an abnormal analysis of the repeated taking life to obtain the abnormal information of the repeated taking life;

[0097] An integration and output module 14, configured to calculate and obtain multiple life abnormal information according to the multiple tool-taking life abnormal information, multiple installation life abnormal information, and repeated taking life abnormal information, and combine the multiple service life loss information as the tool life assessment result for display.

[0098] Among them, the data acquisition module 11 is specifically configured to:

[0099] Through the intelligent tool cabinet, record the tool model, tool number, tool-taking personnel, and tool-taking time when taking a tool, and obtain the tool-return time based on the tool number index when returning the tool, and record it as tool-taking data;

[0100] Based on the tool numbers of multiple tools, integrate the tool-taking data sets of multiple tools to obtain a tool-taking log.

[0101] Among them, the evaluation and analysis module 12 is specifically configured to:

[0102] According to the tool-taking log, extract multiple tool-taking time sets and multiple tool-return time sets of multiple tools, and calculate multiple tool-using time sets according to the tool-taking time and the corresponding tool-return time;

[0103] According to the tool-taking log, extract multiple tool-taking personnel sets of multiple tools;

[0104] According to the multiple tool-using time sets, calculate the multiple total tool-using times of multiple tools;

[0105] Input the multiple total tool-using times into the service life classifier respectively, and map and classify to obtain multiple service life loss information, where the service life classifier includes a mapping table of sample total tool-using time and sample service life loss information.

[0106] Among them, the "performing tool-taking life abnormality analysis and installation life abnormality analysis according to multiple tool-taking personnel sets to obtain multiple tool-taking life abnormality information and multiple installation life abnormality information" includes:

[0107] Respectively obtain the cumulative operation time of each tool-taking personnel in the corresponding tool-taking data at the tool-taking time in the multiple tool-taking personnel sets to obtain multiple cumulative operation time sets;

[0108] According to the multiple cumulative operation time sets, perform tool-taking life abnormality analysis and installation life abnormality analysis respectively to obtain multiple tool-taking life abnormality information and multiple installation life abnormality information.

[0109] Furthermore, the "performing tool-taking life abnormality analysis and installation life abnormality analysis respectively according to multiple cumulative operation time sets" includes:

[0110] Input each cumulative operation time into the tool-taking abnormality rate classifier, and output multiple tool-taking life abnormality rate sets of multiple tools, where the tool-taking abnormality rate classifier includes the mapping relationship between the sample cumulative operation time and the sample tool-taking life abnormality rate;

[0111] According to the multiple sets of abnormal tool-taking life rates respectively, calculate multiple sets of normal tool-taking life rates, and calculate the multiple abnormal tool-taking damage rates of multiple tools as multiple pieces of abnormal tool-taking life information, as shown in the following formula:

[0112] ;

[0113] Among them, is the abnormal tool-taking damage rate, is the first normal tool-taking life rate in the set of normal tool-taking life rates, is the Mth normal tool-taking life rate, and M is the number of normal tool-taking life rates in the set of normal tool-taking life rates;

[0114] Input each cumulative operation time into the installation abnormality rate classifier, and output multiple sets of installation life abnormality rates of multiple tools. Among them, the installation abnormality rate classifier includes the mapping relationship between the sample cumulative operation time and the sample installation life abnormality rate;

[0115] According to the multiple sets of installation life abnormality rates respectively, calculate multiple sets of installation life normal rates, and calculate the multiple installation damage abnormality rates of multiple tools as multiple pieces of installation life abnormality information.

[0116] Among them, the repeated tool-taking analysis module 13 is specifically used for:

[0117] Extract the tool-taking data of the same tool-taking personnel repeatedly taking the same type of tool within a preset time range according to the tool-taking log to obtain a repeated tool-taking data set;

[0118] Extract the cumulative operation time of the tool-taking personnel in the tool-taking data set when taking the first tool to obtain the repeated cumulative operation time;

[0119] Input the repeated cumulative operation time into the discrimination abnormality rate classifier, output the tool discrimination abnormality rate, and obtain the repeated tool-taking life abnormality information of the first tool. Among them, the discrimination abnormality rate classifier includes the mapping relationship between the sample repeated cumulative operation time and the sample tool discrimination abnormality rate.

[0120] Among them, the integrated output module 14 is specifically used for:

[0121] Calculate the total abnormality rates of multiple tools according to the tool-taking life abnormality information, multiple installation life abnormality information, and repeated tool-taking life abnormality information, obtain the total abnormality rates of multiple tools as multiple pieces of life abnormality information;

[0122] Combine the multiple pieces of life abnormality information and multiple pieces of service life loss information as the tool life evaluation results of multiple tools for display.

[0123] In summary, the embodiments of the present application at least have the following technical effects:

[0124] Compared with the prior art, in the present application, the data acquisition module monitors and records the tool taking log when a tool is taken; the evaluation and analysis module extracts multiple tool taking time sets and multiple tool taking personnel sets of multiple tools according to the tool taking log, performs service life loss analysis according to the multiple tool taking time sets to obtain multiple service life loss information, and performs tool taking life anomaly analysis and installation life anomaly analysis according to the multiple tool taking personnel sets to obtain multiple tool taking life anomaly information and multiple installation life anomaly information; the repeated taking analysis module extracts the repeated taking data set in which the same type of tool is taken multiple times within a preset time range according to the tool taking log, extracts the corresponding tool taking personnel to obtain the repeated taking personnel set, and performs repeated taking life anomaly analysis to obtain repeated taking life anomaly information; the integration and output module calculates and obtains multiple life anomaly information according to the multiple tool taking life anomaly information, multiple installation life anomaly information and repeated taking life anomaly information, combines the multiple service life loss information, and uses it as the tool life evaluation result for display. Based on the data collected from the tool taking log, the present application fully considers the differential impact of personnel operation behavior on tool wear, analyzes the tool wear situation from multiple dimensions, and outputs the tool life evaluation result, providing an accurate, timely and effective reference for tool maintenance and replacement.

[0125] It should be noted that in the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0126] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for realizing the process Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0128] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more blocks

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions in the process Figure 1 one or more processes and / or blocks Figure 1 the steps of the functions specified in one or more blocks

[0130] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic inventive concept

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations

Claims

1. A tool life assessment method combined with a tool log, characterized in that: The method comprises: When the tool is taken, a tool taking log is monitored and recorded, wherein each data in the tool taking log includes the tool model, tool number, person taking the tool, tool taking time and tool returning time; According to the tool picking log, multiple tool picking time sets and multiple tool picking personnel sets are extracted, service life loss analysis is performed according to the multiple tool picking time sets to obtain multiple service life loss information, and tool picking life abnormality analysis and installation life abnormality analysis are performed according to the multiple tool picking personnel sets to obtain multiple tool picking life abnormality information and multiple installation life abnormality information, including: Respectively obtain the cumulative operation time of each tool picking personnel in the plurality of tool picking personnel sets at the tool picking time in the corresponding tool picking data to obtain a plurality of cumulative operation time sets; According to multiple accumulated operation time sets, tool extraction life abnormality analysis and installation life abnormality analysis are performed respectively to obtain multiple tool extraction life abnormality information and multiple installation life abnormality information, including: Input each accumulated operation time into a tool picking abnormality rate classifier, and output a plurality of tool picking life abnormality rate sets of a plurality of tools, wherein the tool picking abnormality rate classifier includes a mapping relationship between sample accumulated operation time and sample tool picking life abnormality rate; According to the plurality of tool life abnormality rate sets, a plurality of tool life normal rate sets are calculated respectively, and a plurality of tool damage abnormality rates of a plurality of tools are obtained by calculation as a plurality of tool life abnormality information, as shown in the following formula: ； in, is the abnormal rate of knife damage, is the first normal rate of tool life in the normal rate set of tool life. is the Mth normal tool life rate, M is the number of normal tool life rates in the normal tool life rate set; Input each accumulated operation time into an installation abnormality rate classifier, and output a plurality of installation life abnormality rate sets of a plurality of tools, wherein the installation abnormality rate classifier includes a mapping relationship between sample accumulated operation time and sample installation life abnormality rate; Calculating a plurality of installation life normal rate sets according to the plurality of installation life abnormal rate sets respectively, and calculating and obtaining a plurality of installation damage abnormal rates of a plurality of tools as a plurality of installation life abnormality information; According to the tool taking log, a repeated use data set in which the same type of tool is taken multiple times within a preset time range is extracted, and the corresponding tool taking personnel are extracted to obtain a repeated use personnel set, and repeated use life anomaly analysis is performed to obtain repeated use life anomaly information, including: According to the tool picking log, extract the tool picking data of the same type of tool repeatedly picked up by the same tool picking person within a preset time range to obtain a repeated use data set; Extract the cumulative operation time of the tool picker in the repeated use data set when picking up the first tool to obtain the repeated cumulative operation time; Input the repeated accumulated operation time into the abnormality identification rate classifier, output the obtained tool abnormality identification rate, and obtain the abnormal information of the repeated use life of the first tool, wherein the abnormality identification rate classifier includes the mapping relationship between the sample repeated accumulated operation time and the sample tool abnormality identification rate; Based on the multiple tool removal life abnormality information, multiple installation life abnormality information and repeated use life abnormality information, multiple life abnormality information is calculated and obtained, combined with multiple life loss information, as a tool life evaluation result and displayed.

2. The tool life assessment method in combination with the tool removal log according to claim 1 is characterized in that: Monitor and record the knife retrieval log when the knife is retrieved, including; Through the intelligent tool cabinet, the tool model, tool number, person who takes the tool, and time of taking the tool are recorded when the tool is taken. When the tool is returned, the return time is obtained based on the tool number index and recorded as the tool taking data; Based on the tool numbers of multiple tools, tool picking data sets of multiple tools are integrated to obtain a tool picking log.

3. The tool life assessment method in combination with the tool removal log according to claim 1 is characterized in that: According to the tool picking log, multiple tool picking time sets and multiple tool picking personnel sets are extracted, and service life loss analysis is performed according to the multiple tool picking time sets to obtain multiple service life loss information, including: Extract multiple tool taking time sets and multiple tool return time sets of multiple tools according to the tool taking log, and calculate and obtain multiple tool taking time sets according to the tool taking time and the corresponding tool return time; Extracting multiple sets of tool picking personnel for multiple tools according to the tool picking log; Calculate and obtain a plurality of total access times of a plurality of tools according to the plurality of access time sets; The multiple total usage times are respectively input into a service life classifier, and multiple service life loss information is obtained through mapping and classification, wherein the service life classifier includes a mapping table of sample total usage time and sample service life loss information.

4. The tool life assessment method in combination with the tool removal log according to claim 1 is characterized in that: According to the plurality of tool life abnormality information, the plurality of installation life abnormality information and the repeated use life abnormality information, a plurality of life abnormality information is calculated and obtained, and combined with the plurality of life loss information, as a tool life assessment result, including: Calculate the total abnormality rate of multiple tools according to the tool taking life abnormality information, multiple installation life abnormality information and repeated use life abnormality information, and obtain the total abnormality rate of multiple tools as multiple life abnormality information; The plurality of life abnormality information and the plurality of life loss information are combined and displayed as tool life evaluation results of the plurality of tools.

5. An intelligent tool cabinet for implementing the tool life assessment method as claimed in claim 1, characterized in that: A tool life assessment system combined with a tool log is provided, the system comprising: A data acquisition module is used to monitor and record a knife taking log when the knife is taken, wherein each data in the knife taking log includes the knife taking model, the knife number, the knife taking person, the knife taking time and the knife returning time; An evaluation and analysis module is used to extract multiple sets of use time of multiple tools and multiple sets of tool pickers according to the tool picker log, perform life loss analysis according to the multiple use time sets to obtain multiple life loss information, perform tool picker life abnormality analysis and installation life abnormality analysis according to the multiple tool picker sets to obtain multiple tool picker life abnormality information and multiple installation life abnormality information; A repeated use analysis module is used to extract a repeated use data set in which the same type of tool is used multiple times within a preset time range according to the tool taking log, and extract the corresponding tool taking personnel to obtain a repeated use personnel set, perform repeated use life anomaly analysis, and obtain repeated use life anomaly information; The integrated output module is used to calculate and obtain multiple life abnormality information based on the multiple tool removal life abnormality information, multiple installation life abnormality information and repeated use life abnormality information, and combine multiple life loss information as the tool life evaluation result for display.

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