Tool monitoring threshold value determination method, device and electronic device

Abstract

The application discloses a tool monitoring threshold determination method and device and electronic equipment, and belongs to the technical field of equipment monitoring. The method comprises the following steps: acquiring a machining feature of a reference tool and machining data corresponding to the machining feature; performing threshold calculation on the machining data to determine the correspondence between the machining feature and a monitoring threshold, so as to generate a monitoring model library; based on a target machining feature of a target tool, determining an initial monitoring threshold corresponding to the target machining feature in the monitoring model library; when the target tool is in a machining state, acquiring target machining data corresponding to multiple procedures of the target tool; and adjusting the initial monitoring threshold according to the number of times that the target machining data is greater than or less than the initial monitoring threshold, so as to form a final monitoring threshold.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of device monitoring, and particularly relates to a tool monitoring threshold determination method and device and electronic equipment. BACKGROUND

[0002] The setting of the tool monitoring threshold is a key factor affecting the accuracy of tool monitoring. For the machining process, the size and hardness of the workpiece blank, the running state of the machine tool, the use state of the tool, and the ambient temperature are all changing conditions. Changes in these parameters will directly affect the results of tool monitoring, so it is necessary to adjust the monitoring threshold in real time.

[0003] In the prior art, the initial threshold is manually set by the experience of the operator, and is manually adjusted for a long time and multiple times subsequently to finally obtain a monitoring threshold meeting the actual requirements. In the above method, the accuracy of the initial threshold is low, and the subsequent adjustment period is long and inefficient, resulting in high labor costs. SUMMARY

[0004] The purpose of the embodiments of the application is to provide a tool monitoring threshold determination method and device and electronic equipment, which can solve the problems of low accuracy of the initial threshold and long adjustment period in the prior art tool monitoring threshold determination technology.

[0005] In a first aspect, the embodiments of the application provide a tool monitoring threshold determination method, which comprises:

[0006] Obtaining the machining feature of a reference tool and machining data corresponding to the machining feature;

[0007] Performing threshold calculation on the machining data to determine the correspondence between the machining feature and the monitoring threshold, so as to generate a monitoring model library;

[0008] Based on the target machining feature of a target tool, determining an initial monitoring threshold corresponding to the target machining feature in the monitoring model library;

[0009] When the target tool is in a machining state, obtaining target machining data corresponding to multiple procedures of the target tool;

[0010] According to the number of times that the target machining data is greater than or less than the initial monitoring threshold, adjusting the initial monitoring threshold to form a final monitoring threshold.

[0011] In a second aspect, the embodiments of the application provide a tool monitoring threshold determination device, which comprises:

[0012] A first obtaining module is configured to obtain the machining feature of a reference tool and machining data corresponding to the machining feature;

[0013] generating a monitoring model library by performing threshold calculation on the processing data and determining the correspondence between the processing features and the monitoring thresholds;

[0014] a first determining module configured to determine an initial monitoring threshold corresponding to a target processing feature of a target tool in the monitoring model library based on the target processing feature of the target tool;

[0015] a second acquiring module configured to acquire target processing data corresponding to a plurality of procedures of the target tool when the target tool is in a processing state;

[0016] a second determining module configured to adjust the initial monitoring threshold to form a final monitoring threshold according to the number of times that the target processing data is greater than or less than the initial monitoring threshold.

[0017] In a third aspect, an electronic device is provided, which includes a processor and a memory. The memory stores programs or instructions executable on the processor. When the programs or instructions are executed by the processor, the steps of the tool monitoring threshold determination method according to the first aspect are implemented.

[0018] In a fourth aspect, a readable storage medium is provided, which stores programs or instructions. When the programs or instructions are executed by a processor, the steps of the tool monitoring threshold determination method according to the first aspect are implemented.

[0019] In the embodiments of the present application, a monitoring model library is established, which includes the correspondence between the processing features of a tool and the monitoring thresholds. When a new tool needs to be set with a monitoring threshold, the corresponding monitoring threshold in the monitoring model library is selected as an initial monitoring threshold according to the processing feature of the new tool, which greatly improves the accuracy of the monitoring threshold. Thus, the workload required for adjusting the initial monitoring threshold is reduced, the adjustment period is shortened, and the efficiency of determining the final monitoring threshold is improved. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of the tool monitoring threshold determination method provided by the embodiments of the present application is provided.

[0021] Figure 2 A flowchart of the tool monitoring threshold determination method provided by the embodiments of the present application is provided.

[0022] Figure 3 A structural diagram of the tool monitoring threshold determination device provided by the embodiments of the present application is provided.

[0023] Figure 4 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0025] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually a class and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents a "or" relationship between the front and rear associated objects.

[0026] The method provided by the embodiments of the present application will be described in detail below with reference to the drawings, through specific embodiments and their application scenarios.

[0027] As shown in Figure 1 The tool monitoring threshold determination method provided by the embodiments of the present application includes the following steps:

[0028] Step S1, acquiring a machining feature of a reference tool and machining data corresponding to the machining feature,

[0029] It can be understood that the machining feature of the reference tool determines the machining data of the machining process of the reference tool. The machining feature includes machining process feature, tool feature, material feature, and cooling feature. The machining process feature includes machining type, machining procedure, spindle speed, and feed speed. The machining type includes milling, drilling, tapping, reaming, boring, chamfering, and turning. The machining procedure includes rough machining, semi-finishing machining, and finishing machining. The spindle speed can be a program specified speed, and the feed speed can be a program specified speed.

[0030] The tool feature includes tool type, tool material, tool specification, and tool life. The tool type includes integral type and indexable type. The tool material includes tool body material and coating material. The tool life can be the maximum tool life set by the tool.

[0031] The material features include workpiece material and workpiece hardness, and the workpiece material includes cast iron, aluminum, steel, etc. The workpiece hardness includes a hardness range of the workpiece. The cooling features include a cooling mode and a cooling liquid type, and the cooling mode includes internal cooling, external cooling, no cooling, mixed cooling, etc. The cooling liquid type includes water-based, emulsified, and oil-based. The machining data of the reference tool can be a tool spindle load.

[0032] In step S2, threshold calculation is performed on the machining data to determine a correspondence between the machining features and the monitoring threshold values, so as to generate a monitoring model library.

[0033] The machining data corresponding to different machining features of the reference tool is subjected to threshold calculation, the monitoring threshold value range corresponding to different machining features of the reference tool is obtained, a correspondence between the machining features and the monitoring threshold value range is formed, and thus the monitoring model library is formed. The monitoring model library includes the machining features and the monitoring threshold value range.

[0034] It should be noted that, in the process of threshold calculation on the machining data, the overall monitoring threshold value range of the machining data can be calculated, or only representative data such as average data and extreme values in the machining data can be subjected to threshold range calculation. Analysis of the representative data can reduce the amount of data to be analyzed and improve efficiency.

[0035] In step S3, based on target machining features of a target tool, an initial monitoring threshold value corresponding to the target machining features is determined in the monitoring model library.

[0036] When a new tool, i.e., a target tool, needs to be subjected to monitoring threshold value setting, target machining features of the target tool are obtained, the monitoring threshold value range corresponding to the target machining features is found in the monitoring model library, and a monitoring threshold value is selected from the monitoring threshold value range corresponding to the target machining features as an initial monitoring threshold value. Similarly, when the monitoring threshold value of the target tool is selected, the overall monitoring threshold value of the machining data of the target tool can be selected, or representative data in the machining data of the target tool can be selected.

[0037] According to the correspondence between the machining features and the monitoring threshold value range in the monitoring model library, the initial monitoring threshold value of the new tool is selected. Compared with the way of selecting the initial monitoring threshold value range by the experience of an operator, the method for determining the monitoring threshold value of the tool provided in the embodiments of the present application greatly improves the accuracy of the initial monitoring threshold value, thereby reducing the workload required for subsequent adjustment of the initial monitoring threshold value and shortening the adjustment period.

[0038] In step S4, when the target tool is in a machining state, target machining data corresponding to multiple procedures of the target tool is obtained.

[0039] It can be understood that in actual work process, there will be accidental factors, so it is necessary to comprehensively analyze the machining data of multiple process procedures of the tool, and try to reduce the influence of accidental factors, so as to determine the final monitoring threshold of the tool.

[0040] For example, the machining data of rough machining process, semi-finishing machining process and finishing machining process of the target tool is obtained. In the multiple machining processes of the target tool, all machining data can be selected as the corresponding target machining data, that is, the whole machining data is analyzed to determine the overall monitoring threshold, or representative data in the machining data corresponding to each machining process can be selected as the target machining data.

[0041] Step S5, adjusting the initial monitoring threshold according to the number of times that the target machining data is greater than or less than the initial monitoring threshold to form a final monitoring threshold.

[0042] It can be understood that if the number of times that the target machining data corresponding to multiple machining processes is greater than or less than the initial monitoring threshold is too much, it means that the monitoring effect of the initial monitoring threshold is poor, and the initial monitoring threshold needs to be adjusted. If the number of times that the target machining data is greater than the initial monitoring threshold is too much, the initial monitoring threshold can be increased to form a new monitoring threshold; if the number of times that the target machining data is less than the initial monitoring threshold is too much, the initial monitoring threshold can be reduced to form a new monitoring threshold. In the machining process of the target tool, the target machining data and the initial monitoring threshold or the new monitoring threshold are compared and analyzed every preset time period, and the initial monitoring threshold or the new monitoring threshold is adjusted. The adjustment process is repeated, and finally the boundary threshold range meeting the actual requirements can be obtained.

[0043] Through the above steps, a monitoring model library is established, which includes the corresponding relationship between the machining characteristics of the tool and the monitoring threshold. When a new tool needs to be set with a monitoring threshold, the corresponding monitoring threshold in the monitoring model library is selected as the initial monitoring threshold according to the machining characteristics of the new tool, which greatly improves the accuracy of the monitoring threshold. Thus, the workload required for adjusting the initial monitoring threshold is reduced, the adjustment period is shortened, and the efficiency of determining the final monitoring threshold is improved.

[0044] Optionally, the machining data includes an empty sample curve of the reference tool in an empty state and multiple machining sample curves of the reference tool in a machining state.

[0045] The no-load sample curve represents a curve formed by the tool spindle load data when the reference tool is in an idle state without machining a workpiece. The machining sample curve represents a curve formed by the tool spindle load data corresponding to different machining processes when the reference tool is machining a workpiece. In addition, the no-load sample curve and the machining sample curve can be subjected to data filtering to reduce the influence of irrelevant data and further improve the accuracy of the monitoring threshold obtained by threshold calculation. In the following description, the no-load sample curve and the machining sample curve are curves after data filtering.

[0046] Based on the above embodiment, in step S2, threshold calculation is performed on the machining data to determine the correspondence between the machining features and the monitoring threshold, including:

[0047] The first machining data corresponding to each of the plurality of machining sample curves at the same time is obtained, and the reference sample curve is determined according to the first machining data,

[0048] The data corresponding to the target time of the reference sample curve is the average of the data corresponding to the target time of each of the plurality of machining sample curves, and the target time is any time. For example, the data of three sample curves at the first time are a, b, and c, respectively, and the data of the reference sample curve corresponding to the three sample curves at the first time is (a+b+c) / 3.

[0049] Based on the reference sample curve and the no-load sample curve, a target value distance coefficient of the reference tool is determined, the target value distance coefficient is used to represent the difference between the target data of the reference tool in the machining state and the target data of the reference tool in the no-load state, and the target data includes average data, maximum data, and minimum data,

[0050] It can be understood that the no-load sample curve represents the data of the reference tool in the no-load state, and therefore the no-load sample curve does not change. However, the data of the plurality of machining sample curves corresponding to the reference tool in different machining processes and different degrees of wear is certainly different, and the selection of the monitoring threshold is also different. Therefore, in the process of establishing the monitoring model library, it is necessary to establish the correspondence between the target value distance coefficient of the tool and the monitoring threshold.

[0051] Different target data is selected, and the target value distance coefficient is also different. When the target data is average data, the target data of the reference sample curve is the average of all data of the reference sample curve, and the target data of the no-load sample curve is the average of all data of the no-load sample curve. Similarly, when the target data is maximum data or minimum data, the target data corresponding to the reference sample curve is the maximum value or the minimum value of all data, and the no-load sample curve is the same.

[0052] determining an upper boundary range and a lower boundary range of the target data when the reference tool is in the machining state based on the idle sample curve, the reference sample curve, the plurality of machining sample curves and a preset safety coefficient,

[0053] It can be understood that a boundary range of one data includes upper and lower sides. When the target data is average data, the boundary range includes an upper boundary range and a lower boundary range. When the target data is maximum data, only the upper boundary range of the boundary range of the target data is considered, and the lower boundary range does not affect the maximum data. Similarly, when the target data is minimum data, only the lower boundary range of the boundary range of the target data is considered.

[0054] determining a target value monitoring coefficient when the reference tool is in the machining state based on the upper boundary range and the lower boundary range of the target data and the target value distance coefficient, the idle sample curve and the reference sample curve, and a monitoring threshold range corresponding to the target value monitoring coefficient.

[0055] The monitoring model library includes machining features, the relationship between the target value distance coefficient and the target value monitoring coefficient and the relationship between the target value monitoring coefficient and the monitoring threshold range under different machining features. When a new tool is monitored and a monitoring threshold is set, only the machining feature and the target value distance coefficient of the new tool need to be obtained, and an initial monitoring threshold can be accurately set.

[0056] Optionally, the target value distance coefficient of the reference tool is determined based on the reference sample curve and the idle sample curve, and the target value distance coefficient is determined by:

[0057] obtaining a first target value corresponding to target data of the reference sample curve and a second target value corresponding to target data of the idle sample curve,

[0058] The first target value represents the target data of the reference sample curve, the second target value represents the target data of the idle sample curve, and the types of the target data corresponding to the first target value and the second target value are consistent. The target data can be average data, maximum data or minimum data.

[0059] The target value distance coefficient is determined based on the first target value and the second target value. The target value distance coefficient is a ratio of a first difference to the first target value, and the first difference is a difference between the first target value and the second target value.

[0060] The calculation formula of the target value distance coefficient is as follows:

[0061] xecm = (csecm - nsecm) / csecm

[0062] Wherein, xecm represents a target value distance coefficient, csecm represents a first target value of a reference sample curve, and nsecm represents a second target value of an empty sample curve.

[0063] Through the above steps, the difference between the target data of the tool in the machining state and the target data of the reference tool in the empty state is determined.

[0064] Optionally, the upper boundary range and the lower boundary range of the target data of the reference tool in the machining state are determined based on the empty sample curve, the reference sample curve, the plurality of machining sample curves, and a preset safety coefficient, and the method comprises the following steps.

[0065] Obtaining the maximum target data and the minimum target data of each machining sample curve,

[0066] Taking three machining sample curves as an example, the target data corresponding to each of the three machining sample curves is obtained, and the maximum value and the minimum value of the three target data are determined as the maximum target data and the minimum target data, respectively.

[0067] The upper boundary range is determined based on the maximum target data, the preset safety coefficient, the first target value, and the second target value, and the upper boundary range comprises a maximum upper boundary and a minimum upper boundary,

[0068] The calculation formula of the minimum upper boundary is as follows:

[0069] ecm+LV01 = border ECM max + δ (csecm - nsecm) (1)

[0070] The calculation formula of the maximum upper boundary is as follows:

[0071] ecm+LV100 = csecm + 2 (csecm - nsecm)

[0072] Wherein, ecm+LV01 represents the minimum upper boundary, border ECM max represents the maximum target data, δ represents the preset safety coefficient, the preset safety coefficient can be 0.05, csecm represents the first target value, nsecm represents the second target value, and ecm+LV100 represents the maximum upper boundary.

[0073] The lower boundary range is determined based on the minimum target data, the preset safety coefficient, the first target value, and the second target value, and the lower boundary range comprises a maximum lower boundary and a minimum lower boundary,

[0074] The formula for calculating the maximum lower boundary is as follows:

[0075] ecm-LV01=border ECM min-δ(csecm-nsecm) (2)

[0076] The formula for calculating the minimum lower boundary is as follows:

[0077] ecm-LV100=0.95necm

[0078] Where ecm-LV01 represents the maximum lower boundary, border ECM min represents the minimum target data, δ represents the preset safety factor, which can be 0.05, csecm represents the first target value, and nsecm represents the second target value. ecm-LV100 represents the minimum lower boundary.

[0079] Optionally, if the target data is average data,

[0080] The determination of the target value monitoring coefficient and the monitoring threshold range corresponding to the target value monitoring coefficient based on the upper and lower boundary ranges of the target data when the reference tool is in the machining state, the target value distance coefficient, the no-load sample curve, and the baseline sample curve includes:

[0081] If the minimum upper boundary of the monitoring value is equal to the minimum upper boundary, a first target value monitoring coefficient is determined. The minimum upper boundary of the monitoring value is determined by the first target value, the second target value, and the first target value monitoring coefficient.

[0082] The formula for calculating the minimum upper boundary of the monitoring is as follows:

[0083] ecm+LV01=csecm+yecm1(csecm-nsecm) (3)

[0084] Wherein, ecm+LV01 represents the minimum upper boundary of the monitoring, csecm represents the first target value, nsecm represents the second target value, and yecm1 represents the coefficient of the first target value.

[0085] The value of yecm1 can be determined based on the fact that the upper boundary of the monitoring system and the minimum upper boundary are equal, i.e., based on the above formulas (1) and (3).

[0086] If the maximum lower boundary of the monitoring value is equal to the maximum lower boundary, a second target value monitoring coefficient is determined. The maximum lower boundary of the monitoring value is determined by the first target value, the second target value, and the second target value monitoring coefficient.

[0087] The calculation formula of the monitoring maximum lower boundary is as follows:

[0088] ecm-LV01=csecm-yecm2(csecm-nsecm) (4)

[0089] Wherein, ecm-LV01 represents the monitoring maximum lower boundary, csecm represents the first target value, nsecm represents the second target value, and yecm2 represents the second target value coefficient.

[0090] The value of yecm2 can be determined based on the monitoring maximum lower boundary being equal to the maximum lower boundary, that is, based on the above formula (2) and formula (4).

[0091] The average of the first target value monitoring coefficient and the second target value monitoring coefficient is obtained to determine the target value monitoring coefficient,

[0092] The target value monitoring coefficient yecm=(yecm1+yecm2) / 2.

[0093] Based on the target value monitoring coefficient, the first target value, and the second target value, the standard minimum upper boundary and the standard maximum lower boundary are determined,

[0094] The target value monitoring coefficient is substituted into the formula (3) to obtain the standard minimum upper boundary. The target value monitoring coefficient is substituted into the formula (4) to obtain the standard maximum lower boundary.

[0095] The standard minimum upper boundary and the maximum upper boundary are determined according to a linear relationship and a preset level to determine a plurality of upper boundary monitoring thresholds of different levels,

[0096] Taking the preset level 100 as an example, the standard minimum upper boundary is the upper boundary monitoring threshold of level 1, the maximum upper boundary is the upper boundary monitoring threshold of level 100, and the remaining 98 upper boundary monitoring thresholds of levels 2-99 are determined according to a linear relationship.

[0097] The standard maximum lower boundary and the minimum lower boundary are determined according to a linear relationship and a preset level to determine a plurality of lower boundary monitoring thresholds of different levels, the number of the lower boundary monitoring thresholds is consistent with and one-to-one corresponds to the number of the upper boundary monitoring thresholds, and the monitoring threshold range includes the upper boundary monitoring threshold and the lower boundary monitoring threshold.

[0098] Taking the preset level 100 as an example, the standard maximum lower boundary is the lower boundary monitoring threshold of level 1, the minimum lower boundary is the lower boundary monitoring threshold of level 100, and the remaining 98 lower boundary monitoring thresholds of levels 2-99 are determined according to a linear relationship.

[0099] It should be noted that when setting the initial monitoring threshold for a new tool, such as the target tool, the upper boundary monitoring threshold of the preset level and the lower boundary monitoring threshold of the preset level can be selected from the upper boundary monitoring threshold of the level 1-100 and the lower boundary monitoring threshold of the level 1-100, that is, the levels of the upper and lower boundary monitoring thresholds of the target tool are consistent at the same time.

[0100] Optionally, in the case of the target data being minimum data,

[0101] The target value monitoring coefficient and the monitoring threshold range corresponding to the target value monitoring coefficient of the reference tool in the machining state are determined based on the upper boundary range and the lower boundary range of the target data when the reference tool is in the machining state, the target value distance coefficient, the idle sample curve and the reference sample curve, and the target value monitoring coefficient is determined based on the first target value, the second target value and the target value distance coefficient.

[0102] In the case of monitoring the maximum lower boundary and the maximum lower boundary being equal, the target value monitoring coefficient is determined, and the monitoring maximum lower boundary is determined by the first target value, the second target value and the target value monitoring coefficient.

[0103] A plurality of lower boundary monitoring thresholds of different levels are determined according to a linear relationship and a preset level based on the maximum lower boundary and the minimum lower boundary, and the monitoring threshold range includes the lower boundary monitoring threshold.

[0104] In the case of the target data being minimum data, the influence of the upper boundary monitoring threshold can be excluded, only the lower boundary monitoring threshold is calculated, and the data processing amount is reduced. It should be noted that in the case of the target data being minimum data, the calculation method of the lower boundary monitoring threshold is similar to the calculation process when the target data is average data, which will not be described here.

[0105] Optionally, in the case of the target data being maximum data,

[0106] The target value monitoring coefficient and the monitoring threshold range corresponding to the target value monitoring coefficient of the reference tool in the machining state are determined based on the upper boundary range and the lower boundary range of the target data when the reference tool is in the machining state, the target value distance coefficient, the idle sample curve and the reference sample curve, and the target value monitoring coefficient is determined based on the first target value, the second target value and the target value distance coefficient.

[0107] In the case of monitoring the minimum upper boundary and the minimum upper boundary being equal, the target value monitoring coefficient is determined, and the monitoring minimum upper boundary is determined by the first target value, the second target value and the target value distance coefficient.

[0108] The maximum upper boundary and the minimum upper boundary are determined as a plurality of upper boundary monitoring thresholds of different levels in a linear relationship and a preset level, and the monitoring threshold range includes the upper boundary monitoring threshold.

[0109] In the case of the target data being maximum data, the influence of the lower boundary monitoring threshold can be excluded, and only the upper boundary monitoring threshold is calculated, further reducing the data processing amount. It should be noted that in the case of the target data being maximum data, the calculation method of the upper boundary monitoring threshold is similar to the calculation process when the target data is average data, which will not be described here.

[0110] Optionally, the threshold calculation on the processing data to determine the correspondence between the processing features and the monitoring threshold comprises:

[0111] A plurality of value ranges of the target value distance coefficient are obtained,

[0112] The target value distance coefficient represents the difference between the target data when the tool is in a processing state and the target data when the tool is in an idle state, and the value range is (0, 1). For example, three value ranges are selected from (0, 1), [0.01, 0.3), [0.3, 0.5), and [0.5, 1). If the target value distance coefficient is less than 0.01, it indicates that the data feature relationship is not obvious, and therefore it is not necessary to consider.

[0113] Based on the target value distance coefficient and the target value monitoring coefficient, a target value monitoring coefficient corresponding to each target value distance coefficient in a target value range is determined, and an average monitoring coefficient corresponding to the target value range is determined according to the target value monitoring coefficient corresponding to each target value distance coefficient. The target value range is any one of the plurality of value ranges.

[0114] It should be noted that the average monitoring coefficient is the average value of the target value monitoring coefficient corresponding to each target value distance coefficient in the target value range. When setting the initial monitoring threshold for a large number of new tools, only the processing features and the target value distance coefficient of the tool need to be obtained, the corresponding average monitoring coefficient is determined according to the value range of the target value distance coefficient, and the monitoring threshold range is obtained according to the average monitoring coefficient, without calculating the target value monitoring coefficient corresponding to each tool in the large number of new tools, further reducing the data processing amount.

[0115] Optionally, as Figure 2 As shown in FIG. 3, step S3, based on the target processing features of the target tool, an initial monitoring threshold corresponding to the target processing features is determined in the monitoring model library, comprising:

[0116] Step S31, obtaining first processing data corresponding to the target processing features,

[0117] The first processing data includes a no-load sample curve of the target tool and a plurality of processing sample curves. The no-load sample curve is obtained when the target tool is in a no-load state, and the processing sample curves are obtained during the first processing of the target tool. It should be noted that, based on the target processing feature, the value range of the target value distance coefficient corresponding to the target processing feature can be determined in the monitoring model library.

[0118] In step S32, the target value distance coefficient of the target tool is determined based on the first processing data.

[0119] The specific steps of obtaining the target value distance coefficient are consistent with the above steps, and will not be described here.

[0120] In step S33, the average monitoring coefficient of the target tool is determined in the monitoring model library based on the target value distance coefficient of the target tool.

[0121] According to the value range corresponding to the target processing feature and the target value distance coefficient of the target tool, the average monitoring coefficient of the target tool can be determined.

[0122] In step S34, the monitoring threshold range of the target tool is determined based on the average monitoring coefficient of the target tool and the target processing data.

[0123] The process of determining the corresponding monitoring threshold range according to the average monitoring coefficient and the target processing data has been described above, and will not be described here. It should be noted that in actual application, the average data, the maximum data and the minimum data of the target tool can be monitored at the same time, and the monitoring threshold range corresponding to each of the three target data types is obtained, and each monitoring threshold range includes a plurality of levels.

[0124] In step S35, the initial monitoring threshold of the target data of the target tool is determined based on the preset initial level and the monitoring threshold range.

[0125] For example, the monitoring threshold range has 100 levels, and the preset initial level can be 30. The upper and lower boundary values corresponding to the level 30 are taken as the initial monitoring threshold.

[0126] Optionally, in step S4, when the target tool is in a processing state, a plurality of target processing data corresponding to a plurality of processes of the target tool is obtained, including:

[0127] When the target tool is in a processing state, a plurality of processing data segments corresponding to a plurality of processes of the target tool are obtained.

[0128] After setting the initial monitoring threshold for the target tool, the actual machining process of the target tool is monitored based on the initial monitoring threshold. A large number of machining data curves corresponding to the actual machining process of the target tool are obtained. The large number of machining data is compared and analyzed based on the initial monitoring threshold.

[0129] The target data corresponding to each of the plurality of machining data segments is determined as the target machining data,

[0130] The target data in each machining data segment, that is, only the average data, maximum data, and minimum data of the target tool are monitored, and only the average data, maximum data, and minimum data are compared with the corresponding average data monitoring threshold, maximum data monitoring threshold, and minimum data monitoring threshold. The data amount to be monitored is greatly reduced by filtering out redundant data.

[0131] Optionally, in step S5, the initial monitoring threshold is adjusted according to the number of times that the target machining data is greater than or less than the initial monitoring threshold to form a final monitoring threshold, comprising:

[0132] In a case where the number of times that the plurality of target machining data exceeds the initial monitoring threshold reaches a first preset number of times, an average of the target machining data corresponding to each of the first preset number of times is obtained,

[0133] The average of the target machining data corresponding to each of the first preset number of times is determined as the final monitoring threshold of the target tool.

[0134] It can be understood that for different target data, the target machining data exceeding the initial monitoring threshold can be greater than or less than. When the number of times that the target machining data exceeds the corresponding initial monitoring threshold reaches a first preset number of times, it indicates that the initial monitoring threshold is not accurate enough and cannot play a monitoring role. Therefore, the target machining data of the target tool each time exceeding the corresponding initial monitoring threshold is recorded, and a first target average of the recorded target machining data is calculated. A first target level is determined according to the first target average, and the final monitoring threshold corresponding to the first target level is greater than the first target average and closest to the first target average.

[0135] In a case where the number of times that the plurality of target machining data does not exceed the initial monitoring threshold reaches a second preset number of times, an average of the target machining data corresponding to each of the second preset number of times is obtained,

[0136] The average of the target machining data corresponding to each of the second preset number of times is determined as the final monitoring threshold of the target tool.

[0137] Similarly, if the target processing data does not exceed the initial monitoring threshold value for the second preset number of times, it indicates that the initial monitoring threshold value is set too widely and cannot effectively monitor. Based on the average of the target processing data corresponding to each of the second preset number of times, a second target level is determined. Optionally, the level difference between the second target level and the initial level corresponding to the initial monitoring threshold value needs to be greater than a preset minimum level difference. When the level difference between the second target level and the initial level is less than the preset minimum level difference, the level obtained by adding the preset minimum level difference to the initial level is taken as a new second target level, and the monitoring threshold value corresponding to the new second target level is the final monitoring threshold value.

[0138] The tool monitoring threshold value determination method provided by the embodiments of the present application can be executed by a tool monitoring threshold value determination device. In the embodiments of the present application, the tool monitoring threshold value determination method is executed by the tool monitoring threshold value determination device as an example, and the tool monitoring threshold value determination device is described in combination with the accompanying drawings. Figure 3 The tool monitoring threshold value determination device 300 provided by the embodiments of the present application is described. The device comprises:

[0139] The first acquisition module 301 is configured to acquire a processing feature of a reference tool and processing data corresponding to the processing feature;

[0140] The generation module 302 is configured to perform threshold value calculation on the processing data, determine the correspondence between the processing feature and the monitoring threshold value, and generate a monitoring model library;

[0141] The first determination module 303 is configured to determine an initial monitoring threshold value corresponding to a target processing feature of a target tool in the monitoring model library based on the target processing feature;

[0142] The second acquisition module 304 is configured to acquire target processing data corresponding to a plurality of working procedures of the target tool when the target tool is in a processing state;

[0143] The second determination module 305 is configured to adjust the initial monitoring threshold value according to the number of times that the target processing data is greater than or less than the initial monitoring threshold value, to form a final monitoring threshold value.

[0144] Optionally, the processing data comprises an idle sample curve of the reference tool in an idle state and a plurality of processing sample curves of the reference tool in a processing state;

[0145] The generation module is further configured to:

[0146] acquire first processing data corresponding to each of the plurality of processing sample curves at the same time, and determine a reference sample curve based on the first processing data;

[0147] determine a target value distance coefficient of the reference tool based on the reference sample curve and the idle sample curve, the target value distance coefficient being used to represent a difference degree between target data of the reference tool in a machining state and target data of the reference tool in an idle state, the target data including average data, maximum data and minimum data;

[0148] determine an upper boundary range and a lower boundary range of the target data of the reference tool in the machining state based on the idle sample curve, the reference sample curve, the plurality of machining sample curves and a preset safety coefficient;

[0149] determine a target value monitoring coefficient of the reference tool in the machining state and a monitoring threshold range corresponding to the target value monitoring coefficient based on the upper boundary range and the lower boundary range of the target data, the target value distance coefficient, the idle sample curve and the reference sample curve.

[0150] Optionally, the generating module is further configured to:

[0151] obtain a first target value corresponding to target data of the reference sample curve and a second target value corresponding to target data of the idle sample curve;

[0152] determine the target value distance coefficient based on the first target value and the second target value, the target value distance coefficient being a ratio of a first difference value to the first target value, the first difference value being a difference between the first target value and the second target value.

[0153] Optionally, the generating module is further configured to:

[0154] obtain maximum target data and minimum target data of each machining sample curve;

[0155] determine the upper boundary range based on the maximum target data, the preset safety coefficient, the first target value and the second target value, the upper boundary range including a maximum upper boundary and a minimum upper boundary;

[0156] determine the lower boundary range based on the minimum target data, the preset safety coefficient, the first target value and the second target value, the lower boundary range including a maximum lower boundary and a minimum lower boundary.

[0157] Optionally, in a case where the target data is average data,

[0158] the generating module is further configured to:

[0159] determining a first target value monitoring coefficient in a case of monitoring a minimum upper limit and the minimum upper limit being equal, the monitoring minimum upper limit being determined by the first target value, the second target value and the first target value coefficient;

[0160] determining a second target value monitoring coefficient in a case of monitoring a maximum lower limit and the maximum lower limit being equal, the monitoring maximum lower limit being determined by the first target value, the second target value and the second target value monitoring coefficient;

[0161] obtaining an average of the first target value monitoring coefficient and the second target value monitoring coefficient to determine the target value monitoring coefficient;

[0162] determining a standard minimum upper limit and a standard maximum lower limit based on the target value monitoring coefficient, the first target value and the second target value;

[0163] determining a plurality of upper limit monitoring thresholds of different levels according to a linear relationship and preset levels based on the standard minimum upper limit and the maximum upper limit;

[0164] determining a plurality of lower limit monitoring thresholds of different levels according to a linear relationship and preset levels based on the standard maximum lower limit and the minimum lower limit, the number of the lower limit monitoring thresholds being consistent with and one-to-one corresponding to the number of the upper limit monitoring thresholds, the monitoring threshold range including the upper limit monitoring thresholds and the lower limit monitoring thresholds.

[0165] Optionally, in a case of the target data being minimum data,

[0166] The generating module is further configured to:

[0167] determining the target value monitoring coefficient in a case of monitoring a maximum lower limit and the maximum lower limit being equal, the monitoring maximum lower limit being determined by the first target value, the second target value and the target value monitoring coefficient;

[0168] determining a plurality of lower limit monitoring thresholds of different levels according to a linear relationship and preset levels based on the maximum lower limit and the minimum lower limit, the monitoring threshold range including the lower limit monitoring thresholds.

[0169] Optionally, in a case of the target data being maximum data,

[0170] The generating module is further configured to:

[0171] determining the target value monitoring coefficient in a case of monitoring a minimum upper limit and the minimum upper limit being equal, the monitoring minimum upper limit being determined by the first target value, the second target value and the target value monitoring coefficient;

[0172] determining a plurality of upper boundary monitoring thresholds of different levels according to the maximum upper boundary and the minimum upper boundary in a linear relationship and preset levels, wherein the monitoring thresholds range includes the upper boundary monitoring thresholds.

[0173] Optionally, the generation module is further configured to:

[0174] obtain a plurality of value ranges of the target value distance coefficient;

[0175] determine a target value monitoring coefficient corresponding to each target value distance coefficient in a target value range based on the target value distance coefficient and the target value monitoring coefficient, and determine an average monitoring coefficient corresponding to the target value range according to the target value monitoring coefficient corresponding to each target value distance coefficient, wherein the target value range is any one of the plurality of value ranges.

[0176] Optionally, the first determination module is further configured to:

[0177] obtain first machining data corresponding to the target machining feature;

[0178] determine a target value distance coefficient of the target tool based on the first machining data;

[0179] determine an average monitoring coefficient of the target tool in the monitoring model library based on the target value distance coefficient of the target tool;

[0180] determine a monitoring threshold range of the target tool based on the average monitoring coefficient of the target tool and the target machining data;

[0181] determine an initial monitoring threshold of target data of the target tool based on a preset initial level and the monitoring threshold range.

[0182] Optionally, the second acquisition module is further configured to:

[0183] when the target tool is in a machining state, obtain machining data segments corresponding to a plurality of processes of the target tool respectively;

[0184] determine the target data corresponding to the plurality of machining data segments respectively as the target machining data.

[0185] Optionally, the second determination module is further configured to:

[0186] when a number of times that a plurality of the target machining data exceeds the initial monitoring threshold reaches a first preset number of times, obtain an average number of the target machining data corresponding to each time in the first preset number of times,

[0187] determine the average of the target machining data corresponding to each of the second preset number of times as the final monitoring threshold of the target tool.

[0188] In a case where the number of times that the plurality of target machining data does not exceed the initial monitoring threshold reaches a second preset number of times, obtain the average of the target machining data corresponding to each of the second preset number of times,

[0189] determine the average of the target machining data corresponding to each of the second preset number of times as the final monitoring threshold of the target tool.

[0190] The device for determining the monitoring threshold of the tool provided in the embodiments of the present application can accurately determine the initial monitoring threshold of the tool by establishing the monitoring model library, and subsequently compare the initial monitoring threshold with the actual machining data of the tool to determine the final monitoring threshold.

[0191] It should be noted that the device for determining the monitoring threshold of the tool provided in the embodiments of the present application can implement all the technical processes of the method for determining the monitoring threshold of the tool and achieve the same technical effects. To avoid repetition, no further description is given here.

[0192] The device in the embodiments of the present application can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other device other than a terminal. For example, the electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a Mobile Internet Device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc. The non-mobile electronic device can also be a server, a Network Attached Storage (NAS), a personal computer (PC), a television (TV), a teller machine, or a self-service machine, etc. The embodiments of the present application are not limited in this regard.

[0193] Optionally, as Figure 4As shown, the electronic device 400 includes a processor 401 and a memory 402. The memory 402 stores programs or instructions executable by the processor 401. When the programs or instructions are executed by the processor 401, the programs or instructions implement each step of the method for determining a monitoring threshold of a tool according to the embodiments of the present application, and achieve the same technical effects. To avoid repetition, details are not described herein.

[0194] It should be noted that the electronic device in the embodiments of the present application includes the mobile electronic device and the non-mobile electronic device.

[0195] The embodiments of the present application further provide a readable storage medium. The readable storage medium stores programs or instructions. When the programs or instructions are executed by a processor, the programs or instructions implement each process of the method for determining a monitoring threshold of a tool according to the embodiments of the present application, and achieve the same technical effects. To avoid repetition, details are not described herein.

[0196] The processor is the processor of the electronic device described in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0197] It should be noted that, in this document, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus including the element. In addition, it should be pointed out that the scope of the methods and apparatuses in the embodiments of the present application is not limited to the order of functions shown or discussed, but can also include functions performed substantially simultaneously or in reverse order, for example, the described method can be performed in an order different from that described, and various steps can be added, omitted or combined. In addition, the features described with reference to some examples can be combined in other examples.

[0198] Those skilled in the art can clearly understand the above-mentioned embodiment method can be realized by means of software and the necessary general hardware platform, of course, also can be through hardware, but in many cases the former is the better embodiment. Based on such understanding, the technical solutions of the present application essentially or say the part of the prior art to make contributions can be embodied in the form of computer software products, the computer software product is stored in a storage medium (such as ROM / RAM, magnetic disc, optical disc), including a number of instructions to make a terminal (may be a mobile phone, computer, server, air conditioner, or network equipment, etc.) executes the method described in various embodiments of the present application.

[0199] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited to this, any skilled in the art within the scope of the technology disclosed in the present application, can easily think of changes or replacement, should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for determining the monitoring threshold of a cutting tool, characterized in that, The method includes: Obtain the machining features of the reference tool and the machining data corresponding to the machining features; Threshold calculations are performed on the processing data to determine the correspondence between the processing features and the monitoring thresholds, so as to generate a monitoring model library; Based on the target machining characteristics of the target tool, an initial monitoring threshold corresponding to the target machining characteristics is determined in the monitoring model library; When the target tool is in the machining state, acquire target machining data corresponding to multiple operations of the target tool; The initial monitoring threshold is adjusted based on the number of times the target processing data is greater than or less than the initial monitoring threshold to form the final monitoring threshold; The machining data includes an unloaded sample curve of the reference tool in an unloaded state and multiple machining sample curves of the reference tool in a machining state. The step of calculating a threshold on the processing data to determine the correspondence between the processing features and the monitoring threshold includes: Obtain the first processing data corresponding to each of the multiple processing sample curves at the same time, and determine the benchmark sample curve based on the first processing data; Based on the benchmark sample curve and the no-load sample curve, the target value distance coefficient of the reference tool is determined. The target value distance coefficient is used to represent the degree of difference between the target data when the reference tool is in the machining state and the target data when the reference tool is in the no-load state. The target data includes average data, maximum data and minimum data. Based on the unloaded sample curve, the reference sample curve, the multiple machining sample curves, and the preset safety factor, the upper and lower boundary ranges of the target data when the reference tool is in the machining state are determined. Based on the upper and lower boundary ranges of the target data, the target value distance coefficient, the no-load sample curve, and the reference sample curve, the target value monitoring coefficient when the reference tool is in the machining state, and the monitoring threshold range corresponding to the target value monitoring coefficient are determined. The step of determining the target value distance coefficient of the reference tool based on the benchmark sample curve and the unloaded sample curve includes: Obtain the first target value corresponding to the target data of the benchmark sample curve and the second target value corresponding to the target data of the unloaded sample curve; Based on the first target value and the second target value, a target value distance coefficient is determined. The target value distance coefficient is the ratio of a first difference to the first target value, where the first difference is the difference between the first target value and the second target value. The process of determining the upper and lower boundary ranges of the target data when the reference tool is in the machining state, based on the unloaded sample curve, the reference sample curve, the multiple machining sample curves, and a preset safety factor, includes: Obtain the maximum and minimum target data for each processed sample curve; Based on the maximum target data, the preset safety factor, the first target value, and the second target value, the upper boundary range is determined, and the upper boundary range includes the maximum upper boundary and the minimum upper boundary. Based on the minimum target data, the preset safety factor, the first target value, and the second target value, the lower boundary range is determined, and the lower boundary range includes the maximum lower boundary and the minimum lower boundary.

2. The method for determining the monitoring threshold of a cutting tool as described in claim 1, characterized in that, When the target data is average data, The determination of the target value monitoring coefficient and the monitoring threshold range corresponding to the target value monitoring coefficient based on the upper and lower boundary ranges of the target data when the reference tool is in the machining state, the target value distance coefficient, the no-load sample curve, and the baseline sample curve includes: If the minimum upper boundary of the monitoring is equal to the minimum upper boundary, a first target value monitoring coefficient is determined, wherein the minimum upper boundary of the monitoring is determined by the first target value, the second target value and the first target value monitoring coefficient; If the maximum lower boundary of the monitoring is equal to the maximum lower boundary, a second target value monitoring coefficient is determined, wherein the maximum lower boundary of the monitoring is determined by the first target value, the second target value and the second target value monitoring coefficient. The average of the first target value monitoring coefficient and the second target value monitoring coefficient is obtained to determine the target value monitoring coefficient; Based on the target value monitoring coefficient, the first target value, and the second target value, the standard minimum upper boundary and the standard maximum lower boundary are determined. Based on the linear relationship and preset levels, the minimum upper boundary and the maximum upper boundary of the standard are used to determine multiple upper boundary monitoring thresholds of different levels. The standard maximum lower boundary and the minimum lower boundary are used to determine multiple lower boundary monitoring thresholds of different levels according to a linear relationship and a preset level. The number of lower boundary monitoring thresholds is consistent with and corresponds one-to-one with the number of upper boundary monitoring thresholds. The monitoring threshold range includes the upper boundary monitoring thresholds and the lower boundary monitoring thresholds.

3. The method for determining the monitoring threshold of a cutting tool as described in claim 1, characterized in that, When the target data is the minimum data, The determination of the target value monitoring coefficient and the monitoring threshold range corresponding to the target value monitoring coefficient based on the upper and lower boundary ranges of the target data when the reference tool is in the machining state, the target value distance coefficient, the no-load sample curve, and the baseline sample curve includes: When the maximum lower boundary of the monitoring is equal to the maximum lower boundary, the target value monitoring coefficient is determined, wherein the maximum lower boundary of the monitoring is determined by the first target value, the second target value and the target value monitoring coefficient. Based on a linear relationship and a preset level, the maximum lower boundary and the minimum lower boundary are used to determine multiple lower boundary monitoring thresholds of different levels, and the monitoring threshold range includes the lower boundary monitoring thresholds.

4. The method for determining the monitoring threshold of a cutting tool as described in claim 1, characterized in that, When the target data is the maximum data, The determination of the target value monitoring coefficient and the monitoring threshold range corresponding to the target value monitoring coefficient based on the upper and lower boundary ranges of the target data when the reference tool is in the machining state, the target value distance coefficient, the no-load sample curve, and the baseline sample curve includes: When the monitoring minimum upper boundary is equal to the monitoring minimum upper boundary, the target value monitoring coefficient is determined, wherein the monitoring minimum upper boundary is determined by the first target value, the second target value and the target value monitoring coefficient. Based on a linear relationship and a preset level, the maximum upper boundary and the minimum upper boundary are used to determine multiple upper boundary monitoring thresholds of different levels, and the monitoring threshold range includes the upper boundary monitoring thresholds.

5. The method for determining the monitoring threshold of a cutting tool as described in any one of claims 1 to 4, characterized in that, The step of calculating a threshold on the processing data to determine the correspondence between the processing features and the monitoring threshold includes: Obtain multiple value ranges for the distance coefficient of the target value; Based on the target value distance coefficient and the target value monitoring coefficient, the target value monitoring coefficient corresponding to each target value distance coefficient within the target value range is determined. The average monitoring coefficient corresponding to the target value range is determined according to the target value monitoring coefficient corresponding to each target value distance coefficient, wherein the target value range is any one of the plurality of value ranges.

6. The method for determining the monitoring threshold of a cutting tool as described in claim 5, characterized in that, The determination of an initial monitoring threshold corresponding to the target machining feature in the monitoring model library based on the target machining feature of the target tool includes: Obtain the first processing data corresponding to the target processing feature; Based on the first processing data, the target value distance coefficient of the target tool is determined; Based on the target value distance coefficient of the target tool, the average monitoring coefficient of the target tool is determined in the monitoring model library; Based on the average monitoring coefficient of the target tool and the target machining data, the monitoring threshold range of the target tool is determined; Based on the preset initial level and the monitoring threshold range, the initial monitoring threshold of the target data of the target tool is determined.

7. The method for determining the monitoring threshold of a cutting tool as described in claim 1, characterized in that, The step of acquiring target machining data corresponding to multiple operations of the target tool when the target tool is in the machining state includes: When the target tool is in the machining state, acquire the machining data segments corresponding to each of the multiple processes of the target tool; The target data corresponding to each of the multiple processing data segments is determined as the target processing data, and the target data includes average data, maximum data, and minimum data.

8. The method for determining the monitoring threshold of a cutting tool as described in claim 7, characterized in that, The step of adjusting the initial monitoring threshold based on the number of times the target processing data is greater than or less than the initial monitoring threshold to form a final monitoring threshold includes: If the number of times the target processing data exceeds the initial monitoring threshold reaches a first preset number, then the average number of the target processing data corresponding to each of the first preset number of times is obtained. The average of the target machining data corresponding to each of the first preset number of times is determined as the final monitoring threshold of the target tool; If, after a second preset number of times the number of times the target processing data does not exceed the initial monitoring threshold is reached, the average number of the target processing data corresponding to each of the second preset number of times is obtained. The average of the target machining data corresponding to each of the second preset number of times is determined as the final monitoring threshold of the target tool.

9. A device for determining the monitoring threshold of a cutting tool, characterized in that, The device includes: The first acquisition module is used to acquire the machining features of the reference tool and the machining data corresponding to the machining features; The generation module is used to perform threshold calculations on the processing data, determine the correspondence between the processing features and the monitoring thresholds, and generate a monitoring model library. The first determining module is used to determine an initial monitoring threshold corresponding to the target machining feature in the monitoring model library based on the target machining feature of the target tool. The second acquisition module is used to acquire target machining data corresponding to multiple operations of the target tool when the target tool is in the machining state; The second determining module is used to adjust the initial monitoring threshold based on the number of times the target processing data is greater than or less than the initial monitoring threshold, so as to form a final monitoring threshold. The machining data includes an unloaded sample curve of the reference tool in an unloaded state and multiple machining sample curves of the reference tool in a machining state. The generation module is also used for: Obtain the first processing data corresponding to each of the multiple processing sample curves at the same time, and determine the benchmark sample curve based on the first processing data; Based on the benchmark sample curve and the no-load sample curve, the target value distance coefficient of the reference tool is determined. The target value distance coefficient is used to represent the degree of difference between the target data when the reference tool is in the machining state and the target data when the reference tool is in the no-load state. The target data includes average data, maximum data and minimum data. Based on the unloaded sample curve, the reference sample curve, the multiple machining sample curves, and the preset safety factor, the upper and lower boundary ranges of the target data when the reference tool is in the machining state are determined. Based on the upper and lower boundary ranges of the target data, the target value distance coefficient, the no-load sample curve, and the reference sample curve, the target value monitoring coefficient when the reference tool is in the machining state, and the monitoring threshold range corresponding to the target value monitoring coefficient are determined. The generation module is also used for: Obtain the first target value corresponding to the target data of the benchmark sample curve and the second target value corresponding to the target data of the unloaded sample curve; Based on the first target value and the second target value, a target value distance coefficient is determined. The target value distance coefficient is the ratio of a first difference to the first target value, where the first difference is the difference between the first target value and the second target value. The generation module is also used for: Obtain the maximum and minimum target data for each processed sample curve; Based on the maximum target data, the preset safety factor, the first target value, and the second target value, the upper boundary range is determined, and the upper boundary range includes the maximum upper boundary and the minimum upper boundary. Based on the minimum target data, the preset safety factor, the first target value, and the second target value, the lower boundary range is determined, and the lower boundary range includes the maximum lower boundary and the minimum lower boundary.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the tool monitoring threshold determination method as described in any one of claims 1 to 8.

11. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the tool monitoring threshold determination method as described in any one of claims 1 to 8.

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