Intelligent cutter management method and system

Abstract

The invention relates to the technical field of intelligent management, in particular to an intelligent cutter management method and system. Through quantitative analysis of basic data of the cutters, the cutters are divided into high-value cutters, conventional cutters and low-value cutters, dynamic visual trigger analysis is performed in combination with stored data, the basic acquisition precision of the high-value cutters is higher, the conventional cutters are adjusted according to needs, and the low-value cutters adopt common monitoring, so that management omissions caused by insufficient acquisition of the high-value cutters are avoided, and the management efficiency is improved. Invalid occupation of resources by low-value tools is reduced, and accurate allocation of the collected resources is realized; according to the method, the cooperative priority value is generated through self-adaptive transfer analysis and integration of the pick-and-place priority, the time urgency rate and the tool value, so that the transfer sequence is determined, the number of AGVs, high-value tools and emergency requirements are matched for priority transfer, transportation resources are reasonably allocated, the pick-and-place waiting time is shortened, the tool circulation efficiency is improved, and the efficient management requirement is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent management, in particular to a tool intelligent management method and system. BACKGROUND

[0002] In the field of tool management, the traditional tool management system usually collects the image of the tool through a camera component for management analysis when managing the tool. However, the tool management library often reserves a large number of tools, and the volume of the tool is small, which leads to the need for high-precision camera requirements or the installation of a large number of cameras to achieve comprehensive image collection in the image collection process. Such a way will cause serious resource waste and greatly increase the cost, which cannot meet the requirements of modern tool management for high efficiency and low cost. Therefore, there is an urgent need for a tool intelligent management method and system that can optimize the image collection method and reduce resource waste and cost increase. SUMMARY

[0003] The present application provides a tool intelligent management method and system to solve the above technical problems.

[0004] The present application provides a tool intelligent management method and system to solve the above technical problems. Step S1: Obtain tool basic data and tool storage data, and perform initial analysis on the basic data of the tool to obtain tool initial labels, and associate the tool initial labels and tool storage data corresponding to each tool.

[0005] As a further improvement of the present application, the tool basic data is initially analyzed as follows: The tool basic information includes tool model, precision grade and use information; the cutting precision corresponding to each tool is obtained based on the precision grade, and the cutting precision is divided into multiple cutting precision intervals according to a preset precision interval, and each cutting precision interval is set with a precision value, the median cutting precision of each cutting precision interval is obtained, and each median cutting precision is arranged in ascending order to obtain median cutting precision sorting information, and the higher the sorting, the higher the precision value; the tool usage frequency corresponding to each tool is obtained based on the use information, and similarly, the corresponding usage frequency interval and frequency value are obtained; the precision value and frequency value corresponding to each tool are calculated and summed to obtain the tool value.

[0006] The tool value corresponding to the preset value monitoring threshold is obtained, when the tool value is less than the value monitoring threshold, the tool initial label of the corresponding tool is marked as a low value tool, when the tool value is greater than the value monitoring threshold, the overflow value is obtained by difference calculation of the tool value and the value monitoring threshold, the overflow value threshold given by the pre-design is obtained, when the overflow value is less than the overflow value threshold, the tool initial label of the corresponding tool is marked as a regular tool, and when the overflow value is greater than the overflow value threshold, the corresponding tool initial label is marked as a high value tool.

[0007] Step S2: obtaining the tool initial label of each tool and identifying the tool initial label, when the tool initial label corresponds to a high value tool and a regular tool, obtaining the tool storage data corresponding to the tool initial label and performing dynamic visual trigger analysis on the tool storage data to obtain a high-precision dynamic visual strategy, and when the tool initial label corresponds to a low value tool, marking the region corresponding to the low value tool as an ordinary monitoring strategy.

[0008] As a further improvement of the application, the tool storage data is analyzed by dynamic visual trigger analysis, and the specific analysis is as follows: According to the static vibration frequency data, impedance data, sealed gas pressure data and infrared radiation data of the tool storage data; According to the static vibration frequency, the measured static frequency and the measured decay time corresponding to each tool are obtained, the standard frequency corresponding to the measured static frequency is obtained, the frequency difference is obtained by difference calculation of the measured static frequency and the standard frequency, and the measured frequency deviation rate is obtained by ratio calculation of the frequency difference and the standard frequency; the standard decay time corresponding to the measured decay time is obtained, and the decay time deviation rate corresponding to the measured decay time is obtained in the same way; the measured frequency deviation rate and the decay time deviation rate are substituted into the formula of the Euclidean distance model The micro-vibration abnormal value is calculated ; wherein, respectively represent the measured frequency deviation rate and the decay time deviation rate; represents the sign function; Both are preset calculation weights.

[0009] According to the impedance data, the impedance change rate and the impedance parameter of each tool are obtained; according to the impedance parameter, the maximum impedance point, the minimum impedance point and the impedance mean value are obtained, the impedance fluctuation value is obtained by difference calculation of the maximum impedance point and the minimum impedance point, and the impedance uniformity deviation value is obtained by ratio calculation of the impedance fluctuation value and the impedance mean value; the impedance change rate and the impedance uniformity deviation value are combined with the formula of the Sigmoid function to calculate the impedance abnormal value zk; wherein, respectively represent the impedance change rate and the impedance uniformity deviation value; represents the Sigmoid function; Used to reinforce situations where both parameters are abnormal.

[0010] Based on the sealing air pressure data, the air pressure fluctuation values ​​for each tool during the corresponding analysis period are obtained. The air pressure gradient change rate is calculated by comparing the air pressure fluctuation value with the duration of the corresponding analysis period. The air pressure gradient change rate is then combined with the error function using the formula... The calculated pressure gradient anomaly value qy is obtained; where Q qy The pressure gradient change rate is represented by erf(x), which is the error function; C represents the sealing coefficient. All of these are pre-set weighting coefficients.

[0011] The radiation intensity and ambient temperature data corresponding to each tool are obtained based on infrared radiation data. The deviation rate of radiation intensity is calculated by comparing it with the corresponding preset radiation standard intensity. The tool temperature difference value is obtained based on the ambient temperature data, and the temperature difference concentration area is obtained. The ratio of the temperature difference concentration area to the effective area corresponding to each tool in the database is calculated to obtain the temperature difference concentration degree. The radiation deviation rate and temperature difference concentration are combined with an exponential function to obtain the formula. The infrared radiation anomaly value hw was calculated; where, These are respectively represented as radiation deviation rate and temperature difference concentration; Represented as an exponentially decaying function; All of these are pre-set weighting factors.

[0012] Furthermore, the micro-vibration anomalies, impedance anomalies, pressure gradient anomalies, and infrared radiation anomalies are normalized and their values ​​are taken. The normalized values ​​of each sub-anomaly are then denoted as follows: Using the formula of the modified weighted Mahalanobis distance model The comprehensive outlier value Y is obtained through calculation. Z ;in, ; K represents the sub-state weights corresponding to each sub-outlier; i Represented as reliability coefficient; Represented as a standard deviation function; The standard deviation weight is used to represent the weight; a high-precision dynamic vision strategy is obtained by analyzing the comprehensive outliers based on the initial tool label.

[0013] Furthermore, the overall outliers are analyzed based on the initial tool labels. The specific analysis process is as follows: A1: Identify the initial tool label for each tool. If the tool corresponds to a high-value tool, execute A2; if the tool corresponds to a regular tool, execute A3.

[0014] A2: Obtain the comprehensive abnormal value of each high-value tool, and obtain the abnormal state of each sub-abnormal value based on the comprehensive abnormal value. Count the number of sub-abnormal values ​​with abnormal states to obtain the sub-state number. When the sub-state number is 1, the high-value dynamic strategy is low-precision acquisition; when the sub-state number is 2-3, the high-value dynamic strategy is directional medium-precision acquisition; when the sub-state number is 4, the high-value dynamic strategy is high-definition focused acquisition.

[0015] A3: Obtain the comprehensive abnormal value of each conventional tool, and obtain the number of sub-states as above. When the number of sub-states is 1, the conventional dynamic strategy is no adjustment required; when the number of sub-states is 2, the conventional dynamic strategy is directional low-precision acquisition; when the number of sub-states is 3, the conventional dynamic strategy is directional medium-precision acquisition; when the number of sub-states is 4, the conventional dynamic strategy is high-definition focused acquisition.

[0016] A4: A high-precision dynamic vision strategy is obtained by combining high-value dynamic strategies and conventional dynamic strategies.

[0017] Step S3: Obtain the high-precision dynamic vision strategy corresponding to each tool, and adapt the acquisition resources based on the high-precision dynamic vision strategy; it should be noted that the basic acquisition accuracy set for high-value tools is greater than that for conventional tools.

[0018] Step S4: Based on the high-precision dynamic vision strategy, perform image verification on the corresponding tool to obtain the tool verification status, and output the tool verification status as a status report.

[0019] A second aspect of the present invention provides an intelligent tool management system, comprising: The data sensing module acquires basic tool data and tool storage data through a pre-deployed sensor array, and then sends the basic tool data and tool storage data to the tool information management module.

[0020] The trigger acquisition control module is used to receive basic tool data and stored tool data, obtain a high-precision dynamic vision strategy based on the basic tool data and stored tool data, and input the high-precision dynamic vision strategy into the dynamic high-definition camera group for strategy execution.

[0021] The adaptive transfer control module receives tool pick-up and drop information, performs adaptive transfer analysis on the tool pick-up and drop information to obtain a transfer control strategy, and performs adaptive transfer control based on the transfer control strategy. Specifically, the module uses a pre-deployed warehouse robot to pick up tools at a preset position according to the tool transfer sequence of the transfer control strategy; and uses a pre-deployed AGV transport group to coordinate and match the corresponding number of AGV transport vehicles to perform the transport operation based on the transport demand.

[0022] As a further improvement to the present invention, adaptive transfer analysis is performed on the tool pick-up and drop information, and the specific analysis method is as follows: Based on tool pick-up and drop information, the pick-up and drop priorities and time requirements are obtained. A priority index is assigned to each priority level, with the index increasing as the priority level increases. The remaining pick-up and drop time is calculated as the required time, and the ratio of this ratio yields the time urgency rate. Furthermore, the initial tool labels are obtained, and a pre-designed precision priority index is assigned to each initial tool label. The precision priority values ​​for low-value tools, conventional tools, and high-value tools increase in that order. The precision priority index corresponding to the initial tool label for the pick-up and drop information is also obtained. Finally, the priority index, time urgency rate, and precision priority value are normalized and their values ​​are calculated using a weighted average formula. The cooperative priority value P is calculated. 协同 ;in, These are respectively represented as priority index, precision priority value, and time urgency rate; All are preset weighting factors; The process is divided into pick-up and place-up execution periods based on a pre-set time interval. The pick-up and place-up tools corresponding to the current pick-up and place-up execution period are obtained, and the coordination priority value of each pick-up and place-up tool is obtained. The tools are sorted in descending order according to the coordination priority value to obtain the coordination ranking of the pick-up and place-up tools. The tool transfer order is generated based on the coordination ranking.

[0023] The number of tools picked up and placed during the current pick-up and place execution period is counted. The number of tools picked up and placed is divided into multiple intervals. A corresponding transportation demand is set for each interval. The current number of tools picked up and placed is matched with each interval to obtain the corresponding transportation demand. The transportation demand corresponds to the number of AGV transport vehicles for transporting tools. The transportation demand and the tool transfer order are used as the corresponding transfer control strategy.

[0024] The user interaction module is used to provide a user interface and obtain the user's tool picking and placing information, and send the tool picking and placing information to the adaptive transfer control module.

[0025] The beneficial effects of the technical solution provided by this invention compared with the prior art are as follows: 1. This invention quantifies and analyzes basic tool data, classifying tools into three categories: high-value, conventional, and low-value. It combines stored data with dynamic visual trigger analysis, resulting in higher accuracy in basic tool acquisition for high-value tools, on-demand adjustment for conventional tools, and ordinary monitoring for low-value tools. This avoids management oversights caused by insufficient acquisition of high-value tools and reduces the ineffective use of resources by low-value tools, thus achieving precise allocation of acquisition resources.

[0026] 2. This invention uses adaptive transfer analysis to generate a collaborative priority value by comprehensively considering pick-up and put-down priorities, time urgency, and tool value. This value determines the transfer order and matches the number of AGVs, prioritizing the transfer of high-value tools and urgent needs. This rationally allocates transportation resources, reduces pick-up and put-down waiting time, improves tool flow efficiency, and meets the needs of efficient management. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. The following drawings are not deliberately drawn to scale according to the actual size, but are intended to show the main idea of ​​this application.

[0028] Figure 1 This is a flowchart of an intelligent tool management method according to the present invention; Figure 2 This is a schematic diagram of a tool intelligent management system according to the present invention; Figure 3 This is a schematic diagram of the warehouse robot of the present invention; Figure 4 This is a schematic diagram of the AGV transport vehicle of the present invention; Figure 5 This is a schematic diagram of the actual warehouse robot of the present invention; Figure 6 This is a schematic diagram of the AGV transport vehicle of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 In one embodiment of the present invention, a method for intelligent management of cutting tools includes the following steps: Step S1: Tool information initialization: Obtain basic tool data and tool storage data, perform initial analysis on the basic tool data to obtain initial tool labels, and associate the initial tool labels and tool storage data corresponding to each tool.

[0031] An initial analysis of the tool's basic data was performed, and the specific analysis is as follows: The basic tool information includes tool model, precision grade, and usage information. Based on the precision grade of each tool, the corresponding cutting precision is obtained. The cutting precision is divided into multiple cutting precision intervals according to preset precision intervals. A precision value is assigned to each cutting precision interval, and the median cutting precision of each interval is obtained. These median cutting precisions are then arranged in ascending order to obtain median cutting precision ranking information. Based on this ranking information, higher rankings correspond to higher precision values. Based on the usage information of each tool, the corresponding tool usage frequency is obtained. Similarly, the corresponding usage frequency intervals and frequency values ​​are obtained. The tool value is calculated and summed by combining the precision value and frequency value of each tool.

[0032] The tool value is obtained from a preset value monitoring threshold. When the tool value is less than the value monitoring threshold, the tool's initial label is marked as a low-value tool. When the tool value is greater than the value monitoring threshold, the difference between the tool value and the value monitoring threshold is calculated to obtain the overflow value. The pre-designed overflow value threshold is obtained. When the overflow value is less than the overflow value threshold, the tool's initial label is marked as a regular tool. When the overflow value is greater than the overflow value threshold, the tool's initial label is marked as a high-value tool.

[0033] Step S2, Dynamic Vision Trigger Decision: Obtain the initial tool label of each tool and identify the initial tool label. When the initial tool label corresponds to a high-value tool or a conventional tool, obtain the corresponding tool storage data and perform dynamic vision trigger analysis on the tool storage data to obtain a high-precision dynamic vision strategy. When the initial tool label corresponds to a low-value tool, mark the area corresponding to the low-value tool as a normal monitoring strategy.

[0034] Dynamic visual trigger analysis is performed on the tool storage data, and the specific analysis is as follows: Based on the static vibration frequency data, impedance data, sealing air pressure data, and infrared radiation data stored in the tool; Based on the static vibration frequency, the measured static frequency and measured decay time corresponding to each tool are obtained. The standard frequency corresponding to the measured static frequency is then obtained. The difference between the measured static frequency and the standard frequency is calculated to obtain the frequency difference, and the ratio of the frequency difference to the standard frequency is calculated to obtain the measured frequency deviation rate. Similarly, the standard decay time corresponding to the measured decay time is obtained. The difference between the measured decay time and the standard decay time is calculated to obtain the decay time difference, and the ratio of the decay time difference to the standard decay time is calculated to obtain the decay time deviation rate. The measured frequency deviation rate and decay time deviation rate are then substituted into the formula of the Euclidean distance model. The micro-vibration anomaly value was calculated. ;in, These are represented as measured frequency deviation rate and decay time deviation rate, respectively. Represented as a symbolic function, used to amplify the effect of double anomalies; All are preset calculation weights; micro-vibration anomalies correspond to the degree of tool clamping.

[0035] The impedance change rate and impedance parameters of each tool are obtained from the impedance data. The maximum impedance point, minimum impedance point, and average impedance value are obtained from the impedance parameters. The impedance fluctuation value is calculated by subtracting the maximum and minimum impedance points. The impedance uniformity deviation value is calculated by comparing the impedance fluctuation value with the average impedance value. The impedance change rate and impedance uniformity deviation value are then combined with the formula of the Sigmoid function. The impedance anomaly value zk is obtained through calculation; where, These are represented as the rate of change of impedance and the deviation of impedance uniformity, respectively. Represented as the Sigmoid function, it is used to mitigate the extreme effects of a single uniformity deviation; Used to reinforce cases where both parameters are abnormal, for example When all values ​​exceed the preset threshold, the sub-state values ​​increase significantly.

[0036] Based on the sealing air pressure data, the air pressure fluctuation values ​​for each tool during the corresponding analysis period are obtained. The air pressure gradient change rate is calculated by comparing the air pressure fluctuation value with the duration of the corresponding analysis period. The air pressure gradient change rate is then combined with the error function using the formula... The calculated pressure gradient anomaly value qy is obtained; where Q qy The rate of change of the pressure gradient is represented by erf(x), which is the error function used to amplify the cumulative effect of slow leakage, such as Q. qy When the pressure is 2Pa / h, the error function value increases linearly with time; C represents the sealing coefficient, which takes a value of 0-1. When completely sealed, C=1, and when completely leaking, C=0. All of these are pre-set weighting coefficients.

[0037] The radiation intensity and ambient temperature data corresponding to each tool are obtained based on infrared radiation data. The deviation rate of radiation intensity is calculated by comparing it with the corresponding preset radiation standard intensity. The tool temperature difference value is obtained based on the ambient temperature data, and the temperature difference concentration area is obtained. The ratio of the temperature difference concentration area to the effective area corresponding to each tool in the database is calculated to obtain the temperature difference concentration degree. The radiation deviation rate and temperature difference concentration are combined with an exponential function to obtain the formula. The infrared radiation anomaly value hw was calculated; where, These are respectively represented as radiation deviation rate and temperature difference concentration; It is represented as an exponential decay function, used to mitigate the impact of low-risk, dispersed temperature differences; All of these are pre-set weighting factors.

[0038] The micro-vibration anomalies, impedance anomalies, pressure gradient anomalies, and infrared radiation anomalies were normalized and their values ​​were taken. The normalized values ​​of each sub-anomaly were then denoted as follows: Using the formula of the modified weighted Mahalanobis distance model The comprehensive outlier value Y was calculated. Z ;in, ; K represents the sub-state weights corresponding to each sub-outlier; i Reliability coefficient, denoted by K, is based on the data acquisition frequency. For example, a acquisition frequency greater than or equal to 10 times / hour. i =1, otherwise K i =0.7, to avoid misjudgment of low-frequency data; It is expressed as a standard deviation function, which reflects the consistency of fluctuations among sub-outliers; the greater the fluctuation, the more unstable it is. The standard deviation weight is used to enhance the synergistic effect of multiple outliers; a high-precision dynamic vision strategy is obtained by analyzing the comprehensive outliers based on the initial tool label.

[0039] The analysis of comprehensive outliers based on the initial tool label is as follows: A1: Identify the initial tool label for each tool. If the tool corresponds to a high-value tool, execute A2; if the tool corresponds to a regular tool, execute A3.

[0040] A2: Obtain the comprehensive abnormal value of each high-value tool, and obtain the abnormal state of each sub-abnormal value based on the comprehensive abnormal value. Count the number of sub-abnormal values ​​with abnormal states to obtain the sub-state number. When the sub-state number is 1, the high-value dynamic strategy is low-precision acquisition; when the sub-state number is 2-3, the high-value dynamic strategy is directional medium-precision acquisition; when the sub-state number is 4, the high-value dynamic strategy is high-definition focused acquisition.

[0041] A3: Obtain the comprehensive abnormal value of each conventional tool, and obtain the number of sub-states as above. When the number of sub-states is 1, the conventional dynamic strategy is no adjustment required; when the number of sub-states is 2, the conventional dynamic strategy is directional low-precision acquisition; when the number of sub-states is 3, the conventional dynamic strategy is directional medium-precision acquisition; when the number of sub-states is 4, the conventional dynamic strategy is high-definition focused acquisition.

[0042] A4: A high-precision dynamic vision strategy is obtained by combining high-value dynamic strategies and conventional dynamic strategies.

[0043] Step S3, Adaptive Camera Acquisition Resource Matching: Obtain the high-precision dynamic vision strategy corresponding to each tool and perform acquisition resource matching based on the high-precision dynamic vision strategy; it should be noted that the basic acquisition accuracy set for high-value tools is greater than that for conventional tools; allocate acquisition resources according to the strategy, prioritizing the accuracy of high-value tools, and adjusting conventional tools as needed to improve resource utilization efficiency. For example, high-value tool A uses 2K resolution and 5 minutes / acquisition; conventional tool B uses 1080P resolution and 30 minutes / acquisition to avoid resource redundancy.

[0044] Step S4, Tool Status Linkage Management: Based on the high-precision dynamic vision strategy, the corresponding tool is image-verified to obtain the tool verification status, and the tool verification status is output as a status report; by outputting the status report through image verification, the tool status is visualized and managed, and anomalies are detected in a timely manner.

[0045] Please see Figure 2 The present invention also provides an intelligent tool management system, including a data sensing module, a trigger acquisition control module, an adaptive transfer control module, and a user interaction module.

[0046] The data sensing module acquires basic tool data and tool storage data through a pre-deployed sensor array, and then sends the basic tool data and tool storage data to the tool information management module.

[0047] The trigger acquisition control module receives basic tool data and stored tool data, and obtains a high-precision dynamic vision strategy based on the basic tool data and stored tool data. The high-precision dynamic vision strategy is then input into the dynamic high-definition camera group for strategy execution.

[0048] The adaptive transfer control module receives tool pick-up and drop information, performs adaptive transfer analysis on the tool pick-up and drop information to obtain a transfer control strategy, and performs adaptive transfer control based on the transfer control strategy. Specifically, the pre-deployed warehouse robot picks up the tool at a preset position according to the tool transfer sequence of the transfer control strategy; and the pre-deployed AGV transport group adjusts and matches the corresponding number of AGV transport vehicles to perform the transport operation based on the transport demand.

[0049] An adaptive transfer analysis is performed on the tool handling information, and the specific analysis method is as follows: Based on tool pick-up and drop information, the pick-up and drop priorities and time requirements are obtained. A priority index is assigned to each priority level, with the index increasing as the priority level increases. The remaining pick-up and drop time is calculated as the required time, and the ratio of this ratio yields the time urgency rate. Furthermore, the initial tool labels are obtained, and a pre-designed precision priority index is assigned to each initial tool label. The precision priority values ​​for low-value tools, conventional tools, and high-value tools increase in that order. The precision priority index corresponding to the initial tool label for the pick-up and drop information is also obtained. Finally, the priority index, time urgency rate, and precision priority value are normalized and their values ​​are calculated using a weighted average formula. The cooperative priority value P is calculated. 协同 ;in, These are respectively represented as priority index, precision priority value, and time urgency rate; All are preset weighting factors; The process is divided into pick-up and place-up execution periods based on a pre-set time interval. The pick-up and place-up tools corresponding to the current pick-up and place-up execution period are obtained, and the coordination priority value of each pick-up and place-up tool is obtained. The tools are sorted in descending order according to the coordination priority value to obtain the coordination ranking of the pick-up and place-up tools. The tool transfer order is generated based on the coordination ranking.

[0050] The number of tools picked up and placed during the current pick-up and place execution period is counted. The number of tools picked up and placed is divided into multiple intervals. A corresponding transportation demand is set for each interval. The current number of tools picked up and placed is matched with each interval to obtain the corresponding transportation demand. The transportation demand corresponds to the number of AGV transport vehicles for transporting tools. The transportation demand and the tool transfer order are used as the corresponding transfer control strategy.

[0051] The user interaction module provides a user interface and obtains the user's tool handling information, and sends the tool handling information to the adaptive transfer control module.

[0052] Please see Figures 3-4 As the execution component for tool handling, the warehouse robotic arm operates according to the tool transfer sequence in the transfer control strategy. With preset positioning accuracy, it precisely grips the corresponding tool at the designated location, especially for high-value tools, where its higher operational precision prevents damage during handling. Furthermore, the robotic arm coordinates with the transport rhythm of the AGV (Automated Guided Vehicle) to complete the handling actions, ensuring a seamless transition of tools from storage to the transport vehicle. By operating in a priority-based manner, redundant actions in the handling process are reduced, improving the accuracy and efficiency of tool handling and providing stable support for subsequent transportation processes.

[0053] AGV transport vehicles execute transport operations based on transport control strategies generated by an adaptive transport control module. They allocate the corresponding number of vehicles according to the transport demand during the current pick-up and drop-off period, avoiding wasted or insufficient transport capacity. Simultaneously, they strictly adhere to the tool transport sequence, prioritizing the transport of high-value tools and time-sensitive pick-up and drop-off needs. By precisely matching the transport quantity and sequence, AGV transport vehicles ensure the timeliness and rationality of tool transport, reduce transport waiting time, improve overall tool flow efficiency, and avoid the risk of high-value tools being bumped or damaged due to disordered transport.

[0054] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent management of cutting tools, characterized in that, Includes the following steps: Step S1: Obtain basic tool data and tool storage data; perform initial analysis on the basic tool data to obtain initial tool labels; and associate the initial tool labels and tool storage data corresponding to each tool. Step S2: Identify the initial tool label. When the initial tool label is a high-value tool or a conventional tool, obtain the corresponding tool storage data and perform dynamic visual trigger analysis on the tool storage data to obtain a high-precision dynamic visual strategy. When the initial tool label corresponds to a low-value tool, mark the area corresponding to the low-value tool as a normal monitoring strategy. Step S3: Obtain the high-precision dynamic vision strategy corresponding to each tool, and adapt the acquisition resources based on the high-precision dynamic vision strategy; Step S4: Based on the high-precision dynamic vision strategy, perform image verification on the corresponding tool to obtain the tool verification status, and output the tool verification status as a status report.

2. The intelligent tool management method according to claim 1, characterized in that, The initial analysis of the tool's basic data is as follows: Based on the precision level of each tool, the corresponding cutting precision is obtained. The cutting precision is divided according to a preset precision interval to obtain multiple cutting precision intervals. A precision value is assigned to each cutting precision interval, and the median cutting precision of each cutting precision interval is obtained. The median cutting precision is then arranged in ascending order to obtain the median cutting precision ranking information. Based on the usage information of each tool, the corresponding tool usage frequency is obtained. Similarly, the corresponding usage frequency interval and frequency value are obtained. The precision value and frequency value of each tool are calculated and summed to obtain the tool value. The tool value is obtained from a preset value monitoring threshold. When the tool value is less than the value monitoring threshold, the tool's initial label is marked as a low-value tool. When the tool value is greater than the value monitoring threshold, the difference between the tool value and the value monitoring threshold is calculated to obtain the overflow value. The pre-designed overflow value threshold is obtained. When the overflow value is less than the overflow value threshold, the tool's initial label is marked as a regular tool. When the overflow value is greater than the overflow value threshold, the tool's initial label is marked as a high-value tool.

3. The intelligent tool management method according to claim 1, characterized in that, The tool storage data is subjected to dynamic visual trigger analysis, and the specific analysis is as follows: Based on the static vibration frequency data, impedance data, sealing air pressure data, and infrared radiation data stored in the tool; Based on the static vibration frequency, the measured static frequency and measured decay time corresponding to each tool are obtained. The standard frequency corresponding to the measured static frequency is obtained. The difference between the measured static frequency and the standard frequency is calculated to obtain the frequency difference. The ratio of the frequency difference to the standard frequency is calculated to obtain the measured frequency deviation rate. The standard decay time corresponding to the measured decay time is obtained. Similarly, the decay time deviation rate corresponding to the measured decay time is obtained; the micro-vibration anomaly value is calculated by substituting the measured frequency deviation rate and decay time deviation rate into the formula of the Euclidean distance model. The impedance change rate and impedance parameters of each tool are obtained based on the impedance data; The maximum impedance point, minimum impedance point, and average impedance value are obtained based on the impedance parameters. The impedance fluctuation value is calculated by the difference between the maximum and minimum impedance points. The impedance uniformity deviation value is calculated by the ratio of the impedance fluctuation value to the average impedance value. The impedance anomaly value is calculated by combining the impedance change rate and the impedance uniformity deviation value with the Sigmoid function. Based on the sealing air pressure data, the air pressure fluctuation value of each tool during the corresponding analysis period is obtained. The air pressure gradient change rate is calculated by the ratio of the air pressure fluctuation value to the duration of the corresponding analysis period. The air pressure gradient change rate is combined with the error function to calculate the air pressure gradient anomaly value. The radiation intensity and ambient temperature data corresponding to each tool are obtained from the infrared radiation data; the radiation intensity deviation rate is obtained by calculating the deviation between the radiation intensity and the corresponding preset radiation standard intensity; the tool temperature difference value is obtained based on the ambient temperature data and the temperature difference concentration area is obtained; the temperature difference concentration area is calculated by comparing the ratio of the temperature difference concentration area with the effective area corresponding to each tool in the database; the infrared radiation anomaly value is obtained by combining the radiation deviation rate and the temperature difference concentration with an exponential function.

4. The intelligent tool management method according to claim 3, characterized in that, The micro-vibration anomalies, impedance anomalies, air pressure gradient anomalies, and infrared radiation anomalies are normalized and their values ​​are taken. The normalized values ​​of each sub-anomaly are then substituted into the formula of the modified weighted Mahalanobis distance model to calculate the comprehensive anomaly value. The comprehensive anomaly value is analyzed based on the initial tool label to obtain a high-precision dynamic vision strategy.

5. The intelligent tool management method according to claim 4, characterized in that, The analysis of comprehensive outliers based on the initial tool label is as follows: A1: Identify the initial tool label for each tool. If the tool corresponds to a high-value tool, proceed to step A2; if the tool corresponds to a regular tool, proceed to step A3. A2: Obtain the comprehensive outlier values ​​of each high-value tool and perform high-value dynamic strategy analysis to obtain the high-value dynamic strategy; A3: Obtain the comprehensive outlier values ​​of each conventional tool and perform conventional dynamic strategy analysis to obtain the conventional dynamic strategy; A4: A high-precision dynamic vision strategy is obtained by combining high-value dynamic strategies and conventional dynamic strategies.

6. The intelligent tool management method according to claim 5, characterized in that, The specific process of step A2 is as follows: Based on the comprehensive anomaly value corresponding to the high-value tool, obtain the anomaly state of each sub-anomaly value, count the number of sub-anomaly values ​​whose anomaly state is abnormal to obtain the number of sub-states, when the number of sub-states is 1, the high-value dynamic strategy is low-precision acquisition; when the number of sub-states is 2-3, the high-value dynamic strategy is directional medium-precision acquisition; when the number of sub-states is 4, the high-value dynamic strategy is high-definition focused acquisition.

7. The intelligent tool management method according to claim 1, characterized in that, Step A3 specifically involves: obtaining the abnormal state of each sub-abnormal value based on the comprehensive abnormal value corresponding to the conventional tool; counting the number of sub-abnormal values ​​whose abnormal state is abnormal to obtain the number of sub-states; when the number of sub-states is 1, the conventional dynamic strategy is no adjustment required; when the number of sub-states is 2, the conventional dynamic strategy is directional low-precision acquisition; when the number of sub-states is 3, the conventional dynamic strategy is directional medium-precision acquisition; and when the number of sub-states is 4, the conventional dynamic strategy is high-definition focused acquisition.

8. An intelligent management system for cutting tools, characterized in that, It includes a data sensing module, a trigger acquisition control module, an adaptive transfer control module, and a user interaction module, so that the intelligent tool management system executes the intelligent tool management method as described in any one of claims 1-7.

9. The intelligent tool management system according to claim 8, characterized in that, The data sensing module acquires basic tool data and stored tool data through a pre-deployed sensor array, and sends the basic tool data and stored tool data to the tool information management module. The trigger acquisition control module is used to receive basic tool data and stored tool data, obtain a high-precision dynamic vision strategy based on the basic tool data and stored tool data, and input the high-precision dynamic vision strategy into the dynamic high-definition camera group for strategy execution. The adaptive transfer control module is used to receive tool pick-up and drop information, perform adaptive transfer analysis on the tool pick-up and drop information to obtain transfer control strategy, and perform adaptive transfer control based on the transfer control strategy. The user interaction module is used to provide a user interface and obtain the user's tool picking and placing information, and send the tool picking and placing information to the adaptive transfer control module.

10. The intelligent tool management system according to claim 9, characterized in that, The tool handling information is subjected to adaptive transfer analysis, and the specific analysis method is as follows: Based on the tool pick-up and drop information, the pick-up and drop priority and pick-up and drop time requirements are obtained. A priority index is set for each pick-up and drop priority, and the value of the priority index increases as the pick-up and drop priority increases. The remaining pick-up and drop time and the required pick-up and drop time are obtained through the pick-up and drop time requirements. The time urgency rate is calculated by the ratio of the remaining time and the required time. The initial tool label corresponding to the pick-up and drop tool is obtained, and the precision priority index corresponding to each tool initial label is obtained. The precision priority index corresponding to the tool initial label of the pick-up and drop tool is obtained. The priority index, time urgency rate and precision priority value are normalized and their values ​​are taken. The collaborative priority value is calculated using a weighted calculation formula. The process is divided into pick-up and place-up execution periods based on a pre-set time interval. The pick-up and place-up tools corresponding to the current pick-up and place-up execution period are obtained, and the coordination priority value of each pick-up and place-up tool is obtained. The tools are sorted in descending order according to the coordination priority value to obtain the coordination ranking of the pick-up and place-up tools. The tool transfer order is generated based on the coordination ranking. The number of tools picked up and placed during the current pick-up and place execution period is counted. The number of tools picked up and placed is divided into multiple intervals. A corresponding transportation demand is set for each interval. The current number of tools picked up and placed is matched with each interval to obtain the corresponding transportation demand. The transportation demand and the tool transfer order are used as the corresponding transfer control strategy.

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