Cutter service life evaluation method combined with cutter taking log and intelligent cutter cabinet

Abstract

The invention provides a tool service life evaluation method in combination with a tool taking log and an intelligent tool cabinet. The method comprises the following steps: monitoring and recording a tool taking log when a tool is taken, wherein each piece of data comprises a tool taking model, a tool number, a tool taking person, tool taking time and tool returning time; according to the tool taking log, multiple taking time sets and multiple tool taking personnel sets of the multiple tools are extracted, service life loss analysis is carried out according to the multiple taking time sets, and tool taking service life abnormity analysis and installation service life abnormity analysis are carried out according to the multiple tool taking personnel sets; according to the tool taking log, a repeated taking data set is extracted, and repeated taking life abnormity analysis is carried out; and according to the multiple pieces of tool taking service life abnormal information, the installation service life abnormal information and the repeated taking service life abnormal information, the service life loss information is combined to serve as a tool service life evaluation result. The technical problem that in the prior art, a tool life evaluation technology is difficult to accurately, timely and effectively provide reference for tool maintenance and replacement is solved.

Description

Technical Field

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

[0002] In the prior art, tool life assessment is mainly achieved through the combination of cumulative usage time and manual experience judgment. However, this extensive assessment model has obvious technical limitations: first, it relies on manual experience judgment, the assessment accuracy is not high, and it is impossible to provide early warning; second, it only considers the single variable of usage time, ignoring the differentiated impact of personnel operation behavior on tool wear, especially the significant differences in operation links such as tool removal and installation by technicians with different proficiency levels. Therefore, these technical defects lead to a significant deviation between the tool life assessment results and the actual tool wear status, making it difficult to accurately, timely and effectively provide 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 combined with a tool taking log and an intelligent tool cabinet.

[0004] The technical solution of the present invention to solve the above technical problems is as follows: In a first aspect, the present invention provides a tool life assessment method in combination with a tool taking log, comprising: monitoring and recording the tool taking log when the tool is taken, wherein each data in the tool taking log includes the tool taking model, tool number, tool taking person, tool taking time and tool returning time; According to the tool picking log, extract multiple tool picking time sets and multiple tool picking personnel sets, perform service life loss analysis according to the multiple tool picking time sets to obtain multiple service life loss information, perform tool picking life abnormality analysis and installation life abnormality analysis according to the multiple tool picking personnel sets to obtain multiple tool picking life abnormality information and multiple 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; 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.

[0005] In a second aspect, the present invention provides an intelligent tool cabinet, 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.

[0006] The beneficial effects of the present invention are: This application firstly monitors and records the tool taking log when the tool is taken, greatly improving the accuracy of the data, and providing effective data support for subsequent tool life prediction and abnormal detection. Secondly, according to the tool taking log, multiple tool taking time sets and multiple tool taking personnel sets are extracted, and the service life loss analysis is performed according to the multiple tool taking time sets to obtain multiple service life loss information, and according to the multiple tool taking personnel sets, the tool taking life abnormality analysis and installation life abnormality analysis are performed to obtain multiple tool taking life abnormality information and multiple installation life abnormality information, which fully considers the difference in the probability of tool damage caused by technicians with different proficiency levels during the tool taking and installation process, and improves the evaluation precision and accuracy. Then, according to the tool taking log, the repeated use data set in which the same model tool is taken multiple times within the preset time range is extracted, and the corresponding tool taking personnel are extracted to obtain the repeated use personnel set, and the repeated use life abnormality analysis is performed to obtain the repeated use life abnormality information, which considers the risk of abnormal tool loss caused by personnel operation factors and improves the accuracy of tool life evaluation. Finally, 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 combined with multiple life loss information as the tool life assessment result, and accurate life assessment results of multiple tools are output to provide a reference for tool maintenance and replacement.

[0007] Through the above-mentioned technical scheme, this application fully considers the differentiated impact of personnel operating behavior on tool wear based on the data collected from the tool removal log, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic flow chart of a tool life assessment method combined with a tool log provided by the present invention; Figure 2 This is a structural schematic diagram of an intelligent tool cabinet provided by the present invention.

[0009] In the accompanying drawings, the components represented by the reference numerals are as follows: Data collection module 11, evaluation and analysis module 12, repeated use analysis module 13, integrated output module 14. DETAILED DESCRIPTION

[0010] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0011] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0012] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0013] Embodiment 1, as Figure 1As shown, an embodiment of the present invention provides a tool life assessment method combined with a tool log, comprising: S100: monitoring and recording a tool taking log when a tool is taken, wherein each data in the tool taking log includes the tool taking model, tool number, tool taking person, tool taking time and tool returning time; In the prior art, the management of cutting tools is mainly carried out by manually recording the usage time of the cutting tools and judging their damage conditions based on manual experience for regular replacement. However, manual data recording has the problems of inaccurate data recording and difficulty in data traceability.

[0014] To address the above issues, this application uses an intelligent tool cabinet to record five key data items, including tool model, tool number, person who picks up the tool, tool picking time, and tool returning time, when the tool is taken and returned. Based on the tool numbers of multiple tools, the tool picking data sets of multiple tools are integrated to obtain a tool picking log, which provides effective data support for subsequent tool life prediction and anomaly detection analysis.

[0015] Specifically, step S10 in the method includes: 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.

[0016] In the embodiment of the present application, firstly, the key data is recorded by the intelligent tool cabinet when the tool is taken and returned: when taking, the tool model (such as a turning tool), tool number (such as CD001), person who takes the tool (such as OP-12), and time of taking the tool (such as a start timestamp accurate to the minute) are recorded; when returning, the corresponding tool is indexed by the tool number to obtain the return time (such as a deadline timestamp accurate to the minute). Among them, the intelligent tool cabinet records relevant information when the tool is taken and returned through Internet of Things technology (such as RFID scanning, QR code scanning). The tool number (such as CD001) is the identity card of the tool. The one-to-one mapping relationship between the tool number and other data during the entire life cycle of the tool ensures the integrity and traceability of each tool usage record.

[0017] Furthermore, based on the tool numbers of multiple tools, the corresponding tool picking data sets are integrated to obtain a tool picking log. Each piece of data in the tool picking log includes the tool picking model, tool number, tool picking person, tool picking time, and tool return time. Exemplarily, a piece of data in the tool picking log is: tool picking model (turning tool), tool number (CD001), tool picking person (OP-12), tool picking time (2025-04-08 13:15), tool return time (2025-04-10 10:30).

[0018] In summary, compared with the existing technology, the present application has greatly improved the accuracy and intelligence level of data recording by collecting relevant data through the intelligent tool cabinet, and realized the accurate tracking of the tool throughout its life cycle based on the tool number, thereby accurately establishing the tool retrieval log of the tool throughout its life cycle, providing effective data support for subsequent tool life prediction and abnormality detection and analysis, and effectively improving the accuracy and intelligence level of tool management.

[0019] S200: extracting multiple sets of tool use time and multiple sets of tool use personnel according to the tool use log, performing service life loss analysis according to the multiple sets of tool use time to obtain multiple service life loss information, performing tool use life abnormality analysis and installation life abnormality analysis according to the multiple sets of tool use personnel to obtain multiple tool use life abnormality information and multiple installation life abnormality information; Traditional tool life assessment methods only consider a single variable, namely, duration of use. They ignore the differentiated impact of personnel operating behaviors on tool wear, especially the significant differences in tool removal, installation and other operations among technicians with different proficiency levels, resulting in low accuracy and precision of the assessment results.

[0020] In view of the above problems, the present application first extracts multiple sets of tool access time and multiple sets of tool access personnel according to the tool access log. Then, multiple total access times are respectively input into the service life classifier, and service life loss analysis is performed to obtain multiple service life loss information. Then, according to multiple sets of tool access personnel, each cumulative operation time is respectively input into the tool access abnormality rate classifier and the installation abnormality rate classifier, and multiple tool access life abnormality rate sets and multiple installation life abnormality rate sets of multiple tools are output to obtain multiple tool access life abnormality information and multiple installation life abnormality information.

[0021] Specifically, step S20 in the method includes: 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.

[0022] In the embodiment of the present application, firstly, a plurality of access time sets are extracted based on the tool access log, and the tool life loss analysis is performed accordingly. Specifically, firstly, a plurality of tool access time sets and a plurality of tool return time sets of a plurality of tools are extracted from the tool access log with the tool number as the index, and then a plurality of access time sets are calculated based on the tool access time and the corresponding tool return time, and a plurality of total access time of a plurality of tools are obtained by cumulative summation. Then, the plurality of total access times of the plurality of tools are respectively input into the service life classifier, and a plurality of service life loss information is obtained according to the mapping classification. Among them, the service life classifier includes a mapping table of sample total access time and sample service life loss information. Further, the sample service life loss information reflects the tool life loss information through quantified numbers, for example, the tool life loss percentage (sample total access time / sample theoretical total life) reflects the tool loss rate, specifically, firstly, 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 access time and the theoretical service life of the sample tool. For example, if the total usage time of a sample is 10 hours and the theoretical total life of the sample is 200 hours, then the service life loss information of the sample is 5%.

[0023] Exemplarily, all the tool picking and returning times of the tool are extracted from the tool picking log using the tool number CD001, the precise duration of each use is calculated and the cumulative sum is used to obtain the total tool usage time of 86.7h, and then the total usage time of 86.7h is input into the service life classifier. The theoretical total life preset in the classifier is 200h. Based on the mapping relationship between the sample total usage time and the sample service life loss information, the tool service life loss information is calculated to be 43.35%.

[0024] Secondly, based on the tool picking log, multiple tool picking personnel sets are extracted. The tool picking personnel sets include the cumulative working time of each tool picking personnel at the tool picking time in the corresponding tool picking data. Then, based on the multiple tool picking personnel sets, tool picking life abnormality analysis and installation life abnormality analysis are performed to obtain multiple tool picking life abnormality information and multiple installation life abnormality information.

[0025] Specifically, the “performing an abnormal analysis of tool taking life and an abnormal analysis of installation life according to multiple sets of tool taking personnel, and obtaining multiple abnormal information of tool taking life and multiple abnormal information of installation life” includes: 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 a plurality of accumulated operation time sets, tool taking life abnormality analysis and installation life abnormality analysis are respectively performed to obtain a plurality of tool taking life abnormality information and a plurality of installation life abnormality information.

[0026] In the embodiment of the present application, firstly, based on the aforementioned multiple tool-picking personnel sets, all personnel who have operated the tool in the corresponding tool-picking data and their total cumulative working time are obtained to obtain multiple cumulative working time sets. Wherein, the cumulative working time set includes the cumulative working time of multiple tool-picking personnel, which is obtained by extracting the historical retrieval and return records of each operator in the tool-picking log. Specifically, firstly, all the historical retrieval and return records of each tool-picking personnel are extracted through indexes, and the working time of each tool-picking is calculated (the tool return time minus the tool picking time), and then all the single tool picking times are accumulated and summed to obtain the cumulative working time of each tool-picking personnel.

[0027] For example, all historical retrieval and return records of a technician numbered OP-12 are detected through the index in the tool retrieval log: CD001 (tool number) was retrieved at 14:00 on 2024-05-05, and the tool was returned at 17:00 on 2024-05-05; CD002 (tool number) was retrieved at 10:00 on 2024-05-06, and the tool was returned at 11:00 on 2024-05-06; CD003 (tool number) was retrieved at 15:00 on 2024-05-07, and the tool was returned at 18:00 on 2024-05-07… By calculating the working time for each tool retrieval (the tool return time minus the tool retrieval time), and then accumulating and summing them up, the accumulated working time of OP-12 is 200 hours.

[0028] Secondly, according to multiple cumulative operation time sets, tool picking life abnormality analysis and installation life abnormality analysis are performed respectively to obtain multiple tool picking life abnormality information and multiple installation life abnormality information. Among them, the tool life abnormality information and installation life abnormality information are obtained by calculating the probability of tool wear and tear during tool picking and installation. This is because the tool may be damaged by bumps during the tool picking process, and the tool may also be damaged by bumps during the installation process. The longer the cumulative working time, the more skilled the technical personnel, and the smaller the probability of damaging the tool during the tool picking and installation process; the shorter the cumulative working time, the less skilled the technical personnel, and the greater the probability of damaging the tool during the tool picking and installation process. Therefore, based on multiple tool picking personnel sets of multiple tools, the probability of wear and tear during the tool picking and installation process of multiple tools is analyzed respectively, thereby obtaining multiple tool picking life abnormality information and multiple installation life abnormality information.

[0029] Furthermore, the “performing tool removal life abnormality analysis and installation life abnormality analysis respectively according to multiple accumulated operation time sets” includes: 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; A plurality of installation life normal rate sets are calculated respectively according to the plurality of installation life abnormal rate sets, and a plurality of installation damage abnormal rates of a plurality of tools are obtained by calculation as a plurality of installation life abnormality information.

[0030] In the embodiment of the present application, firstly, the knife taking life anomaly analysis is performed by the knife taking abnormality rate classifier. Specifically, each cumulative operation time is input into the knife taking abnormality rate classifier, and the sample knife taking life anomaly rate corresponding to the closest sample cumulative operation time is indexed, and the corresponding multiple knife taking life anomaly rate sets are obtained by output. Wherein, the knife taking abnormality rate classifier includes the mapping relationship between the sample cumulative operation time and the sample knife taking life anomaly rate, and the mapping relationship is established based on the different knife taking life anomaly rates of technicians with different cumulative operation times in the knife taking process. This is because the knife may be bumped and damaged during the knife taking process. The longer the cumulative working time, the more skilled the technician is, and the smaller the probability of damaging the knife during the knife taking process. The shorter the cumulative working time, the less skilled the technician is, and the greater the probability of damaging the knife during the knife taking process. Therefore, the knife taking life anomaly rate can be represented by collecting the probability of technicians with different cumulative operation times damaging the knife during the knife taking process, and thus establish a mapping relationship between the sample cumulative operation time and the sample knife taking life anomaly rate.

[0031] Exemplarily, the probability of damaging the tool during tool removal by technicians with different cumulative working times is manually collected: the probability of damaging the tool during tool removal by a technician with a cumulative working time of 1000 hours is 1%, and the probability of damaging the tool during tool removal by a technician with a cumulative working time of 100 hours is 10%. Thus, a mapping relationship between the cumulative working time (1000 hours) and the abnormal rate of tool removal life (1%) and the cumulative working time (100 hours) and the abnormal rate of tool removal life (10%) is established.

[0032] Further, according to the multiple tool-taking life abnormality rate sets, multiple tool-taking life normal rate sets are calculated respectively, and multiple tool-taking damage abnormality rates of multiple tools are calculated and obtained as multiple tool-taking life abnormality information. Wherein, the tool-taking life normal rate set is obtained by calculating the complement of the corresponding tool-taking life abnormality rate set. For example, the tool-taking life abnormality rate set of a certain tool is [12%, 18%], and its complement is calculated as [88%, 82%], thereby obtaining the corresponding tool-taking life normal rate set as [88%, 82%]. The tool-taking damage abnormality rate is obtained by calculating the tool-taking life normal rate set. Specifically, first, the joint probability of M tool-taking life normal rates in the tool-taking life normal rate set is calculated by multiplication. This joint probability represents the probability that the tool remains normal during the M tool-taking operations. The multiplication is used because the use of the tool by different technicians is independent of each other. Then, the tool-taking damage abnormality rate is obtained by calculating the complement of the joint probability of M tool-taking life normal rates. For example, if the normal tool life rate set of a certain tool is [88%, 82%], then its tool damage abnormality rate is: .

[0033] Secondly, the installation life anomaly analysis is performed using the same ideas and methods as the above-mentioned tool life anomaly analysis. First, each cumulative operation time is input into the installation anomaly rate classifier, and multiple installation life anomaly rate sets of multiple tools are obtained as output. The installation anomaly rate classifier includes a mapping relationship between the sample cumulative operation time and the sample installation life anomaly rate.

[0034] Specifically, each cumulative operation time is input into the installation abnormality rate classifier, and the sample installation life abnormality rate corresponding to the closest sample cumulative operation time is indexed, and the corresponding multiple installation life abnormality rate sets are output. Among them, the installation abnormality rate classifier includes a mapping relationship between the sample cumulative operation time and the sample installation life abnormality rate. The mapping relationship is established based on the different installation life abnormality rates of technicians with different cumulative operation times during the installation process. This is because the tool may be damaged by bumping during the installation process. The longer the cumulative working time, the more skilled the technician is, and the smaller the probability of damaging the tool during the installation process. The shorter the cumulative working time, the less skilled the technician is, and the greater the probability of damaging the tool during the installation process. Therefore, the installation life abnormality rate can be represented by collecting the probability of technicians with different cumulative operation times damaging the tool during the installation process, and thus establishing a mapping relationship between the sample cumulative operation time and the sample installation life abnormality rate.

[0035] Exemplarily, the probability of damaging the tool during installation by technicians with different cumulative working times is manually collected: the probability of damaging the tool during installation by a technician with a cumulative working time of 1000 hours is 5%, and the probability of installing the tool during tool removal by a technician with a cumulative working time of 100 hours is 20%. Thus, a mapping relationship between the cumulative working time (1000 hours) and the installation life abnormality rate (5%) and the cumulative working time (100 hours) and the installation life abnormality rate (10%) is established.

[0036] Further, according to the multiple installation life abnormality rate sets, multiple installation life normal rate sets are calculated respectively, and multiple installation damage abnormality rates of multiple tools are calculated as multiple installation life abnormality information. Wherein, the tool life normal rate set is obtained by calculating the complement of the corresponding installation life abnormality rate set. For example, the installation life abnormality rate set of a certain tool is [15%, 20%], and its complement is calculated as [85%, 80%], thereby obtaining the corresponding installation life normal rate set as [85%, 80%]. The installation damage abnormality rate is obtained by calculating the installation life normal rate set. Specifically, first, the joint probability of M installation life normal rates in the installation life normal rate set 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 abnormality rate is obtained by calculating the complement of the joint probability of M installation life normal rates. For example, the installation life normal rate set of a certain tool is [85%, 80%], then its installation damage abnormality rate is: .

[0037] In summary, compared to the prior art, this application extracts multiple sets of tool use time and multiple sets of tool pickers based on the tool picker log, performs life loss analysis based on the multiple sets of use time, obtains multiple life loss information, and outputs quantified life loss information to intuitively reflect tool wear. Then, based on the multiple sets of tool pickers, tool picker life anomaly analysis and installation life anomaly analysis are performed to obtain multiple tool picker life anomaly information and multiple installation life anomaly information, fully considering the differences in the probability of tool damage during tool picker and installation by technicians with different proficiency levels, and improving the evaluation precision and accuracy.

[0038] S300: extracting a repeated use data set in which the same type of tool is taken multiple times within a preset time range according to the tool taking log, extracting corresponding tool taking personnel to obtain a repeated use personnel set, performing repeated use life anomaly analysis, and obtaining repeated use life anomaly information; In the prior art, when a knife-picking person uses a knife of the same model multiple times in a short period of time, this indicates that the previously used knife may be damaged, so another knife of the same model needs to be used again. However, there are also cases where the knife-picking person makes a wrong judgment, that is, the knife is not damaged but is judged to be damaged. The accuracy of judging the damage of the knife is highly correlated with the proficiency of the knife-picking person, that is, the longer the cumulative working time, the more experienced the knife-picking person is, and the smaller the probability of misjudgment. The shorter the cumulative working time, the less experienced the knife-picking person is, and the greater the probability of misjudgment.

[0039] In response to the above problems, the present application extracts a repeated use data set in which the same model of tool is used multiple times within a preset time range based on the tool picking log, and extracts the corresponding tool picking personnel to obtain a repeated use personnel set, performs repeated use life anomaly analysis, and obtains repeated use life anomaly information.

[0040] Specifically, step S30 in the method includes: 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; The repeated accumulated operation time is input into the abnormality identification rate classifier, and the tool abnormality identification rate is output to obtain the repeated use life abnormality information 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.

[0041] In an embodiment of the present application, firstly, according to the tool taking log, the tool taking data of the same tool taking personnel repeatedly taking the same type of tool within a preset time range (such as within 10 minutes) is extracted to obtain a repeated use data set. Among them, the repeated use data set includes the tool taking personnel and their historical retrieval records. Exemplarily, within 10 minutes, the tool taking personnel OP-03 took the same type of turning tool three times, so the historical retrieval record data of OP-03 is extracted in the tool taking log: 2024-05-05 14:00 to take the tool, 2024-05-05 17:00 to return the tool; 2024-05-06 10:00 to take the tool, 2024-05-05 11:00 to return the tool... Thus, the repeated use data set of the tool taking personnel OP-03 is obtained.

[0042] Secondly, the cumulative working time of the tool picker in the repeated use data set when picking up the first tool is extracted to obtain the repeated cumulative working time. The repeated cumulative working time is the historical cumulative working time of the tool picker when picking up the tool for the first time, which can reflect the proficiency of the tool picker at that time, and the proficiency can reflect the accuracy of the tool picker in identifying the damage of the tool. For example, the cumulative working time of the tool picker OP-03 in the repeated use data set when picking up the tool for the first time is 50 hours.

[0043] Finally, the repeated cumulative operation time is input into the identification abnormality classifier, and the tool identification abnormality is obtained by output. The tool identification abnormality is taken as 1 minus the tool identification abnormality as the probability of damage in repeated use of the tool, and as the abnormal information of the repeated use life of the first tool. Among them, the identification abnormality classifier includes the mapping relationship between the sample repeated cumulative operation time and the sample tool identification abnormality, and the mapping relationship is established based on the different probability of error in the identification of the tool picking process by the tool picking personnel with different cumulative operation times. This is because the accuracy of the identification of the tool damage is highly related to the proficiency of the tool picking personnel. The longer the cumulative working time, the more experienced the tool picking personnel, and the smaller the probability of error in identification. The shorter the cumulative working time, the less experienced the tool picking personnel, and the greater the probability of error in identification. Therefore, the accuracy of the identification of the tool damage in the tool picking process can be collected by technicians with different cumulative operation times, and then the tool identification abnormality is obtained by (tool identification abnormality = 1-the accuracy of the identification of the tool damage), and thus the mapping relationship between the sample repeated cumulative operation time and the sample tool identification abnormality is established.

[0044] Exemplarily, data are manually collected on the accuracy of technicians with different cumulative working hours in identifying the damage of the tool during tool removal: the technician with a cumulative working time of 1000 hours has a 95% accuracy in identifying the damage of the tool during tool removal, and the technician with a cumulative working time of 100 hours has a 60% accuracy in identifying the damage of the tool during tool removal. Thus, a mapping relationship between the cumulative working time (1000 hours) tool identification abnormality rate (5%) and the cumulative working time (100 hours) tool identification abnormality rate (40%) is established.

[0045] In summary, compared with the prior art, this application extracts the repeated use data set of the same type of tool that has been used multiple times within a preset time range based on the tool taking log, and extracts the corresponding tool taking personnel to obtain the repeated use personnel set, performs repeated use life anomaly analysis, and obtains repeated use life anomaly information. It realizes the accurate identification and probability prediction of abnormal repeated use behavior, and takes into account the risk of abnormal tool loss caused by human operation factors, thereby improving the accuracy of tool life assessment.

[0046] S400: 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 display them as tool life evaluation results in combination with multiple life loss information.

[0047] In the existing technology, the assessment of tool life is based on the method of cumulative usage time + manual regular inspection, which only takes into account the time factor and ignores the risk of abnormal tool wear caused by human operation factors. As a result, the accuracy and precision of tool life assessment are not high, and it cannot provide a timely and effective reference for tool maintenance and replacement.

[0048] In response to the above problems, the present application integrates the aforementioned data: multiple service life loss information, multiple tool removal life abnormality information, multiple installation life abnormality information and repeated use life abnormality information, as the life assessment results of multiple tools, and outputs and displays them.

[0049] Specifically, step S40 in the method includes: 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.

[0050] In the embodiment of the present application, firstly, according to the tool taking life abnormality information, multiple installation life abnormality information and repeated use life abnormality information, the total abnormality rate of multiple tools is calculated to obtain the total abnormality rate of multiple tools as multiple life abnormality information. The total abnormality rate of multiple tools can be obtained by giving different weights w to the tool taking life abnormality information, the installation life abnormality information and the repeated use life abnormality information in advance. 1 、w 2 、w 3 (W 1 +w 2 +w 3 =1), and then the weighted geometric mean algorithm is used to obtain. For example, the weights of 0.4, 0.4, and 0.2 are respectively given to the abnormal information of tool life, installation life, and repeated use life of a certain tool, and the total abnormal rate is 13.3% using the weighted geometric mean algorithm.

[0051] Secondly, the tool life evaluation results of multiple tools are displayed in combination with the multiple life abnormality information and the multiple life loss information, so as to provide a reference for tool maintenance and replacement. For example, the life evaluation result of a tool includes life abnormality information of 13.3% and life loss information of 43.35%.

[0052] In summary, this application integrates multiple service life loss information, multiple tool removal life abnormality information, multiple installation life abnormality information and repeated use life abnormality information as the life assessment results of multiple tools, providing a reference for tool maintenance and replacement.

[0053] In summary, the embodiments of the present application have at least the following technical effects: Compared with the existing technology, this application greatly improves the accuracy of data recording by collecting relevant data through the intelligent tool cabinet, thereby establishing a tool retrieval log for the entire life cycle of the tool, providing effective data support for subsequent tool life prediction and anomaly detection.

[0054] Secondly, based on the tool picking log, multiple tool picking time sets and multiple tool picking personnel sets are extracted, and service life loss analysis is performed based on the multiple tool picking time sets to obtain multiple service life loss information. The tool loss situation can be intuitively reflected by outputting quantified service life loss information. Then, based on the multiple tool picking personnel sets, tool picking life abnormality analysis and installation life abnormality analysis are performed to obtain multiple tool picking life abnormality information and multiple installation life abnormality information. The differences in the probability of tool damage during tool picking and installation by technicians with different proficiency levels are fully considered, and the evaluation precision and accuracy are improved.

[0055] Thirdly, according to the tool taking log, the repeated use data set of the same type of tool that was taken multiple times within a preset time range was extracted, and the corresponding tool taking personnel were extracted to obtain the repeated use personnel set, and repeated use life anomaly analysis was performed to obtain repeated use life anomaly information. This achieved accurate identification and probability prediction of abnormal repeated use behavior, and took into account the risk of abnormal tool loss caused by human operation factors, thereby improving the accuracy of tool life assessment.

[0056] Finally, by integrating multiple service life loss information, multiple tool removal life abnormality information, multiple installation life abnormality information and repeated use life abnormality information, the life assessment results of multiple tools are used to provide a reference for tool maintenance and replacement.

[0057] Through the above-mentioned technical scheme, this application fully considers the differentiated impact of personnel operating behavior on tool wear based on the data collected from the tool removal log, 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.

[0058] Embodiment 2, as Figure 2 As shown, based on the same inventive concept of a tool life assessment method combined with a tool taking log provided in Embodiment 1, an embodiment of the present invention further provides an intelligent tool cabinet, comprising: The data acquisition module 11 is used to monitor and record the 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 12 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 a life loss analysis according to the multiple use time sets to obtain multiple life loss information, perform a tool picker life abnormality analysis and an 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; The repeated use analysis module 13 is used to extract the 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 the repeated use personnel set, perform repeated use life anomaly analysis, and obtain repeated use life anomaly information; The integrated output module 14 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 display them as tool life evaluation results in combination with multiple life loss information.

[0059] Wherein, the data acquisition module 11 is specifically used for: 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.

[0060] The evaluation and analysis module 12 is specifically used for: 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.

[0061] The “performing an abnormal analysis of tool taking life and an abnormal analysis of installation life according to multiple sets of tool taking personnel to obtain multiple abnormal information of tool taking life and multiple abnormal information of installation life” includes: 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 a plurality of accumulated operation time sets, tool taking life abnormality analysis and installation life abnormality analysis are respectively performed to obtain a plurality of tool taking life abnormality information and a plurality of installation life abnormality information.

[0062] Furthermore, the “performing tool removal life abnormality analysis and installation life abnormality analysis respectively according to multiple accumulated operation time sets” includes: 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; A plurality of installation life normal rate sets are calculated respectively according to the plurality of installation life abnormal rate sets, and a plurality of installation damage abnormal rates of a plurality of tools are obtained by calculation as a plurality of installation life abnormality information.

[0063] The repeated use analysis module 13 is specifically used for: 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; The repeated accumulated operation time is input into the abnormality identification rate classifier, and the tool abnormality identification rate is output to obtain the repeated use life abnormality information 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.

[0064] The integrated output module 14 is specifically used for: 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.

[0065] In summary, the embodiments of the present application have at least the following technical effects: Compared with the prior art, the present application monitors and records the tool taking log when the tool is taken through the data acquisition module; extracts multiple tool taking time sets and multiple tool taking personnel sets of multiple tools according to the tool taking log through the evaluation and analysis module, performs service life loss analysis according to the multiple tool taking time sets, obtains multiple service life loss information, performs tool taking life abnormality analysis and installation life abnormality analysis according to the multiple tool taking personnel sets, obtains multiple tool taking life abnormality information and multiple installation life abnormality information; extracts the repeated use data set in which the same model of tool is taken multiple times within a preset time range according to the tool taking log through the repeated use analysis module, extracts the corresponding tool taking personnel to obtain the repeated use personnel set, performs repeated use life abnormality analysis, and obtains repeated use life abnormality information; calculates and obtains multiple life abnormality information according to the multiple tool taking life abnormality information, multiple installation life abnormality information and repeated use life abnormality information through the integrated output module, combines the multiple service life loss information, and displays them as the tool life evaluation result. This application is based on the data collected from the tool removal log, fully considering the differentiated impact of personnel operating behavior on tool wear, and analyzing the tool wear situation from multiple dimensions to output the tool life assessment results, providing accurate, timely and effective reference for tool maintenance and replacement.

[0066] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0067] 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 an entirely hardware embodiment, an entirely 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.) that contain computer-usable program code.

[0068] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented 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, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0069] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or a plurality of flows and / or blocks

[0071] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concepts.

[0072] 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, extract multiple tool picking time sets and multiple tool picking personnel sets, perform service life loss analysis according to the multiple tool picking time sets to obtain multiple service life loss information, perform tool picking life abnormality analysis and installation life abnormality analysis according to the multiple tool picking personnel sets to obtain multiple tool picking life abnormality information and multiple 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; 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 multiple sets of tool picking personnel, tool picking life anomaly analysis and installation life anomaly analysis are performed to obtain multiple tool picking life anomaly information and multiple installation life anomaly 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 a plurality of accumulated operation time sets, tool taking life abnormality analysis and installation life abnormality analysis are respectively performed to obtain a plurality of tool taking life abnormality information and a plurality of installation life abnormality information.

5. The tool life assessment method in combination with the tool removal log according to claim 4 is characterized in that: According to multiple cumulative operation time sets, the tool life abnormality analysis and installation life abnormality analysis are performed separately, 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; A plurality of installation life normal rate sets are calculated respectively according to the plurality of installation life abnormal rate sets, and a plurality of installation damage abnormal rates of a plurality of tools are obtained by calculation as a plurality of installation life abnormality information.

6. The tool life assessment method in combination with a tool log according to claim 1, characterized in that: 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 taking log, extract the tool taking data of the same type of tool repeatedly taken by the same tool taking 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; The repeated accumulated operation time is input into the abnormality identification rate classifier, and the tool abnormality identification rate is output to obtain the repeated use life abnormality information 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.

7. The tool life assessment method in combination with a tool log according to claim 1, 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.

8. An intelligent tool cabinet, 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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